From a2373c6d0853e3d68f2993775f8f8120ebbd75f2 Mon Sep 17 00:00:00 2001
From: IvorySQL Actions Bot <actions-bot@users.noreply.github.com>
Date: Fri, 20 Mar 2026 02:56:41 +0000
Subject: [PATCH] docs: Regenerate Antora site from IvorySQL/ivorysql_docs
 commit bfecf522b84c408446385a7e6fef30b49340fd2c

---
 .../ivorysql-doc/master/about_ivorysql.html   |   3 +
 .../master/architecture/dual_mode_design.html |   3 +
 .../master/architecture/dual_parser.html      |   7 +-
 .../master/architecture/framework_design.html |   3 +
 .../master/architecture/guc_framework.html    |   3 +
 .../master/architecture/initdb_process.html   |   3 +
 .../ivorysql_cloud_installation.html          |   3 +
 .../cloud_platform/ivorysql_cloud_usage.html  |   3 +
 .../cn/ivorysql-doc/master/cluster_setup.html |   3 +
 .../call_into.html                            |   3 +
 .../case_conversion.html                      |   3 +
 .../empty_string_to_null.html                 |   3 +
 .../force_view.html                           |   3 +
 .../function_procedure.html                   |   3 +
 .../like_operator.html                        |   3 +
 .../nested_function.html                      |   3 +
 .../nls_parameter.html                        |   3 +
 .../out_parameter.html                        |   3 +
 .../compatibility_features_design/rowid.html  |   3 +
 .../sys_guid_function.html                    |   3 +
 .../type_rowtype.html                         |   3 +
 .../docker_podman_deployment.html             |   3 +
 .../docker_swarm_compose_deployment.html      |   3 +
 .../containerization/k8s_deployment.html      |   3 +
 .../containerization/operator_deployment.html |   3 +
 .../asciidoc_syntax_reference.html            |   3 +
 .../community_contribution_guide.html         |   3 +
 .../ivorysql-doc/master/contribution/faq.html |   3 +
 .../contribution/issue_submission_guide.html  |   3 +
 .../cpu_architecture_adaptation.html          |   3 +
 .../os_architecture_adaptation.html           |   3 +
 .../ivorysql-doc/master/developer_guide.html  |   3 +
 .../ecosystem_overview.html                   |  10 +
 .../ecosystem_components/pg_ai_query.html     |   3 +
 .../master/ecosystem_components/pg_cron.html  |   3 +
 .../ecosystem_components/pg_stat_monitor.html | 590 ++++++++++++++
 .../master/ecosystem_components/pgaudit.html  |   3 +
 .../master/ecosystem_components/pgddl.html    |   3 +
 .../master/ecosystem_components/pgroonga.html |   3 +
 .../ecosystem_components/pgrouting.html       |   3 +
 .../ecosystem_components/pgsql_http.html      |   3 +
 .../master/ecosystem_components/pgvector.html |   3 +
 .../ecosystem_components/plpgsql_check.html   |   3 +
 .../master/ecosystem_components/postgis.html  |   3 +
 .../ecosystem_components/system_stats.html    |   3 +
 .../master/ecosystem_components/wal2json.html |   7 +-
 docs/cn/ivorysql-doc/master/gb18030.html      |   3 +
 .../getting-started/daily_maintenance.html    |   3 +
 .../getting-started/daily_monitoring.html     |   3 +
 .../master/getting-started/quick_start.html   |   3 +
 .../master/installation_guide.html            |   3 +
 .../ivorysql-doc/master/migration_guide.html  |   3 +
 .../ivorysql-doc/master/operation_guide.html  | 305 +++++--
 .../oracle_builtin_functions/sys_context.html |   3 +
 .../oracle_builtin_functions/userenv.html     |   3 +
 .../oracle_compatibility/anonymous_block.html |   3 +
 .../builtin_types_functions.html              |   3 +
 .../compat_call_into.html                     |  13 +-
 .../compat_case_conversion.html               |   3 +
 .../compat_empty_string_to_null.html          |   3 +
 .../compat_force_view.html                    |   3 +
 .../compat_function_procedure.html            |   3 +
 .../compat_like_operator.html                 |   3 +
 .../compat_nested_function.html               |   3 +
 .../compat_nls_parameter.html                 |   3 +
 .../compat_out_parameter.html                 |   3 +
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 .../oracle_compatibility/compat_sys_guid.html |   3 +
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 .../master/oracle_compatibility/sequence.html |   3 +
 .../oracle_compatibility/xml_functions.html   |   3 +
 .../pg_reference/pg_functions_reference.html  |   3 +
 .../pg_reference/pg_parameters_reference.html |   3 +
 .../master/problem_report_guide.html          |   3 +
 .../cn/ivorysql-doc/master/release_notes.html |   3 +
 .../ivorysql-doc/master/tools_reference.html  |   3 +
 docs/cn/ivorysql-doc/master/welcome.html      |   3 +
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 docs/cn/ivorysql-doc/v5.3/7.22.html           |  10 +-
 docs/cn/ivorysql-doc/v5.3/_exports/index.pdf  | Bin 27514342 -> 27569520 bytes
 docs/cn/ivorysql-doc/v5.3/ivorysql.pdf        | Bin 27514342 -> 27569520 bytes
 docs/cn/search-index.js                       |   2 +-
 docs/cn/sitemap.xml                           | 766 +++++++++---------
 .../ivorysql-doc/master/about_ivorysql.html   |   3 +
 .../master/architecture/dual_mode_design.html |   3 +
 .../master/architecture/dual_parser.html      |   7 +-
 .../master/architecture/framework_design.html |   3 +
 .../master/architecture/guc_framework.html    |   3 +
 .../master/architecture/initdb_process.html   |   3 +
 .../ivorysql_cloud_installation.html          |   3 +
 .../cloud_platform/ivorysql_cloud_usage.html  |   3 +
 .../en/ivorysql-doc/master/cluster_setup.html |   3 +
 .../call_into.html                            |   3 +
 .../case_conversion.html                      |   3 +
 .../empty_string_to_null.html                 |   3 +
 .../force_view.html                           |   3 +
 .../function_procedure.html                   |   3 +
 .../like_operator.html                        |   3 +
 .../nested_function.html                      |   3 +
 .../nls_parameter.html                        |   3 +
 .../out_parameter.html                        |   3 +
 .../compatibility_features_design/rowid.html  |   3 +
 .../sys_guid_function.html                    |   3 +
 .../type_rowtype.html                         |   3 +
 .../docker_podman_deployment.html             |   3 +
 .../docker_swarm_compose_deployment.html      |   3 +
 .../containerization/k8s_deployment.html      |   3 +
 .../containerization/operator_deployment.html |   3 +
 .../community_contribution_guide.html         |   3 +
 .../ivorysql-doc/master/contribution/faq.html |   3 +
 .../cpu_architecture_adaptation.html          |   3 +
 .../os_architecture_adaptation.html           |   3 +
 .../ivorysql-doc/master/developer_guide.html  |   3 +
 .../ecosystem_overview.html                   |  10 +
 .../ecosystem_components/pg_ai_query.html     |   3 +
 .../master/ecosystem_components/pg_cron.html  |   3 +
 .../ecosystem_components/pg_stat_monitor.html | 570 +++++++++++++
 .../master/ecosystem_components/pgaudit.html  |   3 +
 .../master/ecosystem_components/pgddl.html    |   3 +
 .../master/ecosystem_components/pgroonga.html |   3 +
 .../ecosystem_components/pgrouting.html       |   3 +
 .../ecosystem_components/pgsql_http.html      |   3 +
 .../master/ecosystem_components/pgvector.html |   3 +
 .../ecosystem_components/plpgsql_check.html   |   3 +
 .../master/ecosystem_components/postgis.html  |   3 +
 .../ecosystem_components/system_stats.html    |   3 +
 .../master/ecosystem_components/wal2json.html |   7 +-
 docs/en/ivorysql-doc/master/gb18030.html      |   3 +
 .../getting-started/daily_maintenance.html    |   3 +
 .../getting-started/daily_monitoring.html     |   3 +
 .../master/getting-started/quick_start.html   |   3 +
 .../master/installation_guide.html            |   3 +
 .../ivorysql-doc/master/migration_guide.html  |   3 +
 .../ivorysql-doc/master/operation_guide.html  | 307 +++++--
 .../oracle_builtin_functions/sys_context.html |   3 +
 .../oracle_builtin_functions/userenv.html     |   3 +
 .../oracle_compatibility/anonymous_block.html |   3 +
 .../builtin_types_functions.html              |   3 +
 .../compat_call_into.html                     |  13 +-
 .../compat_case_conversion.html               |   3 +
 .../compat_empty_string_to_null.html          |   3 +
 .../compat_force_view.html                    |   3 +
 .../compat_function_procedure.html            |   3 +
 .../compat_like_operator.html                 |   3 +
 .../compat_nested_function.html               |   3 +
 .../compat_nls_parameter.html                 |   3 +
 .../compat_out_parameter.html                 |   3 +
 .../oracle_compatibility/compat_rowid.html    |   3 +
 .../oracle_compatibility/compat_sys_guid.html |   3 +
 .../compat_type_rowtype.html                  |   3 +
 .../invisible_column.html                     |   3 +
 .../master/oracle_compatibility/package.html  |   3 +
 .../master/oracle_compatibility/port_ip.html  |   3 +
 .../master/oracle_compatibility/sequence.html |   3 +
 .../oracle_compatibility/xml_functions.html   |   3 +
 .../en/ivorysql-doc/master/release_notes.html |   3 +
 .../ivorysql-doc/master/tools_reference.html  |   3 +
 docs/en/ivorysql-doc/master/welcome.html      |   3 +
 docs/en/ivorysql-doc/v5.3/4.4.html            | 304 +++++--
 docs/en/ivorysql-doc/v5.3/7.22.html           |  10 +-
 docs/en/ivorysql-doc/v5.3/_exports/index.pdf  | Bin 21087503 -> 21141385 bytes
 docs/en/ivorysql-doc/v5.3/ivorysql.pdf        | Bin 21087503 -> 21141385 bytes
 docs/en/search-index.js                       |   2 +-
 docs/en/sitemap.xml                           | 722 +++++++++--------
 167 files changed, 3441 insertions(+), 952 deletions(-)
 create mode 100644 docs/cn/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html
 create mode 100644 docs/en/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html

diff --git a/docs/cn/ivorysql-doc/master/about_ivorysql.html b/docs/cn/ivorysql-doc/master/about_ivorysql.html
index 234238b1..dcb10ce5 100644
--- a/docs/cn/ivorysql-doc/master/about_ivorysql.html
+++ b/docs/cn/ivorysql-doc/master/about_ivorysql.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/architecture/dual_mode_design.html b/docs/cn/ivorysql-doc/master/architecture/dual_mode_design.html
index bab4dd0d..932024f6 100644
--- a/docs/cn/ivorysql-doc/master/architecture/dual_mode_design.html
+++ b/docs/cn/ivorysql-doc/master/architecture/dual_mode_design.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/architecture/dual_parser.html b/docs/cn/ivorysql-doc/master/architecture/dual_parser.html
index d270044d..ad740ae9 100644
--- a/docs/cn/ivorysql-doc/master/architecture/dual_parser.html
+++ b/docs/cn/ivorysql-doc/master/architecture/dual_parser.html
@@ -5,7 +5,7 @@
     <meta name="viewport" content="width=device-width,initial-scale=1">
     <title>Oracle兼容框架之双parser :: IvorySQL文档中心</title>
     <link rel="canonical" href="https://docs.ivorysql.org/ivorysql-doc/master/architecture/dual_parser.html">
-    <link rel="prev" href="../ecosystem_components/wal2json.html">
+    <link rel="prev" href="../ecosystem_components/pg_stat_monitor.html">
     <link rel="next" href="framework_design.html">
     <meta name="generator" content="Antora 3.1.7">
     <link rel="stylesheet" href="../../../_/css/site.css">
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -507,7 +510,7 @@ <h2 id="服务器端编程语言词法语法分离"><a class="anchor" href="#服
 </div>
 </div>
 <nav class="pagination">
-  <span class="prev"><a href="../ecosystem_components/wal2json.html">wal2json</a></span>
+  <span class="prev"><a href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a></span>
   <span class="next"><a href="framework_design.html">框架设计</a></span>
 </nav>
 </article>
diff --git a/docs/cn/ivorysql-doc/master/architecture/framework_design.html b/docs/cn/ivorysql-doc/master/architecture/framework_design.html
index ed7ea2e5..2f4c0de5 100644
--- a/docs/cn/ivorysql-doc/master/architecture/framework_design.html
+++ b/docs/cn/ivorysql-doc/master/architecture/framework_design.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/architecture/guc_framework.html b/docs/cn/ivorysql-doc/master/architecture/guc_framework.html
index 2455b393..82b9c300 100644
--- a/docs/cn/ivorysql-doc/master/architecture/guc_framework.html
+++ b/docs/cn/ivorysql-doc/master/architecture/guc_framework.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/architecture/initdb_process.html b/docs/cn/ivorysql-doc/master/architecture/initdb_process.html
index 32157b6b..deaef7cd 100644
--- a/docs/cn/ivorysql-doc/master/architecture/initdb_process.html
+++ b/docs/cn/ivorysql-doc/master/architecture/initdb_process.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html b/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html
index b5477ac7..c623ac11 100644
--- a/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html
+++ b/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html b/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html
index 5ebf2574..573de6d6 100644
--- a/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html
+++ b/docs/cn/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/cluster_setup.html b/docs/cn/ivorysql-doc/master/cluster_setup.html
index 85af5412..bda15ed7 100644
--- a/docs/cn/ivorysql-doc/master/cluster_setup.html
+++ b/docs/cn/ivorysql-doc/master/cluster_setup.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/call_into.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/call_into.html
index 72d318c6..f186ba80 100644
--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/call_into.html
+++ b/docs/cn/ivorysql-doc/master/compatibility_features_design/call_into.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/case_conversion.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/case_conversion.html
index 5a649693..03562ea2 100644
--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/case_conversion.html
+++ b/docs/cn/ivorysql-doc/master/compatibility_features_design/case_conversion.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html
index d6d6acc3..41d56fab 100644
--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html
+++ b/docs/cn/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/force_view.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/force_view.html
index 32fda71e..61785f69 100644
--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/force_view.html
+++ b/docs/cn/ivorysql-doc/master/compatibility_features_design/force_view.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/function_procedure.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/function_procedure.html
index 551ca75b..cce86c01 100644
--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/function_procedure.html
+++ b/docs/cn/ivorysql-doc/master/compatibility_features_design/function_procedure.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/like_operator.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/like_operator.html
index e60c1e12..2ceb3eaf 100644
--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/like_operator.html
+++ b/docs/cn/ivorysql-doc/master/compatibility_features_design/like_operator.html
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
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--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/nested_function.html
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/out_parameter.html
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
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 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/rowid.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/rowid.html
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--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/rowid.html
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/sys_guid_function.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/sys_guid_function.html
index 8e23873f..4ed1457b 100644
--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/sys_guid_function.html
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/compatibility_features_design/type_rowtype.html b/docs/cn/ivorysql-doc/master/compatibility_features_design/type_rowtype.html
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--- a/docs/cn/ivorysql-doc/master/compatibility_features_design/type_rowtype.html
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   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/containerization/docker_podman_deployment.html b/docs/cn/ivorysql-doc/master/containerization/docker_podman_deployment.html
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--- a/docs/cn/ivorysql-doc/master/containerization/docker_podman_deployment.html
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html b/docs/cn/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/containerization/k8s_deployment.html b/docs/cn/ivorysql-doc/master/containerization/k8s_deployment.html
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--- a/docs/cn/ivorysql-doc/master/containerization/k8s_deployment.html
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/containerization/operator_deployment.html b/docs/cn/ivorysql-doc/master/containerization/operator_deployment.html
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/contribution/community_contribution_guide.html b/docs/cn/ivorysql-doc/master/contribution/community_contribution_guide.html
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   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/contribution/faq.html b/docs/cn/ivorysql-doc/master/contribution/faq.html
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/contribution/issue_submission_guide.html b/docs/cn/ivorysql-doc/master/contribution/issue_submission_guide.html
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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 </ul>
diff --git a/docs/cn/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html b/docs/cn/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html b/docs/cn/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html
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     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/developer_guide.html b/docs/cn/ivorysql-doc/master/developer_guide.html
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   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html b/docs/cn/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html
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   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -527,6 +530,13 @@ <h2 id="ivorysql生态插件适配列表" class="discrete">IvorySQL生态插件
 <td class="tableblock halign-left valign-top"><p class="tableblock">AI驱动的自然语言转SQL扩展，支持多种大语言模型</p></td>
 <td class="tableblock halign-left valign-top"><p class="tableblock">AI辅助查询、自然语言数据库交互</p></td>
 </tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock">12</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><a href="pg_stat_monitor.html" class="xref page">pg_stat_monitor</a></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">2.3.1</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">收集性能统计数据，并通过统一视图和直方图形式直观展示查询性能指标。</p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">性能监控</p></td>
+</tr>
 </tbody>
 </table>
 <div class="paragraph">
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   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
   </li>
 </ul>
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   <li class="nav-item" data-depth="4">
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
   </li>
 </ul>
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+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width,initial-scale=1">
+    <title>pg_stat_monitor :: IvorySQL文档中心</title>
+    <link rel="canonical" href="https://docs.ivorysql.org/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html">
+    <link rel="prev" href="wal2json.html">
+    <link rel="next" href="../architecture/dual_parser.html">
+    <meta name="generator" content="Antora 3.1.7">
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+  <nav class="navbar">
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+      <a class="navbar-item" href="https://docs.ivorysql.org">IvorySQL文档中心</a>
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+  </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../compatibility_features_design/type_rowtype.html">%TYPE、%ROWTYPE</a>
+  </li>
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+    <a class="nav-link" href="../compatibility_features_design/nls_parameter.html">NLS 参数</a>
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+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../compatibility_features_design/force_view.html">Force View</a>
+  </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../compatibility_features_design/case_conversion.html">大小写转换</a>
+  </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../compatibility_features_design/sys_guid_function.html">sys_guid 函数</a>
+  </li>
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+    <a class="nav-link" href="../compatibility_features_design/empty_string_to_null.html">空字符串转null</a>
+  </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../compatibility_features_design/call_into.html">CALL INTO</a>
+  </li>
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+<ul class="nav-list">
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+    <a class="nav-link" href="../oracle_compatibility/compat_case_conversion.html">1、大小写转换</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_like_operator.html">2、LIKE操作符</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/anonymous_block.html">3、匿名块</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_function_procedure.html">4、函数与存储过程</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/builtin_types_functions.html">5、内置数据类型与内置函数</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/port_ip.html">6、端口与IP</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/xml_functions.html">7、XML函数</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/sequence.html">8、sequence</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/package.html">9、包</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/invisible_column.html">10、不可见列</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_rowid.html">11、RowID</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_out_parameter.html">12、OUT 参数</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_type_rowtype.html">13、%TYPE、%ROWTYPE</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_nls_parameter.html">14、NLS 参数</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_force_view.html">15、Force View</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_nested_function.html">16、嵌套子函数</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_sys_guid.html">17、sys_guid 函数</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_empty_string_to_null.html">18、空字符串转null</a>
+  </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../oracle_compatibility/compat_call_into.html">19、CALL INTO</a>
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+<article class="doc">
+<h1 class="page">pg_stat_monitor</h1>
+<div class="sect1">
+<h2 id="概述"><a class="anchor" href="#概述"></a>1. 概述</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>pg_stat_monitor 是 PostgreSQL 的查询性能监控工具。它能收集性能统计数据，并通过统一视图和直方图形式直观展示查询性能指标。</p>
+</div>
+<div class="paragraph">
+<p>该工具可帮助数据库用户全面掌握查询来源、执行情况、计划统计与详细信息，以及查询元数据。这极大地提升了可观测性，使用户能够有效调试和优化查询性能。</p>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="安装"><a class="anchor" href="#安装"></a>2. 安装</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>IvorySQL的安装包里已经集成了pg_stat_monitor插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装pg_stat_monitor。如您使用源码方式安装了IvorySQL，可以继续通过源码方式继续安装pg_stat_monitor插件，IvorySQL社区为您提供了源码安装步骤：</p>
+</div>
+<div class="paragraph">
+<p>要从源代码构建 pg_stat_monitor，您需要以下组件:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>git</p>
+</li>
+<li>
+<p>make</p>
+</li>
+<li>
+<p>gcc</p>
+</li>
+<li>
+<p>pg_config</p>
+</li>
+</ul>
+</div>
+<div class="paragraph">
+<p>您可以从 <a href="https://github.com/Percona/pg_stat_monitor/releases">GitHub 上的发布页面</a> 下载 pg_stat_monitor 指定版本的源代码，或者使用 git 命令：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">git clone https://github.com/percona/pg_stat_monitor.git</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>编译并安装扩展程序，假如环境中已经安装了IvorySQL，安装路径为/usr/ivory-5</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">cd pg_stat_monitor
+export PG_CONFIG=/usr/ivory-5/bin/pg_config
+make USE_PGXS=1
+make USE_PGXS=1 install</code></pre>
+</div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="加载模块"><a class="anchor" href="#加载模块"></a>3. 加载模块</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>在启动时，将 pg_stat_monitor 添加到 shared_preload_libraries 配置参数中，以加载该库。这是因为 pg_stat_monitor 需要额外的共享内存。</p>
+</div>
+<div class="paragraph">
+<p>修改 shared_preload_libraries 参数：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-conf hljs" data-lang="conf">shared_preload_libraries = 'pg_stat_monitor';</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>注意：若 shared_preload_libraries 参数中已存在其他模块（例如 pg_stat_statements），需用逗号分隔全部列出。pg_stat_monitor 必须排在 pg_stat_statements 之后，例如：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-conf hljs" data-lang="conf">shared_preload_libraries = 'liboracle_parser, ivorysql_ora, pg_stat_statements, pg_stat_monitor';</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>配置完成后，重启 IvorySQL实例使配置生效。</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">pg_ctl restart -D data</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>将 pg_stat_monitor 添加至 shared_preload_libraries 后，该扩展会立即开始收集所有现有数据库的统计信息。要查看监控数据，需在每个待监控的数据库中创建视图。</p>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="创建扩展视图"><a class="anchor" href="#创建扩展视图"></a>4. 创建扩展视图</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>请使用具有超级用户或数据库所有者权限的账户执行以下操作。以超级用户身份连接至目标数据库，运行以下命令创建扩展：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">CREATE EXTENSION pg_stat_monitor;</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>完成设置后，即可查看pg_stat_monitor收集的统计信息。</p>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="使用"><a class="anchor" href="#使用"></a>5. 使用</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>以获取查询执行时间信息为例，连接数据库执行以下SQL：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">SELECT  userid,  total_exec_time, min_exec_time, max_exec_time, mean_exec_time, query FROM pg_stat_monitor;</code></pre>
+</div>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-text hljs" data-lang="text">userid | total_exec_time | min_exec_time | max_exec_time | mean_exec_time |                                            query
+--------+-----------------+---------------+---------------+----------------+----------------------------------------------------------------------------------------------
+     10 |        1.532168 |      0.749108 |       0.78306 |       0.766084 | SELECT userid,  datname, queryid, substr(query,$1, $2) AS query, calls FROM pg_stat_monitor
+     10 |        0.755857 |      0.755857 |      0.755857 |       0.755857 | SELECT application_name, client_ip, substr(query,$1,$2) as query FROM pg_stat_monitor
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_exec_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+(4 rows)</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>更多关于pg_stat_monitor的使用，请参阅 pg_stat_monitor <a href="https://docs.percona.com/pg-stat-monitor/user_guide.html">官方文档</a></p>
+</div>
+</div>
+</div>
+<nav class="pagination">
+  <span class="prev"><a href="wal2json.html">wal2json</a></span>
+  <span class="next"><a href="../architecture/dual_parser.html">双parser</a></span>
+</nav>
+</article>
+  </div>
+</main>
+</div>
+<footer class="footer">
+
+</footer>
+<script id="site-script" src="../../../_/js/site.js" data-ui-root-path="../../../_"></script>
+<script async src="../../../_/js/vendor/highlight.js"></script>
+<script src="../../../_/js/vendor/lunr.js"></script>
+<script src="../../../_/js/vendor/lunr-languages.js"></script>
+<script src="../../../_/js/search-ui.js" id="search-ui-script" data-site-root-path="../../.." data-snippet-length="100" data-stylesheet="../../../_/css/search.css"></script>
+<script async src="../../../search-index.js"></script>
+  </body>
+</html>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html
index bdbdc7a1..3032b706 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html
index 4127a976..dfb31fb6 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html
index 1e7cb9fe..b4a8deac 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html
index 6a593207..1d406a61 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html
index 87ef7bae..eb033f0b 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html
index da0a1cf6..c55de330 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html b/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html
index 566d090c..9f20f9ba 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html b/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html
index a700d8e1..f869edf3 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html b/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html
index fcef0246..7d53501d 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html b/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html
index 750738fd..011b4bc1 100644
--- a/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html
+++ b/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html
@@ -6,7 +6,7 @@
     <title>wal2json :: IvorySQL文档中心</title>
     <link rel="canonical" href="https://docs.ivorysql.org/ivorysql-doc/master/ecosystem_components/wal2json.html">
     <link rel="prev" href="system_stats.html">
-    <link rel="next" href="../architecture/dual_parser.html">
+    <link rel="next" href="pg_stat_monitor.html">
     <meta name="generator" content="Antora 3.1.7">
     <link rel="stylesheet" href="../../../_/css/site.css">
     <script>var uiRootPath = '../../../_'</script>
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item is-current-page" data-depth="4">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -607,7 +610,7 @@ <h2 id="使用"><a class="anchor" href="#使用"></a>3. 使用</h2>
 </div>
 <nav class="pagination">
   <span class="prev"><a href="system_stats.html">system_stats</a></span>
-  <span class="next"><a href="../architecture/dual_parser.html">双parser</a></span>
+  <span class="next"><a href="pg_stat_monitor.html">pg_stat_monitor</a></span>
 </nav>
 </article>
   </div>
diff --git a/docs/cn/ivorysql-doc/master/gb18030.html b/docs/cn/ivorysql-doc/master/gb18030.html
index 5f5ed3cf..2699e9e9 100644
--- a/docs/cn/ivorysql-doc/master/gb18030.html
+++ b/docs/cn/ivorysql-doc/master/gb18030.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html b/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html
index 2a0e5fdf..a14f101a 100644
--- a/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html
+++ b/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html b/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html
index a620438b..67ddc5f2 100644
--- a/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html
+++ b/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/getting-started/quick_start.html b/docs/cn/ivorysql-doc/master/getting-started/quick_start.html
index 2cc642d5..a857a02f 100644
--- a/docs/cn/ivorysql-doc/master/getting-started/quick_start.html
+++ b/docs/cn/ivorysql-doc/master/getting-started/quick_start.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/installation_guide.html b/docs/cn/ivorysql-doc/master/installation_guide.html
index 334e365e..813ec796 100644
--- a/docs/cn/ivorysql-doc/master/installation_guide.html
+++ b/docs/cn/ivorysql-doc/master/installation_guide.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/migration_guide.html b/docs/cn/ivorysql-doc/master/migration_guide.html
index ad4a07c6..c52c76e8 100644
--- a/docs/cn/ivorysql-doc/master/migration_guide.html
+++ b/docs/cn/ivorysql-doc/master/migration_guide.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/operation_guide.html b/docs/cn/ivorysql-doc/master/operation_guide.html
index b5802554..206a6d4c 100644
--- a/docs/cn/ivorysql-doc/master/operation_guide.html
+++ b/docs/cn/ivorysql-doc/master/operation_guide.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -701,7 +704,211 @@ <h3 id="通过复制升级数据"><a class="anchor" href="#通过复制升级数
 </div>
 </div>
 <div class="sect1">
-<h2 id="管理ivorysql版本"><a class="anchor" href="#管理ivorysql版本"></a>2. 管理IvorySQL版本</h2>
+<h2 id="使用-pg_upgrade-将-postgresql-升级到-ivorysql"><a class="anchor" href="#使用-pg_upgrade-将-postgresql-升级到-ivorysql"></a>2. 使用 pg_upgrade 将 PostgreSQL 升级到 IvorySQL</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p><code>pg_upgrade</code> 支持将原生 PostgreSQL 集群升级到 IvorySQL。当源集群为原生 PostgreSQL ，而目标为 IvorySQL 集群时，必须使用 <code>-g</code>（<code>--using-ora-pg</code>）参数。</p>
+</div>
+<div class="paragraph">
+<p>从 PostgreSQL 升级到 IvorySQL 属于“跨产品”迁移，建议在生产环境操作前完成完整的测试验证，并制定详细的回滚方案。</p>
+</div>
+<div class="sect2">
+<h3 id="参数说明"><a class="anchor" href="#参数说明"></a>2.1. 参数说明</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 25%;">
+<col style="width: 75%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">参数</th>
+<th class="tableblock halign-left valign-top">说明</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code> / <code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">表示源集群为 PostgreSQL ，而目标为 IvorySQL 集群。启用此选项后，<code>pg_upgrade</code> 在连接两端集群时统一使用 PostgreSQL 标准端口，从而正确处理升级。</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="升级步骤"><a class="anchor" href="#升级步骤"></a>2.2. 升级步骤</h3>
+<div class="sect3">
+<h4 id="第一步初始化新-ivorysql-集群"><a class="anchor" href="#第一步初始化新-ivorysql-集群"></a>2.2.1. 第一步：初始化新 IvorySQL 集群</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash"># 使用 IvorySQL 的 initdb 初始化新集群
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第二步停止源-postgresql-集群"><a class="anchor" href="#第二步停止源-postgresql-集群"></a>2.2.2. 第二步：停止源 PostgreSQL 集群</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第三步运行升级检查可选"><a class="anchor" href="#第三步运行升级检查可选"></a>2.2.3. 第三步：运行升级检查（可选）</h4>
+<div class="paragraph">
+<p>使用 <code>-c</code> 仅做兼容性检查，不修改任何数据：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # 源 PG 可执行文件目录
+  -B /opt/ivorysql/bin \            # 目标 IvorySQL 可执行文件目录
+  -d /data/pg/data \                # 源 PG 数据目录
+  -D /data/ivorysql/data \          # 目标 IvorySQL 数据目录
+  -g \                              # 源为 PostgreSQL，目标为 IvorySQL
+  -c                                # 仅检查，不升级</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第四步执行升级"><a class="anchor" href="#第四步执行升级"></a>2.2.4. 第四步：执行升级</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第五步启动-ivorysql-并验证"><a class="anchor" href="#第五步启动-ivorysql-并验证"></a>2.2.5. 第五步：启动 IvorySQL 并验证</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# 验证数据库是否正常
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第六步清理旧集群"><a class="anchor" href="#第六步清理旧集群"></a>2.2.6. 第六步：清理旧集群</h4>
+<div class="paragraph">
+<p>升级完成后，<code>pg_upgrade</code> 会生成一个清理脚本：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">./delete_old_cluster.sh</code></pre>
+</div>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="常用参数速查"><a class="anchor" href="#常用参数速查"></a>2.3. 常用参数速查</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 20%;">
+<col style="width: 40%;">
+<col style="width: 40%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">参数</th>
+<th class="tableblock halign-left valign-top">长选项</th>
+<th class="tableblock halign-left valign-top">说明</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-b</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群可执行文件目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-B</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群可执行文件目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-d</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群数据目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-D</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群数据目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源为 PostgreSQL 升级到 IvorySQL</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-p</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-P</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群 PG 端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群 Oracle 端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-Q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群 Oracle 端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-j</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--jobs</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">并行进程数</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-k</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--link</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">使用硬链接替代文件复制</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-c</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--check</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">仅检查兼容性，不执行升级</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-v</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--verbose</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">输出详细日志</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="注意事项"><a class="anchor" href="#注意事项"></a>2.4. 注意事项</h3>
+<div class="ulist">
+<ul>
+<li>
+<p><code>-g</code> 参数仅在 <strong>源为纯 PostgreSQL 集群</strong>、目标为 <strong>IvorySQL</strong> 时使用。若源集群已是 IvorySQL，则无需此参数。</p>
+</li>
+<li>
+<p>升级期间源集群和目标集群均须处于停止状态。</p>
+</li>
+<li>
+<p>升级前务必对源集群做完整备份。</p>
+</li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="管理ivorysql版本"><a class="anchor" href="#管理ivorysql版本"></a>3. 管理IvorySQL版本</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL基于PostgreSQL开发，版本更新频率与PostgreSQL版本更新频率保持一致，每年更新一个大版本，每季度更新一个小版本。IvorySQL目前发布的版本有1.0到5.0，分别基于PostgreSQL 14.0到18.0进行开发，最新版本为IvorySQL 5.0，基于PostgreSQL 18.0进行开发。IvorySQL 的所有版本全部都做到了向下兼容。相关版本特性可以查看 <a href="https://www.ivorysql.org/zh-CN/releases-page">官网</a>。</p>
@@ -709,7 +916,7 @@ <h2 id="管理ivorysql版本"><a class="anchor" href="#管理ivorysql版本"></a
 </div>
 </div>
 <div class="sect1">
-<h2 id="管理ivorysql数据库访问"><a class="anchor" href="#管理ivorysql数据库访问"></a>3. 管理IvorySQL数据库访问</h2>
+<h2 id="管理ivorysql数据库访问"><a class="anchor" href="#管理ivorysql数据库访问"></a>4. 管理IvorySQL数据库访问</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL使用 <strong>角色</strong> 的概念管理数据库访问权限。一个角色可以看成是一个数据库用户或者是数据库用户组，这取决于角色被怎样设置。角色可以拥有数据库对象（例如，表和函数）并且能够把那些对象上的权限赋予给其他角色来控制谁能访问哪些对象。此外，还可以把一个角色中的 <strong>成员资格</strong> 授予给另一个角色，这样允许成员角色使用被赋予给另一个角色的权限。</p>
@@ -777,21 +984,21 @@ <h2 id="管理ivorysql数据库访问"><a class="anchor" href="#管理ivorysql
 </div>
 </div>
 <div class="sect1">
-<h2 id="定义数据对象"><a class="anchor" href="#定义数据对象"></a>4. 定义数据对象</h2>
+<h2 id="定义数据对象"><a class="anchor" href="#定义数据对象"></a>5. 定义数据对象</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL基于PostgreSQL，具有完整的SQL，其定义数据对象可以参考 <a href="https://www.postgresql.org/docs/current/ddl.html">手册</a>。在此基础之上，IvorySQL为兼容Oracle，还做了一些Oracle专有数据对象的兼容。</p>
 </div>
 <div class="sect2">
-<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>4.1. VARCHAR2</h3>
+<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>5.1. VARCHAR2</h3>
 <div class="sect3">
-<h4 id="概述"><a class="anchor" href="#概述"></a>4.1.1. 概述</h4>
+<h4 id="概述"><a class="anchor" href="#概述"></a>5.1.1. 概述</h4>
 <div class="paragraph">
 <p>具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="语法"><a class="anchor" href="#语法"></a>4.1.2. 语法</h4>
+<h4 id="语法"><a class="anchor" href="#语法"></a>5.1.2. 语法</h4>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">VARCHAR2(size)</code></pre>
@@ -799,7 +1006,7 @@ <h4 id="语法"><a class="anchor" href="#语法"></a>4.1.2. 语法</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="用例"><a class="anchor" href="#用例"></a>4.1.3. 用例</h4>
+<h4 id="用例"><a class="anchor" href="#用例"></a>5.1.3. 用例</h4>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">create table test(a varchar2(5));
@@ -823,7 +1030,7 @@ <h4 id="用例"><a class="anchor" href="#用例"></a>4.1.3. 用例</h4>
 </div>
 </div>
 <div class="sect1">
-<h2 id="查询数据"><a class="anchor" href="#查询数据"></a>5. 查询数据</h2>
+<h2 id="查询数据"><a class="anchor" href="#查询数据"></a>6. 查询数据</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL基于PostgreSQL开发，具有完全的SQL，查询数据具体的操作可以参考 <a href="https://www.postgresql.org/docs/current/queries.html">手册</a>。</p>
@@ -831,7 +1038,7 @@ <h2 id="查询数据"><a class="anchor" href="#查询数据"></a>5. 查询数据
 </div>
 </div>
 <div class="sect1">
-<h2 id="使用外部数据"><a class="anchor" href="#使用外部数据"></a>6. 使用外部数据</h2>
+<h2 id="使用外部数据"><a class="anchor" href="#使用外部数据"></a>7. 使用外部数据</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL实现了部分的SQL/MED规定，允许我们使用普通SQL查询来访问位于IvorySQL之外的数据。这种数据被称为外部数据（注意这种用法不要和外键混淆，后者是数据库中的一种约束）。</p>
@@ -848,7 +1055,7 @@ <h2 id="使用外部数据"><a class="anchor" href="#使用外部数据"></a>6.
 </div>
 </div>
 <div class="sect1">
-<h2 id="备份与恢复"><a class="anchor" href="#备份与恢复"></a>7. 备份与恢复</h2>
+<h2 id="备份与恢复"><a class="anchor" href="#备份与恢复"></a>8. 备份与恢复</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>由于包含着有价值的数据，IvorySQL数据库应当被定期地备份。虽然过程相当简单，但清晰地理解其底层技术和假设是非常重要的。</p>
@@ -870,7 +1077,7 @@ <h2 id="备份与恢复"><a class="anchor" href="#备份与恢复"></a>7. 备份
 </ul>
 </div>
 <div class="sect2">
-<h3 id="sql转储"><a class="anchor" href="#sql转储"></a>7.1. SQL转储</h3>
+<h3 id="sql转储"><a class="anchor" href="#sql转储"></a>8.1. SQL转储</h3>
 <div class="paragraph">
 <p>SQL 转储方法的思想是创建一个由SQL命令组成的文件，当把这个文件回馈给服务器时，服务器将利用其中的SQL命令重建与转储时状态一样的数据库。 IvorySQL为此提供了工具pg_dump。这个工具的基本用法是：</p>
 </div>
@@ -898,7 +1105,7 @@ <h3 id="sql转储"><a class="anchor" href="#sql转储"></a>7.1. SQL转储</h3>
 <p>​    由pg_dump创建的备份在内部是一致的， 也就是说，转储表现了pg_dump开始运行时刻的数据库快照，且在pg_dump运行过程中发生的更新将不会被转储。pg_dump工作的时候并不阻塞其他的对数据库的操作。（但是会阻塞那些需要排它锁的操作，比如大部分形式的ALTER TABLE）</p>
 </div>
 <div class="sect3">
-<h4 id="从转储中恢复"><a class="anchor" href="#从转储中恢复"></a>7.1.1. 从转储中恢复</h4>
+<h4 id="从转储中恢复"><a class="anchor" href="#从转储中恢复"></a>8.1.1. 从转储中恢复</h4>
 <div class="paragraph">
 <p>pg_dump生成的文本文件可以由psql程序读取。 从转储中恢复的常用命令是：</p>
 </div>
@@ -940,7 +1147,7 @@ <h4 id="从转储中恢复"><a class="anchor" href="#从转储中恢复"></a>7.1
 </div>
 </div>
 <div class="sect3">
-<h4 id="使用pg_dumpall"><a class="anchor" href="#使用pg_dumpall"></a>7.1.2. 使用pg_dumpall</h4>
+<h4 id="使用pg_dumpall"><a class="anchor" href="#使用pg_dumpall"></a>8.1.2. 使用pg_dumpall</h4>
 <div class="paragraph">
 <p>pg_dump每次只转储一个数据库，而且它不会转储关于角色或表空间（因为它们是集簇范围的）的信息。为了支持方便地转储一个数据库集簇的全部内容，提供了pg_dumpall程序。pg_dumpall备份一个给定集簇中的每一个数据库，并且也保留了集簇范围的数据，如角色和表空间定义。该命令的基本用法是：</p>
 </div>
@@ -968,7 +1175,7 @@ <h4 id="使用pg_dumpall"><a class="anchor" href="#使用pg_dumpall"></a>7.1.2.
 </div>
 </div>
 <div class="sect3">
-<h4 id="处理大型数据库"><a class="anchor" href="#处理大型数据库"></a>7.1.3. 处理大型数据库</h4>
+<h4 id="处理大型数据库"><a class="anchor" href="#处理大型数据库"></a>8.1.3. 处理大型数据库</h4>
 <div class="paragraph">
 <p>在一些具有最大文件尺寸限制的操作系统上创建大型的pg_dump输出文件可能会出现问题。幸运地是，pg_dump可以写出到标准输出，因此你可以使用标准Unix工具来处理这种潜在的问题。有几种可能的方法：</p>
 </div>
@@ -1059,7 +1266,7 @@ <h4 id="处理大型数据库"><a class="anchor" href="#处理大型数据库"><
 </div>
 </div>
 <div class="sect2">
-<h3 id="文件系统级别备份"><a class="anchor" href="#文件系统级别备份"></a>7.2. 文件系统级别备份</h3>
+<h3 id="文件系统级别备份"><a class="anchor" href="#文件系统级别备份"></a>8.2. 文件系统级别备份</h3>
 <div class="paragraph">
 <p>另外一种备份策略是直接复制 IvorySQL用于存储数据库中数据的文件，你可以采用任何你喜欢的方式进行文件系统备份，例如：</p>
 </div>
@@ -1098,7 +1305,7 @@ <h3 id="文件系统级别备份"><a class="anchor" href="#文件系统级别备
 </div>
 </div>
 <div class="sect2">
-<h3 id="连续归档和时间点恢复pitr"><a class="anchor" href="#连续归档和时间点恢复pitr"></a>7.3. 连续归档和时间点恢复（PITR）</h3>
+<h3 id="连续归档和时间点恢复pitr"><a class="anchor" href="#连续归档和时间点恢复pitr"></a>8.3. 连续归档和时间点恢复（PITR）</h3>
 <div class="paragraph">
 <p>在任何时间，IvorySQL在数据集簇目录的pg_wal/子目录下都保持有一个预写式日志（WAL）。这个日志存在的目的是为了保证崩溃后的安全：如果系统崩溃，可以“重放”从最后一次检查点以来的日志项来恢复数据库的一致性。该日志的存在也使得第三种备份数据库的策略变得可能：我们可以把一个文件系统级别的备份和WAL文件的备份结合起来。当需要恢复时，我们先恢复文件系统备份，然后从备份的WAL文件中重放来把系统带到一个当前状态。这种方法比之前的方法管理起来要更复杂，但是有其显著的优点：</p>
 </div>
@@ -1131,7 +1338,7 @@ <h3 id="连续归档和时间点恢复pitr"><a class="anchor" href="#连续归
 </div>
 </div>
 <div class="sect1">
-<h2 id="装卸数据"><a class="anchor" href="#装卸数据"></a>8. 装卸数据</h2>
+<h2 id="装卸数据"><a class="anchor" href="#装卸数据"></a>9. 装卸数据</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>copy 在 IvorySQL表和标准文件之间移动数据。COPY TO 把一个表的内容复制到一个文件，而COPY FROM 则从一个文件复制数据到一个表（把数据追加到表中原有数据）。COPY TO 也能复制一个SELECT查询的结果。</p>
@@ -1146,7 +1353,7 @@ <h2 id="装卸数据"><a class="anchor" href="#装卸数据"></a>8. 装卸数据
 <p>​    运行COPY的每个后端将在pg_stat_progress_copy视图中报告其进度。</p>
 </div>
 <div class="sect2">
-<h3 id="大纲"><a class="anchor" href="#大纲"></a>8.1. 大纲</h3>
+<h3 id="大纲"><a class="anchor" href="#大纲"></a>9.1. 大纲</h3>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">COPY table_name [ ( column_name [, ...] ) ]
@@ -1178,7 +1385,7 @@ <h3 id="大纲"><a class="anchor" href="#大纲"></a>8.1. 大纲</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="输出"><a class="anchor" href="#输出"></a>8.2. 输出</h3>
+<h3 id="输出"><a class="anchor" href="#输出"></a>9.2. 输出</h3>
 <div class="paragraph">
 <p>在成功完成时，一个COPY命令会返回一个形为</p>
 </div>
@@ -1193,7 +1400,7 @@ <h3 id="输出"><a class="anchor" href="#输出"></a>8.2. 输出</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="注解"><a class="anchor" href="#注解"></a>8.3. 注解</h3>
+<h3 id="注解"><a class="anchor" href="#注解"></a>9.3. 注解</h3>
 <div class="paragraph">
 <p>COPY TO只能用于普通表，而不能用于视图，并且不能从子表或子分区复制行。 例如，COPY table TO 复制与SELECT * FROM ONLY table 相同的行。 语法COPY (SELECT * FROM table) TO &#8230;&#8203; 可用于转储一个继承层次结构、分区表或视图中的所有行。</p>
 </div>
@@ -1238,9 +1445,9 @@ <h3 id="注解"><a class="anchor" href="#注解"></a>8.3. 注解</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="文件格式"><a class="anchor" href="#文件格式"></a>8.4. 文件格式</h3>
+<h3 id="文件格式"><a class="anchor" href="#文件格式"></a>9.4. 文件格式</h3>
 <div class="sect3">
-<h4 id="文本格式"><a class="anchor" href="#文本格式"></a>8.4.1. 文本格式</h4>
+<h4 id="文本格式"><a class="anchor" href="#文本格式"></a>9.4.1. 文本格式</h4>
 <div class="paragraph">
 <p>在使用text格式时，读取或写入的是一个文本文件，其中每一行就是表中的一行。一行中的列被定界字符分隔。列值本身是由输出函数产生的或者是可被输入函数接受的属于每个属性数据类型的字符串。在为空值的列的位置使用指定的空值串。如果输入文件的任何行包含比预期更多或者更少的列，COPY FROM将会抛出一个错误。</p>
 </div>
@@ -1317,7 +1524,7 @@ <h4 id="文本格式"><a class="anchor" href="#文本格式"></a>8.4.1. 文本
 </div>
 </div>
 <div class="sect3">
-<h4 id="csv格式"><a class="anchor" href="#csv格式"></a>8.4.2. CSV格式</h4>
+<h4 id="csv格式"><a class="anchor" href="#csv格式"></a>9.4.2. CSV格式</h4>
 <div class="paragraph">
 <p>这种格式选项被用于导入和导出很多其他程序（例如电子表格）使用的逗号 分隔值（CSV）文件格式。不同于 IvorySQL标准文本格式使用的转义 规则，它产生并且识别一般的 CSV 转义机制。</p>
 </div>
@@ -1356,7 +1563,7 @@ <h4 id="csv格式"><a class="anchor" href="#csv格式"></a>8.4.2. CSV格式</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="二进制格式"><a class="anchor" href="#二进制格式"></a>8.4.3. 二进制格式</h4>
+<h4 id="二进制格式"><a class="anchor" href="#二进制格式"></a>9.4.3. 二进制格式</h4>
 <div class="paragraph">
 <p>binary格式选项导致所有数据被以二进制格式 而不是文本格式存储/读取。它比文本和CSV格式要 快一些，但是二进制格式文件在不同的机器架构和 IvorySQL 版本之间的可移 植性要差些。还有，二进制格式与数据格式非常相关。例如不能从 一个smallint列中输出二进制数据并且把它读入到一个 integer列中，虽然这样做在文本格式中是可行的。</p>
 </div>
@@ -1434,7 +1641,7 @@ <h4 id="二进制格式"><a class="anchor" href="#二进制格式"></a>8.4.3. 
 </div>
 </div>
 <div class="sect2">
-<h3 id="示例"><a class="anchor" href="#示例"></a>8.5. 示例</h3>
+<h3 id="示例"><a class="anchor" href="#示例"></a>9.5. 示例</h3>
 <div class="paragraph">
 <p>下面的例子使用竖线（|）作为域定界符把一个表复制到客户端：</p>
 </div>
@@ -1509,13 +1716,13 @@ <h3 id="示例"><a class="anchor" href="#示例"></a>8.5. 示例</h3>
 </div>
 </div>
 <div class="sect1">
-<h2 id="性能管理"><a class="anchor" href="#性能管理"></a>9. 性能管理</h2>
+<h2 id="性能管理"><a class="anchor" href="#性能管理"></a>10. 性能管理</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>查询性能可能受多种因素影响。其中一些因素可以由用户控制，而其他的则属于系统下层设计的基本原理。</p>
 </div>
 <div class="sect2">
-<h3 id="使用explain"><a class="anchor" href="#使用explain"></a>9.1. 使用EXPLAIN</h3>
+<h3 id="使用explain"><a class="anchor" href="#使用explain"></a>10.1. 使用EXPLAIN</h3>
 <div class="paragraph">
 <p>IvorySQL为每个收到查询产生一个查询计划。 选择正确的计划来匹配查询结构和数据的属性对于好的性能来说绝对是最关键的，因此系统包含了一个复杂的规划器来尝试选择好的计划。 你可以使用EXPLAIN命令察看规划器为任何查询生成的查询计划。 阅读查询计划是一门艺术，它要求一些经验来掌握，但是本节只试图覆盖一些基础。</p>
 </div>
@@ -1523,7 +1730,7 @@ <h3 id="使用explain"><a class="anchor" href="#使用explain"></a>9.1. 使用EX
 <p>​    这些例子使用EXPLAIN的默认“text”输出格式，这种格式紧凑并且便于阅读。如果你想把EXPLAIN的输出交给一个程序做进一步分析，你应该使用它的某种机器可读的输出格式（XML、JSON 或 YAML）。</p>
 </div>
 <div class="sect3">
-<h4 id="explain基础"><a class="anchor" href="#explain基础"></a>9.1.1. EXPLAIN基础</h4>
+<h4 id="explain基础"><a class="anchor" href="#explain基础"></a>10.1.1. EXPLAIN基础</h4>
 <div class="paragraph">
 <p>查询计划的结构是一个计划结点的树。最底层的结点是扫描结点：它们从表中返回未经处理的行。 不同的表访问模式有不同的扫描结点类型：顺序扫描、索引扫描、位图索引扫描。 也还有不是表的行来源，例如VALUES子句和FROM中返回集合的函数，它们有自己的结点类型。如果查询需要连接、聚集、排序、或者在未经处理的行上的其它操作，那么就会在扫描结点之上有其它额外的结点来执行这些操作。 并且，做这些操作通常都有多种方法，因此在这些位置也有可能出现不同的结点类型。 EXPLAIN给计划树中每个结点都输出一行，显示基本的结点类型和计划器为该计划结点的执行所做的开销估计。 第一行（最上层的结点）是对该计划的总执行开销的估计；计划器试图最小化的就是这个数字。</p>
 </div>
@@ -1858,7 +2065,7 @@ <h4 id="explain基础"><a class="anchor" href="#explain基础"></a>9.1.1. EXPLAI
 </div>
 </div>
 <div class="sect3">
-<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EXPLAIN ANALYZE</h4>
+<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>10.1.2. EXPLAIN ANALYZE</h4>
 <div class="paragraph">
 <p>可以通过使用EXPLAIN的ANALYZE选项来检查规划器估计值的准确性。通过使用这个选项，EXPLAIN会实际执行该查询，然后显示真实的行计数和在每个计划结点中累计的真实运行时间，还会有一个普通EXPLAIN显示的估计值。例如，我们可能得到这样一个结果：</p>
 </div>
@@ -2063,7 +2270,7 @@ <h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EX
 </div>
 </div>
 <div class="sect3">
-<h4 id="警告"><a class="anchor" href="#警告"></a>9.1.3. 警告</h4>
+<h4 id="警告"><a class="anchor" href="#警告"></a>10.1.3. 警告</h4>
 <div class="paragraph">
 <p>在两种有效的方法中EXPLAIN ANALYZE所度量的运行时间可能偏离同一个查询的正常执行。首先，由于不会有输出行被递交给客户端，网络传输开销和 I/O 转换开销没有被包括在内。其次，由EXPLAIN ANALYZE所增加的度量开销可能会很可观，特别是在操作系统调用gettimeofday()很慢的机器上。你可以使用pg_test_timing工具来度量在你的系统上的计时开销。</p>
 </div>
@@ -2103,9 +2310,9 @@ <h4 id="警告"><a class="anchor" href="#警告"></a>9.1.3. 警告</h4>
 </div>
 </div>
 <div class="sect2">
-<h3 id="规划器使用的统计信息"><a class="anchor" href="#规划器使用的统计信息"></a>9.2. 规划器使用的统计信息</h3>
+<h3 id="规划器使用的统计信息"><a class="anchor" href="#规划器使用的统计信息"></a>10.2. 规划器使用的统计信息</h3>
 <div class="sect3">
-<h4 id="单列统计信息"><a class="anchor" href="#单列统计信息"></a>9.2.1. 单列统计信息</h4>
+<h4 id="单列统计信息"><a class="anchor" href="#单列统计信息"></a>10.2.1. 单列统计信息</h4>
 <div class="paragraph">
 <p>如我们在上一节所见，查询规划器需要估计一个查询要检索的行数，这样才能对查询计划做出好的选择。 本节对系统用于这些估计的统计信息进行一个快速的介绍。</p>
 </div>
@@ -2173,7 +2380,7 @@ <h4 id="单列统计信息"><a class="anchor" href="#单列统计信息"></a>9.2
 </div>
 </div>
 <div class="sect3">
-<h4 id="扩展统计信息"><a class="anchor" href="#扩展统计信息"></a>9.2.2. 扩展统计信息</h4>
+<h4 id="扩展统计信息"><a class="anchor" href="#扩展统计信息"></a>10.2.2. 扩展统计信息</h4>
 <div class="paragraph">
 <p>常常可以看到由于查询子句中用到的多个列相互关联而运行着糟糕的执行计划的慢查询。规划器通常会假设多个条件是彼此独立的，这种假设在列值相互关联的情况下是不成立的。由于常规的统计信息天然的针对个体列的性质，它们无法捕捉到跨列关联的知识。不过，IvorySQL有能力计算多元统计信息，它能捕捉这类信息。</p>
 </div>
@@ -2190,7 +2397,7 @@ <h4 id="扩展统计信息"><a class="anchor" href="#扩展统计信息"></a>9.2
 <p>​    下面的小节介绍当前支持的扩展统计信息类型。</p>
 </div>
 <div class="sect4">
-<h5 id="函数依赖"><a class="anchor" href="#函数依赖"></a>9.2.2.1. 函数依赖</h5>
+<h5 id="函数依赖"><a class="anchor" href="#函数依赖"></a>10.2.2.1. 函数依赖</h5>
 <div class="paragraph">
 <p>最简单的一类扩展统计信息跟踪函数依赖，这是在数据库范式定义中使用的概念。如果列a的值的知识足以决定列b的值，即不会有两个行具有相同的a值但是有不同的b值，我们就说列b函数依赖于列a。在一个完全规范化的数据库中，函数依赖应该仅存在于主键和超键上。不过，实际上很多数据集合会由于各种原因无法被完全规范化，常见的例子是为了性能而有意地反规范化。即使在一个完全规范化的数据库中，也会有某些列之间的部分关联，这些可以表达成部分函数依赖。</p>
 </div>
@@ -2225,7 +2432,7 @@ <h5 id="函数依赖"><a class="anchor" href="#函数依赖"></a>9.2.2.1. 函数
 <p>在为涉及函数依赖列的查询计算选择度时，规划器会使用依赖系数来调整针对条件的选择度估计，这样就不会产生低估。</p>
 </div>
 <div class="sect5">
-<h6 id="函数依赖的限制"><a class="anchor" href="#函数依赖的限制"></a>9.2.2.1.1. 函数依赖的限制</h6>
+<h6 id="函数依赖的限制"><a class="anchor" href="#函数依赖的限制"></a>10.2.2.1.1. 函数依赖的限制</h6>
 <div class="paragraph">
 <p>当前只有在考虑简单等值条件（将列与常量值比较）和具有常量值的IN 子句时，函数依赖才适用。不会使用它们来改进比较两个列或者比较列和表达式的等值条件的估计， 也不会用它们来改进范围子句、LIKE或者任何其他类型的条件。</p>
 </div>
@@ -2254,7 +2461,7 @@ <h6 id="函数依赖的限制"><a class="anchor" href="#函数依赖的限制"><
 </div>
 </div>
 <div class="sect4">
-<h5 id="多元可区分值计数"><a class="anchor" href="#多元可区分值计数"></a>9.2.2.2. 多元可区分值计数</h5>
+<h5 id="多元可区分值计数"><a class="anchor" href="#多元可区分值计数"></a>10.2.2.2. 多元可区分值计数</h5>
 <div class="paragraph">
 <p>单列统计信息存储每一列中可区分值的数量。在组合多个列（例如GROUP BY a, b）时，如果规划器只有单列统计数据，则对可区分值数量的估计常常会错误，导致选择不好的计划。</p>
 </div>
@@ -2287,7 +2494,7 @@ <h5 id="多元可区分值计数"><a class="anchor" href="#多元可区分值计
 </div>
 </div>
 <div class="sect4">
-<h5 id="多元mcv列表"><a class="anchor" href="#多元mcv列表"></a>9.2.2.3. 多元MCV列表</h5>
+<h5 id="多元mcv列表"><a class="anchor" href="#多元mcv列表"></a>10.2.2.3. 多元MCV列表</h5>
 <div class="paragraph">
 <p>为每列存储的另一种统计信息是频繁值列表。 这样可以对单个列进行非常准确的估计，但是对于在多个列上具有条件的查询，可能会导致严重的错误估计。</p>
 </div>
@@ -2332,7 +2539,7 @@ <h5 id="多元mcv列表"><a class="anchor" href="#多元mcv列表"></a>9.2.2.3.
 </div>
 </div>
 <div class="sect2">
-<h3 id="用显示join子句控制规划器"><a class="anchor" href="#用显示join子句控制规划器"></a>9.3. 用显示JOIN子句控制规划器</h3>
+<h3 id="用显示join子句控制规划器"><a class="anchor" href="#用显示join子句控制规划器"></a>10.3. 用显示JOIN子句控制规划器</h3>
 <div class="paragraph">
 <p>我们可以在一定程度上用显式JOIN语法控制查询规划器。要明白为什么需要它，我们首先需要一些背景知识。</p>
 </div>
@@ -2429,18 +2636,18 @@ <h3 id="用显示join子句控制规划器"><a class="anchor" href="#用显示jo
 </div>
 </div>
 <div class="sect2">
-<h3 id="填充一个数据库"><a class="anchor" href="#填充一个数据库"></a>9.4. 填充一个数据库</h3>
+<h3 id="填充一个数据库"><a class="anchor" href="#填充一个数据库"></a>10.4. 填充一个数据库</h3>
 <div class="paragraph">
 <p>第一次填充数据库时可能需要插入大量的数据。本节包含一些如何让这个处理尽可能高效的建议。</p>
 </div>
 <div class="sect3">
-<h4 id="禁用自动提交"><a class="anchor" href="#禁用自动提交"></a>9.4.1. 禁用自动提交</h4>
+<h4 id="禁用自动提交"><a class="anchor" href="#禁用自动提交"></a>10.4.1. 禁用自动提交</h4>
 <div class="paragraph">
 <p>在使用多个INSERT时，关闭自动提交并且只在最后做一次提交（在普通 SQL 中，这意味着在开始发出BEGIN并且在结束时发出COMMIT。某些客户端库可能背着你就做了这些，在这种情况下你需要确定在你需要做这些时该库确实帮你做了）。如果你允许每一个插入都被独立地提交，IvorySQL要为每一个被增加的行做很多工作。在一个事务中做所有插入的一个额外好处是：如果一个行的插入失败则所有之前插入的行都会被回滚，这样你不会被卡在部分载入的数据中。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="使用copy"><a class="anchor" href="#使用copy"></a>9.4.2. 使用COPY</h4>
+<h4 id="使用copy"><a class="anchor" href="#使用copy"></a>10.4.2. 使用COPY</h4>
 <div class="paragraph">
 <p>使用COPY在一条命令中装载所有记录，而不是一系列INSERT命令。 COPY命令是为装载大量行而优化过的； 它没INSERT那么灵活，但是在大量数据装载时导致的负荷也更少。 因为COPY是单条命令，因此使用这种方法填充表时无须关闭自动提交。</p>
 </div>
@@ -2455,7 +2662,7 @@ <h4 id="使用copy"><a class="anchor" href="#使用copy"></a>9.4.2. 使用COPY</
 </div>
 </div>
 <div class="sect3">
-<h4 id="移除索引"><a class="anchor" href="#移除索引"></a>9.4.3. 移除索引</h4>
+<h4 id="移除索引"><a class="anchor" href="#移除索引"></a>10.4.3. 移除索引</h4>
 <div class="paragraph">
 <p>如果你正在载入一个新创建的表，最快的方法是创建该表，用COPY批量载入该表的数据，然后创建表需要的任何索引。在已存在数据的表上创建索引要比在每一行被载入时增量地更新它更快。</p>
 </div>
@@ -2464,7 +2671,7 @@ <h4 id="移除索引"><a class="anchor" href="#移除索引"></a>9.4.3. 移除
 </div>
 </div>
 <div class="sect3">
-<h4 id="移除外键约束"><a class="anchor" href="#移除外键约束"></a>9.4.4. 移除外键约束</h4>
+<h4 id="移除外键约束"><a class="anchor" href="#移除外键约束"></a>10.4.4. 移除外键约束</h4>
 <div class="paragraph">
 <p>和索引一样，“成批地”检查外键约束比一行行检查效率更高。 因此，先删除外键约束、载入数据然后重建约束会很有用。 同样，载入数据和约束缺失期间错误检查的丢失之间也存在平衡。</p>
 </div>
@@ -2473,19 +2680,19 @@ <h4 id="移除外键约束"><a class="anchor" href="#移除外键约束"></a>9.4
 </div>
 </div>
 <div class="sect3">
-<h4 id="增加-maintenance_work_mem"><a class="anchor" href="#增加-maintenance_work_mem"></a>9.4.5. 增加 maintenance_work_mem</h4>
+<h4 id="增加-maintenance_work_mem"><a class="anchor" href="#增加-maintenance_work_mem"></a>10.4.5. 增加 maintenance_work_mem</h4>
 <div class="paragraph">
 <p>在载入大量数据时，临时增大maintenance_work_mem配置变量可以改进性能。这个参数也可以帮助加速CREATE INDEX命令和ALTER TABLE ADD FOREIGN KEY命令。 它不会对COPY本身起很大作用，所以这个建议只有在你使用上面的一个或两个技巧时才有用。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="增加max_wal_size"><a class="anchor" href="#增加max_wal_size"></a>9.4.6. 增加max_wal_size</h4>
+<h4 id="增加max_wal_size"><a class="anchor" href="#增加max_wal_size"></a>10.4.6. 增加max_wal_size</h4>
 <div class="paragraph">
 <p>临时增大max_wal_size配置变量也可以让大量数据载入更快。 这是因为向IvorySQL中载入大量的数据将导致检查点的发生比平常（由checkpoint_timeout配置变量指定）更频繁。无论何时发生一个检查点时，所有脏页都必须被刷写到磁盘上。 通过在批量数据载入时临时增加max_wal_size，所需的检查点数目可以被缩减。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="禁用wal归档和流复制"><a class="anchor" href="#禁用wal归档和流复制"></a>9.4.7. 禁用WAL归档和流复制</h4>
+<h4 id="禁用wal归档和流复制"><a class="anchor" href="#禁用wal归档和流复制"></a>10.4.7. 禁用WAL归档和流复制</h4>
 <div class="paragraph">
 <p>当使用 WAL 归档或流复制向一个安装中载入大量数据时，在录入结束后执行一次新的基础备份比处理大量的增量 WAL 数据更快。为了防止载入时记录增量 WAL，通过将wal_level设置为minimal、将archive_mode设置为off以及将max_wal_senders设置为零来禁用归档和流复制。 但需要注意的是，修改这些设置需要重启服务。</p>
 </div>
@@ -2494,13 +2701,13 @@ <h4 id="禁用wal归档和流复制"><a class="anchor" href="#禁用wal归档和
 </div>
 </div>
 <div class="sect3">
-<h4 id="事后运行analyze"><a class="anchor" href="#事后运行analyze"></a>9.4.8. 事后运行ANALYZE</h4>
+<h4 id="事后运行analyze"><a class="anchor" href="#事后运行analyze"></a>10.4.8. 事后运行ANALYZE</h4>
 <div class="paragraph">
 <p>不管什么时候你显著地改变了表中的数据分布后，我们都强烈推荐运行ANALYZE。这包括向表中批量载入大量数据。运行ANALYZE（或者VACUUM ANALYZE）保证规划器有表的最新统计信息。 如果没有统计数据或者统计数据过时，那么规划器在查询规划时可能做出很差劲决定，导致在任意表上的性能低下。需要注意的是，如果启用了 autovacuum 守护进程，它可能会自动运行ANALYZE。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="关于pg_dump的一些注记"><a class="anchor" href="#关于pg_dump的一些注记"></a>9.4.9. 关于pg_dump的一些注记</h4>
+<h4 id="关于pg_dump的一些注记"><a class="anchor" href="#关于pg_dump的一些注记"></a>10.4.9. 关于pg_dump的一些注记</h4>
 <div class="paragraph">
 <p>pg_dump生成的转储脚本自动应用上面的若干个（但不是全部）技巧。 要尽可能快地载入pg_dump转储，你需要手工做一些额外的事情（请注意，这些要点适用于恢复一个转储，而不是创建它的时候。同样的要点也适用于使用psql载入一个文本转储或用pg_restore从一个pg_dump归档文件载入）。</p>
 </div>
@@ -2537,7 +2744,7 @@ <h4 id="关于pg_dump的一些注记"><a class="anchor" href="#关于pg_dump的
 </div>
 </div>
 <div class="sect2">
-<h3 id="非持久设置"><a class="anchor" href="#非持久设置"></a>9.5. 非持久设置</h3>
+<h3 id="非持久设置"><a class="anchor" href="#非持久设置"></a>10.5. 非持久设置</h3>
 <div class="paragraph">
 <p>持久性是数据库的一个保证已提交事务的记录的特性（即使是发生服务器崩溃或断电）。 然而，持久性会明显增加数据库的负荷，因此如果你的站点不需要这个保证，IvorySQL可以被配置成运行更快。在这种情况下，你可以调整下列配置来提高性能。除了下面列出的，在数据库软件崩溃的情况下也能保证持久性。当这些设置被使用时，只有突然的操作系统停止会产生数据丢失或损坏的风险。</p>
 </div>
diff --git a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html
index 398bb80c..9b71d98a 100644
--- a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html
+++ b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html
index 2ec60dd1..a5d51bbe 100644
--- a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html
+++ b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html
index 88ea6bb0..2ab9c024 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html
index 8383830b..4937dfe7 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html
index e12ecbc6..6ab625f4 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -574,7 +577,15 @@ <h3 id="无参和默认参数调用"><a class="anchor" href="#无参和默认参
 <h3 id="在参数或into子句中引用绑定变量"><a class="anchor" href="#在参数或into子句中引用绑定变量"></a>3.4. 在参数或INTO子句中引用绑定变量</h3>
 <div class="listingblock">
 <div class="content">
-<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- 设置输入绑定变量
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- 创建独立函数 stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- 设置输入绑定变量
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- 使用绑定变量作为参数调用函数
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
index 94dbd492..dec3b68b 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
index 9fd8dad4..f36f1e39 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
index d76e3ce3..4b4a5981 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
index 10b113e4..8e32dd49 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
index 9b923587..e851c1d9 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
index c7d8b2fb..7fc90c55 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
index b4a9a34c..b42df5d6 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
index 55456ec2..7d191d40 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
index f8422af1..53acde24 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
index 07b11542..33fcfafc 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
index a273afbd..dd1cd4af 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html
index 62b3bcda..3b04acf1 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html
index ab0340c3..4f0b8122 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html
index 0313aa44..713bdf5c 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/sequence.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/sequence.html
index d4599942..d875dfdc 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/sequence.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/sequence.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html
index 5282b332..fd18b331 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/pg_reference/pg_functions_reference.html b/docs/cn/ivorysql-doc/master/pg_reference/pg_functions_reference.html
index 281cea86..eaf99c6f 100644
--- a/docs/cn/ivorysql-doc/master/pg_reference/pg_functions_reference.html
+++ b/docs/cn/ivorysql-doc/master/pg_reference/pg_functions_reference.html
@@ -175,6 +175,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/pg_reference/pg_parameters_reference.html b/docs/cn/ivorysql-doc/master/pg_reference/pg_parameters_reference.html
index 9a4668f0..3269e9a6 100644
--- a/docs/cn/ivorysql-doc/master/pg_reference/pg_parameters_reference.html
+++ b/docs/cn/ivorysql-doc/master/pg_reference/pg_parameters_reference.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="../welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/problem_report_guide.html b/docs/cn/ivorysql-doc/master/problem_report_guide.html
index ed552c2d..abcb3fe2 100644
--- a/docs/cn/ivorysql-doc/master/problem_report_guide.html
+++ b/docs/cn/ivorysql-doc/master/problem_report_guide.html
@@ -174,6 +174,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/release_notes.html b/docs/cn/ivorysql-doc/master/release_notes.html
index b29ef795..7b8a3865 100644
--- a/docs/cn/ivorysql-doc/master/release_notes.html
+++ b/docs/cn/ivorysql-doc/master/release_notes.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/tools_reference.html b/docs/cn/ivorysql-doc/master/tools_reference.html
index dcf9e42d..6b17f403 100644
--- a/docs/cn/ivorysql-doc/master/tools_reference.html
+++ b/docs/cn/ivorysql-doc/master/tools_reference.html
@@ -176,6 +176,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/master/welcome.html b/docs/cn/ivorysql-doc/master/welcome.html
index 4118714c..a9752dc8 100644
--- a/docs/cn/ivorysql-doc/master/welcome.html
+++ b/docs/cn/ivorysql-doc/master/welcome.html
@@ -175,6 +175,9 @@ <h3 class="title"><a href="welcome.html">文档中心</a></h3>
   <li class="nav-item" data-depth="4">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="4">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/cn/ivorysql-doc/v5.3/4.4.html b/docs/cn/ivorysql-doc/v5.3/4.4.html
index 29c5ccf6..409ac35d 100644
--- a/docs/cn/ivorysql-doc/v5.3/4.4.html
+++ b/docs/cn/ivorysql-doc/v5.3/4.4.html
@@ -715,7 +715,211 @@ <h3 id="通过复制升级数据"><a class="anchor" href="#通过复制升级数
 </div>
 </div>
 <div class="sect1">
-<h2 id="管理ivorysql版本"><a class="anchor" href="#管理ivorysql版本"></a>2. 管理IvorySQL版本</h2>
+<h2 id="使用-pg_upgrade-将-postgresql-升级到-ivorysql"><a class="anchor" href="#使用-pg_upgrade-将-postgresql-升级到-ivorysql"></a>2. 使用 pg_upgrade 将 PostgreSQL 升级到 IvorySQL</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p><code>pg_upgrade</code> 支持将原生 PostgreSQL 集群升级到 IvorySQL。当源集群为原生 PostgreSQL ，而目标为 IvorySQL 集群时，必须使用 <code>-g</code>（<code>--using-ora-pg</code>）参数。</p>
+</div>
+<div class="paragraph">
+<p>从 PostgreSQL 升级到 IvorySQL 属于“跨产品”迁移，建议在生产环境操作前完成完整的测试验证，并制定详细的回滚方案。</p>
+</div>
+<div class="sect2">
+<h3 id="参数说明"><a class="anchor" href="#参数说明"></a>2.1. 参数说明</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 25%;">
+<col style="width: 75%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">参数</th>
+<th class="tableblock halign-left valign-top">说明</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code> / <code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">表示源集群为 PostgreSQL ，而目标为 IvorySQL 集群。启用此选项后，<code>pg_upgrade</code> 在连接两端集群时统一使用 PostgreSQL 标准端口，从而正确处理升级。</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="升级步骤"><a class="anchor" href="#升级步骤"></a>2.2. 升级步骤</h3>
+<div class="sect3">
+<h4 id="第一步初始化新-ivorysql-集群"><a class="anchor" href="#第一步初始化新-ivorysql-集群"></a>2.2.1. 第一步：初始化新 IvorySQL 集群</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash"># 使用 IvorySQL 的 initdb 初始化新集群
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第二步停止源-postgresql-集群"><a class="anchor" href="#第二步停止源-postgresql-集群"></a>2.2.2. 第二步：停止源 PostgreSQL 集群</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第三步运行升级检查可选"><a class="anchor" href="#第三步运行升级检查可选"></a>2.2.3. 第三步：运行升级检查（可选）</h4>
+<div class="paragraph">
+<p>使用 <code>-c</code> 仅做兼容性检查，不修改任何数据：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # 源 PG 可执行文件目录
+  -B /opt/ivorysql/bin \            # 目标 IvorySQL 可执行文件目录
+  -d /data/pg/data \                # 源 PG 数据目录
+  -D /data/ivorysql/data \          # 目标 IvorySQL 数据目录
+  -g \                              # 源为 PostgreSQL，目标为 IvorySQL
+  -c                                # 仅检查，不升级</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第四步执行升级"><a class="anchor" href="#第四步执行升级"></a>2.2.4. 第四步：执行升级</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第五步启动-ivorysql-并验证"><a class="anchor" href="#第五步启动-ivorysql-并验证"></a>2.2.5. 第五步：启动 IvorySQL 并验证</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# 验证数据库是否正常
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="第六步清理旧集群"><a class="anchor" href="#第六步清理旧集群"></a>2.2.6. 第六步：清理旧集群</h4>
+<div class="paragraph">
+<p>升级完成后，<code>pg_upgrade</code> 会生成一个清理脚本：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">./delete_old_cluster.sh</code></pre>
+</div>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="常用参数速查"><a class="anchor" href="#常用参数速查"></a>2.3. 常用参数速查</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 20%;">
+<col style="width: 40%;">
+<col style="width: 40%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">参数</th>
+<th class="tableblock halign-left valign-top">长选项</th>
+<th class="tableblock halign-left valign-top">说明</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-b</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群可执行文件目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-B</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群可执行文件目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-d</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群数据目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-D</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群数据目录</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源为 PostgreSQL 升级到 IvorySQL</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-p</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-P</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群 PG 端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">源集群 Oracle 端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-Q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">目标集群 Oracle 端口</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-j</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--jobs</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">并行进程数</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-k</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--link</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">使用硬链接替代文件复制</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-c</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--check</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">仅检查兼容性，不执行升级</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-v</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--verbose</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">输出详细日志</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="注意事项"><a class="anchor" href="#注意事项"></a>2.4. 注意事项</h3>
+<div class="ulist">
+<ul>
+<li>
+<p><code>-g</code> 参数仅在 <strong>源为纯 PostgreSQL 集群</strong>、目标为 <strong>IvorySQL</strong> 时使用。若源集群已是 IvorySQL，则无需此参数。</p>
+</li>
+<li>
+<p>升级期间源集群和目标集群均须处于停止状态。</p>
+</li>
+<li>
+<p>升级前务必对源集群做完整备份。</p>
+</li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="管理ivorysql版本"><a class="anchor" href="#管理ivorysql版本"></a>3. 管理IvorySQL版本</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL基于PostgreSQL开发，版本更新频率与PostgreSQL版本更新频率保持一致，每年更新一个大版本，每季度更新一个小版本。IvorySQL目前发布的版本有1.0到5.3，分别基于PostgreSQL 14.0到18.3进行开发，最新版本为IvorySQL 5.3，基于PostgreSQL 18.3进行开发。IvorySQL 的所有版本全部都做到了向下兼容。相关版本特性可以查看 <a href="https://www.ivorysql.org/zh-CN/releases-page">官网</a>。</p>
@@ -723,7 +927,7 @@ <h2 id="管理ivorysql版本"><a class="anchor" href="#管理ivorysql版本"></a
 </div>
 </div>
 <div class="sect1">
-<h2 id="管理ivorysql数据库访问"><a class="anchor" href="#管理ivorysql数据库访问"></a>3. 管理IvorySQL数据库访问</h2>
+<h2 id="管理ivorysql数据库访问"><a class="anchor" href="#管理ivorysql数据库访问"></a>4. 管理IvorySQL数据库访问</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL使用 <strong>角色</strong> 的概念管理数据库访问权限。一个角色可以看成是一个数据库用户或者是数据库用户组，这取决于角色被怎样设置。角色可以拥有数据库对象（例如，表和函数）并且能够把那些对象上的权限赋予给其他角色来控制谁能访问哪些对象。此外，还可以把一个角色中的 <strong>成员资格</strong> 授予给另一个角色，这样允许成员角色使用被赋予给另一个角色的权限。</p>
@@ -791,21 +995,21 @@ <h2 id="管理ivorysql数据库访问"><a class="anchor" href="#管理ivorysql
 </div>
 </div>
 <div class="sect1">
-<h2 id="定义数据对象"><a class="anchor" href="#定义数据对象"></a>4. 定义数据对象</h2>
+<h2 id="定义数据对象"><a class="anchor" href="#定义数据对象"></a>5. 定义数据对象</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL基于PostgreSQL，具有完整的SQL，其定义数据对象可以参考 <a href="https://www.postgresql.org/docs/current/ddl.html">手册</a>。在此基础之上，IvorySQL为兼容Oracle，还做了一些Oracle专有数据对象的兼容。</p>
 </div>
 <div class="sect2">
-<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>4.1. VARCHAR2</h3>
+<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>5.1. VARCHAR2</h3>
 <div class="sect3">
-<h4 id="概述"><a class="anchor" href="#概述"></a>4.1.1. 概述</h4>
+<h4 id="概述"><a class="anchor" href="#概述"></a>5.1.1. 概述</h4>
 <div class="paragraph">
 <p>具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="语法"><a class="anchor" href="#语法"></a>4.1.2. 语法</h4>
+<h4 id="语法"><a class="anchor" href="#语法"></a>5.1.2. 语法</h4>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">VARCHAR2(size)</code></pre>
@@ -813,7 +1017,7 @@ <h4 id="语法"><a class="anchor" href="#语法"></a>4.1.2. 语法</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="用例"><a class="anchor" href="#用例"></a>4.1.3. 用例</h4>
+<h4 id="用例"><a class="anchor" href="#用例"></a>5.1.3. 用例</h4>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">create table test(a varchar2(5));
@@ -837,7 +1041,7 @@ <h4 id="用例"><a class="anchor" href="#用例"></a>4.1.3. 用例</h4>
 </div>
 </div>
 <div class="sect1">
-<h2 id="查询数据"><a class="anchor" href="#查询数据"></a>5. 查询数据</h2>
+<h2 id="查询数据"><a class="anchor" href="#查询数据"></a>6. 查询数据</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL基于PostgreSQL开发，具有完全的SQL，查询数据具体的操作可以参考 <a href="https://www.postgresql.org/docs/current/queries.html">手册</a>。</p>
@@ -845,7 +1049,7 @@ <h2 id="查询数据"><a class="anchor" href="#查询数据"></a>5. 查询数据
 </div>
 </div>
 <div class="sect1">
-<h2 id="使用外部数据"><a class="anchor" href="#使用外部数据"></a>6. 使用外部数据</h2>
+<h2 id="使用外部数据"><a class="anchor" href="#使用外部数据"></a>7. 使用外部数据</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL实现了部分的SQL/MED规定，允许我们使用普通SQL查询来访问位于IvorySQL之外的数据。这种数据被称为外部数据（注意这种用法不要和外键混淆，后者是数据库中的一种约束）。</p>
@@ -862,7 +1066,7 @@ <h2 id="使用外部数据"><a class="anchor" href="#使用外部数据"></a>6.
 </div>
 </div>
 <div class="sect1">
-<h2 id="备份与恢复"><a class="anchor" href="#备份与恢复"></a>7. 备份与恢复</h2>
+<h2 id="备份与恢复"><a class="anchor" href="#备份与恢复"></a>8. 备份与恢复</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>由于包含着有价值的数据，IvorySQL数据库应当被定期地备份。虽然过程相当简单，但清晰地理解其底层技术和假设是非常重要的。</p>
@@ -884,7 +1088,7 @@ <h2 id="备份与恢复"><a class="anchor" href="#备份与恢复"></a>7. 备份
 </ul>
 </div>
 <div class="sect2">
-<h3 id="sql转储"><a class="anchor" href="#sql转储"></a>7.1. SQL转储</h3>
+<h3 id="sql转储"><a class="anchor" href="#sql转储"></a>8.1. SQL转储</h3>
 <div class="paragraph">
 <p>SQL 转储方法的思想是创建一个由SQL命令组成的文件，当把这个文件回馈给服务器时，服务器将利用其中的SQL命令重建与转储时状态一样的数据库。 IvorySQL为此提供了工具pg_dump。这个工具的基本用法是：</p>
 </div>
@@ -912,7 +1116,7 @@ <h3 id="sql转储"><a class="anchor" href="#sql转储"></a>7.1. SQL转储</h3>
 <p>​    由pg_dump创建的备份在内部是一致的， 也就是说，转储表现了pg_dump开始运行时刻的数据库快照，且在pg_dump运行过程中发生的更新将不会被转储。pg_dump工作的时候并不阻塞其他的对数据库的操作。（但是会阻塞那些需要排它锁的操作，比如大部分形式的ALTER TABLE）</p>
 </div>
 <div class="sect3">
-<h4 id="从转储中恢复"><a class="anchor" href="#从转储中恢复"></a>7.1.1. 从转储中恢复</h4>
+<h4 id="从转储中恢复"><a class="anchor" href="#从转储中恢复"></a>8.1.1. 从转储中恢复</h4>
 <div class="paragraph">
 <p>pg_dump生成的文本文件可以由psql程序读取。 从转储中恢复的常用命令是：</p>
 </div>
@@ -954,7 +1158,7 @@ <h4 id="从转储中恢复"><a class="anchor" href="#从转储中恢复"></a>7.1
 </div>
 </div>
 <div class="sect3">
-<h4 id="使用pg_dumpall"><a class="anchor" href="#使用pg_dumpall"></a>7.1.2. 使用pg_dumpall</h4>
+<h4 id="使用pg_dumpall"><a class="anchor" href="#使用pg_dumpall"></a>8.1.2. 使用pg_dumpall</h4>
 <div class="paragraph">
 <p>pg_dump每次只转储一个数据库，而且它不会转储关于角色或表空间（因为它们是集簇范围的）的信息。为了支持方便地转储一个数据库集簇的全部内容，提供了pg_dumpall程序。pg_dumpall备份一个给定集簇中的每一个数据库，并且也保留了集簇范围的数据，如角色和表空间定义。该命令的基本用法是：</p>
 </div>
@@ -982,7 +1186,7 @@ <h4 id="使用pg_dumpall"><a class="anchor" href="#使用pg_dumpall"></a>7.1.2.
 </div>
 </div>
 <div class="sect3">
-<h4 id="处理大型数据库"><a class="anchor" href="#处理大型数据库"></a>7.1.3. 处理大型数据库</h4>
+<h4 id="处理大型数据库"><a class="anchor" href="#处理大型数据库"></a>8.1.3. 处理大型数据库</h4>
 <div class="paragraph">
 <p>在一些具有最大文件尺寸限制的操作系统上创建大型的pg_dump输出文件可能会出现问题。幸运地是，pg_dump可以写出到标准输出，因此你可以使用标准Unix工具来处理这种潜在的问题。有几种可能的方法：</p>
 </div>
@@ -1073,7 +1277,7 @@ <h4 id="处理大型数据库"><a class="anchor" href="#处理大型数据库"><
 </div>
 </div>
 <div class="sect2">
-<h3 id="文件系统级别备份"><a class="anchor" href="#文件系统级别备份"></a>7.2. 文件系统级别备份</h3>
+<h3 id="文件系统级别备份"><a class="anchor" href="#文件系统级别备份"></a>8.2. 文件系统级别备份</h3>
 <div class="paragraph">
 <p>另外一种备份策略是直接复制 IvorySQL用于存储数据库中数据的文件，你可以采用任何你喜欢的方式进行文件系统备份，例如：</p>
 </div>
@@ -1112,7 +1316,7 @@ <h3 id="文件系统级别备份"><a class="anchor" href="#文件系统级别备
 </div>
 </div>
 <div class="sect2">
-<h3 id="连续归档和时间点恢复pitr"><a class="anchor" href="#连续归档和时间点恢复pitr"></a>7.3. 连续归档和时间点恢复（PITR）</h3>
+<h3 id="连续归档和时间点恢复pitr"><a class="anchor" href="#连续归档和时间点恢复pitr"></a>8.3. 连续归档和时间点恢复（PITR）</h3>
 <div class="paragraph">
 <p>在任何时间，IvorySQL在数据集簇目录的pg_wal/子目录下都保持有一个预写式日志（WAL）。这个日志存在的目的是为了保证崩溃后的安全：如果系统崩溃，可以“重放”从最后一次检查点以来的日志项来恢复数据库的一致性。该日志的存在也使得第三种备份数据库的策略变得可能：我们可以把一个文件系统级别的备份和WAL文件的备份结合起来。当需要恢复时，我们先恢复文件系统备份，然后从备份的WAL文件中重放来把系统带到一个当前状态。这种方法比之前的方法管理起来要更复杂，但是有其显著的优点：</p>
 </div>
@@ -1145,7 +1349,7 @@ <h3 id="连续归档和时间点恢复pitr"><a class="anchor" href="#连续归
 </div>
 </div>
 <div class="sect1">
-<h2 id="装卸数据"><a class="anchor" href="#装卸数据"></a>8. 装卸数据</h2>
+<h2 id="装卸数据"><a class="anchor" href="#装卸数据"></a>9. 装卸数据</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>copy 在 IvorySQL表和标准文件之间移动数据。COPY TO 把一个表的内容复制到一个文件，而COPY FROM 则从一个文件复制数据到一个表（把数据追加到表中原有数据）。COPY TO 也能复制一个SELECT查询的结果。</p>
@@ -1160,7 +1364,7 @@ <h2 id="装卸数据"><a class="anchor" href="#装卸数据"></a>8. 装卸数据
 <p>​    运行COPY的每个后端将在pg_stat_progress_copy视图中报告其进度。</p>
 </div>
 <div class="sect2">
-<h3 id="大纲"><a class="anchor" href="#大纲"></a>8.1. 大纲</h3>
+<h3 id="大纲"><a class="anchor" href="#大纲"></a>9.1. 大纲</h3>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">COPY table_name [ ( column_name [, ...] ) ]
@@ -1192,7 +1396,7 @@ <h3 id="大纲"><a class="anchor" href="#大纲"></a>8.1. 大纲</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="输出"><a class="anchor" href="#输出"></a>8.2. 输出</h3>
+<h3 id="输出"><a class="anchor" href="#输出"></a>9.2. 输出</h3>
 <div class="paragraph">
 <p>在成功完成时，一个COPY命令会返回一个形为</p>
 </div>
@@ -1207,7 +1411,7 @@ <h3 id="输出"><a class="anchor" href="#输出"></a>8.2. 输出</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="注解"><a class="anchor" href="#注解"></a>8.3. 注解</h3>
+<h3 id="注解"><a class="anchor" href="#注解"></a>9.3. 注解</h3>
 <div class="paragraph">
 <p>COPY TO只能用于普通表，而不能用于视图，并且不能从子表或子分区复制行。 例如，COPY table TO 复制与SELECT * FROM ONLY table 相同的行。 语法COPY (SELECT * FROM table) TO &#8230;&#8203; 可用于转储一个继承层次结构、分区表或视图中的所有行。</p>
 </div>
@@ -1252,9 +1456,9 @@ <h3 id="注解"><a class="anchor" href="#注解"></a>8.3. 注解</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="文件格式"><a class="anchor" href="#文件格式"></a>8.4. 文件格式</h3>
+<h3 id="文件格式"><a class="anchor" href="#文件格式"></a>9.4. 文件格式</h3>
 <div class="sect3">
-<h4 id="文本格式"><a class="anchor" href="#文本格式"></a>8.4.1. 文本格式</h4>
+<h4 id="文本格式"><a class="anchor" href="#文本格式"></a>9.4.1. 文本格式</h4>
 <div class="paragraph">
 <p>在使用text格式时，读取或写入的是一个文本文件，其中每一行就是表中的一行。一行中的列被定界字符分隔。列值本身是由输出函数产生的或者是可被输入函数接受的属于每个属性数据类型的字符串。在为空值的列的位置使用指定的空值串。如果输入文件的任何行包含比预期更多或者更少的列，COPY FROM将会抛出一个错误。</p>
 </div>
@@ -1331,7 +1535,7 @@ <h4 id="文本格式"><a class="anchor" href="#文本格式"></a>8.4.1. 文本
 </div>
 </div>
 <div class="sect3">
-<h4 id="csv格式"><a class="anchor" href="#csv格式"></a>8.4.2. CSV格式</h4>
+<h4 id="csv格式"><a class="anchor" href="#csv格式"></a>9.4.2. CSV格式</h4>
 <div class="paragraph">
 <p>这种格式选项被用于导入和导出很多其他程序（例如电子表格）使用的逗号 分隔值（CSV）文件格式。不同于 IvorySQL标准文本格式使用的转义 规则，它产生并且识别一般的 CSV 转义机制。</p>
 </div>
@@ -1370,7 +1574,7 @@ <h4 id="csv格式"><a class="anchor" href="#csv格式"></a>8.4.2. CSV格式</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="二进制格式"><a class="anchor" href="#二进制格式"></a>8.4.3. 二进制格式</h4>
+<h4 id="二进制格式"><a class="anchor" href="#二进制格式"></a>9.4.3. 二进制格式</h4>
 <div class="paragraph">
 <p>binary格式选项导致所有数据被以二进制格式 而不是文本格式存储/读取。它比文本和CSV格式要 快一些，但是二进制格式文件在不同的机器架构和 IvorySQL 版本之间的可移 植性要差些。还有，二进制格式与数据格式非常相关。例如不能从 一个smallint列中输出二进制数据并且把它读入到一个 integer列中，虽然这样做在文本格式中是可行的。</p>
 </div>
@@ -1448,7 +1652,7 @@ <h4 id="二进制格式"><a class="anchor" href="#二进制格式"></a>8.4.3. 
 </div>
 </div>
 <div class="sect2">
-<h3 id="示例"><a class="anchor" href="#示例"></a>8.5. 示例</h3>
+<h3 id="示例"><a class="anchor" href="#示例"></a>9.5. 示例</h3>
 <div class="paragraph">
 <p>下面的例子使用竖线（|）作为域定界符把一个表复制到客户端：</p>
 </div>
@@ -1523,13 +1727,13 @@ <h3 id="示例"><a class="anchor" href="#示例"></a>8.5. 示例</h3>
 </div>
 </div>
 <div class="sect1">
-<h2 id="性能管理"><a class="anchor" href="#性能管理"></a>9. 性能管理</h2>
+<h2 id="性能管理"><a class="anchor" href="#性能管理"></a>10. 性能管理</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>查询性能可能受多种因素影响。其中一些因素可以由用户控制，而其他的则属于系统下层设计的基本原理。</p>
 </div>
 <div class="sect2">
-<h3 id="使用explain"><a class="anchor" href="#使用explain"></a>9.1. 使用EXPLAIN</h3>
+<h3 id="使用explain"><a class="anchor" href="#使用explain"></a>10.1. 使用EXPLAIN</h3>
 <div class="paragraph">
 <p>IvorySQL为每个收到查询产生一个查询计划。 选择正确的计划来匹配查询结构和数据的属性对于好的性能来说绝对是最关键的，因此系统包含了一个复杂的规划器来尝试选择好的计划。 你可以使用EXPLAIN命令察看规划器为任何查询生成的查询计划。 阅读查询计划是一门艺术，它要求一些经验来掌握，但是本节只试图覆盖一些基础。</p>
 </div>
@@ -1537,7 +1741,7 @@ <h3 id="使用explain"><a class="anchor" href="#使用explain"></a>9.1. 使用EX
 <p>​    这些例子使用EXPLAIN的默认“text”输出格式，这种格式紧凑并且便于阅读。如果你想把EXPLAIN的输出交给一个程序做进一步分析，你应该使用它的某种机器可读的输出格式（XML、JSON 或 YAML）。</p>
 </div>
 <div class="sect3">
-<h4 id="explain基础"><a class="anchor" href="#explain基础"></a>9.1.1. EXPLAIN基础</h4>
+<h4 id="explain基础"><a class="anchor" href="#explain基础"></a>10.1.1. EXPLAIN基础</h4>
 <div class="paragraph">
 <p>查询计划的结构是一个计划结点的树。最底层的结点是扫描结点：它们从表中返回未经处理的行。 不同的表访问模式有不同的扫描结点类型：顺序扫描、索引扫描、位图索引扫描。 也还有不是表的行来源，例如VALUES子句和FROM中返回集合的函数，它们有自己的结点类型。如果查询需要连接、聚集、排序、或者在未经处理的行上的其它操作，那么就会在扫描结点之上有其它额外的结点来执行这些操作。 并且，做这些操作通常都有多种方法，因此在这些位置也有可能出现不同的结点类型。 EXPLAIN给计划树中每个结点都输出一行，显示基本的结点类型和计划器为该计划结点的执行所做的开销估计。 第一行（最上层的结点）是对该计划的总执行开销的估计；计划器试图最小化的就是这个数字。</p>
 </div>
@@ -1872,7 +2076,7 @@ <h4 id="explain基础"><a class="anchor" href="#explain基础"></a>9.1.1. EXPLAI
 </div>
 </div>
 <div class="sect3">
-<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EXPLAIN ANALYZE</h4>
+<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>10.1.2. EXPLAIN ANALYZE</h4>
 <div class="paragraph">
 <p>可以通过使用EXPLAIN的ANALYZE选项来检查规划器估计值的准确性。通过使用这个选项，EXPLAIN会实际执行该查询，然后显示真实的行计数和在每个计划结点中累计的真实运行时间，还会有一个普通EXPLAIN显示的估计值。例如，我们可能得到这样一个结果：</p>
 </div>
@@ -2077,7 +2281,7 @@ <h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EX
 </div>
 </div>
 <div class="sect3">
-<h4 id="警告"><a class="anchor" href="#警告"></a>9.1.3. 警告</h4>
+<h4 id="警告"><a class="anchor" href="#警告"></a>10.1.3. 警告</h4>
 <div class="paragraph">
 <p>在两种有效的方法中EXPLAIN ANALYZE所度量的运行时间可能偏离同一个查询的正常执行。首先，由于不会有输出行被递交给客户端，网络传输开销和 I/O 转换开销没有被包括在内。其次，由EXPLAIN ANALYZE所增加的度量开销可能会很可观，特别是在操作系统调用gettimeofday()很慢的机器上。你可以使用pg_test_timing工具来度量在你的系统上的计时开销。</p>
 </div>
@@ -2117,9 +2321,9 @@ <h4 id="警告"><a class="anchor" href="#警告"></a>9.1.3. 警告</h4>
 </div>
 </div>
 <div class="sect2">
-<h3 id="规划器使用的统计信息"><a class="anchor" href="#规划器使用的统计信息"></a>9.2. 规划器使用的统计信息</h3>
+<h3 id="规划器使用的统计信息"><a class="anchor" href="#规划器使用的统计信息"></a>10.2. 规划器使用的统计信息</h3>
 <div class="sect3">
-<h4 id="单列统计信息"><a class="anchor" href="#单列统计信息"></a>9.2.1. 单列统计信息</h4>
+<h4 id="单列统计信息"><a class="anchor" href="#单列统计信息"></a>10.2.1. 单列统计信息</h4>
 <div class="paragraph">
 <p>如我们在上一节所见，查询规划器需要估计一个查询要检索的行数，这样才能对查询计划做出好的选择。 本节对系统用于这些估计的统计信息进行一个快速的介绍。</p>
 </div>
@@ -2187,7 +2391,7 @@ <h4 id="单列统计信息"><a class="anchor" href="#单列统计信息"></a>9.2
 </div>
 </div>
 <div class="sect3">
-<h4 id="扩展统计信息"><a class="anchor" href="#扩展统计信息"></a>9.2.2. 扩展统计信息</h4>
+<h4 id="扩展统计信息"><a class="anchor" href="#扩展统计信息"></a>10.2.2. 扩展统计信息</h4>
 <div class="paragraph">
 <p>常常可以看到由于查询子句中用到的多个列相互关联而运行着糟糕的执行计划的慢查询。规划器通常会假设多个条件是彼此独立的，这种假设在列值相互关联的情况下是不成立的。由于常规的统计信息天然的针对个体列的性质，它们无法捕捉到跨列关联的知识。不过，IvorySQL有能力计算多元统计信息，它能捕捉这类信息。</p>
 </div>
@@ -2204,7 +2408,7 @@ <h4 id="扩展统计信息"><a class="anchor" href="#扩展统计信息"></a>9.2
 <p>​    下面的小节介绍当前支持的扩展统计信息类型。</p>
 </div>
 <div class="sect4">
-<h5 id="函数依赖"><a class="anchor" href="#函数依赖"></a>9.2.2.1. 函数依赖</h5>
+<h5 id="函数依赖"><a class="anchor" href="#函数依赖"></a>10.2.2.1. 函数依赖</h5>
 <div class="paragraph">
 <p>最简单的一类扩展统计信息跟踪函数依赖，这是在数据库范式定义中使用的概念。如果列a的值的知识足以决定列b的值，即不会有两个行具有相同的a值但是有不同的b值，我们就说列b函数依赖于列a。在一个完全规范化的数据库中，函数依赖应该仅存在于主键和超键上。不过，实际上很多数据集合会由于各种原因无法被完全规范化，常见的例子是为了性能而有意地反规范化。即使在一个完全规范化的数据库中，也会有某些列之间的部分关联，这些可以表达成部分函数依赖。</p>
 </div>
@@ -2239,7 +2443,7 @@ <h5 id="函数依赖"><a class="anchor" href="#函数依赖"></a>9.2.2.1. 函数
 <p>在为涉及函数依赖列的查询计算选择度时，规划器会使用依赖系数来调整针对条件的选择度估计，这样就不会产生低估。</p>
 </div>
 <div class="sect5">
-<h6 id="函数依赖的限制"><a class="anchor" href="#函数依赖的限制"></a>9.2.2.1.1. 函数依赖的限制</h6>
+<h6 id="函数依赖的限制"><a class="anchor" href="#函数依赖的限制"></a>10.2.2.1.1. 函数依赖的限制</h6>
 <div class="paragraph">
 <p>当前只有在考虑简单等值条件（将列与常量值比较）和具有常量值的IN 子句时，函数依赖才适用。不会使用它们来改进比较两个列或者比较列和表达式的等值条件的估计， 也不会用它们来改进范围子句、LIKE或者任何其他类型的条件。</p>
 </div>
@@ -2268,7 +2472,7 @@ <h6 id="函数依赖的限制"><a class="anchor" href="#函数依赖的限制"><
 </div>
 </div>
 <div class="sect4">
-<h5 id="多元可区分值计数"><a class="anchor" href="#多元可区分值计数"></a>9.2.2.2. 多元可区分值计数</h5>
+<h5 id="多元可区分值计数"><a class="anchor" href="#多元可区分值计数"></a>10.2.2.2. 多元可区分值计数</h5>
 <div class="paragraph">
 <p>单列统计信息存储每一列中可区分值的数量。在组合多个列（例如GROUP BY a, b）时，如果规划器只有单列统计数据，则对可区分值数量的估计常常会错误，导致选择不好的计划。</p>
 </div>
@@ -2301,7 +2505,7 @@ <h5 id="多元可区分值计数"><a class="anchor" href="#多元可区分值计
 </div>
 </div>
 <div class="sect4">
-<h5 id="多元mcv列表"><a class="anchor" href="#多元mcv列表"></a>9.2.2.3. 多元MCV列表</h5>
+<h5 id="多元mcv列表"><a class="anchor" href="#多元mcv列表"></a>10.2.2.3. 多元MCV列表</h5>
 <div class="paragraph">
 <p>为每列存储的另一种统计信息是频繁值列表。 这样可以对单个列进行非常准确的估计，但是对于在多个列上具有条件的查询，可能会导致严重的错误估计。</p>
 </div>
@@ -2346,7 +2550,7 @@ <h5 id="多元mcv列表"><a class="anchor" href="#多元mcv列表"></a>9.2.2.3.
 </div>
 </div>
 <div class="sect2">
-<h3 id="用显示join子句控制规划器"><a class="anchor" href="#用显示join子句控制规划器"></a>9.3. 用显示JOIN子句控制规划器</h3>
+<h3 id="用显示join子句控制规划器"><a class="anchor" href="#用显示join子句控制规划器"></a>10.3. 用显示JOIN子句控制规划器</h3>
 <div class="paragraph">
 <p>我们可以在一定程度上用显式JOIN语法控制查询规划器。要明白为什么需要它，我们首先需要一些背景知识。</p>
 </div>
@@ -2443,18 +2647,18 @@ <h3 id="用显示join子句控制规划器"><a class="anchor" href="#用显示jo
 </div>
 </div>
 <div class="sect2">
-<h3 id="填充一个数据库"><a class="anchor" href="#填充一个数据库"></a>9.4. 填充一个数据库</h3>
+<h3 id="填充一个数据库"><a class="anchor" href="#填充一个数据库"></a>10.4. 填充一个数据库</h3>
 <div class="paragraph">
 <p>第一次填充数据库时可能需要插入大量的数据。本节包含一些如何让这个处理尽可能高效的建议。</p>
 </div>
 <div class="sect3">
-<h4 id="禁用自动提交"><a class="anchor" href="#禁用自动提交"></a>9.4.1. 禁用自动提交</h4>
+<h4 id="禁用自动提交"><a class="anchor" href="#禁用自动提交"></a>10.4.1. 禁用自动提交</h4>
 <div class="paragraph">
 <p>在使用多个INSERT时，关闭自动提交并且只在最后做一次提交（在普通 SQL 中，这意味着在开始发出BEGIN并且在结束时发出COMMIT。某些客户端库可能背着你就做了这些，在这种情况下你需要确定在你需要做这些时该库确实帮你做了）。如果你允许每一个插入都被独立地提交，IvorySQL要为每一个被增加的行做很多工作。在一个事务中做所有插入的一个额外好处是：如果一个行的插入失败则所有之前插入的行都会被回滚，这样你不会被卡在部分载入的数据中。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="使用copy"><a class="anchor" href="#使用copy"></a>9.4.2. 使用COPY</h4>
+<h4 id="使用copy"><a class="anchor" href="#使用copy"></a>10.4.2. 使用COPY</h4>
 <div class="paragraph">
 <p>使用COPY在一条命令中装载所有记录，而不是一系列INSERT命令。 COPY命令是为装载大量行而优化过的； 它没INSERT那么灵活，但是在大量数据装载时导致的负荷也更少。 因为COPY是单条命令，因此使用这种方法填充表时无须关闭自动提交。</p>
 </div>
@@ -2469,7 +2673,7 @@ <h4 id="使用copy"><a class="anchor" href="#使用copy"></a>9.4.2. 使用COPY</
 </div>
 </div>
 <div class="sect3">
-<h4 id="移除索引"><a class="anchor" href="#移除索引"></a>9.4.3. 移除索引</h4>
+<h4 id="移除索引"><a class="anchor" href="#移除索引"></a>10.4.3. 移除索引</h4>
 <div class="paragraph">
 <p>如果你正在载入一个新创建的表，最快的方法是创建该表，用COPY批量载入该表的数据，然后创建表需要的任何索引。在已存在数据的表上创建索引要比在每一行被载入时增量地更新它更快。</p>
 </div>
@@ -2478,7 +2682,7 @@ <h4 id="移除索引"><a class="anchor" href="#移除索引"></a>9.4.3. 移除
 </div>
 </div>
 <div class="sect3">
-<h4 id="移除外键约束"><a class="anchor" href="#移除外键约束"></a>9.4.4. 移除外键约束</h4>
+<h4 id="移除外键约束"><a class="anchor" href="#移除外键约束"></a>10.4.4. 移除外键约束</h4>
 <div class="paragraph">
 <p>和索引一样，“成批地”检查外键约束比一行行检查效率更高。 因此，先删除外键约束、载入数据然后重建约束会很有用。 同样，载入数据和约束缺失期间错误检查的丢失之间也存在平衡。</p>
 </div>
@@ -2487,19 +2691,19 @@ <h4 id="移除外键约束"><a class="anchor" href="#移除外键约束"></a>9.4
 </div>
 </div>
 <div class="sect3">
-<h4 id="增加-maintenance_work_mem"><a class="anchor" href="#增加-maintenance_work_mem"></a>9.4.5. 增加 maintenance_work_mem</h4>
+<h4 id="增加-maintenance_work_mem"><a class="anchor" href="#增加-maintenance_work_mem"></a>10.4.5. 增加 maintenance_work_mem</h4>
 <div class="paragraph">
 <p>在载入大量数据时，临时增大maintenance_work_mem配置变量可以改进性能。这个参数也可以帮助加速CREATE INDEX命令和ALTER TABLE ADD FOREIGN KEY命令。 它不会对COPY本身起很大作用，所以这个建议只有在你使用上面的一个或两个技巧时才有用。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="增加max_wal_size"><a class="anchor" href="#增加max_wal_size"></a>9.4.6. 增加max_wal_size</h4>
+<h4 id="增加max_wal_size"><a class="anchor" href="#增加max_wal_size"></a>10.4.6. 增加max_wal_size</h4>
 <div class="paragraph">
 <p>临时增大max_wal_size配置变量也可以让大量数据载入更快。 这是因为向IvorySQL中载入大量的数据将导致检查点的发生比平常（由checkpoint_timeout配置变量指定）更频繁。无论何时发生一个检查点时，所有脏页都必须被刷写到磁盘上。 通过在批量数据载入时临时增加max_wal_size，所需的检查点数目可以被缩减。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="禁用wal归档和流复制"><a class="anchor" href="#禁用wal归档和流复制"></a>9.4.7. 禁用WAL归档和流复制</h4>
+<h4 id="禁用wal归档和流复制"><a class="anchor" href="#禁用wal归档和流复制"></a>10.4.7. 禁用WAL归档和流复制</h4>
 <div class="paragraph">
 <p>当使用 WAL 归档或流复制向一个安装中载入大量数据时，在录入结束后执行一次新的基础备份比处理大量的增量 WAL 数据更快。为了防止载入时记录增量 WAL，通过将wal_level设置为minimal、将archive_mode设置为off以及将max_wal_senders设置为零来禁用归档和流复制。 但需要注意的是，修改这些设置需要重启服务。</p>
 </div>
@@ -2508,13 +2712,13 @@ <h4 id="禁用wal归档和流复制"><a class="anchor" href="#禁用wal归档和
 </div>
 </div>
 <div class="sect3">
-<h4 id="事后运行analyze"><a class="anchor" href="#事后运行analyze"></a>9.4.8. 事后运行ANALYZE</h4>
+<h4 id="事后运行analyze"><a class="anchor" href="#事后运行analyze"></a>10.4.8. 事后运行ANALYZE</h4>
 <div class="paragraph">
 <p>不管什么时候你显著地改变了表中的数据分布后，我们都强烈推荐运行ANALYZE。这包括向表中批量载入大量数据。运行ANALYZE（或者VACUUM ANALYZE）保证规划器有表的最新统计信息。 如果没有统计数据或者统计数据过时，那么规划器在查询规划时可能做出很差劲决定，导致在任意表上的性能低下。需要注意的是，如果启用了 autovacuum 守护进程，它可能会自动运行ANALYZE。</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="关于pg_dump的一些注记"><a class="anchor" href="#关于pg_dump的一些注记"></a>9.4.9. 关于pg_dump的一些注记</h4>
+<h4 id="关于pg_dump的一些注记"><a class="anchor" href="#关于pg_dump的一些注记"></a>10.4.9. 关于pg_dump的一些注记</h4>
 <div class="paragraph">
 <p>pg_dump生成的转储脚本自动应用上面的若干个（但不是全部）技巧。 要尽可能快地载入pg_dump转储，你需要手工做一些额外的事情（请注意，这些要点适用于恢复一个转储，而不是创建它的时候。同样的要点也适用于使用psql载入一个文本转储或用pg_restore从一个pg_dump归档文件载入）。</p>
 </div>
@@ -2551,7 +2755,7 @@ <h4 id="关于pg_dump的一些注记"><a class="anchor" href="#关于pg_dump的
 </div>
 </div>
 <div class="sect2">
-<h3 id="非持久设置"><a class="anchor" href="#非持久设置"></a>9.5. 非持久设置</h3>
+<h3 id="非持久设置"><a class="anchor" href="#非持久设置"></a>10.5. 非持久设置</h3>
 <div class="paragraph">
 <p>持久性是数据库的一个保证已提交事务的记录的特性（即使是发生服务器崩溃或断电）。 然而，持久性会明显增加数据库的负荷，因此如果你的站点不需要这个保证，IvorySQL可以被配置成运行更快。在这种情况下，你可以调整下列配置来提高性能。除了下面列出的，在数据库软件崩溃的情况下也能保证持久性。当这些设置被使用时，只有突然的操作系统停止会产生数据丢失或损坏的风险。</p>
 </div>
diff --git a/docs/cn/ivorysql-doc/v5.3/7.22.html b/docs/cn/ivorysql-doc/v5.3/7.22.html
index 0307d3ca..7b5bac4c 100644
--- a/docs/cn/ivorysql-doc/v5.3/7.22.html
+++ b/docs/cn/ivorysql-doc/v5.3/7.22.html
@@ -588,7 +588,15 @@ <h3 id="无参和默认参数调用"><a class="anchor" href="#无参和默认参
 <h3 id="在参数或into子句中引用绑定变量"><a class="anchor" href="#在参数或into子句中引用绑定变量"></a>3.4. 在参数或INTO子句中引用绑定变量</h3>
 <div class="listingblock">
 <div class="content">
-<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- 设置输入绑定变量
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- 创建独立函数 stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- 设置输入绑定变量
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- 使用绑定变量作为参数调用函数
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/cn/ivorysql-doc/v5.3/_exports/index.pdf b/docs/cn/ivorysql-doc/v5.3/_exports/index.pdf
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diff --git a/docs/cn/ivorysql-doc/v5.3/ivorysql.pdf b/docs/cn/ivorysql-doc/v5.3/ivorysql.pdf
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diff --git a/docs/cn/search-index.js b/docs/cn/search-index.js
index 55a3b71f..e6b22c97 100644
--- a/docs/cn/search-index.js
+++ b/docs/cn/search-index.js
@@ -1 +1 @@
-antoraSearch.initSearch(lunr, 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IvorySQL简介","hash":"ivorysql简介","id":1},{"text":"1.1. 概述","hash":"概述","id":2},{"text":"1.2. 产品目标和范围","hash":"产品目标和范围","id":3},{"text":"1.3. 核心特性","hash":"核心特性","id":4},{"text":"1.4. 竞争优势","hash":"竞争优势","id":5},{"text":"1.5. 技术生态","hash":"技术生态","id":6},{"text":"1.6. 核心应用场景","hash":"核心应用场景","id":7},{"text":"2. 主要、基本功能","hash":"主要基本功能","id":8},{"text":"3. 与Oracle的兼容性","hash":"与oracle的兼容性","id":9}]},"2":{"id":2,"text":"yum源快速安装数据库，请参考从yum源安装ivorysql数据库。 想要获取更多安装方式，请参考安装指南。 主节点数据库需要安装并启动 集群中各节点均需关闭防火墙才能正常通信。 $ sudo systemctl stop firewalld 为了从主节点向备节点搭建流复制，主节点需要对data目录下的postgresql.conf和pg_hba.conf两个文件进行配置。 postgresql.conf 将以下配置追加到postgresql.conf文件末尾： listen_addresses = '*' max_connections = 100 wal_level = replica max_wal_senders = 5 hot_standby = on pg_hba.conf 将以下配置追加到pg_hba.conf文件末尾： host all all 0.0.0.0/0 trust host replication all 0.0.0.0/0 trust 示例中的pg_hba的配置，仅作为demo用来测试，这种配置会导致数据库密码失效，请根据环境实际情况进行配置 $ pg_ctl restart Yum源快速安装数据库，请参考从yum源安装ivorysql数据库。 想要获取更多安装方式，请参考安装指南。 备节点数据库只需要安装，不需要启动 集群中各节点均需关闭防火墙才能正常通信。 $ sudo systemctl stop firewalld 在备节点上执行以下命令，创建一个主节点的基础备份，即搭建流复制： $ sudo pg_basebackup -F p -P -X fetch -R -h <primary_ip> -p <primary_port> -U ivorysql -D /usr/local/ivorysql/ivorysql-5/data -h为主节点ip； -p为主节点数据库端口号，默认为5432； -U为数据库用户； -D为要创建的备节点数据库数据目录。 其他参数使用方法，请使用pg_basebackup --help命令获取。 将以下配置写入~/.bash_profile文件： PATH=/usr/local/ivorysql/ivorysql-5/bin:$PATH export PATH PGDATA=/usr/local/ivorysql/ivorysql-5/data export PGDATA source该文件使环境变量生效： $ source ~/.bash_profile $ pg_ctl -D /usr/local/ivorysql/ivorysql-5/data start 在主节点上执行以下命令可以看到walsender： $ ps -ef |grep postgres ... ivorysql 11176 8067 0 21:54 ? 00:00:00 postgres: walsender ivorysql 192.168.31.102(53416) streaming 0/7000060... 而备节点则可看到walreceiver： $ ps -ef | grep postgres ... ivorysql 6567 6139 0 21:54 ? 00:00:00 postgres: walreceiver streaming 0/7000060 ... 在主节点上psql连接数据库，并查看集群状态： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# select * from pg_stat_replication; pid | usesysid | usename | application_name | client_addr | client_hostname | client_port | backend_start | backend_ xmin | state | sent_lsn | write_lsn | flush_lsn | replay_lsn | write_lag | flush_lag | replay_lag | sync_priority | sync_state | reply_time -------+----------+----------+------------------+----------------+-----------------+-------------+-------------------------------+--------- -----+-----------+-----------+-----------+-----------+------------+-----------+-----------+------------+---------------+------------+------ ------------------------- 11176 | 10 | ivorysql | walreceiver | 192.168.31.102 | | 53416 | 2024-12-18 21:54:52.041847-05 | | streaming | 0/7000148 | 0/7000148 | 0/7000148 | 0/7000148 | | | | 0 | async | 2024- 12-18 22:52:07.325111-05 (1 row) 这里192.168.31.102为备节点的ip，async表示数据同步方式为异步流复制。 === 使用集群 集群中所有的写操作均在主节点执行，读操作则主备节点都可以执行。主节点的数据通过流复制同步到备节点。主节点写操作的结果在任何一个备节点都能够查询到。 例如，在主节点创建一个新的数据库test，并在主节点进行查询： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# create database test; CREATE DATABASE ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows) 在备节点查询： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows)","title":"集群搭建","component":"ivorysql-doc","version":"master","name":"cluster_setup","url":"/ivorysql-doc/master/cluster_setup.html","titles":[{"text":"1. 主节点","hash":"主节点","id":1},{"text":"1.1. 安装并启动数据库","hash":"安装并启动数据库","id":2},{"text":"1.2. 关闭防火墙","hash":"关闭防火墙","id":3},{"text":"1.3. 配置文件","hash":"配置文件","id":4},{"text":"1.4. 重启主节点数据库服务","hash":"重启主节点数据库服务","id":5},{"text":"2. 备节点","hash":"备节点","id":6},{"text":"2.1. 安装数据库","hash":"安装数据库","id":7},{"text":"2.2. 关闭防火墙","hash":"关闭防火墙-2","id":8},{"text":"2.3. 搭建流复制","hash":"搭建流复制","id":9},{"text":"2.4. 配置环境变量","hash":"配置环境变量","id":10},{"text":"2.5. 启动备节点数据库服务","hash":"启动备节点数据库服务","id":11},{"text":"3. 集群的使用","hash":"集群的使用","id":12},{"text":"3.1. 查看集群状态","hash":"查看集群状态","id":13}]},"3":{"id":3,"text":"IvorySQL在开源PostgreSQL数据库的基础上提供独特的附加功能。 IvorySQL致力于通过创新和建立在开源数据库解决方案之上为其终端用户提供价值。我们的目标是为中小型企业提供一个具有高性能、可扩展性、可靠性和易于使用的解决方案。 IvorySQL提供的扩展功能将使用户能够建立高性能和可扩展的PostgreSQL数据库集群，具有更好的数据库兼容性和管理。这简化了从其他DBMS迁移到PostgreSQL的过程，增强了数据库管理经验。 在对原有的PostgreSQL改动最小的前提下，实现对Oracle的兼容。我们需要实现双parser、双端口、模式PL/pgSQL实现PL/iSQL的框架。实现流程图如下： 下图描述了对PostgreSQL现有系统表的变化以及所做的补充。 看看你能否访问数据库服务器的第一个例子就是试着创建一个数据库。一台运行着的IvorySQL服务器可以管理许多数据库。通常我们会为每个项目和每个用户单独使用一个数据库。 你的站点管理员可能已经为你创建了可以使用的数据库。如果这样你就可以省略这一步，并且跳到下一节。 要创建一个新的数据库，在我们这个例子里叫`mydb`，你可以使用下面的命令： $ createdb mydb 如果不产生任何响应则表示该步骤成功，你可以跳过本节的剩余部分。 如果你看到类似下面这样的信息： createdb: command not found 那么就是IvorySQL没有安装好。或者是根本没安装，或者是你的shell搜索路径没有设置正确。尝试用绝对路径调用该命令试试： $ /usr/local/pgsql/bin/createdb mydb 在你的站点上这个路径可能不一样。和你的站点管理员联系或者看看安装指导获取正确的位置。 另外一种响应可能是这样： createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory Is the server running locally and accepting connections on that socket? 这意味着该服务器没有启动，或者在`createdb`期望去连接它的时候没有在监听。同样，你也要查看安装指导或者咨询管理员。 另外一个响应可能是这样： createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: FATAL: role \"joe\" does not exist 在这里提到了你自己的登录名。如果管理员没有为你创建IvorySQL用户帐号，就会发生这些现象。（IvorySQL用户帐号和操作系统用户帐号是不同的。） 如果你是管理员，参阅 第 22 章 获取创建用户帐号的帮助。你需要变成安装IvorySQL的操作系统用户的身份（通常是 postgres）才能创建第一个用户帐号。也有可能是赋予你的IvorySQL用户名和你的操作系统用户名不同；这种情况下，你需要使用`-U`选项或者使用`PGUSER`环境变量指定你的IvorySQL用户名。 如果你有个数据库用户帐号，但是没有创建数据库所需要的权限，那么你会看到下面的信息： createdb: error: database creation failed: ERROR: permission denied to create database 并非所有用户都被许可创建新数据库。如果IvorySQL拒绝为你创建数据库，那么你需要让站点管理员赋予你创建数据库的权限。出现这种情况时请咨询你的站点管理员。如果你自己安装了IvorySQL，那么你应该以你启动数据库服务器的用户身份登录然后参考手册完成权限的赋予工作。http://www.postgresql.org/docs/17/tutorial-createdb.html#ftn.id-1.4.3.4.10.4[[1\\]] 你还可以用其它名字创建数据库。IvorySQL允许你在一个站点上创建任意数量的数据库。数据库名必须是以字母开头并且小于63个字符长。一个方便的做法是创建和你当前用户名同名的数据库。许多工具假设该数据库名为缺省数据库名，所以这样可以节省你的敲键。要创建这样的数据库，只需要键入： $ createdb 如果你再也不想使用你的数据库了，那么你可以删除它。比如，如果你是数据库`mydb`的所有人（创建人），那么你就可以用下面的命令删除它： $ dropdb mydb （对于这条命令而言，数据库名不是缺省的用户名，因此你就必须声明它）。这个动作将在物理上把所有与该数据库相关的文件都删除并且不可取消，因此做这中操作之前一定要考虑清楚。 更多关于 createdb 和 dropdb 的信息可以分别在 createdb和 dropdb中找到。 你可以通过指定表的名字和所有列的名字及其类型来创建表∶ CREATE TABLE weather ( city varchar(80), temp_lo int, -- 最低温度 temp_hi int, -- 最高温度 prcp real, -- 湿度 date date ); 你可以在 psql 输入这些命令以及换行符。psql 可以识别该命令直到分号才结束。 你可以在SQL命令中自由使用空白（即空格、制表符和换行符）。这就意味着你可以用和上面不同的对齐方式键入命令，或者将命令全部放在一行中。两个划线（“--”）引入注释。任何跟在它后面直到行尾的东西都会被忽略。SQL是对关键字和标识符大小写不敏感的语言，只有在标识符用双引号包围时才能保留它们的大小写（上例没有这么做）。 varchar(80) 指定了一个可以存储最长80个字符的任意字符串的数据类型。int 是普通的整数类型。real 是一种用于存储单精度浮点数的类型。date 类型应该可以自解释（没错，类型为`date`的列名字也是`date`。这么做可能比较方便或者容易让人混淆 — 你自己选择）。 IvorySQL支持标准的SQL类型`int`、smallint、real、double precision、char(N)、varchar(N)、date、time、timestamp`和`interval，还支持其他的通用功能的类型和丰富的几何类型。IvorySQL中可以定制任意数量的用户定义数据类型。因而类型名并不是语法关键字，除了SQL标准要求支持的特例外。 第二个例子将保存城市和它们相关的地理位置： CREATE TABLE cities ( name varchar(80), location point ); 类型`point`就是一种IvorySQL特有数据类型的例子。 最后，我们还要提到如果你不再需要某个表，或者你想以不同的形式重建它，那么你可以用下面的命令删除它： DROP TABLE tablename; 当一个表被创建后，它不包含任何数据。在数据库发挥作用之前，首先要做的是插入数据。一次插入一行数据。你也可以在一个命令中插入多行，但不能插入不完整的行。即使只知道其中一些列的值，也必须创建完整的行。 要创建一个新行，使用 INSERT命令。这条命令要求提供表的名字和其中列的值。例如，考虑 第 5 章中的产品表： CREATE TABLE products ( product_no integer, name text, price numeric ); CREATE TABLE new_products ( product_no int , name varchar(255), price DECIMAL(10, 2), release_date DATE ); 一个插入一行的命令将是： INSERT INTO products VALUES (1, 'Cheese', 9.99); 数据的值是按照这些列在表中出现的顺序列出的，并且用逗号分隔。通常，数据的值是文字（常量），但也允许使用标量表达式。 上面的语法的缺点是你必须知道表中列的顺序。要避免这个问题，你也可以显式地列出列。例如，下面的两条命令都有和上文那条 命令一样的效果： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99); INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1); 许多用户认为明确列出列的名字是个好习惯。 如果你没有获得所有列的值，那么你可以省略其中的一些。在这种情况下，这些列将被填充为它们的缺省值。例如： INSERT INTO products (product_no, name) VALUES (1, 'Cheese'); INSERT INTO products VALUES (1, 'Cheese'); 第二种形式是IvorySQL的一个扩展。它从使用给出的值从左开始填充列，有多少个给出的列值就填充多少个列，其他列的将使用缺省值。 为了保持清晰，你也可以显式地要求缺省值，用于单个的列或者用于整个行： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', DEFAULT); INSERT INTO products DEFAULT VALUES; 你可以在一个命令中插入多行： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99), (2, 'Bread', 1.99), (3, 'Milk', 2.99); INSERT INTO new_products (product_no, name, price, release_date) VALUES (1, 'A', 100.00, '2025-05-29'), (2, 'B', 150.50, '2024-11-20'), (3, 'C', 200.75, '2025-05-29'); 也可以插入查询的结果（可能没有行、一行或多行）： INSERT INTO products (product_no, name, price) SELECT product_no, name, price FROM new_products WHERE release_date = '2025-05-29'; 这提供了用于计算要插入的行的SQL查询机制（ 第 7 章）的全部功能。 提示 在一次性插入大量数据时，考虑使用 COPY命令。它不如 INSERT命令那么灵活，但是更高效。 两个查询的结果可以用集合操作并、交、差进行组合。语法是: query1 UNION [ALL] query2 query1 INTERSECT [ALL] query2 query1 EXCEPT [ALL] query2 query1*和query2*都是可以使用以上所有特性的查询。集合操作也可以嵌套和级连，例如: query1 UNION query2 UNION query3 实际执行的是： (query1 UNION query2) UNION query3 UNION`有效地把*`query2的结果附加到query1*的结果上（不过我们不能保证这就是这些行实际被返回的顺序）。此外，它将删除结果中所有重复的行，就像`DISTINCT`做的那样，除非你使用了`UNION ALL`。 INTERSECT`返回那些同时存在于*`query1和query2*的结果中的行，除非声明了`INTERSECT ALL`，否则所有重复行都被消除。 EXCEPT`返回所有在*`query1的结果中但是不在query2的结果中的行（有时侯这叫做两个查询的*差）。同样的，除非声明了`EXCEPT ALL`，否则所有重复行都被消除。 为了计算两个查询的并、交、差，这两个查询必须是“并操作兼容的”，也就意味着它们都返回同样数量的列，并且对应的列有兼容的数据类型，如 第 10.5 节中描述的那样。 当优化器判断对于某一个特定的查询，并行查询是最快的执行策略时，优化器将创建一个查询计划。该计划包括一个 *Gather*或者*Gather Merge*节点。下面是一个简单的例子： EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%'; QUERY PLAN ------------------------------------------------------------------------------------- Gather (cost=1000.00..217018.43 rows=1 width=97) Workers Planned: 2 -> Parallel Seq Scan on pgbench_accounts (cost=0.00..216018.33 rows=1 width=97) Filter: (filler ~~ '%x%'::text) (4 rows) 在所有的情形下，`Gather`或*Gather Merge*节点都只有一个子计划，它是将被并行执行的计划的一部分。如果`Gather`或*Gather Merge*节点位于计划树的最顶层，那么整个查询将并行执行。如果它位于计划树的其他位置，那么只有查询中在它之下的那一部分会并行执行。在上面的例子中，查询只访问了一个表，因此除`Gather`节点本身之外只有一个计划节点。因为该计划节点是`Gather`节点的孩子节点，所以它会并行执行。 使用 EXPLAIN命令, 你能看到规划器选择的工作者数量。当查询执行期间到达`Gather`节点时，实现用户会话的进程将会请求和规划器选中的工作者数量一样多的 后台工作者进程。规划器将考虑使用的后台工作者的数量被限制为最多 max_parallel_workers_per_gather个。任何时候能够存在的后台工作者进程的总数由 max_worker_processes和 max_parallel_workers限制。因此，一个并行查询可能会使用比规划中少的工作者来运行，甚至有可能根本不使用工作者。最优的计划可能取决于可用的工作者的数量，因此这可能会导致不好的查询性能。如果这种情况经常发生，那么就应当考虑一下提高`max_worker_processes`和`max_parallel_workers`的值，这样更多的工作者可以同时运行；或者降低`max_parallel_workers_per_gather`，这样规划器会要求少一些的工作者。 为一个给定并行查询成功启动的后台工作者进程都将会执行计划的并行部分。这些工作者的领导者也将执行该计划，不过它还有一个额外的任务：它还必须读取所有由工作者产生的元组。当整个计划的并行部分只产生了少量元组时，领导者通常将表现为一个额外的加速查询执行的工作者。反过来，当计划的并行部分产生大量的元组时，领导者将几乎全用来读取由工作者产生的元组并且执行`Gather`或`Gather Merge`节点上层计划节点所要求的任何进一步处理。在这些情况下，领导者所作的执行并行部分的工作将会很少。 当计划的并行部分的顶层节点是`Gather Merge`而不是`Gather`时，它表示每个执行计划并行部分的进程会产生有序的元组，并且领导者执行一种保持顺序的合并。相反，`Gather`会以任何方便的顺序从工作者读取元组，这会破坏可能已经存在的排序顺序。 有几种设置会导致查询规划器在任何情况下都不生成并行查询计划。为了让并行查询计划能够被生成，必须配置好下列设置。 max_parallel_workers_per_gather必须被设置为大于零的值。这是一种特殊情况，更加普遍的原则是所用的工作者数量不能超过`max_parallel_workers_per_gather`所配置的数量。 此外，系统一定不能运行在单用户模式下。因为在单用户模式下，整个数据库系统运行在单个进程中，没有后台工作者进程可用。 如果下面的任一条件为真，即便对一个给定查询通常可以产生并行查询计划，规划器都不会为它产生并行查询计划： 查询要写任何数据或者锁定任何数据库行。如果一个查询在顶层或者CTE中包含了数据修改操作，那么不会为该查询产生并行计划。一种例外是，CREATE TABLE …​ AS、`SELECT INTO`以及`CREATE MATERIALIZED VIEW`这些创建新表并填充它的命令可以使用并行计划。 查询可能在执行过程中被暂停。只要在系统认为可能发生部分或者增量式执行，就不会产生并行计划。例如：用 DECLARE CURSOR创建的游标将永远不会使用并行计划。类似地，一个`FOR x IN query LOOP .. END LOOP`形式的 PL/pgSQL 循环也永远不会使用并行计划，因为当并行查询进行时，并行查询系统无法验证循环中的代码执行起来是安全的。 使用了任何被标记为`PARALLEL UNSAFE`的函数的查询。大多数系统定义的函数都被标记为`PARALLEL SAFE`，但是用户定义的函数默认被标记为`PARALLEL UNSAFE`。参见 第 15.4 节中的讨论。 该查询运行在另一个已经存在的并行查询内部。例如，如果一个被并行查询调用的函数自己发出一个 SQL 查询，那么该查询将不会使用并行计划。这是当前实现的一个限制，但是或许不值得移除这个限制，因为它会导致单个查询使用大量的进程。 即使对于一个特定的查询已经产生了并行查询计划，在一些情况下执行时也不会并行执行该计划。如果发生这种情况，那么领导者将会自己执行该计划在`Gather`节点之下的部分，就好像`Gather`节点不存在一样。上述情况将在满足下面的任一条件时发生： 因为后台工作者进程的总数不能超过 max_worker_processes，导致不能得到后台工作者进程。 由于为并行查询目的启动的后台工作者数量不能超过 max_parallel_workers这一限制而不能得到后台工作者。 客户端发送了一个执行消息，并且消息中要求取元组的数量不为零。执行消息可见 扩展查询协议中的讨论。因为 libpq当前没有提供方法来发送这种消息，所以这种情况只可能发生在不依赖 libpq 的客户端中。如果这种情况经常发生，那在它可能发生的会话中设置 max_parallel_workers_per_gather为零是一个很好的主意，这样可以避免产生连续运行时次优的查询计划。 因为每个工作者只执行完成计划的并行部分，所以不可能简单地产生一个普通查询计划并使用多个工作者运行它。每个工作者都会产生输出结果集的一个完全，因而查询并不会比普通查询运行得更快甚至还会产生不正确的结果。相反，计划的并行部分一定被查询优化器在内部当作一个*部分计划*，即它必须被构建出来，这样每一个执行该计划的进程将以无重复地方式产生输出行的一个子集，即保证每一个所需要的输出行正好只被一个合作进程生成。通常，这意味着该查询的驱动表上的扫描必须是一种可并行的扫描。 当前支持下列可并行的表扫描。 在一个*并行顺序扫描\\*中，表块将在合作进程之间被划分。一次会分发一个块，这样对表的访问还是保持顺序方式。 在一个*并行位图堆扫描\\*中，一个进程被选为领导者。这个进程执行对一个或者多个索引的扫描并且构建出一个位图指示需要访问哪些表块。这些表块接着会在合作进程之间划分（和并行顺序扫描中一样）。换句话说，堆扫描以并行方式进行但底层的索引扫描不是并行。 在一个*并行索引扫描\\*或者*并行只用索引的扫描\\*中，合作进程轮流从索引读取数据。当前，并行索引扫描仅有B-树索引支持。每一个进程将认领一个索引块并且扫描和返回该索引块引用的所有元组，其他进程可以同时地从一个不同的索引块返回元组。并行B-树扫描的结果会以每个工作者进程内的顺序返回。 其他扫描类型（例如非B-树索引的扫描）可能会在未来支持并行扫描。 正如在非并行计划中那样，驱动表可能被使用嵌套循环、哈希连接或者归并连接连接到一个或者多个其他表。连接的内侧可以是任何类型的被规划器支持的非并行计划，假设它能够安全地在并行工作者中运行。根据连接类型，内侧还可以是一种并行计划。 在一个*嵌套循环连接\\*中，内侧总是非并行的。尽管它会被完全执行，如果内侧是一个索引扫描也会很高效，因为外侧元组以及在索引中查找值的循环会被划分到多个合作进程。 在一个*归并连接\\*中，内侧总是一个非并行计划并且因此会被完全执行。这可能是不太高效的，特别是在排序必须被执行时，因为在每一个合作进程中工作数据和结果数据是重复的。 在一个哈希连接\\（没有“并行”前缀）中，每个合作进程都会完全执行内侧以构建哈希表的相同。如果哈希表很大或者该计划开销很大，这种方式就很低效。在一个并行哈希连接\\*中，内侧是一个*并行哈希\\，它把构建共享哈希表的工作划分到多个合作进程。 IvorySQL通过按两个阶段进行聚集来支持并行聚集。首先，每个参与到查询并行部分的进程执行一个聚集步骤，为该进程注意到的每个分组产生一个部分结果。这在计划中反映为一个`Partial Aggregate`节点。然后，部分结果通过`Gather`或者`Gather Merge`被传输到领导者。最后，领导者对来自所有工作者的结果进行重新聚集得到最终的结果。这在计划中反映为一个`Finalize Aggregate`节点。 因为`Finalize Aggregate`节点运行在领导者进程上，如果查询产生的分组数相对于其输入行数来说比较大，则查询规划器不会喜欢它。例如，在最坏的情况下，`Finalize Aggregate`节点看到的分组数可能与所有工作者进程在`Partial Aggregate`阶段看到的输入行数一样多。对于这类情况，使用并行聚集显然得不到性能收益。查询规划器会在规划过程中考虑这一点并且不太会在这种情况下选择并行聚集。 并行聚集并非在所有情况下都被支持。每一个聚集都必须是对并行 安全的并且必须有一个组合函数。如果该聚集有一个类型为`internal`的转移状态，它必须有序列化和反序列化函数。更多细节请参考 CREATE AGGREGATE。如果任何聚集函数调用包含`DISTINCT`或`ORDER BY`子句，则不支持并行聚集。对于有序集聚集或者当查询涉及`GROUPING SETS`时，也不支持并行聚集。只有在查询中涉及的所有连接也是该计划并行部分的组成部分时，才能使用并行聚集。 只要当IvorySQL需要从多个源中整合行到一个单一结果集时，它会使用`Append`或`MergeAppend`计划节点。在实现`UNION ALL`或扫描分区表时常常会发生这种情况。就像这些节点可以被用在任何其他计划中一样，它们可以被用在并行计划中。不过，在并行计划中，规划器使用的是`Parallel Append`节点。 当一个`Append`节点被用在并行计划中时，每个进程将按照子计划出现的顺序执行子计划，这样所有的参与进程会合作执行第一个子计划直到它被完成，然后同时移动到第二个计划。而在使用`Parallel Append`时，执行器将把它的子计划尽可能均匀地散布在参与进程中，这样多个子计划会被同时执行。这避免了竞争，也避免了子计划在那些不执行它的进程中产生启动代价。 此外，和常规的`Append`节点不同（在并行计划中使用时仅有部分子计划），Parallel Append`节点既可以有部分子计划也可以有非部分子计划。非部分子计划将仅被单个进程扫描，因为扫描它们不止一次会产生重复的结果。因此涉及到追加多个结果集的计划即使在没有有效的部分计划可用时，也能实现粗粒度的并行。例如，考虑一个针对分区表的查询，它只能通过使用一个不支持并行扫描的索引来实现。规划器可能会选择常规`Index Scan`计划的`Parallel Append。每个索引扫描必须被单一的进程执行完，但不同的扫描可以由不同的进程同时执行。 enable_parallel_append可以被用来禁用这种特性。 如果我们想要一个查询能产生并行计划但事实上又没有产生，可以尝试减小 parallel_setup_cost或者 parallel_tuple_cost。当然，这个计划可能比规划器优先产生的顺序计划还要慢，但也不总是如此。如果将这些设置为很小的值（例如把它们设置为零）也不能得到并行计划，那就可能是有某种原因导致查询规划器无法为你的查询产生并行计划。可能的原因可见 第 15.2 节和 第 15.4 节。 在执行一个并行计划时，可以用`EXPLAIN (ANALYZE,VERBOSE)`来显示每个计划节点在每个工作者上的统计信息。这些信息有助于确定是否所有的工作被均匀地分发到所有计划节点以及从总体上理解计划的性能特点。 ABORT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ABORT`回滚当前事务并且导致由该事务所作的所有更新被丢弃。这个命令的行为与标准SQL命令 `ROLLBACK的行为一样，并且只是为了历史原因存在。 WORK TRANSACTION 可选关键词。它们没有效果。 AND CHAIN 如果规定了`AND CHAIN` ，新事务立即启动，具有与刚刚完成的事务相同的事务特征(参见 http://www.postgresql.org/docs/17/sql-set-transaction.html[`SET TRANSACTION`])。否则，不会启动新事务。 使用 COMMIT成功地终止一个事务。 在一个事务块之外发出`ABORT`会发出一个警告消息并且不会产生效果。 中止所有更改： ABORT; 这个命令是一个因为历史原因而存在的IvorySQL扩展。`ROLLBACK`是等效的标准SQL命令。 BEGIN [ WORK | TRANSACTION ] [ transaction_mode [, ...] ] 其中 transaction_mode 是以下之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE BEGIN 开始一个事务块，也就是说所有 BEGIN 命令之后的所有语句将被在一个事务中执行，直到给出一个显式的 COMMIT或者 ROLLBACK。默认情况下（没有 BEGIN ），IvorySQL在“自动提交”模式中执行事务，也就是说每个语句都在自己的事务中执行并且在语句结束时隐式地执行一次提交（如果执行成功，否则会完成一次回滚）。 在一个事务块内的语句会执行得更快，因为事务的开始/提交也要求可观的CPU和磁盘活动。在进行多个相关更改时，在一个事务内执行多个语句也有助于保证一致性：在所有相关更新还没有完成之前，其他会话将不能看到中间状态。 如果指定了隔离级别、读/写模式或者延迟模式，新事务也会有那些特性，就像执行了 SET TRANSACTION一样。 WORK``TRANSACTION 可选的关键词。它们没有效果。 这个语句其他参数的含义请参考 SET TRANSACTION。 START TRANSACTION具有和`BEGIN` 相同的功能。 使用 COMMIT或者 ROLLBACK来终止一个事务块。 在已经在一个事务块中时发出`BEGIN`将惹出一个警告消息。事务状态不会被影响。要在一个事务块中嵌套事务，可以使用保存点（见 SAVEPOINT）。 由于向后兼容的原因，连续的 transaction_modes 之间的逗号可以被省略。 开始一个事务块： BEGIN; BEGIN 是一种IvorySQL语言扩展。它等效于SQL标准的命令 START TRANSACTION，它的参考页包含额外的兼容性信息。 DEFERRABLE transaction_mode 是一种IvorySQL语言扩展。 附带地，`BEGIN`关键词被用于嵌入式SQL中的一种不同目的。在移植数据库应用时，我们建议小心对待事务语义。 `COMMIT`提交当前事务。所有由该事务所作的更改会变得对他人可见并且被保证在崩溃发生时仍能持久。 WORK TRANSACTION 可选的关键词。它们没有效果。 AND CHAIN 如果指定了 AND CHAIN，则立即启动与刚刚完成的事务具有相同事务特征（参见 SET TRANSACTION）的新事务。否则，没有新事务被启动。 使用 ROLLBACK中止一个事务。 当不在一个事务内时发出 COMMIT 不会产生危害，但是它会产生一个警告消息。当`COMMIT AND CHAIN`不在事务内时是一个错误。 要提交当前事务并且让所有更改持久化： COMMIT; 命令 `COMMIT`符合SQL标准。表单`COMMIT TRANSACTION`为IvorySQL扩展。 COMMIT PREPARED transaction_id `COMMIT PREPARED`提交一个处于预备状态的事务。 transaction_id 要被提交的事务的事务标识符。 要提交一个预备的事务，你必须是原先执行该事务的同一用户或者超级用户。但是不需要处于执行该事务的同一会话中。 这个命令不能在一个事务块中执行。该预备事务将被立刻提交。 pg_prepared_xacts系统视图中列出了所有当前可用的预备事务。 提交由事务标识符`foobar`标识的事务： COMMIT PREPARED 'foobar'; COMMIT PREPARED 是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 END [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] END 提交当前事务。所有该事务做的更改便得对他人可见并且被保证发生崩溃时仍然是持久的。这个命令是一种IvorySQL扩展，它等效于 COMMIT。 WORK TRANSACTION 可选关键词，它们没有效果。 AND CHAIN 如果规定了`AND CHAIN`，则立即启动与刚完成事务具有相同事务特征(参见 SET TRANSACTION)的新事务。否则，没有新事务被启动。 使用 ROLLBACK可以中止一个事务。 当不在一个事务中时发出`END`没有危害，但是会产生一个警告消息。 要提交当前事务并且让所有更改持久化： END; END 是一种IvorySQL扩展，它提供和 COMMIT等效的功能，后者在SQL标准中指定。 PREPARE TRANSACTION transaction_id `PREPARE TRANSACTION`为两阶段提交准备当前事务。在这个命令之后，该事务不再与当前会话关联。相反，它的状态被完全存储在磁盘上，并且有很高的可能性它会被提交成功（即便在请求提交前发生数据库崩溃）。 一旦被准备好，事务稍后就可以分别用 COMMIT PREPARED或者 ROLLBACK PREPARED提交或者回滚。可以从任何会话而不仅仅是执行原始事务的会话中发出这些命令。 从发出命令的会话的角度来看，`PREPARE TRANSACTION`不像`ROLLBACK`命令：在执行它之后，就没有活跃的当前事务，并且该预备事务的效果也不再可见（如果该事务被提交，效果将重新变得可见）。 如果由于任何原因`PREPARE TRANSACTION`命令失败，它会变成一个`ROLLBACK`：当前事务会被取消。 transaction_id 一个任意的事务标识符，`COMMIT PREPARED`或者`ROLLBACK PREPARED`以后将用这个标识符来标识这个事务。该标识符必须写成一个字符串,并且长度必须小于200字节。它也不能与任何当前已经准备好的事务的标识符相同。 PREPARE TRANSACTION`并不是设计为在应用或者交互式会话中使用。它的目的是允许一个外部事务管理器在多个数据库或者其他事务性来源之间执行原子的全局事务。除非你在编写一个事务管理器，否则你可能不会用到`PREPARE TRANSACTION。 这个命令必须在一个事务块中使用。事务块用 BEGIN开始。 当前在已经执行过任何涉及到临时表或者会话的临时命名空间、创建带`WITH HOLD`的游标或者执行`LISTEN`、`UNLISTEN`或 `NOTIFY`的事务中，不允许`PREPARE`该事务。这些特性与当前会话绑定得太过紧密，所以对一个要被准备的事务来说没有什么用处。 如果用`SET`（不带`LOCAL`选项）修改过事务的任何运行时参数，这些效果会持续到`PREPARE TRANSACTION`之后，并且将不会被后续的任何`COMMIT PREPARED`或`ROLLBACK PREPARED`所影响。因此，在这一 方面`PREPARE TRANSACTION`的行为更像`COMMIT`而不是`ROLLBACK`。 所有当前可用的准备好事务被列在 pg_prepared_xacts系统视图中。 让一个事务处于准备好状态太久是不明智的。这将会干扰`VACUUM`回收存储的能力，并且在极限情况下可能导致数据库关闭以阻止事务ID回卷（见 第 25.1.5 节）。还要记住，该事务会继续持有它已经持有的锁。该特性的设计用法是，只要一个外部事务管理器已经验证其他数据库也准备好了要提交，一个准备好的事务将被正常地提交或者回滚。 如果没有建立一个外部事务管理器来跟踪准备好的事务并且确保它们被迅速地结束，最好禁用准备好事务特性（设置 max_prepared_transactions为零）。这将防止意外地创建准备好事务，不然该事务有可能被忘记并且最终导致问题。 为两阶段提交准备当前事务，使用`foobar`作为事务标识符： PREPARE TRANSACTION 'foobar'; `PREPARE TRANSACTION`是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 ROLLBACK [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] `ROLLBACK`回滚当前事务并且导致该事务所作的所有更新都被抛弃。 WORK TRANSACTION 可选关键词，没有效果。 AND CHAIN 如果指定了`AND CHAIN`，则立即启动与刚刚完成事务具有相同事务特征（参见 SET TRANSACTION）的新事务。否则，不会启动任何新事务。 使用 COMMIT可成功地终止一个事务。 在一个事务块之外发出`ROLLBACK`会发出一个警告并且不会有效果。事务块之外的`ROLLBACK AND CHAIN`是一个错误。 要中止所有更改： ROLLBACK; 命令`ROLLBACK`符合SQL标准。窗体`ROLLBACK TRANSACTION`是一个IvorySQL扩展。 ROLLBACK PREPARED transaction_id `ROLLBACK PREPARED`回滚一个处于准备好状态的事务。 transaction_id 要被回滚的事务的事务标识符。 要回滚一个准备好的事务，你必须是原先执行该事务的同一个用户或者是一个超级用户。但是你必须处在执行该事务的同一个会话中。 这个命令不能在一个事务块内被执行。准备好的事务会被立刻回滚。 pg_prepared_xacts系统视图中列出了当前可用的所有准备好的事务。 用事务标识符`foobar`回滚对应的事务： ROLLBACK PREPARED 'foobar'; `ROLLBACK PREPARED`是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 SAVEPOINT savepoint_name `SAVEPOINT`在当前事务中建立一个新保存点。 保存点是事务内的一种特殊标记，它允许所有在它被建立之后执行的命令被回滚，把该事务的状态恢复到它处于保存点时的样子。 savepoint_name 给新保存点的名字。 使用 ROLLBACK TO回滚到一个保存点。使用 RELEASE SAVEPOINT销毁一个保存点，但保持在它被建立之后执行的命令的效果。 保存点只能在一个事务块内建立。可以在一个事务内定义多个保存点。 要建立一个保存点并且后来撤销在它建立之后执行的所有命令的效果： BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); ROLLBACK TO SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); COMMIT; 上面的事务将插入值 1 和 3，但不会插入 2。 要建立并且稍后销毁一个保存点： BEGIN; INSERT INTO table1 VALUES (3); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (4); RELEASE SAVEPOINT my_savepoint; COMMIT; 上面的事务将插入 3 和 4。 当建立另一个同名保存点时，SQL要求之前的那个保存点自动被销毁。在IvorySQL中，旧的保存点会被保留，不过在进行回滚或释放时只能使用最近的那一个（用 `RELEASE SAVEPOINT`释放较新的保存点将会导致较旧的保存点再次变得可以被`ROLLBACK TO SAVEPOINT`和`RELEASE SAVEPOINT`访问）。在其他方面，`SAVEPOINT`完全符合SQL。 SET CONSTRAINTS { ALL | name [, ...] } { DEFERRED | IMMEDIATE } `SET CONSTRAINTS`设置当前事务内约束检查的行为。`IMMEDIATE`约束在每个语句结束时被检查。`DEFERRED`约束直到事务提交时才被检查。每个约束都有自己的`IMMEDIATE`或`DEFERRED`模式。 在创建时，一个约束会被给定三种特性之一：DEFERRABLE INITIALLY DEFERRED、DEFERRABLE INITIALLY IMMEDIATE`或者`NOT DEFERRABLE。第三类总是`IMMEDIATE`并且不会受到`SET CONSTRAINTS`命令的影响。前两类在每个事务开始时都处于指定的模式，但是它们的行为可以在一个事务内用`SET CONSTRAINTS`更改。 带有一个约束名称列表的`SET CONSTRAINTS`只更改那些约束（都必须是可延迟的）的模式。每一个约束名称都可以是模式限定的。如果没有指定模式名称，则当前的模式搜索路径将被用来寻找第一个匹配的名称。`SET CONSTRAINTS ALL`更改所有可延迟约束的模式。 当`SET CONSTRAINTS`把一个约束的模式从`DEFERRED`改成`IMMEDIATE`时，新模式会有追溯效果：任何还没有解决的数据修改（本来会在事务结束时被检查）会转而在`SET CONSTRAINTS`命令的执行期间被检查。如果任何这种约束被违背，SET CONSTRAINTS`将会失败（并且不会改变该约束模式）。这样，`SET CONSTRAINTS 可以被用来在一个事务中的特定点强制进行约束检查。 当前，只有`UNIQUE`、PRIMARY KEY、REFERENCES（外键）以及`EXCLUDE`约束受到这个设置的影响。`NOT NULL`以及`CHECK`约束总是在一行被插入或修改时立即检查（不是在语句结束时）。没有被声明为`DEFERRABLE`的唯一和排除约束也会被立刻检查。 被声明为“约束触发器”的触发器的引发也受到这个设置的控制 — 它们会在相关约束被检查的同时被引发。 因为IvorySQL并不要求约束名称在模式内唯一（但是在表内要求唯一），可能有多于一个约束匹配指定的约束名称。在这种情况下`SET CONSTRAINTS`将会在所有的匹配上操作。对于一个非模式限定的名称，一旦在搜索路径中的某个模式中发现一个或者多个匹配，路径中后面的模式将不会被搜索。 这个命令只修改当前事务内约束的行为。在事务块外部发出这个命令会产生一个警告并且也不会有任何效果。 这个命令符合SQL标准中定义的行为，但有一点限制：在 IvorySQL中，它不会应用在`NOT NULL`和`CHECK`约束上。还有，IvorySQL会立刻检查非可延迟的唯一约束，而不是按照标准建议的在语句结束时检查。 SET TRANSACTION transaction_mode [, ...] SET TRANSACTION SNAPSHOT snapshot_id SET SESSION CHARACTERISTICS AS TRANSACTION transaction_mode [, ...] 其中transaction_mode是下列之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE `SET TRANSACTION`命令设置当前会话的特性。`SET SESSION CHARACTERISTICS`设置一个会话后续事务的默认事务特性。在个体事务中可以用`SET TRANSACTION`覆盖这些默认值。 可用的事务特性是事务隔离级别、事务访问模式（读/写或只读）以及可延迟模式。此外，可以选择一个快照，不过只能用于当前事务而不能作为会话默认值。 一个事务的隔离级别决定当其他事务并行运行时该事务能看见什么数据： READ COMMITTED 一个语句只能看到在它开始前提交的行。这是默认值。 REPEATABLE READ 当前事务的所有语句只能看到这个事务中执行的第一个查询或者数据修改语句之前提交的行。 SERIALIZABLE 当前事务的所有语句只能看到这个事务中执行的第一个查询或者数据修改语句之前提交的行。如果并发的可序列化事务间的读写模式可能导致一种那些事务串行（一次一个）执行时不可能出现的情况，其中之一将会被回滚并且得到一个 `serialization_failure`错误。 SQL标准定义了一种额外的级别：READ UNCOMMITTED。在IvorySQL中`READ UNCOMMITTED`被视作`READ COMMITTED`。 一个事务执行了第一个查询或者数据修改语句（SELECT、INSERT、DELETE、UPDATE、FETCH`或`COPY）之后就无法更改事务隔离级别。更多有关事务隔离级别和并发控制的信息可见 第 13 章。 事务的访问模式决定该事务是否为读/写或者只读。读/写是默认值。当一个事务为只读时，如果SQL命令`INSERT`、UPDATE、DELETE`和`COPY FROM`要写的表不是一个临时表，则它们不被允许。不允许`CREATE、ALTER`以及`DROP`命令。不允许`COMMENT、GRANT、REVOKE、TRUNCATE。如果`EXPLAIN ANALYZE`和`EXECUTE`要执行的命令是上述命令之一，则它们也不被允许。这是一种高层的只读概念，它不能阻止所有对磁盘的写入。 只有事务也是`SERIALIZABLE`以及`READ ONLY`时，`DEFERRABLE`事务属性才会有效。当一个事务的所有这三个属性都被选择时，该事务在第一次获取其快照时可能会阻塞，在那之后它运行时就不会有`SERIALIZABLE`事务的开销并且不会有任何牺牲或者被一次序列化失败取消的风险。这种模式很适合于长时间运行的报表或者备份。 SET TRANSACTION SNAPSHOT`命令允许新的事务使用与一个现有事务相同的*快照*运行。已经存在的事务必须已经把它的快照用`pg_export_snapshot 函数（见 第 9.27.5 节）导出。该函数会返回一个快照标识符，SET TRANSACTION SNAPSHOT`需要被给定一个快照标识符来指定要导入的快照。在这个命令中该标识符必须被写成一个字符串，例如’000003A1-1'。SET TRANSACTION SNAPSHOT`只能在一个事务的开始执行，并且要在该事务的第一个查询或者数据修改语句（`SELECT、INSERT、DELETE、UPDATE、FETCH`或 `COPY）之前执行。此外，该事务必须已经被设置为`SERIALIZABLE`或者`REPEATABLE READ`隔离级别（否则，该快照将被立刻抛弃，因为`READ COMMITTED`模式会为每一个命令取一个新快照）。如果导入事务使用了`SERIALIZABLE`隔离级别，那么导入快照的事务必须也使用该隔离级别。还有，一个非只读可序列化事务不能导入来自只读事务的快照。 如果执行`SET TRANSACTION`之前没有`START TRANSACTION`或者`BEGIN`，它会发出一个警告并且不会有任何效果。 可以通过在`BEGIN`或者`START TRANSACTION`中指定想要的`transaction_modes来省掉`SET TRANSACTION。但是在`SET TRANSACTION SNAPSHOT`中该选项不可用。 会话默认的事务模式也可以通过配置参数 default_transaction_isolation、http://www.postgresql.org/docs/17/runtime-config-client.html#GUC-DEFAULT-TRANSACTION-READ-ONLY[default_transaction_read_only]和 default_transaction_deferrable来设置或检查（实际上`SET SESSION CHARACTERISTICS`只是用`SET`设置这些变量的等效体）。这意味着可以通过配置文件、ALTER DATABASE 等方式设置默认值。详见 第 20 章。 当前事务的模式可以类似的通过配置参数 transaction_isolation、 transaction_read_only、和 transaction_deferrable来设置或检查。设置这其中一个参数的作用与相应的`SET TRANSACTION`选项相同，在它何时可以完成方面，也有相同的限制。但是，这些参数不能在配置文件中设置，或者从活动SQL以外的任何来源来设置。 要用一个已经存在的事务的同一快照开始一个新事务，首先要从该现有事务导出快照。这将会返回快照标识符，例如： BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SELECT pg_export_snapshot(); pg_export_snapshot --------------------- 00000003-0000001B-1 (1 row) 然后在一个新开始的事务的开头把该快照标识符用在一个`SET TRANSACTION SNAPSHOT`命令中： BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SET TRANSACTION SNAPSHOT '00000003-0000001B-1'; SQL标准中定义了这些命令，不过`DEFERRABLE`事务模式和`SET TRANSACTION SNAPSHOT`形式除外，这两者是IvorySQL扩展。 SERIALIZABLE`是标准中默认的事务隔离级别。在IvorySQL中默认值是普通的`READ COMMITTED，但是你可以按上述的方式更改。 在SQL标准中，可以用这些命令设置一个其他的事务特性：诊断区域的尺寸。这个概念与嵌入式SQL有关，并且因此没有在IvorySQL服务器中实现。 SQL标准要求连续的 transaction_modes 之间有逗号，但是出于历史原因IvorySQL允许省略逗号。 START TRANSACTION [ transaction_mode [, ...] ] 其中 transaction_mode 是下列之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE 这个命令开始一个新的事务块。如果指定了隔离级别、读写模式或者可延迟模式，新的事务将会具有这些特性，就像执行了 SET TRANSACTION一样。这和 BEGIN命令一样。 这些参数对于这个语句的含义可参考 SET TRANSACTION。 在标准中，没有必要发出`START TRANSACTION`来开始一个事务块：任何SQL命令会隐式地开始一个块。IvorySQL的行为可以被视作在每个命令之后隐式地发出一个没有跟随在`START TRANSACTION`（或者`BEGIN`）之后的`COMMIT`并且因此通常被称作 “自动提交”。为了方便，其他关系型数据库系统也可能会提供自动提交特性。 DEFERRABLE transaction_mode 是一种IvorySQL语言扩展。 SQL标准要求在连续的 transaction_modes 之间有逗号，但是由于历史原因IvorySQL允许省略逗号。 SQL输入由一个 命令 序列组成。一个命令由一个 记号 的序列构成，并由一个分号（“;”）终结。输入流的末端也会标志一个命令的结束。具体哪些记号是合法的与具体命令的语法有关。 一个记号可以是一个 关键词、一个 标识符、一个 带引号的标识符、一个 literal（或常量）或者一个特殊字符符号。记号通常以空白（空格、制表符、新行）来分隔，但在无歧义时并不强制要求如此（唯一的例子是一个特殊字符紧挨着其他记号）。 例如，下面是一个（语法上）合法的SQL输入： SELECT * FROM MY_TABLE; UPDATE MY_TABLE SET A = 5; INSERT INTO MY_TABLE VALUES (3, 'hi there'); 这是一个由三个命令组成的序列，每一行一个命令（尽管这不是必须地，在同一行中可以有超过一个命令，而且命令还可以被跨行分割）。 另外，注释 也可以出现在SQL输入中。它们不是记号，它们和空白完全一样。 根据标识命令、操作符、参数的记号不同，SQL的语法不很一致。最前面的一些记号通常是命令名，因此在上面的例子中我们通常会说一个“SELECT”、一个“UPDATE”和一个“INSERT”命令。但是例如`UPDATE`命令总是要求一个`SET`记号出现在一个特定位置，而`INSERT`则要求一个`VALUES`来完成命令。每个命令的精确语法规则在 第 VI 部分中介绍。 上例中的`SELECT`、`UPDATE`或`VALUES`记号是 关键词 的例子，即SQL语言中具有特定意义的词。记号`MY_TABLE`和`A`则是 标识符 的例子。它们标识表、列或者其他数据库对象的名字，取决于使用它们的命令。因此它们有时也被简称为“名字”。关键词和标识符具有相同的词法结构，这意味着我们无法在没有语言知识的前提下区分一个标识符和关键词。一个关键词的完整列表可以在 附录 C中找到。 SQL标识符和关键词必须以一个字母（a-z，也可以是带变音符的字母和非拉丁字母）或一个下划线（ _ ）开始。后续字符可以是字母、下划线（_）、数字（0-9）或美元符号（$）。注意根据SQL标准的字母规定，美元符号是不允许出现在标识符中的，因此它们的使用可能会降低应用的可移植性。SQL标准不会定义包含数字或者以下划线开头或结尾的关键词，因此这种形式的标识符不会与未来可能的标准扩展冲突。 系统中一个标识符的长度不能超过`NAMEDATALEN`-1字节，在命令中可以写超过此长度的标识符，但是它们会被截断。默认情况下，`NAMEDATALEN`的值为64，因此标识符的长度上限为63字节。如果这个限制有问题，可以在`src/include/pg_config_manual.h`中修改`NAMEDATALEN`常量。 关键词和不被引号修饰的标识符是大小写不敏感的。因此： UPDATE MY_TABLE SET A = 5; 可以等价地写成： uPDaTE my_TabLE SeT a = 5; 一个常见的习惯是将关键词写成大写，而名称写成小写，例如： UPDATE my_table SET a = 5; 这里还有第二种形式的标识符：受限标识符*或*被引号修饰的标识符。它是由双引号（\"）包围的一个任意字符序列。一个受限标识符总是一个标识符而不会是一个关键字。因此`\"select\"`可以用于引用一个名为“select”的列或者表，而一个没有引号修饰的`select`则会被当作一个关键词，从而在本应使用表或列名的地方引起解析错误。在上例中使用受限标识符的例子如下： UPDATE \"my_table\" SET \"a\" = 5; 受限标识符可以包含任何字符，除了代码为0的字符（如果要包含一个双引号，则写两个双引号）。这使得可以构建原本不被允许的表或列的名称，例如包含空格或花号的名字。但是长度限制依然有效。 引用标识符也使其区分大小写，而未引用的名称总是折叠成小写。例如，标识符`FOO`、foo`和`\"foo\"`在IvorySQL中被认为是相同的，但是`\"Foo\"`和`\"FOO\"`与这三个不同，并且彼此不同。（在IvorySQL中，将不带引号的名称折叠为小写与SQL标准不兼容，SQL标准规定不带引号的名称应折叠为大写。因此，根据标准，`foo`应等同于`\"FOO\"`而不是`\"foo\"。如果您想编写可移植应用程序，建议您始终引用某个特定的名称，或者永远不要引用它。） 一种受限标识符的变体允许包括转义的用代码点标识的Unicode字符。这种变体以`U&`（大写或小写U跟上一个花号）开始，后面紧跟双引号修饰的名称，两者之间没有任何空白，如`U&\"foo\"`（注意这里与操作符`&`似乎有一些混淆，但是在`&`操作符周围使用空白避免了这个问题）。在引号内，Unicode字符可以以转义的形式指定：反斜线接上4位16进制代码点号码或者反斜线和加号接上6位16进制代码点号码。例如，标识符`\"data\"`可以写成： U&\"d\\0061t\\+000061\" 下面的例子用斯拉夫语字母写出了俄语单词“slon”（大象）： U&\"\\0441\\043B\\043E\\043D\" 如果希望使用其他转义字符来代替反斜线，可以在字符串后使用`UESCAPE`子句，例如： U&\"d!0061t!+000061\" UESCAPE '!' 转义字符可以是除了16进制位、加号、单引号、双引号、空白字符之外的任意单个字符。请注意，转义字符在 UESCAPE 之后用单引号而不是双引号书写。 为了在标识符中包括转义字符本身，将其写两次即可。 4位或6位转义形式都可以被用来定义UTF-16代理对来组成代码点大于U+FFFF的字符，尽管6位形式的存在使得这种做法变得不必要（代理对并不被直接存储，而是绑定成一个单独的代码点）。 如果服务器编码不是UTF-8，则由其中一个转义序列标识的Unicode代码点转换为实际的服务器编码；如果不可能，则报告错误。 在IvorySQL中有三种 隐式类型常量：字符串、位串和数字。常量也可以被指定显式类型，这可以使得它被更精确地展示以及更有效地处理。这些选择将会在后续小节中讨论。 在SQL中，一个字符串常量是一个由单引号（ ' ）包围的任意字符序列，例如’This is a string'`。为了在一个字符串中包括一个单引号，可以写两个相连的单引号，例如’Dianne''s horse'`。注意这和一个双引号（\"）不同。 两个只由空白及至少一个新行分隔的字符串常量会被连接在一起，并且将作为一个写在一起的字符串常量来对待。例如： SELECT 'foo' 'bar'; 等同于： SELECT 'foobar'; 但是： SELECT 'foo' 'bar'; 则不是合法的语法（这种有些奇怪的行为是SQL指定的，IvorySQL遵循了该标准）。 IvorySQL也接受“转义”字符串常量，这也是SQL标准的一个扩展。一个转义字符串常量可以通过在开单引号前面写一个字母`E`（大写或小写形式）来指定，例如`E’foo'`（当一个转义字符串常量跨行时，只在第一个开引号之前写`E`）。在一个转义字符串内部，一个反斜线字符（\\）会开始一个C风格的 反斜线转义 序列，在其中反斜线和后续字符的组合表示一个特殊的字节值（如 表 4.1中所示）。 表 4.1. 反斜线转义序列 反斜线转义序列 解释 \\b 退格 \\f 换页 \\n 换行 \\r 回车 \\t 制表符 *`o*, `*`oo*, `*`ooo*` (o = 0–7) 八进制字节值 \\x*`h, `\\x`hh` (h* = 0–9, A–F) 十六进制字节值 \\u*`xxxx, `\\U`xxxxxxxx` (x* = 0–9, A–F) 16 或 32-位十六进制 Unicode 字符值 跟随在一个反斜线后面的任何其他字符被当做其字面意思。因此，要包括一个反斜线字符，请写两个反斜线（\\\\）。在一个转义字符串中包括一个单引号除了普通方法’'`之外，还可以写成`\\'`。 你要负责保证你创建的字节序列由服务器字符集编码中合法的字符组成，特别是在使用八进制或十六进制转义时。一个有用的替代方法是使用Unicode转义或替代的Unicode转义语法，如 第 4.1.2.3 节中所述；然后服务器将检查字符转换是否可行。 小心 如果配置参数 standard_conforming_strings为`off`，那么IvorySQL对常规字符串常量和转义字符串常量中的反斜线转义都识别。不过，在IvorySQL中该参数的默认值为`on`，意味着只在转义字符串常量中识别反斜线转义。这种行为更兼容标准，但是可能打断依赖于历史行为（反斜线转义总是会被识别）的应用。作为一种变通，你可以设置该参数为`off`，但是最好迁移到符合新的行为。如果你需要使用一个反斜线转义来表示一个特殊字符，为该字符串常量写上一个`E`。在`standard_conforming_strings`之外，配置参数 escape_string_warning和 backslash_quote也决定了如何对待字符串常量中的反斜线。代码零的字符不能出现在一个字符串常量中。 IvorySQL也支持另一种类型的字符串转义语法，它允许用代码点指定任意Unicode字符。一个Unicode转义字符串常量开始于`U&`（大写或小写形式的字母U，后跟花号），后面紧跟着开引号，之间没有任何空白，例如`U&'foo'`（注意这产生了与操作符`&`的混淆。在操作符周围使用空白来避免这个问题）。在引号内，Unicode字符可以通过写一个后跟4位十六进制代码点编号或者一个前面有加号的6位十六进制代码点编号的反斜线来指定。例如，字符串’data'`可以被写为 U&'d\\0061t\\+000061' 下面的例子用斯拉夫字母写出了俄语的单词“slon”（大象）： U&'\\0441\\043B\\043E\\043D' 如果想要一个不是反斜线的转义字符，可以在字符串之后使用`UESCAPE`子句来指定，例如： U&'d!0061t!+000061' UESCAPE '!' 转义字符可以是出一个十六进制位、加号、单引号、双引号或空白字符之外的任何单一字符。 要在一个字符串中包括一个表示其字面意思的转义字符，把它写两次。 4位或6位转义形式可用于指定UTF-16代理项对，以组成代码点大于U+FFFF的字符，尽管从技术上讲，6位形式的可用性使得这是不必要的（代理项对不是直接存储的，而是合并到单个代码点中。） 如果服务器编码不是UTF-8，则由这些转义序列之一标识的Unicode代码点将转换为实际的服务器编码；如果不可能，则会报告错误。 此外，字符串常量的Unicode转义语法仅在配置参数 standard_conforming_strings开启时才有效。这是因为否则这种语法可能会混淆解析SQL语句的客户端，可能导致SQL注入和类似的安全问题。如果该参数设置为off，则此语法将被拒绝并显示错误消息。 虽然用于指定字符串常量的标准语法通常都很方便，但是当字符串中包含了很多单引号或反斜线时很难理解它，因为每一个都需要被双写。要在这种情形下允许可读性更好的查询，IvorySQL提供了另一种被称为“美元引用”的方式来书写字符串常量。一个美元引用的字符串常量由一个美元符号（$）、一个可选的另个或更多字符的“标签”、另一个美元符号、一个构成字符串内容的任意字符序列、一个美元符号、开始这个美元引用的相同标签和一个美元符号组成。例如，这里有两种不同的方法使用美元引用指定字符串“Dianne’s horse”： $$Dianne's horse$$ $SomeTag$Dianne's horse$SomeTag$ 注意在美元引用字符串中，单引号可以在不被转义的情况下使用。事实上，在一个美元引用字符串中不需要对字符进行转义：字符串内容总是按其字面意思写出。反斜线不是特殊的，并且美元符号也不是特殊的，除非它们是匹配开标签的一个序列的一部分。 可以通过在每一个嵌套级别上选择不同的标签来嵌套美元引用字符串常量。这最常被用在编写函数定义上。例如： $function$ BEGIN RETURN ($1 ~ $q$[\\t\\r\\n\\v\\\\]$q$); END; $function$ 这里，序列`$q$[\\t\\r\\n\\v\\\\]$q$表示一个美元引用的文字串[\\t\\r\\n\\v\\\\]，当该函数体被IvorySQL执行时它将被识别。但是因为该序列不匹配外层的美元引用的定界符$function$`，它只是一些在外层字符串所关注的常量中的字符而已。 一个美元引用字符串的标签（如果有）遵循一个未被引用标识符的相同规则，除了它不能包含一个美元符号之外。标签是大小写敏感的，因此`$tag$String content$tag$是正确的，但是$TAG$String content$tag$`不正确。 一个跟着一个关键词或标识符的美元引用字符串必须用空白与之分隔开，否则美元引用定界符可能会被作为前面标识符的一部分。 美元引用不是SQL标准的一部分，但是在书写复杂字符串文字方面，它常常是一种比兼容标准的单引号语法更方便的方法。当要表示的字符串常量位于其他常量中时它特别有用，这种情况常常在过程函数定义中出现。如果用单引号语法，上一个例子中的每个反斜线将必须被写成四个反斜线，这在解析原始字符串常量时会被缩减到两个反斜线，并且接着在函数执行期间重新解析内层字符串常量时变成一个。 位串常量看起来像常规字符串常量在开引号之前（中间无空白）加了一个`B`（大写或小写形式），例如`B'1001'`。位串常量中允许的字符只有`0`和`1`。 作为一种选择，位串常量可以用十六进制记号法指定，使用一个前导`X`（大写或小写形式）,例如`X'1FF'`。这种记号法等价于一个用四个二进制位取代每个十六进制位的位串常量。 两种形式的位串常量可以以常规字符串常量相同的方式跨行继续。美元引用不能被用在位串常量中。 在这些一般形式中可以接受数字常量： digits digits.[digits][e[+-]digits] [digits].digits[e[+-]digits] digitse[+-]digits 其中 digits 是一个或多个十进制数字（0到9）。如果使用了小数点，在小数点前面或后面必须至少有一个数字。如果存在一个指数标记（e），在其后必须跟着至少一个数字。在该常量中不能嵌入任何空白或其他字符。注意任何前导的加号或减号并不实际被考虑为常量的一部分，它是一个应用到该常量的操作符。 这些是合法数字常量的例子： 42 3.5 4. .001 5e2 1.925e-3 如果一个不包含小数点和指数的数字常量的值适合类型`integer`（32 位），它首先被假定为类型`integer`。否则如果它的值适合类型`bigint`（64 位），它被假定为类型`bigint`。再否则它会被取做类型`numeric`。包含小数点和/或指数的常量总是首先被假定为类型`numeric`。 一个数字常量初始指派的数据类型只是类型转换算法的一个开始点。在大部分情况中，常量将被根据上下文自动被强制到最合适的类型。必要时，你可以通过造型它来强制一个数字值被解释为一种指定数据类型。例如，你可以这样强制一个数字值被当做类型`real`（float4）： REAL '1.23' -- string style 1.23::REAL -- IvorySQL (historical) style 这些实际上只是接下来要讨论的一般造型记号的特例。 一种任意类型的一个常量可以使用下列记号中的任意一种输入： type 'string' 'string'::type CAST ( 'string' AS type ) 字符串常量的文本被传递到名为 type 的类型的输入转换例程中。其结果是指定类型的一个常量。如果对该常量的类型没有歧义（例如，当它被直接指派给一个表列时），显式类型造型可以被忽略，在那种情况下它会被自动强制。 字符串常量可以使用常规SQL记号或美元引用书写。 也可以使用一个类似函数的语法来指定一个类型强制： typename ( 'string' ) 但是并非所有类型名都可以用在这种方法中，详见 第 4.2.9 节。 如 第 4.2.9 节中讨论的，::、CAST()`以及函数调用语法也可以被用来指定任意表达式的运行时类型转换。要避免语法歧义，type 'string'语法只能被用来指定简单文字常量的类型。type 'string'` 语法上的另一个限制是它无法对数组类型工作，指定一个数组常量的类型可使用`::`或`CAST()`。 CAST()`语法符合SQL。`type 'string'`语法是该标准的一般化：SQL指定这种语法只用于一些数据类型，但是IvorySQL允许它用于所有类型。带有::`的语法是IvorySQL的历史用法，就像函数调用语法一样。 一个操作符名是最多`NAMEDATALEN`-1（默认为63）的一个字符序列，其中的字符来自下面的列表： \\+ - * / < > = ~ ! @ # % ^ & | ` ? 不过，在操作符名上有一些限制： --`和/*`不能在一个操作符名的任何地方出现，因为它们将被作为一段注释的开始。 一个多字符操作符名不能以`+或-`结尾，除非该名称也至少包含这些字符中的一个： ~ ! @ # % ^ & | ` ? 例如，@-`是一个被允许的操作符名，但*-`不是。这些限制允许IvorySQL解析SQL兼容的查询而不需要在记号之间有空格。 当使用非SQL标准的操作符名时，你通常需要用空格分隔相邻的操作符来避免歧义。例如，如果你定义了一个名为`@的前缀操作符，你不能写`X*@Y，你必须写`X* @Y`来确保IvorySQL把它读作两个操作符名而不是一个。 一些不是数字字母的字符有一种不同于作为操作符的特殊含义。这些字符的详细用法可以在描述相应语法元素的地方找到。这一节只是为了告知它们的存在以及总结这些字符的目的。 跟随在一个美元符号（ $ ）后面的数字被用来表示在一个函数定义或一个预备语句中的位置参数。在其他上下文中该美元符号可以作为一个标识符或者一个美元引用字符串常量的一部分。 圆括号（ () ）具有它们通常的含义，用来分组表达式并且强制优先。在某些情况中，圆括号被要求作为一个特定 SQL 命令的固定语法的一部分。 方括号（ [] ）被用来选择一个数组中的元素。更多关于数组的信息见 第 8.15 节。 逗号（ , ）被用在某些语法结构中来分割一个列表的元素。 分号（ ; ）结束一个 SQL 命令。它不能出现在一个命令中间的任何位置，除了在一个字符串常量中或者一个被引用的标识符中。 冒号（ : ）被用来从数组中选择“切片”（见 第 8.15 节）。在某些 SQL 的“方言”（例如嵌入式 SQL）中，冒号被用来作为变量名的前缀。 星号（ * ）被用在某些上下文中标记一个表的所有域或者组合值。当它被用作一个聚集函数的参数时，它还有一种特殊的含义，即该聚集不要求任何显式参数。 句点（ . ）被用在数字常量中，并且被用来分割模式、表和列名。 一段注释是以双横杠开始并且延伸到行结尾的一个字符序列，例如： -- This is a standard SQL comment 另外，也可以使用 C 风格注释块： /* multiline comment * with nesting: /* nested block comment */ */ 这里该注释开始于 / 并且延伸到匹配出现的 /。这些注释块可按照SQL标准中指定的方式嵌套，但和C中不同。这样我们可以注释掉一大段可能包含注释块的代码。 在进一步的语法分析前，注释会被从输入流中被移除并且实际被替换为空白。 表 4.2显示了IvorySQL中操作符的优先级和结合性。大部分操作符具有相同的优先并且是左结合的。操作符的优先级和结合性被硬写在解析器中。如果您希望以不同于优先级规则所暗示的方式解析具有多个运算符的表达式，请添加括号。 表 4.2. 操作符优先级（从高到低） 操作符/元素 结合性 描述 . 左 表/列名分隔符 :: 左 IvorySQL-风格的类型转换 [ ] 左 数组元素选择 + - 右 一元加、一元减 ^ 左 指数 * / % 左 乘、除、模 + - 左 加、减 （任意其他操作符） 左 所有其他本地以及用户定义的操作符 BETWEEN IN LIKE ILIKE SIMILAR 范围包含、集合成员关系、字符串匹配 < > = ⇐ >= <> 比较操作符 IS ISNULL NOTNULL IS TRUE、IS FALSE、IS NULL、`IS DISTINCT FROM`等 NOT 右 逻辑否定 AND 左 逻辑合取 OR 左 逻辑析取 注意该操作符有限规则也适用于与上述内建操作符具有相同名称的用户定义的操作符。例如，如果你为某种自定义数据类型定义了一个“”操作符，它将具有和内建的“”操作符相同的优先级，不管你的操作符要做什么。 当一个模式限定的操作符名被用在`OPERATOR`语法中时，如下面的例子： SELECT 3 OPERATOR(pg_catalog.+) 4; `OPERATOR`结构被用来为“任意其他操作符”获得 表 4.2中默认的优先级。不管出现在`OPERATOR()`中的是哪个指定操作符，这都是真的。 注意 版本9.5之前的PostgreSQL使用的操作符优先级规则略有不同。特别是，⇐、>= 和 `<>`习惯于被当作普通操作符，`IS`测试习惯于具有较高的优先级。并且在一些认为`NOT`比`BETWEEN`优先级高的情况下，`NOT BETWEEN`和相关的结构的行为不一致。为了更好地兼容SQL标准并且减少对 逻辑上等价的结构不一致的处理，这些规则也得到了修改。在大部分情况下，这些变化不会导致行为上的变化，或者可能会产生“no such operator”错误，但可以通过增加圆括号解决。不过在一些极端情况中，查询可能在没有被报告解析错误的情况下发生行为的改变。 值表达式被用于各种各样的环境中，例如在`SELECT`命令的目标列表中、作为`INSERT`或`UPDATE`中的新列值或者若干命令中的搜索条件。为了区别于一个表表达式（是一个表）的结果，一个值表达式的结果有时候被称为一个 标量。值表达式因此也被称为 标量表达式（或者甚至简称为 表达式）。表达式语法允许使用算数、逻辑、集合和其他操作从原始部分计算值。 一个值表达式是下列之一： 一个常量或文字值 一个列引用 在一个函数定义体或预备语句中的一个位置参数引用 一个下标表达式 一个域选择表达式 一个操作符调用 一个函数调用 一个聚集表达式 一个窗口函数调用 一个类型转换 一个排序规则表达式 一个标量子查询 一个数组构造器 一个行构造器 另一个在圆括号（用来分组子表达式以及重载优先级）中的值表达式 在这个列表之外，还有一些结构可以被分类为一个表达式，但是它们不遵循任何一般语法规则。这些通常具有一个函数或操作符的语义并且在 第 9 章中的合适位置解释。一个例子是`IS NULL`子句。 我们已经在 第 4.1.2 节中讨论过常量。下面的小节会讨论剩下的选项。 一个列可以以下面的形式被引用： correlation.columnname correlation 是一个表（有可能以一个模式名限定）的名字，或者是在`FROM`子句中为一个表定义的别名。如果列名在当前索引所使用的表中都是唯一的，关联名称和分隔用的句点可以被忽略（另见 第 7 章）。 一个位置参数引用被用来指示一个由SQL语句外部提供的值。参数被用于SQL函数定义和预备查询中。某些客户端库还支持独立于SQL命令字符串来指定数据值，在这种情况中参数被用来引用那些线外数据值。一个参数引用的形式是： $number 例如，考虑一个函数`dept`的定义： CREATE FUNCTION dept(text) RETURNS dept AS $$ SELECT * FROM dept WHERE name = $1 $$ LANGUAGE SQL; 这里 $1 引用函数被调用时第一个函数参数的值。 如果一个表达式得到了一个数组类型的值，那么可以抽取出该数组值的一个特定元素： expression[subscript] 或者抽取出多个相邻元素（一个“数组切片”）： expression[lower_subscript:upper_subscript] （这里，方括号 [ ] 表示其字面意思）。每一个 下标 自身是一个表达式，它将四舍五入到最接近的整数值。 通常，数组 表达式 必须被加上括号，但是当要被加下标的表达式只是一个列引用或位置参数时，括号可以被忽略。还有，当原始数组是多维时，多个下标可以被连接起来。例如： mytable.arraycolumn[4] mytable.two_d_column[17][34] $1[10:42] (arrayfunction(a,b))[42] 最后一个例子中的圆括号是必需的。详见 第 8.15 节。 如果一个表达式得到一个组合类型（行类型）的值，那么可以抽取该行的指定域: expression.fieldname 通常行 表达式 必须被加上括号，但是当该表达式是仅从一个表引用或位置参数选择时，圆括号可以被忽略。例如： mytable.mycolumn $1.somecolumn (rowfunction(a,b)).col3 （因此，一个被限定的列引用实际上只是域选择语法的一种特例）。一种重要的特例是从一个组合类型的表列中抽取一个域： (compositecol).somefield (mytable.compositecol).somefield 这里需要圆括号来显示 compositecol 是一个列名而不是一个表名，在第二种情况中则是显示 mytable 是一个表名而不是一个模式名。 你可以通过书写 .* 来请求一个组合值的所有域： (compositecol).* 这种记法的行为根据上下文会有不同，详见 第 8.16.5 节。 对于一次操作符调用，有两种可能的语法： expression operator expression（二元中缀操作符） operator expression（一元前缀操作符） 其中 operator 记号遵循 第 4.1.3 节 的语法规则，或者是关键词`AND`、`OR`和`NOT`之一，或者是一个如下形式的受限定操作符名： OPERATOR(schema.operatorname) 哪个特定操作符存在以及它们是一元的还是二元的取决于由系统或用户定义的那些操作符。 第 9 章 描述了内建操作符。 一个函数调用的语法是一个函数的名称（可能受限于一个模式名）后面跟上封闭于圆括号中的参数列表： function_name ([expression [, expression ... ]] ) 例如，下面会计算 2 的平方根： sqrt(2) 当在一个某些用户不信任其他用户的数据库中发出查询时，在编写函数调用时应遵守 第 10.3 节 中的安全防范措施。 内建函数的列表在 第 9 章 中。其他函数可以由用户增加。 参数可以有选择地被附加名称。详见 第 4.3 节。 注意 一个采用单一组合类型参数的函数可以被有选择地称为域选择语法，并且反过来域选择可以被写成函数的风格。也就是说，记号`col(table)`和`table.col`是可以互换的。这种行为是非 SQL 标准的但是在IvorySQL中被提供，因为它允许函数的使用来模拟“计算域”。 一个 聚集表达式 表示在由一个查询选择的行上应用一个聚集函数。一个聚集函数将多个输入减少到一个单一输出值，例如对输入的求和或平均。一个聚集表达式的语法是下列之一： aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ FILTER ( WHERE filter_clause ) ] 这里 aggregate_name 是一个之前定义的聚集（可能带有一个模式名限定），并且 expression 是任意自身不包含聚集表达式的值表达式或一个窗口函数调用。可选的 order_by_clause 和 filter_clause 描述如下。 第一种形式的聚集表达式为每一个输入行调用一次聚集。第二种形式和第一种相同，因为 ALL 是默认选项。第三种形式为输入行中表达式的每一个可区分值（或者对于多个表达式是值的可区分集合）调用一次聚集。第四种形式为每一个输入行调用一次聚集，因为没有特定的输入值被指定，它通常只对于 count() 聚集函数有用。最后一种形式被用于 *有序集 聚集函数，其描述如下。 大部分聚集函数忽略空输入，这样其中一个或多个表达式得到空值的行将被丢弃。除非另有说明，对于所有内建聚集都是这样。 例如，count(*) 得到输入行的总数。 count(f1) 得到输入行中 f1 为非空的数量，因为 count 忽略空值。而 count(distinct f1) 得到 f1 的非空可区分值的数量。 一般地，交给聚集函数的输入行是未排序的。在很多情况中这没有关系，例如不管接收到什么样的输入， min 总是产生相同的结果。但是，某些聚集函数（例如 array_agg 和 string_agg ）依据输入行的排序产生结果。当使用这类聚集时，可选的 order_by_clause 可以被用来指定想要的顺序。order_by_clause 与查询级别的 ORDER BY 子句（如 第 7.5 节 所述）具有相同的语法，除非它的表达式总是仅有表达式并且不能是输出列名称或编号。例如： SELECT array_agg(a ORDER BY b DESC) FROM table; 在处理多参数聚集函数时，注意 ORDER BY 出现在所有聚集参数之后。例如，要这样写： SELECT string_agg(a, ',' ORDER BY a) FROM table; 而不能这样写： SELECT string_agg(a ORDER BY a, ',') FROM table; -- 不正确 后者在语法上是合法的，但是它表示用两个`ORDER BY`键来调用一个单一参数聚集函数（第二个是无用的，因为它是一个常量）。 如果在 order_by_clause 之外指定了 DISTINCT ，那么所有的 ORDER BY 表达式必须匹配聚集的常规参数。也就是说，你不能在 DISTINCT 列表没有包括的表达式上排序。 注意 在一个聚集函数中指定 DISTINCT 以及 ORDER BY 的能力是一种IvorySQL扩展。按照到目前为止的描述，如果一般目的和统计性聚集中 排序是可选的，在要为它排序输入行时可以在该聚集的常规参数 列表中放置 ORDER BY 。有一个聚集函数的子集叫做 有序集聚集 ，它要求一个 order_by_clause，通常是因为该聚集的计算只对其输入行的特定顺序有意义。有序集聚集的典 型例子包括排名和百分位计算。按照上文的最后一种语法，对于 一个有序集聚集， order_by_clause 被写在 WITHIN GROUP (…​) 中。 order_by_clause 中的表达式 会像普通聚集参数一样对每一个输入行计算一次，按照每个 order_by_clause 的要求排序并 且交给该聚集函数作为输入参数（这和非 WITHIN GROUP order_by_clause 的情况不同，在其中表达 式的结果不会被作为聚集函数的参数）。如果有在 WITHIN GROUP 之前的参数表达式，会把它们称 为 直接参数 以便与列在 order_by_clause 中的 聚集参数 相区分。与普通聚集参数不同，针对 每次聚集调用只会计算一次直接参数，而不是为每一个输入行 计算一次。这意味着只有那些变量被 GROUP BY 分组时，它们才能包含这些变量。这个限制同样适用于根本不在 一个聚集表达式内部的直接参数。直接参数通常被用于百分数 之类的东西，它们只有作为每次聚集计算用一次的单一值才有意 义。直接参数列表可以为空，在这种情况下，写成 () 而不是 (*)（实际上 IvorySQL接受两种拼写，但是只有第一种符合 SQL 标准）。 有序集聚集的调用例子： SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_cont ----------------- 50489 这会从表 households 的 income 列得到第 50 个百分位或者中位的值。 这里`0.5`是一个直接参数，对于百分位部分是一个 在不同行之间变化的值的情况它没有意义。 如果指定了 FILTER ，那么只有对 filter_clause 计算为真的输入行会被交给该聚集函数，其他行会被丢弃。例如： SELECT count(*) AS unfiltered, count(*) FILTER (WHERE i < 5) AS filtered FROM generate_series(1,10) AS s(i); unfiltered | filtered ------------+---------- 10 | 4 (1 row) 预定义的聚集函数在 第 9.21 节 中描述。其他聚集函数可以由用户增加。 一个聚集表达式只能出现在 SELECT 命令的结果列表或是 HAVING 子句中。在其他子句（如 WHERE ）中禁止使用它，因为那些子句的计算在逻辑上是在聚集的结果被形成之前。 当一个聚集表达式出现在一个子查询中（见 第 4.2.11 节 和 第 9.23 节），聚集通常在该子查询的行上被计算。但是如果该聚集的参数（以及 filter_clause，如果有）只包含外层变量则会产生一个异常：该聚集则属于最近的那个外层，并且会在那个查询的行上被计算。该聚集表达式从整体上则是对其所出现于的子查询的一种外层引用，并且在那个子查询的任意一次计算中都作为一个常量。只出现在结果列表或 HAVING 子句的限制适用于该聚集所属的查询层次。 一个*窗口函数调用*表示在一个查询选择的行的某个部分上应用一个聚集类的函数。和非窗口聚集函数调用不同，这不会被约束为将被选择的行分组为一个单一的输出行 — 在查询输出中每一个行仍保持独立。不过，窗口函数能够根据窗口函数调用的分组声明（ PARTITION BY 列表）访问属于当前行所在分组中的所有行。一个窗口函数调用的语法是下列之一： function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) 其中 window_definition 的语法是 [ existing_window_name ] [ PARTITION BY expression [, ...] ] [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ] [ frame_clause ] 可选的 frame_clause 是下列之一 { RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ] { RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end [ frame_exclusion ] 其中 frame_start 和 frame_end 可以是下面形式中的一种 UNBOUNDED PRECEDING offset PRECEDING CURRENT ROW offset FOLLOWING UNBOUNDED FOLLOWING 而 frame_exclusion 可以是下列之一 EXCLUDE CURRENT ROW EXCLUDE GROUP EXCLUDE TIES EXCLUDE NO OTHERS 这里，expression 表示任何自身不含有窗口函数调用的值表达式。 window_name 是对定义在查询的 WINDOW 子句中的一个命名窗口声明的引用。还可以使用在 WINDOW 子句中定义命名窗口的相同语法在圆括号内给定一个完整的 window_definition，详见 SELECT 参考页。值得指出的是，OVER wname 并不严格地等价于 OVER (wname …​)，后者表示复制并修改窗口定义，并且在被引用窗口声明包括一个帧子句时会被拒绝。 PARTITION BY 选项将查询的行分组成为 分区，窗口函数会独立地处理它们。PARTITION BY 工作起来类似于一个查询级别的 GROUP BY 子句，不过它的表达式总是只是表达式并且不能是输出列的名称或编号。如果没有 PARTITION BY，该查询产生的所有行被当作一个单一分区来处理。ORDER BY 选项决定被窗口函数处理的一个分区中的行的顺序。它工作起来类似于一个查询级别的 ORDER BY 子句，但是同样不能使用输出列的名称或编号。如果没有 ORDER BY，行将被以未指定的顺序被处理。 frame_clause 指定构成 窗口帧 的行集合，它是当前分区的一个子集，窗口函数将作用在该帧而不是整个分区。帧中的行集合会随着哪一行是当前行而变化。在 RANGE、ROWS 或者 GROUPS 模式中可以指定帧，在每一种情况下，帧的范围都是从 frame_start 到 frame_end。如果 frame_end 被省略，则末尾默认为 CURRENT ROW。 UNBOUNDED PRECEDING 的一个 frame_start 表示该帧开始于分区的第一行，类似地 UNBOUNDED FOLLOWING 的一个 frame_end 表示该帧结束于分区的最后一行。 在 RANGE 或 GROUPS 模式中，CURRENT ROW 的一个 frame_start 表示帧开始于当前行的第一个 平级 行（被窗口的 ORDER BY 子句排序为与当前行等效的行），而 CURRENT ROW 的一个 frame_end 表示帧结束于当前行的最后一个平级行。在 ROWS 模式中，CURRENT ROW 就表示当前行。 在 offset PRECEDING 以及 offset FOLLOWING 帧选项中，offset 必须是一个不包含任何变量、聚集函数或者窗口函数的表达式。offset 的含义取决于帧模式： 在 ROWS 模式中， offset 必须得到一个非空、非负的整数，并且该选项表示帧开始于当前行之前或者之后指定数量的行。 在 GROUPS 模式中，offset 也必须得到一个非空、非负的整数，并且该选项表示帧开始于当前行的平级组之前或者之后指定数量的*平级组*，这里平级组是在 ORDER BY 顺序中等效的行集合（要使用 GROUPS 模式，在窗口定义中就必须有一个 ORDER BY 子句）。 在 RANGE 模式中，这些选项要求 ORDER BY 子句正好指定一列。offset 指定当前行中那一列的值与它在该帧中前面或后面的行中的列值的最大差值。offset 表达式的数据类型会随着排序列的数据类型而变化。对于数字的排序列，它通常是与排序列相同的类型，但对于日期时间排序列它是一个 interval。例如，如果排序列是类型 date 或者 timestamp，我们可以写 RANGE BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING。offset 仍然要求是非空且非负，不过“非负”的含义取决于它的数据类型。 在任何一种情况下，到帧末尾的距离都受限于到分区末尾的距离，因此对于离分区末尾比较近的行来说，帧可能会包含比较少的行。 注意在 ROWS 以及 GROUPS 模式中， 0 PRECEDING 和 0 FOLLOWING 与 CURRENT ROW 等效。通常在 RANGE 模式中，这个结论也成立（只要有一种合适的、与数据类型相关的“零”的含义）。 frame_exclusion 选项允许当前行周围的行被排除在帧之外，即便根据帧的开始和结束选项应该把它们包括在帧中。EXCLUDE CURRENT ROW 会把当前行排除在帧之外。EXCLUDE GROUP 会把当前行以及它在顺序上的平级行都排除在帧之外。EXCLUDE TIES 把当前行的任何平级行都从帧中排除，但不排除当前行本身。EXCLUDE NO OTHERS 只是明确地指定不排除当前行或其平级行的这种默认行为。 默认的帧选项是 RANGE UNBOUNDED PRECEDING，它和 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW 相同。如果使用 ORDER BY，这会把该帧设置为从分区开始一直到当前行的最后一个 ORDER BY 平级行的所有行。如果不使用 ORDER BY，就意味着分区中所有的行都被包括在窗口帧中，因为所有行都成为了当前行的平级行。 限制是 frame_start 不能是 UNBOUNDED FOLLOWING 、frame_end 不能是 UNBOUNDED PRECEDING，并且在上述 frame_start 和 frame_end 选项的列表中 frame_end 选择不能早于 frame_start 选择出现 — 例如不允许 RANGE BETWEEN CURRENT ROW AND offset PRECEDING，但允许 ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING ，虽然它不会选择任何行。 如果指定了 FILTER ，那么只有对 filter_clause 计算为真的输入行会被交给该窗口函数，其他行会被丢弃。只有是聚集的窗口函数才接受 FILTER 。 内建的窗口函数在 表 9.60 中介绍。用户可以加入其他窗口函数。此外，任何内建的或者用户定义的通用聚集或者统计性聚集都可以被用作窗口函数（有序集和假想集聚集当前不能被用作窗口函数）。 使用 * 的语法被用来把参数较少的聚集函数当作窗口函数调用，例如 count(*) OVER (PARTITION BY x ORDER BY y)。星号（*）通常不被用于窗口相关的函数。窗口相关的函数不允许在函数参数列表中用 DISTINCT 或 ORDER BY。 只有在 SELECT 列表和查询的 ORDER BY 子句中才允许窗口函数调用。 更多关于窗口函数的信息可以在 第 3.5 节、 第 9.22 节 以及 第 7.2.5 节 中找到。 一个类型造型指定从一种数据类型到另一种数据类型的转换。IvorySQL接受两种等价的类型造型语法： CAST ( expression AS type ) expression::type CAST 语法遵从 SQL，而用 :: 的语法是IvorySQL的历史用法。 当一个造型被应用到一种未知类型的值表达式上时，它表示一种运行时类型转换。只有已经定义了一种合适的类型转换操作时，该造型才会成功。注意这和常量的造型（如 第 4.1.2.7 节 中所示）使用不同。应用于一个未修饰串文字的造型表示一种类型到一个文字常量值的初始赋值，并且因此它将对任意类型都成功（如果该串文字的内容对于该数据类型的输入语法是可接受的）。 如果一个值表达式必须产生的类型没有歧义（例如当它被指派给一个表列），通常可以省略显式类型造型，在这种情况下系统会自动应用一个类型造型。但是，只有对在系统目录中被标记为“OK to apply implicitly”的造型才会执行自动造型。其他造型必须使用显式造型语法调用。这种限制是为了防止出人意料的转换被无声无息地应用。 还可以用像函数的语法来指定一次类型造型： typename ( expression ) 不过，这只对那些名字也作为函数名可用的类型有效。例如，double precision 不能以这种方式使用，但是等效的 float8 可以。还有，如果名称 interval、time 和 timestamp 被用双引号引用，那么由于语法冲突的原因，它们只能以这种风格使用。因此，函数风格的造型语法的使用会导致不一致性并且应该尽可能被避免。 注意 函数风格的语法事实上只是一次函数调用。当两种标准造型语法之一被用来做一次运行时转换时，它将在内部调用一个已注册的函数来执行该转换。简而言之，这些转换函数具有和它们的输出类型相同的名字，并且因此“函数风格的语法”无非是对底层转换函数的一次直接调用。显然，一个可移植的应用不应当依赖于它。详见 CREATE CAST。 COLLATE 子句会重载一个表达式的排序规则。它被追加到它适用的表达式： expr COLLATE collation 这里 collation 可能是一个受模式限定的标识符。COLLATE 子句比操作符绑得更紧，需要时可以使用圆括号。 如果没有显式指定排序规则，数据库系统会从表达式所涉及的列中得到一个排序规则，如果该表达式没有涉及列，则会默认采用数据库的默认排序规则。 COLLATE 子句的两种常见使用是重载 ORDER BY 子句中的排序顺序，例如： SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE \"C\"; 以及重载具有区域敏感结果的函数或操作符调用的排序规则，例如： SELECT * FROM tbl WHERE a > 'foo' COLLATE \"C\"; 注意在后一种情况中，COLLATE 子句被附加到我们希望影响的操作符的一个输入参数上。COLLATE 子句被附加到该操作符或函数调用的哪个参数上无关紧要，因为被操作符或函数应用的排序规则是考虑所有参数得来的，并且一个显式的 COLLATE 子句将重载所有其他参数的排序规则（不过，附加非匹配 COLLATE 子句到多于一个参数是一种错误。详见 第 24.2 节）。因此，这会给出和前一个例子相同的结果： SELECT * FROM tbl WHERE a COLLATE \"C\" > 'foo'; 但是这是一个错误： SELECT * FROM tbl WHERE (a > 'foo') COLLATE \"C\"; 因为它尝试把一个排序规则应用到 > 操作符的结果，而它的数据类型是非可排序数据类型 boolean。 一个标量子查询是一种圆括号内的普通 SELECT 查询，它刚好返回一行一列（关于书写查询可见 第 7 章）。`SELECT`查询被执行并且该单一返回值被使用在周围的值表达式中。将一个返回超过一行或一列的查询作为一个标量子查询使用是一种错误（但是如果在一次特定执行期间该子查询没有返回行则不是错误，该标量结果被当作为空）。该子查询可以从周围的查询中引用变量，这些变量在该子查询的任何一次计算中都将作为常量。对于其他涉及子查询的表达式还可见 第 9.23 节。 例如，下列语句会寻找每个州中最大的城市人口： SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name) FROM states; 一个数组构造器是一个能构建一个数组值并且将值用于它的成员元素的表达式。一个简单的数组构造器由关键词 ARRAY、一个左方括号 [ 、一个用于数组元素值的表达式列表（用逗号分隔）以及最后的一个右方括号 ] 组成。例如： SELECT ARRAY[1,2,3+4]; array --------- {1,2,7} (1 row) 默认情况下，数组元素类型是成员表达式的公共类型，使用和 UNION 或 CASE 结构（见 第 10.5 节）相同的规则决定。你可以通过显式将数组构造器造型为想要的类型来重载，例如： SELECT ARRAY[1,2,22.7]::integer[]; array ---------- {1,2,23} (1 row) 这和把每一个表达式单独地造型为数组元素类型的效果相同。关于造型的更多信息请见 第 4.2.9 节。 多维数组值可以通过嵌套数组构造器来构建。在内层的构造器中，关键词 ARRAY 可以被忽略。例如，这些语句产生相同的结果： SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]]; array --------------- {{1,2},{3,4}} (1 row) SELECT ARRAY[[1,2],[3,4]]; array --------------- {{1,2},{3,4}} (1 row) 因为多维数组必须是矩形的，处于同一层次的内层构造器必须产生相同维度的子数组。任何被应用于外层 ARRAY 构造器的造型会自动传播到所有的内层构造器。 多维数组构造器元素可以是任何得到一个正确种类数组的任何东西，而不仅仅是一个子- ARRAY 结构。例如： CREATE TABLE arr(f1 int[], f2 int[]); INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]); SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr; array ------------------------------------------------ {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}} (1 row) 你可以构造一个空数组，但是因为无法得到一个无类型的数组，你必须显式地把你的空数组造型成想要的类型。例如： SELECT ARRAY[]::integer[]; array ------- {} (1 row) 也可以从一个子查询的结果构建一个数组。在这种形式中，数组构造器被写为关键词 ARRAY 后跟着一个加了圆括号（不是方括号）的子查询。例如： SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%'); array ------------------------------------------------------------------ {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412} (1 row) SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i)); array ---------------------------------- {{1,2},{2,4},{3,6},{4,8},{5,10}} (1 row) 子查询必须返回一个单一列。如果子查询的输出列是非数组类型， 结果的一维数组将为该子查询结果中的每一行有一个元素， 并且有一个与子查询的输出列匹配的元素类型。如果子查询的输出列 是一种数组类型，结果将是同类型的一个数组，但是要高一个维度。 在这种情况下，该子查询的所有行必须产生同样维度的数组，否则结果 就不会是矩形形式。 用 ARRAY 构建的一个数组值的下标总是从一开始。更多关于数组的信息，请见 第 8.15 节。 一个行构造器是能够构建一个行值（也称作一个组合类型）并用值作为其成员域的表达式。一个行构造器由关键词 ROW、一个左圆括号、用于行的域值的零个或多个表达式（用逗号分隔）以及最后的一个右圆括号组成。例如： SELECT ROW(1,2.5,'this is a test'); 当在列表中有超过一个表达式时，关键词 ROW 是可选的。 一个行构造器可以包括语法 rowvalue .*，它将被扩展为该行值的元素的一个列表，就像在一个顶层 SELECT 列表（见 第 8.16.5 节）中使用 .* 时发生的事情一样。例如，如果表 t 有列 f1 和 f2，那么这些是相同的： SELECT ROW(t.*, 42) FROM t; SELECT ROW(t.f1, t.f2, 42) FROM t; 注意 在PostgreSQL 8.2 以前，.* 语法不会在行构造器中被扩展，这样写 ROW(t.*, 42) 会创建一个有两个域的行，其第一个域是另一个行值。新的行为通常更有用。如果你需要嵌套行值的旧行为，写内层行值时不要用 .*，例如 ROW(t, 42)。 默认情况下，由一个 ROW 表达式创建的值是一种匿名记录类型。如果必要，它可以被造型为一种命名的组合类型 — 或者是一个表的行类型，或者是一种用 CREATE TYPE AS 创建的组合类型。为了避免歧义，可能需要一个显式造型。例如： CREATE TABLE mytable(f1 int, f2 float, f3 text); CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- 不需要造型因为只有一个 getf1() 存在 SELECT getf1(ROW(1,2.5,'this is a test')); getf1 ------- 1 (1 row) CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric); CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- 现在我们需要一个造型来指示要调用哪个函数： SELECT getf1(ROW(1,2.5,'this is a test')); ERROR: function getf1(record) is not unique SELECT getf1(ROW(1,2.5,'this is a test')::mytable); getf1 ------- 1 (1 row) SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype)); getf1 ------- 11 (1 row) 行构造器可以被用来构建存储在一个组合类型表列中的组合值，或者被传递给一个接受组合参数的函数。还有，可以比较两个行值，或者用 IS NULL 或 IS NOT NULL 测试一个行，例如： SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows 详见 第 9.24 节。如 第 9.23 节 中所讨论的，行构造器也可以被用来与子查询相连接。 子表达式的计算顺序没有被定义。特别地，一个操作符或函数的输入不必按照从左至右或其他任何固定顺序进行计算。 此外，如果一个表达式的结果可以通过只计算其一部分来决定，那么其他子表达式可能完全不需要被计算。例如，如果我们写： SELECT true OR somefunc(); 那么 somefunc() 将（可能）完全不被调用。如果我们写成下面这样也是一样： SELECT somefunc() OR true; 注意这和一些编程语言中布尔操作符从左至右的“短路”不同。 因此，在复杂表达式中使用带有副作用的函数是不明智的。在 WHERE 和 HAVING 子句中依赖副作用或计算顺序尤其危险，因为在建立一个执行计划时这些子句会被广泛地重新处理。这些子句中布尔表达式（ AND / OR / NOT 的组合）可能会以布尔代数定律所允许的任何方式被重组。 当有必要强制计算顺序时，可以使用一个 CASE 结构（见 第 9.18 节）。例如，在一个 WHERE 子句中使用下面的方法尝试避免除零是不可靠的： SELECT ... WHERE x > 0 AND y/x > 1.5; 但是这是安全的： SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END; 一个以这种风格使用的 CASE 结构将使得优化尝试失败，因此只有必要时才这样做（在这个特别的例子中，最好通过写 y > 1.5*x 来回避这个问题）。 不过，CASE 不是这类问题的万灵药。上述技术的一个限制是， 它无法阻止常量子表达式的提早计算。如 第 38.7 节 中所述，当查询被规划而不是被执行时，被标记成 IMMUTABLE 的函数和操作符可以被计算。因此 SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab; 很可能会导致一次除零失败，因为规划器尝试简化常量子表达式。即便是 表中的每一行都有 x > 0（这样运行时永远不会进入到 ELSE 分支）也是这样。 虽然这个特别的例子可能看起来愚蠢，没有明显涉及常量的情况可能会发生 在函数内执行的查询中，因为因为函数参数的值和本地变量可以作为常量 被插入到查询中用于规划目的。例如，在PL/pgSQL函数 中，使用一个 IF -THEN -ELSE 语句来 保护一种有风险的计算比把它嵌在一个 CASE 表达式中要安全得多。 另一个同类型的限制是，一个 CASE 无法阻止其所包含的聚集表达式 的计算，因为在考虑 SELECT 列表或 HAVING 子句中的 其他表达式之前，会先计算聚集表达式。例如，下面的查询会导致一个除零错误， 虽然看起来好像已经这种情况加以了保护： SELECT CASE WHEN min(employees) > 0 THEN avg(expenses / employees) END FROM departments; min() 和 avg() 聚集会在所有输入行上并行地计算， 因此如果任何行有 employees 等于零，在有机会测试 min() 的结果之前，就会发生除零错误。取而代之的是，可以使用 一个 WHERE 或 FILTER 子句来首先阻止有问题的输入行到达一个聚集函数。 IvorySQL允许带有命名参数的函数被使用 位置 或 命名 记号法调用。命名记号法对于有大量参数的函数特别有用，因为它让参数和实际参数之间的关联更明显和可靠。在位置记号法中，书写一个函数调用时，其参数值要按照它们在函数声明中被定义的顺序书写。在命名记号法中，参数根据名称匹配函数参数，并且可以以任何顺序书写。对于每一种记法，还要考虑函数参数类型的效果，这些在 第 10.3 节 有介绍。 在任意一种记号法中，在函数声明中给出了默认值的参数根本不需要在调用中写出。但是这在命名记号法中特别有用，因为任何参数的组合都可以被忽略。而在位置记号法中参数只能从右往左忽略。 IvorySQL也支持 混合 记号法，它组合了位置和命名记号法。在这种情况中，位置参数被首先写出并且命名参数出现在其后。 下列例子将展示所有三种记号法的用法： CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false) RETURNS text AS $$ SELECT CASE WHEN $3 THEN UPPER($1 || ' ' || $2) ELSE LOWER($1 || ' ' || $2) END; $$ LANGUAGE SQL IMMUTABLE STRICT; 函数 concat_lower_or_upper 有两个强制参数，a 和 b。此外，有一个可选的参数 uppercase，其默认值为 false。a 和 b 输入将被串接，并且根据 uppercase 参数被强制为大写或小写形式。这个函数的剩余细节对这里并不重要（详见 第 38 章）。 在IvorySQL中，位置记号法是给函数传递参数的传统机制。一个例子： SELECT concat_lower_or_upper('Hello', 'World', true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 所有参数被按照顺序指定。结果是大写形式，因为 uppercase 被指定为 true。另一个例子： SELECT concat_lower_or_upper('Hello', 'World'); concat_lower_or_upper ----------------------- hello world (1 row) 这里，uppercase 参数被忽略，因此它接收它的默认值 false，并导致小写形式的输出。在位置记号法中，参数可以按照从右往左被忽略并且因此而得到默认值。 在命名记号法中，每一个参数名都用 ⇒ 指定来把它与参数表达式分隔开。例如： SELECT concat_lower_or_upper(a => 'Hello', b => 'World'); concat_lower_or_upper ----------------------- hello world (1 row) 再次，参数 uppercase 被忽略，因此它被隐式地设置为 false。使用命名记号法的一个优点是参数可以用任何顺序指定，例如： SELECT concat_lower_or_upper(a => 'Hello', b => 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) SELECT concat_lower_or_upper(a => 'Hello', uppercase => true, b => 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 为了向后兼容性，基于 \":=\" 的旧语法仍被支持： SELECT concat_lower_or_upper(a := 'Hello', uppercase := true, b := 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 混合记号法组合了位置和命名记号法。不过，正如已经提到过的，命名参数不能超越位置参数。例如： SELECT concat_lower_or_upper('Hello', 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 在上述查询中，参数 a 和 b 被以位置指定，而 uppercase 通过名字指定。在这个例子中，这只增加了一点文档。在一个具有大量带默认值参数的复杂函数中，命名的或混合的记号法可以节省大量的书写并且减少出错的机会。 注意 命名的和混合的调用记号法当前不能在调用聚集函数时使用（但是当聚集函数被用作窗口函数时它们可以被使用）。 ALTER TABLE [ IF EXISTS ] [ ONLY ] name [ * ] action; action: ADD ( add_coldef [, ... ] ) | MODIFY ( modify_coldef [, ... ] ) | DROP [ COLUMN ] ( column_name [, ... ] ) add_coldef: column_name data_type modify_coldef: column_name data_type alter_using alter_using: USING expression name 表名. column_name 列名. data_type 列类型. expression 值表达式. ADD keyword 增加表的列, 可以增加一个列或多个列. MODIFY keyword 修改表的列, 可以修改一个列或多个列. DROP keyword 删除表的列, 可以删除一个列或多个列. USING keyword 修改列的值. ADD: create table tb_test1(id int, flg char(10)); alter table tb_test1 add (name varchar); ALTER TABLE tb_test1 ADD adress varchar, ADD num int, ADD flg1 char; \\d tb_test1 Table \"public.tb_test1\" Column | Type | Collation | Nullable | Default --------+-----------------+-----------+----------+--------- id | pg_catalog.int4 | | | flg | char(10) | | | name | varchar2(4000) | | | adress | varchar2(4000) | | | num | pg_catalog.int4 | | | flg1 | char(1) | | | MODIFY: create table tb_test2(id int, flg char(10), num varchar); insert into tb_test2 values('1', 2, '3'); ALTER TABLE tb_test2 ALTER COLUMN id TYPE char; \\d tb_test2 Table \"public.tb_test2\" Column | Type | Collation | Nullable | Default --------+----------------+-----------+----------+--------- id | char(1) | | | flg | char(10) | | | num | varchar2(4000) | | | DROP: create table tb_test3(id int, flg1 char(10), flg2 char(11), flg3 char(12), flg4 char(13), flg5 char(14), flg6 char(15)); ALTER TABLE tb_test3 DROP id; \\d tb_test3 Table \"public.tb_test3\" Column | Type | Collation | Nullable | Default --------+----------+-----------+----------+--------- flg1 | char(10) | | | flg2 | char(11) | | | flg3 | char(12) | | | flg4 | char(13) | | | flg5 | char(14) | | | flg6 | char(15) | | | [ WITH [ RECURSIVE ] with_query [, ...] ] DELETE [ FROM ] [ ONLY ] table_name [ * ] [ [ AS ] alias ] [ USING using_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name 表名. alias 表别名. using_list 一个表表达式的列表，它允许在WHERE条件中出现 来自其他表的列. condition 一个返回boolean类型值的表达式. cursor_name 要在WHERE CURRENT OF情况中使用的游标的名称. output_expression 在每一行被删除后，会被DELETE计算并且返回的表达式. output_name 被返回列的名称. create table tb_test4(id int, flg char(10)); insert into tb_test4 values(1, '2'), (5, '6'); delete from tb_test4 where id = 1; table tb_test4; id | flg ----+------------ 5 | 6 (1 row) [ WITH [ RECURSIVE ] with_query [, ...] ] UPDATE [ ONLY ] table_name [ * ] [ [ AS ] alias ] SET { [ table_name | alias ] column_name = { expression | DEFAULT } | ( [ table_name | alias ] column_name [, ...] ) = [ ROW ] ( { expression | DEFAULT } [, ...] ) | ( [ table_name | alias ] column_name [, ...] ) = ( sub-SELECT ) } [, ...] [ FROM from_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name 表名. alias 表别名. column_name 列名. expression 值表达式. sub-SELECT select子句. from_list 表表达式. condition 一个返回boolean类型值的表达式. cursor_name 要在WHERE CURRENT OF情况中使用的游标 的名称. output_expression 在每一行被删除后，会被DELETE计算并且返回的表达式. output_name 被返回列的名称. create table tb_test5(id int, flg char(10)); insert into tb_test5 values(1, '2'), (3, '4'), (5, '6'); update tb_test5 a set a.id = 33 where a.id = 3; table tb_test5; Id | flg ----+------------ 1 | 2 5 | 6 33 | 4 (3 rows) set compatible_mode to oracle; create table students(student_id varchar(20) primary key , student_name varchar(40), student_pid int); select student_id,student_name from students group by student_id; ERROR: column \"students.student_name\" must appear in the GROUP BY clause or be used in an aggregate function SELECT 100 AS value FROM DUAL UNION SELECT 200 AS value FROM DUAL UNION SELECT 100 AS value FROM DUAL; value ------- 100 200 (2 rows) select_statement MINUS [ ALL | DISTINCT ] select_statement; select_statement 任何没有ORDER BY、LIMIT、 FOR NO KEY UPDATE、FOR UPDATE、 FOR SHARE和FOR KEY SHARE子句的 SELECT语句. ALL keyword 包含重复行结果. DISTINCT keyword 显示的消除重复行. select * from generate_series(1, 3) g(i) MINUS select * from generate_series(1, 3) g(i) where i = 1; i --- 2 3 (2 rows) 使用 q\\' 转义特殊字符。q\\' 转义字符通常在! [] {} () \\<> 和其他转义字符之后使用, 您也可以使用 \\, 也可以使用字母, 数字, \\=, +, -, *, \\&, \\$, \\%, #, 等, 不允许使用空格。 select q''' is goog ''; ?column? ------------ ' is goog (1 row) SELECT [ database {schema} | schema ] sequence {nextval | currval}; sequence 序列名. create sequence sq; select sq.nextval; nextval ----------- 1 (1 row) select sq.currval; nextval ----------- 1 (1 row) 将给定的不带时区的时间戳转换为指定的带时区的时间戳，如果指定时区或者时间戳为NULL，则返回NULL。 参数 描述 day 不带时区的时间戳 tz 指定的时区 select from_tz('2021-11-08 09:12:39','Asia/Shanghai') from dual; from_tz ----------------------------------- 2021-11-08 09:12:39 Asia/Shanghai (1 row) select from_tz('2021-11-08 09:12:39','SAST') from dual; from_tz -------------------------- 2021-11-08 09:12:39 SAST select from_tz(NULL,'SAST') from dual; from_tz --------- (1 row) select from_tz('2021-11-08 09:12:31',NULL) from dual; from_tz --------- (1 row) 获取当前数据库系统的时间戳。 select systimestamp(); systimestamp ------------------------------- 2021-12-02 14:38:59.879642+08 (1 row) select systimestamp; statement_timestamp ------------------------------- 2021-12-02 14:39:33.262828+08 将给定的带时区的时间戳转换为不带时区的UTC时间。 参数 描述 day 需要转化带时区的时间戳 select sys_extract_utc('2018-03-28 11:30:00.00 +09:00'::timestamptz) from dual; sys_extract_utc --------------------- 2018-03-28 02:30:00 (1 row) select sys_extract_utc(NULL) from dual; sys_extract_utc ----------------- (1 row) 获取当前会话的时区。 select sessiontimezone() from dual; sessiontimezone ----------------- PRC (1 row) set timezone to UTC; select sessiontimezone(); sessiontimezone ----------------- UTC (1 row) next_day 返回由格式名相同的第一个工作日的日期，该日期晚于当前日期。 无论日期的数据类型如何，返回类型始终为 DATE。 返回值具有与参数日期相同的小时、分钟和秒部分。 参数 描述 value 开始时间戳 weekday 星期几，可以是 \"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" 或者 0,1,2,3,4,5,6,0代表星期天 select next_day(to_timestamp('2020-02-29 14:40:50', 'YYYY-MM-DD HH24:MI:SS'), 'Tuesday') from dual; next_day --------------------- 2020-03-03 14:40:50 (1 row) select next_day('2020-07-01 19:43:51 +8'::timestamptz, 1) from dual; next_day --------------------- 2020-07-05 19:43:51 (1 row) last_day返回档期日期所在月份的最后一天。 参数 描述 value 指定的时间戳 select last_day(timestamp '2020-05-17 13:27:19') from dual; last_day --------------------- 2020-05-31 13:27:19 (1 row) select last_day('2020-11-29 19:20:40 +08'::timestamptz) from dual; last_day --------------------- 2020-11-30 19:20:40 (1 row) add_months 返回日期加上整数月份。 date 参数可以是日期时间值或任何可以隐式转换为 DATE 的值。 整数参数可以是整数或任何可以隐式转换为整数的值。 参数 描述 day oracle.date类型，需要被改变的时间戳 value 一个整形数据，需要增加的月数 select add_months(date '2020-02-15',7) from dual; add_months --------------------- 2020-09-15 00:00:00 (1 row) select add_months(timestamp '2018-12-15 19:12:09',12) from dual; add_months --------------------- 2019-12-15 19:12:09 (1 row) sysdate 返回数据库服务器的操作系统时间。 select sysdate; statement_sysdate --------------------- 2021-12-09 16:20:34 (1 row) select sysdate(); sysdate --------------------- 2021-12-09 16:21:39 (1 row) 转换第一个时区的时间到第二个时区的时间. 时区包括了 \"ast\", \"adt\", \"bst\", \"bdt\", \"cst\", \"cdt\", \"est\", \"edt\", \"gmt\", \"hst\", \"hdt\", \"mst\", \"mdt\", \"nst\", \"pst\", \"pdt\", \"yst\", \"ydt\". 参数 描述 day 需要被转换的时间戳 tz1 时间戳的时区 tz2 目标时区 select new_time(timestamp '2020-12-12 17:45:18', 'AST', 'ADT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'BST', 'BDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'CST', 'CDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) trunc函数返回一个日期, 按照指定格式被截断, fmt包括 \"Y\", \"YY\", \"YYY\", \"YYYY\",\"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". 参数 描述 value 指定的日期（oracle.date, timestamp, timestamptz） fmt 指定的格式, 如果被省略, 默认为 \"DDD\" select trunc(timestamp '2020-07-28 19:16:12', 'Q'); trunc --------------------- 2020-07-01 00:00:00 (1 row) select trunc(timestamptz '2020-09-27 18:30:21 + 08', 'MONTH'); trunc ------------------------ 2020-09-01 00:00:00+08 (1 row) round函数返回一个日期,按照指定的格式四舍五入, fmt 包括了 \"Y\", \"YY\", \"YYY\", \"YYYY\",\"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". 参数 描述 value 被转换的日期（oracle.date, timestamp, timestamptz） fmt 指定的格式, 如果被省略, 默认为 \"DDD\" select round(timestamp '2050-06-12 16:40:55', 'IYYY'); round --------------------- 2050-01-03 00:00:00 (1 row) TO_CHAR（str,[fmt]） 根据给定的格式将输入参数转换为 TEXT 数据类型的值。 如果省略 fmt，则数据将转换为系统默认格式的 TEXT 值。 如果 str 为 null，则该函数返回 null。 str 输入参数 （任意类型）。 fmt 输入格式参数,详见格式fmt。 select to_char('3 2:20:05'); to_char ----------------- 3 days 02:20:05 (1 row) select to_char('4.00'::numeric); to_char --------- 4 (1 row) select to_char(NULL); to_char --------- (1 row) select to_char(123,'xx'); to_char --------- 7b (1 row) TO_NUMBER(str,[fmt1]) 根据给定的格式将输入参数 str 转换为 NUMREIC 数据类型的值。 如果省略 fmt1，则数据将转换为系统默认格式的 NUMERIC 值。 如果 str 是 NUMERIC，则该函数返回 str。如果 str 计算结果为 null，则该函数返回 null。 如果它不能转换为 NUMERIC 数据类型，则该函数返回错误。 str 输入参数包括以下数据类型（double precision，numeric，text，integer等，但必须隐式转换为numeric）。 fmt1 输入格式参数，详见格式fmt1。 select to_number(1210.73::numeric, 9999.99::numeric); to_number ----------- 1210.73 (1 row) select to_number(NULL); to_number ----------- (1 row) select to_number('123'::text); to_number ----------- 123 (1 row) TO_DATE(str,[fmt]) 根据给定的格式将输入参数 str 转换为日期数据类型的值。 如果省略 fmt，则数据将转换为系统默认格式的日期值。 如果 str 为 null，则该函数返回 null。 如果 fmt 是 J，对于 Julian，则 char 必须是整数。 如果无法转换为 DATE，则该函数返回错误。 str 输入参数（integer，text，可以隐式转换为上述类型，符合日期格式的字符串）。 fmt 输入格式参数，详见格式fmt。 select to_date('50-11-28 ','RR-MM-dd '); to_date --------------------- 1950-11-28 00:00:00 (1 row) select to_date(2454336, 'J'); to_date --------------------- 2007-08-23 00:00:00 (1 row) select to_date('2019/11/22', 'yyyy-mm-dd'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('20-11-28 10:14:22','YY-MM-dd hh24:mi:ss'); to_date --------------------- 2020-11-28 10:14:22 (1 row) select to_date('2019/11/22'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('2019/11/27 10:14:22'); to_date --------------------- 2019-11-27 10:14:22 (1 row) select to_date('2020','RR'); to_date --------------------- 2020-01-01 00:00:00 (1 row) select to_date(NULL); to_date --------- (1 row) select to_date('-4712-07-23 14:31:23', 'syyyy-mm-dd hh24:mi:ss'); to_date ---------------------- -4712-07-23 14:31:23 (1 row) TO_TIMESTAMP(str,[fmt]) 根据给定的格式将输入参数 str 转换为不带时区的时间戳。 如果省略 fmt，则数据将转换为系统默认格式中不带时区值的时间戳。 如果 str 为 null，则该函数返回 null。 如果无法转换为不带时区的时间戳，则该函数返回错误。 str 输入参数（double precision,text，可以隐式转换为上述类型）。 fmt 输入格式参数，详见格式fmt。 select to_timestamp(1212121212.55::numeric); to_timestamp --------------------------- 2008-05-30 12:20:12.55 (1 row) select to_timestamp('2020/03/03 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp ------------------------ 2020-03-03 13:13:18 (1 row) select to_timestamp(NULL,NULL); to_timestamp -------------- (1 row) TO_YMINTERVAL(str) 将输入参数 str 时间间隔转换为年到月范围内的时间间隔。 只处理年月，其他部分省略。 如果输入参数为NULL，函数返回NULL，如果输入参数格式错误，函数返回错误。 str 输入参数（text，可以隐式转换为文本类型，必须是时间间隔格式。 兼容 SQL 标准的 SQL 间隔格式， ISO 持续时间格式与 ISO 8601:2004 标准兼容）。 select to_yminterval('P1Y-2M2D'); to_yminterval --------------- +000000000-10 (1 row) select to_yminterval('P1Y2M2D'); to_yminterval --------------- +000000001-02 (1 row) select to_yminterval('-01-02'); to_yminterval --------------- -000000001-02 (1 row) TO_DSINTERVAL(str) 将输入参数 str 的时间间隔转换为天到秒范围内的时间间隔。 输入参数包括：日、时、分、秒和微秒。 如果输入参数为NULL，函数返回NULL，如果输入参数包含年月或格式错误，函数返回错误。 str 输入参数（text，可以隐式转换为文本类型，必须是时间间隔格式。 兼容 SQL 标准的 SQL 间隔格式， ISO 持续时间格式与 ISO 8601:2004 标准兼容）。 select to_dsinterval('100 00 :02 :00'); to_dsinterval ------------------------------- +000000100 00:02:00.000000000 (1 row) select to_dsinterval('-100 00:02:00'); to_dsinterval ------------------------------- -000000100 00:02:00.000000000 (1 row) select to_dsinterval(NULL); to_dsinterval --------------- (1 row) TO_TIMESTAMP_TZ(str,[fmt]) 根据给定的格式将输入参数 str 转换为带时区的时间戳。 如果省略 fmt，则数据将转换为具有系统默认格式带时区值的时间戳。 如果 str 为 null，则该函数返回 null。 如果无法转换为带时区的时间戳，则该函数返回错误。 str 输入参数（text，可以隐式转换为文本类型）。 fmt 输入格式参数，详见格式fmt。 select to_timestamp_tz('2019','yyyy'); to_timestamp_tz ------------------------ 2019-01-01 00:00:00+08 (1 row) select to_timestamp_tz('2019-11','yyyy-mm'); to_timestamp_tz ------------------------ 2019-11-01 00:00:00+08 (1 row) select to_timestamp_tz('2003/12/13 10:13:18 +7:00'); to_timestamp_tz ------------------------ 2003-12-13 11:13:18+08 (1 row) select to_timestamp_tz('2019/12/13 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp_tz ------------------------ 2019-12-13 13:13:18+08 (1 row) select to_timestamp_tz(NULL); to_timestamp_tz ----------------- (1 row) GREATEST(expr1,expr2,…​) 获取一个或多个表达式的输入列表中的最大值。 如果任何 expr 的计算结果为 NULL，则该函数返回 NULL。 expr1` 输入参数（任意类型）。 `expr2` 输入参数（任意类型）。 `... select greatest('a','b','A','B'); greatest ---------- b (1 row) select greatest(',','.','/',';','!','@','?'); greatest ---------- @ (1 row) select greatest('瀚','高','数','据','库'); greatest ---------- 高 (1 row) SELECT greatest('HARRY', 'HARRIOT', 'HARRA'); greatest ---------- HARRY (1 row) SELECT greatest('HARRY', 'HARRIOT', NULL); greatest ---------- (1 row) SELECT greatest(1.1, 2.22, 3.33); greatest ---------- 3.33 (1 row) SELECT greatest('A', 6, 7, 5000, 'E', 'F','G') A; a --- G (1 row) LEAST(expr1,expr2,…​) 获取一个或多个表达式的输入列表中的最小值。 如果任何 expr 的计算结果为 NULL，则该函数返回 NULL。 expr1` 输入参数（任意类型）。 `expr2` 输入参数（任意类型）。 `... SELECT least(1,' 2', '3' ); least ------- 1 (1 row) SELECT least(NULL, NULL, NULL); least ------- (1 row) SELECT least('A', 6, 7, 5000, 'E', 'F','G') A; a ------ 5000 (1 row) select least(1,3,5,10); least ------- 1 (1 row) select least('a','A','b','B'); least ------- A (1 row) select least(',','.','/',';','!','@'); least ------- ! (1 row) select least('瀚','高','据','库'); least ------- 库 (1 row) SELECT least('HARRY', 'HARRIOT', NULL); least ------- (1 row) 模式 描述 HH 一天中的小时 （01-12） HH12 一天中的小时 （01-12） HH24 一天中的小时 （00-23） MI 分钟 （00-59）minute (00-59) SS 秒（00-59） MS 毫秒（000-999） US 微秒（000000-999999） SSSS 午夜后的秒（0-86399） AM, am, PM or pm 正午指示器（不带句号） A.M., a.m., P.M. or p.m. 正午指示器（带句号） Y,YYY 带逗号的年（4 位或者更多位） YYYY 年（4 位或者更多位） YYY 年的后三位 YY 年的后两位 Y 年的最后一位 IYYY ISO 8601 周编号方式的年（4 位或更多位） IYY ISO 8601 周编号方式的年的最后 3 位 IY ISO 8601 周编号方式的年的最后 2 位 I ISO 8601 周编号方式的年的最后一位 BC, bc, AD或者ad 纪元指示器（不带句号） B.C., b.c., A.D.或者a.d. 纪元指示器（带句号） MONTH 全大写形式的月名（空格补齐到 9 字符） Month 全首字母大写形式的月名（空格补齐到 9 字符） month 全小写形式的月名（空格补齐到 9 字符） MON 简写的大写形式的月名（英文 3 字符，本地化长度可变） Mon 简写的首字母大写形式的月名（英文 3 字符，本地化长度可变） mon 简写的小写形式的月名（英文 3 字符，本地化长度可变） MM 月编号（01-12） DAY 全大写形式的日名（空格补齐到 9 字符） Day 全首字母大写形式的日名（空格补齐到 9 字符） day 全小写形式的日名（空格补齐到 9 字符） DY 简写的大写形式的日名（英语 3 字符，本地化长度可变） Dy 简写的首字母大写形式的日名（英语 3 字符，本地化长度可变） dy 简写的小写形式的日名（英语 3 字符，本地化长度可变） DDD 一年中的日（001-366） IDDD ISO 8601 周编号方式的年中的日（001-371，年的第 1 日时第一个 ISO 周的周一） DD 月中的日（01-31） D 周中的日，周日（1）到周六（7） ID 周中的 ISO 8601 日，周一（1）到周日（7） W 月中的周（1-5）（第一周从该月的第一天开始） WW 年中的周数（1-53）（第一周从该年的第一天开始） IW ISO 8601 周编号方式的年中的周数（01 - 53；新的一年的第一个周四在第一周） CC 世纪（2 位数）（21 世纪开始于 2001-01-01） J 儒略日（从午夜 UTC 的公元前 4714 年 11 月 24 日开始的整数日数） Q 季度（to_date和to_timestamp会忽略） RM 大写形式的罗马计数法的月（I-XII；I 是 一月） rm 小写形式的罗马计数法的月（i-xii；i 是 一月） TZ 大写形式的时区名称 tz 小写形式的时区名称 OF 时区偏移量 模式 描述 9 带有指定位数的值 0 带前导零的值 . (period) 小数点 , (comma) 分组（千）分隔符 PR 尖括号内的负值 S 带符号的数字（使用区域） L 货币符号（使用区域） D 小数点（使用区域） G 分组分隔符（使用区域） MI 在指定位置的负号（如果数字 < 0） PL 在指定位置的正号（如果数字 > 0） SG 在指定位置的正/负号 RN 罗马数字（输入在 1 和 3999 之间） TH or th 序数后缀 V 移动指定位数（参阅注解） EEEE 科学记数的指数 NLS_LENGTH_SEMANTICS 使您能够使用字节或字符长度语义创建 CHAR 和 VARCHAR2 列。 现有列不受影响。 在这种情况下，默认语义是 BYTE。 SET NLS_LENGTH_SEMANTICS TO [NONE | BYTE | CHAR]; BYTE:数据以字节长度来存储。 CHAR:数据以字符长度来存储。 NONE:数据使用原生PostgreSQL存储方式。 create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 SET NLS_LENGTH_SEMANTICS TO BYTE; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- byte (1 row) insert into test values ('李老师您好'); 2021-12-14 15:28:11.906 HKT [6774] ERROR: value too long for type varchar2(5 byte) 2021-12-14 15:28:11.906 HKT [6774] STATEMENT: insert into test values ('李老师您好'); ERROR: value too long for type varchar2(5 byte) 具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。 VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 PL/iSQL 是 IvorySQL 的过程语言，用于为 IvorySQL 编写自定义函数、过程和包。 PL/iSQL 派生自 PostgreSQL 的 PL/pgsql，并增加了一些功能，但在语法上 PL/iSQL 更接近 Oracle 的 PL/SQL。 本文档描述了 PL/iSQL 程序的基本结构和构造。 iSQL 是一种程序化的块结构语言，支持四种不同的 程序类型，即 PACKAGES、PROCEDURES、FUNCTIONS 和 TRIGGERS。 iSQL 对每种类型的受支持程序使用相同的块结构。 一个块最多由三个部分组成：声明部分，可执行文件，和异常部分。 而声明和异常部分是可选的。 [DECLARE declarations] BEGIN statements [ EXCEPTION WHEN <exception_condition> THEN statements] END; 一个块至少可以由一个可执行部分组成 在 BEGIN 和 END 关键字中包含一个或多个 iSQL 语句。 CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS BEGIN NULL; END; / 所有关键字都不区分大小写。 标识符被隐式转换为小写，除非双引号， 就像它们在普通 SQL 命令中一样。 声明部分可用于声明变量和游标，并取决于使用块的上下文， 声明部分可以以关键字 DECLARE 开头。 CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS DECLARE quantity integer := 30; c_row pg_class%ROWTYPE; r_cursor refcursor; CURSOR c1 RETURN pg_proc%ROWTYPE; BEGIN NULL; end; / 可选的异常部分也可以包含在 BEGIN - END 块中。 异常部分以关键字 EXCEPTION 开始，一直持续到它出现的块的末尾。 如果块内的语句抛出异常，程序控制转到异常部分，根据异常和异常部分的内容，可能会或不会处理抛出的异常。 CREATE OR REPLACE FUNCTION reraise_test() RETURN void AS BEGIN BEGIN RAISE syntax_error; EXCEPTION WHEN syntax_error THEN BEGIN raise notice 'exception % thrown in inner block, reraising', sqlerrm; RAISE; EXCEPTION WHEN OTHERS THEN raise notice 'RIGHT - exception % caught in inner block', sqlerrm; END; END; EXCEPTION WHEN OTHERS THEN raise notice 'WRONG - exception % caught in outer block', sqlerrm; END; / 注意 与 PL/pgSQL 类似，PL/iSQL 使用 BEGIN/END 对语句进行分组， 并且不要将它们与用于事务控制的同名 SQL 命令混淆。 PL/iSQL 的 BEGIN/END 仅用于分组； 他们不开始或结束事务 要从 psql 客户端创建 PL/iSQL 程序，您可以使用类似于 PL/pgSQL 的$$语法 CREATE FUNCTION func() RETURNS void as $$ .. end$$ language plisql; 或者，您可以使用不带 $$ 的 Oracle 兼容语法的引用和语言规范， 并使用 /（正斜杠） 结束程序定义。 /（正斜杠）必须在换行符上 CREATE FUNCTION func() RETURN void AS … END; / CREATE [OR REPLACE] PROCEDURE procedure_name [(parameter_list)] is [DECLARE] -- variable declaration BEGIN -- stored procedure body END; / CREATE [OR REPLACE] FUNCTION function_name ([parameter_list]) RETURN return_type AS [DECLARE] -- variable declaration BEGIN -- function body return statement END; / CREATE [ OR REPLACE ] PACKAGE [schema.] *package_name* [invoker_rights_clause] [IS | AS] item_list[, item_list ...] END [*package_name*]; invoker_rights_clause: AUTHID [CURRENT_USER | DEFINER] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] function_declaration: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype; procedure_declaration: PROCEDURE procedure_name [(parameter_declaration[, ...])] type_definition: record_type_definition | ref_cursor_type_definition cursor_declaration: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype; item_declaration: cursor_declaration | cursor_variable_declaration | record_variable_declaration | variable_declaration | record_type_definition: TYPE record_type IS RECORD ( variable_declaration [, variable_declaration]... ) ; ref_cursor_type_definition: TYPE type IS REF CURSOR [ RETURN type%ROWTYPE ]; cursor_variable_declaration: curvar curtype; record_variable_declaration: recvar { record_type | rowtype_attribute | record_type%TYPE }; variable_declaration: varname datatype [ [ NOT NULL ] := expr ] parameter_declaration: parameter_name [IN] datatype [[:= | DEFAULT] expr] CREATE [ OR REPLACE ] PACKAGE BODY [schema.] package_name [IS | AS] [item_list[, item_list ...]] | item_list_2 [, item_list_2 ...] [initialize_section] END [package_name]; initialize_section: BEGIN statement[, ...] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] item_list_2: [ function_declaration function_definition procedure_declaration procedure_definition cursor_definition ] function_definition: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype [IS | AS] [declare_section] body; procedure_definition: PROCEDURE procedure_name [(parameter_declaration[, ...])] [IS | AS] [declare_section] body; cursor_definition: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype IS select_statement; body: BEGIN statement[, ...] END [name]; statement: [<<LABEL>>] pl_statments[, ...]; { CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... [ START WITH condition ] | START WITH condition CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... } CONNECT BY 查询语法以 CONNECT BY 关键字开头，这些关键字定义了父行和子行之间的分层相互依赖关系。 必须通过在 CONNECT BY 子句的条件部分指定 PRIOR 关键字来进一步限定结果. PRIOR PRIOR 关键字是一元运算符，它将前一行与当前行联系起来。 此关键字可以用在相等条件的左边或右边。 START WITH 该子句指定从哪一行开始层次结构。 NOCYCLE 无操作语句。 目前只有语法支持。 该子句表示即使存在循环也返回数据。 LEVEL 返回层次结构中当前行的级别，从根节点的 1 开始，之后每级别递增 1。 CONNECT_BY_ROOT expr 返回层次结构中当前行的父列。 SYS_CONNECT_BY_PATH(col, chr) 它是一个返回从根到当前节点的列值的函数，由字符“chr”分隔。 目前此功能有以下限制： 附加列可用于大多数表达式，如函数调用、CASE 语句和通用表达式，但有一些不受支持的列，如 ROW、TYPECAST、COLLATE、GROUPING 子句等 两个或多个列相同的情况下，可能需要输出列名，例如 > SELECT CONNECT_BY_ROOT col AS \"col1\", CONNECT_BY_ROOT col AS \"col2\" .... 不支持间接运算符或“*” 不支持循环检测（Loop detection） CREATE UNIQUE INDEX [IF NOT EXISTS] name ON table_name [USING method] (columns) GLOBAL CREATE UNIQUE INDEX myglobalindex on mytable(bid) GLOBAL; 在创建全局唯一索引期间，系统会对所有现有分区执行索引扫描，如果发现来自其他分区的重复项而不是当前分区，则会引发错误。例如： 命令 create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); create table gidxpart2 partition of gidxpart for values from (100000) to (199999); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); insert into gidxpart (a, b, c) values (150000, 572814, 'inserted second on gidxpart2'); create unique index on gidxpart (b) global; 输出 ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated. 在全局唯一索引创建过程中，系统会对所有现有分区执行索引扫描，如果在其他分区而不是当前分区中发现重复项，则会引发错误。 命令 create table gidx_part (a int, b int, c text) partition by range (a); create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (10); create table gidxpart2 partition of gidxpart for values from (10) to (100); create unique index gidx_u on gidxpart using btree(b) global; insert into gidxpart values (1, 1, 'first'); insert into gidxpart values (11, 11, 'eleventh'); insert into gidxpart values (2, 11, 'duplicated (b)=(11) on other partition'); 输出 ERROR: duplicate key value violates unique constraint \"gidxpart2_b_idx\" DETAIL: Key (b)=(11) already exists. 将新表附加到具有全局唯一索引的分区表时，系统将对所有现有分区进行重复检查。 如果在现有分区中发现与附加表中的元组匹配的重复项，则会引发错误并且附加失败。 附加需要所有现有分区上的共享锁（SHARE LOCK）。 如果其中一个分区正在进行并发 INSERT，则附加将等待它先完成。 这可以在未来的版本中改进 运行命令 create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); create unique index on gidxpart (b) global; create table gidxpart2 (a int, b int, c text); insert into gidxpart2 (a, b, c) values (150000, 572814, 'dup inserted on gidxpart2'); alter table gidxpart attach partition gidxpart2 for values from (100000) to (199999); 输出 ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated.","title":"开发者指南","component":"ivorysql-doc","version":"master","name":"developer_guide","url":"/ivorysql-doc/master/developer_guide.html","titles":[{"text":"1. 概览","hash":"概览","id":1},{"text":"1.1. 架构概述","hash":"架构概述","id":2},{"text":"1.2. 系统表的变化","hash":"系统表的变化","id":3},{"text":"2. 数据库建模（第一章创建库+第二章创建表）","hash":"数据库建模第一章创建库第二章创建表","id":4},{"text":"2.1. 创建一个数据库","hash":"创建一个数据库","id":5},{"text":"2.2. 创建一个新表","hash":"创建一个新表","id":6},{"text":"3. 写入数据（SQL写入）参考第 6 章 数据操纵","hash":"写入数据sql写入参考第-6-章-数据操纵","id":7},{"text":"4. 查询数据 参考 第七章查询的组合查询  第十五章 并行查询","hash":"查询数据-参考-第七章查询的组合查询-第十五章-并行查询","id":8},{"text":"4.1. 组合查询","hash":"组合查询","id":9},{"text":"4.2. 并行查询","hash":"并行查询","id":10},{"text":"4.2.1. 并行查询如何工作","hash":"并行查询如何工作","id":11},{"text":"4.2.2. 何时会用到并行查询？","hash":"何时会用到并行查询","id":12},{"text":"4.2.3. 并行计划","hash":"并行计划","id":13},{"text":"4.2.3.1. 并行扫描","hash":"并行扫描","id":14},{"text":"4.2.3.2. 并行连接","hash":"并行连接","id":15},{"text":"4.2.3.3. 并行聚集","hash":"并行聚集","id":16},{"text":"4.2.3.4. 并行Append","hash":"并行append","id":17},{"text":"4.2.3.5. 并行计划小贴士","hash":"并行计划小贴士","id":18},{"text":"5. 事务（参考Sql命令）","hash":"事务参考sql命令","id":19},{"text":"5.1. 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COMMIT PREPARED — 提交一个早前为两阶段提交预备的事务","hash":"commit-prepared-提交一个早前为两阶段提交预备的事务","id":40},{"text":"5.4.1. 大纲","hash":"大纲-4","id":41},{"text":"5.4.2. 描述","hash":"描述-3","id":42},{"text":"5.4.3. 参数","hash":"参数-4","id":43},{"text":"5.4.4. 注解","hash":"注解-4","id":44},{"text":"5.4.5. 例子","hash":"例子-2","id":45},{"text":"5.4.6. 兼容性","hash":"兼容性-4","id":46},{"text":"5.5. END - 提交当前事务","hash":"end-提交当前事务","id":47},{"text":"5.5.1. 大纲","hash":"大纲-5","id":48},{"text":"5.5.2. 描述","hash":"描述-4","id":49},{"text":"5.5.3. 参数","hash":"参数-5","id":50},{"text":"5.5.4. 注解","hash":"注解-5","id":51},{"text":"5.5.5. 示例","hash":"示例-3","id":52},{"text":"5.5.6. 兼容性","hash":"兼容性-5","id":53},{"text":"5.6. PREPARE TRANSACTION — 为两阶段提交准备当前事务","hash":"prepare-transaction-为两阶段提交准备当前事务","id":54},{"text":"5.6.1. 大纲","hash":"大纲-6","id":55},{"text":"5.6.2. 描述","hash":"描述-5","id":56},{"text":"5.6.3. 参数","hash":"参数-6","id":57},{"text":"5.6.4. 注解","hash":"注解-6","id":58},{"text":"5.6.5. 小心","hash":"小心","id":59},{"text":"5.6.6. 例子","hash":"例子-3","id":60},{"text":"5.6.7. 兼容性","hash":"兼容性-6","id":61},{"text":"5.7. ROLLBACK — 中止当前事务","hash":"rollback-中止当前事务","id":62},{"text":"5.7.1. 大纲","hash":"大纲-7","id":63},{"text":"5.7.2. 描述","hash":"描述-6","id":64},{"text":"5.7.3. 参数","hash":"参数-7","id":65},{"text":"5.7.4. 注解","hash":"注解-7","id":66},{"text":"5.7.5. 示例","hash":"示例-4","id":67},{"text":"5.7.6. 兼容性","hash":"兼容性-7","id":68},{"text":"5.8. 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SET TRANSACTION — 设置当前事务的特性","hash":"set-transaction-设置当前事务的特性","id":88},{"text":"5.11.1. 大纲","hash":"大纲-11","id":89},{"text":"5.11.2. 描述","hash":"描述-10","id":90},{"text":"5.11.3. 注解","hash":"注解-11","id":91},{"text":"5.11.4. 示例","hash":"示例-6","id":92},{"text":"5.11.5. 兼容性","hash":"兼容性-11","id":93},{"text":"5.12. START TRANSACTION — 开始一个事务块","hash":"start-transaction-开始一个事务块","id":94},{"text":"5.12.1. 大纲","hash":"大纲-12","id":95},{"text":"5.12.2. 描述","hash":"描述-11","id":96},{"text":"5.12.3. 参数","hash":"参数-10","id":97},{"text":"5.12.4. 兼容性","hash":"兼容性-12","id":98},{"text":"6. Sql参考（第 4 章 SQL语法）","hash":"sql参考第-4-章-sql语法","id":99},{"text":"6.1. 词法结构","hash":"词法结构","id":100},{"text":"6.1.1. 标识符和关键词","hash":"标识符和关键词","id":101},{"text":"6.1.2. 常量","hash":"常量","id":102},{"text":"6.1.2.1. 字符串常量","hash":"字符串常量","id":103},{"text":"6.1.2.2. 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PACKAGE BODY","hash":"package-body","id":261},{"text":"7.14. 层级查询","hash":"层级查询","id":262},{"text":"7.14.1. 语法","hash":"语法-8","id":263},{"text":"7.14.2. 附加列","hash":"附加列","id":264},{"text":"7.14.3. 限制","hash":"限制","id":265},{"text":"8. 全局唯一索引","hash":"全局唯一索引","id":266},{"text":"8.1. 创建全局唯一索引","hash":"创建全局唯一索引","id":267},{"text":"8.1.1. 语法","hash":"语法-9","id":268},{"text":"8.1.2. 示例","hash":"示例-23","id":269},{"text":"8.1.3. 全局唯一性保证","hash":"全局唯一性保证","id":270},{"text":"8.2. 插入和更新","hash":"插入和更新","id":271},{"text":"8.2.1. 插入和更新的全局唯一性保证","hash":"插入和更新的全局唯一性保证","id":272},{"text":"8.2.2. 示例","hash":"示例-24","id":273},{"text":"8.3. 附加和分离","hash":"附加和分离","id":274},{"text":"8.3.1. 附加语句的全球唯一性保证","hash":"附加语句的全球唯一性保证","id":275},{"text":"8.3.2. 示例","hash":"示例-25","id":276}]},"4":{"id":4,"text":"PostgreSQL 服务端提供了对 GB18030 字符集的全面支持。GB18030是中国国家标准，旨在包含所有汉字和多种少数民族文字，实现与Unicode的统一。在PostgreSQL中正确配置和使用GB18030字符集，对于处理和存储需要符合此标准的中文数据至关重要。 服务端 GB18030 支持应当具备以下特性： 1. 支持 GB18030 作为服务端编码：initdb -E GB18030 可用，SHOW server_encoding 显示为 GB18030。 2. 提供 GB18030 <→ UTF8 的双向转换。 3. 支持多字节边界判定。 PostgreSQL已支持GB18030-2000版本作为客户端编码，通过扩展的方式支持GB18030_2022字符集与UTF的转换。 修改pg_enc来实现可指定GB18030作为服务端编码，PostgreSQL编码框架中增加底层函数以供pg内核调用。 设置一个全局变量is_load_gb18030_2022,默认为true,当用户指定-E选项时,在get_encoding_id中判断其设置的是否为gb18030_2022,如果是,将其字符串转为gb18030,然后将is_load_gb18030_2022 设为true,如果-E 选项为Gb18030,将其设为false。 在适当位置判断是否要加载插件,如果是,执行load_gb18030_2022,并将ivorysql.conf中的shared_preload_library添加gb18030_2022。 if (encoding_name && *encoding_name) { encoding_name_modify = pg_strdup(encoding_name); if(pg_strcasecmp(encoding_name,\"gb18030_2022\") == 0) { encoding_name_modify = pg_strdup(\"gb18030\"); is_load_gb18030_2022 = true; } else if(pg_strcasecmp(encoding_name,\"gb18030\") == 0) is_load_gb18030_2022 = false; if ((enc = pg_valid_server_encoding((const char *)encoding_name_modify)) >= 0) return enc; } wchar.c，增加 GB18030 的函数指针： pg_gb180302wchar_with_len(const unsigned char *from, pg_wchar *to, int len) gb18030 → wchar pg_wchar2gb18030_with_len(const pg_wchar *from, unsigned char *to, int len) wchar → gb18030 pg_gb18030_mblen(const unsigned char *s)：返回 1/2/4。 pg_gb18030_dsplen(const unsigned char *s)：ASCII 显示宽度 1；其它按 1 处理（与UTF8一致）。 pg_gb18030_verifier(const unsigned char *s, int len)：校验字节范围，拒绝非法序列。 接收数据: 如果一个使用 UTF-8 编码的客户端连接上来，服务端在接收到数据后，会调用其内部的 utf8_to_gb18030 函数，将数据转换为 GB18030 格式，然后才进行验证和存储。 发送数据: 当该客户端执行 SELECT 查询时，服务端会从磁盘/内存中读取原生的 GB18030 数据，然后调用 gb18030_to_utf8 函数将其转换为 UTF-8 格式，最后再通过网络协议发送给客户端。 新增GB18030-2022.xml数据文件，通过perl脚本解析为map文件，提供 gb18030_to_utf8() 与 utf8_to_gb18030()，优先表驱动，覆盖不到的区间通过算法映射。 static inline uint32 unicode_to_utf8word(uint32 c) { uint32 word; if (c <= 0x7F) { word = c; } else if (c <= 0x7FF) { word = (0xC0 | ((c >> 6) & 0x1F)) << 8; word |= 0x80 | (c & 0x3F); } else if (c <= 0xFFFF) { word = (0xE0 | ((c >> 12) & 0x0F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } else { word = (0xF0 | ((c >> 18) & 0x07)) << 24; word |= (0x80 | ((c >> 12) & 0x3F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } return word; } static uint32 conv_18030_2022_to_utf8(uint32 code) { #define conv18030(minunicode, mincode, maxcode) \\ if (code >= mincode && code <= maxcode) \\ return unicode_to_utf8word(gb_linear(code) - gb_linear(mincode) + minunicode) conv18030(0x0452, 0x8130D330, 0x8136A531); conv18030(0x2643, 0x8137A839, 0x8138FD38); conv18030(0x361B, 0x8230A633, 0x8230F237); conv18030(0x3CE1, 0x8231D438, 0x8232AF32); conv18030(0x4160, 0x8232C937, 0x8232F837); conv18030(0x44D7, 0x8233A339, 0x8233C931); conv18030(0x478E, 0x8233E838, 0x82349638); conv18030(0x49B8, 0x8234A131, 0x8234E733); conv18030(0x9FA6, 0x82358F33, 0x8336C738); conv18030(0xE865, 0x8336D030, 0x84308534); conv18030(0xFA2A, 0x84309C38, 0x84318537); conv18030(0xFFE6, 0x8431A234, 0x8431A439); conv18030(0x10000, 0x90308130, 0xE3329A35); /* No mapping exists */ return 0; }","title":"GB18030 字符集服务端支持","component":"ivorysql-doc","version":"master","name":"gb18030","url":"/ivorysql-doc/master/gb18030.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. initdb时通过-E指定GB18030或GB18030_2022","hash":"initdb时通过-e指定gb18030或gb18030_2022","id":3},{"text":"2.2. 多字节处理","hash":"多字节处理","id":4},{"text":"2.3. 与客户端的交互","hash":"与客户端的交互","id":5}]},"5":{"id":5,"text":"IvorySQL安装方式包括以下5种： yum源安装 docker安装 rpm安装 源码安装 deb安装 本章将详细介绍每种方式的安装、运行及卸载过程。想要更快获得IvorySQL，请参阅快速开始。 同样，安装前请先创建一个用户，并赋予其root权限，安装、使用和卸载均以该用户执行。这里以ivorysql用户为例。 创建或编辑IvorySQL yum源配置文件/etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 保存退出后，安装IvorySQL5 $ sudo dnf install -y ivorysql5-5.0 从Docker Hub上获取IvorySQL镜像 $ docker pull ivorysql/ivorysql:5.0-ubi8 运行IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 -e参数说明 参数名 是否必填 描述 IVORYSQL_USER 否 数据库用户，默认 ivorysql IVORYSQL_PASSWORD 是 数据库用户密码 IVORYSQL_DB 否 数据库名称，默认 ivorysql POSTGRES_HOST_AUTH_METHOD 否 修改主机身份验证方式，参考值：md5 POSTGRES_INITDB_ARGS 否 为initdb是添加额外参数，参考值：\"--data-checksums\" PGDATA 否 将数据目录定义到其他路径文件夹下（例如子目录），默认 /var/lib/ivorysql/data POSTGRES_INITDB_WALDIR 否 定义IvorySQL transaction文件夹路径，默认在数据目录（PGDATA）的子目录中。 不推荐将POSTGRES_HOST_AUTH_METHOD参数填写为trust，这样将会使IVORYSQL_PASSWORD设置失效。 如果POSTGRES_HOST_AUTH_METHOD参数设置为scram-sha-256，同时需将POSTGRES_INITDB_ARGS设置为—​auth-host=scram-sha-256，才会使数据库正确进行初始化。 安装依赖 $ sudo dnf install -y lz4 libicu libxslt python3 获取rpm包 $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-9d890e9-20251120.x86_64.rpm 安装rpm包 使用以下命令安装所有rpm包： $ sudo yum --disablerepo=* localinstall *.rpm 数据库将被安装在/usr/ivory-5/路径下。 安装依赖 $ sudo dnf install -y bison readline-devel zlib-devel openssl-devel uuid-devel $ sudo dnf groupinstall -y 'Development Tools' 获取IvorySQL源代码 $ git clone https://github.com/IvorySQL/IvorySQL.git $ cd IvorySQL $ git checkout -b IVORY_REL_5_STABLE origin/IVORY_REL_5_STABLE 配置 在IvorySQL目录下，执行以下命令进行配置，请使用—​prefix指定安装目录: $ ./configure --prefix=/usr/local/ivorysql/ivorysql-5 编译 执行以下命令进行编译： $ make 编译完毕，安装之前可先执行make check或make all-check-world测试刚刚编译的结果。 安装 执行以下命令安装，数据库将被安装在上述由—​prefix指定的路径下： $ sudo make install 安装依赖 $ sudo apt -y install pkg-config libreadline-dev libicu-dev libldap2-dev uuid-dev tcl-dev libperl-dev python3-dev bison flex openssl libssl-dev libpam-dev libxml2-dev libxslt-dev libossp-uuid-dev libselinux-dev gettext 获取deb包 $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-a50789d-20250304.amd64.deb 安装deb包 $ sudo dpkg -i IvorySQL-5.0-a50789d-20250304.amd64.deb 数据库将被安装在/usr/ivory-5/路径下。 参考yum源安装、rpm安装、源码安装、deb安装的用户，需要手动启动数据库。 赋权 执行以下命令为安装用户赋权，示例用户为ivorysql，安装目录为/usr/ivory-5/： $ sudo chown -R ivorysql:ivorysql /usr/ivory-5/ 配置环境变量 将以下配置写入用户的~/.bash_profile文件并使用source命令该文件使环境变量生效： PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile 数据库初始化 $ mkdir /usr/ivory-5/data $ initdb -D /usr/ivory-5/data 其中-D参数用来指定数据库的数据目录。更多参数使用方法，请使用initdb --help命令获取。 启动数据库服务 $ pg_ctl -D /usr/ivory-5/data -l ivory.log start 其中-D参数用来指定数据库的数据目录，如果[配置环境变量] 配置了PGDATA，则该参数可以省略。-l参数用来指定日志文件。更多参数使用方法，请使用pg_ctl --help命令获取。 查看确认数据库启动成功： $ ps -ef | grep postgres ivorysql 130427 1 0 02:45 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 130428 130427 0 02:45 ? 00:00:00 postgres: checkpointer ivorysql 130429 130427 0 02:45 ? 00:00:00 postgres: background writer ivorysql 130431 130427 0 02:45 ? 00:00:00 postgres: walwriter ivorysql 130432 130427 0 02:45 ? 00:00:00 postgres: autovacuum launcher ivorysql 130433 130427 0 02:45 ? 00:00:00 postgres: logical replication launcher ivorysql 130445 130274 0 02:45 pts/1 00:00:00 grep --color=auto postgres psql连接数据库： $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# 其中-d参数用来指定想要连接到的数据库名称。IvorySQL默认使用ivorysql数据库，但较低版本的IvorySQL首次使用时需用户先连接postgres数据库，然后自己创建ivorysql数据库。较高版本的IvorySQL则已为用户创建好ivorysql数据库，可以直接连接。 更多参数使用方法，请使用psql --help命令获取。 Docker运行IvorySQL时，需要添加额外参数，参考：psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 使用任何一种方法卸载前请先停止数据库服务并做好数据备份。 执行以下命令依次卸载： $ sudo dnf remove -y ivorysql5-5.0 执行以下命令，使IvorySQL容器停止运行，并删除IvorySQL容器和镜像： $ docker stop ivorysql $ docker rm ivorysql $ docker rmi ivorysql/ivorysql:5.0-ubi8 执行以下命令卸载并清理文件夹： $ sudo yum remove --disablerepo=* ivorysql5\\* $ sudo rm -rf /usr/ivory-5 执行以下命令卸载数据库并清理文件夹： $ sudo make uninstall $ make clean $ sudo rm -rf /usr/ivory-5 执行以下命令卸载数据库并清理文件夹： $ sudo dpkg -P IvorySQL-5.0 $ sudo rm -rf /usr/ivory-5","title":"安装指南","component":"ivorysql-doc","version":"master","name":"installation_guide","url":"/ivorysql-doc/master/installation_guide.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. yum源安装","hash":"yum源安装","id":2},{"text":"3. docker安装","hash":"docker安装","id":3},{"text":"4. rpm安装","hash":"rpm安装","id":4},{"text":"5. 源码安装","hash":"源码安装","id":5},{"text":"6. deb安装","hash":"deb安装","id":6},{"text":"7. 启动数据库","hash":"启动数据库","id":7},{"text":"8. 数据库连接","hash":"数据库连接","id":8},{"text":"9. 卸载IvorySQL","hash":"卸载ivorysql","id":9},{"text":"9.1. yum源安装的卸载","hash":"yum源安装的卸载","id":10},{"text":"9.2. docker安装的卸载","hash":"docker安装的卸载","id":11},{"text":"9.3. rpm安装的卸载","hash":"rpm安装的卸载","id":12},{"text":"9.4. 源码安装的卸载","hash":"源码安装的卸载","id":13},{"text":"9.5. deb安装的卸载","hash":"deb安装的卸载","id":14}]},"6":{"id":6,"text":"数据库迁移是指将数据从一个数据库转移到另一个数据库的过程，两端的数据库可能是PostgreSQL, IvorySQL, MySQL, Oracle, SQL Server等。迁移过程是一个具有挑战性的复杂过程，需要对数据库的原理以及各自的特性了如指掌。如果应用已经部署到生产环境并处于正常运行状态，为了保持业务不中断运行，并且不发生数据丢失，数据库迁移后需进行平滑的应用迁移。 迁移后数据库和系统应符合以下要求： 迁移后的数据库系统应完全承载原数据库系统的数据。避免迁移过程中数据丢失导致新的数据库系统数据不完整。 迁移后的数据库系统应完全适配原有数据库的功能。避免迁移后因数据类型或语法、函数的不支持，且无替代方案，导致整个业务系统的无法运行或抛错。 迁移后的数据库应适配整个业务系统的上下游，稳定可靠的保障整个业务系统的运行。 迁移后的数据库各方面综合性能不能弱于原数据库，为整个业务系统提供性能保证。 Ora2Pg 是一个免费的工具，用于将 Oracle 数据库迁移到 IvorySQL 兼容的模式。它连接您的 Oracle 数据库，自动扫描并提取它的结构或数据，然后生成可以装载到 IvorySQL 数据库的 SQL 脚本。Ora2Pg 可以从逆向工程 Oracle 数据库到大型企业数据库迁移，或者简单地将一些 Oracle 数据复制到 IvorySQL 数据库中。它非常容易使用，并且不需要任何 Oracle 数据库知识，而不需要提供连接到Oracle数据库所需的参数。 Ora2Pg 由一个 Perl 脚本（ora2pg）以及一个 Perl 模块（ Ora2Pg.pm）组成，唯一需要做的事情就是修改它的配置文件 ora2pg.conf，设置连接 Oracle 数据库的 DSN 和一个可选的 SCHEMA 名称。完成之后，只需要设置导出的类型：TABLE（包括约束和索引）、VIEW、MVIEW、TABLESPACE、SEQUENCE、INDEXES、TRIGGER、GRANT、FUNCTION、PROCEDURE、PACKAGE、PARTITION、TYPE、INSERT 或 COPY、FDW、QUERY、KETTLE 以及 SYNONYM。 默认情况下，Ora2Pg 导出一个 SQL 文件，可以通过 IvorySQL 客户端工具 psql 执行导出的SQL文件。当进行数据迁移时，可以在配置文件中设置一个目标数据库的 DSN，直接将数据从Oracle导入到 IvorySQL 数据库中。 对象 ora2pg是否支持 view 是 trigger 是，某些情况下需要手工修改脚本 sequence 是 function 是 procedure 是，某些情况下需要手工修改脚本 type 是，某些情况下需要手工修改脚本 materialized view 是，某些情况下需要手工修改脚本 linux环境 Oracle版本 IvorySQL版本 Centos Stream 9 19.0.0.0 4.2 [root@localhost /]# dnf install -y perl perl-ExtUtils-CBuilder perl-ExtUtils-MakeMaker [root@localhost /]# perl -v This is perl 5, version 16, subversion 3 (v5.16.3) built for x86_64-linux-thread-multi (with 44 registered patches, see perl -V for more detail) Copyright 1987-2012, Larry Wall Perl may be copied only under the terms of either the Artistic License or the GNU General Public License, which may be found in the Perl 5 source kit. Complete documentation for Perl, including FAQ lists, should be found on this system using \"man perl\" or \"perldoc perl\". If you have access to the Internet, point your browser at http://www.perl.org/, the Perl Home Page. DBI，Database Independent Interface，是 Perl 语言连接数据库的接口 下载地址 https://mirrors.sjtug.sjtu.edu.cn/cpan/modules/by-module/Plack/TIMB/DBI-1.643.tar.gz # tar zxvf DBI-1.643.tar.gz # cd DBI-1.643/ # perl Makefile.PL # make && make install 下载地址：https://sourceforge.net/projects/ora2pg/ 设置环境变量; 加载环境变量;因为必须定义ORACLE_HOME环境变量;本例在ivorysql用户下配置环境变量 export ORACLE_HOME=/opt/oracle/product/19c/dbhome_1 # tar -zxvf DBD-Oracle-1.76.tar.gz # source /home/postgres/.bashrc # cd DBD-Oracle-1.76 # perl Makefile.PL # make && make install 下载地址：https://sourceforge.net/projects/ora2pg/ # tar -xjf ora2pg-24.0.tar.bz2 # cd ora2pg-24.0 # perl Makefile.PL # make && make install 默认安装在/usr/local/bin/目录下 检查软件环境： # vi check.pl #!/usr/bin/perl use strict; use ExtUtils::Installed; my $inst= ExtUtils::Installed->new(); my @modules = $inst->modules(); foreach(@modules) { my $ver = $inst->version($_) || \"???\"; printf(\"%-12s -- %s\\n\", $_, $ver); } exit; # perl check.pl DBD::Oracle -- 1.76 DBD::Pg -- 3.8.0 DBI -- 1.643 Ora2Pg -- 24.0 Perl -- 5.16.3 设置环境变量 export PERL5LIB=<your_install_dir> #export PERL5LIB=/usr/local/bin/ === 源端准备工作 更新oracle统计信息 提高性能 BEGIN DBMS_STATS.GATHER_SCHEMA_STATS('SH'); DBMS_STATS.GATHER_SCHEMA_STATS('SCOTT'); DBMS_STATS.GATHER_SCHEMA_STATS('HR'); DBMS_STATS.GATHER_DATABASE_STATS; DBMS_STATS.GATHER_DICTIONARY_STATS; END;/ 查询源端对象的类型 ```bash SYS@PROD1>set pagesize 200 SYS@PROD1>select distinct OBJECT_TYPE from dba_objects where OWNER in ('SH','SCOTT','HR') ; OBJECT_TYPE ------------------- INDEX PARTITION TABLE PARTITION SEQUENCE PROCEDURE LOB X TRIGGER DIMENSION X MATERIALIZED VIEW TABLE INDEX VIEW 11 rows selected. 配置ora2pg.conf: 默认情况下，Ora2Pg会查找/etc/ora2pg/ora2pg.conf配置文件，如果文件存在，您只需执行：/usr/local/bin/ora2pg cat /etc/ora2pg/ora2pg.conf.dist | grep -v ^# |grep -v ^$ >ora2pg.conf vi ora2pg.conf # cat ora2pg.conf ORACLE_HOME /opt/oracle/product/19c/dbhome_1 ORACLE_DSN dbi:Oracle:host=localhost;sid=ORCLCDB;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 # 将用户导入到PostgreSQL数据库中 DISABLE_UNLOGGED 1 #避免将NOLOGGING属性设为UNLOGGED SKIP fkeys ukeys checks #跳过外键 唯一 和检查约束 TYPE TABLE,VIEW,GRANT,SEQUENCE,TABLESPACE,PROCEDURE,TRIGGER,FUNCTION,PACKAGE,PARTITION,TYPE,MVIEW,QUERY,DBLINK,SYNONYM,DIRECTORY,TEST,TEST_VIEW NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh.sql 只能同时执行一种类型的导出，因此TYPE指令必须是唯一的。如果您有多个，则只会在文件中找到最后一个。但我测试就可以同时导出多个类型的。 请注意，您可以通过向TYPE指令提供以逗号分隔的导出类型列表来链接多个导出，但在这种情况下，您不能将COPY或INSERT与其他导出类型一起使用。 某些导出类型不能或不应该直接加载到 IvorySQL 数据库中，仍然需要很少的手动编辑。GRANT，TABLESPACE，TRIGGER，FUNCTION，PROCEDURE，TYPE，QUERY和PACKAGE导出类型就是这种情况，特别是如果您有PLSQL代码或Oracle特定SQL。 对于TABLESPACE，您必须确保系统上存在文件路径，对于SYNONYM，您可以确保对象的所有者和模式对应于新的PostgreSQL数据库设计。 建议导出表结构时，一个类型一个类型的操作，避免其它错误相互影响。 设置Oracle数据库DSN后，您可以执行ora2pg以查看它是否有效： # ora2pg -t SHOW_VERSION -c ora2pg.conf Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 估算从Oracle到PostgreSQL的迁移过程的成本并不容易。为了获得对此迁移成本的良好评估，Ora2Pg将检查所有数据库对象，所有函数和存储过程，以检测是否仍有一些对象和PL / SQL代码无法由Ora2Pg自动转换。 Ora2Pg具有内容分析模式，该模式检查Oracle数据库以生成有关Oracle数据库包含的内容和无法导出的内容的文本报告。 # ora2pg -t SHOW_REPORT --estimate_cost -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 11/11 objects types (100.0%) end of objects auditing. ------------------------------------------------------------------------------- Ora2Pg v24.0 - Database Migration Report ------------------------------------------------------------------------------- Version Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 Schema SH Size 287.25 MB ------------------------------------------------------------------------------- Object Number Invalid Estimated cost Comments Details ------------------------------------------------------------------------------- DATABASE LINK 0 0 0 Database links will be exported as SQL/MED IvorySQL's Foreign Data Wrapper (FDW) extensions using oracle_fdw. DIMENSION 5 0 0 GLOBAL TEMPORARY TABLE 0 0 0 Global temporary table are not supported by PostgreSQL and will not be exported. You will have to rewrite some application code to match the PostgreSQL temporary table behavior. INDEX 20 0 3.4 14 index(es) are concerned by the export, others are automatically generated and will do so on PostgreSQL. Bitmap will be exported as btree_gin index(es) and hash index(es) will be exported as b-tree index(es) if any. Domain index are exported as b-tree but commented to be edited to mainly use FTS. Cluster, bitmap join and IOT indexes will not be exported at all. Reverse indexes are not exported too, you may use a trigram-based index (see pg_trgm) or a reverse() function based index and search. Use 'varchar_pattern_ops', 'text_pattern_ops' or 'bpchar_pattern_ops' operators in your indexes to improve search with the LIKE operator respectively into varchar, text or char columns. 11 bitmap index(es). 1 domain index(es). 2 b-tree index(es). INDEX PARTITION 196 0 0 Only local indexes partition are exported, they are build on the column used for the partitioning. JOB 0 0 0 Job are not exported. You may set external cron job with them. MATERIALIZED VIEW 2 0 6 All materialized view will be exported as snapshot materialized views, they are only updated when fully refreshed. SYNONYM 0 0 0 SYNONYMs will be exported as views. SYNONYMs do not exists with PostgreSQL but a common workaround is to use views or set the PostgreSQL search_path in your session to access object outside the current schema. TABLE 11 0 1.1 1 external table(s) will be exported as standard table. See EXTERNAL_TO_FDW configuration directive to export as file_fdw foreign tables or use COPY in your code if you just want to load data from external files. Total number of rows: 1063384. Top 10 of tables sorted by number of rows:. sales has 918843 rows. costs has 82112 rows. customers has 55500 rows. supplementary_demographics has 4500 rows. times has 1826 rows. promotions has 503 rows. products has 72 rows. countries has 23 rows. channels has 5 rows. sales_transactions_ext has 0 rows. Top 10 of largest tables:. TABLE PARTITION 56 0 5.6 Partitions are exported using table inheritance and check constraint. Hash and Key partitions are not supported by PostgreSQL and will not be exported. 56 RANGE partitions.. VIEW 1 0 1 Views are fully supported but can use specific functions. ------------------------------------------------------------------------------- Total 291 0 17.10 17.10 cost migration units means approximatively 1 man-day(s). The migration unit was set to 5 minute(s) ------------------------------------------------------------------------------ Migration level : A-1 ------------------------------------------------------------------------------- Migration levels: A - Migration that might be run automatically B - Migration with code rewrite and a human-days cost up to 5 days C - Migration with code rewrite and a human-days cost above 5 days Technical levels: 1 = trivial: no stored functions and no triggers 2 = easy: no stored functions but with triggers, no manual rewriting 3 = simple: stored functions and/or triggers, no manual rewriting 4 = manual: no stored functions but with triggers or views with code rewriting 5 = difficult: stored functions and/or triggers with code rewriting ------------------------------------------------------------------------------- # ora2pg -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 12/12 tables (100.0%) end of table export. [========================>] 1/1 views (100.0%) end of output. [========================>] 0/0 sequences (100.0%) end of output. [========================>] 0/0 procedures (100.0%) end of procedures export. [========================>] 0/0 triggers (100.0%) end of output. [========================>] 0/0 functions (100.0%) end of functions export. [========================>] 0/0 packages (100.0%) end of output. [========================>] 56/56 partitions (100.0%) end of output. [========================>] 0/0 types (100.0%) end of output. [========================>] 2/2 materialized views (100.0%) end of output. [========================>] 0/0 dblink (100.0%) end of output. [========================>] 0/0 synonyms (100.0%) end of output. [========================>] 2/2 directory (100.0%) end of output. Fixing function calls in output files.... 配置ora2pg.conf 的TYPE 为COPY 或 INSERT # cp ora2pg.conf sh_data.conf # vi sh_data.conf ORACLE_HOME /opt/oracle/product/19c/dbhome_1 ORACLE_DSN dbi:Oracle:host=localhost;sid=ORCLCDB;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 DISABLE_UNLOGGED 1 SKIP fkeys ukeys checks TYPE COPY NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh_data.sql 导出数据 # ora2pg -c sh_data.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 5/5 rows (100.0%) Table CHANNELS (5 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1995 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_H1_1997 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1996 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). …………………………………………………………… [========================>] 4500/4500 rows (100.0%) Table SUPPLEMENTARY_DEMOGRAPHICS (4500 recs/sec) [=======================> ] 1061558/1063384 total rows (99.8%) - (45 sec., avg: 23590 recs/sec). [========================>] 1826/1826 rows (100.0%) Table TIMES (1826 recs/sec) [========================>] 1063384/1063384 total rows (100.0%) - (45 sec., avg: 23630 recs/sec). [========================>] 1063384/1063384 rows (100.0%) on total estimated data (45 sec., avg: 23630 recs/sec) Fixing function calls in output files... 查看导出的文件： [root@test01 ora2pg]# ls -lrt *.sql -rw-r--r-- 1 root root 15716 Jul 2 21:21 TABLE_sh.sql -rw-r--r-- 1 root root 858 Jul 2 21:21 VIEW_sh.sql -rw-r--r-- 1 root root 2026 Jul 2 21:21 TABLESPACE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 SEQUENCE_sh.sql -rw-r--r-- 1 root root 2382 Jul 2 21:21 GRANT_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 TRIGGER_sh.sql -rw-r--r-- 1 root root 346 Jul 2 21:21 PROCEDURE_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 PACKAGE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 FUNCTION_sh.sql -rw-r--r-- 1 root root 6771 Jul 2 21:21 PARTITION_sh.sql -rw-r--r-- 1 root root 341 Jul 2 21:21 TYPE_sh.sql -rw-r--r-- 1 root root 342 Jul 2 21:21 QUERY_sh.sql -rw-r--r-- 1 root root 950 Jul 2 21:21 MVIEW_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 SYNONYM_sh.sql -rw-r--r-- 1 root root 926 Jul 2 21:21 DIRECTORY_sh.sql -rw-r--r-- 1 root root 343 Jul 2 21:21 DBLINK_sh.sql -rw-r--r-- 1 root root 55281235 Jul 2 17:11 sh_data.sql 以同样的方法分别导出HR，SCOTT用户数据。 创建ORCL 数据库 # su - ivorysql Last login: Tue Jul 2 20:04:30 CST 2019 on pts/3 $ createdb orcl $ psql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Collate | Ctype | ICU Locale | Locale Provider | Access privileges -----------+----------+-----------+---------+-------+------------+-----------------+----------------------- ivorysql | ivorysql | SQL_ASCII | C | C | | libc | orcl | ivorysql | SQL_ASCII | C | C | | libc | postgres | ivorysql | SQL_ASCII | C | C | | libc | template0 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql (5 rows) ivorysql=# 创建SH,HR,SCOTT 用户： $ psql orcl psql (18.0) Type \"help\" for help. orcl=# orcl=# create user sh with password 'sh'; CREATE ROLE 由于有物化视图，在TABLE_sh.sql 里包含了物化视图的索引，会创建失败。需先创建表，在创建物化视图，最后创建索引。 取消物化视图索引，后面单独创建： CREATE INDEX fw_psc_s_mv_chan_bix ON fweek_pscat_sales_mv (channel_id); CREATE INDEX fw_psc_s_mv_promo_bix ON fweek_pscat_sales_mv (promo_id); CREATE INDEX fw_psc_s_mv_subcat_bix ON fweek_pscat_sales_mv (prod_subcategory); CREATE INDEX fw_psc_s_mv_wd_bix ON fweek_pscat_sales_mv (week_ending_day); CREATE TEXT SEARCH CONFIGURATION en (COPY = pg_catalog.english); ALTER TEXT SEARCH CONFIGURATION en ALTER MAPPING FOR hword, hword_part, word WITH unaccent, english_stem; psql orcl -f tab.sql ALTER TABLE PARTITION sh.sales OWNER TO sh; COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT ALTER TABLE ALTER TABLE ALTER TABLE ……………………………… cat psql orcl -f GRANT_sh.sql CREATE USER SH WITH PASSWORD 'change_my_secret' LOGIN; ALTER TABLE sh.fweek_pscat_sales_mv OWNER TO sh; GRANT ALL ON sh.fweek_pscat_sales_mv TO sh; 物化视图需要相关查询权限,所以导入权限,注意这里要跟上用户 $ psql orcl sh -f MVIEW_sh.sql SELECT 0 SELECT 0 CREATE INDEX CREATE INDEX CREATE INDEX CREATE INDEX $ psql orcl -f VIEW_sh.sql SET SET SET CREATE VIEW $ psql orcl -f PARTITION_sh.sql SET SET SET CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE ………………………… $ psql orcl -f sh_data.sql SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 4500 SET COPY 1826 COMMIT 源库,目标端抽取部份对象对比： SYS@PROD1>select count(*) from sh.products; COUNT(*) ---------- 72 orcl=# select count(*) from sh.products; count ------- 72 (1 row) --------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.channels; COUNT(*) ---------- 5 orcl=# select count(*) from sh.channels; count ------- 5 (1 row) -------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.customers ; COUNT(*) ---------- 55500 orcl=# select count(*) from sh.customers ; count ------- 55500 (1 row) 使用时，两个选项—​project_base和—​init_project向Ora2Pg表明他必须创建一个项目模板，其中包含工作树，配置文件和从Oracle数据库导出所有对象的脚本。 生成通用配置文件。 1.创建脚本export_schema.sh以自动执行所有导出。2.创建脚本import_all.sh以自动执行所有导入。例： mkdir -p /ora2pg/migration # ora2pg --project_base /ora2pg/migration/ --init_project test_project Creating project test_project. /ora2pg/migration//test_project/ schema/ dblinks/ directories/ functions/ grants/ mviews/ packages/ partitions/ procedures/ sequences/ synonyms/ tables/ tablespaces/ triggers/ types/ views/ sources/ functions/ mviews/ packages/ partitions/ procedures/ triggers/ types/ views/ data/ config/ reports/ Generating generic configuration file Creating script export_schema.sh to automate all exports. Creating script import_all.sh to automate all imports.","title":"迁移指南","component":"ivorysql-doc","version":"master","name":"migration_guide","url":"/ivorysql-doc/master/migration_guide.html","titles":[{"text":"1. 迁移概述","hash":"迁移概述","id":1},{"text":"2. 迁移工具Ora2Pg","hash":"迁移工具ora2pg","id":2},{"text":"3. 迁移Oracle数据库至IvorySQL","hash":"迁移oracle数据库至ivorysql","id":3},{"text":"3.1. 环境准备","hash":"环境准备","id":4},{"text":"3.2. 依赖环境安装","hash":"依赖环境安装","id":5},{"text":"3.2.1. 安装Perl","hash":"安装perl","id":6},{"text":"3.2.2. 安装DBI模块","hash":"安装dbi模块","id":7},{"text":"3.2.3. 安装DBD-Oracle","hash":"安装dbd-oracle","id":8},{"text":"3.3. 安装Ora2pg","hash":"安装ora2pg","id":9},{"text":"3.4. ora2pg导出表结构","hash":"ora2pg导出表结构","id":10},{"text":"3.4.1. 测试连接","hash":"测试连接","id":11},{"text":"3.4.2. 迁移成本评估","hash":"迁移成本评估","id":12},{"text":"3.4.3. 导出SH表构","hash":"导出sh表构","id":13},{"text":"3.4.4. 导出SH用户数据","hash":"导出sh用户数据","id":14},{"text":"3.5. 在IvorySQL环境中创建orcl库","hash":"在ivorysql环境中创建orcl库","id":15},{"text":"4. 迁移门户","hash":"迁移门户","id":16},{"text":"4.1. 导入表结构","hash":"导入表结构","id":17},{"text":"4.2. 给对象授权","hash":"给对象授权","id":18},{"text":"4.3. 导入物化视图结构","hash":"导入物化视图结构","id":19},{"text":"4.4. 导入视图","hash":"导入视图","id":20},{"text":"4.5. 导入分区表","hash":"导入分区表","id":21},{"text":"4.6. 导入数据","hash":"导入数据","id":22},{"text":"5. 数据验证","hash":"数据验证","id":23},{"text":"6. 生成迁移模板","hash":"生成迁移模板","id":24}]},"7":{"id":7,"text":"由于IvorySQL基于PostgreSQL开发而成，运维人员在阅读理解本节内容时，建议同时参阅 手册。 IvorySQL版本号由主要版本和次要版本组成。例如，IvorySQL 3.2 中的3是主要版本，2是次要版本。 ​发布次要版本是不会改变内存的存储格式，因此总是和相同的主要版本兼容。例如，IvorySQL 3.2 和Ivory SQL 3.0 以及后续的 Ivory SQL 3.x 兼容。对于这些兼容版本的升级非常简单，只要关闭数据库服务，安装替换二进制的可执行文件，重新启动服务即可。 ​接下来，我们主要讨论IvorySQL的跨版本升级问题，例如，从 IvorySQL 2.3 升级到 IvorySQL 3.4 。主要版本的升级可能会修改内部数据的存储格式，因此需要执行额外的操作。常用的跨版本升级方法和适用场景如下： 升级方法 适用场景 停机时间 通过pg_dumpall升级数据 中小型数据库，例如小于100GB支持跨平台数据迁移 取决于数据库的大小 pg_upgrade工具 大中型数据库，例如大于100GB本机就地升级 几分钟 逻辑复制 大中型数据库，例如大于100GB跨平台支持 几秒钟 注意事项： 新的主版本通常会引入一些用户可见的不兼容性，因此可能需要应用程序编程上的改变。所有用户可见的更改都被列在 说明中，请特别注意标有“Migration”的小节。尽管你可能从一个主版本升级到另一个，而不用升级中间版本，你应该阅读全部中间版本的主要发行说明。 传统的跨版本升级方法利用pg_dump/pg_dumpall逻辑备份导出数据库，然后在新版本中通过pg_restore进行还原。导出旧版本数据库时推荐使用新版本的pg_dump/pg_dumpall工具，可以利用最新的并行导出和还原功能，同时可以减少数据库膨胀问题。 ​逻辑备份与还原非常简单但速度比较慢，停机时间取决于数据库的大小，因此适合中小型数据库的升级。 ​下面介绍这种升级方法的具体操作，假如当前IvorySQL软件的安装目录位于/usr/local/pgsql，同时数据目录位于/usr/local/pgsql/data，我们在同一台服务器上进行升级。 执行逻辑备份之前停止应用程序，确保没有数据更新，因为备份开始后的更新不会被导出。如有必要，可以修改/usr/local/pgsql/data/pg_hba.conf文件禁止其他人访问数据库。然后备份数据库： pg_dumpall > outputfile 如果已经安装了新版本的IvorySQL，可以使用新版本的pg_dumpall命令备份旧版本数据库。 2.停止旧版本的后台服务： pg_ctl stop 或者通过其他方式停止后台服务。 3.如果安装目录没有包含特定版本标识，可以将目录改名，必要时可以再修改回来。可以使用类似以下的命令重命名目录： mv /usr/local/pgsql /usr/local/pgsql.old 4.安装新版本IvorySQL软件，假如安装目录仍然是/usr/local/pgsql。 5.初始化一个新的数据库集群，需要使用数据库专用用户（通常是postgres；如果升级版本，应该已经存在该用户）执行操作： /usr/local/pgsql/bin/initdb -D /usr/local/pgsql/data 6.将旧版本配置文件pg_hba.conf 和 postgresql.conf 等中的改动在对应的新配置文件中再次进行修改。 7.使用数据库专用用户启动新版本的后台服务： /usr/local/pgsql/bin/postgres -D /usr/local/pgsql/data 8.最后利用新版本的psql命令还原数据： /usr/local/pgsql/bin/psql -d postgres -f outputfile 为了减少停机时间，可以将新版本的IvorySQL安装到另一个目录，同时使用不同的端口启动服务。然后同时执行数据库的导出和导入： pg_dumpall -p 5432 | psql -d postgres -p 5433 ​执行以上操作时，新旧版本的后台服务同时运行，新版本使用5433端口，旧版本使用5432端口。 pg_upgrade 工具是PostgreSQL 内置的跨版本升级工具，能够对数据库就地升级，不需要执行导出和导入操作。IvorySQL源自于PG，因此也能够使用pg_upgrade 工具进行大版本升级。 下面简要介绍一下CentOS8平台上如何使用 pg_upgrade 将IvorySQL升级到最新的5.0版本。 pg_upgrade 提供了升级前的兼容性检查（-c 或者 --check 选项）功能，可以发现插件、数据类型不兼容等问题。如果指定了—​link 选项，新版本服务可以直接使用原有的数据库文件而不需要执行复制，通常可以在几分钟内完成升级操作。 常用的参数包括： -b bindir，--old-bindir=bindir：旧的 IvorySQL 可执行文件目录； -B bindir，--new-bindir=bindir：新的 IvorySQL 可执行文件目录； -d configdir，--old-datadir=configdir：旧版本的数据目录； -D configdir，--new-datadir=configdir：新版本的数据目录； -c，--check：只检查升级兼容性，不更改任何数据； -k，--link：硬链接方式升级； 升级准备： 首先停止旧版本的IvorySQL4.6数据库： /usr/ivory-4/bin/pg_ctl -D ./data stop 然后安装新版本的IvorySQL5.0数据库： dnf install -y ivorysql5-5.0 初始化新版IvorySQL5.0数据目录： /usr/ivory-5/bin/initdb -D ./data 检查版本兼容性： /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ --check 最后出现 “Clusters are compatible” 表明两个版本之间的数据不存在兼容性问题，可以进行升级。 正式升级： /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ 看到 Upgrade Complete 说明升级已经顺利完成。 更新统计信息： pg_upgrade 会创建新的系统表，并重用旧的数据进行升级，统计信息并不会随升级过程迁移，所以在启用新版本之前，应该首先重新收集统计信息，避免没有统计信息导致错误的查询计划。 启动新版本数据库 /usr/ivory-5/bin/pg_ctl -D ./data -l logfile start 手动运行vacuum命令 vacuum --all --analyze-in-stage -h 127.0.0.1 -p 1521 升级后清理 rm -rf /home/ivorysql/test/4.6/data pg_upgrade 文档概述了上述所需的步骤。 我们也可以使用IvorySQL的已更新版本逻辑复制来创建一个后备服务器，逻辑复制支持在不同主版本的IvorySQL之间的复制。后备服务器可以在同一台计算机或者不同的计算机上。一旦它和主服务器（运行旧版本的IvorySQL）同步好，你可以切换主机并且将后备服务器作为主机，然后关闭旧的数据库实例。这样一种切换使得一次升级的停机时间只有数秒。 这种升级方法可以用内置的逻辑复制工具和外部的逻辑复制系统如pglogical，Slony，Londiste，和Bucardo。 IvorySQL基于PostgreSQL开发，版本更新频率与PostgreSQL版本更新频率保持一致，每年更新一个大版本，每季度更新一个小版本。IvorySQL目前发布的版本有1.0到5.0，分别基于PostgreSQL 14.0到18.0进行开发，最新版本为IvorySQL 5.0，基于PostgreSQL 18.0进行开发。IvorySQL 的所有版本全部都做到了向下兼容。相关版本特性可以查看 官网。 IvorySQL使用 角色 的概念管理数据库访问权限。一个角色可以看成是一个数据库用户或者是数据库用户组，这取决于角色被怎样设置。角色可以拥有数据库对象（例如，表和函数）并且能够把那些对象上的权限赋予给其他角色来控制谁能访问哪些对象。此外，还可以把一个角色中的 成员资格 授予给另一个角色，这样允许成员角色使用被赋予给另一个角色的权限。 角色的概念把“用户”和“组”的概念都包括在内。 数据库角色在概念上已经完全与操作系统用户独立开来。 事实上可能维护一个对应关系会比较方便，但是这并非必需。 数据库角色在一个数据库集簇安装范围内是全局的（而不是独立数据库内）。 要创建一个角色，可使用CREATE ROLE SQL 命令： CREATE ROLE name; name遵循 SQL 标识符的规则：或是未经装饰没有特殊字符，或是用双引号包围（实际上，你将总是给该命令要加上额外选项，例如LOGIN。更多细节可见下文）。 要移除一个已有的角色，使用相似的DROP ROLE命令： DROP ROLE name; 为了方便，createuser和dropuser程序被提供作为这些 SQL 命令的包装器，它们可以从 shell 命令行调用： createuser name dropuser name 要决定现有角色的集合，检查pg_roles系统目录，例如： SELECT rolname FROM pg_roles; psql程序的\\du元命令也可以用来列出现有角色。 为了引导数据库系统，一个刚刚被初始化好的系统总是包含一个预定义角色。这个角色总是一个“superuser”，并且默认情况下（除非在运行initdb时修改）它的名字和初始化数据库集簇的操作系统用户相同。习惯上，这个角色将被命名为postgres。为了创建更多角色，你首先必须以初始角色的身份连接。 每一个到数据库服务器的连接都是使用某个特定角色名建立的，并且这个角色决定发起连接的命令的初始访问权限。要使用一个特定数据库连接的角色名由客户端指示，该客户端以一种应用相关的风格发起连接请求。例如，psql程序使用-U命令行选项来指定要以哪个角色连接。很多应用假定该名字默认是当前操作系统用户（包括createuser和psql）。因此在角色和操作系统用户之间维护一个名字对应关系通常是很方便的。 一个给定客户端连接能够用来连接的数据库角色的集合由该客户端的认证设置决定，因此，一个客户端不止限于以匹配其操作系统用户的角色连接，就像一个人的登录名不需要匹配她的真实名字一样。因为角色身份决定一个已连接客户端可用的权限集合，在设置一个多用户环境时要小心地配置权限。 一个数据库角色可以有一些属性，它们定义角色的权限并且与客户端认证系统交互。 把用户分组在一起来便于管理权限常常很方便：那样，权限可以被授予一整个组或从一整个组回收。在IvorySQL中通过创建一个表示组的角色来实现，并且然后将在该组角色中的 成员关系 授予给单独的用户角色。 由于角色可以拥有数据库对象并且能持有访问其他对象的特权，删除一个角色常常并非一次DROP ROLE就能解决。 任何被该用户所拥有的对象必须首先被删除或者转移给其他拥有者，并且任何已被授予给该角色的权限必须被收回。 更多有关数据库访问管理的细节，可以参阅 手册。 IvorySQL基于PostgreSQL，具有完整的SQL，其定义数据对象可以参考 手册。在此基础之上，IvorySQL为兼容Oracle，还做了一些Oracle专有数据对象的兼容。 具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。 VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 IvorySQL基于PostgreSQL开发，具有完全的SQL，查询数据具体的操作可以参考 手册。 IvorySQL实现了部分的SQL/MED规定，允许我们使用普通SQL查询来访问位于IvorySQL之外的数据。这种数据被称为外部数据（注意这种用法不要和外键混淆，后者是数据库中的一种约束）。 外部数据可以在一个外部数据包装器的帮助下被访问。一个外部数据包装器是一个库，它可以与一个外部数据源通讯，并隐藏连接到数据源和从它获取数据的细节。在contrib模块中有一些外部数据包装器，参见 文档。其他类型的外部数据包装器可以在第三方产品中找到。如果这些现有的外部数据包装器都不能满足你的需要，可以自己编写一个，参见 手册。 要访问外部数据，我们需要建立一个外部服务器对象，它根据它所支持的外部数据包装器所使用的一组选项定义了如何连接到一个特定的外部数据源。接着我们需要创建一个或多个外部表，它们定义了外部数据的结构。一个外部表可以在查询中像一个普通表一样地使用，但是在IvorySQL服务器中外部表没有存储数据。不管使用什么外部数据包装器，IvorySQL会要求外部数据包装器从外部数据源获取数据，或者在更新命令的情况下传送数据到外部数据源。 访问远程数据可能需要在外部数据源的授权。这些信息通过一个用户映射提供，它基于当前的IvorySQL角色提供了附加的数据例如用户名和密码。 由于包含着有价值的数据，IvorySQL数据库应当被定期地备份。虽然过程相当简单，但清晰地理解其底层技术和假设是非常重要的。 有三种不同的基本方法来备份 IvorySQL 数据： SQL转储 文件系统级备份 连续归档 SQL 转储方法的思想是创建一个由SQL命令组成的文件，当把这个文件回馈给服务器时，服务器将利用其中的SQL命令重建与转储时状态一样的数据库。 IvorySQL为此提供了工具pg_dump。这个工具的基本用法是： pg_dump dbname > dumpfile 正如你所见，pg_dump把结果输出到标准输出。我们后面将看到这样做有什么用处。 尽管上述命令会创建一个文本文件，pg_dump可以用其他格式创建文件以支持并行 和细粒度的对象恢复控制。 ​ pg_dump是一个普通的IvorySQL客户端应用（尽管是个 相当聪明的东西）。这就意味着你可以在任何可以访问该数据库的远端主机上进行备份工作。但是请记住 pg_dump不会以任何特殊权限运行。具体说来，就是它必须要有你想备份的表的读权限，因此为了备份整个数据库你几乎总是必须以一个数据库超级用户来运行它（如果你没有足够的特权来备份整个数据库，你仍然可以使用诸如-n schema 或-t table选项来备份该数据库中你能够访问的部分）。 ​ 要声明pg_dump连接哪个数据库服务器，使用命令行选项-h host和 -p port。 默认主机是本地主机或你的HOST环境变量指定的主机。 类似地，默认端口是环境变量PORT或（如果PORT不存在）内建的默认值。 （服务器通常有相同的默认值，所以还算方便。） ​ pg_dump默认使用与当前操作系统用户名同名的数据库用户名进行连接。 要使用其他名字，要么声明-U选项，要么设置环境变量PGUSER。请注意pg_dump的连接也要通过客户认证机制。 ​ pg_dump对于其他备份方法的一个重要优势是，pg_dump的输出可以很容易地在新版本的IvorySQL中载入，而文件级备份和连续归档都是极度的服务器版本限定的。pg_dump也是唯一可以将一个数据库传送到一个不同机器架构上的方法，例如从一个32位服务器到一个64位服务器。 ​ 由pg_dump创建的备份在内部是一致的， 也就是说，转储表现了pg_dump开始运行时刻的数据库快照，且在pg_dump运行过程中发生的更新将不会被转储。pg_dump工作的时候并不阻塞其他的对数据库的操作。（但是会阻塞那些需要排它锁的操作，比如大部分形式的ALTER TABLE） pg_dump生成的文本文件可以由psql程序读取。 从转储中恢复的常用命令是： psql dbname < dumpfile 其中dumpfile就是pg_dump命令的输出文件。这条命令不会创建数据库dbname，你必须在执行psql前自己从template0创建（例如，用命令createdb -T template0 dbname）。psql支持类似pg_dump的选项用以指定要连接的数据库服务器和要使用的用户名。参阅psql的手册获取更多信息。 非文本文件转储可以使用pg_restore工具来恢复。 ​ 在开始恢复之前，转储库中对象的拥有者以及在其上被授予了权限的用户必须已经存在。如果它们不存在，那么恢复过程将无法将对象创建成具有原来的所属关系以及权限（有时候这就是你所需要的，但通常不是）。 ​ 默认情况下，psql脚本在遇到一个SQL错误后会继续执行。你也许希望在遇到一个SQL错误后让psql退出，那么可以设置ON_ERROR_STOP变量来运行psql，这将使psql在遇到SQL错误后退出并返回状态3： psql --set ON_ERROR_STOP=on dbname < infile 不管怎样，你将只能得到一个部分恢复的数据库。作为另一种选择，你可以指定让整个恢复作为一个单独的事务运行，这样恢复要么完全完成要么完全回滚。这种模式可以通过向psql传递-1或—​single-transaction命令行选项来指定。在使用这种模式时，注意即使是很小的一个错误也会导致运行了数小时的恢复被回滚。但是，这仍然比在一个部分恢复后手工清理复杂的数据库要更好。 ​ pg_dump和psql读写管道的能力使得直接从一个服务器转储一个数据库到另一个服务器成为可能，例如： pg_dump -h host1 dbname | psql -h host2 dbname 重要：pg_dump产生的转储是相对于template0。这意味着在template1中加入的任何语言、过程等都会被pg_dump转储。结果是，如果在恢复时使用的是一个自定义的template1，你必须从template0创建一个空的数据库，正如上面的例子所示。 ​ 一旦完成恢复，在每个数据库上运行ANALYZE是明智的举动，这样优化器就有有用的统计数据了。 pg_dump每次只转储一个数据库，而且它不会转储关于角色或表空间（因为它们是集簇范围的）的信息。为了支持方便地转储一个数据库集簇的全部内容，提供了pg_dumpall程序。pg_dumpall备份一个给定集簇中的每一个数据库，并且也保留了集簇范围的数据，如角色和表空间定义。该命令的基本用法是： pg_dumpall > dumpfile 转储的结果可以使用psql恢复： psql -f dumpfile postgres （实际上，你可以指定恢复到任何已有数据库名，但是如果你正在将转储载入到一个空集簇中则通常要用（postgres）。在恢复一个pg_dumpall转储时常常需要具有数据库超级用户访问权限，因为它需要恢复角色和表空间信息。如果你在使用表空间，请确保转储中的表空间路径适合于新的安装。 pg_dumpall工作时会发出命令重新创建角色、表空间和空数据库，接着为每一个数据库pg_dump。这意味着每个数据库自身是一致的，但是不同数据库的快照并不同步。 集簇范围的数据可以使用pg_dumpall的—​globals-only选项来单独转储。如果在单个数据库上运行pg_dump命令，上述做法对于完全备份整个集簇是必需的。 在一些具有最大文件尺寸限制的操作系统上创建大型的pg_dump输出文件可能会出现问题。幸运地是，pg_dump可以写出到标准输出，因此你可以使用标准Unix工具来处理这种潜在的问题。有几种可能的方法： 使用压缩转储。你可以使用你喜欢的压缩程序，例如gzip： pg_dump dbname | gzip > filename.gz 恢复： gunzip -c filename.gz | psql dbname 或者： cat filename.gz | gunzip | psql dbname 使用split。split命令允许你将输出分割成较小的文件以便能够适应底层文件系统的尺寸要求。例如，让每一块的大小为2G字节： pg_dump dbname | split -b 2G - filename 恢复： cat filename* | psql dbname 如果使用GNU split，可能会把它和gzip一起使用： pg_dump dbname | split -b 2G -−filter='gzip > $FILE.gz' 它可以使用zcat恢复。 使用pg_dump的自定义转储格式。. 如果 IvorySQL 所在的系统上安装了zlib压缩库，自定义转储格式将在写出数据到输出文件时对其压缩。这将产生和使用gzip时差不多大小的转储文件，但是这种方式的一个优势是其中的表可以被有选择地恢复。下面的命令使用自定义转储格式来转储一个数据库： pg_dump -Fc dbname > filename ​自定义格式的转储不是psql的脚本，只能通过pg_restore恢复，例如： pg_restore -d dbname filename ​更多细节可以参阅 手册。 ​对于非常大型的数据库，你可能需要将split配合其他两种方法之一进行使用。 ​使用pg_dump的并行转储特性。 为了加快转储一个大型数据库的速度，你可以使用pg_dump的并行模式。它将同时转储多个表。你可以使用-j参数控制并行度。并行转储只支持“目录”归档格式。 pg_dump -j num -F d -f out.dir dbname ​你可以使用pg_restore -j来以并行方式恢复一个转储。它只能适合于“自定义”归档或者“目录”归档，但不管归档是否由pg_dump -j创建。 另外一种备份策略是直接复制 IvorySQL用于存储数据库中数据的文件，你可以采用任何你喜欢的方式进行文件系统备份，例如： tar -cf backup.tar /usr/local/pgsql/data 但是这种方法有两个限制，使得这种方法不实用，或者说至少比pg_dump方法差： 为了得到一个可用的备份，数据库服务器必须被关闭。例如阻止所有连接的半路措施是不起作用的（部分原因是tar和类似工具无法得到文件系统状态的一个原子的快照，还有服务器内部缓冲的原因）。不用说，在恢复数据之前你也需要关闭服务器。 如果你已经深入地了解了数据库的文件系统布局的细节，你可能会有兴趣尝试通过相应的文件或目录来备份或恢复特定的表或数据库。这种方法也不会起作用，因为包含在这些文件中的信息只有配合提交日志文件（pg_xact/*）才有用，提交日志文件包含了所有事务的提交状态。一个表文件只有和这些信息一起才有用。当然也不可能只恢复一个表及相关的pg_xact数据，因为这会导致数据库集簇中所有其他表变得无用。因此文件系统备份值适合于完整地备份或恢复整个数据库集簇。 ​另一种文件系统备份方法是创建一个数据目录的“一致快照”，如果文件系统支持此功能（并且你相信它的实现正确）。典型的过程是创建一个包含数据库的卷的“冻结快照”，然后从该快照复制整个数据目录（如上，不能是部分复制）到备份设备，最后释放冻结快照。即使在数据库服务器运行时，这种方式也有效。但是，以这种方式创建的备份保存的文件看起来就像数据库没有被正确关闭时的状态。因此，当你从备份数据上启动数据库服务器时，它会认为上一次的服务器实例崩溃了并尝试重放WAL日志。这不是问题，只是需要注意（当然WAL文件必须要包括在备份中）。你可以在拍摄快照之前执行一次CHECKPOINT以便节省恢复时间。 ​如果你的数据库跨越多个文件系统，可能没有任何方式可以对所有卷获得完全同步的冻结快照。例如，如果你的数据文件和WAL日志放置在不同的磁盘上，或者表空间在不同的文件系统中，可能没有办法使用快照备份，因为快照必须是同步的。在这些情况下，一定要仔细阅读你的文件系统文档以了解其对一致快照技术的支持。 ​如果没有可能获得同步快照，一种选择是将数据库服务器关闭足够长的时间以建立所有的冻结快照。另一种选择是执行一次连续归档基础备份，因为这种备份对于备份期间发生的文件系统改变是免疫的。这要求在备份过程中允许连续归档，恢复时使用连续归档恢复。 ​还有一种选择是使用rsync来执行一次文件系统备份。其做法是先在数据库服务器运行时执行rsync，然后关闭数据库服务器足够长时间来做一次rsync --checksum （--checksum是必需的，因为rsync的文件修改 时间粒度只能精确到秒）。第二次rsync会比第一次快，因为它只需要传送相对很少的数据，由于服务器是停止的，所以最终结果将是一致的。这种方法允许在最小停机时间内执行一次文件系统备份。 注意一个文件系统备份通常会比一个SQL转储体积更大（例如pg_dump不需要转储索引的内容，而是转储用于重建索引的命令）。但是，做一次文件系统备份可能更快。 在任何时间，IvorySQL在数据集簇目录的pg_wal/子目录下都保持有一个预写式日志（WAL）。这个日志存在的目的是为了保证崩溃后的安全：如果系统崩溃，可以“重放”从最后一次检查点以来的日志项来恢复数据库的一致性。该日志的存在也使得第三种备份数据库的策略变得可能：我们可以把一个文件系统级别的备份和WAL文件的备份结合起来。当需要恢复时，我们先恢复文件系统备份，然后从备份的WAL文件中重放来把系统带到一个当前状态。这种方法比之前的方法管理起来要更复杂，但是有其显著的优点： 我们不需要一个完美的一致的文件系统备份作为开始点。备份中的任何内部不一致性将通过日志重放（这和崩溃恢复期间发生的并无显著不同）来修正。因此我们不需要文件系统快照功能，只需要tar或一个类似的归档工具。 由于我们可以结合一个无穷长的WAL文件序列用于重放，可以通过简单地归档WAL文件来达到连续备份。这对于大型数据库特别有用，因为在其中不方便频繁地进行完全备份。 并不需要一直重放WAL项一直到最后。我们可以在任何点停止重放，并得到一个数据库在当时的一致快照。这样，该技术支持时间点恢复： 在得到你的基础备份以后，可以将数据库恢复到它在其后任何时间的状态。 如果我们连续地将一系列WAL文件输送给另一台已经载入了相同基础备份文件的机器， 我们就得到了一个热后备系统： 在任何时间点我们都能提出第二台机器， 它差不多是数据库的当前副本。 注意: pg_dump 和 pg_dumpall不会产生文件系统级别的备份，并且不能用于连续归档方案。这类转换是逻辑的并且不包含足够的信息用于WAL重放。 ​ 就简单的文件系统备份技术来说，这种方法只能支持整个数据库集簇的恢复，却无法支持其中一个子集的恢复。另外，它需要大量的归档存储：一个基础备份的体积可能很庞大，并且一个繁忙的系统将会产生大量需要被归档的WAL流量。尽管如此，在很多需要高可靠性的情况下，它是首选的备份技术。 ​ 要使用连续归档（也被很多数据库厂商称为“在线备份”）成功地恢复，你需要一个从基础备份时间开始的连续的归档WAL文件序列。为了开始，在你建立第一个基础备份之前，你应该建立并测试用于归档WAL文件的过程。对应地，我们首先讨论归档WAL文件的机制。关于如何建立归档和备份的方式以及操作过程中的要点，请参阅 手册。 copy 在 IvorySQL表和标准文件之间移动数据。COPY TO 把一个表的内容复制到一个文件，而COPY FROM 则从一个文件复制数据到一个表（把数据追加到表中原有数据）。COPY TO 也能复制一个SELECT查询的结果。 ​ 如果指定了一个列列表，COPY TO将只把指定列的数据复制到文件。对于COPY FROM，文件中的每个字段将按顺序插入到指定列中。COPY FROM命令的列列表中没有指定的表列则会采纳其默认值。 ​ 带一个文件名的COPY指示IvorySQL服务器直接从一个文件读取或者写入到一个文件。该文件必须是IvorySQL用户（运行服务器的用户ID）可访问的并且应该以服务器的视角来指定其名称。当指定了PROGRAM时，服务器执行给定的命令并且从该程序的标准输出读取或者写入到该程序的标准输入。该程序必须以服务器的视角指定，并且必须是IvorySQL用户可执行的。在指定STDIN或者STDOUT时，数据会通过客户端和服务器之间的连接传输。 ​ 运行COPY的每个后端将在pg_stat_progress_copy视图中报告其进度。 COPY table_name [ ( column_name [, ...] ) ] FROM { 'filename' | PROGRAM 'command' | STDIN } [ [ WITH ] ( option [, ...] ) ] [ WHERE condition ] COPY { table_name [ ( column_name [, ...] ) ] | ( query ) } TO { 'filename' | PROGRAM 'command' | STDOUT } [ [ WITH ] ( option [, ...] ) ] 其中 option 可以是下列之一： FORMAT format_name FREEZE [ boolean ] DELIMITER 'delimiter_character' NULL 'null_string' HEADER [ boolean ] QUOTE 'quote_character' ESCAPE 'escape_character' FORCE_QUOTE { ( column_name [, ...] ) | * } FORCE_NOT_NULL ( column_name [, ...] ) FORCE_NULL ( column_name [, ...] ) ENCODING 'encoding_name' 详细参数设置，请参阅 手册。 在成功完成时，一个COPY命令会返回一个形为 COPY count count 是被复制的行数。 注意： 如果命令不是COPY …​ TO STDOUT或者等效的psql命令\\copy …​ to stdout, psql将只打印这个命令标签。这是为了防止弄混命令标签和刚刚打印的数据。 COPY TO只能用于普通表，而不能用于视图，并且不能从子表或子分区复制行。 例如，COPY table TO 复制与SELECT * FROM ONLY table 相同的行。 语法COPY (SELECT * FROM table) TO …​ 可用于转储一个继承层次结构、分区表或视图中的所有行。 COPY FROM可以被用于普通表、外部表、分区表或者具有INSTEAD OF INSERT触发器的视图。 ​ 你必须拥有被COPY TO读取的表上的选择特权， 以及被COPY FROM插入的表上的插入特权。 拥有在命令中列出的列上的特权就足够了。 ​ 如果对表启用了行级安全性，相关的SELECT策略将应用于COPY table TO语句。当前，有行级安全性的表不支持COPY FROM。不过可以使用等效的INSERT语句。 ​ COPY命令中提到的文件会被服务器（而不是 客户端应用）直接读取或写入。因此它们必须位于数据库服务器（不是客户 端）的机器上或者是数据库服务器可以访问的。它们必须是 IvorySQL 用户（运行服务器的用户 ID）可访问的并且是可读或者可写的。类似地，用PROGRAM 指定的命令也会由服务器（不是客户端应用）直接执行，它也必须是 IvorySQL 用户可以执行的。 只允许数据库超级用户或者授予了角色pg_read_server_files、 pg_write_server_files及pg_execute_server_program 之一的用户COPY一个文件或者命令， 因为它允许读取或者写入服务器有特权访问的任何文件或者运行服务器有特权访问的程序。 ​ 不要把COPY和 psql指令 \\copy 弄混。\\copy会调用 COPY FROM STDIN或者COPY TO STDOUT，然后读取/存储一个 psql客户端可访问的文件中的数据。 因此，在使用\\copy时，文件的可访 问性和访问权利取决于客户端而不是服务器。 ​ 我们推荐在COPY中使用的文件名总是 指定为一个绝对路径。在COPY TO的 情况下服务器会强制这一点，但是对于 COPY FROM你可以选择从一个用相对 路径指定的文件中读取。该路径将根据服务器进程（而不是客户端） 的工作目录（通常是集簇的数据目录）解释。 ​ 用PROGRAM执行一个命令可能会受到操作系统 的访问控制机制（如 SELinux）的限制。 ​ COPY FROM将调用目标表上的任何触发器 和检查约束。但是它不会调用规则。 ​ 对于标识列，COPY FROM命令将总是写上输入数据中提供的列值，这和INSERT的选项OVERRIDING SYSTEM VALUE的行为一样。 ​ COPY输入和输出受到 DateStyle的影响。为了确保到其他 可能使用非默认DateStyle设置的 IvorySQL 安装的可移植性，在使用 COPY TO前应该把 DateStyle设置为ISO。避免转储把 IntervalStyle设置为 sql_standard的数据也是一个好主意，因为负的区间值可能会 被具有不同IntervalStyle设置的服务器解释错误。 ​ 即使数据会被服务器直接从一个文件读取或者写入一个文件而不通过 客户端，输入数据也会被根据ENCODING选项或者当前 客户端编码解释，并且输出数据会被根据ENCODING或 者当前客户端编码进行编码。 ​ COPY会在第一个错误处停止操作。这在 COPY TO的情况下不会导致问题，但是 在COPY FROM中目标表将已经收到了一 些行。这些行将不会变得可见或者可访问，但是它们仍然占据磁盘空间。 如果在一次大型的复制操作中出现错误，这可能浪费相当可观的磁盘空间。 你可能希望调用VACUUM来恢复被浪费的 空间。 ​ FORCE_NULL和FORCE_NOT_NULL可以被同时 用在同一列上。这会导致把已被引用的空值串转换为空值并且把未引用的空值 串转换为空串。 在使用text格式时，读取或写入的是一个文本文件，其中每一行就是表中的一行。一行中的列被定界字符分隔。列值本身是由输出函数产生的或者是可被输入函数接受的属于每个属性数据类型的字符串。在为空值的列的位置使用指定的空值串。如果输入文件的任何行包含比预期更多或者更少的列，COPY FROM将会抛出一个错误。 ​ 数据的结束可以表示为一个只包含反斜线-点号（\\.）的单一行。从一个文件读取时，数据结束标记并不是必要的，因为文件结束符就已经足够用了。只有使用3.0客户端协议之前的客户端应用复制数据时才需要它。 ​ 反斜线字符（\\）可以被用在COPY数据中来引用被用作行或者列界定符的字符。特别地，如果下列字符作为一个列值的一部分出现，它们 必须被前置一个反斜线：反斜线本身、新行、回车以及 当前的定界符字符。 ​ COPY TO会不加任何反斜线返回指定的空值串。 相反，COPY FROM会在移除反斜线之前把输入 与空值串相匹配。因此，一个空值串（例如\\N）不会与实 际的数据值\\N（它会被表示为\\\\N）搞混。 ​ COPY FROM识别下列特殊的反斜线序列： 序列 表示 \\b 退格（ASCII 8） \\f 换页（ASCII 12） \\n 新行（ASCII 10） \\r 回车（ASCII 13） \\t 制表（ASCII 9） \\v 纵向制表（ASCII 11） \\digits 反斜线后跟一到三个八进制数字表示该数字代码对应的字节 \\xdigits 反斜线加x后跟一到三个十六进制数字表示该数字代码对应的字节 当前，COPY TO不会发出一个八进制或十六进制位 反斜线序列，但是它确实把上面列出的其他序列用于那些控制字符。 ​ 任何上述表格中没有提到的其他反斜线字符将被当作表示其本身。不过，要注意 增加不必要的反斜线，因为那可能意外地产生一个匹配数据结束标记（ \\.）或者空值串（默认是\\N）的字符串。这些字符串 将在完成任何其他反斜线处理之前被识别。 ​ 强烈建议产生COPY数据的应用把数据新行和回车分别 转换为\\n和\\r序列。当前可以把一个数据回车表示为 一个反斜线和回车，把一个数据新行表示为一个反斜线和新行。不过，未来的发行 可能不会接受这些表示。如果在不同的机器之间（例如从 Unix 到 Windows） 传输COPY文件，它们也很容易受到破坏。 ​ 所有反斜杠序列都在编码转换后进行解释。 用八进制和十六进制数字反斜杠序列指定的字节必须在数据库编码中形成有效字符。 ​ COPY TO将用一个 Unix 风格的新行（ “\\n”）终止每一行。运行在 Microsoft Windows 上的服务器则会输出回车/新行（“\\r\\n”），不过只对 COPY到一个服务器文件这样做。为了做到跨平台一致， COPY TO STDOUT总是发送“\\n”而 不管服务器平台是什么。COPY FROM能够处理以 新行、回车或者回车/新行结尾的行。为了减少由作为数据的未加反斜线的新行 或者回车带来的风险，如果输出中的行结束并不完全相似， COPY FROM将会抱怨。 这种格式选项被用于导入和导出很多其他程序（例如电子表格）使用的逗号 分隔值（CSV）文件格式。不同于 IvorySQL标准文本格式使用的转义 规则，它产生并且识别一般的 CSV 转义机制。 ​ 每个记录中的值用DELIMITER字符分隔。如果值包含 定界符字符、QUOTE字符、NULL字符串、 一个回车或者换行字符，那么整个值会被加上QUOTE字符 作为前缀或者后缀，并且在该值内QUOTE字符或者 ESCAPE字符的任何一次出现之前放上转义字符。在输出 指定列中非NULL值时，还可以使用 FORCE_QUOTE来强制加上引用。 CSV格式没有标准方式来区分NULL值和空字符串。 IvorySQL的COPY用引用来处理 这种区分工作。NULL被按照NULL参数字符串输出 并且不会被引用，而匹配NULL参数字符串的非NULL 值会被加上引用。例如，使用默认设置时，NULL被写作一个未 被引用的空字符串，而一个空字符串数据值会被写成带双引号（\"\"）。 值的读取遵循类似的规则。你可以用FORCE_NOT_NULL来防止 对指定列的NULL输入比较。你还可以使用 FORCE_NULL把带引用的空值字符串数据值转换成NULL。 ​ 因为反斜线在CSV格式中不是一种特殊字符，数据结束标记 \\.也可以作为一个数据值出现。为了避免任何解释误会，在 一行上作为孤项出现的\\.数据值输出时会自动被引用，并且 输入时如果被引用，则不会被解释为数据结束标记。如果正在载入一个由 另一个应用创建的文件并且其中具有一个未被引用的列且可能具有 \\.值，你可能需要在输入文件中引用该值。 注意 CSV格式中，所有字符都是有意义的。一个被空白或者其他 非 DELIMITER字符围绕的引用值将包括那些字符。在导入 来自用空白填充CSV行到固定长度的系统的数据时，这可能 会导致错误。如果出现这种情况，在导入数据到 IvorySQL.之前，你可能需要预处理该 CSV文件以移除拖尾的空白。 注意 CSV 格式将识别并且产生带有包含嵌入的回车和换行的引用值的 CSV 文件。因此文件并不限于文本格式文件的每个表行一行的形式。 注意 很多程序会产生奇怪的甚至偶尔是不合常理的 CSV 文件，因此该文件 格式更像是一种习惯而不是标准。因此你可能会碰到一些无法使用这种 机制导入的文件，并且COPY也可能产生其他程序无 法处理的文件。 binary格式选项导致所有数据被以二进制格式 而不是文本格式存储/读取。它比文本和CSV格式要 快一些，但是二进制格式文件在不同的机器架构和 IvorySQL 版本之间的可移 植性要差些。还有，二进制格式与数据格式非常相关。例如不能从 一个smallint列中输出二进制数据并且把它读入到一个 integer列中，虽然这样做在文本格式中是可行的。 ​ binary文件格式由一个文件头、零个或者更多个包含 行数据的元组以及一个文件尾构成。头部和数据都以网络字节序表示。 文件头 文件头由 15 字节的固定域构成，后面跟着一个变长的头部扩展区。 固定域有： 签名 11-字节的序列PGCOPY\\n\\377\\r\\n\\0 — 注意 零字节是签名的一个必要的部分（该签名是为了能容易地发现文件被 无法正确处理 8 位字符编码的传输所破坏。这个签名将被行尾翻译过 滤器、删除零字节、删除高位或者奇偶修改等改变）。 标志域 32-位整数位掩码，用以表示该文件格式的重要方面。位被编号为 从 0 （LSB）到 31（MSB）。 注意这个域以网络字节序存放（最高有效位在前），所有该文件格式 中使用的整数域都是这样。16-31 位被保留用来表示严重的文件格式 问题， 读取者如果在这个范围内发现预期之外的被设置位，它应该 中止。0-15 位被保留用来表示向后兼容的格式问题，读取者应该简单 地略过这个范围内任何预期之外的被设置位。当前只定义了一个标志 位，其他位必须为零： 位 16 如果为 1，表示数据中包含 OID；如果为 0，则不包含。IvorySQL不再支持Oid系统列，但是格式仍然包含该指示符。 头部扩展区长度 32-为整数，表示头部剩余部分的以字节计的长度，不包括其本身。 当前，这个长度为零，并且其后就紧跟着第一个元组。未来对该 格式的更改可能会允许在头部中表示额外的数据。如果读取者不知 道要对头部扩展区数据做什么，可以安静地跳过它。 ​ 头部扩展区域被预期包含一个能自我解释的块的序列。 该标志域并不想告诉读取者扩展数据是什么。详细的 头部扩展内容的设计留给后来的发行去做。 这种设计允许向后兼容的头部增加（增加头部扩展块或者设置低位标志位）以及 非向后兼容的更改（设置高位标志位来表示这类更改并且在需要时向扩展区域 中增加支持数据）。 元组 每一个元组由一个表示元组中域数量的 16 位整数计数开始（当前，一个表中 的所有元组都应该具有相同的计数，但是这可能不会总是为真）。然后是元组 中的每一个域，它是一个 32 位的长度字，后面则跟随着这么多个字节的域数 据（长度字不包括其本身，并且可以是零）。作为一种特殊情况，-1 表示一个 NULL 域值。在 NULL 情况下，后面不会跟随值字节。 ​ 在域之间没有对齐填充或者任何其他额外的数据。 ​ 当前，一个二进制格式文件中的所有数据值都被假设为二进制格式（格式代码一）。 可以预见未来的扩展可能会增加一个允许独立指定各列的格式代码的头部域。 ​ 要为实际的元组数据决定合适的二进制格式，你应该参考 IvorySQL源码，特别是用于各列 数据类型的*send和*recv函数（通常可 以在源码的src/backend/utils/adt/目录中找到 这些函数）。 ​ 如果文件中包含 OID，OID 域会紧跟在域计数字之后。它是一个普通域， 不过它没有被包含在域计数中。注意IvorySQL当前版本不支持oid系统列。 文件尾 文件位由一个包含 -1 的 16 位整数字组成。这很容易与一个 元组的域计数字区分开。 ​ 如果一个域计数字不是 -1 也不是期望的列数，读取者应该报告错误。 这提供了一种针对某种数据不同步的额外检查。 下面的例子使用竖线（|）作为域定界符把一个表复制到客户端： COPY country TO STDOUT (DELIMITER '|'); ​ 从一个文件中复制数据到country表中： COPY country TO STDOUT (DELIMITER '|'); ​ 只把名称以 'A' 开头的国家复制到一个文件中： COPY (SELECT * FROM country WHERE country_name LIKE 'A%') TO '/usr1/proj/bray/sql/a_list_countries.copy'; ​ 要复制到一个压缩文件中，你可以用管道把输出导到一个外部压缩程序： COPY country TO PROGRAM 'gzip > /usr1/proj/bray/sql/country_data.gz'; ​ 这里是一个适合于从STDIN复制到表中的数据： AF AFGHANISTAN AL ALBANIA DZ ALGERIA ZM ZAMBIA ZW ZIMBABWE 注意每一行上的空白实际是一个制表符。 ​ 下面是用二进制格式输出的相同数据。该数据是用 Unix 工具 od -c过滤后显示的。该表具有三列， 第一列类型是char(2)，第二列类型是text， 第三列类型是integer。所有行在第三列都是空值。 0000000 P G C O P Y \\n 377 \\r \\n \\0 \\0 \\0 \\0 \\0 \\0 0000020 \\0 \\0 \\0 \\0 003 \\0 \\0 \\0 002 A F \\0 \\0 \\0 013 A 0000040 F G H A N I S T A N 377 377 377 377 \\0 003 0000060 \\0 \\0 \\0 002 A L \\0 \\0 \\0 007 A L B A N I 0000100 A 377 377 377 377 \\0 003 \\0 \\0 \\0 002 D Z \\0 \\0 \\0 0000120 007 A L G E R I A 377 377 377 377 \\0 003 \\0 \\0 0000140 \\0 002 Z M \\0 \\0 \\0 006 Z A M B I A 377 377 0000160 377 377 \\0 003 \\0 \\0 \\0 002 Z W \\0 \\0 \\0 \\b Z I 0000200 M B A B W E 377 377 377 377 377 377 剩余的详细信息可以参阅 手册。 查询性能可能受多种因素影响。其中一些因素可以由用户控制，而其他的则属于系统下层设计的基本原理。 IvorySQL为每个收到查询产生一个查询计划。 选择正确的计划来匹配查询结构和数据的属性对于好的性能来说绝对是最关键的，因此系统包含了一个复杂的规划器来尝试选择好的计划。 你可以使用EXPLAIN命令察看规划器为任何查询生成的查询计划。 阅读查询计划是一门艺术，它要求一些经验来掌握，但是本节只试图覆盖一些基础。 ​ 这些例子使用EXPLAIN的默认“text”输出格式，这种格式紧凑并且便于阅读。如果你想把EXPLAIN的输出交给一个程序做进一步分析，你应该使用它的某种机器可读的输出格式（XML、JSON 或 YAML）。 查询计划的结构是一个计划结点的树。最底层的结点是扫描结点：它们从表中返回未经处理的行。 不同的表访问模式有不同的扫描结点类型：顺序扫描、索引扫描、位图索引扫描。 也还有不是表的行来源，例如VALUES子句和FROM中返回集合的函数，它们有自己的结点类型。如果查询需要连接、聚集、排序、或者在未经处理的行上的其它操作，那么就会在扫描结点之上有其它额外的结点来执行这些操作。 并且，做这些操作通常都有多种方法，因此在这些位置也有可能出现不同的结点类型。 EXPLAIN给计划树中每个结点都输出一行，显示基本的结点类型和计划器为该计划结点的执行所做的开销估计。 第一行（最上层的结点）是对该计划的总执行开销的估计；计划器试图最小化的就是这个数字。 ​ 这里是一个简单的例子，只是用来显示输出看起来是什么样的： EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) 由于这个查询没有WHERE子句，它必须扫描表中的所有行，因此计划器只能选择使用一个简单的顺序扫描计划。被包含在圆括号中的数字是（从左至右）： 估计的启动开销。在输出阶段可以开始之前消耗的时间，例如在一个排序节点里执行排序的时间。 估计的总开销。这个估计值基于的假设是计划结点会被运行到完成， 即所有可用的行都被检索。不过实际上一个结点的父节点可能很快停止读取所有可用的行（见下面的LIMIT例子）。 这个计划结点输出行数的估计值。同样，也假定该结点能运行到完成。 预计这个计划结点输出的行平均宽度（以字节计算）。 ​ 开销是用规划器的开销参数所决定的捏造单位来衡量的。传统上以取磁盘页面为单位来度量开销； 也就是seq_page_cost将被按照习惯设为1.0，其它开销参数将相对于它来设置。 本节的例子都假定这些参数使用默认值。 ​ 有一点很重要：一个上层结点的开销包括它的所有子结点的开销。还有一点也很重要：这个开销只反映规划器关心的东西。特别是这个开销没有考虑结果行传递给客户端所花费的时间，这个时间可能是实际花费时间中的一个重要因素；但是它被规划器忽略了，因为它无法通过修改计划来改变（我们相信，每个正确的计划都将输出同样的行集）。 ​ 行数值有一些小技巧，因为它不是计划结点处理或扫描过的行数，而是该结点发出的行数。这通常比被扫描的行数少一些， 因为有些被扫描的行会被应用于此结点上的任意WHERE子句条件过滤掉。 理想中顶层的行估计会接近于查询实际返回、更新、删除的行数。 ​ 回到我们的例子： EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) 这些数字的产生非常直接。如果你执行： SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1'; 你会发现tenk1有358个磁盘页面和10000行。 开销被计算为 （页面读取数*seq_page_cost）+（扫描的行数*cpu_tuple_cost）。默认情况下，seq_page_cost是1.0，cpu_tuple_cost是0.01， 因此估计的开销是 (358 * 1.0) + (10000 * 0.01) = 458。 ​ 现在让我们修改查询并增加一个WHERE条件： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000; QUERY PLAN ------------------------------------------------------------ Seq Scan on tenk1 (cost=0.00..483.00 rows=7001 width=244) Filter: (unique1 < 7000) 请注意EXPLAIN输出显示WHERE子句被当做一个“过滤器”条件附加到顺序扫描计划结点。 这意味着该计划结点为它扫描的每一行检查该条件，并且只输出通过该条件的行。因为WHERE子句的存在，估计的输出行数降低了。不过，扫描仍将必须访问所有 10000 行，因此开销没有被降低；实际上开销还有所上升（准确来说，上升了 10000 * cpu_operator_cost）以反映检查WHERE条件所花费的额外 CPU 时间。 ​ 这条查询实际选择的行数是 7000，但是估计的rows只是个近似值。如果你尝试重复这个试验，那么你很可能得到略有不同的估计。 此外，这个估计会在每次ANALYZE命令之后改变， 因为ANALYZE生成的统计数据是从该表中随机采样计算的。 ​ 现在，让我们把条件变得更严格： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) 这里，规划器决定使用一个两步的计划：子计划结点访问一个索引来找出匹配索引条件的行的位置，然后上层计划结点实际地从表中取出那些行。独立地抓取行比顺序地读取它们的开销高很多，但是不是所有的表页面都被访问，这么做实际上仍然比一次顺序扫描开销要少（使用两层计划的原因是因为上层规划结点把索引标识出来的行位置在读取之前按照物理位置排序，这样可以最小化单独抓取的开销。结点名称里面提到的“位图”是执行该排序的机制）。 ​ 现在让我们给WHERE子句增加另一个条件： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND stringu1 = 'xxx'; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.04..229.43 rows=1 width=244) Recheck Cond: (unique1 < 100) Filter: (stringu1 = 'xxx'::name) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) 新增的条件stringu1 = 'xxx’减少了估计的输出行计数， 但是没有减少开销，因为我们仍然需要访问相同的行集合。 请注意，stringu1子句不能被应用为一个索引条件，因为这个索引只是在unique1列上。 它被用来过滤从索引中检索出的行。因此开销实际上略微增加了一些以反映这个额外的检查。 ​ 在某些情况下规划器将更倾向于一个“simple”索引扫描计划： EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42; QUERY PLAN -------------------------------------------------------------------​----------- Index Scan using tenk1_unique1 on tenk1 (cost=0.29..8.30 rows=1 width=244) Index Cond: (unique1 = 42) 在这类计划中，表行被按照索引顺序取得，这使得读取它们开销更高，但是其中有一些是对行位置排序的额外开销。 你很多时候将在只取得一个单一行的查询中看到这种计划类型。 它也经常被用于拥有匹配索引顺序的ORDER BY子句的查询中， 因为那样就不需要额外的排序步骤来满足ORDER BY。在此示例中，添加 ORDER BY unique1将使用相同的计划，因为索引已经隐式提供了请求的排序。 ​ 规划器可以通过多种方式实现ORDER BY子句。上面的例子表明，这样的排序子句可以隐式实现。 规划器还可以添加一个明确的sort步骤： EXPLAIN SELECT * FROM tenk1 ORDER BY unique1; QUERY PLAN ------------------------------------------------------------------- Sort (cost=1109.39..1134.39 rows=10000 width=244) Sort Key: unique1 -> Seq Scan on tenk1 (cost=0.00..445.00 rows=10000 width=244) 如果计划的一部分保证对所需排序键的前缀进行排序，那么计划器可能会决定使用incremental sort步骤： EXPLAIN SELECT * FROM tenk1 ORDER BY four, ten LIMIT 100; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Limit (cost=521.06..538.05 rows=100 width=244) -> Incremental Sort (cost=521.06..2220.95 rows=10000 width=244) Sort Key: four, ten Presorted Key: four -> Index Scan using index_tenk1_on_four on tenk1 (cost=0.29..1510.08 rows=10000 width=244) 与常规排序相比，增量排序允许在对整个结果集进行排序之前返回元组，这尤其可以使用LIMIT查询进行优化。 它还可以减少内存使用和将排序溢出到磁盘的可能性，但其代价是将结果集拆分为多个排序批次的开销增加。 ​ 如果在WHERE引用的多个行上有独立的索引，规划器可能会选择使用这些索引的一个 AND 或 OR 组合： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​------------------ Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) Index Cond: (unique2 > 9000) 但是这要求访问两个索引，所以与只使用一个索引并把其他条件作为过滤器相比，它不一定能胜出。如果你变动涉及到的范围，你将看到计划也会相应改变。 下面是一个例子，它展示了LIMIT的效果： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------ Limit (cost=0.29..14.48 rows=2 width=244) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..71.27 rows=10 width=244) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) 这是和上面相同的查询，但是我们增加了一个LIMIT这样不是所有的行都需要被检索，并且规划器改变了它的决定。注意索引扫描结点的总开销和行计数显示出好像它会被运行到完成。但是，限制结点在检索到这些行的五分之一后就会停止，因此它的总开销只是索引扫描结点的五分之一，并且这是查询的实际估计开销。之所以用这个计划而不是在之前的计划上增加一个限制结点是因为限制无法避免在位图扫描上花费启动开销，因此总开销会是超过那种方法（25个单位）的某个值。 ​ 让我们尝试连接两个表，使用我们已经讨论过的列： EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..118.62 rows=10 width=488) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) Index Cond: (unique2 = t1.unique2) 在这个计划中，我们有一个嵌套循环连接结点，它有两个表扫描作为输入或子结点。该结点的摘要行的缩进反映了计划树的结构。连接的第一个（或“outer”）子结点是一个与前面见到的相似的位图扫描。它的开销和行计数与我们从SELECT …​ WHERE unique1 < 10得到的相同，因为我们将WHERE子句unique1 < 10用在了那个结点上。t1.unique2 = t2.unique2子句现在还不相关，因此它不影响 outer 扫描的行计数。嵌套循环连接结点将为从 outer 子结点得到的每一行运行它的第二个（或“inner”）子结点。当前 outer 行的列值可以被插入 inner 扫描。这里，来自 outer 行的t1.unique2值是可用的，所以我们得到的计划和开销与前面见到的简单SELECT …​ WHERE t2.unique2 = constant情况相似（估计的开销实际上比前面看到的略低，是因为在t2上的重复索引扫描会利用到高速缓存）。循环结点的开销则被以 outer 扫描的开销为基础设置，外加对每一个 outer 行都要进行一次 inner 扫描 （10 * 7.87），再加上用于连接处理一点 CPU 时间。 ​ 在这个例子里，连接的输出行计数等于两个扫描的行计数的乘积，但通常并不是所有的情况中都如此， 因为可能有同时提及两个表的 额外WHERE子句，并且因此它只能被应用于连接点，而不能影响任何一个输入扫描。这里是一个例子： EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t2.unique2 < 10 AND t1.hundred < t2.hundred; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..49.46 rows=33 width=488) Join Filter: (t1.hundred < t2.hundred) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Materialize (cost=0.29..8.51 rows=10 width=244) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..8.46 rows=10 width=244) Index Cond: (unique2 < 10) 条件t1.hundred < t2.hundred不能在tenk2_unique2索引中被测试，因此它被应用在连接结点。这缩减了连接结点的估计输出行计数，但是没有改变任何输入扫描。 ​ 注意这里规划器选择了“物化”连接的 inner 关系，方法是在它的上方放了一个物化计划结点。这意味着t2索引扫描将只被做一次，即使嵌套循环连接结点需要读取其数据十次（每个来自 outer 关系的行都要读一次）。物化结点在读取数据时将它保存在内存中，然后在每一次后续执行时从内存返回数据。 ​ 在处理外连接时，你可能会看到连接计划结点同时附加有“连接过滤器”和普通“过滤器”条件。连接过滤器条件来自于外连接的ON子句，因此一个无法通过连接过滤器条件的行也能够作为一个空值扩展的行被发出。但是一个普通过滤器条件被应用在外连接条件之后并且因此无条件移除行。在一个内连接中这两种过滤器类型没有语义区别。 ​ 如果我们把查询的选择度改变一点，我们可能得到一个非常不同的连接计划： EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Hash Join (cost=230.47..713.98 rows=101 width=488) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) -> Hash (cost=229.20..229.20 rows=101 width=244) -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) 这里规划器选择了使用一个哈希连接，在其中一个表的行被放入一个内存哈希表，在这之后其他表被扫描并且为每一行查找哈希表来寻找匹配。同样要注意缩进是如何反映计划结构的：tenk1上的位图扫描是哈希结点的输入，哈希结点会构造哈希表。然后哈希表会返回给哈希连接结点，哈希连接结点将从它的 outer 子计划读取行，并为每一个行搜索哈希表。 ​ 另一种可能的连接类型是一个归并连接，如下所示： EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Merge Join (cost=198.11..268.19 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Sort (cost=197.83..200.33 rows=1000 width=244) Sort Key: t2.unique2 -> Seq Scan on onek t2 (cost=0.00..148.00 rows=1000 width=244) 归并连接要求它的输入数据被按照连接键排序。在这个计划中，tenk1数据被使用一个索引扫描排序，以便能够按照正确的顺序来访问行。但是对于onek则更倾向于一个顺序扫描和排序，因为在那个表中有更多行需要被访问（对于很多行的排序，顺序扫描加排序常常比一个索引扫描好，因为索引扫描需要非顺序的磁盘访问）。 ​ 一种查看变体计划的方法是强制规划器丢弃它认为开销最低的任何策略，这可以使用启用/禁用标志实现例如，如果我们并不认同在前面的例子中顺序扫描加排序是处理表onek的最佳方法，我们可以尝试： SET enable_sort = off; EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​----------------------- Merge Join (cost=0.56..292.65 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Index Scan using onek_unique2 on onek t2 (cost=0.28..224.79 rows=1000 width=244) 这显示规划器认为用索引扫描来排序onek的开销要比用顺序扫描加排序的方式高大约12%。当然，下一个问题是是否真的是这样。我们可以通过使用EXPLAIN ANALYZE来仔细研究一下，如下文所述。 可以通过使用EXPLAIN的ANALYZE选项来检查规划器估计值的准确性。通过使用这个选项，EXPLAIN会实际执行该查询，然后显示真实的行计数和在每个计划结点中累计的真实运行时间，还会有一个普通EXPLAIN显示的估计值。例如，我们可能得到这样一个结果： EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Nested Loop (cost=4.65..118.62 rows=10 width=488) (actual time=0.128..0.377 rows=10 loops=1) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) (actual time=0.057..0.121 rows=10 loops=1) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) (actual time=0.024..0.024 rows=10 loops=1) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) (actual time=0.021..0.022 rows=1 loops=10) Index Cond: (unique2 = t1.unique2) Planning time: 0.181 ms Execution time: 0.501 ms 注意“actual time”值是以毫秒计的真实时间，而cost估计值被以捏造的单位表示，因此它们不大可能匹配上。在这里面要查看的最重要的一点是估计的行计数是否合理地接近实际值。在这个例子中，估计值都是完全正确的，但是在实际中非常少见。 在某些查询计划中，可以多次执行一个子计划结点。例如，inner 索引扫描可能会因为上层嵌套循环计划中的每一个 outer 行而被执行一次。在这种情况下，loops值报告了执行该结点的总次数，并且 actual time 和行数值是这些执行的平均值。这是为了让这些数字能够与开销估计被显示的方式有可比性。将这些值乘上loops值可以得到在该结点中实际消耗的总时间。在上面的例子中，我们在执行tenk2的索引扫描上花费了总共 0.220 毫秒。 ​ 在某些情况中，EXPLAIN ANALYZE会显示计划结点执行时间和行计数之外的额外执行统计信息。例如，排序和哈希结点提供额外的信息： EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2 ORDER BY t1.fivethous; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------------​------ Sort (cost=717.34..718.09 rows=101 width=488) (actual time=7.761..7.774 rows=100 loops=1) Sort Key: t1.fivethous Sort Method: quicksort Memory: 77kB -> Hash Join (cost=230.47..713.98 rows=101 width=488) (actual time=0.711..7.427 rows=100 loops=1) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) (actual time=0.007..2.583 rows=10000 loops=1) -> Hash (cost=229.20..229.20 rows=101 width=244) (actual time=0.659..0.659 rows=100 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 28kB -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) (actual time=0.080..0.526 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.049..0.049 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.194 ms Execution time: 8.008 ms 排序结点显示使用的排序方法（尤其是，排序是在内存中还是磁盘上进行）和需要的内存或磁盘空间量。哈希结点显示了哈希桶的数量和批数，以及被哈希表所使用的内存量的峰值（如果批数超过一，也将会涉及到磁盘空间使用，但是并没有被显示）。 ​ 另一种类型的额外信息是被一个过滤器条件移除的行数： EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE ten < 7; QUERY PLAN -------------------------------------------------------------------​-------------------------------------- Seq Scan on tenk1 (cost=0.00..483.00 rows=7000 width=244) (actual time=0.016..5.107 rows=7000 loops=1) Filter: (ten < 7) Rows Removed by Filter: 3000 Planning time: 0.083 ms Execution time: 5.905 ms 这些值对于被应用在连接结点上的过滤器条件特别有价值。只有在至少有一个被扫描行或者在连接结点中一个可能的连接对被过滤器条件拒绝时，“Rows Removed”行才会出现。 ​ 一个与过滤器条件相似的情况出现在“有损”索引扫描中。例如，考虑这个查询，它搜索包含一个指定点的多边形： EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Seq Scan on polygon_tbl (cost=0.00..1.05 rows=1 width=32) (actual time=0.044..0.044 rows=0 loops=1) Filter: (f1 @> '((0.5,2))'::polygon) Rows Removed by Filter: 4 Planning time: 0.040 ms Execution time: 0.083 ms 规划器认为（非常正确）这个采样表太小不值得劳烦一次索引扫描，因此我们得到了一个普通的顺序扫描，其中的所有行都被过滤器条件拒绝。但是如果我们强制使得一次索引扫描可以被使用，我们看到： SET enable_seqscan TO off; EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------- Index Scan using gpolygonind on polygon_tbl (cost=0.13..8.15 rows=1 width=32) (actual time=0.062..0.062 rows=0 loops=1) Index Cond: (f1 @> '((0.5,2))'::polygon) Rows Removed by Index Recheck: 1 Planning time: 0.034 ms Execution time: 0.144 ms 这里我们可以看到索引返回一个候选行，然后它会被索引条件的重新检查拒绝。这是因为一个 GiST 索引对于多边形包含测试是 “有损的”：它确实返回覆盖目标的多边形的行，然后我们必须在那些行上做精确的包含性测试。 EXPLAIN有一个BUFFERS选项可以和ANALYZE一起使用来得到更多运行时统计信息： EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) (actual time=0.323..0.342 rows=10 loops=1) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) Buffers: shared hit=15 -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) (actual time=0.309..0.309 rows=0 loops=1) Buffers: shared hit=7 -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Buffers: shared hit=2 -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) (actual time=0.227..0.227 rows=999 loops=1) Index Cond: (unique2 > 9000) Buffers: shared hit=5 Planning time: 0.088 ms Execution time: 0.423 ms BUFFERS提供的数字帮助我们标识查询的哪些部分是对 I/O 最敏感的。 ​记住因为EXPLAIN ANALYZE实际运行查询，任何副作用都将照常发生，即使查询可能输出的任何结果被丢弃来支持打印EXPLAIN数据。如果你想要分析一个数据修改查询而不想改变你的表，你可以在分析完后回滚命令，例如： BEGIN; EXPLAIN ANALYZE UPDATE tenk1 SET hundred = hundred + 1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------- Update on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=14.628..14.628 rows=0 loops=1) -> Bitmap Heap Scan on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=0.101..0.439 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.079 ms Execution time: 14.727 ms ROLLBACK; 正如在这个例子中所看到的，当查询是一个INSERT、UPDATE或DELETE命令时，应用表更改的实际工作由顶层插入、更新或删除计划结点完成。这个结点之下的计划结点执行定位旧行以及/或者计算新数据的工作。因此在上面，我们看到我们已经见过的位图表扫描，它的输出被交给一个更新结点，更新结点会存储被更新过的行。还有一点值得注意的是，尽管数据修改结点可能要可观的运行时间（这里，它消耗最大份额的时间），规划器当前并没有对开销估计增加任何东西来说明这些工作。这是因为这些工作对每一个正确的查询计划都得做，所以它不影响计划的选择。 ​ 当一个UPDATE或者DELETE命令影响继承层次时， 输出可能像这样： EXPLAIN UPDATE parent SET f2 = f2 + 1 WHERE f1 = 101; QUERY PLAN -------------------------------------------------------------------​---------------- Update on parent (cost=0.00..24.53 rows=4 width=14) Update on parent Update on child1 Update on child2 Update on child3 -> Seq Scan on parent (cost=0.00..0.00 rows=1 width=14) Filter: (f1 = 101) -> Index Scan using child1_f1_key on child1 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child2_f1_key on child2 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child3_f1_key on child3 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) 在这个例子中，更新节点需要考虑三个子表以及最初提到的父表。因此有四个输入 的扫描子计划，每一个对应于一个表。为清楚起见，在更新节点上标注了将被更新 的相关目标表，显示的顺序与相应的子计划相同（这些标注是从 PostgreSQL 9.5 开始新增的，在以前的版本中读者必须通过 观察子计划才能知道这些目标表）。 ​ EXPLAIN ANALYZE显示的 Planning time是从一个已解析的查询生成查询计划并进行优化 所花费的时间，其中不包括解析和重写。 ​ EXPLAIN ANALYZE显示的Execution time包括执行器的启动和关闭时间，以及运行被触发的任何触发器的时间，但是它不包括解析、重写或规划的时间。如果有花在执行BEFORE执行器的时间，它将被包括在相关的插入、更新或删除结点的时间内；但是用来执行AFTER 触发器的时间没有被计算，因为AFTER触发器是在整个计划完成后被触发的。在每个触发器（BEFORE或AFTER）也被独立地显示。注意延迟约束触发器将不会被执行，直到事务结束，并且因此根本不会被EXPLAIN ANALYZE考虑。 在两种有效的方法中EXPLAIN ANALYZE所度量的运行时间可能偏离同一个查询的正常执行。首先，由于不会有输出行被递交给客户端，网络传输开销和 I/O 转换开销没有被包括在内。其次，由EXPLAIN ANALYZE所增加的度量开销可能会很可观，特别是在操作系统调用gettimeofday()很慢的机器上。你可以使用pg_test_timing工具来度量在你的系统上的计时开销。 ​ EXPLAIN结果不应该被外推到与你实际测试的非常不同的情况。例如，一个很小的表上的结果不能被假定成适合大型表。规划器的开销估计不是线性的，并且因此它可能为一个更大或更小的表选择一个不同的计划。一个极端例子是，在一个只占据一个磁盘页面的表上，你将几乎总是得到一个顺序扫描计划，而不管索引是否可用。规划器认识到它在任何情况下都将采用一次磁盘页面读取来处理该表，因此用额外的页面读取去查看一个索引是没有价值的（我们已经在前面的polygon_tbl例子中见过）。 ​ 在一些情况中，实际的值和估计的值不会匹配得很好，但是这并非错误。一种这样的情况发生在计划结点的执行被LIMIT或类似的效果很快停止。例如，在我们之前用过的LIMIT查询中： EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------ Limit (cost=0.29..14.71 rows=2 width=244) (actual time=0.177..0.249 rows=2 loops=1) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..72.42 rows=10 width=244) (actual time=0.174..0.244 rows=2 loops=1) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) Rows Removed by Filter: 287 Planning time: 0.096 ms Execution time: 0.336 ms 索引扫描结点的估计开销和行计数被显示成好像它会运行到完成。但是实际上限制结点在得到两个行之后就停止请求行，因此实际的行计数只有 2 并且运行时间远低于开销估计所建议的时间。这并非估计错误，这仅仅一种估计值和实际值显示方式上的不同。 ​ 归并连接也有类似的现象。如果一个归并连接用尽了一个输入并且其中的最后一个键值小于另一个输入中的下一个键值，它将停止读取另一个输入。在这种情况下，不会有更多的匹配并且因此不需要扫描第二个输入的剩余部分。这会导致不读取一个子结点的所有内容，其结果就像在LIMIT中所提到的。另外，如果 outer （第一个）子结点包含带有重复键值的行，inner（第二个）子结点会被倒退并且被重新扫描来找能匹配那个键值的行。EXPLAIN ANALYZE会统计相同 inner 行的重复发出，就好像它们是真实的额外行。当有很多 outer 重复时，对 inner 子计划结点所报告的实际行计数会显著地大于实际在 inner 关系中的行数。 ​ 由于实现的限制，BitmapAnd 和 BitmapOr 结点总是报告它们的实际行计数为零。 ​ 通常，EXPLAIN将显示规划器生成的每个计划节点。 但是，在某些情况下，执行器可以不执行某些节点，因为根据规划时不可用的参数值能确定这些节点无法产生任何行。 （当前，这仅会在扫描分区表的Append或MergeAppend节点的子节点中发生。） 发生这种情况时，将从EXPLAIN输出中省略这些计划节点，并显示Subplans Removed：N的标识。 如我们在上一节所见，查询规划器需要估计一个查询要检索的行数，这样才能对查询计划做出好的选择。 本节对系统用于这些估计的统计信息进行一个快速的介绍。 ​ 统计信息的一个部分就是每个表和索引中的项的总数，以及每个表和索引占用的磁盘块数。这些信息保存在pg_class表的reltuples和relpages列中。 我们可以用类似下面的查询查看这些信息： SELECT relname, relkind, reltuples, relpages FROM pg_class WHERE relname LIKE 'tenk1%'; relname | relkind | reltuples | relpages ----------------------+---------+-----------+---------- tenk1 | r | 10000 | 358 tenk1_hundred | i | 10000 | 30 tenk1_thous_tenthous | i | 10000 | 30 tenk1_unique1 | i | 10000 | 30 tenk1_unique2 | i | 10000 | 30 (5 rows) 这里我们可以看到tenk1包含 10000 行， 它的索引也有这么多行，但是索引远比表小得多（不奇怪）。 ​ 出于效率考虑，reltuples和relpages不是实时更新的 ，因此它们通常包含有些过时的值。它们被VACUUM、ANALYZE和几个 DDL 命令（例如CREATE INDEX）更新。一个不扫描全表的VACUUM或ANALYZE操作（常见情况）将以它扫描的部分为基础增量更新reltuples计数，这就导致了一个近似值。在任何情况中，规划器将缩放它在pg_class中找到的值来匹配当前的物理表尺寸，这样得到一个较紧的近似。 ​ 大多数查询只是检索表中行的一部分，因为它们有限制要被检查的行的WHERE子句。 因此规划器需要估算WHERE子句的选择度，即符合WHERE子句中每个条件的行的比例。 用于这个任务的信息存储在pg_statistic系统目录中。 在pg_statistic中的项由ANALYZE和VACUUM ANALYZE命令更新， 并且总是近似值（即使刚刚更新完）。 ​ 除了直接查看pg_statistic之外， 手工检查统计信息的时候最好查看它的视图pg_stats。pg_stats被设计为更容易阅读。 而且，pg_stats是所有人都可以读取的，而pg_statistic只能由超级用户读取（这样可以避免非授权用户从统计信息中获取一些其他人的表的内容的信息。pg_stats视图被限制为只显示当前用户可读的表）。例如，我们可以： SELECT attname, inherited, n_distinct, array_to_string(most_common_vals, E'\\n') as most_common_vals FROM pg_stats WHERE tablename = 'road'; attname | inherited | n_distinct | most_common_vals ---------+-----------+------------+------------------------------------ name | f | -0.363388 | I- 580 Ramp+ | | | I- 880 Ramp+ | | | Sp Railroad + | | | I- 580 + | | | I- 680 Ramp name | t | -0.284859 | I- 880 Ramp+ | | | I- 580 Ramp+ | | | I- 680 Ramp+ | | | I- 580 + | | | State Hwy 13 Ramp (2 rows) 注意，这两行显示的是相同的列，一个对应开始于road表（inherited=t）的完全继承层次， 另一个只包括road表本身（inherited=f）。 ​ ANALYZE在pg_statistic中存储的信息量（特别是每个列的most_common_vals中的最大项数和histogram_bounds数组）可以用ALTER TABLE SET STATISTICS命令为每一列设置， 或者通过设置配置变量default_statistics_target进行全局设置。 目前的默认限制是 100 个项。提升该限制可能会让规划器做出更准确的估计（特别是对那些有不规则数据分布的列）， 其代价是在pg_statistic中消耗了更多空间，并且需要略微多一些的时间来计算估计数值。 相比之下，比较低的限制可能更适合那些数据分布比较简单的列。 更多规划器对统计信息的使用可以参阅 手册。 常常可以看到由于查询子句中用到的多个列相互关联而运行着糟糕的执行计划的慢查询。规划器通常会假设多个条件是彼此独立的，这种假设在列值相互关联的情况下是不成立的。由于常规的统计信息天然的针对个体列的性质，它们无法捕捉到跨列关联的知识。不过，IvorySQL有能力计算多元统计信息，它能捕捉这类信息。 ​ 由于可能的列组合数非常巨大，所以不可能自动计算多元统计信息。可以创建扩展统计信息对象（更常被称为统计信息对象）来指示服务器获得跨感兴趣列集合的统计信息。 ​ 统计信息对象可以使用CREATE STATISTICS命令创建。这样一个对象的创建仅仅是创建了一个目录项来表示对统计信息有兴趣。实际的数据收集是由ANALYZE（或者是一个手工命令，或者是后台的自动分析）执行的。收集到的值可以在pg_statistic_ext_data目录中看到。 ​ ANALYZE基于它用来计算常规单列统计信息的表行样本来计算扩展统计信息。由于样本的尺寸会随着表或者表列的统计信息目标（如前一节所述）增大而增加，更大的统计信息目标通常将会导致更准确的扩展统计信息，同时也会导致更多花在计算扩展统计信息之上的时间。 ​ 下面的小节介绍当前支持的扩展统计信息类型。 最简单的一类扩展统计信息跟踪函数依赖，这是在数据库范式定义中使用的概念。如果列a的值的知识足以决定列b的值，即不会有两个行具有相同的a值但是有不同的b值，我们就说列b函数依赖于列a。在一个完全规范化的数据库中，函数依赖应该仅存在于主键和超键上。不过，实际上很多数据集合会由于各种原因无法被完全规范化，常见的例子是为了性能而有意地反规范化。即使在一个完全规范化的数据库中，也会有某些列之间的部分关联，这些可以表达成部分函数依赖。 ​ 函数依赖的存在直接影响了特定查询中估计的准确性。如果一个查询包含独立列和依赖列上的条件，依赖列上的条件不会进一步降低结果的尺寸。但是如果没有函数依赖的知识，查询规划器将假定条件是独立的，导致对结果尺寸的低估。 ​ 要告知规划器有关函数依赖的信息，ANALYZE可以收集跨列依赖的测度。评估所有列组之间的依赖程度可能会昂贵到不可实现，因此数据收集被限制为针对那些在一个统计信息对象中一起出现的列组（用dependencies选项定义）。建议只对强相关的列组创建dependencies统计信息，以避免ANALYZE以及后期查询规划中不必要的开销。 ​ 这里是一个收集函数依赖统计信息的例子： CREATE STATISTICS stts (dependencies) ON city, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxname, stxkeys, stxddependencies FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts'; stxname | stxkeys | stxddependencies ---------+---------+------------------------------------------ stts | 1 5 | {\"1 => 5\": 1.000000, \"5 => 1\": 0.423130} (1 row) 这里可以看到列1（邮编）完全决定列5（城市），因此系数为1.0，而城市仅决定42%的邮编，意味着有很多城市（58%）有多个邮编。 在为涉及函数依赖列的查询计算选择度时，规划器会使用依赖系数来调整针对条件的选择度估计，这样就不会产生低估。 当前只有在考虑简单等值条件（将列与常量值比较）和具有常量值的IN 子句时，函数依赖才适用。不会使用它们来改进比较两个列或者比较列和表达式的等值条件的估计， 也不会用它们来改进范围子句、LIKE或者任何其他类型的条件。 ​ 在用函数依赖估计时，规划器假定在涉及的列上的条件是兼容的并且因此是冗余的。如果它们是不兼容的，正确的估计将是零行，但是那种可能性不会被考虑。例如，给定一个这样的查询 SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '94105'; 规划器将会忽视city子句，因为它不改变选择度，这是正确的。不过，即便真地只有零行满足下面的查询，规划器也会做出同样的假设 SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '90210'; 不过，函数依赖统计信息无法提供足够的信息来排除这种情况。 ​ 在很多实际情况中，这种假设通常是能满足的。例如，在应用程序中可能有一个GUI仅允许选择兼容的城市和邮编值用在查询中。但是如果不是这样，函数依赖可能就不是一个可行的选项。 单列统计信息存储每一列中可区分值的数量。在组合多个列（例如GROUP BY a, b）时，如果规划器只有单列统计数据，则对可区分值数量的估计常常会错误，导致选择不好的计划。 ​ 为了改进这种估计，ANALYZE可以为列组收集可区分值统计信息。和以前一样，为每一种可能的列组合做这件事情是不切实际的，因此只会为一起出现在一个统计信息对象（用ndistinct选项定义）中的列组收集数据。将会为列组中列出的列的每一种可能的组合都收集数据。 ​ 继续之前的例子，ZIP代码表中的可区分值计数可能像这样： CREATE STATISTICS stts2 (ndistinct) ON city, state, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxkeys AS k, stxdndistinct AS nd FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts2'; -[ RECORD 1 ]------------------------------------------------------​-- k | 1 2 5 nd | {\"1, 2\": 33178, \"1, 5\": 33178, \"2, 5\": 27435, \"1, 2, 5\": 33178} (1 row) 这表示有三种列组合有33178个可区分值：ZIP代码和州、ZIP代码和城市、ZIP代码+城市+周（事实上对于表中给定的一个唯一的ZIP代码，它们本来就应该是相等的）。另一方面，城市和州的组合只有27435个可区分值。 ​ 建议只对实际用于分组的列组合以及分组数错误估计导致了糟糕计划的列组合创建ndistinct统计信息对象。否则，ANALYZE循环只会被浪费。 为每列存储的另一种统计信息是频繁值列表。 这样可以对单个列进行非常准确的估计，但是对于在多个列上具有条件的查询，可能会导致严重的错误估计。 ​ 为了改善这种估计，ANALYZE可以收集列组合上的MCV列表。 与功能依赖和n-distinct系数类似，对每种可能的列分组进行此操作都是不切实际的。 在这种情况下，甚至更是如此，因为MCV列表（与功能依赖性和n-distinct系数不同）存储了公共列值。 因此，仅收集在使用mcv选项定义的统计对象中同时出现的那些列组的数据。 ​ 继续前面的示例，邮政编码表的MCV列表可能类似于以下内容（与更简单的统计信息不同，它需要一个函数来检查MCV内容）: CREATE STATISTICS stts3 (mcv) ON city, state FROM zipcodes; ANALYZE zipcodes; SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid), pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts3'; index | values | nulls | frequency | base_frequency -------+------------------------+-------+-----------+---------------- 0 | {Washington, DC} | {f,f} | 0.003467 | 2.7e-05 1 | {Apo, AE} | {f,f} | 0.003067 | 1.9e-05 2 | {Houston, TX} | {f,f} | 0.002167 | 0.000133 3 | {El Paso, TX} | {f,f} | 0.002 | 0.000113 4 | {New York, NY} | {f,f} | 0.001967 | 0.000114 5 | {Atlanta, GA} | {f,f} | 0.001633 | 3.3e-05 6 | {Sacramento, CA} | {f,f} | 0.001433 | 7.8e-05 7 | {Miami, FL} | {f,f} | 0.0014 | 6e-05 8 | {Dallas, TX} | {f,f} | 0.001367 | 8.8e-05 9 | {Chicago, IL} | {f,f} | 0.001333 | 5.1e-05 ... (99 rows) 这表明城市和州的最常见组合是华盛顿特区，实际频率（在样本中）约为0.35%。 组合的基本频率（根据简单的每列频率计算）仅为0.0027％，导致两个数量级的低估。 ​ 建议仅在实际在条件中一起使用的列的组合上创建MCV统计对象，对于这些组合，错误估计组数会导致糟糕的执行计划。 否则，只会浪费ANALYZE和规划时间。 我们可以在一定程度上用显式JOIN语法控制查询规划器。要明白为什么需要它，我们首先需要一些背景知识。 在一个简单的连接查询中，例如： SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; 规划器可以自由地按照任何顺序连接给定的表。例如，它可以生成一个使用WHERE条件a.id = b.id连接 A 到 B 的查询计划，然后用另外一个WHERE条件把 C 连接到这个连接表。或者它可以先连接 B 和 C 然后再连接 A 得到同样的结果。 或者也可以连接 A 到 C 然后把结果与 B 连接 — 不过这么做效率不好，因为必须生成完整的 A 和 C 的迪卡尔积，而在WHERE子句中没有可用条件来优化该连接（IvorySQL执行器中的所有连接都发生在两个输入表之间， 所以它必须以这些形式之一建立结果）。 重要的一点是这些不同的连接可能性给出在语义等效的结果，但在执行开销上却可能有巨大的差别。 因此，规划器会对它们进行探索并尝试找出最高效的查询计划。 ​ 当一个查询只涉及两个或三个表时，那么不需要考虑很多连接顺序。但是可能的连接顺序数随着表数目的增加成指数增长。 当超过十个左右的表以后，实际上根本不可能对所有可能性做一次穷举搜索，甚至对六七个表都需要相当长的时间进行规划。 当有太多的输入表时，IvorySQL规划器将从穷举搜索切换为一种遗传概率搜索，它只需要考虑有限数量的可能性（切换的阈值用geqo_threshold运行时参数设置）。遗传搜索用时更少，但是并不一定会找到最好的计划。 ​ 当查询涉及外连接时，规划器比处理普通（内）连接时拥有更小的自由度。例如，考虑： SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); 尽管这个查询的约束表面上和前一个非常相似，但它们的语义却不同， 因为如果 A 里有任何一行不能匹配 B 和 C的连接表中的行，它也必须被输出。因此这里规划器对连接顺序没有什么选择：它必须先连接 B 到 C，然后把 A 连接到该结果上。 相应地，这个查询比前面一个花在规划上的时间更少。在其它情况下，规划器就有可能确定多种连接顺序都是安全的。例如，给定： SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id); 将 A 首先连接到 B 或 C 都是有效的。当前，只有FULL JOIN完全约束连接顺序。大多数涉及LEFT JOIN或RIGHT JOIN的实际情况都在某种程度上可以被重新排列。 显式连接语法（INNER JOIN、CROSS JOIN或无修饰的JOIN）在语义上和FROM中列出输入关系是一样的， 因此它不约束连接顺序。 即使大多数类型的JOIN并不完全约束连接顺序，但仍然可以指示IvorySQL查询规划器将所有JOIN子句当作有连接顺序约束来对待。例如，这里的三个查询在逻辑上是等效的： SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); 但如果我们告诉规划器遵循JOIN的顺序，那么第二个和第三个还是要比第一个花在规划上的时间少。 这个效果对于只有三个表的连接而言是微不足道的，但对于数目众多的表，可能就是救命稻草了。 ​ 要强制规划器遵循显式JOIN的连接顺序， 我们可以把运行时参数join_collapse_limit设置为 1(其它可能值在下文讨论)。 ​ 你不必为了缩短搜索时间来完全约束连接顺序，因为可以在一个普通FROM列表里使用JOIN操作符。例如，考虑： SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...; 如果设置join_collapse_limit = 1，那么这就强迫规划器先把 A 连接到 B， 然后再连接到其它的表上，但并不约束它的选择。在这个例子中，可能的连接顺序的数目减少了 5 倍。 ​ 按照这种方法约束规划器的搜索是一个有用的技巧，不管是对减少规划时间还是对引导规划器生成好的查询计划。 如果规划器按照默认选择了一个糟糕的连接顺序，你可以通过JOIN语法强迫它选择一个更好的顺序 — 假设你知道一个更好的顺序。我们推荐进行实验。 ​ 一个非常相近的影响规划时间的问题是把子查询压缩到它们的父查询中。例如，考虑： SELECT * FROM x, y, (SELECT * FROM a, b, c WHERE something) AS ss WHERE somethingelse; 这种情况可能在使用包含连接的视图时出现；该视图的SELECT规则将被插入到引用视图的地方，得到与上文非常相似的查询。 通常，规划器会尝试把子查询压缩到父查询里，得到： SELECT * FROM x, y, a, b, c WHERE something AND somethingelse; 这样通常会生成一个比独立的子查询更好些的计划（例如，outer 的WHERE条件可能先把 X 连接到 A 上，这样就消除了 A 中的许多行， 因此避免了形成子查询的全部逻辑输出）。但是同时，我们增加了规划的时间； 在这里，我们用五路连接问题替代了两个独立的三路连接问题。这样的差别是巨大的，因为可能的计划数的是按照指数增长的。 如果有超过from_collapse_limit个FROM项将会导致父查询，规划器将尝试通过停止提升子查询来避免卡在巨大的连接搜索问题中。你可以通过调高或调低这个运行时参数在规划时间和计划的质量之间取得平衡。 from_collapse_limit和join_collapse_limit的命名相似，因为它们做的几乎是同一件事：一个控制规划器何时将把子查询“平面化”，另外一个控制何时把显式连接平面化。通常，你要么把join_collapse_limit设置成和from_collapse_limit一样（这样显式连接和子查询的行为类似）， 要么把join_collapse_limit设置为 1（如果你想用显式连接控制连接顺序）。 但是你可以把它们设置成不同的值，这样你就可以细粒度地调节规划时间和运行时间之间的平衡。 第一次填充数据库时可能需要插入大量的数据。本节包含一些如何让这个处理尽可能高效的建议。 在使用多个INSERT时，关闭自动提交并且只在最后做一次提交（在普通 SQL 中，这意味着在开始发出BEGIN并且在结束时发出COMMIT。某些客户端库可能背着你就做了这些，在这种情况下你需要确定在你需要做这些时该库确实帮你做了）。如果你允许每一个插入都被独立地提交，IvorySQL要为每一个被增加的行做很多工作。在一个事务中做所有插入的一个额外好处是：如果一个行的插入失败则所有之前插入的行都会被回滚，这样你不会被卡在部分载入的数据中。 使用COPY在一条命令中装载所有记录，而不是一系列INSERT命令。 COPY命令是为装载大量行而优化过的； 它没INSERT那么灵活，但是在大量数据装载时导致的负荷也更少。 因为COPY是单条命令，因此使用这种方法填充表时无须关闭自动提交。 如果你不能使用COPY，那么使用PREPARE来创建一个预备INSERT语句也有所帮助，然后根据需要使用EXECUTE多次。这样就避免了重复分析和规划INSERT的负荷。不同接口以不同的方式提供该功能， 可参阅接口文档中的“预备语句”。 ​请注意，在载入大量行时，使用COPY几乎总是比使用INSERT快， 即使使用了PREPARE并且把多个插入被成批地放入一个单一事务。 ​同样的事务中，COPY比更早的CREATE TABLE或TRUNCATE命令更快。 在这种情况下，不需要写 WAL，因为在一个错误的情况下，包含新载入数据的文件不管怎样都将被移除。不过，只有当wal_level设置为minimal（此时所有的命令必须写 WAL）时才会应用这种考虑。 如果你正在载入一个新创建的表，最快的方法是创建该表，用COPY批量载入该表的数据，然后创建表需要的任何索引。在已存在数据的表上创建索引要比在每一行被载入时增量地更新它更快。 ​ 如果你正在对现有表增加大量的数据，删除索引、载入表然后重新创建索引可能是最好的方案。 当然，在缺少索引的期间，其它数据库用户的数据库性能将会下降。 我们在删除唯一索引之前还需要仔细考虑清楚，因为唯一约束提供的错误检查在缺少索引的时候会丢失。 和索引一样，“成批地”检查外键约束比一行行检查效率更高。 因此，先删除外键约束、载入数据然后重建约束会很有用。 同样，载入数据和约束缺失期间错误检查的丢失之间也存在平衡。 ​更重要的是，当你在已有外键约束的情况下向表中载入数据时， 每个新行需要一个在服务器的待处理触发器事件（因为是一个触发器的触发会检查行的外键约束）列表的条目。载入数百万行会导致触发器事件队列溢出可用内存， 造成不能接受的交换或者甚至是命令的彻底失败。因此在载入大量数据时，可能需要（而不仅仅是期望）删除并重新应用外键。如果临时移除约束不可接受，那唯一的其他办法可能是就是将载入操作分解成更小的事务。 在载入大量数据时，临时增大maintenance_work_mem配置变量可以改进性能。这个参数也可以帮助加速CREATE INDEX命令和ALTER TABLE ADD FOREIGN KEY命令。 它不会对COPY本身起很大作用，所以这个建议只有在你使用上面的一个或两个技巧时才有用。 临时增大max_wal_size配置变量也可以让大量数据载入更快。 这是因为向IvorySQL中载入大量的数据将导致检查点的发生比平常（由checkpoint_timeout配置变量指定）更频繁。无论何时发生一个检查点时，所有脏页都必须被刷写到磁盘上。 通过在批量数据载入时临时增加max_wal_size，所需的检查点数目可以被缩减。 当使用 WAL 归档或流复制向一个安装中载入大量数据时，在录入结束后执行一次新的基础备份比处理大量的增量 WAL 数据更快。为了防止载入时记录增量 WAL，通过将wal_level设置为minimal、将archive_mode设置为off以及将max_wal_senders设置为零来禁用归档和流复制。 但需要注意的是，修改这些设置需要重启服务。 除了避免归档器或 WAL 发送者处理 WAL 数据的时间之外，这样做将实际上使某些命令更快，因为如果wal_level是minimal并且当前子事务（或顶级事务）创建或截断了它们更改的表或索引，则它们根本不编写 WAL。（通过在最后执行一个fsync而不是写 WAL，它们能以更小地代价保证崩溃安全）。 不管什么时候你显著地改变了表中的数据分布后，我们都强烈推荐运行ANALYZE。这包括向表中批量载入大量数据。运行ANALYZE（或者VACUUM ANALYZE）保证规划器有表的最新统计信息。 如果没有统计数据或者统计数据过时，那么规划器在查询规划时可能做出很差劲决定，导致在任意表上的性能低下。需要注意的是，如果启用了 autovacuum 守护进程，它可能会自动运行ANALYZE。 pg_dump生成的转储脚本自动应用上面的若干个（但不是全部）技巧。 要尽可能快地载入pg_dump转储，你需要手工做一些额外的事情（请注意，这些要点适用于恢复一个转储，而不是创建它的时候。同样的要点也适用于使用psql载入一个文本转储或用pg_restore从一个pg_dump归档文件载入）。 默认情况下，pg_dump使用COPY，并且当它在生成一个完整的模式和数据转储时， 它会很小心地先装载数据，然后创建索引和外键。因此在这种情况下，一些指导方针是被自动处理的。你需要做的是： 为maintenance_work_mem和max_wal_size设置适当的（即比正常值大的）值。 如果使用 WAL 归档或流复制，在转储时考虑禁用它们。在载入转储之前，可通过将archive_mode设置为off、将wal_level设置为minimal以及将max_wal_senders设置为零（在录入dump前）来实现禁用。 之后，将它们设回正确的值并执行一次新的基础备份。 采用pg_dump和pg_restore的并行转储和恢复模式进行实验并且找出要使用的最佳并发任务数量。通过使用-j选项的并行转储和恢复应该能为你带来比串行模式高得多的性能。 考虑是否应该在一个单一事务中恢复整个转储。要这样做，将-1或—​single-transaction命令行选项传递给psql或pg_restore。 当使用这种模式时，即使是一个很小的错误也会回滚整个恢复，可能会丢弃已经处理了很多个小时的工作。根据数据间的相关性， 可能手动清理更好。如果你使用一个单一事务并且关闭了 WAL 归档，COPY命令将运行得最快。 如果在数据库服务器上有多个 CPU 可用，可以考虑使用pg_restore的—​jobs选项。这允许并行数据载入和索引创建。 之后运行ANALYZE。 一个只涉及数据的转储仍将使用COPY，但是它不会删除或重建索引，并且它通常不会触碰外键。 因此当载入一个只有数据的转储时，如果你希望使用那些技术，你需要负责删除并重建索引和外键。在载入数据时增加max_wal_size仍然有用，但是不要去增加maintenance_work_mem；不如说在以后手工重建索引和外键时你已经做了这些。并且不要忘记在完成后执行ANALYZE。 持久性是数据库的一个保证已提交事务的记录的特性（即使是发生服务器崩溃或断电）。 然而，持久性会明显增加数据库的负荷，因此如果你的站点不需要这个保证，IvorySQL可以被配置成运行更快。在这种情况下，你可以调整下列配置来提高性能。除了下面列出的，在数据库软件崩溃的情况下也能保证持久性。当这些设置被使用时，只有突然的操作系统停止会产生数据丢失或损坏的风险。 将数据库集簇的数据目录放在一个内存支持的文件系统上（即RAM磁盘）。这消除了所有的数据库磁盘I/O,但将数据存储限制到可用的内存量（可能有交换区）。 关闭fsync； 不需要将数据刷入磁盘。 关闭synchronous_commit；可能不需要在每次提交时强制把WAL写入磁盘。这种设置可能会在数据库崩溃时带来事务丢失的风险（但是没有数据破坏）。 关闭full_page_writes； 不需要警惕部分页面写入。 增加max_wal_size和checkpoint_timeout； 这会降低检查点的频率，但会增加/pg_wal的存储要求。 创建不做日志的表来避免WAL写入，不过这会让表在崩溃时不安全。","title":"运维管理指南","component":"ivorysql-doc","version":"master","name":"operation_guide","url":"/ivorysql-doc/master/operation_guide.html","titles":[{"text":"1. 升级IvorySQL版本","hash":"升级ivorysql版本","id":1},{"text":"1.1. 升级方案概述","hash":"升级方案概述","id":2},{"text":"1.2. 通过pg_dumpall升级数据","hash":"通过pg_dumpall升级数据","id":3},{"text":"1.3. 利用pg_upgrade 工具进行升级","hash":"利用pg_upgrade-工具进行升级","id":4},{"text":"1.4. 通过复制升级数据","hash":"通过复制升级数据","id":5},{"text":"2. 管理IvorySQL版本","hash":"管理ivorysql版本","id":6},{"text":"3. 管理IvorySQL数据库访问","hash":"管理ivorysql数据库访问","id":7},{"text":"4. 定义数据对象","hash":"定义数据对象","id":8},{"text":"4.1. 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CSV格式","hash":"csv格式","id":28},{"text":"8.4.3. 二进制格式","hash":"二进制格式","id":29},{"text":"8.5. 示例","hash":"示例","id":30},{"text":"9. 性能管理","hash":"性能管理","id":31},{"text":"9.1. 使用EXPLAIN","hash":"使用explain","id":32},{"text":"9.1.1. EXPLAIN基础","hash":"explain基础","id":33},{"text":"9.1.2. 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事后运行ANALYZE","hash":"事后运行analyze","id":52},{"text":"9.4.9. 关于pg_dump的一些注记","hash":"关于pg_dump的一些注记","id":53},{"text":"9.5. 非持久设置","hash":"非持久设置","id":54}]},"8":{"id":8,"text":"本文详细描述如何提交一个问题。 以IvorySQL为例，提交一个bug report IvorySQL仓库： https://github.com/IvorySQL/IvorySQL 点击New issue 1、bug report Title: 标题 ## Bug Report 对bug进行描述 ### IvorySQL Version 在IvorySQL哪个版本发现的问题 ### OS Version (uname -a) 系统版本 ### Configuration options ( config.status --config ) 配置参数 ### Current Behavior 当前的结果 ### Expected behavior/code 期望的结果 ### Step to reproduce 复现步骤 ### Additional context that can be helpful for identifying the problem 有助于识别问题的其它信息 2、Enhancement Title: 标题 ## Enhancement 对于期望强化的功能作一个描述 3、Feature Request Title: 标题 ## Feature Request 描述你期望实现的一个功能 点击 submit new issue 按钮, 提交即可。 提交问题以后，如果问题的描述和复现步骤清晰明确和可定位，会有人直接定位和解决该问题。但也有可能会出现，负责跟进该问题的开发者需要您提供更加详细的信息的情况，也感谢您的配合。","title":"问题提交指南","component":"ivorysql-doc","version":"master","name":"problem_report_guide","url":"/ivorysql-doc/master/problem_report_guide.html","titles":[{"text":"1. 确认问题所属仓库","hash":"确认问题所属仓库","id":1},{"text":"2. 创建新的Issue","hash":"创建新的issue","id":2},{"text":"2.1. 第1步 进入New issue 页面：","hash":"第1步-进入new-issue-页面","id":3},{"text":"2.2. 第2步：选择需要填写的issue类型","hash":"第2步选择需要填写的issue类型","id":4},{"text":"2.3. 第3步：提交","hash":"第3步提交","id":5}]},"9":{"id":9,"text":"[发布日期：2025 年 11 月 25 日] IvorySQL 5.0 基于 PostgreSQL 18.0，带来更强的 Oracle 兼容能力、PL/iSQL 增强以及全新的全球化特性，同时对打包、自动化和工具链进行全面更新。 有关完整更新列表，请访问我们的 文档站点。 PostgreSQL 18.0 新增异步 I/O（AIO）子系统，可提升顺序扫描、位图堆扫描、vacuum 等操作的性能。 pg_upgrade 现在会保留优化器统计信息。 支持 \"skip scan\" 查找，使多列 B-tree 索引能够在更多场景下使用。 提供用于生成按时间排序 UUID 的 uuidv7() 函数。 支持虚拟生成列（在读取时计算值），并将其设为生成列的默认模式。 增加 OAuth 认证能力。 在 INSERT、UPDATE、DELETE 和 MERGE 的 RETURNING 子句中支持 OLD 和 NEW。 对 PRIMARY KEY、UNIQUE 与 FOREIGN KEY 引入时间区间约束。 更多细节请参阅 PostgreSQL 18.0 发布说明。 Oracle 兼容 ROWID：Feature #126 让 IvorySQL 行标识符与 Oracle 语义保持一致，便于跨数据库工具协同。 PL/iSQL CALL 调用语法：Feature #764 新增 Oracle 风格的 CALL 入口，实现存储过程一致的调用体验。 PL/iSQL %ROWTYPE：Feature #765 允许变量复用整张表或游标行的结构，便于紧凑编写 PL/iSQL。 PL/iSQL %TYPE：Feature #766 支持变量继承既有列或变量的类型，降低类型漂移风险。 区分大小写兼容开关：Feature #767 在需要时可保留标识符大小写，以匹配 Oracle 行为。 NLS 参数兼容性：Feature #768 支持 NLS_DATE_FORMAT、NLS_TIMESTAMP_FORMAT 等 Oracle 风格 NLS 设置。 空字符串转 NULL：Feature #769 将长度为零的字符串转换为 NULL，以遵循 Oracle 的兼容规则。 解析器切换能力：Feature #770 可在 Oracle 与 PostgreSQL 解析器之间切换，实现会话级灵活性。 GB18030 数据库编码：Feature #771 为中国市场提供 GB18030 初始化和创建数据库选项。 Oracle 兼容 SYS_GUID：Feature #773 实现 Oracle SYS_GUID 函数，生成基于 RAW 的 GUID。 Oracle 兼容 SYS_CONTEXT：Feature #774 提供 Oracle SYS_CONTEXT API，用于查询会话与环境元数据。 Oracle 兼容 USERENV：Feature #775 引入 USERENV 函数，使会话可检查 Oracle 风格的上下文信息。 Oracle 兼容函数语法：Feature #776 支持 EDITIONABLE/NONEDITIONABLE、RETURN、IS、OUT …​ NOCOPY 等 Oracle 结构。 Oracle 兼容过程语法：Feature #777 支持包含 Oracle 选项的过程 DDL、EXEC 调用以及 ALTER PROCEDURE。 libpq OUT 参数传递：Feature #778 扩展客户端协议，使 OUT 参数可像 OCI 一样被消费。 过程 OUT 参数：Feature #779 存储过程现在可按 Oracle 约定声明 IN、OUT、IN OUT 模式。 函数 OUT 参数：Feature #780 函数支持 Oracle 风格 OUT（含 IN OUT）参数。 嵌套子程序：Feature #781 允许在子程序内部定义函数或过程，并支持重载。 Oracle 兼容 INSTR：Feature #782 与 Oracle INSTR 行为保持一致，覆盖子串搜索与位置判断。 Oracle 兼容 FORCE VIEW：Feature #783 允许在引用对象尚未存在时创建视图，重现 Oracle FORCE 选项。 Oracle 兼容 LIKE 运算符：Feature #784 对齐 Oracle 的通配符语义，确保匹配行为可预测。 在线体验：IvorySQL v5.0：Feature #887 上线交互式浏览器环境，用户可实时体验与评估 IvorySQL v5.0，无需安装。 全平台打包：Feature #949 为 X86、ARM、MIPS、龙芯架构等平台提供多架构安装介质。 容器化部署支持（Docker Compose & Docker Swarm）： 支持在 Docker Swarm 与 Docker Compose 中部署单实例数据库与高可用集群。 容器化部署支持（Kubernetes 基础版）： 使用 Helm 在 Kubernetes（K8S）中部署单实例数据库与高可用集群。 发布 IvorySQL Operator v5（Kubernetes 进阶版）： Operator v5 适配 IvorySQL v5.0，并同步升级系统组件版本与数据库扩展版本。 发布 IvorySQL Cloud v5（统一全生命周期与可视化控制平面）： 提供可视化托管控制平面，覆盖订阅、全生命周期编排以及生态集成。 pg_cron：Feature #882 通过 pg_cron 集成在数据库层内执行计划任务。 pgAudit：Feature #929 借助 PostgreSQL 标准日志能力输出详尽的会话 / 对象审计记录。 PostGIS：Feature #880 提供空间数据处理与地理分析能力。 pgRouting：Feature #881 引入网络与路径分析能力。 PGroonga：Feature #879 增强全文检索。 ddlx：Feature #877 支持 ddlx，便于高级模式洞察与自动化 DDL 生成。 pgsql-http：Feature #883 允许数据库内部发起 HTTP/HTTPS 请求，实现与外部 Web 服务的无缝通信。 system_stats：Feature #946 通过 system_stats 提供系统级统计信息。 plpgsql_check：Feature #915 在运行前对 PL/pgSQL 函数做静态分析，定位错误、警告与潜在问题。 pgvector：Feature #878 融合 pgvector，为 AI/ML 工作负载提供原生向量相似度检索。 修复 unused_oids 与 duplicate_oids 目录工具，使头文件扫描能准确检测冲突且无误报：Issue #841 为 libpq/ivytest 产物新增 .gitignore，避免生成的二进制与日志污染开发树：Issue #843 扩展 GitHub 工作流回归测试，覆盖 --with-libnuma 配置，防止未来在启用 NUMA 的主机上出问题：Issue #869 让 psql 用户可以通过 \\h create package 获取 CREATE PACKAGE 语法帮助，补齐 PL/iSQL 包的 CLI 文档：Issue #936 排除 MainLoop 悬空指针引发的并发压力下间歇性段错误：Issue #898 修复 oracle_test/modules/*/sql 的测试框架假设，让 Oracle 兼容测试套件再次端到端运行：Issue #897 更新 README.md 与 README_CN.md，同步 IvorySQL v5 特性、打包与快速上手信息：Issue #896 更正全局唯一索引的强制机制，使相关回归测试在所有支持平台上稳定通过：Issue #894 IvorySQL 的主要代码仓库： IvorySQL 数据库源码：https://github.com/IvorySQL/IvorySQL IvorySQL 官网：https://github.com/IvorySQL/Ivory-www IvorySQL 文档：https://github.com/IvorySQL/IvorySQL-docs IvorySQL Docker：https://github.com/IvorySQL/docker_library 以下人员（按字母顺序）作为补丁作者、提交者、审阅者、测试者或问题报告者，为本次发布做出了贡献。 ccwxl Cédric Villemain elodiefb Grant Zhou Imran Zaheer luss Martin Gerhardy msdnchina omstack otegami rophy shlei6067 sjw1933 Yasir Hussain Shah 初少林 崇鹏豪 高雪玉 矫顺田 类延良 李苑 梁翔宇 刘晓辉 吕新杰 牛世继 彭冲 潘振浩 石卓妍 隋戈 陶郑 童水森 王硕 薛晓刚 严少安 杨世华 赵法威","title":"","component":"ivorysql-doc","version":"master","name":"release_notes","url":"/ivorysql-doc/master/release_notes.html","titles":[{"text":"1. 版本概览","hash":"版本概览","id":1},{"text":"2. 增强内容","hash":"增强内容","id":2},{"text":"3. 新特性","hash":"新特性","id":3},{"text":"3.1. 新增21 项 Oracle 兼容能力","hash":"新增21-项-oracle-兼容能力","id":4},{"text":"3.2. 在线体验与多平台发行包","hash":"在线体验与多平台发行包","id":5},{"text":"3.3. 云原生与容器化","hash":"云原生与容器化","id":6},{"text":"3.4. 新增 10 个 PostgreSQL 扩展","hash":"新增-10-个-postgresql-扩展","id":7},{"text":"4. 缺陷修复","hash":"缺陷修复","id":8},{"text":"5. 源码仓库","hash":"源码仓库","id":9},{"text":"6. 贡献者名单","hash":"贡献者名单","id":10}]},"10":{"id":10,"text":"这部份包含IvorySQL客户端应用和工具的参考信息。不是所有这些命令都是通用工具，某些需要特殊权限。这些应用的共同特征是它们可以被运行在任何主机上，而不管数据库服务器在哪里。 当在命令行上指定用户和数据库名时，它们的大小写会被保留 — 空格或特殊字符的出现可能需要使用引号。表名和其他标识符的大小写不会被保留并且可能需要使用引号。 分类 工具名称 描述 客户端工具 clusterdb clusterdb是一个工具，它用来对一个IvorySQL数据库中的表进行重新聚簇。它会寻找之前已经被聚簇过的表，并且再次在最后使用过的同一个索引上对它们重新聚簇。没有被聚簇过的表将不会被影响。clusterdb是SQL命令 CLUSTER 的一个包装器。在通过这个工具和其他方法访问服务器来聚簇数据库之间没有实质性的区别。 createdb createdb创建一个新的IvorySQL数据库。通常，执行这个命令的数据库用户将成为新数据库的所有者。但是，如果执行用户具有合适的权限，可以通过 -O 选项指定一个不同的所有者。createdb是SQL命令 CREATE DATABASE的一个包装器。在通过这个工具和其他方法访问服务器来创建数据库之间没有实质性的区别。 createuser createuser创建一个新的IvorySQL用户（或者更准确些，是一个角色）。只有超级用户和具有 CREATEROLE 特权的用户才能创建新用户，因此createuser必须被以上两种用户调用。如果你希望创建一个新的超级用户，你必须作为一个超级用户连接，而不仅仅是具有 CREATEROLE 特权。作为一个超级用户意味着绕过数据库中所有访问权限检查的能力，因此超级用户访问权限不能轻易被授予。createuser是SQL命令 CREATE ROLE的一个包装器。在通过这个工具和其他方法访问服务器来创建用户之间没有实质性的区别。 dropdb ropdb毁掉一个现有的IvorySQL数据库。执行这个命令的用户必须是一个数据库超级用户或该数据库的拥有者。dropdb是SQL命令 DROP DATABASE 的一个包装器。在通过这个工具和其他方法访问服务器来删除数据库之间没有实质性的区别。 dropuser dropuser移除一个已有的IvorySQL用户。只有超级用户以及具有 CREATEROLE 特权的用户能够移除IvorySQL用户（要移除一个超级用户，你必须自己是一个超级用户）。dropuser是SQL命令 DROP ROLE 的一个包装器。在通过这个工具和其他方法访问服务器来删除用户之间没有实质性的区别。 ecpg ecpg 是用于 C 程序的嵌入式 SQL 预处理器。它通过将 SQL 调用替换为特殊函数调用把带有嵌入式 SQL 语句的 C 程序转换为普通 C 代码。输出文件可以被任何 C 编译器工具链处理。ecpg 将把命令行中给出的每一个输入文件转换为相应的 C 输出文件。 如果输入文件名没有任何扩展名，则假定为 .pgc。文件扩展名将由 .c 替换以构造输出文件名。 但是输出文件名可以使用 -o 选项覆盖。如果输入文件名只是 -，ecpg 从标准输入 读取程序（并写入标准输出，除非用 -o 重写）。 pg_amcheck pg_amcheck支持对一个或多个数据库运行 amcheck 的损坏检查函数，并提供选项来选择要检查的模式、表和索引、要执行的检查类型以及是否并行执行检查，如果是，按并行数建立连接并使用。当前仅支持表关系和btree索引。其他关系类型将自动跳过。如果指定了 dbname，则它应该是要检查的单个数据库的名称，并且不应该存在其他数据库选择选项。否则，如果存在任何数据库选择选项，将检查所有匹配的数据库。如果不存在此类选项，将选中默认数据库。数据库选择选项包括 --all，--database 和 --exclude-database。它们还包括 --relation，--exclude-relation， --table，--exclude-table，--index，和 --exclude-index，但仅当这些选项与三段式模式一起使用时（例如，mydb*.myschema*.myrel*）。最后，它们包括 --schema 和 --exclude-schema 当这些选项与两段式模式一起使用时（例如 mydb*.myschema* ）。 pg_basebackup pg_basebackup被用于获得一个正在运行的IvorySQL数据库集簇的基础备份。获得这些备份不会影响数据库的其他客户端，并且可以被用于时间点恢复，以及用作一个日志传送或流复制后备服务器的开始点。pg_basebackup对数据库群集的文件进行精确复制，同时确保服务器自动进入和退出备份模式。备份总是从整个数据库集簇获得，不可能备份单个数据库或数据库对象。关于选择性备份，必须使用一个像 pg_dump 的工具。备份通过一个使用复制协议常规IvorySQL连接制作。该连接必须由一个具有 REPLICATION 权限或者具有超级用户权限的用户ID建立，并且 pg_hba.conf必须允许该复制连接。该服务器还必须被配置，使 max_wal_senders 设置得足够高以提供至少一个walsender用于备份以及一个用于WAL流（如果使用流）。在同一时间可以有多个 pg_basebackup 运行，但是从性能的角度来说，只进行一次备份并且复制结果通常更好。pg_basebackup不仅能从主控机也能从后备机创建一个基础备份。要从后备机获得一个备份，设置后备机让它能接受复制连接（也就是，设置 max_wal_senders 和 hot_standby，并且适当配置其 pg_hba.conf ）。你将也需要在主控机上启用 full_page_writes。注意在来自后备机的备份中有一些限制：不会在被备份的数据库集簇中创建备份历史文件。 pg_basebackup不能强制后备服务器在备份结束时切换到新的WAL文件。 当正在使用 -X none 时，如果服务器上的写活动比较低，pg_basebackup可能需要等待很长时间，以便切换和归档备份所需要的最后的WAL文件。 在这种情况下，在主服务器上运行 pg_switch_wal 以触发立即的WAL文件切换可能是有用的。 如果在备份期间后备机被提升为主控机，备份会失败。 备份所需的所有 WAL 记录必须包含足够的全页写，这要求你在主控机上启用 full_page_writes 并且不使用一个类似pg_compresslog的工具以 archive_command 从 WAL 文件中移除全页写。每当pg_basebackup进行基本备份时，服务器的 pg_stat_progress_basebackup 视图将报告备份的进度。 pgbench pgbench是一种在IvorySQL上运行基准测试的简单程序。它可能在并发的数据库会话中一遍一遍地运行相同序列的 SQL 命令，并且计算平均事务率（每秒的事务数）。默认情况下，pgbench会测试一种基于 TPC-B 但是要更宽松的场景，其中在每个事务中涉及五个 SELECT、 UPDATE 以及 INSERT 命令。但是，通过编写自己的事务脚本文件很容易用来测试其他情况。 pg_config pg_config工具打印当前安装版本的IvorySQL的配置参数。它的设计目的之一是便于想与IvorySQL交互的软件包能够找到所需的头文件和库。 pg_dump pg_dumppg_dump是用于备份一种IvorySQL数据库的工具。即使数据库正在被并发使用，它也能创建一致的备份。pg_dump不阻塞其他用户访问数据库（读取或写入）。pg_dump只转储单个数据库。要备份一个集簇或者集簇中 对于所有数据库公共的全局对象（例如角色和表空间），应使用 pg_dumpall。转储可以被输出到脚本或归档文件格式。脚本转储是包含 SQL 命令的纯文本文件，它们可以用来重构数据库到它被转储时的状态。要从这样一个脚本恢复，将它输入到 psql。脚本文件甚至可以被用来在其他机器和其他架构上重构数据库。在经过一些修改后，甚至可以在其他 SQL 数据库产品上重构数据库。另一种可选的归档文件格式必须与 pg_restore 配合使用来重建数据库。它们允许pg_restore能选择恢复什么，或者甚至在恢复之前对条目重排序。归档文件格式被设计为在架构之间可移植。当使用归档文件格式之一并与pg_restore组合时，pg_dump提供了一种灵活的归档和传输机制。pg_dump可以被用来备份整个数据库，然后pg_restore可以被用来检查归档并/或选择数据库的哪些部分要被恢复。最灵活的输出文件格式是“自定义”格式（ -Fc ）和“目录”格式（ -Fd ）。它们允许选择和重排序所有已归档项、支持并行恢复并且默认是压缩的。“目录”格式是唯一一种支持并行转储的格式。当运行pg_dump时，我们应该检查输出中有没有任何警告（打印在标准错误上） pg_dumpall pg_dumpall工具可以一个集簇中所有的IvorySQL数据库写出到（“转储”）一个脚本文件。该脚本文件包含可以用作 psql 的输入SQL命令来恢复数据库。它会对集簇中的每个数据库调用 pg_dump 来完成该工作。pg_dumpall还转储对所有数据库公用的全局对象（ pg_dump 不保存这些对象），也就是说数据库角色和表空间都会被转储。 目前这包括适数据库用户和组、表空间以及适合所有数据库的访问权限等属性。因为pg_dumpall从所有数据库中读取表，所以你很可能需要以一个数据库超级用户的身份连接以便生成完整的转储。同样，你也需要超级用户特权执行保存下来的脚本，这样才能增加角色和组以及创建数据库。SQL 脚本将被写出到标准输出。使用 -f / --file 选项或者 shell 操作符可以把它重定向到一个文件。pg_dumpall需要多次连接到IvorySQL服务器（每个数据库一次）。如果你使用口令认证，可能每次都会要求口令。这种情况下使用一个 ~/.pgpass 会比较方便 pg_isready pg_isready是一个用来检查一个IvorySQL数据库服务器的连接状态的工具。其退出状态指定了连接检查的结果。 pg_receivewal pg_receivewal被用来从一个运行着的IvorySQL集簇以流的方式得到预写式日志。预写式日志会被使用流复制协议以流的方式传送，并且被写入到文件的一个本地目录。这个目录可以被用作归档位置来做一次使用时间点恢复的恢复。当预写式日志在服务器上被产生时，pg_receivewal实时以流的方式传输预写式日志，并且不像 archive_command 那样等待段完成。由于这个原因，在使用pg_receivewal时不必设置 archive_timeout。与 IvorySQL 后备服务器上的 WAL 接收进程不同，pg_receivewal默认只在一个 WAL 文件被关闭时才刷入 WAL 数据。要实时刷入 WAL 数据，必须指定选项 --synchronous。 由于pg_receivewal不应用于WAL，当 synchronous_commit 等于 remote_apply 时，你将不允许它成为同步备用。 如果发生这样的情况，它将成为一个永远不能拉起的备用数据库，并且会导致事务提交阻塞。 为了避免这种情况，你应该为 synchronous_standby_names 配置一个适当的值，或规定为pg_receivewal 的 application_name 与它不匹配，或将 synchronous_commit 的值更改为 remote_apply 以外的内容。预写式日志在一个常规IvorySQL连接上被以流式传送，并且使用复制协议。连接必须由一个具有 REPLICATION 权限的用户或者一个超级用户建立，并且 pg_hba.conf 必须允许复制连接。服务器也必须被配置一个足够高的 max_wal_senders 来至少留出一个可用会话给流。首先，扫描WAL段文件所写入的目录，并发现最新完成的段文件，作为下一个段文件的开始的起始点。 这是独立计算的，根据用于压缩每个段的压缩方法。 如果用前面的方法无法计算出起点，最新的WAL刷写位置用作由服务器通过 IDENTIFY_SYSTEM 命令的报告。 pg_recvlogical pg_recvlogical 控制逻辑解码复制槽以及来自这种复制槽的流数据。它会创建一个复制模式的连接，因此它受到和 pg_receivewal 相同的约束，还有逻辑复制的约束。pg_recvlogical 与逻辑解码SQL接口的peek和get模式没有等效性。它咋接收到数据以及干净地退出时，它会惰性地发送数据的确认。为了检查一个槽上还未消费的待处理数据，可以使用 pg_logical_slot_peek_changes。 pg_restore pg_restore是一个用来从 pg_dump 创建的非文本格式归档恢复IvorySQL数据库的工具。它将发出必要的命令把该数据库重建成它被保存时的状态。这些归档文件还允许pg_restore选择恢复哪些内容或者在恢复前对恢复项重排序。这些归档文件被设计为可以在不同的架构之间迁移。pg_restore可以在两种模式下操作。如果指定了一个数据库名称，pg_restore会连接那个数据库并且把归档内容直接恢复到该数据库中。否则，会创建一个脚本，其中包含着重建该数据库所必要的 SQL 命令，它会被写入到一个文件或者标准输出。这个脚本输出等效于pg_dump的纯文本输出格式。因此，一些控制输出的选项与pg_dump的选项类似。显然，pg_restore无法恢复不在归档文件中的信息。例如，如果归档使用“以 INSERT 命令转储数据”选项创建， pg_restore将无法使用 COPY 语句装载数据。 pg_verifybackup pg_verifybackup用于根据备份时服务器生成的 backup_manifest 检查使用 pg_basebackup 进行的数据库群集备份的完整性。备份必须以“普通”格式存储；“tar”格式的备份可以在解压缩后进行检查。需要注意的是，由pg_verifybackup执行的验证不包括也不可能包括运行中的服务器在尝试使用备份时执行的所有检查。 即使使用此工具，也应执行测试还原，并验证生成的数据库是否按预期工作，以及它们是否包含正确的数据。但是，pg_verifybackup可以检测到由于存储问题或用户错误而经常出现的许多问题。备份验证分四个阶段进行。首先，pg_verifybackup 读取 backup_manifest 文件。如果该文件不存在、无法读取、格式不正确或无法根据其内部校验和进行验证，pg_verifybackup 将以致命错误终止。其次，pg_verifybackup 将尝试验证当前存储在磁盘上的数据文件是否与服务器打算发送的数据文件完全相同，下面将介绍一些例外情况。 除了少数例外，额外和丢失的文件将被检测到。此步骤将忽略 postgresql.auto.conf、standby.signal 和 recovery.signal 的存在与否或对其的任何修改，因为预计这些文件可能是在备份过程中创建或修改的。它也不会抱怨目标目录中的 backup_manifest 文件或 pg_wal 中的任何内容，即使这些文件不会列在备份清单中。只检查文件；不验证目录的存在与否，除非间接验证：如果目录丢失，则它应该包含的任何文件也必然会丢失。 psql psql是一个IvorySQL的基于终端的前端。它让你能交互式地键入查询，把它们发送给IvorySQL，并且查看查询结果。或者，输入可以来自于一个文件或者命令行参数。此外，psql还提供一些元命令和多种类似 shell 的特性来为编写脚本和自动化多种任务提供便利。 reindexdb reindexdb是用于重建一个IvorySQL数据库中索引的工具。reindexdb是 SQL 命令 REINDEX的一个包装器。 在通过这个工具和其他方法访问服务器来重索引数据库之间没有实质性的区别。 vacuumdb vacuumdb是用于清理一个IvorySQL数据库的工具。vacuumdb也将产生由IvorySQL查询优化器所使用的内部统计信息。 服务器应用 initdb initdb 创建一个新的IvorySQL数据库集簇。一个数据库集簇是由一个单一服务器实例管理的数据库的集合。一个数据库集簇的创建包括创建存放数据库数据的目录、生成共享目录表（属于整个集簇而不是任何特定数据库的表）并且创建 template1 和 postgres 数据库。当你后来创建一个新的数据库时，任何在 template1 数据库中的东西都会被复制（因此，任何已安装在 template1 中的东西都会被自动地复制到后来创建的每一个数据库中）。 postgres 数据库是便于用户、工具和第三方应用使用的默认数据库。尽管 initdb 将尝试创建指定的数据目录，它可能没有权限（如果想要的数据目录的父目录被根用户拥有）。要在这样一种设置中初始化，作为 root 创建一个空数据目录，然后使用 chown 将该目录赋予给数据库用户账户，再然后 su 成为该数据库用户并运行 initdb。initdb 必须以将拥有该服务器进程的用户运行，因为该服务器需要访问 initdb 创建的文件和目录。因为该服务器不能作为 root 运行，你不能以 root 运行 initdb （事实上它会拒绝这样做）。由于安全原因，由 initdb 创建的新集簇默认将只能由集簇拥有者访问。--allow-group-access 选项允许与集簇拥有者同组的任何用户读取集簇中的文件。这对非特权用户执行备份很有用。initdb 初始化该数据库集簇的默认区域和字符集编码。当一个数据库被创建时，其字符集编码、排序顺序（ LC_COLLATE ）和字符集类（ LC_CTYPE ，例如大写、小写、数字）可以被独立设置。initdb 为 template1 数据库确定那些设置，它们将作为所有其他数据库的默认值。要修改默认排序顺序或字符集类，使用 --lc-collate 和 --lc-ctype 选项。除 C 或 POSIX 之外的排序顺序也有性能罚值。由于这些原因，在运行 initdb 时选择正确的区域很重要。余下的区域分类可以在服务器启动之后改变。你也可以使用 --locale 来为所有区域分类设置默认值，包括排序顺序和字符集类。所有服务器区域值（ lc_* ）可以通过 SHOW ALL 显示。要修改默认编码，使用 --encoding 。 pg_archivecleanup pg_archivecleanup被设计用作 archive_cleanup_command 在作为后备服务器运行时来清理 WAL 文件归档。 pg_archivecleanup也可以被用作一个单独的程序来清理 WAL 文件归档。要配置一个后备服务器以使用pg_archivecleanup，把下面 的内容放在 postgresql.conf 配置文件中：archive_cleanup_command = 'pg_archivecleanup archivelocation %r' 其中 archivelocation 是要从中移除 WAL 段文件的目录。当被用在 archive_cleanup_command中时，所有逻辑上在 %r 参数的值之前的 WAL 文件都将被从 archivelocation 移除。这能最小化需要被保留的文件数量， 同时能保留崩溃后重启的能力。如果对于这台特定的后备服务器， archivelocation 是一个短暂需要的区域，使用这个参数就是合适的，但是当 archivelocation 要用作一个长期的 WAL 归档 区域或者当多个后备服务器正在从这个归档位置恢复时，使用这个参数就 不合适。当被用作一个单独的程序时，所有逻辑上在 oldestkeptwalfile 之前的 WAL 文件将被从 archivelocation 中移除。在这种模式中，如果指定了 .partial 或者 .backup 文件名，则 只有该文件前缀将被用作 oldestkeptwalfile。这种对 .backup 文件名的处理允许你移除所有在一个特定基础备份之前归档的 WAL 文件而不出错。 pg_checksums pg_checksums在IvorySQL集簇中检查、启用或禁用数据校验和。运行pg_checksums之前，必须彻底关闭服务器。验证校验和时，如果没有校验和错误，则退出状态为零，如果检测到至少一个校验和失败，则退出状态为非零。启用或禁用校验和时，如果操作失败，则退出状态为非零。验证校验和时，集簇中的每个文件都要被扫描。启用校验和时，集簇中的每个文件都会被原地重写。禁用校验和时，仅更新 pg_control 文件。 pg_controldata pg_controldata 打印在 initdb 期间初始化的信息，例如目录版本。它也显示关于预写式日志和检查点处理的信息。这种信息是集簇范围的，并且不针对任何一个数据库。这个工具只能由初始化集簇的用户运行，因为它要求对数据目录的读访问。你可以在命令行中指定数据目录，或者使用环境变量 PGDATA。这个工具支持选项 -V 和 --version，它们打印pg_controldata版本并退出。它也支持选项 -? 和 --help，它们输出支持的参数。 pg_ctl pg_ctl是一个用于初始化IvorySQL数据库集簇，启动、停止或重启IvorySQL数据库服务器（ postgres），或者显示一个正在运行服务器的状态的工具。尽管服务器可以被手工启动，pg_ctl包装了重定向日志输出以及正确地从终端和进程组脱离等任务。它也提供了方便的选项用来控制关闭。 pg_resetwal pg_resetwal 会清除预写式日志（WAL）并且有选择地重置存储在 pg_control 文件中的一些其他控制信息。如果这些文件已经被损坏，某些时候就需要这个功能。当服务器由于这样的损坏而无法启动时，这只应该被用作最后的手段。在运行这个命令之后，就可能可以启动服务器，但是记住数据库可能包含由于部分提交事务产生的不一致数据。你应当立刻转储你的数据、运行 initdb 并且重新载入。重新载入后，检查不一致并且根据需要修复之。这个工具只能被安装服务器的用户运行，因为它要求对数据目录的读写访问。出于安全原因，你必须在命令行中指定数据目录。pg_resetwal 不使用环境变量 PGDATA。如果 pg_resetwal 抱怨它无法为 pg_control 决定合法数据，你可以通过指定 -f （强制）选项强制它继续。在这种情况下，丢失的数据将被替换为看似合理的值。可以期望大部分域是匹配的，但是下一个 OID、下一个事务 ID 和纪元、下一个多事务 ID 和偏移以及 WAL 开始位置域可能还是需要人工协助。这些域可以使用下面讨论的选项设置。如果你不能为所有这些域决定正确的值，-f 还是可以被使用，但是恢复的数据库还是值得怀疑：一次立即的转储和重新载入是势在必行的。在你转储之前不要在该数据库中执行任何数据修改操作，因为任何这样的动作都可能使破坏更严重。 pg_rewind pg_rewind是用于在集簇的时间线分叉以后，同步一个 IvorySQL 集簇和同一集簇的另一份拷贝的工具。一种典型的场景是在失效后让一个旧的主服务器重新上线，同时有一个后备机跟随着新的主机。成功回放后，目标数据目录的状态类似于源数据目录的基本备份。与进行新的基本备份或使用rsync等工具不同，pg_rewind不需要比较或复制集群中未更改的关系块。仅复制现有关系文件中更改的块；所有其他文件（包括新的关系文件、配置文件和WAL段）都将被完整复制。因此，当数据库很大并且集群之间只有一小部分块不同时，倒带操作比其他方法要快得多。pg_rewind检查源集簇和目标集簇的时间线历史来判断它们在哪一点分叉，并且期望在目标集簇的 pg_wal 目录中找到 WAL 来返回到分叉点。分叉点可能会在目标时间线、源时间线或者它们的共同祖先上找到。在典型的失效场景中，目标集簇在分叉后很快就被关闭，这不是问题，但是如果目标集簇在分叉后已经运行了很长时间，旧的 WAL 文件可能已经不存在了。在这样的情况下，您可以手动将它们从WAL存档复制到 pg_wal 目录，或使用 -c 选项运行pg_rewind以自动从WAL存档检索它们。pg_rewind的使用并不限于失效的场景，例如一个后备服务器可能被提升、运行一些写事务，然后被倒回再次成为一个后备。在运行pg_rewind之后，需要完成WAL重放以使数据目录处于一致状态。当目标服务器再次启动时，它将进入归档恢复，并从分歧点之前的最后一个检查点重放源服务器中生成的所有 WAL。当pg_rewind被运行时有某些 WAL 在源服务器上不可用，并且因此无法被pg_rewind会话所复制，则在目标服务器被启动时必须让这些 WAL 可用。 这可以通过在目标数据目录中创建一个 recovery.signal 文件并且在 postgresql.conf 中配置适合的 restore_command 来实现。pg_rewind要求目标服务器在 postgresql.conf 中启用了 wal_log_hints 选项，或者在用initdb初始化集簇时启用了数据校验。目前默认情况下这两者都没有被打开。http://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-FULL-PAGE-WRITES[full_page_writes] 也必须被设置为 on，这是默认的。 pg_test_fsync pg_test_fsync是想告诉你在特定的系统上，哪一种 wal_sync_method 最快，还可以在发生认定的 I/O 问题时提供诊断信息。不过，pg_test_fsync 显示的区别可能不会在真实的数据库吞吐量上产生显著的区别，特别是由于 很多数据库服务器被它们的预写日志限制了速度。 pg_test_fsync为 wal_sync_method 报告以微秒计的平均文件同步操作时间， 也能被用来提示用于优化 commit_delay 值的方法。 pg_test_timing pg_test_timing是一种度量在你的系统上计时开销以及确认系统时间绝不会回退的工具。收集计时数据很慢的系统会给出不太准确的 EXPLAIN ANALYZE 结果。 pg_upgrade pg_upgrade允许存储在IvorySQL数据文件中的数据被升级到一个较晚 的IvorySQL主版本而无需进行主版本升级通常所需的数据转储/重载。 对于次版本升级则不需要这个程序。IvorySQL主发行版本通常会加入新的特性，这些新特性常常会更改系统表的布局，但是内部数据存储格式很少会改变。pg_upgrade 使用这一事实来通过创建新系统表并且重用旧的用户数据文件来执行快速升级。 如果未来的主发行版本更改了数据存储格式，导致旧数据格式不可读，那么 pg_upgrade将无法用于此类升级。（社区将努力避免这种情况）。pg_upgrade会尽力（例如通过检查兼容的编译时设 置）确保新旧集簇在二进制上也是兼容的，包括 32/64 位二进制。保持外部模块也是二进制兼容的也很重要，不过pg_upgrade无法检查这一点。 pg_waldump pg_waldump 显示预写式日志（WAL），它主要用于调试或者教育目的。这个工具只能由安装该服务器的用户运行，因为它要求对数据目录的只读访问。 postgres postgres 是IvorySQL数据库服务器。一个客户端应用为了能访问一个数据库，它会（通过一个网络或者本地）连接到一个运行着的 postgres 实例。该 postgres 实例接着会开始一个独立的服务器进程来处理该连接。一个 postgres 实例总是管理正好一个数据库集簇的数据。一个数据库集簇是一个数据库的集合，它们被存储在一个共同的文件系统位置（“数据区”）上。 一个系统上可以同时运行多个 postgres 实例，只要它们使用不同的数据区和不同的通信端口（见下文）。postgres 启动时需要知道数据区的位置，该位置必须通过 -D 选项或 PGDATA 环境变量指定，对此是没有默认值的。通常，-D 或 PGDATA 会直接指向由 initdb 创建的数据区目录。默认情况下，postgres 会在前台启动并将日志消息打印到标准错误流。但在实际应用中，postgres 应当作为一个后台进程启动，而且多数是在系统启动时自动启动。postgres 还能在单用户模式中被调用。这种模式的主要用途是在启动过程中由 initdb 使用。有时候它也被用于调试或者灾难性恢复。注意，运行一个单用户模式服务器并不真地适合调试服务器，因为不会发生实际的进程间通信和锁定。当从 shell 中调用单用户模式时，用户可以输入查询并且结果会被以一种更适合开发者阅读（不适合普通用户）的形式打印在屏幕上。在单用户模式中，会话用户将被设置为 ID 为 1 的用户，并且这个用户会被隐式地赋予超级用户权限。该用户不必实际存在，因此单用户模式运行可以被用来对某些意外损坏的系统目录进行手工恢复。 clusterdb [connection-option…​] [ --verbose | -v ] [ --table | -t table ] …​ [dbname] clusterdb` [*`connection-option`*...] [ `--verbose` | `-v` ] `--all` | `-a clusterdb接受下列命令行参数： -a --all 聚簇所有数据库。 [-d] dbname [--dbname=] dbname 当不使用 -a/ --all 时，指定要被聚簇的数据库名称。 如果数据库名称未指定，则从环境变量 PGDATABASE 中读取数据库名称。 如果该环境变量也没有被设置，则使用为连接指定的用户名作数据库名。 dbname 可以是 connection string。 如果是这样，连接时的字符串参数将覆盖所有冲突的命令行选项。 -e --echo 回显clusterdb生成并发送给服务器的命令。 -q --quiet 不显示进度消息。 -t table --table=table 只聚簇 table。可以通过写多个 -t 开关来聚簇多个表。 -v --verbose 在处理期间打印详细信息。 -V --version 打印clusterdb版本并退出。 -? --help 显示关于clusterdb命令行参数的帮助并退出。clusterdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制clusterdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，clusterdb将自动提示要求一个口令。但是，clusterdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db= dbname 当使用 -a / --all 时，指定要连接到的数据库名称来发现哪些其他数据库应该被聚簇。 如果没有指定，将使用 postgres 数据库。而如果它也不存在，将使用 template1。 这可以是 connection string。如果是这样，连接时的字符串参数将覆盖所有冲突的命令行选项。 另外，连接到其他数据库时，除了数据库名字本身其他的连接时字符串参数将被重新使用。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可选的值为 `always` ， `auto` ， `never` 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 CLUSTER 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要聚簇数据库 test： $ clusterdb test 要聚簇在数据库 xyzzy 中的一个表 foo： $ clusterdb --table=foo xyzzy createdb [connection-option…​] [option…​] [dbname [description]] createdb接受下列命令行参数： dbname 指定要被创建的数据库名。该名称必须在这个集簇中所有IvorySQL数据库中唯一。默认是创建一个与当前系统用户同名的数据库。 description 指定与新创建的数据库相关联的一段注释。 -D tablespace --tablespace=tablespace 指定该数据库的默认表空间（这个名称被当做一个双引号引用的标识符处理）。 -e --echo 回显createdb生成并发送到服务器的命令。 -E encoding --encoding=encoding 指定要在这个数据库中使用的字符编码模式。 -l locale --locale=locale 指定要在这个数据库中使用的区域。这等效于同时指定`--lc-collate`和`--lc-ctype`。 --lc-collate=locale 指定要在这个数据库中使用的 LC_COLLATE 设置。 --lc-ctype=locale 指定要在这个数据库中使用的 LC_CTYPE 设置。 -O owner --owner=owner 指定拥有这个新数据库的数据库用户（这个名称被当做一个双引号引用的标识符处理）。 -T template --template=template 指定用于创建这个数据库的模板数据库（这个名称被当做一个双引号引用的标识符处理）。 -V --version 打印createdb版本并退出。 -? --help 显示关于createdb命令行参数的帮助并退出。选项 -D、-l 、 -E 、 -O`和 `-T 对应于底层 SQL 命令 CREATE DATABASE的选项，关于这些选项的信息可见该命令的内容。 createdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制createdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，createdb将自动提示要求一个口令。但是，createdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db=dbname 指定要连接到来发现哪些其他数据库应该被聚簇的数据库名。如果没有指定，将使用 postgres 数据库。而如果它也不存在（或者如果它就是要创建新数据库的名称），将使用 template1。这可以是 connection string。如果是这样，连接时字符串参数将覆盖所有冲突的命令行选项。 PGDATABASE 如果被设置，就是要创建的数据库名，除非在命令行中覆盖。 PGHOST PGPORT PGUSER 默认连接参数。如果没有在命令行或 PGDATABASE 指定要创建的数据库名， PGUSER 也决定要创建的数据库名。 PG_COLOR 规定在诊断消息中是否使用颜色。 可能的值为 always，auto 和 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 CREATE DATABASE和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要使用默认数据库服务器创建数据库 demo： $ createdb demo 要在主机 eden、端口 5000 上使用 template0 模板数据库创建数据库 demo，这里是命令行命令和底层SQL命令： $ createdb -p 5000 -h eden -T template0 -e demo CREATE DATABASE demo TEMPLATE template0; createuser — 定义一个新的IvorySQL用户账户 createuser [connection-option…​] [option…​] [username] createuser接受下列命令行参数： username 指定要被创建的IvorySQL用户的名称。这个名称必须与这个IvorySQL安装中所有现存角色不同。 -c number --connection-limit=number 为该新用户设置一个最大连接数。默认值为不设任何限制。 -d --createdb 新用户将被允许创建数据库。 -D --no-createdb 新用户将不被允许创建数据库。这是默认值。 -e --echo 回显createuser生成并发送给服务器的命令。 -E --encrypted 此选项已过时，但为了实现向后兼容仍然接受。 -g role --role=role 指定一个角色，这个角色将立即加入其中成为其成员。 如果要把这个角色加入到多个角色中作为成员， 可以写多个 -g 开关。 -i --inherit 新角色将自动继承把它作为成员的角色的特权。这是默认值。 -I --no-inherit 新角色将不会自动继承把它作为成员的角色的特权。 --interactive 如果在命令行没有指定用户名，提示要求用户名，并且在命令行没有指定选项 -d / -D、 -r / -R、 -s / `-S`时也提示。 -l --login 新用户将被允许登入（即，该用户名能被用作初始会话用户标识符）。这是默认值。 -L --no-login 新用户将不被允许登入（一个没有登录特权的角色仍然可以作为管理数据库权限的方式而存在）。 -P --pwprompt 如果给定，createuser将发出一个提示要求新用户的口令。如果你没有计划使用口令认证，这就不是必须的。 -r --createrole 新用户将被允许创建新的角色（即，这个用户将具有 CREATEROLE 特权）。 -R --no-createrole 新用户将不被允许创建新角色。这是默认值。 -s --superuser 新用户将成为一个超级用户。 -S --no-superuser 新用户将不会成为一个超级用户。这是默认值。 -V --version 打印createuser版本并退出。 --replication 新用户将具有 REPLICATION 特权，这在 CREATE ROLE 的文档中有更完整的描述。 --no-replication 新用户将不具有 REPLICATION 特权，这在 CREATE ROLE 的文档中有更完整的描述。 -? --help 显示有关createuser命令行参数的帮助并退出。 createuser也接受下列命令行参数作为连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接（不是要创建的用户名）。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制createuser在连接到一个数据库之前提示要求一个口令（用来连接到服务器，而不是新用户的口令）。这个选项不是必不可少的，因为如果服务器要求口令认证，createuser将自动提示要求一个口令。但是，createuser将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always， auto 和 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 CREATE ROLE 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要在默认数据库服务器上创建一个用户 joe： $ createuser joe 要在默认数据库服务器上创建一个用户 joe 并提示要求一些额外属性： $ createuser --interactive joe Shall the new role be a superuser? (y/n) n Shall the new role be allowed to create databases? (y/n) n Shall the new role be allowed to create more new roles? (y/n) n 要使用在主机 eden、端口 5000 上的服务器创建同一个用户 joe，并带有显式指定的属性，看看下面的命令： $ createuser -h eden -p 5000 -S -D -R -e joe CREATE ROLE joe NOSUPERUSER NOCREATEDB NOCREATEROLE INHERIT LOGIN; 要创建用户 joe 为一个超级用户并且立刻分配一个口令： $ createuser -P -s -e joe Enter password for new role: xyzzy Enter it again: xyzzy CREATE ROLE joe PASSWORD 'md5b5f5ba1a423792b526f799ae4eb3d59e' SUPERUSER CREATEDB CREATEROLE INHERIT LOGIN; 在上面的例子中，在录入新口令时新口令并没有真正地被回显，但是为了清晰，我们特意把它列了出来。如你所见，该口令在被发送给客户端之前会被加密。 dropdb — 移除一个IvorySQL数据库 dropdb [connection-option…​] [option…​] dbname dropdb接受下列命令行参数： dbname 指定要被移除的数据库的名字。 -e -−echo 回显dropdb生成并发送给服务器的命令。 -f -−force 在删除目标数据库之前，尝试终止与该数据库的所有现有连接。有关此选项的详细信息，请参见 DROP DATABASE。 -i -−interactive 在做任何破坏性的工作之前发出一个验证提示。 -V -−version 打印dropdb版本并退出。 -−if-exists 如果数据库不存在也不抛出一个错误。在这种情况下会发出一个提示。 -? -−help 显示有关dropdb命令行参数的帮助并退出。 dropdb也接受下列命令行参数作为连接参数： -h host -−host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作Unix域套接字的目录。 -p port -−port=port 指定服务器正在监听连接的TCP端口或本地Unix域套接字文件扩展。 -U username -−username=username 要作为哪个用户连接。 -w -−no-password 不发出口令提示。如果服务器要求口令认证并且没有可用的口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W -−password 强制dropdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，dropdb将自动提示要求一个口令。但是，dropdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 -−maintenance-db=dbname 指定一个数据库的名称，将连接到这个数据库以便删除目标数据库。如果没有指定，将使用 postgres 数据库。而如果它也不存在（或者它就是正在被删除的目标数据库），将使用 template1。这个值可以是一个 连接字符串。 如果是这样，连接字符串参数将覆盖任何有冲突的命令行选项。 PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto 以及 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 DROP DATABASE 和 psql 中找找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要在默认数据库服务器上毁掉数据库 demo： $ dropdb demo 要使用在主机 eden、端口5000上的服务器中毁掉数据库 demo，并带有验证和回显，看看下面的命令： $ dropdb -p 5000 -h eden -i -e demo Database \"demo\" will be permanently deleted. Are you sure? (y/n) y DROP DATABASE demo; dropuser — 移除一个IvorySQL用户账户 dropuser [connection-option…​] [option…​] [username] dropuser接受下列命令行参数： username 指定要移除的IvorySQL用户的名字。如果没有在命令行指定并且使用了 -i / -−interactive 选项，你将被提醒要求一个用户名。 -e -−echo 回显dropuser生成并发送给服务器的命令。 -i -−interactive 在实际移除该用户之前提示要求确认，并且在没有在命令行指定用户名提示要求一个用户名。 -V -−version 打印dropuser版本并退出。 -−if-exists 如果用户不存在时不要抛出一个错误。在这种情况下将发出一个提示。 -? -−help 显示有关dropuser命令行参数的帮助并退出。 dropuser也接受下列命令行参数作为连接参数： -h host -−host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作Unix域套接字的目录。 -p port -−port=port 指定服务器正在监听连接的TCP端口或本地Unix域套接字文件扩展。 -U username -−username=username 要作为哪个用户连接。 -w -−no-password 不发出口令提示。如果服务器要求口令认证并且没有可用的口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W -−password 强制dropuser在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，dropuser将自动提示要求一个口令。但是，dropuser将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、auto 以及 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 DROP ROLE 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要从默认数据库服务器移除用户 joe： $ dropuser joe 要使用在主机 eden、端口5000上的服务器移除用户 joe，并带有验证和回显，可使用下面的命令： $ dropuser -p 5000 -h eden -i -e joe Role \"joe\" will be permanently removed. Are you sure? (y/n) y DROP ROLE joe; ecpg — 嵌入式 SQL C 预处理器 ecpg [option…​] file…​ -c 自动从 SQL 代码生成确定的 C 代码。当前，这对 EXEC SQL TYPE 起效。 -C mode 设置一个兼容性模式。mode 可以是 INFORMIX，INFORMIX_SE 或 ORACLE。 -D symbol 定义一个 C 预处理器符号。 -h 处理头文件。指定此选项后，输出文件扩展名变为 .h 而不是 .c，默认输入文件扩展名为 .pgh 而不是 .pgc。此外，将强制启用 -c 选项。 -i 分析系统也包括文件。 -I directory 指定一个额外的包括路径，用来寻找通过 EXEC SQL INCLUDE 包括的文件。默认值是 .（当前目录）、/usr/local/include 、在编译时定义的IvorySQL包括目录（默认：/usr/local/pgsql/include）以及 /usr/include。 -o `filename` 指定 ecpg 应该将它的所有输出写到给定的 filename。写 -o- 将所有输出发送到标准输出。 -r `option` 选择运行时行为。option 可以是下列之一：no_indicator 不使用指示器而使用特殊值来表示空值。历史上曾有数据库使用这种方法。prepare 在使用所有语句之前准备它们。libecpg 将保持一个预备语句的缓冲并当语句再被执行时重用该语句。如果缓冲满了，libecpg 将释放最少使用的语句。`questionmarks`为兼容性原因允许使用问号作为占位符。在很久以前这被用作默认值。 -t 打开事务的自动提交。在这种模式下，每一个 SQL 命令会被自动提交，除非它位于一个显式事务块中。在默认模式中，命令只有当 EXEC SQL COMMIT 被发出时才被提交。 -v 打印额外信息，包括版本和“包括”路径。 --version 打印ecpg版本并退出。 -? --help 显示关于ecpg命令行参数的帮助并退出。 在编译预处理好的 C 代码文件时，编译器需要能够找到IvorySQL包括目录中的ECPG头文件。因此，在调用编译器时，你可能必须使用 -I 选项（例如， -I/usr/local/pgsql/include ）。 使用带有嵌入式 SQL 的 C 代码的程序必须被链接到 libecpg 库，例如使用链接器选项 -L/usr/local/pgsql/lib -lecpg。 适合于安装的这些目录的值可以使用 pg_config 找到。 如果你有一个名为 prog1.pgc 的嵌入式 SQL C 源文件，你可以使用下列命令序列创建一个可执行程序： ecpg prog1.pgc cc -I/usr/local/pgsql/include -c prog1.c cc -o prog1 prog1.o -L/usr/local/pgsql/lib -lecpg pg_amcheck — 在一个或多个IvorySQL数据库中检查损坏 pg_amcheck [option…​] [dbname] 以下命令行选项控制要检查的内容： -a --all 检查所有数据库，但通过`--exclude-database`排除的数据库除外。 -d pattern --database=pattern 检查与指定的 pattern匹配的数据库，但`--exclude-database`排除的数据库除外。可以多次指定此选项。 -D pattern --exclude-database=pattern 排除与给定的 pattern匹配的数据库。可以多次指定此选项。 -i pattern --index=pattern 检查与指定的 pattern匹配的索引，除非它们被排除在外。可以多次指定此选项。这类似于 --relation 选项，只是它只适用于索引，而不适用于表。 -I pattern --exclude-index=pattern 排除与指定的 pattern匹配的索引。可以多次指定此选项。这类似于 --exclude-relation 选项，只是它只适用于索引，而不适用于表。 -r pattern --relation=pattern 检查与指定的 pattern匹配的关系，除非它们被排除在外。可以多次指定此选项。模式可能是非限定的，例如 myrel*，或者它们可能是模式限定的，如 myschema*.myrel* 或数据库限定和模式限定，例如 mydb*.myscheam*.myrel*。数据库限定模式将向要检查的数据库列表中添加匹配的数据库。 -R pattern --exclude-relation=pattern 排除与指定的 pattern匹配的关系。可以多次指定此选项。与`--relation`一样, pattern 可以是非限定的、模式限定的或数据库和模式限定的。 -s pattern --schema=pattern 检查模式中与指定的 pattern匹配的表和索引，除非另有排除。可以多次指定此选项。要仅选择与特定模式匹配的模式中的表，请考虑使用类似 --table=SCHEMAPAT.* --no-dependent-indexes。要仅选择索引，请考虑使用类似 --index=SCHEMAPAT. 的方法。模式模式可以是数据库限定的。例如，你可以编写 --schema=mydb.myschema* 在数据库中选择匹配 mydb* 的模式。 -S pattern --exclude-schema=pattern 排除模式中与指定的 pattern匹配的表和索引。可以多次指定此选项。与`--schema`一样，模式可以是数据库限定的。 -t pattern --table=pattern 检查与指定的 pattern匹配的表，除非它们被排除在外。可以多次指定此选项。这类似于`--relation`选项，只是它只适用于表，而不适用于索引。 -T pattern --exclude-table=pattern 排除与指定的 pattern 匹配的表。可以多次指定此选项。这类似于 --exclude-relation 选项，只是它只适用于表，而不适用于索引。 --no-dependent-indexes 默认情况下，如果选中了一个表，则该表的任何btree索引也将被选中，即使它们没有被诸如 --index 或 --relation 之类的选项显式选择。此选项将抑制该行为。 --no-dependent-toast 默认情况下，如果选中了一个表，则也将检查它的toast表（如果有），即使它没有通过诸如 --table 或 --relation 之类的选项显式选择。此选项将抑制该行为。 --no-strict-names 默认情况下，如果 --database， --table， --index，或 --relation 的参数不匹配任何对象，则这是一个致命错误。此选项将该错误降级为警告。 以下命令行选项用于控制表的检查： --exclude-toast-pointers 默认情况下，每当在表中遇到toast指针时，都会执行查找以确保它引用toast表中明显有效的条目。这些检查可能非常慢，可以使用此选项跳过这些检查。 --on-error-stop 在发现损坏的表的第一页上报告所有损坏后，停止处理该表关系，并转到下一个表或索引。请注意，索引检查总是在第一个损坏页之后停止。此选项仅与表关系相关。 --skip=option 如果给定 all-frozen，表损坏检查将跳过所有表中标记为全部冻结的页面。如果给定了 all-visible，则表损坏检查将跳过所有表中标记为所有可见的页面。默认情况下，不跳过任何页面。这可以指定为 none，但由于这是默认值，因此无需提及。 --startblock=block 从指定的块号开始检查。如果正在检查的表关系的块数少于此数，则会发生错误。此选项不适用于索引，可能仅在检查单个表关系时有用。请参阅 --endblock 了解更多注意事项。 --endblock=`block` 在指定的块号结束检查。如果正在检查的表关系的块数少于此数，则会发生错误。此选项不适用于索引，可能仅在检查单个表关系时有用。如果同时选中常规表和toast表，则此选项将同时适用于这两个表，但在验证toast指针时，仍然可以访问编号更高的toast块，除非使用 --exclude-toast-pointers 抑制。 以下命令行选项用来控制B树索引的检查： --heapallindexed 对于每个选中的索引，使用 amcheck's heapallindexed 选项验证索引中是否存在作为索引元组的所有堆元组。 --parent-check 对于检查的每个btree索引，使用 amcheck 的 bt_index_parent_check 函数，该函数在索引检查期间对父/子关系执行其他检查。默认情况是使用amcheck的 bt_index_check 函数，但请注意，使用 --rootdescend 选项会隐式选择 bt_index_parent_check。 --rootdescend 对于每个选中的索引，通过使用 amcheck's rootdescend 选项从根页面对每个元组执行新搜索，在叶级重新查找元组。使用此选项也会隐式选择 --parent-check 选项。这种形式的验证最初是为了帮助开发btree索引功能而编写的。它在帮助检测实践中发生的损坏类型方面可能用处有限，甚至毫无用处。它还可能导致损坏检查花费相当长的时间，并消耗服务器上相当多的资源。 当指定 --parent-check 选项或 --rootdescend 选项时，对B树索引执行的额外检查需要相对强的关系级锁。这些检查是唯一会阻止 INSERT， UPDATE，和 DELETE 命令并发数据修改的检查。 以下命令行选项用来控制与服务器的连接： -h hostname --host=hostname 指定运行服务器的计算机的主机名。如果该值以斜杠开头，它将用作Unix域套接字的目录。 -p port --port=port 指定服务器正在侦听连接的TCP端口或本地Unix域套接字文件扩展名。 -U --username=username 连接的用户名。 -w --no-password 永远不要发出密码提示。如果服务器需要密码身份验证，并且密码无法通过其他方式（如 .pgpass 文件，连接尝试将失败。在没有用户输入密码的批处理作业和脚本中，此选项非常有用。 -W --password 在连接到数据库之前，强制pg_amcheck提示输入密码。此选项从来都不是必需的，因为如果服务器要求密码身份验证，pg_amcheck将自动提示输入密码。但是，pg_amcheck将浪费一次连接尝试，以发现服务器需要密码。在某些情况下，键入 -W 以避免额外的连接尝试是值得的。 --maintenance-db=dbname 指定用于发现要检查的数据库列表的数据库或 连接字符串。如果既不使用 --all 也不使用任何包含数据库模式的选项，则不需要这种连接，并且该选项不起任何作用。否则，连接到正在检查的数据库时，也将使用此选项值中包含的数据库名称以外的任何连接字符串参数。如果省略此选项，则默认值为 postgres，或如果失败，则为 template1。 其它允许的选项： -e --echo 回显发送到服务器的所有SQL。 -j num --jobs=num 使用 num 个到服务器的并发连接，或每个要检查的对象使用一个连接，以较小者为准。默认情况是使用单个连接。 -P --progress 显示进度信息。进度信息包括已完成检查的关系数以及这些关系的总大小。它还包括最终要检查的关系的总数，以及这些关系的估计大小。 -v --verbose 打印更多消息。特别是，这将为每个正在检查的关系打印一条消息，并将增加服务器错误的详细程度。 -V --version 打印pg_amcheck版本并退出。 --install-missing --install-missing=`schema` 安装检查数据库所需的任何缺少的扩展。如果尚未安装，每个扩展的对象将安装到给定的 schema 中，或者如果未指定则安装到 pg_catalog 模式中。目前，唯一需要的扩展是 amcheck。 -? --help 显示有关pg_amcheck命令行参数的帮助，然后退出。 pg_basebackup — 获得一个IvorySQL集簇的一个基础备份 pg_basebackup [option…​] 下列命令行选项控制输出的位置和格式： -D directory --pgdata=directory 设置目标目录以将输出写入。如果该目录不存在，pg_basebackup将创建该目录（以及所有丢失的父目录）。如果已经存在，则必须为空。当备份处于 tar 模式中，目标目录可以被指定为 - （破折号），从而将 tar 文件写入 stdout。这个选项是必需的。 -F format --format=format 为输出选择格式。format 可以是下列之一： p plain 把输出写成平面文件，使用和源服务器数据目录和表空间相同的布局。 当集簇没有额外表空间时，整个数据库将被放在目标目录中。 如果集簇包含额外的表空间，主数据目录将被放置在目标目录中，但是所有其他表空间将被放在它们位于源服务器上的相同的绝对路径中。 (参见 --tablespace-mapping 来更改。)这是默认格式。t tar 将输出作为tar文件写入目标目录中。 主数据目录的内容将被写入名为 base.tar 的文件，而其他表空间将被写入以该表空间的OID命名的单独的tar文件。如果将目标目录指定为 -（破折号），则tar内容将被写入标准输出，该标准输出适用于管道传输至gzip（例如）。 只有当集簇没有额外表空间并且没有使用WAL流时才允许这样做。 -R --write-recovery-conf 创建一个 standby.signal 文件，并将连接设置附加到目标目录（或使用tar格式的基本存档文件中）的 postgresql.auto.conf 文件中。 这样可以简化使用备份结果设置后备服务器的过程。 postgresql.auto.conf 文件将记录连接设置（如果有）以及pg_basebackup所使用的复制槽，这样流复制后面就会使用相同的设置。 -T olddir=newdir --tablespace-mapping=olddir=newdir 在备份期间将目录 olddir 中的表空间重定位到 newdir 中。为使之有效，olddir 必须与源服务器上定义的表空间的路径规范完全匹配。（但如果备份中没有包含 olddir 中的表空间也不是错误）。同时，newdir 是接收主机文件系统中的目录。与主目标目录一样，newdir 不必已经存在，但是如果确实存在，则必须为空。olddir 和 newdir 必须是绝对路径。如果一个路径凑巧包含了一个 = 符号，可用反斜线对它转义。对于多个表空间可以多次使用这个选项。如果以这种方法重定位一个表空间，主数据目录中的符号链接会被更新成指向新位置。因此新数据目录已经可以被一个所有表空间位于更新后位置的新服务器实例使用。目前，这个选项仅以普通输出格式工作，如果选择了tar格式，请忽略它。 --waldir=waldir 指定用于预写式日志目录的位置。waldir 必须是绝对路径。只有当备份是平面文件模式时才能指定事务日志目录。 设置要写入WAL（预写日志）文件的目录。 默认情况下，WAL文件将放置在目标目录的 pg_wal 子目录中，但是此选项可用于将它们放置在其他位置。waldir 必须是绝对路径。与主目标目录一样，waldir 不必已经存在，但是如果确实存在，则必须为空。只有当备份是平面文件模式时才可以指定此选项。 -X method --wal-method=method 在备份中包括所需的WAL（预写式日志）文件。这包括所有在备份期间产生的预写式日志。除非指定了方法 none，可以在目标目录中启动postmaster而无需参考日志归档，从而使输出成为完全独立的备份。支持下列收集预写式日志的 方法：n none 不要在备份中包括预写式日志。f fetch 在备份末尾收集预写式日志文件。因此，有必要把源服务器的 wal_keep_size 参数设置得足够高，这样在备份末尾之前需要的日志数据不会被移除。如果所需的日志数据在传输之前已被回收，则备份将失败并且无法使用。如果使用tar格式，预写式日志文件将被包含在 base.tar 文件。s stream 在进行备份时，流式传输预写式日志数据。将开启一个到服务器的第二连接并且在运行备份时并行开始流传输预写式日志。因此，它将需要两个复制连接，而不仅仅是一个。 只要客户端可以跟上预写式日志数据，使用此方法就不需要在源服务器上保存额外的预写式日志。如果使用tar格式，预写式日志文件被写入到一个单独的名为 pg_wal.tar 的文件（如果服务器的版本超过10，该文件将被命名为 pg_xlog.tar ）。这个值是默认值。 -z --gzip 启用对 tar 文件输出的 gzip 压缩，使用默认的压缩级别。只有使用 tar 格式时压缩才可用，并且会在所有tar文件名后面自动加上后缀 .gz。 -Z level --compress=level 启用对 tar 文件输出的 gzip 压缩，并且制定压缩机别（0 到 9，0 是不压缩，9 是最佳压缩）。只有使用 tar 格式时压缩才可用，并且会在所有tar文件名后面自动加上后缀 .gz。 下列命令行选项控制备份的生成和程序的调用： -c fast|spread --checkpoint=fast|spread 将检查点模式设置为 fast（立刻）或 spread（默认）。 -C --create-slot 指定在启动备份之前应创建由 --slot 选项命名的复制插槽。如果插槽已存在，则会引发错误。 -l label --label=label 为备份设置标签。如果没有指定，将使用一个默认值“ pg_basebackup base backup ”。 -n --no-clean 默认情况下，当 pg_basebackup 因为一个错误而中止时，它会把它意识到无法完成该工作之前已经创建的目录（例如目标目录和预写式日志目录）都移除。这个选项可以禁止这种清洗，因此可以用于调试。注意不管哪一种方式都不会清除表空间目录。 -N --no-sync 默认情况下，pg_basebackup 将等待所有文件被安全地写到磁盘上。这个选项导致 pg_basebackup 不做这种等待就返回，这样会更快一些，但是也意味着后续发生的操作系统崩溃可能会使得这个基础备份损坏。通常这个选项对测试比较有用，在创建生产安装时不应该使用。 -P --progress 启用进度报告。启用这个选项将在备份期间发表一个大致的进度报告。由于数据库可能在备份期间改变，这仅仅是一种近似并且可能不会刚好在 100% 结束。特别地，当 WAL 日志被包括在备份中时，总数据量无法预先估计，并且在这种情况中估计的目标尺寸会在它经过不带 WAL 的总估计后增加。 -r rate --max-rate=rate 设置从源服务器收集数据的最大传输速率。这有助于限制pg_basebackup对服务器的影响。值以每秒千字节为单位。使用后缀 M 表示每秒兆字节数。后缀 k 也可以接受，没有任何影响。有效值介于每秒32千字节和每秒1024兆字节之间。此选项始终影响数据目录的传输。只有收集方法为 fetch 时，才会影响WAL文件的传输。 -S slotname --slot=slotname 这个选项仅能与 -X stream 一起使用。它导致WAL流使用指定的复制槽。如果该基础备份的目的是被用作一台使用复制槽的流复制后备，则它应该使用与 primary_slot_name 中相同的复制槽名称。这可以确保主服务器不会移除位于该基础备份结束与新备用数据库上流复制开始之间产生的任何所需的WAL数据。指定的复制槽必须已经存在，除非同时使用了选项 -C。如果这个选项没有被指定并且服务器支持临时复制槽（版本10以后），则会自动使用一个临时复制槽来进行WAL流。 -v --verbose 启用冗长模式。将在启动和关闭期间输出一些额外步骤，并且如果进度报告也被启用，还会显示当前正在被处理的确切文件名。 --manifest-checksums=algorithm 指定应应用于备份清单中包含的每个文件的校验和算法。目前，可用的算法有`NONE`、CRC32C、SHA224、SHA256、SHA384`和`SHA512。默认值为`CRC32C`。如果选择了`NONE`，备份清单将不包含任何校验和。否则，它将包含备份中使用指定算法的每个文件的校验和。此外，清单将始终包含自身内容的`SHA256`校验和。`SHA`算法比`CRC32C`算法占用大量CPU，因此选择其中一种算法可能会增加完成备份所需的时间。使用SHA散列函数可为希望验证备份是否遭到篡改的用户提供每个文件的加密安全摘要，而CRC32C算法提供的校验和计算速度更快。它擅长捕获由于意外更改引起的错误，但不能抵抗恶意修改。请注意，为了对有权访问备份的对手有用，备份清单必须安全地存储在其他位置，否则必须经过验证以确保自从进行备份以来未进行任何修改。 pg_verifybackup 可用于根据备份清单检查备份的完整性。 --manifest-force-encode 强制备份清单中的所有文件名采用十六进制编码。如果未指定此选项，则仅对非UTF8文件名进行十六进制编码。此选项主要用于测试读取备份清单文件的工具是否正确处理此情况。 --no-estimate-size 阻止服务器估计将要流式传输的备份数据总量，从而导致 pg_stat_progress_basebackup 视图中的 backup_total 列始终为 NULL 。如果没有此选项，则备份将从枚举整个数据库的大小开始，然后返回并发送实际内容。这可能会使备份花费的时间稍长一些，特别是在发送第一个数据之前，备份将花费更长的时间。 如果估计时间过长，此选项将有助于避免此类估计时间。使用 --progress 时不允许使用此选项。 --no-manifest 禁用备份清单的生成。如果未指定此选项，则服务器将生成并发送备份清单，可以使用 pg_verifybackup 进行验证。清单是备份中存在的每个文件的列表，可能包含的所有WAL文件除外。它还存储大小、上次修改时间和每个文件的可选校验和。 --no-slot 防止为备份创建临时复制插槽。默认情况下，如果选择了日志流，但没有用选项 -S 指定槽名称，则会创建一个临时复制插槽（如果源服务器支持）。这个选项的主要目的是允许在服务器没有空闲复制槽可用时制作基础备份。使用复制槽几乎总是最好的方式，因为它能防止备份期间所需的WAL被删除。 --no-verify-checksums 如果在取基础备份的服务器上启用了校验码验证，则禁用校验码验证。默认情况下，校验码会被验证并且校验码失败将会导致一种非零的退出状态。不过，基础备份在这种情况下将不会被移除，就好像使用了 --no-clean 选项一样。校验和验证失败也将报告在 pg_stat_database 视图中。 以下命令行选项控制到源服务器的连接： -d connstr --dbname=connstr 指定用于连接到服务器的参数，比如 连接字符串；这些将覆盖所有冲突的命令行选项。为了和其他客户端应用一致，该选项被称为 --dbname。但是因为pg_basebackup并不连接到集簇中的任何特定数据库，连接字符串中的任何数据库名将被忽略。 -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。默认值取自 PGHOST 环境变量（如果设置），否则会尝试一个 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。默认用 PGPORT 环境变量中的值（如果设置），或者一个编译在程序中的默认值。 -s interval --status-interval=interval 指定发送回源服务器的状态包之间的秒数。较小的值可以更准确地监视服务器的备份进度。一个零值完全禁用这种周期性的状态更新，不过当服务器需要时还是会有一个更新会被发送来避免超时导致的断开连接。默认值是 10 秒。 -U username --username=username 指定连接的用户名。 -w --no-password 防止发出口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_basebackup在连接到源服务器之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，pg_basebackup将自动提示要求一个口令。但是，pg_basebackup将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 其他选项也可用： -V --version 打印pg_basebackup版本并退出。 -? --help 显示有关pg_basebackup命令行参数的帮助并退出。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 环境变量 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 在备份的开始时，需要在源服务器上执行检查点。这可能需要一点时间（尤其在没有使用选项 --checkpoint=fast 时），在此期间pg_basebackup看起来处于闲置状态。 备份将包括数据目录和表空间中的所有文件，包括配置文件以及由第三方放在该目录中的任何额外文件，不过由IvorySQL管理的特定临时文件除外。但只有常规文件和目录会被拷贝，但用于表空间的符号链接会被保留。指向IvorySQL已知的特定目录的符号链接被拷贝为空目录。其他符号链接和特殊设备文件会被跳过。 在普通格式中，表空间将备份到源服务器上的相同路径，除非使用了 --tablespace-mapping 选项。如果没有这个选项并且表空间正在使用，在同一台服务器上进行普通格式的基础备份将无法工作，因为备份必须要写入到与原始表空间相同的目录位置。 在使用 tar 格式时，用户应负责在启动使用数据的 IvorySQL 服务器前解压每一个 tar 文件。如果有额外的表空间，用于它们的 tar 文件需要被解压到正确的位置。在这种情况下，服务器将根据包含在 base.tar 文件中的 tablespace_map 文件的内容为那些表空间创建符号链接。 pg_basebackup可以和具有相同或较低主版本的服务器一起工作. 如果在源集簇上启用了组权限，pg_basebackup将保留数据文件的组权限。 要创建服务器 mydbserver 的一个基础备份并将它存储在本地目录 /usr/local/pgsql/data 中： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data 要创建本地服务器的一个备份，为其中每一个表空间产生一个压缩过的 tar 文件，并且将它存储在目录 backup 中，在运行期间显示一个进度报告： $ pg_basebackup -D backup -Ft -z -P 要创建一个单一表空间本地数据库的备份并且使用bzip2压缩它： $ pg_basebackup -D - -Ft -X fetch | bzip2 > backup.tar.bz2 （如果在该数据库中有多个表空间，这个命令将失败）。 要创建一个本地数据库的备份，其中 /opt/ts 中的表空间被重定位到 ./backup/ts： $ pg_basebackup -D backup/data -T /opt/ts=$(pwd)/backup/ts pgbench — 在IvorySQL上运行一个基准测试 pgbench -i [option…​] [dbname] pgbench [option…​] [dbname] pgbench -i 会创建四个表 pgbench_accounts、 pgbench_branches、 pgbench_history 以及 pgbench_tellers，如果同名表已经存在会被先删除。如果你已经有同名表，一定注意要使用另一个数据库！ 在默认的情况下“比例因子”为 1，这些表初始包含的行数为： table # of rows --------------------------------- pgbench_branches 1 pgbench_tellers 10 pgbench_accounts 100000 pgbench_history 0 你可以使用 -s （比例因子）选项增加行的数量。-F （填充因子）选项也可以在这里使用。 一旦你完成了必要的设置，你就可以用不包括 -i 的命令运行基准，也就是： pgbench [ options ] dbname 在近乎所有的情况中，你将需要一些选项来做一次有用的测试。最重要的选项是 -c （客户端数量）、 -t （事务数量）、-T （时间限制）以及 -f （指定一个自定义脚本文件）。完整的列表见下文。 下面分成三个部分。数据库初始化期间使用的选项和运行基准时会使用不同的选项，但也有一些选项在两种情况下都使用。 pgbench接受下列命令行初始化参数： dbname 指定要测试的数据库的名称。 如果这个没有被指定，将使用环境变量 PGDATABASE 。 如果那个也没有设定，将使用指定要连接的用户名称。 -i --initialize 要求调用初始化模式。 -I init_steps --init-steps=init_steps 只执行选出的一组普通初始化步骤。init_steps 指定要被执行的初始化步骤，每一个步骤使用一个字符代表。每一个步骤都以指定的顺序被调用。默认是 dtgvp。可用的步骤是：d（删除）删除任何已有的pgbench表。t（创建表）创建标准pgbench场景使用的表，即 pgbench_accounts、pgbench_branches、pgbench_history`以及 `pgbench_tellers。g 或 G（生成数据、客户端或服务器端）生成数据并且装入到标准的表中，替换掉已经存在的任何数据。使用 g（客户端数据生成），数据在 pgbench 客户端生成，然后发送到服务器。这通过 COPY 广泛使用客户端/服务器带宽。使用 g 会导致日志记录每 100,000 行打印一条消息，同时为 pgbench_accounts 表生成数据。使用 G（服务器端数据生成），仅从 pgbench 客户端发送少量查询，然后在服务器中实际生成数据。 此变体不需要大量带宽，但服务器将完成更多工作。 使用 G`会导致日志记录在生成数据时不打印任何进度消息。默认的初始化行为使用客户端数据生成（相当于`g）。v（清理）在标准的表上调用 VACUUM。p（创建主键）在标准的表上创建主键索引。f（创建外键）在标准的表之间创建外键约束（注意这一步默认不会被执行）。 -F fillfactor --fillfactor= fillfactor 用给定的填充因子创建表 pgbench_accounts、pgbench_tellers 以及 pgbench_branches。默认是100。 -n --no-vacuum 在初始化期间不执行清理（这个选项会抑制 v 初始化步骤，即便在`-I` 中指定了该步骤）。 -q --quiet 把记录切换到安静模式，只是每 5 秒产生一个进度消息。默认的记录会每 100,000 行打印一个消息，这经常会在每秒钟输出很多行（特别是在好的硬件上）。如果在 -I 中指定了 G，则此设置无效。 -s scale_factor --scale= scale_factor 将生成的行数乘以比例因子。例如，-s 100 将在 pgbench_accounts 表中创建 10,000,000 行。默认为 1。当比例为 20,000 或更高时，用来保存账号标识符的列（ aid 列）将切换到使用更大的整数（ bigint ），这样才能足以保存账号标识符。 --foreign-keys 在标准的表之间创建外键约束（如果 f 在初始化步骤序列中不存在，这个选项会把它加入）。 --index-tablespace=index_tablespace 在指定的表空间而不是默认表空间中创建索引。 --partition-method=NAME 使用 NAME 方法创建一个分区的 pgbench_accounts 表。 预期值为 range 或 hash。 此选项要求 --partitions 设置为非零。 如果未指定，默认值为 range。 --partitions=NUM 创建一个分区 pgbench_accounts 表，其中 NUM 分区的大小与按比例缩放的帐户数几乎相等。 默认为 0，表示没有分区。 --tablespace=tablespace 在指定的表空间而不是默认表空间中创建表。 --unlogged-tables 把所有的表创建为非日志记录表而不是永久表。 pgbench接受下列命令行基准参数： -b scriptname[@weight] --builtin= scriptname[@weight] 把指定的内建脚本加入到要被执行的脚本列表中。 可用的内建脚本有： tpcb-like、 simple-update 和 select-only 。 这里也接受内建名称无歧义的前缀缩写。 如果用上特殊的名字 list，将会显示内建脚本的列表并且立刻退出。可选的，在 @ 后面写一个整数权重以调整选择这个脚本而不是其它的概率。 默认的权重为1。 详情如下。 -c clients --client= clients 模拟的客户端数量，也就是并发数据库会话数量。默认为 1。 -C --connect 为每一个事务建立一个新连接，而不是只为每个客户端会话建立一个连接。这对于度量连接开销有用。 -d --debug 打印调试输出。 -D varname = value --define= varname = value 定义一个由自定义脚本（见下文）使用的变量。允许多个 -D 选项。 -f filename[@weight] --file= filename[@weight] 把一个从 filename 读到的事务脚本加入到被执行的脚本列表中。可选的，在 @ 后写一个整数权重以调整选择这个脚本而非其他的概率。 默认权重为1. (要使用包含 @ 字符的脚本文件名，附加权重在后面以避免歧义，例如 filen@me@1 。) 详情如下。 -j threads --jobs= threads pgbench中的工作者线程数量。在多 CPU 机器上使用多于一个线程会有用。客户端会尽可能均匀地分布到可用的线程上。默认为 1。 -l --log 把与每一个事务相关的信息写到一个日志文件中。详见下文。 -L limit --latency-limit= limit 对持续超过 limit 毫秒的事务进行独立的计数和报告， 这些事务被认为是 迟到（late） 了的事务。在使用限流措施时（ --rate=…​ ），滞后于计划超过 limit 毫秒并且因此没有希望满足延迟限制的事务根本 不会被发送给服务器。这些事务被认为是 被跳过（skipped） 的事务，它们会被单独计数并且报告。 -M querymode --protocol=querymode 要用来提交查询到服务器的协议： simple ：使用简单查询协议。 extended 使用扩展查询协议。 prepared ：使用带预备语句的扩展查询语句。在 prepared 模式中，pgbench重用从第二次查询迭代开始的语法分析结果，因此pgbench运行速度比其他模式快。默认是简单查询协议。 -n --no-vacuum 在运行测试前不进行清理。如果你在运行一个不包括标准的表 pgbench_accounts 、 pgbench_branches 、 pgbench_history 和 pgbench_tellers 的自定义测试场景时，这个选项是必需的。 -N --skip-some-updates 运行内建的简单更新脚本。这是 -b simple-update 的简写。 -P sec --progress=sec 每 sec 秒显示进度报告。该报告包括运行了多长时间、从上次报告以来的 tps 以及从上次报告以来事务延迟的平均值和标准偏差。如果低于限流值（ -R ），延迟会相对于事务预定的开始时间（而不是实际的事务开始时间）计算，因此其中也包括了平均调度延迟时间。 -r --report-latencies 在基准结束后，报告平均的每个命令的每语句等待时间（从客户端的角度来说是执行时间）。详见下文。 -R rate --rate= rate 按照指定的速率执行事务而不是尽可能快地执行（默认行为）。该速率 以 tps（每秒事务数）形式给定。如果目标速率高于最大可能速率，则 该速率限制不会影响结果。该速率的目标是按照一条泊松分布的调度时间线开始事务。期望的开始 时间表会基于客户端第一次开始的时间（而不是上一个事务结束的时 间）前移。这种方法意味着当事务超过它们的原定结束时间时，更迟的 那些有机会再次追赶上来。当限流措施被激活时，运行结束时报告的事务延迟是从预订的开始时间计 算而来的，因此它包括每一个事务不得不等待前一个事务结束所花的时 间。该等待时间被称作调度延迟时间，并且它的平均值和最大值也会被 单独报告。关于实际事务开始时间的事务延迟（即在数据库中执行事务 所花的时间）可以用报告的延迟减去调度延迟时间计算得到。如果把 --latency-limit 和 --rate 一起使用， 当一个事务在前一个事务结束时已经超过了延迟限制时，它可能会滞后 非常多，因为延迟是从计划的开始时间计算得来。这类事务不会被发送 给服务器，而是一起被跳过并且被单独计数。一个高的调度延迟时间表示系统无法用选定的客户端和线程数按照指定 的速率处理事务。当平均的事务执行时间超过每个事务之间的调度间隔时，每一个后续事务将会落后更多，并且随着测试运行时间越长，调度延迟时间将持续增加。发生这种情况时，你将不得不降低指定的事务速率。 -s scale_factor --scale= scale_factor 在pgbench的输出中报告指定的比例因子。对于内建测试，这并非必需；正确的比例因子将通过对 pgbench_branches 表中的行计数来检测。不过，当只测试自定义基准（ -f 选项）时，比例因子将被报告为 1（除非使用了这个选项）。 -S --select-only 执行内建的只有选择的脚本。是 -b select-only 简写形式。 -t transactions --transactions= transactions 每个客户端运行的事务数量。默认为 10。 -T seconds --time= seconds 运行测试这么多秒，而不是为每个客户端运行固定数量的事务。-t 和 -T 是互斥的。 -v --vacuum-all 在运行测试前清理所有四个标准的表。在没有用 -n 以及 -v 时， pgbench将清理 pgbench_tellers 和 pgbench_branches 表，并且截断 pgbench_history。 --aggregate-interval=`seconds` 聚集区间的长度（单位是秒）。仅可以与 -l 选项一起使用。通过这个选项，日志会包含针对每个区间的概要数据，如下文所述。 --log-prefix=prefix 设置 --log 创建的日志文件的文件名前缀。默认是 pgbench_log。 --progress-timestamp 当显示进度（选项 -P ）时，使用一个时间戳（Unix 时间）取代从运行开始的秒数。单位是秒，在小数点后是毫秒精度。这可以有助于比较多种工具生成的日志。 --random-seed= seed 设置随机数生成器种子。为系统的随机数生成器提供种子，然后随机数生成器会产生一个初始生成器状态序列，每一个线程一个状态。seed 的值可以是： time （默认值，种子基于当前时间）、 rand （使用一种强随机源，如果没有可用的源则失败）或者一个无符号十进制整数值。一个pgbench脚本中会显式（ random…​ 函数）地或者隐式地（如 --rate 使用随机数生成器调度事务）调用随机数生成器。在被明确设置时，用作种子的值会显示在终端上。 还可以通过环境变量 PGBENCH_RANDOM_SEED 提供用于 seed 的值。为了确保所提供的种子影响所有可能的使用，把这个选项放在第一位或者使用环境变量。明确地设置种子允许准确地再生一个 pgbench 运行，对随机数而言。因为随机状态是针对每个线程管理，这意味着如果每一个线程有一个客户端并且没有外部或者数据依赖，则对于一个相同的调用就会有完全相同的 pgbench 运行。从一种统计的角度来看，再生运行不是什么好主意，因为它能隐藏性能可变性或者不正当地改进性能，即通过命中前一次运行的相同页面来改进性能。不过，它也可以对调试起到很大帮助作用，例如重新运行一种导致错误的棘手用例。请善用。 --sampling-rate=`rate` 采样率，在写入数据到日志时被用来减少日志产生的数量。如果给出这个选项，只有指定比例的事务被记录。1.0 表示所有事务都将被记录，0.05 表示只有 5% 的事务会被记录。在处理日志文件时，记得要考虑这个采样率。例如，当计算TPS值时，你需要相应地乘以这个数字（例如，采样率是 0.01，你将只能得到实际TPS的 1/100）。 --show-script= scriptname 在 stderr 上显示内置脚本 scriptname 的实际代码，并立即退出。 pgbench 还接受以下用于连接参数的常见命令行参数： -h hostname --host= hostname 数据库服务器的主机名 -p port --port=port 数据库服务器的端口号 -U login --username= login 要作为哪个用户连接 -V --version 打印pgbench版本并退出。 -? --help 显示有关pgbench命令行参数的信息，并且退出。 成功运行将以状态0退出。 退出状态为1表示静态问题，如无效的命令行选项。 运行过程中的错误，例如数据库错误或脚本中的问题将导致退出状态为2。 在后一种情况下，pgbench将打印部分结果。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数。 此实用程序与大多数其他 IvorySQL 实用程序一样，使用 libpq 支持的环境变量。 环境变量 PG_COLOR 指定是否在诊断消息中使用颜色。 可能的值是 always、 auto 和 never. pg_config — 获取已安装的IvorySQL的信息 pg_config [option…​] 要使用pg_config，提供一个或多个下列选项： --bindir 打印用户可执行文件的位置。例如使用这个选项来寻找 psql 程序。这通常也是 pg_config 程序所在的位置。 --docdir 打印文档文件的位置。 --htmldir 打印 HTML 文档文件的位置。 --includedir 打印客户端接口的 C 头文件的位置。 --pkgincludedir 打印其它 C 头文件的位置。 --includedir-server 打印用于服务器编程的 C 头文件的位置。 --libdir 打印对象代码库的位置。 --pkglibdir 打印动态可载入模块的位置，或者服务器可能搜索它们的位置（其它架构独立数据文件可能也被安装在这个目录）。 --localedir 打印区域支持文件的位置（如果在IvorySQL被编译时没有配置区域支持，这将是一个空字符串）。 --mandir 打印手册页的位置。 --sharedir 打印架构独立支持文件的位置。 --sysconfdir 打印系统范围配置文件的位置。 --pgxs 打印扩展 makefile 的位置。 --configure 打印当IvorySQL被配置编译时给予 configure 脚本的选项。这可以被用来重新得到相同的配置，或者找出是哪个选项编译了一个二进制包（不过注意二进制包通常包含厂商相关的自定补丁）。参见下面的例子。 --cc 打印用来编译IvorySQL的 CC 变量值。这显示被使用的 C 编译器。 --cppflags 打印用来编译IvorySQL的 CPPFLAGS 变量值。这显示在预处理时需要的 C 编译器开关（典型的是 -I 开关）。 --cflags 打印用来编译IvorySQL的 CFLAGS 变量值。这显示被使用的 C 编译器开关。 --cflags_sl 打印用来编译IvorySQL的 CFLAGS_SL 变量值。这显示被用来编译共享库的额外 C 编译器开关。 --ldflags 打印用来编译IvorySQL的 LDFLAGS 变量值。这显示链接器开关。 --ldflags_ex 打印用来编译IvorySQL的 LDFLAGS_EX 变量值。这只显示被用来编译可执行程序的链接器开关。 --ldflags_sl 打印用来编译IvorySQL的 LDFLAGS_SL 变量值。这只显示被用来编译共享库的链接器开关。 --libs 打印用来编译IvorySQL的 LIBS 变量值。这通常包含用于链接到IvorySQL中的外部库的 -l 开关。 --version 打印IvorySQL的版本。 -? --help 显示有关pg_config命令行参数的帮助并退出。 如果给定多于一个选项，将按照相同的顺序打印信息，每行一项。如果没有给定选项，将打印所有可用信息，并带有标签。 要重建当前 IvorySQL 安装的编译配置，可运行下列命令： eval ./configure `pg_config --configure` pg_config --configure 的输出包含 shell 引号，这样带空格的参数可以被正确地表示。因此，为了得到正确的结果需要使用 eval。 pg_dump — 把IvorySQL数据库抽取为一个脚本文件或其他归档文件 pg_dump [connection-option…​] [option…​] [dbname] 下列命令选项控制输出的内容和格式。 dbname 指定要被转储的数据库名。如果没有指定，将使用环境变量 PGDATABASE。如果环境变量也没有设置，则使用指定给该连接的用户名。 -a --data-only 只转储数据，而不转储模式（数据定义）。表数据、大对象和序列值都会被转储。这个选项类似于指定 --section=data，但是由于历史原因又不完全相同。 -b --blobs 在转储中包括大对象。这是当 --schema、 --table 或 --schema-only 被指定时的默认行为。因此，只有在请求转储一个特定方案或者表的情况中， -b 开关才对向转储中加入大对象有用。注意blobs是被考虑的数据，因此在使用 --data-only 时将被包括在内，但在使用 --schema-only 时则不会包括。 -B --no-blobs 在转储中排除大对象。当同时给定 -b 和 -B 时，行为是在数据被转储时输出大对象，请参考 -b 文档。 -c --clean 在输出创建数据库对象的命令之前输出清除（删除）它们的命令 （除非也指定了 --if-exists，如果任何对象不存在于 目的数据库中，恢复可能会产生一些伤害性的错误消息）。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 -C --create 使得在输出的开始是一个创建数据库本身并且重新连接到被创建的数据库的命令（通过这种形式的一个脚本，在运行脚本之前你连接的是目标安装中的哪个数据库都没有关系）。如果也指定了 --clean，脚本会在重新连接到目标数据库之前先删除它然后再重建。通过 --create，输出还会包括数据库的注释（如果有）以及与这个数据库相关的任何配置变量设置，也就是任何提到了这个数据库的 ALTER DATABASE …​ SET …​ 命令和 ALTER ROLE …​ IN DATABASE …​ SET …​ 命令。该数据库本身的访问特权也会被转储，除非指定有 --no-acl。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 -e pattern -−extension=pattern 只转储匹配 pattern 的扩展。 如果未指定此选项，目标数据库中的所有非系统扩展都将被转储。 通过写入多个 -e 开关，可以选择多个扩展。 pattern 参数可以理解为与psql’s \\d 命令使用的规则相同的模式， 所以还可以通过在模式中写入通配符来选择多个扩展。 在使用通配符时，如果需要防止来自扩展通配符的shell，请小心的引用模式。任何由 pg_extension_config_dump 注册的配置关系被包括在转储中，如果他的扩展已经被 -−extension 指定了。注意当指定了 -e ，pg_dump不尝试转储任何所选扩展可能依赖的其他数据库对象。 也就是说，这里不保证指定扩展转储的结果能够由他们自己成功恢复到一个干净的数据库中。 -E encoding --encoding=encoding 以指定的字符集编码创建转储。 在默认情况下，该转储会以该数据库的编码创建（另一种得到相同结果的方式是将 PGCLIENTENCODING 环境变量设置成想要的转储编码）。 -f file --file=file 将输出发送到指定文件。对于基于输出格式的文件这个参数可以被忽略，在那种情况下将使用标准输出。不过对于目录输出格式必须给定这个参数，在目录输出格式中指定的是一个目录而不是一个文件。在这种情况中，该目录会由 pg_dump 创建并且不需要以前就存在。 -F format --format=format 选择输出的格式。format 可以是下列之一： p plain 输出一个纯文本形式的SQL脚本文件（默认值）。c custom 输出一个适合于作为pg_restore输入的自定义格式归档。和目录输出格式一起，这是最灵活的输出格式，它允许在恢复时手动选择和排序已归档的项。这种格式在默认情况还会被压缩。 d directory 输出一个适合作为pg_restore输入的目录格式归档。这将创建一个目录，其中每个被转储的表和大对象都有一个文件，外加一个所谓的目录文件，该文件以一种pg_restore能读取的机器可读格式描述被转储的对象。一个目录格式归档能用标准 Unix 工具操纵，例如一个未压缩归档中的文件可以使用gzip工具压缩。这种格式默认情况下是被压缩的并且也支持并行转储。t tar 输出一个适合于输入到pg_restore中的 tar -格式归档。tar 格式可以兼容目录格式，抽取一个 tar 格式的归档会产生一个合法的目录格式归档。不过，tar 格式不支持压缩。还有，在使用 tar 格式时，表数据项的相对顺序不能在恢复过程中被更改。 -j njobs --jobs=njobs 通过同时归档 njobs 个表来运行并行转储。这个选项可能会减少执行转储所需的时间，但也会增加数据库服务器上的负载。你只能和目录输出格式一起使用这个选项，因为这是唯一一种让多个进程能在同一时间写其数据的输出格式。pg_dump将打开 njobs + 1 个到该数据库的连接，因此确保你的 max_connections 设置足够高以容纳所有的连接。在运行一次并行转储时请求数据库对象上的排他锁可能导致转储失败。 其原因是，pg_dump领导者进程会在工作者进程将要稍后转储的对象上请求共享锁，以便确保在转储运行时不会有人删除它们并让它们出错。 如果另一个客户端接着请求一个表上的排他锁，那个锁将不会被授予但是会被排入队列等待领导者进程的共享锁被释放。 因此，任何其他对该表的访问将不会被授予或者将排在排他锁请求之后。 这包括尝试转储该表的工作者进程。如果没有任何防范措施，这可能会是一种经典的死锁情况。 要检测这种冲突，pg_dump工作者进程使用 NOWAIT 选项请求另一个共享锁。 如果该工作者进程没有被授予这个共享锁，其他某人必定已经在同时请求了一个排他锁并且没有办法继续转储，因此pg_dump除了中止转储之外别无选择。对于一个一致的备份，数据库服务器需要支持同步的快照，在IvorySQL的主服务器和10的后备服务器中引入了一种特性。 有了这种特性，即便数据库客户端使用不同的连接，也可以保证他们看到相同的数据集。 pg_dump -j 使用多个数据库连接，它用领导者进程连接到数据一次，并且为每一个工作者任务再一次连接数据库。 如果没有同步快照特征，在每一个连接中不同的工作者任务将不能被保证看到相同的数据，这可能导致一个不一致的备份。 -n pattern --schema=pattern 只转储匹配 pattern 的模式，这会选择模式本身以及它所包含的所有对象。当没有指定这个选项时，目标数据库中所有非系统模式都将被转储。多个模式可以通过书写多个 -n 开关来选择。另外， pattern 参数可以被解释为一种根据psql的`\\d`命令所用的相同规则（请参见下面的 Patterns）编写的模式，这样多个模式也可以通过在该模式中书写通配字符来选择。在使用通配符时，如果需要阻止 shell 展开通配符需要小心引用该模式，注意当 -n 被指定时，pg_dump不会尝试转储所选模式可能依赖的任何其他数据库对象。因此，无法保证一次指定模式转储的结果能够仅凭其本身被成功地恢复到一个干净的数据库中。注意当 -n 被指定时，非模式对象（如二进制大对象）不会被转储。你可以使用 --blobs 开关将二进制大对象加回到该转储中。 -N pattern --exclude-schema=pattern 不转储匹配 pattern 模式的任何模式。该模式被根据 -n 所用的相同规则被解释。-N 可以被给定多次来排除匹配几个模式中任意一个的模式。当 -n 和 -N 都被给定时，该行为是只转储匹配至少一个 -n 开关但是不匹配 -N 开关的模式。如果只有 -N 而没有 -n，那么匹配 -N 的模式会被从一个正常转储中排除。 -O --no-owner 不输出设置对象拥有关系来匹配原始数据库的命令。默认情况下，pg_dump会发出`ALTER OWNER` 或 SET SESSION AUTHORIZATION 语句来设置被创建的数据库对象的拥有关系。除非该脚本被一个超级用户（或是拥有脚本中所有对象的同一个用户）启动，这些语句都将会失败。要使一个脚本能够被任意用户恢复，但把所有对象的拥有关系都给这个用户，可指定 -O。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用`pg_restore`时指定该选项。 -R --no-reconnect 这个选项已经废弃，但是为了向后兼容仍然能被接受。 -s --schema-only 只转储对象定义（模式），而非数据。这个选项是 --data-only 的逆选项。它和指定 --section=pre-data --section=post-data 相似，但是由于历史原因又不完全相同。（不要把这个选项和 --schema 选项混淆，后者在“schema”的使用上有不同的含义）。要为数据库中表的一个子集排除表数据，见 --exclude-table-data。 -S username --superuser=username 指定要在禁用触发器时使用的超级用户的用户名。只有使用 --disable-triggers 时，这个选项才相关（通常，最好省去这个选项，而作为超级用户来启动结果脚本来取而代之）。 -t pattern --table=pattern 只转储名字匹配 pattern 的表。通过写多个 -t 开关可以选择多个表。另外，pattern 参数可以被解释为一种根据psql’s \\d 命令所用的相同规则编写的模式，这样多个表也可以通过在该模式中书写通配字符来选择。在使用通配符时，如果需要阻止 shell 展开通配符需要小心引用该模式，当 -t 被使用时，-n 和 -N 开关不会有效果，因为被 -t 选择的表将被转储而无视那些开关，并且非表对象将不会被转储。注意当 -t 被指定时，pg_dump不会尝试转储所选表可能依赖的任何其他数据库对象。因此，无法保证一次指定表转储的结果能够仅凭其本身被成功地恢复到一个干净的数据库中。 -T pattern --exclude-table=pattern 不转储匹配 pattern 模式的任何表。该模式被根据 -t 所用的相同规则被解释。-T 可以被给定多次来排除匹配几个模式中任意一个的模式。当 -t 和 -T 都被给定时，该行为是只转储匹配至少一个 -t 开关但是不匹配 -T 开关的表。如果只有 -T 而没有 -t，那么匹配 -T 的表会被从一个正常转储中排除。 -v --verbose 指定冗长模式。这将导致pg_dump向标准错误输出详细的对象注释以及转储文件的开始/停止时间，还有进度消息。 重复该选项会导致在标准错误上出现附加的调试级消息。 -V --version pg_dump版本并退出。 -x --no-privileges --no-acl 防止转储访问特权（授予/收回命令）。 -Z 0..9 --compress=0..9 指定要使用的压缩级别。零意味着不压缩。对于自定义和目录归档格式，这会指定个体表数据段的压缩，并且默认是进行中等级别的压缩。对于纯文本输出，设置一个非零压缩级别会导致整个输出文件被压缩，就好像它被gzip处理过一样，但是默认是不压缩。tar 归档格式当前完全不支持压缩。 --binary-upgrade 这个选项用于就地升级功能。我们不推荐也不支持把它用于其他目的。这个选项在未来的发行中可能被改变而不做通知。 --column-inserts --attribute-inserts 将数据转储为带有显式列名的 INSERT 命令（ INSERT INTO table (column, …​) VALUES …​）。这将使得恢复过程非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。重新加载期间的任何错误都将导致有问题的 INSERT 相关的行将丢失，而不是整个表内容。 --disable-dollar-quoting 这个选项禁止在函数体中使用美元符号引用，并且强制它们使用 SQL 标准字符串语法被引用。 --disable-triggers 只有在创建一个只转储数据的转储时，这个选项才相关。它指示pg_dump包括在数据被重新载入时能够临时禁用目标表上的触发器的命令。如果你在表上有引用完整性检查或其他触发器，并且你在数据重新载入期间不想调用它们，请使用这个选项。当前，为 --disable-triggers 发出的命令必须作为超级用户来执行。因此，你还应当使用 -S 指定一个超级用户名，或者宁可作为一个超级用户启动结果脚本。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 --enable-row-security 只有在转储具有行安全性的表的内容时，这个选项才相关。默认情况下， pg_dump将把 row_security 设置为 off 来确保从该表中转储 出所有的数据。如果用户不具有足够能绕过行安全性的特权，那么会抛出 一个错误这个参数指示pg_dump将 row_security 设置为 on，允许用户只转储该表中 它们能够访问到的部分内容。注意如果当前你使用了这个选项，你可能还想得到 INSERT 格式的转储，因为恢复期间的 COPY FROM 不支持行安全性。 --exclude-table-data=pattern 不转储匹配 pattern 模式的任何表中的数据。该模式根据 -t 的相同规则被解释。--exclude-table-data 可以被给定多次来排除匹配多个模式的表。当你需要一个特定表的定义但不想要其中的数据时，这个选项就有用了。要排除数据库中所有表的数据，见 --schema-only。 --extra-float-digits=ndigits 在转储浮点数据时使用规定的 extra_float_digits 值，而不是最大可用精度。以备份目的生成的常规转储不使用此选项。 --if-exists 时间条件性命令（即增加一个 IF EXISTS 子句）来清除数据库和其他对象。 只有同时指定了 --clean 时，这个选项才可用。 --include-foreign-data=`foreignserver` 使用与 foreignserver 模式匹配的外部服务器转储任何外部表的数据。可以通过编写 多个 --include-foreign-data 开关来选择多个外部服务器。 同样，根据psql’s \\d 命令使用的相同规则， 将 foreignserver 参数解释为模式。 因此也可以通过在模式中写入通配符来选择多个外部服务器。使用通配符时， 如果需要，请小心引用该模式，以防止Shell扩展通配符。 唯一的例外是不允许使用空模式。注意指定 --include-foreign-data 时，pg_dump不会检查外部表是否可写。因此，不能保证可以成功还原外部表转储的结果。 --inserts 将数据转储为 INSERT 命令（而不是 COPY ）。这将使得恢复非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。重新加载期间的任何错误都将导致有问题的 INSERT 相关的行将丢失，而不是整个表内容。注意如果你已经重新安排了列序，该恢复可能会一起失败。--column-inserts 选项对于列序改变是安全的，但是会更慢。 --load-via-partition-root 在为一个分区表转储数据时，让 COPY 语句或者 INSERT 语句把包含它的分区层次的根而不是分区自身作为目标。这导致在数据被装载时，会为每一个行重新确定合适的分区。如果在一台服务器上重新装载数据时会出现行并不是总是落入到和原始服务器上相同的分区中的情况，这个选项就很有用。例如，如果分区列是文本类型并且两个系统中用于排序分区列的排序规则有着不同的定义，就会发生这种情况。在从用这个选项制作的归档恢复时，最好不要使用并行，因为pg_restore将不能准确地知道一个给定的归档数据项将把数据装载到哪个分区中。这会导致效率不高，因为在并行任务见会有锁冲突，或者甚至可能由于在所有的相关数据被装载前建立了外键约束而导致重新装载失败。 --lock-wait-timeout=`timeout` 在转储的开始从不等待共享表锁的获得。而是在指定的 timeout 内不能锁定一个表时失败。超时时长可以用 SET statement_timeout 接受的任何格式指定。 --no-comments 不转储注释。 --no-publications 不转储publication。 --no-security-labels 不转储安全标签。 --no-subscriptions 不转储订阅。 --no-sync 默认情况下，pg_dump 将等待所有文件被安全地写入磁盘。这个选项会让 pg_dump 不等待直接返回，这样会更快，但是也意味着后续的一次操作系统崩溃会让该转储损坏。通常这个选项对测试有用，但是不应该在从生产安装中转储数据时使用。 --no-tablespaces 不要输出选择表空间的命令。通过这个选项，在恢复期间所有的对象都会被创建在任何作为默认的表空间中。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 --no-toast-compression 不要输出命令以设置 TOAST 压缩方法。 采用这个选项，所有列将按默认压缩设置进行恢复。 --no-unlogged-table-data 不转储非日志记录表的内容。这个选项对于表定义（模式）是否被转储没有影响，它只会限制转储表数据。当从一个后备服务器转储时，在非日志记录表中的数据总是会被排除。 --on-conflict-do-nothing 增加 ON CONFLICT DO NOTHING 到 INSERT commands。 除非规定了 --inserts, --column-inserts 或 --rows-per-insert ，否则此选项是无效的. --quote-all-identifiers 强制引用所有标识符。当从IvorySQL主版本与pg_dump不同的服务器上转储一个数据库时或者当输出准备载入到一个具有不同主版本的服务器时，推荐使用这个选项。默认情况下，pg_dump只对在其主版本中是被保留词的标识符加上引号。在转储其他版本服务器时，这种默认行为有时会导致兼容性问题，因为那些版本可能具有些许不同的被保留词集合。使用 --quote-all-identifiers 能阻止这种问题，但代价是转储脚本更难阅读。 --rows-per-insert=nrows 数据转储为 INSERT 命令（而不是 COPY ）。 控制每个 INSERT 命令的最大行数。 指定的值必须大于零。重新加载期间的任何错误都将导致有问题的`INSERT` 相关的行将丢失，而不是整个表内容。 --section=sectionname 只转储命名节。节的名称可以是 pre-data、 data 或 post-data。这个选项可以被指定多次来选择多个节。默认是转储所有节。数据节包含真正的表数据、大对象内容和序列值。数据后项包括索引、触发器、规则和除了已验证检查约束之外的约束的定义。数据前项包括所有其他数据定义项。 --serializable-deferrable 为转储使用一个 可序列化 事务，以保证所使用的快照与后来的数据库状态是一致的。但是这样做是在事务流中等待一个点，在该点上不能存在异常，这样就不会有转储失败或者导致其他事务带着 serialization_failure 回滚的风险。对于一个只为灾难恢复存在的转储，这个选项没什么益处。如果一个转储被用来在原始数据库持续被更新期间载入一份用于报表或其他只读负载的数据库拷贝时，这个选项就有所帮助。如果没有这个选项，转储可能会反映一个与最终提交事务的任何执行序列都不一致的状态。例如，如果使用了批处理技术，一个批处理在转储中可以显示为关闭，而其中的所有项都不出现。如果 pg_dump 被启动时没有读写事务在活动，则这个选项没有什么不同。如果有读写事务在活动，该转储的启动可能会被延迟一段不确定的时间。一旦开始运行，有没有这个开关的表现是相同的。 --snapshot=snapshotname 在做一个数据库的转储时指定一个同步的快照。在需要把转储和一个逻辑复制槽 或者一个并发会话同步时可以用上这个选项。在并行转储的情况下，将使用这个选项指定的快照名而不是取一个新快照。 --strict-names 要求每一个扩展( -e / --extension )，模式（ -n / --schema ）和表（ -t / --table ）限定符匹配要转储的数据库中至少一个扩展/模式/表。 注意，如果没有找到有这样的扩展/模式/表限定符匹配，即便没有 --strict-names ，pg_dump也将生成一个错误。这个选项对 -N / --exclude-schema、-T / --exclude-table 或者 --exclude-table-data 没有效果。无法匹配任何对象的排除模式不会被当作错误。 --use-set-session-authorization 输出 SQL-标准的 SET SESSION AUTHORIZATION 命令取代 ALTER OWNER 命令来确定对象的所有关系。这让该转储更加兼容标准，但是取决于该转储中对象的历史，该转储可能无法正常恢复。而且，一个使用 SET SESSION AUTHORIZATION 的转储将一定会要求超级用户特权来正确地恢复，而 ALTER OWNER 要求更少的特权。 -? --help 显示有关pg_dump命令行参数的帮助并退出。 下列命令行选项控制数据库连接参数。 -d dbname --dbname=dbname 指定要连接的数据库的名称。 这等效于在命令行上将 dbname 指定为第一个非选项参数。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖所有冲突的命令行选项。 -h host --host=host 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线，它被用作一个 Unix 域套接字的目录。默认是从 PGHOST 环境变量中取得（如果被设置），否则将尝试一次 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。默认是放在`PGPORT`环境变量中（如果被设置），否则使用编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_dump在连接到一个数据库之前提示要求一个口令。这个选项从来不是必须的，因为如果服务器要求口令认证，pg_dump将自动提示要求一个口令。但是，pg_dump将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下，值得键入`-W`来避免额外的连接尝试。 --role=rolename 指定一个用来创建该转储的角色名。这个选项导致pg_dump在连接到数据库后发出一个 SET ROLE rolename 命令。当已认证用户（由 -U 指定）缺少pg_dump所需的特权但是能够切换到一个具有所需权利的角色时，这个选项很有用。一些安装有针对直接作为超级用户登录的策略，使用这个选项可以让转储在不违反该策略的前提下完成。 PGDATABASE PGHOST PGOPTIONS PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。. 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 pg_dump在内部执行 SELECT 语句。如果你运行pg_dump时出现问题，确定你能够从正在使用的数据库中选择信息，例如 psql。此外，libpq前端-后端库所使用的任何默认连接设置和环境变量都将适用。 pg_dump的数据库活动会被统计收集器正常地收集。如果不想这样，你可以通过 PGOPTIONS 或 ALTER USER 命令设置参数 track_counts 为假。 如果你的数据库集簇对于 template1 数据库有任何本地添加，要注意将pg_dump的输出恢复到一个真正的空数据库。否则你很可能由于以增加对象的重复定义而得到错误。要创建一个不带任何本地添加的空数据库，从 template0 而不是 template1 复制它，例如： CREATE DATABASE foo WITH TEMPLATE template0; 当一个只含数据的转储被选中并且使用了选项 --disable-triggers 时，pg_dump在开始插入数据之前会发出命令禁用用户表上的触发器，并且接着在数据被插入之后发出命令重新启用它们。如果恢复中途被停止，系统目录可能会停留在一种错误状态。 pg_dump产生的转储文件不包含优化器用来做出查询计划决定的统计信息。因此，在从一个转储文件恢复后运行 ANALYZE 来确保最优性能是明智的。 在转储逻辑复制订阅时，pg_dump将生成使用 connect = false 选项的 CREATE SUBSCRIPTION 命令，这样恢复订阅时不会建立远程连接来创建复制槽或者进行初始的表拷贝。通过这种方式，可以无需到远程服务器的网络访问就能恢复该转储。然后就需要用户以一种合适的方式重新激活订阅。如果涉及到的主机已经改变，连接信息可能也必须被改变。在开启一次新的全表拷贝之前，截断目标表也可能是合适的。 要把一个数据库 mydb 转储到一个 SQL 脚本文件： $ pg_dump mydb > db.sql 要把这样一个脚本重新载入到一个（新创建的）名为 newdb 的数据库中： $ psql -d newdb -f db.sql 要转储一个数据库到一个自定义格式归档文件： $ pg_dump -Fc mydb > db.dump 要转储一个数据库到一个目录格式的归档： $ pg_dump -Fd mydb -f dumpdir 要用 5 个并行的工作者任务转储一个数据库到一个目录格式的归档： $ pg_dump -Fd mydb -j 5 -f dumpdir 要把一个归档文件重新载入到一个（新创建的）名为 newdb 的数据库： $ pg_restore -d newdb db.dump 把一个归档文件重新装载到同一个数据库（该归档正是从这个数据库中转储得来）中，丢掉那个数据库中的当前内容： $ pg_restore -d postgres --clean --create db.dump 要转储一个名为 mytab 的表： $ pg_dump -t mytab mydb > db.sql 要转储 detroit 模式中名称以 emp 开始的所有表，排除名为 employee_log 的表： $ pg_dump -t 'detroit.emp*' -T detroit.employee_log mydb > db.sql 要转储名称以 east 或者 west 开始并且以 gsm 结束的所有模式，排除名称包含词`test`的任何模式： $ pg_dump -n 'east*gsm' -n 'west*gsm' -N '*test*' mydb > db.sql 同样，用正则表达式记号法来合并开关： $ pg_dump -n '(east|west)*gsm' -N '*test*' mydb > db.sql 要转储除了名称以 ts_ 开头的表之外的所有数据库对象： $ pg_dump -T 'ts_*' mydb > db.sql 要在 -t 和相关开关中指定一个大写形式或混合大小写形式的名称，你需要双引用该名称，否则它会被折叠到小写形式 。但是双引号对于 shell 是特殊的，所以反过来它们必须被引用。因此， 要转储一个有混合大小写名称的表，你需要类似这样的东西： $ pg_dump -t \"\\\"MixedCaseName\\\"\" mydb > mytab.sql pg_dumpall — 将一个IvorySQL数据库集簇抽取到一个脚本文件中 pg_dumpall [connection-option…​] [option…​] pg_dumpall工具可以一个集簇中所有的IvorySQL数据库写出到（“转储”）一个脚本文件。该脚本文件包含可以用作 psql 的输入SQL命令来恢复数据库。它会对集簇中的每个数据库调用 pg_dump 来完成该工作。pg_dumpall还转储对所有数据库公用的全局对象（ pg_dump 不保存这些对象），也就是说数据库角色和表空间都会被转储。 目前这包括适数据库用户和组、表空间以及适合所有数据库的访问权限等属性。 因为pg_dumpall从所有数据库中读取表，所以你很可能需要以一个数据库超级用户的身份连接以便生成完整的转储。同样，你也需要超级用户特权执行保存下来的脚本，这样才能增加角色和组以及创建数据库。 SQL 脚本将被写出到标准输出。使用 -f / --file 选项或者 shell 操作符可以把它重定向到一个文件。 pg_dumpall需要多次连接到IvorySQL服务器（每个数据库一次）。如果你使用口令认证，可能每次都会要求口令。这种情况下使用一个 ~/.pgpass 会比较方便。 下列命令行选项用于控制输出的内容和格式。 -a --data-only 只转储数据，不转储模式（数据定义）。 -c --clean 包括在重建数据库之前清除（移除）它们的 SQL 命令。角色和表空间的 DROP 命令也会被加入进来。 -E encoding --encoding=encoding 用指定的字符集编码创建转储。默认情况下，转储使用数据库的编码创建（另一种得到相同结果的方法是设置 PGCLIENTENCODING 环境变量为想要的转储编码）。 -f filename --file=filename 将输出发送到指定的文件中。如果省略，将使用标准输出。 -g --globals-only 只转储全局对象（角色和表空间），而不转储数据库。 -O --no-owner 不输出用于设置对象所有权以符合原始数据库的命令。默认情况下，pg_dumpall发出 ALTER OWNER 或 SET SESSION AUTHORIZATION 语句来设置被创建的模式元素的所有权。除非脚本是由一个超级用户（或者是拥有脚本中所有对象的同一个用户）所运行，这些语句在脚本运行时会失败。要使得一个脚本能被任意用户恢复，但又不想给予该用户所有对象的所有权，可以指定 -O。 -r --roles-only 只转储角色，不转储数据库和表空间。 -s --schema-only 只转储对象定义（模式），不转储数据。 -S username --superuser=username 指定要在禁用触发器时使用的超级用户的用户名。只有使用 --disable-triggers 时，这个选项才相关（通常，最好省去这个选项，而作为超级用户来启动结果脚本来取而代之）。 -t --tablespaces-only 只转储表空间，不转储数据库和角色。 -v --verbose 指定细节模式。这将导致pg_dumpall向标准错误输出详细的对象注释以及转储文件的开始/停止时间，还有进度消息。 重复该选项会导致在标准错误上出现附加的调试级信息。 这个选项还会被传递到pg_dump。 -V --version 打印pg_dumpall版本并退出。 -x --no-privileges --no-acl 防止转储访问特权（授予/收回命令）。 --binary-upgrade 这个选项用于就地升级功能。我们不推荐也不支持把它用于其他目的。这个选项在未来的发行中可能被改变而不做通知。 --column-inserts --attribute-inserts 将数据转储为带有显式列名的 INSERT 命令（INSERT INTO table(column, …​) VALUES …​）。这将使得恢复过程非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。 --disable-dollar-quoting 这个选项禁止在函数体中使用美元符号引用，并且强制它们使用 SQL 标准字符串语法被引用。 --disable-triggers 只有在创建一个只转储数据的转储时，这个选项才相关。它指示pg_dumpall包括在数据被重新载入时能够临时禁用目标表上的触发器的命令。如果你在表上有引用完整性检查或其他触发器，并且你在数据重新载入期间不想调用它们，请使用这个选项。当前，为 --disable-triggers 发出的命令必须作为超级用户来执行。因此，你还应当使用 -S 指定一个超级用户名，或者宁可作为一个超级用户启动结果脚本。 --exclude-database=`pattern` 不要转储名字与 pattern 匹配的数据库。可以通过编写多个 --exclude-database 开关来排除多个模式。 pattern 参数被解释为模式，根据psql的 \\d 命令使用的相同规则 ，因此，通过在模式中编写通配符也可以排除多个数据库。 使用通配符时，请谨慎的引用模式，如果需要防止shell通配符扩展。 --extra-float-digits=ndigits 在转储浮点数据时使用extra_float_digits规定的值，而不是最大可用精度。备份目的进行的常规转储不使用此选项。 --if-exists 时间条件性命令（即增加一个 IF EXISTS 子句）来清除数据库和其他对象。 只有同时指定了 --clean 时，这个选项才可用。 --inserts 将数据转储为 INSERT 命令（而不是 COPY ）。这将使得恢复非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。注意如果你已经重新安排了列序，该恢复可能会一起失败。--column-inserts 选项对于列序改变是安全的，但是会更慢。 --load-via-partition-root 在为一个分区表转储数据时，让 COPY 语句或者 INSERT 语句把包含它的分区层次的根而不是分区自身作为目标。这导致在数据被装载时，会为每一个行重新确定合适的分区。如果在一台服务器上重新装载数据时会出现行并不是总是落入到和原始服务器上相同的分区中的情况，这个选项就很有用。例如，如果分区列是文本类型并且两个系统中用于排序分区列的排序规则有着不同的定义，就会发生这种情况。 --lock-wait-timeout=timeout 在转储的开始从不等待共享表锁的获得。 而是在指定的 timeout 内不能锁定一个表时失败。 超时时长可以用 SET statement_timeout 接受的任何格式指定。 --no-comments 不转储注释。 --no-publications 不转储publication。 --no-role-passwords 不为角色转储口令。在恢复完后，角色的口令将是空口令，并且在设置口令之前口令认证都不会成功。由于指定这个选项时并不需要口令值，角色信息将从目录视图 pg_roles 而不是 pg_authid 中读出。因此，如果对 pg_authid 的访问被某条安全性策略所限制，那么这个选项也会有所帮助。 --no-security-labels 不转储安全标签。 --no-subscriptions 不转储subscription。 --no-sync 默认情况下，pg_dumpall 将等待所有文件被安全地写入到磁盘。这个选项会让 pg_dumpall 不做这种等待而返回，这样会更快，但是意味着后续的操作系统崩溃可能留下被损坏的转储。通常来说，这个选项对测试有用，但不应该在从生产安装中转储数据时使用。 --no-tablespaces 不要输出选择表空间的命令。通过这个选项，在恢复期间所有的对象都会被创建在任何作为默认的表空间中。 -−no-toast-compression 不要输出命令以设置 TOAST 压缩方法。 采用这个选项，所有列将会以默认压缩设置来恢复。 --no-unlogged-table-data 不转储非日志记录表的内容。这个选项对于表定义（模式）是否被转储没有影响，它只会限制转储表数据。 --on-conflict-do-nothing 添加 ON CONFLICT DO NOTHING 到 INSERT 命令。 除非 --inserts 或 --column-inserts 也被规定，否则此选项不生效。 --quote-all-identifiers 强制引用所有标识符。在从一个与pg_dumpall主版本不同的IvorySQL服务器转储数据库时或者要将输出载入到一个不同主版本的服务器时，推荐使用这个选项。默认情况下，pg_dumpall只会对为其主版本中保留词的标识符加上引号。在与其他版本的具有不同保留词集合的服务器交互时，这有时会导致兼容性问题。使用 --quote-all-identifiers 可以阻止这类问题，但是代价是转储脚本会更加难读。 --rows-per-insert=nrows 将数据转储为 INSERT 命令（而不是 COPY ）。控制每个 INSERT 命令的最大行数。 指定的值必须是大于零的数。重新加载期间的任何错误都将导致仅丢失有问题的 INSERT 的行，而不是整个表内容。 --use-set-session-authorization 输出 SQL-标准的 SET SESSION AUTHORIZATION 命令取代 ALTER OWNER 命令来确定对象的所有关系。这让该转储更加兼容标准，但是取决于该转储中对象的历史，该转储可能无法正常恢复。 -? --help 显示有关pg_dumpall命令行参数的帮助并退出。 下列命令行选项控制数据库连接参数。 -d connstr --dbname=connstr 指定用于连接到服务器的参数，比如 连接字符串；这些将覆盖所有冲突的命令行选项。这个选项被称为 --dbname 是为了和其他客户端应用一致，但是因为pg_dumpall需要连接多个数据库，连接字符串中的数据库名将被忽略。使用 -l 选项指定一个数据库，该数据库被用于初始连接，这将转储全局对象并且发现需要转储哪些其他数据库。 -h host --host=host 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线，它被用作一个 Unix 域套接字的目录。默认是从 PGHOST 环境变量中取得（如果被设置），否则将尝试一次 Unix 域套接字连接。 -l dbname --database=dbname 指定要连接到哪个数据库转储全局对象以及发现要转储哪些其他数据库。如果没有指定，将会使用 postgres 数据库，如果 postgres 不存在，就使用 template1。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。默认是放在 PGPORT 环境变量中（如果被设置），否则使用编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_dumpall在连接到一个数据库之前提示要求一个口令。这个选项从来不是必须的，因为如果服务器要求口令认证，pg_dumpall将自动提示要求一个口令。但是，pg_dumpall将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下，值得键入 -W 来避免额外的连接尝试。注意对每个要被转储的数据库，口令提示都会再次出现。通常，最好设置一个 ~/.pgpass 文件来减少手工口令输入。 --role=`rolename` 指定一个用来创建该转储的角色名。这个选项导致pg_dump在连接到数据库后发出一个 SET ROLE rolename 命令。当已认证用户（由 -U 指定）缺少pg_dump所需的特权但是能够切换到一个具有所需权利的角色时，这个选项很有用。一些安装有针对直接作为超级用户登录的策略，使用这个选项可以让转储在不违反该策略的前提下完成。 PGHOST PGOPTIONS PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 因为pg_dumpall在内部调用pg_dump，所以， 一些诊断消息可以参考pg_dump。 即使当用户的目的是把转储脚本恢复到一个空的集簇中，--clean 选项也有用武之地。--clean 的使用让该脚本删除并且重建内建的 postgres 和 template1 数据库，确保这两个数据库保持与源集簇中相同的属性（例如locale和编码）。如果不用这个选项，这两个数据库将保持它们现有的数据库级属性以及任何已有的内容。 一旦恢复，建议在每个数据库上运行 ANALYZE，这样优化器就可以得到有用的统计信息。你也可以运行 vacuumdb -a -z 来分析所有数据库。 不应该预期转储脚本运行到结束都不出错。特别是由于脚本将为源集簇中已有的每一个角色发出 CREATE ROLE 语句，对于bootstrap超级用户当然会得到一个“role already exists”错误，除非目标集簇用一个不同的bootstrap超级用户名完成的初始化。这种错误是无害的并且应该被忽略。--clean 选项的使用很可能会产生额外的有关于不存在对象的无害错误消息，不过可以通过加上 --if-exists 减少这类错误消息。 pg_dumpall要求所有需要的表空间目录在进行恢复之前就必须存在；否则，数据库创建就会由于在非默认位置创建数据库而失败。 要转储所有数据库： $ pg_dumpall > db.out 要从这个文件重新载入数据库，你可以使用： $ psql -f db.out postgres 这里你连接哪一个数据库并不重要，因为由pg_dumpall创建的脚本将包含合适的命令来创建和连接到被保存的数据库。一个例外是，如果指定了 --clean，则开始时必须连接到 postgres 数据库，该脚本将立即尝试删除其他数据库，并且这种动作对于已连接上的这个数据库将会失败。 pg_isready — 检查一个IvorySQL服务器的连接状态 pg_isready [connection-option…​] [option…​] -d dbname --dbname=dbname 指定要连接的数据库名。dbname 可以是连接字符串。如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -h hostname --host=hostname 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。默认值取自 PGPORT 环境变量。如果环境变量没有设置，则默认值使用编译时指定的端口（通常是 5432）。 -q --quiet 不显示状态消息。当脚本编程时有用。 -t seconds --timeout=seconds 尝试连接时，在返回服务器不响应之前等待的最大秒数。设置为 0 则禁用。默认值是 3 秒。 -U username --username=username 作为用户 username 连接数据库，而不是用默认用户。 -V --version 打印pg_isready版本并退出。 -? --help 显示有关pg_isready命令行参数的帮助并退出。 如果服务器正常接受连接，pg_isready返回 0 给 shell；如果服务器拒绝连接（例如处于启动阶段）则返回 1；如果连接尝试没有被相应则返回 2；如果没有尝试（例如由于非法参数）则返回 3。 和大部分其他IvorySQL工具相似，pg_isready 也使用libpq支持的环境变量。 环境变量 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 要获得服务器状态，不一定需要提供正确的用户名、口令或数据库名。 不过，如果提供了不正确的值，服务器将会记录一次失败的连接尝试。 标准用法： $ pg_isready /tmp:5432 - accepting connections $ echo $? 0 使用连接参数运行连接到处于启动中的IvorySQL集簇： $ pg_isready -h localhost -p 5433 localhost:5433 - rejecting connections $ echo $? 1 使用连接参数运行连接到无响应的IvorySQL集簇： $ pg_isready -h someremotehost someremotehost:5432 - no response $ echo $? 2 pg_receivewal — 以流的方式从一个IvorySQL服务器得到预写式日志 pg_receivewal [option…​] -D directory --directory=directory 要把输出写到哪个目录。这个参数是必需的。 -E lsn --endpos=lsn 当接收到达指定的LSN时，自动停止复制并且以正常退出状态0退出。如果有一个记录的LSN正好等于 lsn，则该记录将会被处理。 --if-not-exists 当指定 --create-slot 并且具有指定名称的槽已经存在时不要抛出错误。 -n --no-loop 不要在连接错误上循环。相反，碰到一个错误时立刻退出。 --no-sync 这个选项导致 pg_receivewal 不强制WAL数据被刷回磁盘。这样会更快，但是也意味着接下来的操作系统崩溃会让WAL段损坏。通常，这个选项对于测试有用，但不应该在对生产部署进行WAL归档时使用。这个选项与 --synchronous 不兼容。 -s interval --status-interval=interval 指定发送回服务器的状态包之间的秒数。这允许我们更容易地监控服务器的进度。 一个零值完全禁用这种周期性的状态更新，不过当服务器需要时还是会有一个更新 会被发送来避免超时导致的断开连接。默认值是 10 秒。 -S slotname --slot=slotname 要求pg_receivewal使用一个已有的复制槽。在使用这个选项时， pg_receivewal将会报告给服务器一个刷写位置，指示每一个 段是何时被同步到磁盘的，这样服务器可以在不需要该段时移除它。当pg_receivewal的复制客户端在服务器 上被配置为一个同步后备时，那么使用一个复制槽将会向服务器报告刷写 位置，但只在一个 WAL 文件被关闭时报告。因此，该配置将导致主服务 器上的事务等待很长的时间并且无法令人满意地工作。要让这种配置工作 正确，还必须制定选项 --synchronous （见下文）。 --synchronous 在 WAL 数据被收到后立即刷入到磁盘。还要在刷写后立即向服务器回送 一个状态包（不考虑 --status-interval ）。如果pg_receivewal的复制客户端在服务器 上被配置为一个同步后备，应该指定这个选项来确保向服务器发送及时的反馈。 -v --verbose 启用冗长模式。 -Z level --compress=level 启用预写式日志上的gzip压缩，并且指定压缩级别（0到9，0是不压缩而9是最大压缩）。所有的文件名后都将被追加后缀 .gz。 下列命令行选项控制数据库连接参数。 -d connstr --dbname=connstr 指定用于连接到服务器的参数，作为 连接字符串；这些将覆盖所有冲突的命令行选项。为了和其他客户端应用一致，该选项被称为 --dbname。但是因为pg_receivewal并不连接到集簇中的任何特定数据库，连接字符串中的数据库名将被忽略。 -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。默认值取自 PGHOST 环境变量（如果设置），否则会尝试一个 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。默认用 PGPORT 环境变量中的值（如果设置），或者一个编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_receivewal在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，pg_receivewal将自动提示要求一个口令。但是，pg_receivewal将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 为了控制物理复制槽，pg_receivewal 可以执行下列两种动作之一： --create-slot 用 --slot 中指定的名称创建一个新的物理复制槽， 然后退出。 --drop-slot 删除 --slot 中指定的复制槽，然后退出。 其他选项也可用： -V --version 打印pg_receivewal版本并退出。 -? --help 显示有关pg_receivewal命令行参数的帮助并退出。 在被SIGINT信号终止（没有正常的方式结束它。因此这不是一种错误）时，pg_receivewal将以状态0退出。 对于致命错误或者其他信号，退出状态将不是零。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 环境变量 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 在使用pg_receivewal替代 archive_command 作为主要的 WAL 备份方法时， 强烈建议使用复制槽。否则，服务器可能会在预写式日志文件被备份好之前重用 或者移除它们，因为没有任何信息（不管是来自 archive_command 或是复制槽）能够指示 WAL 流已 经被归档到什么程度。不过要注意，如果接收者没有持续地取走 WAL 数据， 一个复制槽将会填满服务器的磁盘空间。 如果在源集簇上启用了组权限，pg_receivewal将保留接收到的WAL文件上的组权限。 要从位于 mydbserver 的服务器流式传送预写式日志并且将它存储在本地目录`/usr/local/pgsql/archive`： $ pg_receivewal -h mydbserver -D /usr/local/pgsql/archive pg_recvlogical — 控制 IvorySQL 逻辑解码流 pg_recvlogical [option…​] 必须至少要指定下列选项之一来选择一个动作： --create-slot 为 --dbname 指定的数据库用 --slot 指定的名称创建一个新的逻辑复制槽，使用 --plugin 指定的输出插件。 --drop-slot 删除名称由 --slot 指定的复制槽，然后退出。 --start 从 --slot 指定的逻辑复制槽开始进行流式传送更改，一直继续 到被一个信号终止。如果服务器端关机或者断开连接导致更改流结束，会进入一个 循环一直重试，通过指定 --no-loop 可以防止这种情况下进入循环重试。流格式由槽创建时指定的输出插件决定。连接必须是连接到用于创建该槽的同一个数据库上。 --create-slot 和 --start 可以被一起指定。 --drop-slot 不能和另一个动作组合在一起。 下面的命令行选项控制输出的位置和格式以及其他复制行为： -E `lsn` --endpos=`lsn` 在 --start 模式中，当接收过程到达指定的LSN时会自动地停止复制并且以正常的退出状态0退出。如果不处于 --start 模式时指定这个选项，则会发生错误。如果有一个记录的LSN正好等于 lsn，则该记录将被输出。--endpos 不会察觉到事务边界并且可能会在一个事务中间截断输出。任何部分输出的事务都将不会被消费，并且在下一次从该槽中读取时将会重放该事务。单个的消息不会被截断。 -f filename --file=filename 把接收到并且解码好的事务数据写入到一个文件。使用 - 可以写到stdout。 -F interval_seconds --fsync-interval=interval_seconds 指定pg_recvlogical发出 fsync() 调用确保输出文件被安全地刷到磁盘的频度。服务器将会偶尔要求客户端执行一次刷写并且把刷写位置报告给服务器。 这个设置可以在此之外更加频繁地执行刷写。指定间隔为 0 会完全禁止发出 fsync() 调用，但是仍会报告进度给服务器。在这种情况下，发生崩溃会导致数据丢失。 -I lsn --startpos=lsn 在 --start 模式中，从给定的 LSN 开始复制。 --if-not-exists 当指定 --create-slot 并且具有指定名称的槽已经存在时不要抛出错误。 -n --no-loop 当服务器连接丢失时，不要在循环中重试，直接退出。 -o name [=value] --option=name[=value] 如果指定了输出插件，把选项值 value 传递给选项 name。存在哪些选项以及它们的效果 取决于使用的输出插件。 -P plugin --plugin=plugi 在创建一个槽时使用指定的逻辑解码输出插件。如果该槽已经存在，这个选项没有效果。 -s interval_seconds --status-interval=interval_seconds 这个选项和 pg_receivewal 中的同名选项具有相同的效果。请参考那里的描述。 -S slot_name --slot=slot_name 在 --start 模式中，使用名为 slot_name 的已有逻辑复制槽。在 --create-slot 模式中，使用这个名称 创建该槽。在 --drop-slot 模式中，删除这个名称指定的槽。 -v --verbose 开启详细输出模式。 下列命令行选项控制数据库连接参数。 -d database --dbname=database 要连接的数据库。这个选项的详细含义请见动作的描述。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。默认为用户名。 -h hostname-or-ip --host=hostname-or-ip 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线， 它被用作一个 Unix 域套接字的目录。默认是从 `PGHOST`环境变量中取得（如果被设置）， 否则将尝试一次 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。 默认是放在 PGPORT 环境变量中（如果被设置）， 否则使用编译在程序中的默认值。 -U user --username=user 要作为哪个用户连接。默认是用当前操作系统用户名。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有 其他方式提供口令（例如一个 .pgpass 文件）， 那么连接尝试将失败。这个选项对于批处理任务和脚本有用， 因为在其中没有一个用户来输入口令。 -W --password 强制pg_dump在连接到一个数据库之前提示要求一个口令。这个选项不是必须的，因为如果服务器要求口令认证， pg_dump将自动提示要求一个口令。 但是，pg_dump将浪费一次连接尝试 来发现服务器想要一个口令。在某些情况下，值得键入 -W 来避免额外的连接尝试。 还有下列附加选项可用： -V --version 打印pg_recvlogical的版本并且退出。 -? --help 显示关于pg_recvlogical命令行参数的帮助，并且退出。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 环境变量`PG_COLOR`规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 如果在源服务器上启用了组权限，pg_recvlogical将会在接收到的WAL文件上保留组权限。 pg_restore — 从一个由pg_dump创建的归档文件恢复一个IvorySQL数据库 pg_restore [connection-option…​] [option…​] [filename] pg_restore接受下列命令行参数。 filename 指定要被恢复的归档文件（对于一个目录格式的归档则是目录）的位置。如果没有指定，则使用标准输入。 -a --data-only 只恢复数据，不恢复模式（数据定义）。如果在归档中存在，表数据、大对象和序列值会被恢复。这个选项类似于指定 --section=data，但是由于历史原因两者不完全相同。 -c --clean 在重新创建数据库对象之前清除（丢弃）它们（除非使用了 --if-exists，如果有对象在目标数据库中不存在，这可能会生成一些无害的错误消息）。 -C --create 在恢复一个数据库之前县创建它。如果还指定了 --clean，在连接到目标数据库之前丢弃并且重建它。如果使用 --create，pg_restore还会恢复数据库的注释（如果有）以及与其相关的配置变量设置，也就是任何提到过这个数据库的 ALTER DATABASE …​ SET …​ 和 ALTER ROLE …​ IN DATABASE …​ SET …​ 命令。不管是否指定 --no-acl，数据库本身的访问特权都会被恢复。在使用这个选项时，-d 提到的数据库只被用于发出初始的 DROP DATABASE 和 CREATE DATABASE 命令。所有要恢复到该数据库名中的数据都出现在归档中。 -d dbname --dbname=dbname 连接到数据库 dbname 并且直接恢复到该数据库中。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -e --exit-on-error 在发送 SQL 命令到该数据库期间如果碰到一个错误就退出。默认行为是继续并且在恢复结束时显示一个错误计数。 -f filename --file=filename 为生成的脚本指定输出文件，或在与 -l 选项一起使用时为列表指定输出文件。为 stdout用 -。 -F format --format=format 指定归档的格式。并不一定要指定该格式，因为pg_restore将会自动决定格式。如果指定，可以是下列之一：c custom 归档是pg_dump的自定义格式。d directory 归档是一个目录归档。t tar 归档是一个 tar 归档。 -I index --index=index 只恢复提及的索引的定义。可以通过写多个 -I 开关指定多个索引。 -j number-of-jobs --jobs=number-of-jobs 使用并发任务运行pg_restore中最耗时的步骤 — 载入数据、创建索引或者创建约束； 同时，最多使用 number-of-jobs 个并发会话。对于一个运行在多处理器机器上的服务器， 这个选项能够大幅度减少恢复一个大型数据库的时间。当发出脚本而不是直接连接到数据库服务器时，将忽略此选项。每一个任务是一个进程或者一个线程，这取决于操作系统，它们都使用一个独立的服务器连接。这个选项的最佳值取决于服务器、客户端以及网络的硬件设置。因素包括 CPU 的核心数和磁盘设置。一个好的建议是服务器上 CPU 的核心数，但是更大的值在很多情况下也能导致更快的恢复时间。当然，过高的值会由于超负荷反而导致性能降低。这个选项只支持自定义和目录归档格式。输入必须是一个常规文件或目录（例如，不能是一个管道或者标准输出）。还有，多任务不能和选项 --single-transaction 一起用。 -l --list 列出归档的内容的表格。这个操作的输出能被用作 -L 选项的输入。注意如果把 -n 或 -t 这样的过滤开关与 -l 一起使用，它们将会限制列出的项。 -L list-file --use-list=list-file 只恢复在 list-file 中列出的归档元素，并且按照它们出现在该文件中的顺序进行恢复。注意如果把 -n 或 -t 这样的过滤开关与 -L 一起使用，它们将会进一步限制要恢复的项。list-file 通常是编辑一个 -l 操作的输出来创建。行可以被移动或者移除，并且也可以通过在行首放一个（ ; ）将其注释掉。例子见下文。 -n schema --schema=schema 只恢复在被提及的模式中的对象。可以用多个 -n 开关来指定多个模式。这可以与`-t`选项组合在一起只恢复一个指定的表。 -N schema --exclude-schema=schema 不恢复所提及方案中的对象。可以用多个 -N 开关指定多个要被排除的方案。如果对同一个方案名称同时给出了 -n 和 -N，则 -N 会胜出并且该方案会被排除。 -O --no-owner 不要输出将对象的所有权设置为与原始数据库匹配的命令。默认情况下，pg_restore会发出 ALTER OWNER 或者 SET SESSION AUTHORIZATION 语句来设置已创建的模式对象的所有权。除非到该数据库的初始连接是一个超级用户（或者拥有脚本中所有对象的同一个用户）建立的，这些语句将会失败。通过 -O，任何用户名都可以被用于初始连接，并且这个用户将会拥有所有被创建的对象。 -P function-name(argtype [, …​]) --function=function-name(argtype [, …​]) 只恢复被提及的函数。要小心地拼写函数的名称和参数使它们正好就是出现在转储文件的内容表中的名称和参数。可以使用多个 -P 开关指定多个函数。 -R --no-reconnect 这个选项已被废弃，但是出于向后兼容性的目的，系统仍然还接受它。 -s --schema-only 只恢复归档中的模式（数据定义）不恢复数据。这个选项是 --data-only 的逆选项。它与指定 --section=pre-data --section=post-data 相似，但是由于历史原因并不完全相同。（不要把这个选项和 --schema 选项弄混，后者把词“schema”用于一种不同的含义）。 -S username --superuser=username 指定在禁用触发器时要用的超级用户名。只有使用 --disable-triggers 时这个选项才相关。 -t table --table=table 只恢复所提及的表的定义和数据。出于这个目的，“table”包括视图、物化视图、序列和外部表。可以写上多个 -t 开关可以选择多个表。这个选项可以和 -n 选项结合在一起指定一个特定模式中的表。注意在指定 -t 时，pg_restore不会尝试恢复所选表可能依赖的任何其他数据库对象。因此，无法确保能成功地把一个特定表恢复到一个干净的数据库中。注意这个标志的行为和pg_dump的 -t 标志不一样。在pg_restore中当前没有任何通配符匹配的规定，也不能在其 -t 选项中包括模式的名称。而且，虽然pg_dump的 -t 标志也会转储选中表的附属对象（例如索引），但是pg_restore的 -t 标志不包括这些附属对象。 -T trigger --trigger=trigger 只恢复所提及的触发器。可以用多个 -T 开关指定多个触发器。 -v --verbose 指定冗长模式。 这将导致pg_restore输出详细的对象注释和启动/停止时间到输出文件，并将推送消息输出到标准错误。 重复该选项会造成附加的调试级消息出现在标准报错上。 -V --version 打印该pg_restore的版本并退出。 -x --no-privileges --no-acl 阻止恢复访问特权（授予/收回命令）。 -1 --single-transaction 将恢复作为单一事务执行（即把发出的命令包裹在 BEGIN / COMMIT 中）。这可以确保要么所有命令完全成功，要么任何改变都不被应用。这个选项隐含了 --exit-on-error。 --disable-triggers 只有在执行一个只恢复数据的恢复时，这个选项才相关。它指示pg_restore在装载数据时执行命令临时禁用目标表上的触发器。如果你在表上有参照完整性检查或者其他触发器并且你不希望在数据载入期间调用它们时，请使用这个选项。目前，为 --disable-triggers 发出的命令必须以超级用户身份完成。因此你还应该用`-S`指定一个超级用户名，或者更好的方法是以一个IvorySQL超级用户运行pg_restore。 --enable-row-security 只有在恢复具有行安全性的表的内容时，这个选项才相关。默认情况下，pg_restore将把 row_security 设置为 off 来确保所有数据都被恢复到表中。如果用户不拥有足够绕过行安全性的特权，那么会抛出一个错误。这个参数指示pg_restore把 row_security 设置为on允许用户尝试恢复启用了行安全性的表的内容。如果用户没有从转储向表中插入行的权限，这仍将失败。注意当前这个选项还要求转储处于 INSERT 格式，因为 COPY FROM 不支持行安全性。 --if-exists 使用条件命令（即增加一个 IF EXISTS 子句）删除数据库对象。只有指定了 --clean 时，这个选项才有效。 --no-comments 即便归档中包含注释也不输出恢复注释的命令。 --no-data-for-failed-tables 默认情况下，即便表的创建命令失败（例如因为表已经存在），表数据也会被恢复。通过这个选项，对这类表的数据会被跳过。如果目标数据库已经包含了想要的表内容，这种行为又很有有用。例如，IvorySQL扩展（如PostGIS）的辅助表可能已经被载入到目标数据库中，指定这个选项就能阻止把重复的或者废弃的数据载入到这些表中。只有当直接恢复到一个数据库中时这个选项才有效，在产生 SQL脚本输出时这个选项不会产生效果。 --no-publications 即便归档中包含publication也不输出恢复publication的命令。 --no-security-labels 不要输出恢复安全标签的命令，即使归档中包含安全标签。 --no-subscriptions 即便归档中包含subscription也不输出恢复subscription的命令。 --no-tablespaces 不输出命令选择表空间。通过这个选项，所有的对象都会被创建在恢复时的默认表空间中。 --section=`sectionname` 只恢复提及的小节。小节的名称可以是 pre-data、data 或者 post-data。可以把这个选项指定多次来选择多个小节。默认值是恢复所有小节。数据小节包含实际的表数据以及大对象定义。Post-data 项由索引定义、触发器、规则和除已验证的检查约束之外的约束构成。Pre-data 项由所有其他数据定义项构成。 --strict-names 要求每一个模式（ -n / --schema）以及表（ -t / --table）限定词匹配备份文件中至少一个模式/表。 --use-set-session-authorization 输出 SQL 标准的 SET SESSION AUTHORIZATION 命令取代 ALTER OWNER 命令来决定对象拥有权。这会让转储更加兼容标准，但是依赖于转储中对象的历史，可能无法正确恢复。 -? --help 显示有关pg_restore命令行参数的帮助，并且退出。 pg_restore也接受下列用于连接参数的命令行参数： -h host --host=host 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线，它被用作一个 Unix 域套接字的目录。默认是从 PGHOST 环境变量中取得（如果被设置），否则将尝试一次 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。默认是放在 PGPORT 环境变量中（如果被设置），否则使用编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_restore在连接到一个数据库之前提示要求一个口令。这个选项不是必须的，因为如果服务器要求口令认证，pg_restore将自动提示要求一个口令。但是，pg_restore将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下，值得键入 -W 来避免额外的连接尝试。 --role=`rolename` 指定一个用来创建该转储的角色名。这个选项导致pg_restore在连接到数据库后发出一个 SET ROLE rolename 命令。当已认证用户（由 -U 指定）缺少pg_restore所需的特权但是能够切换到一个具有所需权利的角色时，这个选项很有用。一些安装有针对直接作为超级用户登录的策略，使用这个选项可以让转储在不违反该策略的前提下完成。 PGHOST PGOPTIONS PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 当使用 -d 选项指定一个直接数据库连接时，pg_restore在内部执行 SELECT 语句。如果你运行pg_restore时出现问题，确定你能够从正在使用的数据库中选择信息，例如 psql。此外，libpq前端-后端库所使用的任何默认连接设置和环境变量都将适用。 如果你的数据库集簇对于 template1 数据库有任何本地添加，要注意将pg_restore的输出载入到一个真正的空数据库。否则你很可能由于以增加对象的重复定义而得到错误。要创建一个不带任何本地添加的空数据库，从 template0 而不是 template1 复制它，例如： CREATE DATABASE foo WITH TEMPLATE template0; 下面将详细介绍pg_restore的局限性。 在恢复数据到一个已经存在的表中并且使用了选项 --disable-triggers 时，pg_restore会在插入数据之前发出命令禁用用户表上的触发器，然后在完成数据插入后重新启用它们。如果恢复在中途停止，可能会让系统目录处于错误的状态。 pg_restore不能有选择地恢复大对象，例如只恢复特定表的大对象。如果一个归档包含大对象，那么所有的大对象都会被恢复，如果通过 -L、 -t 或者其他选项进行了排除，它们一个也不会被恢复。 一旦完成恢复，应该在每一个被恢复的表上运行 ANALYZE，这样优化器能得到有用的统计信息。 假设我们已经以自定义格式转储了一个叫做 mydb 的数据库： $ pg_dump -Fc mydb > db.dump 要删除该数据库并且从转储中重新创建它： $ dropdb mydb $ pg_restore -C -d postgres db.dump -d 开关中提到的数据库可以是任何已经存在于集簇中的数据库，pg_restore只会用它来为 mydb 发出 CREATE DATABASE 命令。通过 -C，数据总是会被恢复到出现在归档文件的数据库名中。 要把转储重新载入到一个名为 newdb 的新数据库中： $ createdb -T template0 newdb $ pg_restore -d newdb db.dump 注意我们不使用 -C，而是直接连接到要恢复到其中的数据库。还要注意我们是从 template0 而不是 template1 创建了该数据库，以保证它最初是空的。 要对数据库项重排序，首先需要转储归档的表内容： $ pg_restore -l db.dump > db.list 列表文件由一个头部和一些行组成，这些行每一个都用于一个项，例如： ; ; Archive created at Mon Sep 14 13:55:39 2009 ; dbname: DBDEMOS ; TOC Entries: 81 ; Compression: 9 ; Dump Version: 1.10-0 ; Format: CUSTOM ; Integer: 4 bytes ; Offset: 8 bytes ; Dumped from database version: 8.3.5 ; Dumped by pg_dump version: 8.3.8 ; ; ; Selected TOC Entries: ; 3; 2615 2200 SCHEMA - public pasha 1861; 0 0 COMMENT - SCHEMA public pasha 1862; 0 0 ACL - public pasha 317; 1247 17715 TYPE public composite pasha 319; 1247 25899 DOMAIN public domain0 pasha 分号表示开始一段注释，行首的数字表明了分配给每个项的内部归档 ID。 文件中的行可以被注释掉、删除以及重排序。例如： 10; 145433 TABLE map_resolutions postgres ;2; 145344 TABLE species postgres ;4; 145359 TABLE nt_header postgres 6; 145402 TABLE species_records postgres ;8; 145416 TABLE ss_old postgres 把这样一个文件作为pg_restore的输入将会只恢复项 10 和 6，并且先恢复 10 再恢复 6。 $ pg_restore -L db.list db.dump pg_verifybackup — 验证IvorySQL集群的基础备份的完整性 pg_verifybackup [option…​] pg_verifybackup 接受以下命令行参数： -e --exit-on-error 检测到备份问题后立即退出。 如果没有指定这个选项，pg_verifybackup 将在检测到问题后继续检查备份，并将检测到的所有问题报告为错误。 -i path --ignore=path 在将备份中实际存在的数据文件列表与 backup_manifest 文件中列出的数据文件列表进行比较时，忽略指定的文件或目录，该文件或目录应表示为相对路径名。如果指定了目录，则此选项会影响以该位置为根的整个子树。 如果相对路径名与指定的路径名匹配，有关额外文件、丢失文件、文件大小差异或校验和不匹配的投诉将被抑制。 可以多次指定此选项。 -m path --manifest-path=path 使用指定路径的清单文件，而不是位于备份目录根目录中的清单文件。 -n --no-parse-wal 不要试图解析从该备份恢复所需的预写式日志数据。 -q --quiet 成功验证备份后不要打印任何内容。 -s --skip-checksums 不要验证数据文件校验和。但仍检查是否存在文件以及这些文件的大小。这样将会快得多，因为文件本身不需要读取。 -w path --wal-directory=path 尝试解析存储在指定目录中的 WAL 文件，而不是 pg_wal。 如果备份存储在与WAL存档不同的位置，则这可能很有用。 其他选项也可用： -V --version 打印 pg_verifybackup 版本并退出。 -? --help 显示有关pg_verifybackup命令行参数的帮助，然后退出。 要在 mydbserver 上创建服务器的基本备份并验证备份的完整性： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ pg_verifybackup /usr/local/pgsql/data 要在 mydbserver 上创建服务器的基本备份，请将清单移动到备份目录之外的某个位置，并验证备份： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/backup1234 $ mv /usr/local/pgsql/backup1234/backup_manifest /my/secure/location/backup_manifest.1234 $ pg_verifybackup -m /my/secure/location/backup_manifest.1234 /usr/local/pgsql/backup1234 要在忽略手动添加到备份目录的文件的同时验证备份，并跳过校验和验证： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ edit /usr/local/pgsql/data/note.to.self $ pg_verifybackup --ignore=note.to.self --skip-checksums /usr/local/pgsql/data psql — IvorySQL的交互式终端 psql [option…​] [dbname [username]] -a --echo-all 把所有非空输入行按照它们被读入的形式打印到标准输出（不适用于交互式行读取）。这等效于把变量 ECHO 设置为 all。 -A --no-align 切换到非对齐输出模式（默认输出模式是 对齐 的）。这等效于 \\pset format unaligned。 -b --echo-errors 把失败的 SQL 命令打印到标准错误输出。这等效于把变量 ECHO 设置为 errors。 -c command --command=command 指定psql执行一个给定的命令字符串 command。这个选项可以重复多次并且以任何顺序与 -f 选项组合在一起。当 -c 或者 -f 被指定时，psql不会从标准输入读取命令，直到它处理完序列中所有的 -c 和 -f 选项之后终止。command 必须是一个服务器完全可解析的命令字符串（即不包含psql相关的特性）或者单个反斜线命令。因此不能在一个 -c 选项中混合SQL和psql元命令。要那样做，可以使用多个 -c 选项或者把字符串用管道输送到psql中，例如： psql -c '\\x' -c 'SELECT * FROM foo;' ` 或者 `echo '\\x \\\\ SELECT * FROM foo;' | psql ` （ `\\\\ 是分隔符元命令）。每一个被传递给 -c 的SQL命令字符串会被当做一个单独的请求发送给服务器。因此，即便该字符串包括多个SQL命令，服务器也会把它当做一个事务来执行，除非在该字符串中有显式的 BEGIN / COMMIT 命令把它划分成多个事务。此外，psql只会打印出该字符串中最后一个SQL命令的结果。这和从文件中读取同一字符串或者把同一字符串传给psql的标准输出时的行为不同，因为那两种情况下psql会独立地发送每一个SQL命令。由于这种行为，把多于一个SQL命令放在 -c 字符串中通常会得到意料之外的结果。最好使用多个 -c 命令或者把多个命令输送给psql的标准输入，按照上文所说的使用echo或者通过一个shell，例如： `psql <<EOF \\x SELECT * FROM foo; EOF ` --csv 切换到CSV（逗号分隔值）输出模式。 这相当于 \\pset format csv。 -d dbname --dbname=dbname 指定要连接的数据库的名称。这等效于指定 dbname 为命令行上的第一个非选项参数。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -e --echo-queries 也把发送到服务器的所有 SQL 命令复制到标准输出。这等效于把变量 ECHO 设置为 queries。 -E --echo-hidden 回显 \\d 以及其他反斜线命令生成的实际查询。可以用它来学习psql的内部操作。这等效于把变量 ECHO_HIDDEN 设置为 on。 -f filename --file=filename 从文件 filename 而不是标准输入中读取命令。这个选项可以被重复多次，也可以以任意顺序与 -c 选项组合。当 -c 或者 -f 被指定时，psql不会从标准输入读取命令，直到它处理完序列中所有的 -c 和 -f 选项之后终止。除此以外，这个选项很大程度上等价于元命令 \\i。如果 filename 是 - （连字符），那么会读取标准输入直到遇见一个 EOF 指示或者 \\q 元命令。这种方式可以用把自多个文件的输入组合成一种交互式输入。不过注意在这种情况下不会使用 Readline（很像指定了 -n 的情况）。使用这个选项与 psql < filename 有细微的不同。通常，两种形式都可以做到我们所期望的，但是使用 -f 启用了一些好的特性，例如带有行号的错误消息。使用这个选项还有一丝机会可以降低启动开销。在另一方面，使用shell输入重定向的变体（理论上）保证会得到与手工输入时相同的输出。 -F separator --field-separator=separator 使用 separator 作为非对齐输出的域分隔符。这等效于 \\pset fieldsep 或者 \\f。 -h hostname --host=hostname 指定运行服务器的机器的主机名。如果这个值由一个斜线开始，它会被用作 Unix 域套接字的目录。 -H --html 切换到HTML输出模式。这等效于 \\pset format html 或者 \\H 命令。 -l --list 列出所有可用的数据库，然后退出。其他非连接选项会被忽略。这与元命令 \\list 类似。在使用这个选项时，psql将连接到数据库 postgres，除非在命令行上提及一个不同的数据（选项 -d 或非选项参数，可能是通过一个服务项，但不能通过一个环境变量）。 -L filename --log-file=filename 除了把所有查询输出写到普通输出目标之外，还写到文件 filename 中。 -n --no-readline 不使用Readline做行编辑并且不使用命令历史。在剪切和粘贴时，关掉 Tab 展开会有所帮助。 -o `filename` --output=`filename` 把所有查询输出放到文件 filename 中。这等效于命令 \\o。 -p port --port=port 指定服务器用于监听连接的 TCP 端口或者本地 Unix 域套接字文件扩展。默认是 PGPORT 环境变量的值，如果没有设置，则默认为编译时指定的端口号（通常是5432）。 -P assignment --pset=assignment 以 \\pset 的形式指定打印选项。注意，这里你必须用一个等号而不是空格来分隔名称和值。例如，要设置输出格式为LaTeX，应该写成 -P format=latex。 -q --quiet 指定psql应该安静地工作。默认情况下，它会打印出欢迎消息以及多种输出。如果使用了这个选项，以上那些就都不会输出。在使用 -c 选项时，配合这个选项很有用。这等效于设置变量 QUIET 为 on。 -R separator --record-separator=separator 把 separator 用作非对齐输出的记录分隔符。这等效于 \\pset recordsep 命令。 -s --single-step 运行在单步模式中。这意味着在每个命令被发送给服务器之前都会提示用户一个可以取消执行的选项。使用这个选项可以调试脚本。 -S --single-line 运行在单行模式中，其中新行会终止一个 SQL 命令，就像分号的作用一样。注意这种模式被提供给那些坚持使用它的用户，但是并不一定要使用它。特别地，如果在一行中混合了SQL和元命令，那对于没有经的用户来说，它们的执行顺序可能不总是那么清晰。 -t --tuples-only 关闭打印列名和结果行计数页脚等。这等效于 \\t 或者 \\pset tuples_only 命令。 -T table_options --table-attr=table_options 指定要替换HTML table 标签的选项。详见 \\pset tableattr。 -U username --username=username 作为用户 username 而不是默认用户连接到数据库（当然，你必须具有这样做的权限）。 -v assignment --set=assignment --variable=assignment 执行一次变量赋值，和 \\set 元命令相似。注意你必须在命令行上用等号分隔名字和值（如果有）。要重置一个变量，去掉等号就行。要把一个变量置为空值，使用等号但是去掉值。这些赋值在命令行处理期间被完成，因此反映连接状态的变量将在稍后被覆盖。 -V --version 打印psql版本并且退出。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且口令不能从其他来源（例如一个 .pgpass 文件）获得，那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。注意这个选项将对整个会话保持设置，并且因此它会影响元命令 \\connect 的使用，就像初始的连接尝试那样。 -W --password 强制psql在连接到一个数据库之前提示要求一个口令，即使口令不会被使用。如果服务器需要口令认证并且口令不能从其他来源获得，例如 .pgpass 文件，psql 在任何情况下都会提示输入口令。 然而，psql 将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。注意这个选项将对整个会话保持设置，并且因此它会影响元命令 \\connect 的使用，就像初始的连接尝试那样。 -x --expanded 打开扩展表格式模式。这等效于 \\x 或者 \\pset expanded 命令。 -X, --no-psqlrc 不读取启动文件（要么是系统范围的 psqlrc 文件，要么是用户的 ~/.psqlrc 文件）。 -z --field-separator-zero 设置非对齐输出的域分隔符为零字节。这等效于 \\pset fieldsep_zero。 -0 --record-separator-zero 设置非对齐输出的记录分隔符为零字节。例如，这对与 xargs -0 配合有关。这等效于 \\pset recordsep_zero。 -1 --single-transaction 这个选项只能被用于与一个或者多个 -c 以及/或者 -f 选项组合。它会让psql在第一个上述选项之前发出一条 BEGIN 命令并且在最后一个上述选项之后发出一条 COMMIT 命令，这样就把所有的命令都包裹在一个事务中。这个选项可以保证要么所有的命令都成功地完成，要么不应用任何更改。如果命令本身包含 BEGIN 、 COMMIT 或者 ROLLBACK，这个选项将不会得到想要的效果。还有，如果当个命令不能在一个事务块中执行，指定这个选项将导致整个事务失败。 -? --help[=`topic]` 显示有关psql的帮助并且退出。可选的 topic 参数（默认为 options ）选择要解释哪一部分的psql： commands 描述psql的反斜线命令； options 描述可以被传递给psql的命令行选项；而 variables 则显示有关psql配置变量的帮助。 如果psql正常完成，它会向 shell 返回 0。如果它自身发生一个致命错误（例如内存用完、找不到文件），它会返回 1。如果到服务器的连接出问题并且事务不是交互式的，它会返回 2。如果在脚本中发生错误，它会返回 3 并且变量 ON_ERROR_STOP 会被设置。 COLUMNS 如果 \\pset columns 为零，这个环境变量控制用于 wrapped 格式的宽度以及用来确定是否输出需要用到分页器或者切换到扩展自动模式中的垂直格式的宽度。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 PSQL_EDITOR EDITOR VISUAL \\e、 \\ef 以及 \\ev 命令所使用的编辑器。会按照列出的顺序检查这些变量，第一个被设置的将被使用。如果都没有被设置，默认是使用Unix系统上的 vi 或者Windows系统上的 notepad.exe。 PSQL_EDITOR_LINENUMBER_ARG 当 \\e、 \\ef 或者 \\ev 带有一个行号参数时，这个变量指定用于传递起始行号给用户编辑器的命令行参数。对于Emacs或者vi之类的编辑器，这个变量是一个加号。如果需要在选项名称和行号之间有空格，可以在该变量的值中包括一个结尾的空格。例如： PSQL_EDITOR_LINENUMBER_ARG='' PSQL_EDITOR_LINENUMBER_ARG='--line ' ` 在 Unix 系统上默认是 ` （对应于默认编辑器 vi ，且对很多其他常见编辑器可用）。在 Windows 系统上没有默认值。 PSQL_HISTORY 命令历史文件的替代位置。波浪线（ ~ ）扩展会被执行。 PSQL_PAGER PAGER 如果一个查询的结果在屏幕上放不下，它们会通过这个命令分页显示。典型的值是 more 或 less。通过把 PSQL_PAGER 或 PAGER 设置为空字符串可以禁用分页器的使用，调整 \\pset 命令与分页器相关的选项也能达到同样的效果。会按照列出的顺序检查这些变量，第一个被设置的将被使用。如果都没有被设置，则大部分平台上默认使用 more，但在Cygwin上使用 less。 PSQLRC 用户的 .psqlrc 文件的替代位置。波浪线（ ~ ）扩展会被执行。 SHELL 被 \\! 命令执行的命令。 TMPDIR 存储临时文件的目录。默认是 /tmp 。 和大部分其他IvorySQL工具一样，这个工具也使用libpq所支持的环境变量。 psqlrc and ~/.psqlrc 如果没有 -X 选项，在连接到数据库后但在接收正常的命令之前，psql会尝试依次从系统级的启动文件（ psqlrc ）和用户的个人启动文件（ ~/.psqlrc ）中读取并且执行命令。这些文件可以被用来设置客户端或者服务器，通常是一些 \\set 和 SET 命令。系统级的启动文件是 psqlrc，它应该在安装好的IvorySQL的“系统配置”目录中，最可靠的定位方法是运行 pg_config --sysconfdir 。默认情况下，这个目录将是 ../etc/ （相对于包含IvorySQL可执行文件的目录）。可以通过 PGSYSCONFDIR 环境变量显式地设置这个目录的名称。用户个人的启动文件是 .psqlrc，它应该在调用用户的主目录中。在 Windows 上，由于没有用户主目录的概念，个人的启动文件是 %APPDATA%\\postgresql\\psqlrc.conf 。用户启动文件的位置可以通过 PSQLRC 环境变量设置。系统级和用户个人的启动文件都可以弄成是针对特定psql版本的，方法是在文件名后面加上一个横线以及IvorySQL的主、次版本号，版本最为匹配的文件会优先于不那么匹配的文件读入。 .psql_history 命令行历史被存储在文件 ~/.psql_history 中，或者是 Windows 的文件 %APPDATA%\\postgresql\\psql_history 中。历史文件的位置可以通过 HISTFILE psql变量或者 PSQL_HISTORY 环境变量明确的设置。 psql和具有相同主版本或者更老的主版本服务器最为匹配。如果服务器的版本比psql本身要高，则反斜线命令尤其容易失败。运行 SQL 命令并且显示查询结果的一般功能应该也能和具有更新主版本的服务器一起使用，但是并非在所有的情况下都能保证如此。 如果你想用psql连接到多个具有不同主版本的服务器，推荐使用最新版本的psql。或者，你可以为每一个主版本保留一份psql拷贝，并且针对相应的服务器使用匹配的版本。但实际上，这种额外的麻烦是不必要的。 第一个例子展示了如何如何跨越多行输入一个命令。注意提示符的改变： testdb=> CREATE TABLE my_table ( testdb(> first integer not null default 0, testdb(> second text) testdb-> ; CREATE TABLE 现在再看看表定义： testdb=> \\d my_table Table \"public.my_table\" Column | Type | Collation | Nullable | Default --------+---------+-----------+----------+--------- first | integer | | not null | 0 second | text | | | 现在我们把提示符改一改： testdb=> \\set PROMPT1 '%n@%m %~%R%# ' peter@localhost testdb=> 假定已经用数据填充了这个表并且想看看其中的数据： peter@localhost testdb=> SELECT * FROM my_table; first | second -------+-------- 1 | one 2 | two 3 | three 4 | four (4 rows) 你可以用 \\pset 命令以不同的方式显示表： peter@localhost testdb=> \\pset border 2 Border style is 2. peter@localhost testdb=> SELECT * FROM my_table; +-------+--------+ | first | second | +-------+--------+ | 1 | one | | 2 | two | | 3 | three | | 4 | four | +-------+--------+ (4 rows) peter@localhost testdb=> \\pset border 0 Border style is 0. peter@localhost testdb=> SELECT * FROM my_table; first second ----- ------ 1 one 2 two 3 three 4 four (4 rows) peter@localhost testdb=> \\pset border 1 Border style is 1. peter@localhost testdb=> \\pset format csv Output format is csv. peter@localhost testdb=> \\pset tuples_only Tuples only is on. peter@localhost testdb=> SELECT second, first FROM my_table; one,1 two,2 three,3 four,4 peter@localhost testdb=> \\pset format unaligned Output format is unaligned. peter@localhost testdb=> \\pset fieldsep '\\t' Field separator is \" \". peter@localhost testdb=> SELECT second, first FROM my_table; one 1 two 2 three 3 four 4 或者使用短命令： peter@localhost testdb=> \\a \\t \\x Output format is aligned. Tuples only is off. Expanded display is on. peter@localhost testdb=> SELECT * FROM my_table; -[ RECORD 1 ]- first | 1 second | one -[ RECORD 2 ]- first | 2 second | two -[ RECORD 3 ]- first | 3 second | three -[ RECORD 4 ]- first | 4 second | four 此外，可以使用 \\g 为一个查询设置这些输出格式选项： peter@localhost testdb=> SELECT * FROM my_table peter@localhost testdb-> \\g (format=aligned tuples_only=off expanded=on) -[ RECORD 1 ]- first | 1 second | one -[ RECORD 2 ]- first | 2 second | two -[ RECORD 3 ]- first | 3 second | three -[ RECORD 4 ]- first | 4 second | four 这是一个示例，用 \\df 命令以发现名称匹配 int*pl 并且第二参数是 bigint 类型的函数: testdb=> \\df int*pl * bigint List of functions Schema | Name | Result data type | Argument data types | Type ------------+---------+------------------+---------------------+------ pg_catalog | int28pl | bigint | smallint, bigint | func pg_catalog | int48pl | bigint | integer, bigint | func pg_catalog | int8pl | bigint | bigint, bigint | func (3 rows) 如果需要，可以用 \\crosstabview 命令以交叉表的形式显示查询结果： testdb=> SELECT first, second, first > 2 AS gt2 FROM my_table; first | second | gt2 -------+--------+----- 1 | one | f 2 | two | f 3 | three | t 4 | four | t (4 rows) testdb=> \\crosstabview first second first | one | two | three | four -------+-----+-----+-------+------ 1 | f | | | 2 | | f | | 3 | | | t | 4 | | | | t (4 rows) 这第二个例子展示了表的“乘法”（连接），行按照序号降序排序且列按照独立的、升序的方式排序。 testdb=> SELECT t1.first as \"A\", t2.first+100 AS \"B\", t1.first*(t2.first+100) as \"AxB\", testdb(> row_number() over(order by t2.first) AS ord testdb(> FROM my_table t1 CROSS JOIN my_table t2 ORDER BY 1 DESC testdb(> \\crosstabview \"A\" \"B\" \"AxB\" ord A | 101 | 102 | 103 | 104 ---+-----+-----+-----+----- 4 | 404 | 408 | 412 | 416 3 | 303 | 306 | 309 | 312 2 | 202 | 204 | 206 | 208 1 | 101 | 102 | 103 | 104 (4 rows) reindexdb — 重索引一个IvorySQL数据库 reindexdb [connection-option…​] [option…​] [ -S | --schema schema ] …​ [ -t | --table table ] …​ [ -i | --index index ] …​ [dbname] reindexdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all reindexdb [connection-option…​] [option…​] -s | --system [dbname] reindexdb接受下列命令行参数： -a --all 重索引所有数据库。 --concurrently 使用 `CONCURRENTLY` 选项。 请参阅 http://www.postgresql.org/docs/17/sql-reindex.html[REINDEX]，其中详细解释了此选项的所有注意事项。 [-d] dbname dbname 当 -a / --all 未使用时，指定要重新索引的数据库的名称。 如果未指定，则从环境变量 PGDATABASE 中读取数据库名称。 如果未设置，则使用为连接指定的用户名。 dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -e --echo 回显reindexdb生成并发送到服务器的命令。 -i index --index=index 只是重建 *`index`*。可以通过写多个 `-i` 开关来重建多个索引。 -j njobs --jobs=njobs 通过同时运行 njobs 命令并行执行 reindex 命令。 此选项可能会减少处理时间，但也会增加数据库服务器上的负载。reindexdb将打开到数据库的 njobs 连接，因此请确保 max_connections 设置足够高，可以容纳所有连接。请注意，此选项与 --index 和 --system 选项不兼容。 -q --quiet 不显示进度消息。 -s --system 仅索引数据库的系统目录。 -S schema --schema=schema 只对 schema 重建索引。 通过写多个 -S 开关可以指定多个要重建索引的模式。 -t table --table=table 只索引 table。可以通过写多个 -t 开关来重索引多个表。 --tablespace=tablespace 指定要重建索引的表空间。 （这个名字以双引号标识符来处理。） -v --verbose 在处理时打印详细信息。 -V --version 打印reindexdb版本并退出。 -? --help 显示有关reindexdb命令行参数的帮助并退出。 reindexdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制reindexdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，reindexdb将自动提示要求一个口令。但是，reindexdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db=dbname 当使用 -a / --all 时，指定要连接到的数据库名称以发现应重新索引哪些数据库。 如果未指定，将使用 postgres 数据库， 如果不存在，将使用 template1 。 这可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 此外，在连接到其他数据库时，将重新使用除数据库名称本身之外的连接字符串参数。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 REINDEX和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 reindexdb可能需要多次连接到IvorySQL服务器，每一次都会询问一个口令。在这种情况下使用一个 ~/.pgpass 文件会更方便。 要重索引数据库 test： $ reindexdb test 要重索引名为 abcd 的数据库中的表 foo 和索引 bar： $ reindexdb --table foo --index bar abcd vacuumdb — 对一个IvorySQL数据库进行垃圾收集和分析 vacuumdb [connection-option…​] [option…​] [ -t | --table table [( column [,…​] )] ] …​ [dbname] vacuumdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all vacuumdb接受下列命令行参数： -a --all 清理所有数据库。 [-d] dbname dbname 指定要清理或分析的数据库的名称，当不使用 -a / --all`时。 如果未指定，则从环境变量 `PGDATABASE 中读取数据库名称。 如果环境变量也没有设置，指定给该连接的用户名将用作数据库名。 dbname 可以是 connection string。 如果是这样，连接时的字符串参数将覆盖任何冲突的命令行选项。 --disable-page-skipping 根据可见性地图的内容禁用跳过页面。 -e --echo 回显vacuumdb生成并发送给服务器的命令。 -f --full 执行“完全”清理。 -F --freeze 强有力地“冻结”元组。 --force-index-cleanup 总是移除指向死元组的索引条目。 -j njobs --jobs=njobs 通过同时运行 njobs 个命令来并行执行清理或者分析命令。这个选项可能会减少处理的时间， 但是它也会增加数据库服务器的负载。vacuumdb将开启 njobs 个到数据库的连接，因此请确认你的 max_connections 设置足够高以容纳所有的连接。注意如果某些系统目录被并行处理，使用这种模式加上 -f（ FULL ）选项可能会导致死锁失败。 --min-mxid-age mxid_age 仅在multixact ID 年龄至少为 mxid_age 的表上执行清空或分析命令。 此设置对于确定要处理的表的优先级比较有用，以防止multixact ID 回绕。对于此选项的用途，关系的multixact ID年龄是主关系及其关联的TOAST表的年龄中最大的，如果存在的话。由于vacuumdb发出的命令在需要时还将处理TOAST表的关系，它无需单独考虑。 --min-xid-age xid_age 仅在事务ID 年龄至少为 xid_age 的表上执行清空或分析命令。 此设置对于确定要处理的表的优先级比较有用，以防止事务ID 回绕。对于此选项的用途，关系的事务ID年龄是主关系及其关联的TOAST表的年龄中最大的，如果存在的话。由于vacuumdb发出的命令在需要时还将处理TOAST表的关系，它无需单独考虑。 --no-index-cleanup 不移除指向死元组的索引条目。 --no-process-toast 略过与要清理的表有关联的任何 TOAST 表。 --no-truncate 不要在表的结尾截断空页面。 -P parallel_workers --parallel=parallel_workers 指定 parallel vacuum 的并发数量。这允许清理利用多个 CPU 来处理索引。请参见 VACUUM。 -q --quiet 不显示进度消息。 --skip-locked 跳过无法立即锁定以进行处理的关系。 -t `table [ (column [,…​]) ]` --table=`table [ (column [,…​]) ]` 只清理或分析 table。列名只能和 --analyze 或 --analyze-only 选项一起被指定。通过写多个 -t 开关可以清理多个表。提示如果你指定列，你可能必须转义来自 shell 的括号（见下面的例子）。 -v --verbose 在处理期间打印详细信息。 -V --version 打印vacuumdb版本并退出。 -z --analyze 也计算优化器使用的统计信息。 -Z --analyze-only 只计算优化器使用的统计信息（不清理）。 --analyze-in-stages 与 --analyze-only 相似，只计算优化器使用的统计信息（不做清理）。 使用不同的配置设置运行分析的几个（目前是 3个）阶段以更快地产生可用的统计信息。这个选项对分析一个刚从转储恢复或者通过 pg_upgrade 得到的数据库有用。这个选项将尝试尽可能快地创建一些统计信息来让该数据库可用，然后在后续的阶段中产生完整的统计信息。 -? --help 显示有关vacuumdb命令行参数的帮助并退出。 vacuumdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制vacuumdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，vacuumdb将自动提示要求一个口令。但是，vacuumdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db=dbname 当使用 -a / --all 时，指定要连接到的数据库名称以发现应该清理的数据库。 如果未指定，将使用 postgres 数据库，如果不存在，将使用 template1。 这可以是 connection string。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 此外，在连接到其他数据库时，将重新使用除数据库名称本身之外的连接字符串参数。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto 和 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 VACUUM 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 vacuumdb可能需要多次连接到IvorySQL服务器，每次都询问一个口令。在这种情况下有一个 ~/.pgpass 文件会很方便。 要清理数据库 test： $ vacuumdb test 要清理和为优化器分析一个名为 bigdb 的数据库： $ vacuumdb --analyze bigdb 要清理在名为 xyzzy 的数据库中的一个表 foo，并且为优化器分析该表的 bar 列： $ vacuumdb --analyze --verbose --table='foo(bar)' xyzzy initdb — 创建一个新的IvorySQL数据库集簇 initdb [option…​] [ --pgdata | -D ] ` directory` -A authmethod --auth=authmethod 这个选项为本地用户指定在 pg_hba.conf 中使用的默认认证方法（ host 和`local` 行）。initdb 将使用指定的认证方法为非复制连接以及复制连接填充 pg_hba.conf 项。除非你信任你系统上的所有本地用户，不要使用 trust。为了安装方便，trust 是默认值。 --auth-host=authmethod 这个选项为通过 TCP/IP 连接的本地用户指定在 pg_hba.conf 中使用的认证方法（ host 行）。 --auth-local=authmethod 这个选项为通过 Unix 域套接字连接的本地用户指定在 pg_hba.conf 中使用的认证方法（ local 行）。 -D directory --pgdata=directory 这个选项指定数据库集簇应该存放的目录。这是 initdb 要求的唯一信息，但是你可以通过设定 PGDATA 环境变量来避免书写它，这很方便因为之后数据库服务器（ postgres ）可以使用同一个变量来找到数据库目录。 -E encoding --encoding=encoding 选择模板数据库的编码。这也将是后来创建的任何数据库的默认编码，除非你覆盖它。默认值来自于区域，或者如果该值不起作用则为 SQL_ASCII。 -g --allow-group-access 允许与集簇拥有者同组的用户读取 initdb 创建的所有集簇文件。 Windows会忽略此选项，因为它不支持POSIX样式的组权限。 -k --data-checksums 在数据页面上使用校验码来帮助检测 I/O 系统造成的损坏。 启用校验码将会引起显著的性能惩罚。 如果设置，则为所有对象计算校验和，在整个数据库中。 所有校验和失败都将报告在 pg_stat_database 视图。 --locale=locale 为数据库集簇设置默认区域。如果这个选项没有被指定，该区域将从 initdb 所运行的环境中继承。 --lc-collate=locale --lc-ctype=locale --lc-messages=locale --lc-monetary=locale --lc-numeric=locale --lc-time=locale 和 --locale 相似，但是只在指定的分类中设置区域。 --no-locale 等效于 --locale=C。 -N --no-sync 默认情况下，initdb 将等待所有文件被安全地写到磁盘。这个选项会导致 initdb 不等待就返回，这当然更快，但是也意味着一次后续的操作系统崩溃可能让数据目录损坏。通常，这个选项对测试有用，但是不应该在创建生产安装时使用。 -−no-instructions 默认情况下，initdb 将在输出的尾部写入如何启动集群的指令。 这个选项会导致那些指令被抛弃。 这主要由平台特定行为中封装 initdb 的工具来使用，那些指令有可能是错误的。 --pwfile=filename 让 initdb 从一个文件读取数据库超级用户的口令。该文件的第一行被当作口令。 -S --sync-only 安全地把所有数据库文件写入到磁盘并退出。这不会执行任何正常的initdb操作。 -T config --text-search-config=config 设置默认的文本搜索配置。详见 default_text_search_config。 -U username --username=username 选择数据库超级用户的用户名。这个的默认值是实际运行 initdb 的用户的名称。超级用户的名字是什么真的不重要，但是你可以选择保留常用的名字postgres，即使操作系统的用户名不同。 -W --pwprompt 让 initdb 提示要求为数据库超级用户给予一个口令。如果你没有计划使用口令认证，这就不重要。否则在你设置一个口令之前你就无法使用口令认证。 -X directory --waldir=directory 这个选项指定预写式日志会被存储在哪个目录中。 --wal-segsize=`size` 设置 WAL段尺寸，以兆字节为单位。这是WAL日志中每个文件的尺寸。默认的尺寸为16兆字节。该值必须位于2的1次幂和1024次幂（兆字节）之间。这个选项只能在初始化期间设置，并且之后不能更改。调整这个值来控制WAL日志传送或者归档可能会有用。此外，在有大量WAL的数据库中，每个目录中数量巨大的WAL文件可能会成为性能和管理问题。增加WAL文件尺寸将会降低WAL文件的数量。 其他较少使用的选项： -d --debug 打印来自引导后端的调试输出以及普通大众不那么感兴趣的一些消息。引导后端被程序 initdb 用来创建目录表。这个选项会生成大量极端无聊的输出。 -−discard-caches 使用 debug_discard_caches=1 选项运行引导程序后端。 这需要很长时间并且只适用于深度调试。 -L directory 指定 initdb 应从哪里寻找它的输入文件来初始化数据库集簇。这通常没有必要。如果你需要显式指定它们的位置，你应该被告知。 -n --no-clean 默认情况下，当 initdb 确定有一个错误阻止它完整地创建数据库集簇，它会移除在它发现无法完成任务之前创建的任何文件。这个选项会抑制这种整理并且对调试有用。 其他选项： -V --version 打印initdb版本并退出。 -? --help 显示有关initdb命令行参数的帮助并退出。 PGDATA 指定数据库集簇应该被存放的目录，可以使用 -D 选项覆盖。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 TZ 指定创建的数据集簇的默认时区。值应该是一个完整的时区名称 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 initdb 可以通过 pg_ctl initdb 被调用。 pg_archivecleanup — 清理IvorySQL WAL 归档文件 pg_archivecleanup [option…​] archivelocation oldestkeptwalfile pg_archivecleanup接受下列命令行参数： -d 在 stderr 上打印很多调试日志输出。 -n 在 stdout 上打印将被移除的文件的名字（执行一次演习）。 -V --version 打印pg_archivecleanup版本并退出。 -x extension 提供一个扩展名，在决定所有的文件 是否应该被删除之前，将从文件名中剥离这个扩展名。这通常有助于清理已经 存储期间被压缩过并且被压缩程序增加了一个扩展名的归档。例如： -x .gz。 -? --help 显示pg_archivecleanup命令行参数的帮助并退出。 环境变量 PG_COLOR 指定是否在诊断消息中使用颜色。 可能的值是 always, auto 和 never。 在 Linux 或者 Unix 系统上，你可能会用： archive_cleanup_command = 'pg_archivecleanup -d /mnt/standby/archive %r 2>>cleanup.log' 其中归档目录位于后备服务器上，这样 archive_command 通过 NFS 来访问它，但是文件对于后备服务器来说是本地的。这将会 在 cleanup.log 中产生调试输出 从归档目录中移除不再需要的文件 pg_checksums — 在IvorySQL数据库集簇中启用、禁用或检查数据校验和 pg_checksums [option…​] [[ -D | --pgdata ] datadir] 下列命令选项可用： -D directory --pgdata=directory 指定存储数据库集簇的目录。 -c --check 检查校验和。如果未指定其它任何内容，这是缺省模式。 -d --disable 禁用校验和。 -e --enable 启用校验和。 -f filenode --filenode=filenode 仅验证文件节点为 filenode 的关系中的校验和。 -N --no-sync 缺省地，pg_checksums 会等待所有文件安全地写到磁盘上。该选项使得 pg_checksums 不等待就返回，这样更快，但意味着后续如果操作系统崩溃会让更新的数据目录损坏。一般地，该选项对测试有用，但不应用在生产安装上。当使用 --check 时，该选项无效。 -P --progress 启用进度报告。在检查或启用校验和时，打开该选项，会提供进度报告。 -v --verbose 启用详细输出。列出所有检查的文件 。 -V --version 打印pg_checksums版本并退出。 -? --help 显示关于pg_checksums命令行参数的帮助并退出。 PGDATA 指定数据库集簇存储的目录；可以用 -D 选项覆盖。 PG_COLOR 指定是否在诊断消息中使用颜色。可能的值为 always, auto, never。 在大型集簇中启用校验和的时间可能很长。在此操作期间，写到数据目录的集簇或其它程序必须是未启动的，否则可能出现数据丢失。 当复制设置与执行关系文件块的直接拷贝的工具（例如 pg_rewind）一起使用时，启用和禁用校验和会导致以不正确校验和形式出现的页面损坏，如果未在所有节点上执行一致的操作的话。故在复制设置中启用或禁用校验和时，推荐一致地切换所有集簇之前停止所有集簇。此外销毁所有备用数据库，在主数据库上执行操作，最后从头开始重建后备服务器，也是安全的。 如果在启用或禁用校验和时异常终止或杀掉pg_checksums，那么集簇的数据校验和配置保持不变，pg_checksums可以重新运行以执行相同操作。 pg_controldata — 显示一个IvorySQL数据库集簇的控制信息 pg_controldata [option] [[ -D | --pgdata ] datadir] PGDATA 默认的数据目录位置。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 pg_ctl — 初始化、启动、停止或控制一个IvorySQL服务器 pg_ctl init[db] [-D datadir] [-s] [-o initdb-options] pg_ctl start [-D datadir] [-l filename] [-W] [-t seconds] [-s] [-o options] [-p path] [-c] pg_ctl stop [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] pg_ctl restart [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] [-o options] [-c] pg_ctl reload [-D datadir] [-s] pg_ctl status [-D datadir] pg_ctl promote [-D datadir] [-W] [-t seconds] [-s] pg_ctl logrotate [-D datadir] [-s] pg_ctl kill signal_name process_id 在Microsoft Windows上，还有： pg_ctl register [-D datadir] [-N servicename] [-U username] [-P password] [-S a[uto] | d[emand] ] [-e source] [-W] [-t seconds] [-s] [-o options] pg_ctl unregister [-N servicename] -c --core-files 在可行的平台上尝试允许服务器崩溃产生核心文件，方法是提升在核心文件上的任何软性资源限制。这通过允许从一个失败的服务器进程中获得一个栈跟踪而有助于调试或诊断问题。 -D `datadir` --pgdata=`datadir` 指定数据库配置文件的文件系统位置。如果这个选项被忽略，将使用环境变量`PGDATA`。 -l `filename` --log=`filename` 追加服务器日志输出到*`filename`*。如果该文件不存在，它会被创建。umask被设置成 077，这样默认情况下不允许其他用户访问该日志文件。 -m mode --mode=mode 指定关闭模式。mode 可以是 smart、fast 或 immediate，或者这三者之一的第一个字母。如果这个选项被忽略，则 fast 是默认值。 -o options --options=options 指定被直接传递给 postgres 命令的选项。-o 可以被指定多次，所有给定的选项都会被传过去。这些选项应该通常被单引号或双引号包围来确保它们被作为一个组传递。 -o initdb-options --options=initdb-options 指定要直接传递给 initdb 命令的选项。-o 可以被指定多次，所有给定的选项都会被传过去。这些选项应该通常被单引号或双引号包围来确保它们被作为一个组传递。 -p path 指定 postgres 可执行程序的位置。默认情况下，postgres 可执行程序可以从 pg_ctl 相同的目录得到，或者如果没有在那里找到，则在硬写的安装目录中获得。除非你正在做一些不同寻常的事并且得到错误说没有找到 postgres 可执行程序，这个选项不是必需的。在 init 模式中，这个选项类似于指定了 initdb 可执行程序的位置。 -s --silent 只打印错误，不打印信息性的消息。 -t seconds --timeout=seconds 指定等待一个操作完成时要等待的最大秒数（见选项 -w ）。默认为 PGCTLTIMEOUT 环境变量的值，如果该环境变量没有设置则默认为60秒。 -V --version 打印pg_ctl版本并退出。 -w --wait 等待操作完成。模式 start、 stop、 restart、 promote 以及 register 支持这个选项，并且对那些模式是默认的。在等待时，pg_ctl 会一遍又一遍地检查服务器的PID文件，在两次检查之间会休眠一小段时间。当PID文件指示该服务器已经做好准备接受连接时，启动操作被认为完成。当服务器移除PID文件时，关闭操作被认为完成。pg_ctl 会基于启动或关闭的成功与否返回一个退出代码。如果操作在超时时间（见选项 -t ）内未能完成，则 pg_ctl 会以一个非零退出状态退出。但是注意该操作可能会在后台继续进行并且最终取得成功。 -W --no-wait 不等待操作完成。这是选项 -w 的对立面。如果禁用等待，所请求的动作会被触发，但是不会有关于其成功与否的反馈。在这种情况下，可能必须用服务器日志文件或外部监控系统来检查该操作的进度以及成功与否。在以前版本的IvorySQL中，这是除 stop 模式之外的模式的默认选项。 -? --help 显示有关pg_ctl命令行参数的帮助并退出。 如果一个指定的选项有效，但与选中的操作模式无关，则pg_ctl会忽略它。 -e source 作为一个 Windows 服务运行时，pg_ctl用来 在事件日志中记录日志的事件源的名称。默认是 IvorySQL。 注意这只控制由pg_ctl本身发送的消息，一旦开始， 服务器将使用 event_source 参数中指定的事件源。如果服务器在启动时很早（在该参数被设置前）就失败，它可能也会使用默认的事件源名称 IvorySQL 来记录。 -N servicename 要注册的系统服务的名称。这个名称将被用于服务名和显示名。默认是 IvorySQL。 -P password 用于运行该服务的用户的口令。 -S start-type 要注册的系统服务的启动类型。启动类型可以是 auto、 demand 或者两者之一的第一个字母。如果这个选项被忽略，则 auto 是默认值。 -U username 用于运行该服务的用户的用户名。对于域用户，使用格式 DOMAIN\\username。 PGCTLTIMEOUT 等待启动或者关闭完成时要等待的默认秒数限制。如果没有设置， 默认值是 60 秒。 PGDATA 默认的数据目录位置。 大部分的 pg_ctl 模式都要求知道数据目录的位置，因此 -D 选项是必需的，除非 PGDATA 被设置。 和大部分其他IvorySQL工具相似，pg_ctl 也使用libpq支持的环境变量。 更多影响服务器的变量请见 postgres。 postmaster.pid pg_ctl在数据目录中检查这个文件来判断服务器当前是否正在运行。 postmaster.opts 如果这个文件存在于数据目录中，pg_ctl（处于 restart 模式中）将把该文件的内容作为选项传递给postgres，除非通过 -o 选项进行了覆盖。这个文件的内容也会被显示在 status 模式中。 要启动服务器并且等到服务器接受连接： $ pg_ctl start 要使用端口 5433 启动服务器并且运行时不使用 fsync： $ pg_ctl -o \"-F -p 5433\" start 要停止服务器，使用： $ pg_ctl stop -m 选项允许控制服务器如何关闭： $ pg_ctl stop -m smart 重启服务器几乎等价于停止服务器并且再次启动它，不过 pg_ctl 默认会保存并重用被传递给之前的运行实例的命令行选项。要以和之前相同的选项重启服务器，使用： $ pg_ctl restart 但是如果指定了 -o，则会替换任何之前的选项。要使用端口 5433 重启并在重启时禁用 fsync： $ pg_ctl -o \"-F -p 5433\" restart 这里是pg_ctl状态输出的例子： $ pg_ctl status pg_ctl: server is running (PID: 13718) /usr/local/pgsql/bin/postgres \"-D\" \"/usr/local/pgsql/data\" \"-p\" \"5433\" \"-B\" \"128\" 第二行是在重启模式可能被调用的命令行。 pg_resetwal — 重置一个IvorySQL数据库集簇的预写式日志以及其他控制信息 pg_resetwal [ -f | --force ] [ -n | --dry-run ] [option…​] [ -D | --pgdata ] datadir -f --force 即使 pg_resetwal 无法从 pg_control 中确定有效的数据（如前面所解释的），也强迫 pg_resetwal 继续运行。 -n --dry-run -n / --dry-run 选项指示 pg_resetwal 打印从 pg_control 重构出来的值以及要被改变的值，然后不修改任何东西退出。这主要是一个调试工具，但是可以用来在允许 pg_resetwal 真正执行下去之前进行完整性检查。 -V --version 显示版本信息然后退出。 -? --help 显示帮助然后退出。 只有当 pg_resetwal 无法通过读取 pg_control 确定合适的值时，才需要下列选项。安全值可以按下文所述来确定。对于接收数字参数的值，可以使用前缀 0x 指定 16 进制值。 -c xid, xid --commit-timestamp-ids=xid, xid 手工设置提交时间可以检索到的最老的和最新的事务 ID。能检索到提交时间的最老事务 ID 的安全值（第一部分）可以通过在数据目录下 pg_commit_ts 目录中数字上最小的文件名来决定。反过来，能检索到提交时间的最新事务 ID 的安全值（第二部分）可以通过同一个目录中数字上最大的文件名来决定。文件名都是十六进制的。 -e xid_epoch --epoch=xid_epoch 手工设置下一个事务 ID 的 epoch。事务 ID 的 epoch 实际上并没有存储在数据库中的任何地方，除了被 pg_resetwal 设置在这个域中，所以只要关心的是数据库本身，任何值都可以用。你可能需要调整这个值来确保诸如Slony-I和Skytools之类的复制系统正确地工作 — 如果确实需要调整，应该可以从下游的复制数据库的状态中获得一个合适的值。 -l `walfile` --next-wal-file=`walfile` 通过指定下一个WAL段文件名称来手工设置WAL开始位置。下一个WAL段文件的名称应该比当前存在于数据目录下 pg_wal 目录中的任意 WAL 段文件名更大。这些名称也是十六进制的并且有三个部分。第一部分是“时间线 ID”并且通常应该被保持相同。例如，如果 00000001000000320000004A 是 pg_wal 中最大的项，则使用 -l 00000001000000320000004B 或更高的值。注意在使用非默认WAL段尺寸时，WAL文件名中的数字与系统函数和系统视图报告的LSN不同。这个选项要的是WAL文件名而不是LSN。注意 pg_resetwal 本身查看 pg_wal 中的文件并选择一个超出最新现存文件名的默认 -l 设置。因此，只有当你知道 WAL 段文件当前不在 pg_wal 中时，或者当 pg_wal 的内容完全丢失时，才需要对 -l 的手工调整，例如一个离线归档中的项。 -m mxid,mxid --multixact-ids=mxid,mxid 手工设置下一个和最老的多事务 ID。确定下一个多事务 ID（第一部分）的安全值的方法：在数据目录下的 pg_multixact/offsets 目录中查找最大的数字文件名，然后在它的基础上加一并且乘以 65536 (0x10000)。反过来，确定最老的多事务 ID（ -m 的第二部分）的方法：在同一个目录中查找最小的数字文件名并且乘以 65536。文件名是十六进制的数字，因此实现上述方法最简单的方式是以十六进制指定选项值并且追加四个零。 -o oid --next-oid=oid 手工设置下一个 OID。没有相对容易的方法来决定超过数据库中最大 OID 的下一个 OID。但幸运的是正确地得到下一个 OID 设置并不是决定性的。 -O mxoff --multixact-offset=mxoff 手工设置下一个多事务偏移量。确定安全值的方法：查找数据目录下 pg_multixact/members 目录中最大的数字文件名，然后在它的基础上加一并且乘以 52352 (0xCC80)。文件名是十六进制数字。没有像其他选项那样追加零的简单方法。 --wal-segsize=wal_segment_size 设置新的WAL段尺寸，以兆字节为单位。这个值必须被设为2的1次幂和1024次幂（兆字节）之间。更多信息请参考 initdb 的相同选项。注意虽然 pg_resetwal 将把WAL起始地址设置成超过最新的现有WAL段文件，但一些段尺寸的改变可能导致之前的WAL文件名被重用。如果WAL文件名重叠会导致归档策略出现问题，推荐把 -l 和这个选项一起使用来手动设置WAL起始地址。 -u xid --oldest-transaction-id=xid 手工设置最老的未冻结事务ID。一个安全值，可以通过在数据目录下的 pg_xact 目录中查找数字最小的文件名然后乘以1048576 (0x100000)的方式来确定。 注意该文件名是十六进制的。 以十六进制来指定选项值通常也是最简单的。 例如,如果 0007 是 pg_xact 中最小的记录，-u 0x700000 将有效(五个后补零提供适当的乘数)。 -x xid --next-transaction-id=xid 手工设置下一个事务 ID。确定安全值的方法：在数据目录下的 pg_xact 目录中查找最大的数字文件名，然后在它的基础上加一并且乘以 1048576 (0x100000)。注意文件名是十六进制的数字。通常以十六进制的形式指定该选项值也是最容易的。例如，如果 0011 是 pg_xact 中的最大项，-x 0x1200000 就可以（五个尾部的零就表示了前面说的乘数）。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 这个命令不能在服务器正在运行时被使用。如果在数据目录中发现一个服务器锁文件，pg_resetwal 将拒绝启动。如果服务器崩溃那么一个锁文件可能会被留下，在那种情况下你能移除该锁文件来让 pg_resetwal 运行。但是在你那样做之前，再次确认没有服务器进程仍然存活。 pg_resetwal 仅能在具有相同主版本的服务器上使用。 pg_rewind — 把一个IvorySQL数据目录与另一个从该目录中复制出来的数据目录同步 pg_rewind [option…​] { -D | --target-pgdata }*` directory`* { --source-pgdata=`directory` | --source-server=`connstr` } 如果在处理时pg_rewind失败，则目标的数据目录很可能不在可恢复的状态。在这种情况下，推荐创建一个新的备份。 由于 pg_rewind 完全从源复制配置文件，因此可能需要在重新启动目标服务器之前更正用于恢复的配置，特别是当目标服务器作为源的后备服务器重新引入时。 如果在倒带操作完成后重新启动服务器但未配置恢复，则目标可能会再次与主服务器分离。 如果pg_rewind发现它无法直接写入的文件，它将立刻失败。例如当源服务器和目标服务器为只读的SSL密钥及证书使用相同的文件映射，就会发生这种情况。如果在目标服务器上存在这样的文件，推荐在运行pg_rewind之前移除它们。在做了rewind之后，一些那样的文件可能已经被从源服务器拷贝，这样就有必要移除已经拷贝的数据并且恢复到rewind之前使用的链接集合。 pg_rewind接受下列命令行参数： -D directory --target-pgdata=directory 这个选项指定要与源数据目录同步的目标数据目录。在运行pg_rewind之前目标服务器必须被干净地关闭。 --source-pgdata=directory 指定要和目标服务器同步的源服务器的数据目录的文件系统路径。这个选项要求源服务器必须被干净地关闭。 --source-server=`connstr` 指定一个 libpq 连接串用于连接要与目标服务器同步的源IvorySQL服务器。 连接必须是常规（非复制）连接，角色具有足够权限执行源服务器上pg_rewind使用的函数（详请参阅备注部分）或超级用户角色。 这个选项要求源服务器正在运行并且接受连接。 -R --write-recovery-conf 创建 standby.signal 并将连接设置附加到输出目录中的 postgresql.auto.conf 中。--source-server 对于此选项是必需的。 -n --dry-run 做除了实际修改目标目录之外的其他所有事情。 -N --no-sync 默认情况下，pg_rewind 将等待所有文件安全地写入磁盘。 此选项会导致 pg_rewind 不等待即可返回，这更快，但意味着后续操作系统崩溃会使同步数据目录损坏。通常情况，此选项可用于测试，但不应使用于生产安装。 -P --progress 启用进度报告。在从源集簇拷贝数据时，打开这个选项将会发送一个近似的进度报告。 -c --restore-target-wal 如果在 pg_wal 目录中不再可用这些文件，请使用在目标群集配置中定义的 restore_command 从WAL存档中检索WAL文件。 --debug 打印冗长的调试输出，这主要对于调试pg_rewind的开发者有用。 --no-ensure-shutdown pg_rewind 要求目标服务器在倒带之前彻底关闭。 默认情况下，如果目标服务器没有完全关闭，pg_rewind 会以单用户模式启动目标服务器，先完成崩溃恢复，然后将其停止。 通过传递这个选项，如果服务器没有完全关闭，pg_rewind 会跳过这个并立即出错。 在这种情况下，用户应该自己处理这种情况。 -V --version 显示版本信息然后退出。 -? --help 显示帮助然后退出。 在使用 --source-server 选项时，pg_rewind也使用libpq支持的环境变量。 环境变量`PG_COLOR`规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 当使用在线群集作为源执行pg_rewind时，具有充足权限来执行pg_rewind在源群集上使用的函数的角色可以用来代替超级用户。 这里介绍如何创建这样的角色，在这里命名 rewind_user： CREATE USER rewind_user LOGIN; GRANT EXECUTE ON function pg_catalog.pg_ls_dir(text, boolean, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_stat_file(text, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text, bigint, bigint, boolean) TO rewind_user; 当使用近期升级的在线群集作为源执行pg_rewind时，必须在升级后执行 CHECKPOINT 以便其控制文件反映最新的时间线信息， pg_rewind使用这些信息检查目标群集是否可以使用指定的源群集倒回。 其基本思想是从源集簇拷贝所有文件系统级别的改变到目标集簇： 1.以源集簇的时间线历史从目标集簇分叉出来的点之前的最后一个检查点为起点，扫描目标集簇的 WAL 日志。对于每一个 WAL 记录，读取每一个被动过的数据块。这会得到在目标集簇中从源集簇被分支出去以后所有被更改过的数据块列表。如果某些 WAL 文件 不再可用，请尝试使用 -c 选项重新运行 pg_rewind 以在 WAL 存档中搜索丢失的文件。 2.使用直接的文件系统访问（ --source-pgdata ）或者 SQL （ --source-server ），把所有那些更改过的块从源集簇拷贝到目标集簇。 关系文件现在的状态相当于源和目标的WAL时间线偏离点之前最后一个完成的检查点的时刻加上偏离点之后目标上更改的任何块的源上的当前状态。 3.将所有其他文件，包括新的关系文件、WAL段、pg_xact 和配置文件，从源集群复制到目标集群。与基本备份类似，从源集群复制的数据中省略了目录 pg_dynshmem/、 pg_notify/、 pg_replslot/、 pg_serial/、 pg_snapshots/、 pg_stat_tmp/ 以及 pg_subtrans/ 的内容。文件 backup_label、 tablespace_map、 pg_internal.init、 postmaster.opts 以及 postmaster.pid，以及任何以 pgsql_tmp 开始的文件或目录都会被忽略。 4.创建一个 backup_label 文件，在故障转移时创建的检查点处开始WAL重放，并将 pg_control 文件配置为最小一致性LSN，该LSN定义为从活动源回放时的 pg_current_wal_insert_lsn() 结果，或从停止的源回放时的最后一个检查点LSN。 5.启动目标时，IvorySQL将重放所有必需的WAL，从而使数据目录处于一致状态。 pg_test_fsync — 为IvorySQL判断最快的 wal_sync_method pg_test_fsync [option…​] pg_test_fsync接受下列命令行选项： -f --filename 指定要写入测试数据到其中的文件名。这个文件必须位于和 pg_wal 目录所在或者将被放置的同一个文件系统中（ pg_wal 包含WAL文件）。默认是当前 目录中的 pg_test_fsync.out。 -s --secs-per-test 指定每次测试的秒数。每个测试的时间越长，测试的精度就越高，但是 它需要更多时间来运行。默认是 5 秒，这允许程序在 2 分钟以内完成。 -V --version 打印pg_test_fsync版本并且退出。 -? --help 显示有关pg_test_fsync命令行参数的帮助并且退出。 环境变量 PG_COLOR 指定是否在诊断消息中使用颜色。可能的值为 always、 auto 和 never. pg_test_timing — 度量计时开销 pg_test_timing [option…​] pg_test_timing接受下列命令行选项： -d duration --duration=duration 指定测试的持续时间，以秒计。更长的持续时间会给出更好一些的精确度，并且更可能发现系统时钟回退的问题。默认的测试持续时间是 3 秒。 -V --version 打印pg_test_timing版本并退出。 -? --help 显示有关pg_test_timing的命令行参数，然后退出。 好的结果将显示大部分（>90%）的单个计时调用用时都小于 1 微秒。每次循环的平均开销将会更低，低于 100 纳秒。下面的例子来自于一台使用了一份 TSC 时钟源码的 Intel i7-860 系统，它展示了非常好的性能： Testing timing overhead for 3 seconds. Per loop time including overhead: 35.96 ns Histogram of timing durations: &lt; us % of total count 1 96.40465 80435604 2 3.59518 2999652 4 0.00015 126 8 0.00002 13 16 0.00000 2 注意每次循环时间和柱状图用的单位是不同的。循环的解析度可以在几个纳秒（ns），而单个计时调用只能解析到一个微秒（us）。 当查询执行器使用 EXPLAIN ANALYZE 运行一个语句时，单个操作会被计时，总结也会被显示。你的系统的负荷可以通过使用psql程序计数行来检查： CREATE TABLE t AS SELECT * FROM generate_series(1,100000); \\timing SELECT COUNT(*) FROM t; EXPLAIN ANALYZE SELECT COUNT(*) FROM t; i7-860 系统测到运行该计数查询用了 9.8 ms 而 EXPLAIN ANALYZE 版本则需要 16.6 ms，每次处理都在 100,000 行上进行。6.8 ms 的差别意味着在每行上的计时负荷是 68 ns，大概是 pg_test_timing 估计的两倍。即使这样相对少量的负荷也造成了带有计时的计数语句耗时多出了 70%。在更大量的查询上，计时开销带来的问题不会有这么明显。 在一些较新的 Linux 系统上，可以在任何时候更改用来收集计时数据的时钟来源。第二个例子显示了在上述快速结果的相同系统上切换到较慢的 acpi_pm 时间源可能带来的降速： # cat /sys/devices/system/clocksource/clocksource0/available_clocksource tsc hpet acpi_pm # echo acpi_pm > /sys/devices/system/clocksource/clocksource0/current_clocksource # pg_test_timing Per loop time including overhead: 722.92 ns Histogram of timing durations: &lt; us % of total count 1 27.84870 1155682 2 72.05956 2990371 4 0.07810 3241 8 0.01357 563 16 0.00007 3 在这种配置中，上面的例子 EXPLAIN ANALYZE 用了 115.9 ms。其中有 1061 ns 的计时开销，还是用这个工具直接度量结果的一个小倍数。这么多的计时开销意味着实际的查询本身只占了时间的一个很小的分数，大部分的时间都耗在了计时所需的管理开销上。在这种配置中，任何涉及到很多计时操作的`EXPLAIN ANALYZE`都会受到计时开销的显著影响。 FreeBSD 也允许即时更改时间源，并且它会记录在启动期间有关计时器选择的信息： # dmesg | grep \"Timecounter\" Timecounter \"ACPI-fast\" frequency 3579545 Hz quality 900 Timecounter \"i8254\" frequency 1193182 Hz quality 0 Timecounters tick every 10.000 msec Timecounter \"TSC\" frequency 2531787134 Hz quality 800 # sysctl kern.timecounter.hardware=TSC kern.timecounter.hardware: ACPI-fast -> TSC 其他系统可能只允许在启动时设定时间源。在旧的 Linux 系统上，“clock”内核设置是做这类更改的唯一方法。并且即使在一些更近的系统上，对于一个时钟源你将只能看到唯一的选项 \"jiffies\"。Jiffies 是老的 Linux 软件时钟实现，当有足够快的计时硬件支持时，它能够具有很好的解析度，就像在这个例子中： $ cat /sys/devices/system/clocksource/clocksource0/available_clocksource jiffies $ dmesg | grep time.c time.c: Using 3.579545 MHz WALL PM GTOD PIT/TSC timer. time.c: Detected 2400.153 MHz processor. $ pg_test_timing Testing timing overhead for 3 seconds. Per timing duration including loop overhead: 97.75 ns Histogram of timing durations: &lt; us % of total count 1 90.23734 27694571 2 9.75277 2993204 4 0.00981 3010 8 0.00007 22 16 0.00000 1 32 0.00000 1 收集准确的计时信息在计算机上通常是使用具有不同精度的时钟硬件完成的。使用一些硬件，操作系统能几乎直接把系统时钟时间传递给程序。一个系统时钟也可以得自于一块简单地提供计时中断、在某个已知时间区间内的周期性滴答的芯片。在两种情况中，操作系统内核提供一个隐藏这些细节的时钟源。但是时钟源的精确度以及能多快返回结果会根据底层硬件而变化。 不精确的计时能够导致系统不稳定性。对任何时钟源的更改都要仔细地测试。操作系统默认是有时会更倾向于可靠性而不是最好的精确性。并且如果你在使用一个虚拟机器，应查看与之兼容的推荐时间源。在模拟计时器时虚拟硬件面临着额外的困难，并且提供商常常会建议每个操作系统的设置。 时间戳计数器（TSC）时钟源是当前一代 CPU 上最精确的一种。当操作系统支持 TSC 并且 TSC 可靠时，它是跟踪系统时间更好的方式。有多种方式会使 TSC 无法提供准确的计时源，这会让它不可靠。旧的系统能有一种基于 CPU 温度变化的 TSC 时钟，这让它不能用于计时。尝试在一些就的多核 CPU 上使用 TSC 可能在多个核心之间给出不一致的时间报告。这可能导致时间倒退，这个程序会检查这种问题。并且即使最新的系统，在非常激进的节能配置下也可能无法提供准确的 TSC 计时。 更新的操作系统可能检查已知的 TSC 问题并且当它们被发现时切换到一种更慢、更稳定的时钟源。如果你的系统支持 TSC 时间但是并不默认使用它，很可能是由于某种充分的理由才禁用它。某些操作系统可能无法正确地检测所有可能的问题，或者即便在知道 TSC 不精确的情况下也允许使用 TSC。 如果系统上有高精度事件计时器（HPET）并且 TSC 不准确，该系统将会更喜欢 HPET 计时器。计时器芯片本身是可编程的，最高允许 100 纳米的解析度，但是在你的系统时钟中可能见不到那么高的准确度。 高级配置和电源接口（ACPI）提供了一种电源管理（PM）计时器，Linux 把它称之为 acpi_pm。得自于 acpi_pm 的时钟最好时将能提供 300 纳秒的解析度。 在旧的 PC 硬件上使用的计时器包括 8254 可编程区间计时器（PIT）、实时时钟（RTC）、高级可编程中断控制器（APIC）计时器以及 Cyclone 计时器。这些计时器是以毫秒解析度为目标的。 pg_upgrade — 升级IvorySQL服务器实例 pg_upgrade -b oldbindir -B newbindir -d oldconfigdir -D newconfigdir [option…​] pg_upgrade接受下列命令行参数： -b bindir --old-bindir=bindir 旧的 IvorySQL 可执行文件目录； 环境变量 PGBINOLD -B bindir --new-bindir=bindir 新的 IvorySQL 可执行文件目录； 默认为pg_upgrade所在的目录； 环境变量 PGBINNEW -c --check 只检查集簇，不更改任何数据 -d configdir --old-datadir=configdir 旧的集簇数据目录；环境变量 PGDATAOLD -D configdir --new-datadir=configdir 新的集簇数据目录；环境变量 PGDATANEW -j --jobs=`njobs` 要同时使用的进程或线程数 -k --link 使用硬链接来代替将文件拷贝到新集簇 -o options --old-options options 直接传送给旧 postgres 命令的选项，多个选项可以追加在后面 -O options --new-options options 直接传送给新 postgres 命令的选项，多个选项可以追加在后面 -p port --old-port=port 旧的集簇端口号；环境变量 PGPORTOLD -P port --new-port=port 新的集簇端口号；环境变量 PGPORTNEW -r --retain 即使在成功完成后也保留 SQL 和日志文件 -s dir --socketdir=dir 用于升级期间postmaster套接字的目录;默认是当前目录; 环境变量 PGSOCKETDIR -U username --username=username 集簇的安装用户名；环境变量 PGUSER -v --verbose 启用详细的内部日志 -V --version 显示版本信息，然后退出 --clone 使用有效的文件克隆（在一些系统上也被称为“reflinks”），而不是将文件拷贝到新群集。 这可以导致数据文件接近瞬时的复制，从而获得 -k / --link 的速度优势，同时保留旧群集不受影响。文件克隆仅在某些操作系统和文件系统上得到支持。如果选中但不被支持，则 pg_upgrade运行将会出错。 目前，它支持在Linux（内核4.5或更高版本）上的Btrfs和XFS（在文件系统创建reflink支持），以及macOS上的APFS。 -? --help 显示帮助，然后退出 下面是用pg_upgrade执行一次升级的步骤： 1.移动旧集簇（可选） 如果你的安装目录不是版本相关的（例如 /usr/local/pgsql ）， 就有必要移动当前的 IvorySQL 安装目录，以免它干扰新的 IvorySQL安装。一旦当前的 IvorySQL服务器被关闭，就可以安全地重命名 IvorySQL 安装目录。假设旧目录是 /usr/local/pgsql，你可以这样： mv /usr/local/pgsql /usr/local/pgsql.old 来重命名该目录。 2.对于源码安装，编译新版本 用兼容旧集簇的 configure 标记编译新的 IvorySQL 源码。在开始升级之前，pg_upgrade 将检查 pg_controldata 来确保所有设置都是兼容的。 3.安装新的 IvorySQL 二进制文件 安装新服务器的二进制文件和支持文件。pg_upgrade 会被包含在默认的安装中。 对于源码安装，如果你希望把新服务器安装在一个自定义的位置， 可以使用 prefix 变量： make prefix=/usr/local/pgsql.new install 4.初始化新的 IvorySQL 集簇 使用 initdb 初始化新集簇。这里也要使用与 旧集簇相兼容的 initdb 标志。许多预编译的 安装程序会自动做这个步骤。这里没有必要启动新集簇。 5.安装扩展共享对象文件 许多扩展和自定义模块，无论是来自 contrib 或其他源，使用共享对象文件(或DLLs)，例如， pgcrypto.so。 如果旧集群使用过这些，匹配新服务器二进制的共享对象文件，必须安装在新集群中。 通常是通过操作系统命令。 不要加载模式定义，例如 CREATE EXTENSION pgcrypto，因为这些将从旧集群复制。 如果扩展更新是可用的，pg_upgrade将报告这一点，并创建一个脚本，可以稍后运行来更新它们。 6.拷贝定制的全文本检索文件 从旧集群向新集群拷贝任何定制化全文本检索文件(词典、同义词、辞典、停用词) 7.调整认证 pg_upgrade 将会多次连接到旧服务器和新服务器，因此 你可能想要在 pg_hba.conf 中把认证设置成 peer 或者使用一个 ~/.pgpass 文件。 8.停止两个服务器 确认两个数据库服务器都被停止使用，例如在 Unix 上可以： pg_ctl -D /opt/IvorySQL/1.5 stop pg_ctl -D /opt/IvorySQL/2.1 stop 或者在 Windows 上使用正确的服务名： NET STOP postgresql-1.5 NET STOP postgresql-2.1 直到后面的步骤之前，流复制和日志传送后备服务器可以保持运行。 9.为后备服务器升级做准备 升级后备服务器时，请对旧的主集簇和后备集簇运行pg_controldata以验证旧的后备服务器已经完全追上。验证“Latest checkpoint location”值在所有集簇中都匹配（如果旧后备服务器在旧的主服务器之前被关闭或者如果旧的后备服务器仍在运行，则将会出现失配）。此外，请确保在新的主集簇上的 postgresql.conf 文件中，wal_level 未设置为 minimal。 10.运行 pg_upgrade 总是应该运行新服务器而不是旧服务器的pg_upgrade二进制文件。 pg_upgrade要求制定新旧集簇的数据和可执行文件（ bin ）目录。 你也可以指定用户和端口值，以及你是否想要用链接或克隆来取代默认的复制行为对数据文件进行处理。 如果你使用链接模式，升级将会快很多（不需要文件拷贝）并且将使用 更少的磁盘空间，但是在升级后一旦启动新集簇，旧集簇就无法被访问。 链接模式也要求新旧集簇数据目录位于同一个文件系统中（表空间和 pg_wal 可以在不同的文件系统中）。 克隆模式提供了相同的速度以及磁盘空间优势，但不会导致新群集启动后旧群集不可用。 克隆模式还需要新旧数据目录位于同一文件系统中。 此模式仅在某些操作系统和文件系统上可用。 --jobs 选项允许多个 CPU 核心被用来复制/链接文件以及并行地转储和重载数据库模式。这个选项一个比较好的值是 CPU 核心数 和表空间数的最大值。这个选项可以显著地减少升级运行在一台多处理 器机器上的多数据库服务器的时间。 对于 Windows 用户，你必须以一个超级账号登录，并且以 `postgres`用户启动一个 shell 并且设置正确的路径： RUNAS /USER:postgres \"CMD.EXE\" SET PATH=%PATH%;C:\\Program Files\\IvorySQL\\14\\bin; 并且用带引号的目录运行pg_upgrade，例如： pg_upgrade.exe --old-datadir \"C:/Program Files/IvorySQL/1.5/data\" --new-datadir \"C:/Program Files/IvorySQL/2.1/data\" --old-bindir \"C:/Program Files/IvorySQL/1.5/bin\" --new-bindir \"C:/Program Files/IvorySQL/2.1/bin\" 一旦启动，pg_upgrade 将验证两个集簇是否兼容并且 执行升级。你可以使用 pg_upgrade --check 来只执行检查， 这种模式即使在旧服务器还在运行时也能使用。 pg_upgrade --check 也将列出任何在更新后需要做的手工调整。 如果你将要使用链接或克隆模式，你应该使用 --link 或 --clone 选项和 --check 一起来启用链接模式相关的检查。 pg_upgrade 要求在当前目录中的写权限。 显然，没有人可以在升级期间访问这些集簇。pg_upgrade 默认会在端口 50432 上运行服务器来避免意外的客户端连接。在做升级时， 可以对两个集簇使用相同的端口号，因为新旧集簇不会在同时被运行。不过， 在检查一个旧的运行中服务器时，新旧端口号必须不同。 如果在恢复数据库模式时发生错误，pg_upgrade 将会退出 并且你必须按照恢复旧集簇。要再次尝试 pg_upgrade，你将需要修改旧集簇，这样 pg_upgrade 模式会成功恢复。如果问题是一个 contrib 模块， 你可能需要从旧集簇中卸载该模块并且在升级后重新把它安装在新集簇中，不过 这样做的前提是该模块没有被用来存储用户数据。 11.升级流复制和日志传送后备服务器 如果使用链接模式并且有流复制或者日志 传送后备服务器，你可以遵照下面的 步骤对它们进行快速的升级。你将不用在这些后备服务器上运行 pg_upgrade，而是在主服务器上运行rsync。 到这里还不要启动任何服务器。 如果你没有使用链接模式、没有或不想使用rsync或者想用一种更容易的解决方案，请跳过这一节中的过程并且在pg_upgrade完成并且新的主集簇开始运行后重建后备服务器。 在后备服务器上安装新的 IvorySQL 二进制文件 确保新的二进制和支持文件被安装在所有后备服务器上。 确保不存在新的后备机数据目录 确保新的后备机数据目录不存在或者为空。如果 运行过initdb，请删除后备服务器的新数据目录。 安装扩展共享对象文件 在新的后备机上安装和新的主集簇中相同的扩展共享对象文件。 停止后备服务器 如果后备服务器仍在运行，现在使用上述的指令停止它们。 保存配置文件 从旧后备机的配置目录保存任何需要保留的配置文件，例如 `postgresql.conf`（以及它包含的任何文件）、 `postgresql.auto.conf`、`pg_hba.conf`， 因为这些文件在下一步中会被重写或者移除。 运行rsync 在使用链接模式时，后备服务器可以使用rsync快速升级。为了实现这一点，在主服务器上一个高于新旧数据库集簇目录的目录中为每个后备服务器运行这个命令： ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive old_cluster new_cluster remote_dir ``` 其中`old_cluster`和`new_cluster`是相对于主服务器上的当前目录的，而`remote_dir`是后备服务器上高于新旧集簇目录的一个目录。在主服务器和后备服务器上指定目录之下的目录结构必须匹配。指定远程目录的详细情况请参考rsync的手册，例如： ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive /opt/IvorySQL/1.5 \\ /opt/IvorySQL/2.1 standby.example.com:/opt/IvorySQL ``` 可以使用rsync的`--dry-run`选项验证该命令将做的事情。虽然在主服务器上必须为至少一台后备运行rsync，可以在一台已经升级过的后备服务器上运行rsync来升级其他的后备服务器，只要已升级的后备服务器还没有被启动。 这个命令所做的事情是记录由pg_upgrade的链接模式创建的链接，它们连接主服务器上新旧集簇中的文件。该命令接下来在后备服务器的旧集簇中寻找匹配的文件并且为它们在该后备的新集簇中创建链接。主服务器上没有被链接的文件会被从主服务器拷贝到后备服务器（通常都很小）。这提供了快速的后备服务器升级。不幸地是，rsync会不必要地拷贝与临时表和不做日志表相关的文件，因为通常在后备服务器上不存在这些文件。 如果你已经把`pg_wal`放在数据目录外面，也必须在那些目录上运行rsync。 配置流复制和日志传送后备服务器 为日志传送配置服务器（不需要运行`pg_start_backup()` 以及`pg_stop_backup()`或者做文件系统备份，因为从属机 仍在与主机同步）。 12.恢复 pg_hba.conf 如果你修改了 pg_hba.conf，则要将其恢复到原始的设置。 也可能需要调整新集簇中的其他配置文件来匹配旧集簇，例如 postgresql.conf （以及它包含的任何文件）和 postgresql.auto.conf。 13.启动新服务器 现在可以安全地启动新的服务器，并且可以接着启动任何rsync过的后备服务器。 14.升级后处理 如果需要做任何升级后处理，pg_upgrade 将在完成后发出警告。它也将 生成必须由管理员运行的脚本文件。这些脚本文件将连接到每一个需要做 升级后处理的数据库。每一个脚本应该这样运行： ``` psql --username=postgres --file=script.sql postgres ``` 这些脚本可以以任何顺序运行并且在运行之后立即删除。 小心 通常在重建脚本运行完成之前访问重建脚本中引用的表是不安全的，这样做 可能会得到不正确的结果或者很差的性能。没有在重建脚本中引用的表可以 随时被访问。 15.统计信息 由于 pg_upgrade 并未传输优化器统计信息，在升级的尾声 你将被指示运行一个命令来生成这些信息。你可能需要设置连接参数来匹配你的新集簇。 16.删除旧集簇 一旦你对升级表示满意，你就可以通过运行 pg_upgrade 完成时提到的脚本来删除旧集簇的 数据目录（如果在旧数据目录中有用户定义的表空间就不可能实现自动删除）。 你也可以删除旧安装目录（例如 bin、 share ）。 17.恢复到旧集簇 在运行 pg_upgrade 之后，如果你希望恢复到 旧集簇，有几个选项： 如果使用了 --check 选项, 则旧集群没有被修改；它可以被重新启动。 如果 --link 选项没有被使用, 旧集群没有被修改；它可以被重新启动。 如果使用了 --link 选项, 数据文件可能在新旧群集之间共享: 如果 pg_upgrade 在链接启动之前中止，旧集群没有被修改，它可以重新启动。 如果你没有启动新集群，旧集群没有被修改，当链接启动时，一个 .old 后缀会附加到 $PGDATA/global/pg_control。 如果要重用旧集群，从 $PGDATA/global/pg_control 移除 .old 后缀；你就可以重启旧集群。 如果你已经启动新群集，它已经写入了共享文件，并且使用旧群集会不安全。这种情况下，需要从备份中还原旧群集。 pg_upgrade创建不同的工作文件，如模式转储，在当前工作目录中。为了安全，请确保该目录不可被任何其他用户读取或者写入。 pg_upgrade在新旧数据目录中启动短期的postmasters。临时 Unix 套接字文件用于与这些postmasters通信，默认情况下，在当前工作目录中进行。 在某些情况下，当前目录的路径名称可能太长，无法成为有效的套接字名称。这种情况下你可以使用 -s 选项将套接字文件放在某些具有较短路径名称的目录中。 为了安全原因，请确保该目录不可被任何其他用户读取或者写入。（这在 Windows 上不受支持。） 如果失败、重建和重索引会影响你的安装，pg_upgrade 将会报告这些情况。用来重建表和索引的升级后脚本将会自动被建立。 如果你正在尝试自动升级很多集簇，你应该发现具有相同数据库模式的集簇 对所有集簇升级都要求同样的升级后步骤，这是因为升级后步骤是基于数据 库模式而不是用户数据。 对于部署测试，创建一个只有模式的旧集簇副本，在其中插入假数据并且升级。 pg_upgrade不支持包含使用这些 reg* OID-引用 系统数据类型的表列的数据库的升级： regcollation regconfig regdictionary regnamespace regoper regoperator regproc regprocedure (regclass, regrole, and regtype can be upgraded.) 如果你想要使用链接模式并且你不想让你的旧集簇在新集簇启动时被修改，考虑使用克隆模式。 如果(克隆模式)不可用，可以复制一份旧集簇并且在副本上以链接模式进行升级。要创建旧集簇的一 份合法拷贝，可以在服务器运行时使用 rsync 创建旧集簇的 一份脏拷贝，然后关闭旧服务器并且再次运行 rsync --checksum 把更改更新到该拷贝以让其一致（ --checksum 是必要的，因为 rsync 在判断文件修改时间的更改时的精度只能到秒级）。你可能想要排除 一些文件，例如 postmaster.pid。如果你的文件系统支持文 件系统快照或者 copy-on-write 文件副本，你可以使用它们来创建旧集簇和 表空间的一个备份，不过快照和副本必须被同时创建或者在数据库服务器关闭 期间被创建。 pg_waldump — 以人类可读的形式显示一个IvorySQL 数据库集簇的预写式日志 pg_waldump [option…​] [startseg [endseg]] 下列命令行选项控制输出的位置和格式： startseg 从指定的日志段文件开始读取。这也隐含地决定了要搜索文件的路径以及 要使用的时间线。 endseg 在读取指定的日志段文件后停止。 -b --bkp-details 输出有关备份块的细节。 -e end --end=end 在指定的WAL位置停止读取，而不是一直读取到日志流的末尾。 -f --follow 在到达可用 WAL 的末尾之后，保持每秒轮询一次是否有新的 WAL 出现。 -n limit --limit=limit 显示指定数量的记录，然后停止。 -p path --path=path 指定搜索日志段文件的目录或包含这些文件的包含 pg_wal 子目录的目录。 缺省值是在当前目录中搜索，当前目录的 pg_wal 子目录和 PGDATA 的 pg_wal 子目录。 -q --quiet 除错误外，不要打印任何输出。当您想知道一系列WAL记录是否可以成功解析但不关心记录内容时，此选项非常有用。 -r rmgr --rmgr=rmgr 只显示由指定资源管理器生成的记录。如果把 list 作为资源管理器名称 传递给这个选项，则打印出可用资源管理器名称的列表然后退出。 -s start --start=start 要从哪个WAL位置开始读取。默认是从找到的最早的文件的第一个可用日志记录开始。 -t `timeline` --timeline=`timeline` 要从哪个时间线读取日志记录。默认是使用 startseg （如果指定） 中的值，否则默认为 1。 -V --version 打印pg_waldump版本并且退出。 -x xid --xid=xid 只显示用给定事务 ID 标记的记录。 -z --stats[=record] 显示概括统计信息（记录的数量和尺寸以及全页镜像）而不是显示 每个记录。可以选择针对每个记录生成统计信息，而不是针对每个资源管理器生成。 -? --help 显示有关pg_waldump命令行参数的帮助并且退出。 PGDATA 数据目录；另请参阅 -p 选项。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 当服务器正在运行时可能会给出错误的结果。 只有指定的时间线 会被显示（如果没有指定，则显示默认时间线）。 其他时间线上的记录会被忽略。 pg_waldump不能读取具有后缀 .partial 的 WAL 文件。如果需要读取那些文件，需要从文件名中移除 .partial 后缀。 postgres — IvorySQL数据库服务器 postgres [选项…​] postgres 接受下列命令行参数。你也可以通过设置一个配置文件来减少键入大部分这些选项。有些（安全）选项还可以从连接的客户端以一种与应用相关只应用于会话的方法设置。例如，如果设置了 PGOPTIONS 环境变量，那么基于libpq的客户端将都把那个字符串传递给服务器，它将被服务器解释成 postgres 命令行选项。 -B nbuffers 设置被服务器进程使用的共享内存缓冲区数量。这个参数的默认值是initdb自动选择的。指定这个选项等效于设置 shared_buffers 配置参数。 -c name=value 设置一个命名的运行时参数。大多数其它命令行选项实际上都是这种参数赋值的短形式。-c 可以出现多次用于设置多个参数。 -C name 打印命名运行时参数的值，并且退出（详见上面的 -c 选项）。这可以被用在一个运行服务器上，并且从 postgresql.conf 中返回值，这些值可能被在这次调用中的任何参数修改过。它并不反映集簇启动时提供的参数。这个选项用于与一个服务器实例交互的其他程序来查询配置参数值，例如 pg_ctl。面向用户的应用应该使用 SHOW or the pg_settings 视图。 -d debug-level 设置调试级别。数值设置得越高，写到服务器日志的调试输出就越多。取值范围是从 1 到 5。还可以针对某个特定会话使用 -d 0 来阻止父 postgres 进程的服务器日志级别被传播到这个会话。 -D datadir 指定数据库配置文件的文件系统位置。 -e 把默认日期风格设置为“European”，也就是输入日期域的顺序是 DMY。这也导致在一些日期输出格式中把日打印在月之前。 -F 禁用 fsync 调用以提高性能，但是要冒系统崩溃时数据损坏的风险。指定这个选项等效于禁用 fsync 配置参数。 -h hostname 指定 postgres 监听来自客户端应用 TCP/IP 连接的 IP 主机名或地址。该值也可以是一个用逗号分隔的地址列表，或者 * 表示监听所有可用的地址。一个空值表示不监听任何 IP 地址，在这种情况下可以使用 Unix 域套接字连接到服务器。缺省只监听localhost。声明这个选项等效于设置listen_addresses配置参数。默认只监听localhost。指定这个选项等效于设置 listen_addresses 配置参数。 -i 允许远程客户端使用 TCP/IP （互联网域）连接。没有这个选项，将只接受本地连接。这个选项等效于在 postgresql.conf 中或者通过 -h 选项将 listen_addresses 设为 *。这个选项已经被废弃，因为它不允许访问 listen_addresses 的完整功能。所以最好直接设置 listen_addresses。 -k `directory` 指定 postgres 用来监听来自客户端应用连接的 Unix 域套接字的目录。这个值也可以是一个逗号分隔的目录列表。一个空值指定不监听任何 Unix 域套接字，在这种情况下只能用 TCP/IP 套接字来连接到服务器。默认值通常是 /tmp，但是可以在编译的时候修改。指定这个选项等效于设置 unix_socket_directories 配置参数。 -l 启用使用SSL的安全连接。要使这个选项可用，编译IvorySQL时必须打开SSL支持。有关使用SSL的更多信息。 -N max-connections 设置该服务器将接受的最大客户端连接数。该参数的默认值由initdb自动选择。指定这个选项等效于设置 max_connections 配置参数。 -p `port` 指定 postgres 用于监听客户端应用连接的 TCP/IP 端口或本地 Unix 域套接字文件扩展。默认为 PGPORT 环境变量的值。如果 PGPORT 没有设置，那么默认值是编译期间设立的值（通常是 5432）。如果你指定了一个非默认端口，那么所有客户端应用都必须用命令行选项或者 PGPORT 指定同一个端口。 -s 在每条命令结束时打印时间信息和其它统计信息。这个选项对测试基准和调节缓冲区数量有用处。 -S work-mem 指定在使用临时磁盘文件之前排序和散列表使用的基本内存量。 -V --version 打印postgres版本并退出。 --name=value 设置一个命名的运行时参数；其缩写形式是 -c。 --describe-config 这个选项会用制表符分隔的 COPY 格式导出服务器的内部配置变量、描述以及默认值。设计它的目的是用于管理工具。 -? --help 显示有关postgres的命令行参数，并且退出。 这里描述的选项主要被用于调试目的，并且在某些情况下可以协助恢复严重受损的数据库。在生产数据库环境中应该不会去使用它们。在这里列举它们只是为了让IvorySQL系统开发者使用。此外，这些选项可能在将来的版本中更改或删除而不另行通知。 -f { s | i | o | b | t | n | m | h } 禁止某种扫描和连接方法的使用：s 和 i 分别禁用顺序和索引扫描， o、 b 和 t 分别禁用只用索引扫描、位图索引扫描以及 TID 扫描，而 n、 m 和 h 则分别禁用嵌套循环、归并和哈希连接。顺序扫描和嵌套循环连接都不可能完全被禁用。-fs 和 -fn 选项仅仅是在有其他选择时不鼓励优化器使用这些计划类型。 -n 该选项主要用于调试导致服务器进程异常崩溃的问题。对付这种情况的一般策略是通知所有其它服务器进程，让它们终止并且接着重新初始化共享内存和信号量。这是因为一个错误的服务器进程可能在终止之前就已经对共享状态造成了破坏。该选项指定 postgres 将不会重新初始化共享数据结构。一个有经验的系统程序员这时就可以使用调试器检查共享内存和信号量状态。 -O 允许修改系统表的结构。这个选项用于 initdb。 -P 读取系统表时忽略系统索引（但在更改系统表时仍然更新索引）。这在从损坏的系统索引中恢复时有用。 -t pa[rser] | pl[anner] | e[xecutor] 打印与每个主要系统模块相关的查询的时间统计。这个选项不能和 -s 选项一起使用。 -T 该选项主要用于调试导致服务器进程异常崩溃的问题。对付这种情况的一般策略是通知所有其它服务器进程，让它们终止并且接着重新初始化共享内存和信号量。这是因为一个错误的服务器进程可能在终止之前就已经对共享状态造成了破坏。该选项指定 postgres 将通过发送 SIGSTOP 信号停止其他所有服务器进程，但是并不让它们终止。这样就允许系统程序员手动从所有服务器进程收集内核转储。 -v protocol 声明这次会话使用的前/后服务器协议的版本数。该选项仅在内部使用。 -W seconds 在一个新服务器进程被启动时，它实施认证过程之后会延迟这个选项所设置的秒数。这就留出了机会来用一个调试器附着在服务器进程上。 下面的选项仅适用于单用户模式（见 Single-User Mode）。 --single 选择单用户模式。这必须是命令行中的第一个选项。 database 指定要访问的数据库的名称。这必须是命令行中的最后一个参数。如果省略它，则默认为用户名。 -E 在执行命令之前回显所有命令到标准输出。 -j 使用跟着两个新行的分号而不是仅用新行作为命令终止符。 -r filename 将所有服务器日志输出发送到 filename 中。 只有在作为一个命令行选项提供时，这个选项才会兑现。 PGCLIENTENCODING 客户端使用的默认字符编码（客户端可以独立地覆盖它）。这个值也可以在配置文件中设置。 PGDATA 默认的数据目录位置。 PGDATESTYLE DateStyle 运行时参数的默认值（这个环境变量的使用已被废弃）。 PGPORT 默认端口号（在配置文件中设置更好） 一个提到了 semget 或 shmget 的错误消息可能意味着你需要配置内核来提供足够的共享内存和信号量。你也可以通过降低 shared_buffers 值减少IvorySQL的共享内存消耗， 或者降低 max_connections 值减少信号量消耗，这样可以推迟对内核的重新配置。 如果一个消息说另外一个服务器已经在运行，应该仔细地检查，例如根据你的系统可以用命令 $ ps ax | grep postgres 或 $ ps -ef | grep postgres 如果你确信没有冲突的服务器正在运行，那么你可以删除消息中提到的锁文件然后再次尝试。 如果一个失败消息指示它无法绑定到一个端口，可能意味着该端口已经被某些非IvorySQL进程所使用。如果你终止 postgres 并且立即使用相同的端口重启它，你也可能会得到这种错误。在这种情况系，你必须等待几秒直到操作系统关闭该端口，然后再重试。最后，如果你指定了一个操作系统认为需要保留的端口号，你可能也会得到这个错误。例如，很多版本的 Unix 认为低于 1024 的端口号是“可信的”并且只允许 Unix 超级用户访问它们。 实用命令 pg_ctl 可以用来安全方便地启动和关闭 postgres 服务器。 只要有可能，就不要使用 SIGKILL 杀死主 postgres 服务器。这样会阻止 postgres 在终止前释放它持有的系统资源（例如共享内存和信号量）。这样可能会导致启动新的 postgres 进程时出现问题。 要正常地终止 postgres 服务器，可以使用 SIGTERM、 SIGINT 或者 SIGQUIT 信号。第一个在退出前将等待所有客户端终止，第二个将强行断开所有客户端的连接，第三个会不做正确的关闭立即退出并且会导致重启时的恢复。 SIGHUP 信号会重新加载服务器配置文件。也可以向一个单独的服务器进程发送 SIGHUP 信号，但是这样做通常没什么意义。 要取消一个正在运行的查询，可以向运行该查询的进程发送 SIGINT 信号。要干净地终止一个后端进程，可向它发送`SIGTERM`。在 SQL 中可调用的与这两种动作等效的命令可参考 pg_cancel_backend 和 pg_terminate_backend。 postgres 服务器使用 SIGQUIT 来告诉子服务器进程终止但不做正常的清理。该信号不应该被用户使用。向一个服务器进程发送 SIGKILL 也是不明智的 — 主 postgres 进程将把这解释为一次崩溃，并且作为其标准崩溃恢复过程的一部分，它将强制所有的后代进程退出。 -- 选项在FreeBSD或OpenBSD上无法运行，应该使用 -c。这在受影响的系统里是个缺陷； 如果这个缺陷没有被修复，将来的IvorySQL版本将提供一种解决方案。 要启动一个单用户模式的服务器，使用这样的命令 postgres --single -D /usr/local/pgsql/data other-options my_database 用 -D 给服务器提供正确的数据库目录的路径，或者确保环境变量 PGDATA 被设置。同时还要指定你想在其中工作的特定数据库的名字。 通常，单用户模式的服务器会把换行符当做命令输入的终止符。它不明白分号的作用，因为那属于psql。要想把一个命令分成多行，必须在最后一个换行符以外的每个换行符前面敲一个反斜线。这个反斜线和旁边的新行都会被从输入命令中去掉。注意即使在字符串或者注释中也会这样做。 但是如果使用了 -j 命令行选项，那么单个新行将不会终止命令输入。相反，分号-新行-新行的序列才会终止命令输入。也就是说，输入一个紧跟着空行的分号。在这种模式下，反斜线-新行不会被特殊对待。此外，在字符串或者注释内的这类序列也不会被特殊对待。 不管在哪一种输入模式中，如果输入的一个分号不是正好在命令终止符之前或者不是命令终止符的一部分，它会被认为是一个命令分隔符。当真正输入一个命令终止符时，已经输入的多个语句将被作为一个单个事务执行。 要退出会话，输入EOF（通常是 Control + D ）。如果从上一个命令终止符以来已经输入了任何文本，那么EOF将被当作命令终止符，并且如果要退出则需要另一个EOF。 请注意单用户模式的服务器不会提供复杂的行编辑特性（例如没有命令历史）。但用户模式也不会做任何后台处理，例如自动检查点或者复制。 要用默认值在后台启动 postgres： $ nohup postgres >logfile 2>&1 </dev/null & 要用指定端口启动 postgres，例如 1234： $ postgres -p 1234 要使用psql连接到这个服务器，用 -p 选项指定这个端口： $ psql -p 1234 或者设置环境变量 PGPORT： $ export PGPORT=1234 $ psql 命名运行时参数可以用这些形式之一设置： $ postgres -c work_mem=1234 $ postgres --work-mem=1234 两种形式都覆盖 postgresql.conf 中可能存在的 work_mem 设置。请注意在参数名中的下划线在命令行可以写成下划线或连字符。除了用于短期的实验外，更好的习惯是编辑 postgresql.conf 中的设置， 而不是倚赖命令行开关来设置参数。","title":"工具参考","component":"ivorysql-doc","version":"master","name":"tools_reference","url":"/ivorysql-doc/master/tools_reference.html","titles":[{"text":"1. 工具一览表","hash":"工具一览表","id":1},{"text":"2. 客户端工具","hash":"客户端工具","id":2},{"text":"2.1. clusterdb","hash":"clusterdb","id":3},{"text":"2.1.1. 大纲","hash":"大纲","id":4},{"text":"2.1.2. 选项","hash":"选项","id":5},{"text":"2.1.3. 环境","hash":"环境","id":6},{"text":"2.1.4. 诊断","hash":"诊断","id":7},{"text":"2.1.5. 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pg_resetwal","hash":"pg_resetwal","id":156},{"text":"3.6.1. 大纲","hash":"大纲-26","id":157},{"text":"3.6.2. 选项","hash":"选项-24","id":158},{"text":"3.6.3. 环境","hash":"环境-22","id":159},{"text":"3.6.4. 注解","hash":"注解-13","id":160},{"text":"3.7. pg_rewind","hash":"pg_rewind","id":161},{"text":"3.7.1. 大纲","hash":"大纲-27","id":162},{"text":"3.7.2. 警告","hash":"警告-2","id":163},{"text":"3.7.3. 选项","hash":"选项-25","id":164},{"text":"3.7.4. 环境","hash":"环境-23","id":165},{"text":"3.7.5. 注解","hash":"注解-14","id":166},{"text":"3.7.6. 如何工作","hash":"如何工作","id":167},{"text":"3.8. pg_test_fsync","hash":"pg_test_fsync","id":168},{"text":"3.8.1. 大纲","hash":"大纲-28","id":169},{"text":"3.8.2. 选项","hash":"选项-26","id":170},{"text":"3.8.3. 环境","hash":"环境-24","id":171},{"text":"3.9. pg_test_timing","hash":"pg_test_timing","id":172},{"text":"3.9.1. 大纲","hash":"大纲-29","id":173},{"text":"3.9.2. 选项","hash":"选项-27","id":174},{"text":"3.9.3. 用法","hash":"用法","id":175},{"text":"3.9.4. 结果解读","hash":"结果解读","id":176},{"text":"3.9.5. 度量执行器计时开销","hash":"度量执行器计时开销","id":177},{"text":"3.9.6. 改变时间来源Changing time sources","hash":"改变时间来源changing-time-sources","id":178},{"text":"3.9.7. 时钟硬件和计时准确性","hash":"时钟硬件和计时准确性","id":179},{"text":"3.10. pg_upgrade","hash":"pg_upgrade","id":180},{"text":"3.10.1. 大纲","hash":"大纲-30","id":181},{"text":"3.10.2. 选项","hash":"选项-28","id":182},{"text":"3.10.3. 使用","hash":"使用","id":183},{"text":"3.10.4. 注解","hash":"注解-15","id":184},{"text":"3.11. pg_waldump","hash":"pg_waldump","id":185},{"text":"3.11.1. 大纲","hash":"大纲-31","id":186},{"text":"3.11.2. 选项","hash":"选项-29","id":187},{"text":"3.11.3. 环境","hash":"环境-25","id":188},{"text":"3.11.4. 注解","hash":"注解-16","id":189},{"text":"3.12. postgres","hash":"postgres","id":190},{"text":"3.12.1. 大纲","hash":"大纲-32","id":191},{"text":"3.12.2. 选项","hash":"选项-30","id":192},{"text":"3.12.3. 通用选项","hash":"通用选项","id":193},{"text":"3.12.4. 半内部选项","hash":"半内部选项","id":194},{"text":"3.12.5. 用于单用户模式的选项","hash":"用于单用户模式的选项","id":195},{"text":"3.12.6. 环境","hash":"环境-26","id":196},{"text":"3.12.7. 诊断","hash":"诊断-10","id":197},{"text":"3.12.8. 注解","hash":"注解-17","id":198},{"text":"3.12.9. 缺陷","hash":"缺陷","id":199},{"text":"3.12.10. 单用户模式","hash":"单用户模式","id":200},{"text":"3.12.11. 例子","hash":"例子-13","id":201}]},"11":{"id":11,"text":"IvorySQL 是唯一一款基于 PostgreSQL 研发的兼容 Oracle 的数据库。 从 Github 下载源码开始。 请前往 IvorySQL 发布页面。 IvorySQL 项目是瀚高软件提出的一个开源项目，旨在将 Oracle 兼容性功能添加到流行的 PostgreSQL 数据库中。 IvorySQL 开源并且可以免费使用，如果您有任何建议请联系 support@ivorysql.org IvorySQL v5.0 pdf 文档","title":"欢迎来到 IvorySQL","component":"ivorysql-doc","version":"master","name":"welcome","url":"/ivorysql-doc/master/welcome.html","titles":[{"text":"开始","hash":"开始","id":1},{"text":"发布","hash":"发布","id":2},{"text":"关于 IvorySQL","hash":"关于-ivorysql","id":3},{"text":"文档下载","hash":"文档下载","id":4}]},"12":{"id":12,"text":"为了满足PG模式和兼容Oracle模式， IvorySQL在initdb时，可以指定-m参数，来获取PG模式数据库或Oracle兼容模式数据库。 不指定-m参数时，默认为Oracle兼容模式 指定-m参数为pg时，数据库将不再兼容Oracle语法 initdb -m 初始化，需要判断不同的模式，其中Oracle模式下，需要执行postgres_oracle.bki的SQL语句; 启动时会根据初始化模式，判断是否为oracle兼容模式。 database_mode：用于表示初始化模式； database_mode=DB_PG，PG模式，且不可切换； database_mode=DB_ORACLE，Oracle兼容模式； 初始化PG模式： ./initdb -D ../data -m pg 初始化oracle兼容模式： ./initdb -D ../data -m oracle 或 ./initdb -D ../data","title":"双模式设计","component":"ivorysql-doc","version":"master","name":"dual_mode_design","url":"/ivorysql-doc/master/architecture/dual_mode_design.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"13":{"id":13,"text":"双parser框架主要分为两部分，包括SQL端和服务器端编程语言。 基本做法是新增一套兼容Oracle风格的语法和词法，在开启Oracle兼容的情况下，走Oracle风格的语法分析，生成相应的语法树。 具体方法： 在src/backend/下面，新建一个oracle_parser目录，将src/backend/parser/下的scan.l和gram.y复制到该目录下，改名成ora_gram.y和ora_scan.l，添加 Oracle风格的语法和词法分析代码，同时复制keywords.c到该目录下，用来存放自己的关键字。该oracle_parser目录编译成一个动态库 libparser_oracle.so。当开启Oracle兼容的时候，配置文件ivorysql.conf被嵌入到postgresql.conf文件的末尾。配置文件ivorysql.conf中的shared_preload_libraries参数中添加“liboracle_parser”，这样当数据库启动时能够自动载入liboracle_parser动态库。 新增ora_raw_parser 函数指针，当libparser_oracle.so动态库被加载时，该动态库中的 _PG_init() 函数将 oracle_raw_parser() 函数的地址赋值给 ora_raw_parser，_PG_fini()则在兼容模式切换时负责重置 ora_raw_parser 为空。 每一个后端进程调用BackendInitialize()函数，通过后端进程所连接的端口号来设置 port→connmode。如果端口是Oracle兼容端口，则设置connmode为’o’，否则设置为’p’。 这之后PostgresMain()调用InitIvorysql()，如果port→connmode是’o’，则调用函数 SetConfigOption(\"ivorysql.compatible_mode\", \"oracle\", PGC_USERSET, PGC_S_OVERRIDE); 由于这个参数设置了 assign_hook，因此当 SetConfigOption() 函数里执行assign_hook()时，相当于调用 assign_compatible_mode()，从而设置 sql_raw_parser = ora_raw_parser; 在对SQL语句进行分析时，函数pg_parse_query()→raw_parser() 通过函数指针 sql_raw_parser 调用standard parser() 或者 ora_raw_parser()。 下面的图演示了SQL语句分析时发生的事情。 在系统表pg_language中新增pliSQL语言。 具体实现方法： 将PG源码中的plpgsql目录，复制一份，改名为plisql，里面的文件名称，改成plisql开头，因为plpgsql是一个language，改造的plisql也是一个language，因此，plpgsql language的注册函数如plpgsql_validator, plpgsql_call_handler, plpgsql_inline_handler都需要改成plisql开头，里面其他函数的改名都改成以plisql开头。 plisql目录构建为一个插件，initdb时如果数据库模式是Oracle，则创建这个插件。这个插件会将pliSQL语言注册到数据库的系统表中。 pliSQL端没有自己的词法规约，它依赖SQL端的词法规约，因此，针对pliSQL，强制其走兼容Oracle的词法规约，主要改造点是在 plisql_scanner_init 函数中，里面应该调用ora_scanner_init()函数，plisql目录中的 internal_yylex() 函数调用 ora_core_yylex。 pliSQL语法规则在plisql/src/pl_gram.y中，兼容Oracle plSQL块的语法规则都在这个模块中实现。 SQL端创建函数，没有指定language的时候，如果当前是兼容Oracle模式，则默认language是plisql，如果是兼容pg模式，则默认language是plpgsql。oracle_parser中的ora_gram.y默认是plisql，pg parser中的gram.y默认是plpgsql。 匿名块没有指定language的时候，如果是兼容Oracle模式，默认plisql，如果是兼容pg模式，则默认plpgsql。 处理过程的ExecuteDoStmt函数同样根据兼容模式来决定默认language。","title":"Oracle兼容框架之双parser","component":"ivorysql-doc","version":"master","name":"dual_parser","url":"/ivorysql-doc/master/architecture/dual_parser.html","titles":[{"text":"1. SQL端词法语法分离","hash":"sql端词法语法分离","id":1},{"text":"2. 服务器端编程语言词法语法分离","hash":"服务器端编程语言词法语法分离","id":2}]},"14":{"id":14,"text":"在对原有的PostgreSQL改动最小的前提下，实现对Oracle兼容。我们需要实现双parser、双端口、模式PL/PGSQL实现PL/iSQL的框架。实现流程图如下： 保持了Ivorysql 5432端口兼容原有postgres情况，因此 Ivorysql采用另一个独立的端口登录，默认为1521。从该端口登录，默认采用Oracle兼容模式。如果需要从5432端口登录且还要进入兼容模式，则需要通过SET ivorysql.compatible_mode=oracle进行设置。 为了将Oracle语法与PostgreSQL语法间的相互干扰降到最低，因此新增parser模块，用于处理Oracle相关的语法。 新增PL/iSQL过程语言兼容Oracle的PL/SQL过程语言。","title":"Ivorysql框架设计","component":"ivorysql-doc","version":"master","name":"framework_design","url":"/ivorysql-doc/master/architecture/framework_design.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"2.1. 双端口设计","hash":"双端口设计","id":3},{"text":"2.2. parser模块设计","hash":"parser模块设计","id":4},{"text":"2.3. 新增PL/iSQL过程语言","hash":"新增plisql过程语言","id":5}]},"15":{"id":15,"text":"为了兼容Oracle，需要在原有的GUC变量基础之上增加一些用于控制数据库执行结果的变量，以达到和Oracle行为一致的目的。 为了更好地添加兼容的guc参数，以及为了更少地改动pg内核源码，我们需要设计一个框架将guc添加到一个统一的地方。 添加兼容版本的guc参数时，我们需统一在 ivy_guc.c 文件中添加。其中 Ivy_ConfigureNamesBool、Ivy_ConfigureNamesInt、Ivy_ConfigureNamesString、Ivy_ConfigureNamesReal 和 Ivy_ConfigureNamesEnum 分别表示5种不同类型的guc参数，添加guc参数时，将guc的值添加到对应数组中即可。 变量名 描述 ivorysql.compatible_mode 表示当前兼容哪种数据库（pg/oracle），可以通过show命令查看，set命令更改该变量，reset命令重置为连接时的数据库模式，reset all会影响该变量 ivorysql.database_mode 表示当前数据库的模式（pg/oracle），可以通过show命令查看，set/reset/reset all命令不影响该变量 ivorysql.datetime_ignore_nls_mask 表示日期格式是否会受NLS参数影响，默认为0，可以通过set命令设置，reset 命令重置，reset all命令会重置该变量 ivorysql.enable_emptystring_to_NULL 取值为（on/off）,该变量为on时，会将插入的空字符串转成NULL值存储 ivorysql.identifier_case_switch 设置字符大小写转换模式 ivorysql.listen_address 表示兼容模式监听的地址，在初始化数据库时，从ivorysql.conf文件中读取该配置，在配置文件中修改该值，需要重启数据库生效，可以通过show命令查看 ivorysql.port 表示兼容模式下连接的端口号，在初始化数据库时，从ivorysql.conf文件中读取该配置，在配置文件中修改该值，需要重启数据库生效，可以通过show命令查看 nls_date_format 表示默认的日期格式，可以通过show命令查看，默认为‘YYYY-MM-DD’，可以通过set命令设置，可以通过reset命令重置回默认值，reset all 命令会重置该变量 nls_length_semantics 兼容Oracle的同名参数，控制一个字符所占内存的大小 nls_timestamp_format 兼容Oracle的同名参数，控制带时间的日期格式 nls_timestamp_tz_format 兼容Oracle的同名参数，控制带时区的日期格式 shared_preload_libraries 在初始化数据库时，从ivorysql.conf文件中读取，可以通过show命令查看，在配置文件中修改该值，重启数据库生效。 该GUC变量的可选值为0-15 可选值 不经过nls格式化的类型 0 不屏蔽任何类型，所有时间格式均由nls格式化 1 date 2 timestamp 3 date、timestamp 4 timestamptz 5 date、timestamptz 6 timestamp、timestamptz 7 date、timestamp、timestamptz 8 timestampltz 9 date、timestampltz 10 timestamp、timestampltz 11 date、timestamp、timestampltz 12 timestamptz、timestampltz 13 date、timestamptz、timestampltz 14 timestamp、timestamptz、timestampltz 15 date、timestamp、timestamptz、timestampltz 使用方法(以date为例) 查看nls date 的格式及datetime_ignore_nls_mask值 ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# show nls_date_format; nls_date_format ----------------- YYYY-MM-DD (1 row) ivorysql=# show ivorysql.datetime_ignore_nls_mask; ivorysql.datetime_ignore_nls_mask ----------------------------------- 0 (1 row) 创建测试表 ivorysql=# create table test_nls_date(a int, created_at date); CREATE TABLE 插入数据 ivorysql=# insert into test_nls_date values(1, '2024/04/05'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 2024-04-05 (1 row) 修改nls_date_format ivorysql=# set nls_date_format to 'yy-mm-dd'; SET 插入nls格式数据并查看，插入成功 ivorysql=# insert into test_nls_date values(2, '24/04/15'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows) 将date类型改为不经过nls处理，插入相同格式数据，如改成 1 (3,5,7等都可以)，插入数据将报错，nls格式化将不对date的查询结果起效。 ivorysql=# set ivorysql.datetime_ignore_nls_mask to 1; SET ivorysql=# insert into test_nls_date values(3, '24/05/15'); ERROR: date/time field value out of range: \"24/05/15\" LINE 1: insert into test_nls_date values(3, '24/05/15'); ^ HINT: Perhaps you need a different \"datestyle\" setting. ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows)","title":"GUC框架","component":"ivorysql-doc","version":"master","name":"guc_framework","url":"/ivorysql-doc/master/architecture/guc_framework.html","titles":[{"text":"1. 新增GUC变量","hash":"新增guc变量","id":1},{"text":"1.1. 实现","hash":"实现","id":2},{"text":"1.2. 新增变量（目前）","hash":"新增变量目前","id":3},{"text":"1.3. 部分变量使用示例","hash":"部分变量使用示例","id":4},{"text":"1.3.1. ivorysql.datetime_ignore_nls_mask","hash":"ivorysql-datetime_ignore_nls_mask","id":5}]},"16":{"id":16,"text":"IvorySQL 在初始化过程中支持两种数据库模式： PG 模式：保持与原生 PostgreSQL 的兼容性 Oracle 模式（默认）：提供 Oracle 语法兼容及增强功能 用户可通过 initdb 命令参数指定初始化模式，实现不同场景下的兼容性需求。 initdb在开始阶段会解析输入的命令行参数。 参数 功能描述 可选值 默认值 -m 指定数据库模式 oracle/pg oracle -C 设置 Oracle 兼容的大小写转换模式 interchange/normal/lowercase interchange 参数解析流程： 1.继承 PostgreSQL 原有参数处理机制 2.新增模式选择参数 -m 的解析逻辑 3.增加大小写转换参数 -C 的处理模块 执行 setup_data_file_paths() 函数完成关键文件路径配置： if (DB_PG == database_mode) set_input(&bki_file, \"postgres.bki\"); else set_input(&bki_file, \"postgres_oracle.bki\"); 路径检查机制： 1.验证 BKI 文件的存在性（postgres_oracle.bki / postgres.bki） 2.确认配置文件模板的可用性 3.建立与数据库模式对应的系统目录结构 通过 initialize_data_directory() 函数完成核心初始化操作： 调用 create_data_directory() 创建主数据目录（PGDATA）。 通过 create_xlog_or_symlink() 建立 WAL 日志目录。 循环创建 base, global 等标准子目录。 调用set_null_conf()创建空的postgresql.conf。 Oracle 模式下额外创建 ivorysql.conf 配置文件。 调用setup_config()将配置信息写入postgresql.conf。在写入配置时，如果是oracle模式，则会额外向ivorysql.conf中写入配置信息。 执行 bootstrap_template1() 加载对应模式的 BKI 文件初始化template1模板数据库， IvorySQL会额外设置template1模板数据库的数据库模式(oracle/pg)和大小写转换模式。 load_plisql()：安装兼容 Oracle PL/SQL 的 PL/iSQL 过程语言 load_ivorysql_ora()：加载核心Oracle兼容层扩展 make_ivorysql()：创建默认的 ivorysql 数据库 当数据库模式是oracle时，会对用户名进行强制小写转换 提示 在 PostgreSQL 中，\"bki\" 是 \"Bootstrap Kit\" 的简写。Bootstrap Kit 是 PostgreSQL 内部用于引导（bootstrap）数据库系统的一组工具和文件集合。 Bootstrap Kit 主要用于初始化 PostgreSQL 数据库系统的基础结构，包括系统目录（system catalog）、系统表（system table）以及其他必要的系统对象。这些系统目录和表存储了 PostgreSQL 数据库系统的元数据信息，如数据库对象、表结构、索引信息等。 genbki.pl 处理系统表文件如pg_class.h,pg_namespace.h,pg_proc.h等，并生成postgres.bki供initdb加载，主要是加载初始化数据，如创建系统表，系统函数和预定义类型等。 在initdb前的编译阶段，会通过genbki.pl生成postgres.bki 和 postgres_ora.bki，postgres.bki保持PG原样，postgres_ora.bki在pg的基础上增加oracle 兼容的系统对象。","title":"initdb过程","component":"ivorysql-doc","version":"master","name":"initdb_process","url":"/ivorysql-doc/master/architecture/initdb_process.html","titles":[{"text":"1. 参数解析处理","hash":"参数解析处理","id":1},{"text":"2. 文件路径初始化","hash":"文件路径初始化","id":2},{"text":"3. 数据目录初始化流程","hash":"数据目录初始化流程","id":3},{"text":"3.1. 目录结构创建","hash":"目录结构创建","id":4},{"text":"3.2. 配置文件初始化","hash":"配置文件初始化","id":5},{"text":"3.3. 模板数据库引导","hash":"模板数据库引导","id":6},{"text":"3.4. Oracle兼容用户名处理","hash":"oracle兼容用户名处理","id":7}]},"17":{"id":17,"text":"IvorySQL Cloud平台是一个综合性的解决方案，它集成了IvorySQL数据库以及周边生态，以提供全面的数据库和资源管理功能。安装前需要上github编译安装好 前端：https://github.com/IvorySQL/ivory-cloud-web 后端：https://github.com/IvorySQL/ivory-cloud 搭建好K8S集群，并在集群master节点上安装ivory-operator https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE IvorySQL Cloud平台，目前支持Linux系统下的安装，以下为各部分对应的安装包： 组件 安装包 前端 dist 后端 cloudnative-1.0-SNAPSHOT.jar K8S集群 docker.io/ivorysql/ivory-operator:v5.0 docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 另外，云服务平台还需要用户安装以下组件： 后端数据库：负责存储和管理所有与云资源、用户信息、权限控制、计费信息等相关的数据。需要使用PG系列数据库，如PostgreSQL、瀚高数据库、IvorySQL等。 NGINX：支持云服务平台的web服务。 安装前，所有服务器都必须完成以下准备工作。并且将IvorySQL Cloud平台部署在K8S（1.23）的服务器上，且该K8S集群需要有默认的storage class. 所有服务器关闭防火墙，以保证他们之间的网络互通。 systemctl stop firewalld.service systemctl disable firewalld.service IvorySQL Cloud平台的后端数据库需用户自行安装，请参考IvorySQL官网。 # 克隆代码 git clone https://github.com/IvorySQL/ivory-cloud.git # 进入到项目根目录 cd ivory-cloud 请确保ivory-cloud\\cloudnative\\src\\main\\resources\\monitor文件夹，及其所有的子文件夹下的以.sh结尾的文件是unix格式的，如果不是，请执行dos2unix命令转换成unix格式。 dos2unix cloudnative\\src\\main\\resources\\monitor\\* # 编译 mvn clean mvn package -D maven.test.skip=true 打包完成后，可以在 ivory-cloud/cloudnative/target下找到cloudnative-1.0-SNAPSHOT.jar文件 在K8S服务器上执行如下操作： # 创建目录 mkdir -p /home/ivory # 将上一步编译好的文件ivory-cloud/cloudnative/target/cloudnative-1.0-SNAPSHOT.jar上传至上述目录 # 配置文件 ## 创建配置目录 mkdir -p /home/ivory/config ## 上传配置文件 将源代码ivory-cloud/cloudnative/src/main/resources目录里的如下三个配置文件上传至 /home/ivory/config application.yaml application-native.yaml spring_pro_logback.xml ## 修改配置文件，请将url、username、password修改为<<backend-db,后端数据库章节>>安装的数据库的信息。 ## /home/ivory/config/application-native.yaml datasource: druid: db-type: com.alibaba.druid.pool.DruidDataSource driver-class-name: org.postgresql.Driver url: jdbc:postgresql://127.0.0.1:5432/ivorysql username: ivorysql password: \"ivory@123\" # 安装jdk1.8 yum install -y java-1.8.0-openjdk.x86_64 [root@cloud ivory]# pwd /home/ivory/ [root@cloud ivory]# nohup java -jar cloudnative-1.0-SNAPSHOT.jar > log_native 2>&1 & [root@cloud ivory]# ps -ef |grep java root 77494 1 0 10月09 ? 00:03:07 java -jar cloudnative-1.0-SNAPSHOT.jar ## 获取代码 git clone https://github.com/IvorySQL/ivory-cloud-web.git ## 进入项目根目录 cd ivorysql-cloud-web ## 安装依赖 npm install ## 编译打包 npm run build:prod # 创建目录 [root@cloud opt]# mkdir -p /opt/cloud/web # 将前端构建后的dist文件夹置于/opt/cloud/web # 授权 [root@cloud web]# chmod 755 /opt/cloud/web/dist [root@cloud web]# chmod -R 777 /opt/cloud/web/dist 修改文件 [root@cloud dist]# pwd /home/cloud/web/dist [root@cloud dist]# vi config.js var PLATFROM_CONFIG = {}; // ip请更换为当前服务器地址 PLATFROM_CONFIG.baseUrl = \"http://192.168.31.43:8081/cloudapi/api/v1\" //true: need to show \"注册\" on login page //false: don't show \"注册\" on login page globalShowRegister = true //是否隐藏云原生数据库， true: 隐藏云原生数据库；false:显示云原生数据库 disableNative = false // 数据库类型 dbtype = \"IvorySQL\" dbversion = \"5.0\" IvorySQL Cloud平台服务器需要安装nginx，以支持云服务平台的web服务。nginx需要用户自行安装，这里提供一种安装方法作为参考。 [root@cloud web]# wget https://nginx.org/download/nginx-1.20.1.tar.gz [root@cloud web]# ls -lrt 总用量 3924 -rwxrwxr-x. 1 root root 1061461 5月 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 10月 9 16:43 dist.tar.gz drwxrwxrwx. 4 root root 103 10月 21 13:20 dist [root@host30 cloud]# yum -y install pcre-devel [root@host30 cloud]# yum -y install openssl openssl-devel nginx会被安装在configure时由—​prefix指定的目录下，例如这里的/opt/cloud/nginx： ## 解压缩nginx-1.20.1.tar.gz安装包 [root@cloud web]# tar -zxvf nginx-1.20.1.tar.gz ## 解压后生成nginx-1.20.1文件夹 [root@cloud web]# ls -lrt 总用量 3924 -rwxrwxr-x. 1 root root 1061461 5月 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 10月 9 16:43 dist.tar.gz drwxrwxr-x. 9 1001 1001 186 10月 9 16:53 nginx-1.20.1 drwxrwxrwx. 4 root root 103 10月 21 13:20 dist ## 配置导向 [root@cloud web]# cd nginx-1.20.1 [root@cloud nginx-1.20.1]# ./configure --prefix=/opt/cloud/nginx --with-http_ssl_module ## 编译安装 [root@cloud nginx-1.20.1]# make [root@cloud nginx-1.20.1]# make install 配置文件在/opt/cloud/nginx路径下，可以按照github上readme对nginx.conf进行对比修改。ip请配置为当前服务器的ip。 server { listen 9104; server_name 192.168.31.43; location / { root /opt/cloud/web/dist; index index.html index.htm; } error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } } [root@cloud sbin]# pwd /opt/cloud/nginx/sbin [root@cloud sbin]# ./nginx -c /opt/cloud/nginx/conf/nginx.conf [root@cloud sbin]# ps -ef | grep nginx root 2179 131037 0 09:46 pts/1 00:00:00 grep --color=auto nginx root 55047 1 0 10月21 ? 00:00:00 nginx: master process ./nginx -c /opt/cloud/nginx/conf/nginx.conf nobody 55048 55047 0 10月21 ? 00:00:00 nginx: worker process 请自行搭建K8S，此处描述为在K8S集群上安装ivory-operator和load镜像。 参见 https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE 网站上的readme 如果服务器可以直接访问到docker hub,可以跳过该章节。否则需要在所有的K8S集群节点提前load 如下docker镜像 docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/pgbouncer:ubi8-1.23.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 docker.io/prom/prometheus:v2.33.5 docker.io/prom/alertmanager:v0.22.2 docker.io/grafana/grafana:8.5.10","title":"安装说明","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_installation","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html","titles":[{"text":"1. IvorySQL Cloud平台安装","hash":"ivorysql-cloud平台安装","id":1},{"text":"2. 安装前准备","hash":"安装前准备","id":2},{"text":"2.1. 关闭防火墙","hash":"关闭防火墙","id":3},{"text":"2.2. 后端部署","hash":"后端部署","id":4},{"text":"2.2.1. 后端数据库","hash":"后端数据库","id":5},{"text":"2.2.2. 后端服务程序","hash":"后端服务程序","id":6},{"text":"2.2.2.1. 编译后端服务程序","hash":"编译后端服务程序","id":7},{"text":"2.2.2.2. 部署程序","hash":"部署程序","id":8},{"text":"2.2.3. 启动后端服务程序","hash":"启动后端服务程序","id":9},{"text":"2.3. 前端部署","hash":"前端部署","id":10},{"text":"2.3.1. 编译前端服务程序","hash":"编译前端服务程序","id":11},{"text":"2.3.2. 修改目录和文件权限","hash":"修改目录和文件权限","id":12},{"text":"2.3.3. 修改config.js","hash":"修改config-js","id":13},{"text":"2.4. 安装部署nginx","hash":"安装部署nginx","id":14},{"text":"2.4.1. 下载nginx安装包","hash":"下载nginx安装包","id":15},{"text":"2.4.2. 安装相关依赖","hash":"安装相关依赖","id":16},{"text":"2.4.3. 编译安装nginx","hash":"编译安装nginx","id":17},{"text":"2.4.4. 修改配置文件nginx.conf","hash":"修改配置文件nginx-conf","id":18},{"text":"2.4.5. 启动nginx","hash":"启动nginx","id":19},{"text":"2.5. operator部署","hash":"operator部署","id":20},{"text":"2.5.1. 安装ivory-operator","hash":"安装ivory-operator","id":21},{"text":"2.5.2. load镜像","hash":"load镜像","id":22}]},"18":{"id":18,"text":"IvorySQL Cloud是一个基于Web的服务平台，用户可以在任意电脑上通过浏览器进行访问，安装云服务平台的服务器IP为192.168.31.43，然后在浏览器输入http://192.168.31.43:9104/（9104为前端nginx.conf.default中设置的端口）即可进入登录页面页面： 进入登录页面，根据提示输入信息即可访问IvorySQL云服务平台： 点击页面右上角的头像，会显示当前登录用户名和“Log Out”。点击“Log Out”退出当前登录页面。点击用户名则保留当前登录页面： 平台登录admin用户后，点击左侧菜单栏的“K8S集群管理”按钮，进入集群管理页。 点击页面左上角“增加Kubernetes集群”按钮，输入集群信息后提交。 在集群管理页面，可以查看已添加集群的详细信息，同时对集群信息进行编辑和删除操作。 使用demo用户登录平台。 点击左侧菜单栏\"数据库订阅\"按钮，填写需要申请的数据库的各个参数，然后点击\"下一步：确认信息\"。 检查信息后，点击\"确定\" \"确定\"后，会自动跳转到\"数据库管理\"页面，查看订阅任务 显示云服务平台管理的数据库信息。 demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"重启\"选项。 检查信息后，点\"确认\" demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"实例ID\"列。 进入数据库详情页面，点击\"修改密码\"列的图标。 输入新的数据库密码，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"删除实例\"选项。 检查信息后，点击\"确定\"按钮。 此功能需用户自行配置相关插件，如topolvm demo用户登录平台。 点击左侧导航栏\"磁盘扩容\"，选中数据库，点击\"操作\"列的\"修改\"按钮；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"磁盘扩容\"选项。 检查并输入磁盘扩容的大小后，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"规格变更\"，选中数据库，点击\"操作\"列的\"修改\"按钮；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"规格变更\"选项。 检查信息并输入规格变更的大小后，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"数据库备份\"，选中数据库，点击\"操作\"列的\"备份\"；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"备份\"选项。 检查信息并输入备份名称后，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"数据库恢复\"，选中数据库，点击\"操作\"列的\"查看\"按钮；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"恢复\"选项。 选择需要备份的文件后，点\"操作\"列的\"恢复\" 输入需要恢复的数据库信息，密码为备份前的数据库密码 后续参考\"4.1数据库订阅\"的步骤。 demo用户登录平台。 点击左侧导航栏\"监控工具\"，选择数据库所在的集群；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"监控\"选项。 等待监控创建完毕后，再次重复（2）中操作，跳转新页面，输入用户名：admin，密码admin，点击login登录 选择放大镜图标，点击查看监控指标。 demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，选择\"登录\"选项。 跳转新页面后，输入数据库用户名 sysdba@ivyo.com 及数据库密码，点击\"Login\"按钮。 连接数据库，即可进行操作。","title":"使用说明","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_usage","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html","titles":[{"text":"1. 登录和退出","hash":"登录和退出","id":1},{"text":"1.1. 用户登录","hash":"用户登录","id":2},{"text":"1.2. 退出登录","hash":"退出登录","id":3},{"text":"2. 管理员功能","hash":"管理员功能","id":4},{"text":"2.1. 添加集群","hash":"添加集群","id":5},{"text":"2.2. 管理集群","hash":"管理集群","id":6},{"text":"3. demo用户功能","hash":"demo用户功能","id":7},{"text":"3.1. 数据库订阅","hash":"数据库订阅","id":8},{"text":"3.2. 数据库管理","hash":"数据库管理","id":9},{"text":"3.3. 数据库重启","hash":"数据库重启","id":10},{"text":"3.4. 修改密码","hash":"修改密码","id":11},{"text":"3.5. 删除实例","hash":"删除实例","id":12},{"text":"3.6. 磁盘扩容","hash":"磁盘扩容","id":13},{"text":"3.7. 规格变更","hash":"规格变更","id":14},{"text":"3.8. 数据库备份","hash":"数据库备份","id":15},{"text":"3.9. 数据库恢复","hash":"数据库恢复","id":16},{"text":"3.10. 数据库监控","hash":"数据库监控","id":17},{"text":"3.11. 可视化登录工具","hash":"可视化登录工具","id":18}]},"19":{"id":19,"text":"当前，PostgreSQL数据库的CALL语句存在以下限制： 不支持INTO子句； 无法调用有返回值的函数； 无法将结果赋值给客户端变量（即 Oracle 中的绑定变量 / host variables）。 为提升对 Oracle 的兼容性，IvorySQL 实现了对 CALL func(…​) INTO :var; 语法的支持，允许用户通过绑定变量（如 :x）接收函数返回值，并在行为（如精度检查、错误处理）上与 Oracle 保持一致。 由于 PostgreSQL/IvorySQL 本身不支持 SQL 层直接向客户端变量赋值，因此本方案采用 “客户端重写 + 服务端协同” 的方式实现： 当 CALL 语句包含绑定变量（如 :x）时： 客户端将其重写为一个特殊的匿名 PL 块（DO $$ ... $$）； 使用扩展查询协议发送，以便传递参数类型和精度信息； 服务端执行该匿名块，并将结果返回给客户端； 客户端再将结果写入对应的绑定变量。 当 CALL 语句不含绑定变量时： 行为与原生 PostgreSQL 完全一致，使用简单查询协议，不做任何重写。 为了在接口中兼容CALL [INTO]语句，需将其转换为匿名PL/iSQL块，并借助匿名块对OUT参数的支持来实现功能等价。这就要求get_parameter_description函数能够正确识别CALL语句，并在遇到CALL INTO时，返回重写后的PL语句。 相应地，get_hostvariables例程需要将这些信息（如是否为 CALL 语句、是否包含INTO、重写后的语句等）保存到HostVariable结构中。HostVariable的定义如下： typedef struct HostVariable { HostVariableEntry *hostvars; int length; bool isdostmt; bool iscallstmt; // 是否来自 CALL 语句 char *convertcall; // 重写后的语句 } HostVariable; 在服务端需要修改语法解析器部分，在ora_gram.y中添加CALL INTO语法规则，并在action部分生成重写后的PL语句，如“x := add(1,2);” CallStmt: CALL func_application { CallStmt *n = makeNode(CallStmt); n->funccall = castNode(FuncCall, $2); $$ = (Node *)n; } | CALL func_application INTO ORAPARAM { CallStmt *n = makeNode(CallStmt); OraParamRef *hostvar = makeNode(OraParamRef); char *callstr = NULL; n->funccall = castNode(FuncCall, $2); hostvar->number = 0; hostvar->location = @4; hostvar->name = $4; n->hostvariable = hostvar; callstr = pnstrdup(pg_yyget_extra(yyscanner)->core_yy_extra.scanbuf + @2, @3 - @2); n->callinto = psprintf(\"%s := %s;\", $4, callstr); pfree(callstr); $$ = (Node *)n; } ; CallStmt结构需要保存INTO子句和转换后的PL语句 typedef struct CallStmt { NodeTag type; FuncCall *funccall; /* from the parser */ FuncExpr *funcexpr; /* transformed call, with only input args */ List *outargs; /* transformed output-argument expressions */ OraParamRef *hostvariable; /* only used for get_parameter_description() */ char *callinto; /* rewrite CALL INTO to a PL assign stmt */ } CallStmt; 为区分普通 DO 语句和由 CALL 转换而来的匿名块，语法中新增GENERATED FROM CALL关键字： opt_do_from_where: GENERATED FROM CALL { $$ = true; } | /*EMPTY*/ { $$ = false; } ; 生成的 DoStmt 节点将设置 do_from_call = true，供执行器识别。 typedef struct DoStmt { NodeTag type; List *args; /* List of DefElem nodes */ List *paramsmode; /* List of parameters mode */ List *paramslen; /* List of length for parameter datatypes */ bool do_from_call; /* True if DoStmt is come from CallStmt */ } DoStmt; 在IVY接口中，占位符信息是通过一个名为get_parameter_description的集合返回函数（SRF）获取的。该函数需要能够识别输入语句的类型，并在遇到CALL INTO语句时，返回重写后的PL/iSQL赋值语句。 为此，IvorySQL对该函数的返回结构（TupleDesc）进行了扩展：新增了一个hint 字段，专门用于返回CALL INTO语句重写后的PL代码；对于其他类型的语句，该字段保持为NULL。 此外，原函数结果集的第一条元组的第一个字段原本仅用true/false来区分语句是否为匿名块。为了更准确地识别语句类型（尤其是CALL语句），现已将其修改为返回对应解析树的 CommandTag。 所有这些元数据信息最终会被封装到一个用户上下文结构中，以便在 SRF 函数的多次调用之间高效传递和复用。 { OraParamExtralData *extral; const char *cmdtag; char *callintoexpr; } outparam_fctx; 接口层 CALL涉及的ivy前缀的接口包括： IvyStmtExecute IvyStmtExecute2 IvyexecPreparedStatement IvyexecPreparedStatement2 在上述接口中，用户传入的CALL [INTO]语句会被重写为一种“特殊”的匿名块语句。为了明确标识这类由CALL转换而来的匿名块，在接口的语句类型定义中新增了一种专用类型。该类型的作用是在IvyHandleDostmt中正确识别此类语句，并生成形如 DO ... USING … — GENERATED FROM CALL 的执行语句。 typedef enum IvyStmtType { IVY_STMT_UNKNOW, IVY_STMT_DO, IVY_STMT_DOFROMCALL, /* new statement type */ IVY_STMT_DOHANDLED, IVY_STMT_OTHERS } IvyStmtType; 在重写 CALL 语句时，如果遇到调用函数的 CALL INTO 语句，接口需要对绑定变量的顺序进行内部调整。这一调整对用户是完全透明的：用户在绑定参数时，只需按照 CALL 语句中出现的顺序操作即可——即 INTO 子句中的变量在原语句中位于最后。 然而，在重写生成的特殊匿名块中，该 INTO 变量会作为赋值表达式的左值（即第一个参数）出现。因此，接口必须在内部将绑定顺序正确调整，确保执行逻辑与用户预期一致。 所有涉及此逻辑的接口例程都需要实现这一处理，相关例程如下： Ivyreplacenamebindtoposition Ivyreplacenamebindtoposition2 Ivyreplacenamebindtoposition3 在IvyexecPreparedStatement 和 IvyexecPreparedStatement2 这类接口中，用户需要显式提供每个参数的 paramvalues、paramlengths、paramformats 和 parammode。对于 CALL 语句，这些参数数组中的元素顺序必须根据重写后的匿名块结构进行位置调整，以确保绑定与执行逻辑一致。 其中，IvyexecPreparedStatement2 更为特殊：它要求用户额外提供一个 IvyBindOutInfo* 类型的输出绑定列表。该列表不仅用于绑定 OUT 参数，还被 IvyAssignPLISQLOutParameter 在获取 PL/iSQL 过程返回结果时用来识别每个 OUT 参数的数据类型。因此，在处理 CALL语句时，接口会先对用户传入的 IvyBindOutInfo*列表进行位置重排（将INTO对应的输出变量移至首位），再将其写入IvyPreparedStatement语句句柄中，供后续赋值使用。 关于输出参数的精度处理：当CALL语句中的输出绑定变量与实际返回值的精度不匹配时，系统可能报错，也可能自动截断——具体行为取决于绑定变量的数据类型是否与过程/函数声明的参数类型完全一致。 在PL/iSQL inline handler中，每个OUT参数的精确数据类型均可通过ParamListInfo在绑定阶段获取。如果当前执行的匿名块是由CALL语句转换而来的特殊DoStmt，那么在执行赋值时，系统会进行如下判断： 若ParamListInfo中记录的类型与函数/存储过程形参的类型完全相同，则采用强制类型转换赋值； 否则，采用隐式类型转换赋值。 这一机制旨在兼容Oracle的行为，确保在类型不完全匹配时仍能安全、合理地完成赋值。 -- 原始 CALL语句： CALL my_func(:in1, :in2) INTO :out; -- 重写为： do $$BEGIN :out := my_func(:in1, :in2); END$$ using out INOUT, in1 INOUT, in2 INOUT paramslength -1,-1,-1 GENERATED FROM CALL;","title":"CALL INTO","component":"ivorysql-doc","version":"master","name":"call_into","url":"/ivorysql-doc/master/compatibility_features_design/call_into.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 整体设计思路","hash":"整体设计思路","id":2},{"text":"3. 实现原理","hash":"实现原理","id":3},{"text":"3.1. 交互式终端","hash":"交互式终端","id":4},{"text":"3.2. 服务端","hash":"服务端","id":5}]},"20":{"id":20,"text":"为了满足Oracle的引用标识符大小写兼容，ivorysql设计了三种引用标识符的大小写转换模式。 如果在数据库初始化时附加了参数 -C，值可以为 normal/interchange/lowercase，则代码中 Initdb.c→main() 函数会处理该参数，根据参数值设置全局变量 caseswitchmode。然后 initdb 命令会以 -boot 模式启动一个 postgres 进程用于设置 template1 模板数据库，同时赋予参数 -C ivorysql.identifier_case_switch=caseswitchmode 给新进程。 这个新启动的后端进程会通过下面的代码将 identifier_case_switch 信息写入 pg_control 文件： BootstrapModeMain() -> BootStrapXLOG(); /* save database compatible level value */ ControlFile->dbmode = bootstrap_database_mode; ControlFile->casemode = identifier_case_switch; /* some additional ControlFile fields are set in WriteControlFile() */ WriteControlFile(); 当用户使用 pg_ctl 命令启动数据库时，postmaster 进程会读取 pg_control 文件的内容，代码调用路径为： PostmasterMain()-->SetCaseGucOption()-->GetCaseSwitchModeFromControl() 读取到参数值后调用 SetConfigOption() 函数进行赋值。 在每个新的后端进程开始的时候，由于是从 postmaster 进程 fork 而来，会自动拥有相同的 ivorysql.identifier_case_switch 参数值。首先处理 startup 包，其中如果包含 database 或者 user 这两个参数，则根据 identifier_case_switch 对参数值做相应的处理。 源代码为： BackendMain()->BackendInitialize()-->ProcessStartupPacket() 另外，在处理用户的 SQL 语句时，如果包含标识符，也会进行同样的处理，代码分布在： SplitIdentifierString()，quoteOneName() 和 SplitGUCList() 这几个函数中。","title":"引用标识符大小写转换","component":"ivorysql-doc","version":"master","name":"case_conversion","url":"/ivorysql-doc/master/compatibility_features_design/case_conversion.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"21":{"id":21,"text":"在Oracle数据库中，空字符串('')被视为NULL值，这是Oracle的一个重要特性。为了兼容Oracle的这一行为，IvorySQL提供了空字符串转NULL功能。当启用该功能后，SQL语句中的空字符串将自动转换为NULL值，从而保证Oracle应用程序在IvorySQL上的行为一致性。 参数 ivorysql.enable_emptystring_to_NULL 对应的GUC变量为 enable_emptystring_to_NULL。 在文件 ora_scan.l 中可以看到对这个变量的使用： case xe: yylval->str = litbufdup(yyscanner); if (strcmp(yylval->str, \"\") == 0 && ORA_PARSER == compatible_db && enable_emptystring_to_NULL) { return NULL_P; } return SCONST; 其中 xe 代表被引号包围的字符串： <xe> extended quoted strings (support backslash escape sequences) 上面代码的逻辑是，在词法分析时如果遇到空字符串，在空转NULL功能启用的情况下，返回 NULL_P，否则返回 SCONST。 在语法分析文件 ora_gram.y 里，对语句 insert into abc values(''); 是这样解析的： values_clause: VALUES '(' expr_list ')' { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1($3); $$ = (Node *) n; } expr_list: a_expr { $$ = list_make1($1); } a_expr: c_expr { $$ = $1; } c_expr: AexprConst { $$ = $1; } AexprConst: Iconst | Sconst { $$ = makeStringConst($1, @1); } | NULL_P { $$ = makeNullAConst(@1); } 以上代码对词法分析时返回的 NULL_P 或者 SCONST 分别构建对应的节点进行处理。","title":"空字符串转NULL功能","component":"ivorysql-doc","version":"master","name":"empty_string_to_null","url":"/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"22":{"id":22,"text":"Force View 提供 Oracle 兼容的 CREATE [OR REPLACE] FORCE VIEW 与 ALTER VIEW …​ COMPILE 行为，允许开发者先落地视图占位，再在依赖补齐后恢复正常执行。 系统将原始 SQL 及标识符大小写模式写入专属目录项，依赖恢复时可自动或手动编译为普通视图，且保持 Oracle 风格的告警与错误提示。 PG在删除基表时，如果有视图依赖于此基表，则不允许删除，需要使用cascade 关键字全部删除；Oracle删除基表时，允许直接删除基表，而保留force view并将force view 转为无效状态，且创建普通视图后，也可以删除此视图的基表，但把视图转为无效状态。 为了支持新的语法，需要增加对应的语法规则。且FORCE VIEW的状态在多个session之间是共享的，新增一张系统表用来存储FORCE VIEW的相关信息。 src/backend/oracle_parser/ora_gram.y 注册 Force View 关键字。 语法阶段为 ViewStmt→force 赋值，并在 AlterTableStmt 阶段生成 AT_ForceViewCompile 选项。 解析阶段保留 ViewStmt→stmt_literal，后续占位时需要复用原始文本。 /* Insert or update the pg_force_view catalog as needed */ if (need_store) { ...... StoreForceViewQuery(address.objectId, stmt->replace, ident_case, stmt->stmt_literal ? stmt->stmt_literal : queryString); } src/include/nodes/parsenodes.h 为 ViewStmt 增加 bool force、char *stmt_literal 字段。 AT_ForceViewCompile 在 parsenodes.h 与 tablecmds.c 成对定义，确保 ALTER VIEW …​ COMPILE 进入 AlterTable 流程。 parse_analyze 仍按普通路径执行，Force 模式只在解析报错后介入。 DefineView()（src/backend/commands/view.c）统一处理普通与 Force 视图。 代码在 PG_TRY/PG_CATCH 包裹下先尝试普通解析，捕获语义错误后根据 stmt→force 决定是否进入Force View 流程。 成功解析继续调用 StoreViewQuery() 写入 _RETURN 规则，失败则进入 Force View 分支，保证视图对象仍然存在。 src/include/catalog/pg_force_view.h 定义目录表，字段包含视图 OID (fvoid)、标识符大小写模式 (ident_case)、原始 SQL 文本 (source)。 唯一索引 pg_force_view_fvoid_index 搭配 syscache FORCEVIEWOID（src/include/catalog/syscache_info.h）实现按视图 OID 查找。 目录表开启 TOAST，长 SQL 文本不会被截断，可覆盖复杂迁移脚本。 CATALOG(pg_force_view,9120,ForceViewRelationId) { /* oid of force view */ Oid fvoid; /* see IDENT_CASE__xxx constants below */ char ident_case; #ifdef CATALOG_VARLEN /* variable-length fields start here */ /* sql definition */ text source; #endif } FormData_pg_force_view; bool rel_is_force_view(Oid relid) 位于 src/backend/commands/view.c，通过判断 _RETURN 规则是否存在确认视图是否处于 Force 状态。 Force View 仍登记为 relkind = RELKIND_VIEW，避免额外兼容分支。 pg_class.relhasrules 在占位状态设置为 false，配合作为判断依据。 DefineView() 在解析成功后调用 DefineVirtualRelation() 写入 pg_class、pg_attribute 等结构。 StoreViewQuery() 生成 _RETURN 规则并维护依赖关系。 该路径不会触及 pg_force_view，视图完成后立即可用。 CreateForceVirtualPlaceholder()（src/backend/commands/view.c）负责生成或复用占位视图： 若视图不存在，调用 DefineVirtualRelation() 建立基础对象，但不写入 _RETURN 规则。 若已有 Force View，复用当前记录，更新列定义或清理旧元数据。 若已有普通视图且声明了 OR REPLACE，调用 make_view_invalid() 失效原视图，再写入新的占位定义。 StoreForceViewQuery() 将 stmt_literal 与当前 ivorysql.identifier_case_switch 持久化到 pg_force_view，以便后续恢复大小写语义。 视图占位完成后向客户端返回 WARNING: View created with compilation errors，提示仍处于不可用状态。 make_view_invalid() 在依赖对象被 DROP/ALTER 等操作影响时调用。 函数删除 _RETURN 规则、清理 pg_depend 依赖、重置 pg_class.relhasrules 并清空 pg_attribute 列信息。 同步生成 CREATE FORCE VIEW …​ AS <pg_get_viewdef> 文本写入 pg_force_view，ident_case 设置为 IDENT_CASE_UNDEFINE 表示该文本由系统构造。 视图仍然存在并可在元数据中枚举，但实际访问会触发 Force 判定。 _RETURN 缺失导致执行器在打开视图时调用 compile_force_view()，若编译仍失败则抛出 view \"<schema>.<name>\" has errors。 用户可通过 ALTER VIEW …​ COMPILE 或再次 CREATE OR REPLACE FORCE VIEW 来恢复。 parse_relation.c 的 addRangeTableEntry() 和 parse_clause.c 的目标关系打开逻辑在判定 Force View 后调用 compile_force_view()。 该函数重新执行 raw_parser 与 parse_analyze，成功则再安装 _RETURN 规则，失败直接终止当前语句。 AT_ForceViewCompile 在 tablecmds.c Phase 2 阶段执行，先申请 AccessExclusiveLock 再调用 compile_force_view()。 成功编译返回常规 ALTER VIEW 完成信息；失败打印 WARNING: View altered with compilation errors，视图仍保持占位。 compile_force_view() 读取 pg_force_view.source 并构造 ViewStmt，随后调用 compile_force_view_internal()。 函数通过 checkViewColumns() 比对旧列，允许新增列但禁止类型不兼容的替换；新增列由 AT_AddColumnToView() 完成。 _RETURN 规则由 StoreViewQuery() 重新生成，最后 DeleteForceView() 删除目录项，视图恢复为普通状态。","title":"Force View","component":"ivorysql-doc","version":"master","name":"force_view","url":"/ivorysql-doc/master/compatibility_features_design/force_view.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 语法与解析入口","hash":"语法与解析入口","id":3},{"text":"2.1.1. Force View 语法支持","hash":"force-view-语法支持","id":4},{"text":"2.1.2. AST 字段扩展","hash":"ast-字段扩展","id":5},{"text":"2.1.3. 解析入口","hash":"解析入口","id":6},{"text":"2.2. Force View 元数据","hash":"force-view-元数据","id":7},{"text":"2.2.1. pg_force_view 目录","hash":"pg_force_view-目录","id":8},{"text":"2.2.2. 视图状态","hash":"视图状态","id":9},{"text":"2.3. 创建与替换流程","hash":"创建与替换流程","id":10},{"text":"2.3.1. 普通 view","hash":"普通-view","id":11},{"text":"2.3.2. Force view","hash":"force-view","id":12},{"text":"2.4. 依赖失效与回退","hash":"依赖失效与回退","id":13},{"text":"2.4.1. 主动失效逻辑","hash":"主动失效逻辑","id":14},{"text":"2.4.2. 失效后的可见行为","hash":"失效后的可见行为","id":15},{"text":"2.5. 自动与主动编译","hash":"自动与主动编译","id":16},{"text":"2.5.1. 自动编译触发点","hash":"自动编译触发点","id":17},{"text":"2.5.2. 主动编译","hash":"主动编译","id":18},{"text":"2.5.3. 列校验与元数据更新","hash":"列校验与元数据更新","id":19}]},"23":{"id":23,"text":"PostgreSQL支持函数(FUNCTION)和存储过程(PROCEDURE)，但是在语法上和Oracle有差异，为了让Oracle的PLSQL语句可以在IvorySQL上执行，也就是“语法兼容”，IvorySQL采取这样的解决方案：如果Oracle的子句在IvorySQL中存在相同功能的子句，则直接映射为对应的IvorySQL子句，否则只实现其语法，不实现其功能。 PL/iSQL是IvorySQL中的过程语言名称，专门用来兼容Oracle的PLSQL语句。为了兼容Oracle风格的函数与存储过程语法，需要对psql客户端工具、SQL端以及PL/iSQL端做相应处理。 Oracle的sqlplus工具使用斜线(/)来结束函数和存储过程，IvorySQL的客户端工具psql需要兼容同样的语法，也就是说我们常规的遇到分号发送语句给服务端的机制，当遇到Oracle风格的函数和存储过程命令时失效，改为使用斜线(/)发送命令。 为此在 PsqlScanStateData 结构体中增加如下字段： bool cancel_semicolon_terminator; /* not send command when semicolon found */ /* * State to track boundaries of Oracle ANONYMOUS BLOCK. * Case 1: Statements starting with << ident >> is Oracle anonymous block. */ int token_count; /* # of tokens, not blank or newline since start of statement */ bool anonymous_label_start; /* T if the first token is \"<<\" */ bool anonymous_label_ident; /* T if the second token is an identifier */ bool anonymous_label_end; /* T if the third token is \">>\" */ /* * Case 2: DECLARE BEGIN ... END is Oracle anonymous block syntax. * DECLARE can also be a PostgreSQL cursor declaration statement, we need to tell this. */ bool maybe_anonymous_declare_start; /* T if the first token is DECLARE */ int token_cursor_idx; /* the position of keyword CURSOR in SQL statement */ /* * Case 3: DECLARE BEGIN ... END is Oracle anonymous block syntax. * BEGIN can also be a PostgreSQL transaction statement. */ bool maybe_anonymous_begin_start; /* T if the first token is BEGIN */ 同时修改 ora_psqlscan.l，添加和修改相应的词法规则, 以下是代码片段示例： { if (is_oracle_slash(cur_state, cur_state->scanline)) { /* Terminate lexing temporarily */ cur_state->cancel_semicolon_terminator = false; cur_state->maybe_anonymous_declare_start = false; cur_state->maybe_anonymous_begin_start = false; cur_state->anonymous_label_start = false; cur_state->anonymous_label_ident = false; cur_state->anonymous_label_end = false; cur_state->start_state = YY_START; cur_state->token_count = 0; cur_state->token_cursor_idx = 0; cur_state->identifier_count = 0; cur_state->begin_depth = 0; cur_state->ora_plsql_expect_end_symbol = END_SYMBOL_INVALID; return LEXRES_SEMI; } ECHO; 避免将注释等场景中的斜线误判为结束符，为此在 oracle_fe_utils/ora_psqlscan.l 文件中新增 is_oracle_slash 函数接口用于检测： bool is_oracle_slash(PsqlScanState state, const char *line) { bool result = false; switch (state->start_state) { case INITIAL: case xqs: /* treat these like INITIAL */ { int len, i; bool has_slash = false; len = strlen(line); for (i = 0; i < len; i++) { /* allow special char */ if (line[i] == '\\t' || line[i] == '\\n' || line[i] == '\\r' || line[i] == ' ') continue; if (line[i] == '/') { if (has_slash) break; has_slash = true; continue; } /* others */ break; } if (i == len && has_slash) result = true; } break; default: break; } return result; } SQL端要能够识别函数和存储过程的创建语法，这是通过修改ora_base_yylex来实现的。这个函数预取并缓存token，如果是Oracle语法格式则组织一个SCONST发送给PLSQL端，否则从堆栈中获取之前预读的token，按照原生PG的逻辑进行处理。 在ora_base_yy_extra_type数据结构中增加如下字段： /* * The native PG only cache one-token info include yylloc, yylval and token * number in yyextra, IvorySQL cache multiple tokens info using two arrays. */ int max_pushbacks; /* the max size of cache array */ int loc_pushback; /* # of used tokens */ int num_pushbacks; /* # of cached tokens */ int *pushback_token; /* token number array */ TokenAuxData *pushback_auxdata; /* auxdata array */ OraBodyStyle body_style; int body_start; int body_level; 增加token堆栈的操作接口： push_back_token forward_token ora_internal_yylex internal_yylex  ora_base_yylex函数中在创建函数、过程、匿名块时会预读部分token，使用上述结构缓存到堆栈中，是为了构造一个符合Oracle PL/SQL语法的SCONST发送给PL/iSQL端去处理。具体请参考源代码。 该部分主要修改了pl_gram.y文件，以兼容PLSQL的函数和存储过程语法，在不影响PG原生的PL/pgSQL的前提下去兼容Oracle PL/SQL语法形式，如下是DECLARE部分兼容的代码示例，更多请参考IvorySQL源代码。 /* * The declaration section of the outermost block in Oracle does not have the DECLARE keyword. */ ora_outermost_pl_block: ora_decl_sect K_BEGIN proc_sect exception_sect K_END opt_label { PLiSQL_stmt_block *new; new = palloc0(sizeof(PLiSQL_stmt_block)); new->cmd_type = PLISQL_STMT_BLOCK; new->lineno = plisql_location_to_lineno(@2); new->stmtid = ++plisql_curr_compile->nstatements; new->label = $1.label; new->n_initvars = $1.n_initvars; new->initvarnos = $1.initvarnos; new->body = $3; new->exceptions = $4; check_labels($1.label, $6, @6); plisql_ns_pop(); $$ = (PLiSQL_stmt *)new; } ; ora_decl_sect: opt_block_label opt_ora_decl_start opt_ora_decl_stmts { if ($2) { if ($1 == NULL) { plisql_ns_push(NULL, PLISQL_LABEL_BLOCK); } } } opt_ora_decl_stmts { if ($4) { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; /* Remember variables declared in decl_stmts */ $$.n_initvars = plisql_add_initdatums(&($$.initvarnos)); } else { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); $$.n_initvars = 0; if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; $$.initvarnos = NULL; } } ; opt_ora_decl_start: K_DECLARE { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = true; } | /*EMPTY*/ { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = false; } ; opt_ora_decl_stmts: ora_decl_stmts { $$ = true; } | /*EMPTY*/ { $$ = false; } ora_decl_stmts: ora_decl_stmts ora_decl_stmt | ora_decl_stmt ; ora_decl_stmt: decl_statement ;","title":"PL/iSQL函数与存储过程","component":"ivorysql-doc","version":"master","name":"function_procedure","url":"/ivorysql-doc/master/compatibility_features_design/function_procedure.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 客户端工具psql","hash":"客户端工具psql","id":3},{"text":"2.2. SQL端","hash":"sql端","id":4},{"text":"2.3. PL/iSQL端","hash":"plisql端","id":5}]},"24":{"id":24,"text":"Oracle和IvorySQL中的 LIKE 语法是相同的，他们的区别在于表达式的类型，Oracle支持对数字、日期、字符串类型的列用 LIKE 关键字配合通配符来实现模糊查询。原生PostgreSQL只支持字符串类型，不支持日期、数字类型，IvorySQL通过扩展数据类型支持和操作符重载，实现了与 Oracle 兼容的 LIKE 操作符功能。 PostgreSQL的字符串基本类型是text，所以 LIKE 是以text为基础，其他PostgreSQL类型隐式转换成text，不用创建operator就能自动转换；IvorySQL中oracle兼容的字符串类型是varchar2，因此创建一个varchar2的 LIKE 操作符，其他oracle的类型也通过隐式转换成varchar2实现不用创建操作符，也能使用 LIKE 操作符。 在之前实现oracle兼容数据类型时，IvorySQL做了integer，float8，float4 等一些数据类型到varchar2的隐式转换，没有直接到text的。因此实现这些兼容类型的 LIKE 操作符兼容，有两种方式。一种需要针对每个类型添加一个 LIKE 操作符，另一种是做一个基本的varchar2的 LIKE 操作符。在第二种实现方式中，IvorySQL针对float8，integer，number等已经做了向varchar2类型的隐式转换，这些数据类型可以和varchar2用同一个操作符，这样在创建操作符的时候只需要创建varchar2类型的 LIKE 操作符就可以。 可以通过以下SQL语句查看已经存在的类型隐式转换 -- 查询已存在的隐式转换路径(9503为varchar2的OID) SELECT t1.typname AS source_type, t2.typname AS target_type FROM pg_cast C JOIN pg_type t1 ON C.castsource = t1.OID JOIN pg_type t2 ON C.casttarget = t2.OID WHERE C.casttarget = 9503; 经过查看可得，需要进行兼容的类型全部都在该表中做了隐式转换，所以可以直接将这些类型转换为text类型然后进行模糊查询。 CREATE OR REPLACE FUNCTION sys.varchar2like(varchar2, varchar2) RETURNS bool AS $$ SELECT $1::text LIKE $2::text; $$ LANGUAGE SQL IMMUTABLE STRICT; CREATE OPERATOR ~~ ( PROCEDURE = sys.varchar2like, LEFTARG = varchar2, RIGHTARG = varchar2 ); 第一段代码定义了一个名为sys.varchar2like的函数，它接收两个varchar2类型的参数，通过将它们转换为PostgreSQL原生的text类型后执行标准的LIKE模式匹配，最终返回布尔值表示是否匹配。 第二段代码创建了一个名为~~的操作符，它将使用前面定义的varchar2like函数作为实现，并指定该操作符左右两边的参数都必须是varchar2类型。 这样就在IvorySQL中建立了一个与Oracle兼容的LIKE操作符，当用户使用~~操作符时，实际上是通过类型转换后调用PostgreSQL原生的LIKE功能来完成模式匹配，从而实现了Oracle的LIKE语义兼容。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"like_operator","url":"/ivorysql-doc/master/compatibility_features_design/like_operator.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"1.1. 类型转换","hash":"类型转换","id":2},{"text":"1.2. 核心函数实现","hash":"核心函数实现","id":3}]},"25":{"id":25,"text":"嵌套子函数：定义在函数、存储过程或匿名块内部的函数或存储过程，也称为 subproc 或 inner 函数。 父函数：承载嵌套函数的外层函数、存储过程或匿名块，执行过程中负责实际触发子函数调用。 当 DECLARE 块里出现 function …​ is/as begin …​ end 结构时，pl_gram.y 会调用 plisql_build_subproc_function()（对应创建普通函数，这一阶段相当于在 pg_proc 中更新 catalog 中的注册信息）： 在 PLiSQL_function 的 subprocfuncs[] 数组中创建 PLiSQL_subproc_function 结构，记录名称、参数、返回类型、属性等，获得一个下标 fno 作为该子函数的标识。 调用 plisql_check_subprocfunc_properties() 校验声明与定义的合法组合。 保存到父函数的 datum 表：编译期的 PLiSQL_function→datums 描述子函数里的变量、记录字段，PLiSQL_execstate→datums 保存着执行过程中的变量。 如果子函数里使用了多态参数，在语法阶段保存到 subprocfunc→src，同时将 has_poly_argument 设成 true，执行时按不同实参类型重新编译。 父函数的 PLiSQL_function 结构多了一个 subprocfuncs 数组，里面每个元素就是刚才创建的 PLiSQL_subproc_function。 子函数结构体 PLiSQL_subproc_function 有一个哈希表指针 HTAB *poly_tab，默认为空。当子函数里使用了多态函数时，has_poly_argument 为 true，则会在初次编译时初始化 poly_tab。poly_tab 的 key 是 PLiSQL_func_hashkey，记录着子函数的 fno、输入参数类型等； value 是编译好的 PLiSQL_function *（plisql 函数的执行上下文）。 编译过程中，pg 解析器会生成一个 ParseState 结构，plisql_subprocfunc_ref() 会通过 ParseState→p_subprocfunc_hook() 找到父函数的 PLiSQL_function，调用 plisql_ns_lookup() 找到所有同名子函数的 fno，根据参数个数、类型找到最合适的多态函数。 FuncExpr 结构构造时会对子函数进行标记，方便后期执行阶段识别：function_from = FUNC_FROM_SUBPROCFUNC，parent_func 指向父级 PLiSQL_function，funcid = fno。 plisql_call_handler() 当 function_from == FUNC_FROM_SUBPROCFUNC，会用 parent_func + fno 找到对应的 PLiSQL_subproc_function： 如果不是多态：直接复用 subprocfunc→function 里的动作树。 如果是多态：先在 poly_tab 查有没有编译结果；没有就调用 plisql_dynamic_compile_subproc() 编译，放进 poly_tab 缓存。 子函数开始执行之前，plisql_init_subprocfunc_globalvar() 会把父函数的 datum 表中有关的变量 fork 一份，这样子函数可以获取到父函数的变量值，也不会污染父函数的变量空间；执行后由 plisql_assign_out_subprocfunc_globalvar() 把需要回写的变量更新到父函数的 datum 表。 pl_gram.y 新增子过程声明、嵌套定义的产生式，并在创建过程中记录 lastoutvardno、子过程信息等元数据。 支持在子函数内引用父过程变量、子过程以及自定义类型。 当 DECLARE 块内出现 function …​ is/as begin …​ end 结构时，pl_gram.y 会调用 plisql_build_subproc_function() 进行编译： 在 PLiSQL_function 的 subprocfuncs[] 中创建 PLiSQL_subproc_function 结构，记录名称、参数、返回类型和属性，分配下标 fno 作为子函数标识。 调用 plisql_check_subprocfunc_properties() 校验声明与定义属性组合是否合法，防止重复或缺失声明造成的语义错误。 父函数的 Datum 表在编译期和执行期分别缓存子函数能访问的变量： PLiSQL_function→datums 保存子函数编译阶段可见的变量与记录字段信息。 PLiSQL_execstate→datums 在执行阶段持有实时的变量数值，实现运行期访问。 若子函数包含多态参数，语法阶段会： 将子函数源文本拷贝到 subprocfunc→src。 设置 has_poly_argument = true，为后续按实参类型动态编译做好准备。 父函数的 PLiSQL_function 结构新增 subprocfuncs 数组，每个元素对应一个 PLiSQL_subproc_function。 PLiSQL_subproc_function 持有 HTAB *poly_tab 指针；当 has_poly_argument 为 true 时，在首次编译时初始化该缓存，键为 PLiSQL_func_hashkey（子函数 fno + 实参类型），值为编译后的 PLiSQL_function。 编译期间 PostgreSQL 解析器会构造 ParseState，plisql_subprocfunc_ref() 通过 ParseState→p_subprocfunc_hook() 连接父函数，调用 plisql_ns_lookup() 找到同名子函数的全部 fno，并依据参数个数与类型挑选最佳候选，实现重载分发。 构造 FuncExpr 时会标记嵌套调用信息，便于执行阶段识别： function_from = FUNC_FROM_SUBPROCFUNC。 parent_func 指向父级 PLiSQL_function。 funcid = fno，用于快速定位子函数定义。 plisql_subprocfunc_ref() 作为 ParseState→p_subprocfunc_hook 实现入口，复用名称空间查询逻辑。 plisql_get_subprocfunc_detail() 依据参数数量、类型与命名匹配规则挑选最优候选，是嵌套函数重载的关键。 plisql_call_handler() 判断 function_from 后，通过 parent_func + fno 找到目标 PLiSQL_subproc_function。 对普通子函数，直接复用 subprocfunc→function 缓存； 对多态子函数，先查询 poly_tab，未命中时调用 plisql_dynamic_compile_subproc() 动态编译并写入缓存。 plisql_init_subprocfunc_globalvar() 在子函数执行前拷贝父函数 Datum 表中的相关变量，保证子函数读取到外层最新状态。 plisql_assign_out_subprocfunc_globalvar() 在返回前回写 OUT/INOUT 变量，确保父子函数数据一致性且互不污染。 psqlscan.l 调整 proc_func_define_level 和 begin_depth 的入栈/出栈逻辑，确保嵌套函数体整体发送至 SQL 端。 只有当嵌套层级回到 0 且遇到分号时，才触发发送，避免子函数块被拆分。 普通函数通过 funcid 访问 pg_proc；嵌套函数依赖 FuncExpr.parent_func 承载的 PLiSQL_function。 为此实现一组函数指针（plisql_register_internal_func() 注册）供 SQL 层回调，按需获取嵌套函数名称、返回类型与 OUT 参数信息。","title":"嵌套子函数","component":"ivorysql-doc","version":"master","name":"nested_function","url":"/ivorysql-doc/master/compatibility_features_design/nested_function.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 嵌套子函数语法识别","hash":"嵌套子函数语法识别","id":3},{"text":"2.1.1. 识别嵌套写法","hash":"识别嵌套写法","id":4},{"text":"2.1.2. 数据项保存","hash":"数据项保存","id":5},{"text":"2.1.3. 保存多态函数模板","hash":"保存多态函数模板","id":6},{"text":"2.2. 父函数重新编译","hash":"父函数重新编译","id":7},{"text":"2.3. 调用时解析","hash":"调用时解析","id":8},{"text":"3. 模块设计","hash":"模块设计","id":9},{"text":"3.1. PL/iSQL 语法扩展","hash":"plisql-语法扩展","id":10},{"text":"3.2. 数据项保存","hash":"数据项保存-2","id":11},{"text":"3.3. 多态函数模板","hash":"多态函数模板","id":12},{"text":"3.4. 父函数重新编译","hash":"父函数重新编译-2","id":13},{"text":"3.5. 解析器钩子","hash":"解析器钩子","id":14},{"text":"3.6. FuncExpr 标记","hash":"funcexpr-标记","id":15},{"text":"3.7. 嵌套函数查找机制","hash":"嵌套函数查找机制","id":16},{"text":"3.8. 执行路径","hash":"执行路径","id":17},{"text":"3.9. 变量同步","hash":"变量同步","id":18},{"text":"3.10. PSQL 端语句发送","hash":"psql-端语句发送","id":19},{"text":"3.11. SQL 层返回值获取","hash":"sql-层返回值获取","id":20}]},"26":{"id":26,"text":"IvorySQL提供了兼容Oracle的NLS参数功能，包含如下参数。 参数名称 功能描述 ivorysql.datetime_ignore_nls_mask 表示日期格式是否忽略NLS参数影响，默认为0。 nls_length_semantics 兼容Oracle的同名参数，表示char/varchar/varchar2的类型修饰符的大小单位是字节还是字符。 nls_date_format 表示默认的日期格式，可以通过show命令查看，默认为‘YYYY-MM-DD’。 nls_timestamp_format 兼容Oracle的同名参数，控制带时间的日期格式。 nls_timestamp_tz_format 兼容Oracle的同名参数，控制带时区的日期时间格式。 nls_territory 兼容Oracle的同名参数，指定数据库的默认区域。 nls_iso_currency 兼容Oracle的同名参数，指定国家和区域对应的唯一货币符。 nls_currency 兼容Oracle的同名参数，指定显示本地货币的符号，对应数字字符串格式中占位符L。 IvorySQL中的数据类型存在一个属性修饰符 typmod ，是对类型的补充说明，比如在 VARCHAR(n) 类型中，n 就是类型修饰符。 在创建或修改表的列时可以指定长度类型，例如: ivorysql=# create table t1(name varchar2(2 byte)); 对于列类型为 CHAR 、 VARCHAR 和 VARCHAR2 字符型的列，当没有显式指定列的长度类型时，IvorySQL使用 nls_length_semantics 参数的值来决定长度类型，有 byte 和 char 两种值，默认为 byte 。 需要特别注意的是， nls_length_semantics 参数的值仅影响新创建的列，对已经存在的列不会产生任何影响。 在语法解析文件 ora_gram.y中，存在如下代码来根据 nls_length_semantics 把原本的 char/varchar/varchar2 类型改成 oracharchar 或者 oracharbyte ： CharacterWithLength: character '(' Iconst ')' { if (ORA_PARSER == compatible_db) { if (strcmp($1, \"bpchar\")) { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oravarcharchar\"; else $1 = \"oravarcharbyte\"; } else { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oracharchar\"; else $1 = \"oracharbyte\"; } $$ = OracleSystemTypeName($1); $$->typmods = list_make1(makeIntConst($3, @3)); $$->location = @1; } else { ... } } ; IvorySQL 中数据类型 oracharchar 和 oracharbyte 的修饰符输入输出函数包括： oravarcharchartypmodout() oravarcharbytetypmodout() oracharbytetypmodout() oracharchartypmodout() 上面这些函数调用C语言实现的函数 anychar_typmodout() ，后者根据 nls_length_semantics 的值来调整输出的内容是否包含 byte/char 的说明。 nls_length_semantics 另一个作用是限制表中的列长度： 根据上述代码在语法解析文件 ora_gram.y中，如果原本的 varchar 类型被转换成了 oracharchar 类型，则函数 oravarcharchar() 会被调用，而 pg_mbcharcliplen() 函数计算字符长度，而不是字节长度。 Datum oravarcharchar(PG_FUNCTION_ARGS) { VarChar *source = PG_GETARG_VARCHAR_PP(0); int32 typmod = PG_GETARG_INT32(1); bool isExplicit = PG_GETARG_BOOL(2); int32 len, maxlen; size_t maxmblen; char *s_data; len = VARSIZE_ANY_EXHDR(source); s_data = VARDATA_ANY(source); maxlen = typmod - VARHDRSZ; /* No work if typmod is invalid or supplied data fits it already */ if (maxlen < 0 || len <= maxlen) PG_RETURN_VARCHAR_P(source); maxmblen = pg_mbcharcliplen(s_data, len, maxlen); ... } 这个参数被定义为一个int值，低四位分别表示是否在相应的日期时间格式上忽略NLS参数的影响，掩码定义如下： #define ORADATE_MASK 0x01 #define ORATIMESTAMP_MASK 0x02 #define ORATIMESTAMPTZ_MASK 0x04 #define ORATIMESTAMPLTZ_MASK 0x08 在源代码中，这个GUC参数被用于下面这些函数： oradate_in() oratimestamp_in() oratimestampltz_in() oratimestamptz_in() 如果相应的掩码被设置，则调用原生PG的处理函数，否则调用兼容代码并忽略NLS格式。 这三个GUC参数，在函数 ora_do_to_timestamp() 中作为格式字符串，对输入的字符串进行格式检查与模式识别。 下面是其默认值，可以通过设置其值为\"pg\"使其失效。\"pg\"表示禁用NLS特定行为，恢复为PostgreSQL的默认行为。 char *nls_date_format = \"YYYY-MM-DD\"; char *nls_timestamp_format = \"YYYY-MM-DD HH24:MI:SS.FF6\"; char *nls_timestamp_tz_format = \"YYYY-MM-DD HH24:MI:SS.FF6 TZH:TZM\"; 目前，nls_territory 和 nls_iso_currency 支持CHINA与AMERICA两个值。 默认值如下: char *nls_territory = \"AMERICA\"; char *nls_currency = \"$\"; char *nls_iso_currency = \"AMERICA\"; 这三个参数将在oracle兼容函数 to_number() 中被使用。 to_number() 函数尚未实现。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"nls_parameter","url":"/ivorysql-doc/master/compatibility_features_design/nls_parameter.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"1.1. nls_length_semantics 参数","hash":"nls_length_semantics-参数","id":2},{"text":"1.2. GUC参数 datetime_ignore_nls_mask","hash":"guc参数-datetime_ignore_nls_mask","id":3},{"text":"1.3. GUC参数 nls_date_format/nls_timestamp_format/nls_timestamp_tz_format","hash":"guc参数-nls_date_formatnls_timestamp_formatnls_timestamp_tz_format","id":4},{"text":"1.4. GUC参数 nls_currency/nls_iso_currency/nls_territory","hash":"guc参数-nls_currencynls_iso_currencynls_territory","id":5}]},"27":{"id":27,"text":"IvorySQL提供了兼容Oracle的out参数功能，包括含有out参数的函数与过程、匿名块支持out参数、libpq支持out参数。 对于pliSQL函数，在创建函数时，系统表pg_proc中存储函数参数为全部参数个数（包括OUT参数个数）及对应的参数数据类型。 在处理函数参数的interpret_function_parameter_list()函数中，根据参数模式，判断如果是IN OUT，则参数不能有默认值。 在make_return_stmt函数中，去掉如果发现有out参数报错的处理。 通过修改FuncnameGetCandidates函数，在函数查找时匹配包括out参数在内的所有参数。 函数编译时，构造一个row变量来容纳OUT参数变量和返回值变量。修改编译好的函数返回类型（function→fn_rettype）为RECORDOID。 函数执行时，在ExecInitFunc函数中调用新函数ExecInitFuncOutParams， 构造OUT参数计算节点，通过计算函数plisql_out_param将函数返回值和OUT参数值从tuple中分离，并且为OUT参数向外赋值。 为了支持冒号占位符形式的绑定变量，修改ora_scan.l文件，添加语法，遇到冒号占位符返回ORAPARAM。在ora_gram.y文件中，在c_expr和赋值plassign_target的语法中，添加对ORAPARAM添加处理，构造一个OraParamRef节点。 新增 DO + USING 语法： DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, ...] 修改ora_gram.y文件，增加DO+USING语法支持。DoStmt结构体中增加 paramsmode 用于存储匿名块中的绑定变量模式等信息的列表。 对PBE过程的修改包括：exec_parse_message函数中，根据应用接口传过来的参数类型oid，识别出实参的模式；在exec_bind_message函数中，对于匿名块DO+USING，识别出USING后面参数的模式，向执行器传递参数信息。 含OUT参数匿名块的执行 在PortalStart函数中对匿名块语句，调用CreateTupleDescFromParams函数，增加参数构造描述信息。 PLiSQL_function成员fn_prokind增加新值PROKIND_ANONYMOUS_BLOCK用来表示匿名块。 在plisql_exec_function函数中，对有out参数的匿名块做判断，调用plisql_anonymous_return_out_parameter函数完成对OUT参数构造PLiSQL_row类型的变量，最后将row类型的变量求值当作匿名块函数的返回值。 修改Libpq以支持按位置和按参数名字绑定，涉及SQL端，PLiSQL端，libpq接口端。 SQL端：在服务器端实现系统函数 get_parameter_description，该函数根据SQL语句，返回变量名字与位置的关系。这个函数被用在libpq接口函数中。 返回的第一行name显示SQL类型，后面行依次显示占位符名字、位置信息。 ``` ivorysql=# select * from get_parameter_description('insert into t values(:x, :y);'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows) ``` 对于匿名块语句，尚不支持。 PLiSQL端：主要是PL/iSQL块根据参数位置或参数名称调整参数内部标识。 执行函数需要从绑定句柄中获取参数的值与类型的信息； 对out参数的返回列名称做一个特殊处理。如果是out参数，那么其列名称为_column_xxx，其中xxx是out参数的位置，从而根据绑定位置与返回的位置从结果集中给out参数赋值； 在PLiSQL执行端，根据参数名字出现的位置，调整名字转换成内部标识的变量，如$number。在返回到客户端的时候，发送描述信息给libpq， 从而达到返回的列名是由参数名字构造，LIBPQ端根据列名来给out参数赋值。 libpq接口端：提供准备、绑定、执行函数，这些函数与OCI接口相应函数类似。 一般调用流程如下： 使用IvyHandleAlloc分配语句句柄和错误句柄。 调用IvyStmtPrepare准备语句。 调用IvyBindByPos或IvyBindByName 绑定参数。 调用IvyStmtExecute 执行，可重复执行。 调用IvyFreeHandle 释放语句句柄和错误句柄。 另外还实现了Ivyconnectdb，Ivystatus，Ivyexec，IvyresultStatus，IvyCreatePreparedStatement，IvybindOutParameterByPos，IvyexecPreparedStatement，IvyexecPreparedStatement2，Ivynfields，Ivyntuples，Ivyclear等一系列接口函数。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"out_parameter","url":"/ivorysql-doc/master/compatibility_features_design/out_parameter.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"1.1. 含有out参数的函数","hash":"含有out参数的函数","id":2},{"text":"1.2. 匿名块支持out参数","hash":"匿名块支持out参数","id":3},{"text":"1.3. libpq中调用含out参数的函数","hash":"libpq中调用含out参数的函数","id":4}]},"28":{"id":28,"text":"IvorySQL提供了兼容Oracle RowID的功能。RowID是一种伪列，在创建表时由数据库自动生成，对于数据库中的每一行，RowID 伪列返回该行的地址。 RowID 应当具备以下特性： 1. 逻辑地标识每一行，且值唯一 2. 可以通过ROWID快速查询和修改表的其他列，自身不能被插入和修改 3. 用户可以控制是否开启此功能 在IvorySQL中系统列 ctid 字段代表了数据行在表中的物理位置，也就是行标识（tuple identifier），由一对数值组成（块编号和行索引），可以通过ctid快速的查找表中的数据行，这样和Oracle的RowID行为很相似，但是ctid值有可能会改变（例如当update/ vacuum full时），因此ctid不适合作为一个长期的行标识。 我们选择了表的oid加一个序列值组成的复合类型来作为RowID值，其中的序列是系统列。如果RowID功能被开启，则在建表的同时创建一个名为table-id_rowid_seq 的序列。同时在heap_form_tuple构造函数中，为 HeapTupleHeaderData 的长度增加8个字节，并标识td→t_infomask = HEAP_HASROWID 位来表示rowid的存在。 在开启了ROWID的GUC参数或建表时带上 WITH ROWID 选项，或对普通表执行 ALTER TABLE … SET WITH ROWID 时会通过增加序列创建命令来创建一个序列。 /* * Build a CREATE SEQUENCE command to create the sequence object, * and add it to the list of things to be done before this CREATE/ALTER TABLE */ seqstmt = makeNode(CreateSeqStmt); seqstmt->with_rowid = true; seqstmt->sequence = makeRangeVar(snamespace, sname, -1); seqstmt->options = lcons(makeDefElem(\"as\", (Node *) makeTypeNameFromOid(INT8OID, -1), -1), seqstmt->options); seqstmt->options = lcons(makeDefElem(\"nocache\", NULL, -1), seqstmt->options); 同时为了快速通过RowID伪列查询到一行数据，默认会在表的RowID列上创建一个UNIQUE索引，以提供快速查询功能。 RowID列作为系统属性列其实现是通过在 heap.c 中新增一个系统列来实现的。 /* * Compatible Oracle ROWID pseudo column. */ static const FormData_pg_attribute a7 = { .attname = {\"rowid\"}, .atttypid = ROWIDOID, .attlen = -1, .attnum = RowIdAttributeNumber, .attcacheoff = -1, .atttypmod = -1, .attbyval = false, .attalign = TYPALIGN_SHORT, .attstorage = TYPSTORAGE_PLAIN, .attnotnull = true, .attislocal = true, }; 在pg_class系统表中增加一个 bool 类型的字段 relhasrowid，用于标识建表时的 WITH ROWID选项，如果建表时带了WITH ROWID选项，则 relhasrowid为 t，否则为f。 用户在执行 ALTER table … SET WITH ROWID/ WITHOUT ROWID 命令时，也会修改这个值。 /* T if we generate ROWIDs for rows of rel */ bool relhasrowid BKI_DEFAULT(f); 在RowID的存储方面，如果启用了RowID 伪列功能，则在插入表之前 heap_form_tuple函数会根据参数TupleDesc 中tdhasrowid 是否为true 在 HeapTupleHeaderData 中增加8个字节来存储序列值。 在heap_prepare_insert 函数中获取序列的nextval值，存在HeapTupleHeader 相应的位置。 if (relation->rd_rel->relhasrowid) { // Get the sequence next value seqnum = nextval_internal(relation->rd_rowdSeqid, true); // Set the HeapTupleHeader HeapTupleSetRowId(tup, seqnum); }","title":"RowID","component":"ivorysql-doc","version":"master","name":"rowid","url":"/ivorysql-doc/master/compatibility_features_design/rowid.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"29":{"id":29,"text":"IvorySQL的sys_guid() 是一个强大的随机数产生函数，它产生并返回一个由16个字节组成的数据库级别唯一的标识符(原始值)。 IvorySQL的sys_guid()函数通过修改插件uuid-ossp的代码实现。为了充分利用uuid的多种基础库，采用如下逻辑： 如果系统有uuid-ossp，就使用uuid_make(); 如果系统有uuid-e2fs，就使用 uuid_generate_random(); 否则就调用 arc4random(); 同时修改代码使得IvorySQL能够自动载入uuid-ossp插件。","title":"sys_guid() 函数","component":"ivorysql-doc","version":"master","name":"sys_guid_function","url":"/ivorysql-doc/master/compatibility_features_design/sys_guid_function.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"30":{"id":30,"text":"IvorySQL提供了兼容Oracle的plsql数据类型功能，包括%TYPE、%ROWTYPE。 这是一个被动的过程。 当前的实现方案是记录函数（存储过程）与 tablename.columname 的依赖关系。当引用类型发生变化时，根据依赖关系，让函数（存储过程）缓存失效。这样一来，函数被调用时就会执行强制编译。从而确保函数获取最新的变量类型。 在系统表 pg_proc 中添加一个字段 prostatus 用来表示函数（存储过程）状态，有3种状态: validate(v)，invalidate(i)，N/A(n)。函数编译成功后，这个状态会被设置为valid。 在解析函数内容时，plisql_parse_cwordtype，plisql_parse_wordrowtype，plisql_parse_cwordrowtype 等函数识别出%TYPE， %ROWTYPE引用的对象，并记录在 plisql_referenced_object 链表中，最后添加到 pg_depend 系统表中。 增加一种新的依赖类型 DEPENDENCY_TYPE = 't'，表示%TYPE或%ROWTYPE依赖。在添加对象引用关系到pg_depend系统表时，设置依赖类型为 't'。 对表进行操作时（修改表类型、删除表等），查看系统表pg_depend，如果存在依赖类型deptype=’t’，并且依赖对象是函数，则调用函数 plisql_free_function 删除函数缓存，并修改 pg_proc 系统表中的函数状态prostatus 为 N/A(n)。 在 PLiSQL_type 结构体中添加成员 bool notnull; /* * Postgres data type */ typedef struct PLiSQL_type { bool notnull; /* the type is built by variable%type, * isnull or notnull of the variable */ 在负责解析%TYPE类型函数的plisql_parse_wordtype或plisql_parse_cwordtype函数中，判断如果引用变量类型指定了NOT NULL约束，为返回的datatype的成员 bool notnull 属性赋值为true。 在pl_gram.y 文件的 decl_statement 语法中，根据PLiSQL_type 的成员 bool notnull 为变量PLiSQL_variable *var的notnull属性赋值。这样就继承了引用变量的约束。 在 ora_gram.y 中为 func_type 添加%ROWTYPE支持。 | type_function_name attrs '%' ROWTYPE { $$ = makeTypeNameFromNameList(lcons(makeString($1), $2)); $$->row_type = true; $$->location = @1; } 在 TypeName 结构体中添加成员 bool row_type 用来标记是否指定了%ROWTYPE。 typedef struct TypeName { bool pct_type; /* %TYPE specified? */ bool row_type; /* %ROWTYPE specified? */ 在 LookupTypeName 函数中，如果 TypeName 的成员 row_type 为 TRUE，则根据TypeName的成员names中获得schema名与表名，然后获取表的typeoid。 在 ora_gram.y 中添加新的语法，支持 VALUES 后面不带括号’(‘。 values_clause_no_parens: VALUES columnref { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1(list_make1($2)); n->valuesIsrow = true; $$ = (Node *) n; } 为结构体 SelectStmt 添加一个字段 bool valuesIsrow 表示 values 是一个 row。 在为insert语句做 transform 时，也就是函数 transformInsertStmt 中，在处理 INSERT …​ VALUES 语句时，如果 valuesIsrow 为true，调用新函数 transformRowExpression，将 row_variable 转换成等价的 row_variable.field1,…​,row_variable.fieldN。 在UPDATE语句做transform时候，也就是transformUpdateStmt的时候，如果是Oracle兼容模式，调用新添加的 transformIvyUpdateTargetList 函数。 在这个新函数中，对于参数origTlist(即targetList)中没有名字为row的情况，按原来UPDATE的transform流程执行 transformUpdateTargetList 函数。 对参数origTlist中有名字为row的情况：因为PostgreSQL中row可以作为列名，而Oracle 中row是保留关键字，不可以作为列名，所以需要区分row是否是表中的列，如果row不是要更新的表中的列，则调用新函数 transformUpdateRowTargetList 把语句 UPDATE table_name SET ROW = row_variable [WHERE …]; 转换成等价的 UPDATE table_name SET table_name.column1 = row_variable.column1, table_name.column2 = row_variable.filed2,… table_name.columnN = row_variable.columnN [WHERE …]; 如果语句“UPDATE table_name SET ROW = row_variable”中的变量 row_variable不是组合类型，就按原来UPDATE的transform流程执行 transformUpdateTargetList 函数； 如果语句中的变量 row_variable 是组合类型，表中的名为row的那一列也是组合类型，并且两者的类型Oid也一样，则按原来UPDATE的transform流程执行 transformUpdateTargetList 函数； 其他所有情况，调用新函数 transformUpdateRowTargetList 处理；","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"type_rowtype","url":"/ivorysql-doc/master/compatibility_features_design/type_rowtype.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 引用类型发生变化时，如何确保%TYPE声明的变量也随之相应改变","hash":"引用类型发生变化时如何确保type声明的变量也随之相应改变","id":3},{"text":"2.2. %TYPE声明的变量继承引用变量的约束","hash":"type声明的变量继承引用变量的约束","id":4},{"text":"2.3. 表名%ROWTYPE或视图名%ROWTYPE作为函数或存储过程的参数类型和函数返回值类型","hash":"表名rowtype或视图名rowtype作为函数或存储过程的参数类型和函数返回值类型","id":5},{"text":"2.4. INSERT语句增强","hash":"insert语句增强","id":6},{"text":"2.5. UPDATE语句增强","hash":"update语句增强","id":7}]},"31":{"id":31,"text":"从Docker Hub上获取IvorySQL镜像 $ docker pull ivorysql/ivorysql:5.0-ubi8 运行IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 查看IvorySQL容器运行是否成功 $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql 从Docker Hub上获取IvorySQL镜像 [highgo@manager-node1 ~]$ podman pull ivorysql/ivorysql:5.0-ubi8 ✔ docker.io/ivorysql/ivorysql:5.0-ubi8 Trying to pull docker.io/ivorysql/ivorysql:5.0-ubi8... Getting image source signatures Copying blob 5885448c5c88 done | Copying blob 6c502b378234 done | Copying blob 8b4f2b90d6b6 done | Copying blob 9b000f2935f6 done | Copying blob 806f782da874 done | Copying blob e4c51845a9eb done | Copying blob dcb1e9a04275 done | Copying blob 285a279173f8 done | Copying blob 1f6f247b9ae0 done | Copying blob 3cc81bed8614 done | Copying blob 863c87bf25eb done | Copying blob 4f4fb700ef54 done | Copying config 88e1bbeda8 done | Writing manifest to image destination 88e1bbeda81c51d88e12cbd2b19730498f1343d1c64bb3dddc8ffcb08a1f965f 运行IvorySQL $ podman run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=123456 -d ivorysql/ivorysql:5.0-ubi8 查看IvorySQL容器运行是否成功 [highgo@manager-node1 ~]$ podman ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 368dee58d5ef docker.io/ivorysql/ivorysql:5.0-ubi8 postgres 20 seconds ago Up 20 seconds 0.0.0.0:5434->5432/tcp, 1521/tcp, 5866/tcp ivorysql [highgo@manager-node1 ~]$ podman exec -it ivorysql /bin/bash [root@8cc631eb413d /]# ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=#","title":"Docker & Podman 部署IvorySQL","component":"ivorysql-doc","version":"master","name":"docker_podman_deployment","url":"/ivorysql-doc/master/containerization/docker_podman_deployment.html","titles":[{"text":"1. docker方式运行","hash":"docker方式运行","id":1},{"text":"2. podman方式运行","hash":"podman方式运行","id":2}]},"32":{"id":32,"text":"准备三个网络互通的服务器，并搭建swarm集群。 测试集群名称及对应ip地址如下： manager-node1: 192.168.21.205 manager-node2: 192.168.21.164 manager-node3: 192.168.21.51 [root@manager-node1 docker-swarm]# docker node ls ID HOSTNAME STATUS AVAILABILITY MANAGER STATUS ENGINE VERSION y9d9wd9t2ncy4t9bvw6bg9sjs * manager-node1 Ready Active Reachable 26.1.4 iv17o6m9t9e06vd9iu1o6damd manager-node2 Ready Active Leader 25.0.4 vjnax76qj812mlvut6cv4qotl manager-node3 Ready Active Reachable 24.0.6 下载源码 [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-swarm 部署一个三节点的etcd [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-etcd.yml ivoryhac-etcd Creating network ivoryhac-etcd_etcd-net Creating service ivoryhac-etcd_etcd3 Creating service ivoryhac-etcd_etcd1 Creating service ivoryhac-etcd_etcd2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 可自定义数据库外挂目录，修改docker-swarm-ivypatroni.yml中的volumes，修改完成后修改目录权限及属主；示例如下 mkdir -p /home/ivorysql/{data,patroni} chown -R 1000:1000 /home/ivorysql/{data,patroni} chmod 700 /home/ivorysql/{data,patroni} 部署IvorySQL高可用集群 [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-ivypatroni.yml ivoryhac-patroni Since --detach=false was not specified, tasks will be created in the background. In a future release, --detach=false will become the default. Creating service ivoryhac-patroni_ivypatroni1 Creating service ivoryhac-patroni_ivypatroni2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 uzdvjq5j2gwt ivorysql-hac_ivypatroni1 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1521->1521/tcp, *:5866->5866/tcp fr0m9chu3ce8 ivorysql-hac_ivypatroni2 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1522->1521/tcp, *:5867->5866/tcp psql连接数据库的Oracle端口及PG端口 [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) 卸载IvorySQL集群 [root@manager-node1 ~] docker stack rm ivoryhac-patroni [root@manager-node1 ~] docker stack rm ivoryhac-etcd 下载源码 [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-compose 拷贝文件至其他服务器 将etcd和ivypatroni的docker-compose文件，分别拷贝到其他服务器上。 如测试服务器: 192.168.21.205 存放etcd1+ivorypatroni1， 192.168.21.164 存放etcd2+ivorypatroni2， 192.168.21.51 存放etcd3+ivorypatroni3 部署一个三节点的etcd，以node1为例 [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-etcd1.yml up -d [+] Running 1/1 ✔ Container etcd Started 0.1s 部署IvorySQL高可用集群 在每个节点上，部署ivyhac服务 以node1为例 [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-ivypatroni_1.yml up -d [+] Running 1/1 ✔ Container ivyhac1 Started 0.1s [root@manager-node1 docker-compose]# docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 736c0d188bdd ivorysql/docker-compose-ha-cluster:5.0-4.0.6-ubi8 \"/bin/sh /docker-ent…\" 18 seconds ago Up 17 seconds ivyhac1 9d8e04e4f819 quay.io/coreos/etcd:v3.5.8 \"/usr/local/bin/etcd\" 24 minutes ago Up 24 minutes etcd 此时，一主两备集群搭建完成 psql连接数据库的Oracle端口及PG端口 [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) 卸载IvorySQL集群 以node1为例 [root@manager-node1 ~] docker-compose -f ./docker-compose-ivypatroni_1.yml down [root@manager-node1 ~] docker-compose -f ./docker-compose-etcd1.yml down","title":"Docker Swarm & Docker Compose 部署IvorySQL高可用集群","component":"ivorysql-doc","version":"master","name":"docker_swarm_compose_deployment","url":"/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html","titles":[{"text":"1. docker swarm搭建IvorySQL HA Cluster","hash":"docker-swarm搭建ivorysql-ha-cluster","id":1},{"text":"2. docker compose搭建IvorySQL HA Cluster","hash":"docker-compose搭建ivorysql-ha-cluster","id":2}]},"33":{"id":33,"text":"进入k8s集群的master节点上，创建名为ivorysql的namespace [root@k8s-master ~]# kubectl create ns ivorysql 下载最新docker_library代码 [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git 进入单机目录 [root@k8s-master ~]# cd docker_library/k8s-cluster/single [root@k8s-master single]# vim statefulset.yaml #根据个人环境自行修改statefulset中的pvc信息及数据库密码 使用statefulset.yaml创建一个单机pod [root@k8s-master single]# kubectl apply -f statefulset.yaml service/ivorysql-svc created statefulset.apps/ivorysql created 等待pod创建成功 [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 0/1 ContainerCreating 0 47s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 47s NAME READY AGE statefulset.apps/ivorysql 0/1 47s [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 1/1 Running 0 2m39s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 2m39s NAME READY AGE statefulset.apps/ivorysql 1/1 2m39s psql连接IvorySQL的PG端口 [root@k8s-master single]# psql -U ivorysql -p 32106 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# exit psql连接IvorySQL的Oracle端口 [root@k8s-master single]# psql -U ivorysql -p 31887 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) 卸载单机容器 [root@k8s-master single]# kubectl delete -f statefulset.yaml 进入k8s集群的master节点上，创建名为ivorysql的namespace [root@k8s-master ~]# kubectl create ns ivorysql 下载最新docker_library代码 [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git 进入高可用集群目录 [root@k8s-master ~]# cd docker_library/k8s-cluster/ha-cluster/helm_charts [root@k8s-master single]# vim values.yaml #根据个人环境自行修改values.yaml中的pvc信息及集群规模等信息，数据库密码查看templates/secret.yaml并自行修改。 使用 Helm 命令部署高可用集群 [root@k8s-master helm_charts]# helm install ivorysql-ha-cluster -n ivorysql . NAME: ivorysql-ha-cluster LAST DEPLOYED: Wed Sep 10 09:45:36 2025 NAMESPACE: ivorysql STATUS: deployed REVISION: 1 TEST SUITE: None [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 42s pod/ivorysql-patroni-hac-1 0/1 Running 0 18s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 42s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 42s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 1/3 42s 等待所有 Pod 进入“Running”（运行中）状态，即表示集群已部署成功。 [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 88s pod/ivorysql-patroni-hac-1 1/1 Running 0 64s pod/ivorysql-patroni-hac-2 1/1 Running 0 41s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 88s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 88s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 3/3 88s 使用psql连接集群主节点的PG、Oracle端口 [root@k8s-master helm_charts]# psql -U ivorysql -p 32391 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32477 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# 使用psql连接集群备节点的PG、Oracle端口 [root@k8s-master helm_charts]# psql -U ivorysql -p 30111 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32654 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# 卸载高可用集群 [root@k8s-master helm_charts]# helm uninstall ivorysql-ha-cluster -n ivorysql 删除PVC [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-2 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-2 -n ivorysql","title":"k8s部署单机容器和高可用集群","component":"ivorysql-doc","version":"master","name":"k8s_deployment","url":"/ivorysql-doc/master/containerization/k8s_deployment.html","titles":[{"text":"1. 单机容器","hash":"单机容器","id":1},{"text":"2. 高可用集群","hash":"高可用集群","id":2}]},"34":{"id":34,"text":"Fork ivory-operator 仓库 并克隆到本地： YOUR_GITHUB_UN=\"<your GitHub username>\" git clone --depth 1 \"git@github.com:${YOUR_GITHUB_UN}/ivory-operator.git\" cd ivory-operator 执行以下命令完成安装： kubectl apply -k examples/kustomize/install/namespace kubectl apply --server-side -k examples/kustomize/install/default 在本教程中，我们将基于 examples/kustomize/ivory 目录中的示例进行构建。 当引用 YAML 清单中的嵌套对象时，我们将使用类似 kubectl explain 的 . 格式。例如，对于以下 YAML 文件： spec: hippos: appetite: huge 我们会用 spec.hippos.appetite 来表示最深层的元素。 kubectl explain 是一个非常有用的命令。你可以使用它来查看 ivorycluster.ivory-operator.ivorysql.org 自定义资源定义（CRD）的结构： kubectl explain ivorycluster 创建一个 Ivory 集群非常简单。使用 examples/kustomize/ivory 目录中的示例，只需运行： kubectl apply -k examples/kustomize/ivory IVYO 将在 ivory-operator 命名空间中创建一个名为 hippo 的简单 Ivory 集群。你可以通过以下命令跟踪 Ivory 集群的状态： kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo 你也可以使用以下命令跟踪 Ivory Pod 的状态： kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance IVYO 根据 examples/kustomize/ivory 目录中的 Kustomize 清单信息创建了 Ivory 集群。让我们通过查看 examples/kustomize/ivory/ivory.yaml 文件来更好地理解发生了什么： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 当我们运行 kubectl apply 命令时，实际上是在 Kubernetes 中创建了一个 ivorycluster 自定义资源。IVYO 检测到新增的 ivorycluster 资源后，开始创建在 Kubernetes 中运行 Ivory 所需的所有对象！ 还发生了什么？IVYO 从 metadata.name 读取值，为 Ivory 集群命名为 hippo。此外，IVYO 通过查看 spec.image 和 spec.backups.pgbackrest.image 的值，分别确定了 Ivory 和 pgBackRest 使用的容器镜像。spec.postgresVersion 的值也很重要，它帮助 IVYO 跟踪你使用的 Ivory 主版本。 IVYO 通过清单中的 spec.instances 部分知道要创建多少个 Ivory 实例。虽然 name 是可选的，但我们选择将其命名为 instance1。我们也可以在集群初始化期间创建多个副本和实例，但稍后在我们讨论 如何扩展并创建高可用 Ivory 集群 时会详细介绍。 ivorycluster 自定义资源中非常重要的一部分是 dataVolumeClaimSpec 部分。它描述了 Ivory 实例将使用的存储，建模自 Persistent Volume Claim。如果你没有提供 spec.instances.dataVolumeClaimSpec.storageClassName，则将使用 Kubernetes 环境中的默认存储类。 作为创建 Ivory 集群的一部分，我们还指定了备份存档的信息。IVYO 使用 pgBackRest，这是一个开源的备份与恢复工具，专为处理 TB 级备份而设计。在集群初始化期间，我们可以指定备份和归档（https://www.postgresql.org/docs/current/wal-intro.html[预写日志或 WAL]）的存储位置。我们将在本教程的 灾难恢复 部分更深入地讨论 ivorycluster 规范的这一部分，并了解如何将备份存储在 Amazon S3、Google GCS 和 Azure Blob Storage 中。 最常见的原因是 PVC 未绑定。请确保你在任何 volumeClaimSpec 中正确设置了存储选项。你可以随时更新设置并使用 kubectl apply 重新应用更改。 还要确保你有足够的持久卷可用：你的 Kubernetes 管理员可能需要配置更多持久卷。 如果你使用的是 OpenShift，可能需要将 spec.openshift 设置为 true。 创建 Ivory 集群是一回事，连接到它又是另一回事。让我们看看 IVYO 如何让连接 Ivory 集群变得简单！ IVYO 会创建一系列 Kubernetes Service，为访问 Ivory 数据库提供稳定的端点。这些端点让应用程序能够始终如一地连接到数据。要查看可用的 Service，可执行： kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo 输出示例： NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 3h14m hippo-ha-config ClusterIP None <none> <none> 3h14m hippo-pods ClusterIP None <none> <none> 3h14m hippo-primary ClusterIP None <none> 5432/TCP 3h14m hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 3h14m 大多数 Service 用于集群内部管理，无需关注。连接数据库时，只需关注名为 hippo-primary 的 Service。得益于 IVYO，你甚至无需手动指定它——这些信息已被写入 Secret！ 集群初始化时，IVYO 会引导创建一个数据库和用户，供应用程序使用。相关信息保存在名为 <clusterName>-pguser-<userName> 的 Secret 中。对于 hippo 集群，该 Secret 名为 hippo-pguser-hippo，包含以下键值： user：用户账户名 password：用户密码 dbname：用户默认可访问的数据库名 host：数据库主机名（指向主实例的 Service） port：数据库监听端口 uri： PostgreSQL 连接 URI，含完整登录信息 jdbc-uri： PostgreSQL JDBC 连接 URI，供 JDBC 驱动使用 所有连接均通过 TLS 进行。IVYO 自带证书中心（CA），支持使用 Ivory 的 verify-full SSL 模式，防止窃听与中间人攻击。你也可以稍后使用自定义 CA，详见 自定义集群 章节。 默认情况下，IVYO 部署的 Service 类型为 ClusterIP。根据暴露数据库的方式，你可能需要更改为其他 Service 类型 或指定 NodePort 值。 可通过以下字段调整 IVYO 管理的 Service： spec.service：控制连接主库的 Service spec.userInterface.pgAdmin.service：控制 pgAdmin 管理工具的 Service 例如，将主库 Service 改为 NodePort 并指定端口、注解与标签： spec: service: metadata: annotations: my-annotation: value1 labels: my-label: value2 type: NodePort nodePort: 32000 重新应用后，再次查看 Service： kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo 输出示例： NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha NodePort 10.105.57.191 <none> 5432:32000/TCP 48s hippo-ha-config ClusterIP None <none> <none> 48s hippo-pods ClusterIP None <none> <none> 48s hippo-primary ClusterIP None <none> 5432/TCP 48s hippo-replicas ClusterIP 10.106.18.99 <none> 5432/TCP 48s 查看 hippo-ha 的详细信息，顶部将显示自定义注解与标签已生效： Name: hippo-ha Namespace: ivory-operator Labels: my-label=value2 ivory-operator.ivorysql.org/cluster=hippo ivory-operator.ivorysql.org/patroni=hippo-ha Annotations: my-annotation: value1 使用默认 ClusterIP 类型时禁止设置 nodePort；该值必须在合法范围内且未被占用。此处提供的注解与标签优先级最高。若通过外部暴露 Service 并依赖 TLS 验证，需使用 IVYO 的 自定义 TLS 功能。 本教程以 Keycloak（开源身份管理应用）为例。Keycloak 可部署在 Kubernetes 上，并使用 Ivory 作为数据库。以下示例清单将 Keycloak 连接到已运行的 hippo 集群： kubectl apply --filename=- <<EOF apiVersion: apps/v1 kind: Deployment metadata: name: keycloak namespace: ivory-operator labels: app.kubernetes.io/name: keycloak spec: selector: matchLabels: app.kubernetes.io/name: keycloak template: metadata: labels: app.kubernetes.io/name: keycloak spec: containers: - image: quay.io/keycloak/keycloak:latest args: [\"start-dev\"] name: keycloak env: - name: DB_VENDOR value: \"ivory\" - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } - name: KEYCLOAK_ADMIN value: \"admin\" - name: KEYCLOAK_ADMIN_PASSWORD value: \"admin\" - name: KC_PROXY value: \"edge\" ports: - name: http containerPort: 8080 - name: https containerPort: 8443 readinessProbe: httpGet: path: /realms/master port: 8080 restartPolicy: Always EOF 注意环境变量部分： - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } 所有连接信息均来自 hippo-pguser-hippo Secret，无需硬编码或明文传递凭据，即可直接在 GitOps 流水线中使用！ Ivory 以稳定著称，通常“开箱即用”。但在 Kubernetes 这样的分布式环境中，仍可能遇到多种影响可用的事件： 数据库磁盘或其余硬件故障 网络不可达 节点操作系统崩溃 数据文件损坏 整个数据中心失联 Kubernetes 自身组件（如 Service）被误删 此外，常规运维（小版本升级、安全补丁、硬件更换）也会带来计划内停机。 好消息：IVYO 已针对上述场景提供保护。要让集群真正高可用，首先把它扩容。 IVYO 支持两种快速获得 HA 集群的方式： 调大 spec.instances.replicas 在 spec.instances 新增条目 本文采用方法 1，将副本数设为 2。对应 YAML 片段： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: \"{{< param imageIvorySQL >}}\" postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 # <── 新增副本 dataVolumeClaimSpec: accessModes: [\"ReadWriteOnce\"] resources: requests: storage: 1Gi backups: pgbackrest: image: \"{{< param imagePGBackrest >}}\" repos: - name: repo1 volume: volumeClaimSpec: accessModes: [\"ReadWriteOnce\"] resources: requests: storage: 1Gi 应用后稍等片刻，新副本自动初始化。通过以下命令可实时查看实例 Pod： kubectl -n ivory-operator get pods \\ -l ivory-operator.ivorysql.org/cluster=hippo,\\ ivory-operator.ivorysql.org/instance-set 上一篇《连接集群》提到，应用默认通过 hippo-primary Service 读写。我们人为删除它： kubectl -n ivory-operator delete svc hippo-primary 立刻再查询 Service 列表： kubectl -n ivory-operator get svc \\ -l ivory-operator.ivorysql.org/cluster=hippo 可见 hippo-primary 已被 IVYO 秒级重建。多数应用凭借重连逻辑几乎无感知。 首先找到当前主库 Pod 对应的 StatefulSet 名字： PRIMARY_STS=$(kubectl -n ivory-operator get sts \\ -l ivory-operator.ivorysql.org/cluster=hippo,\\ ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[0].metadata.name}') echo $PRIMARY_STS 假设输出为 hippo-instance1-zj5s，直接删除： kubectl -n ivory-operator delete sts \"$PRIMARY_STS\" 再次查看 StatefulSet： kubectl -n ivory-operator get sts \\ -l ivory-operator.ivorysql.org/cluster=hippo IVYO 会立即重建被删对象，并自动将原副本重新加入集群。同时，另一实例已被提升为新主： kubectl -n ivory-operator get pods \\ -l ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[0].metadata.labels.ivory-operator\\.ivorysql\\.org/instance}' 即使 IVYO 进程短暂离线，Patroni 仍能独立完成故障切换，确保应用读写不中断。 IvorySQL 支持同步复制，可进一步降低事务丢失风险。只需在集群里增加： spec: patroni: dynamicConfiguration: synchronous_mode: true 如需强制所有提交都同步到至少一个副本，可再加： synchronous_mode_strict: true Patroni 默认“可用性优先”，当同步副本全部失效时会退化为异步；若业务要求绝对同步，请启用 synchronous_mode_strict，此时无可用同步副本将拒绝写入。 preferredDuringSchedulingIgnoredDuringExecution —— 尽力分散，资源不足时允许同节点 requiredDuringSchedulingIgnoredDuringExecution —— 强制分散，找不到空闲节点则 Pending 示例 —— 强制让同一 instance-set 的 Pod 落在不同节点： affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 将数据库实例固定在带 workload-role=db 标签的节点： affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: workload-role operator: In values: [\"db\"] 相比反亲和的“0 或 1”限制，拓扑分布约束可按比例打散，粒度更细。字段模板： topologySpreadConstraints: - maxSkew: <整数> topologyKey: <标签键> whenUnsatisfiable: <DoNotSchedule | ScheduleAnyway> labelSelector: <对象> 示例 —— 5 个实例 Pod 在 3 节点间尽量均衡： instances: - name: instance1 replicas: 5 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: ivory-operator.ivorysql.org/instance-set: instance1 备份仓库主机（repo-host）也可同理配置，确保多集群场景下仓库 Pod 分散。 业务蒸蒸日上，流量激增，需要给 Ivory 集群扩容，却又担心 resize 造成停机？ IVYO 提供滚动升级机制，能在零感知或毫秒级中断内完成 CPU、内存、磁盘等所有规格的在线调整。 继续阅读前，请确保已按上一章《高可用》部署了 HA 集群（至少 2 副本）。 IVYO 把资源声明分散在多处，保持统一语义（与 Kubernetes 原生 resources 字段一致），并支持 QoS 类别设置： spec.instances.resources └ Ivory 主容器、init 容器、数据迁移 Job 的 CPU / 内存 spec.instances.sidecars.replicaCertCopy.resources └ 副本证书复制 sidecar spec.backups.pgbackrest.repoHost.resources └ pgBackRest 仓库主机及对应 init / 迁移 Job spec.backups.pgbackrest.sidecars.*.resources spec.backups.pgbackrest.jobs.resources spec.backups.pgbackrest.restore.resources spec.dataSource.ivorycluster.resources 示例：把 hippo 每个实例上限调整为 2 CPU、4 GiB 内存 apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: instances: - name: instance1 replicas: 2 resources: # <── 新增或修改 limits: cpu: \"2\" memory: 4Gi dataVolumeClaimSpec: accessModes: [\"ReadWriteOnce\"] resources: requests: storage: 1Gi kubectl apply -k examples/kustomize/ivory ---- 实时观察滚动过程（逐 Pod 重建）： watch kubectl -n ivory-operator get pods \\ -l ivory-operator.ivorysql.org/cluster=hippo,\\ ivory-operator.ivorysql.org/instance \\ -o custom-columns=NAME:.metadata.name,ROLE:.metadata.labels.ivory-operator\\.ivorysql\\.org/role,PHASE:.status.phase ---- 流程解析： 1. 先升级所有 副本 实例 → 新 Pod 就绪后旧 Pod 才删除 2. 执行受控主从切换（switchover）→ 应用仅感受到毫秒级重连 3. 最后升级原主库 → 再次选主完成 要求： - 底层 StorageClass 的 allowVolumeExpansion=true - 只能增不能减 需要调大的字段： spec.instances.dataVolumeClaimSpec.resources.requests.storage （数据目录） spec.backups.pgbackrest.repos[*].volume.volumeClaimSpec…​ （备份仓库） 示例：数据盘 1 GiB → 10 GiB，备份盘 1 GiB → 20 GiB spec: instances: - name: instance1 dataVolumeClaimSpec: resources: requests: storage: 10Gi # 1→10 backups: pgbackrest: repos: - name: repo1 volume: volumeClaimSpec: resources: requests: storage: 20Gi # 1→20 kubectl apply -k examples/kustomize/ivory ---- IVYO 会按 副本优先、主库最后 的顺序触发底层 pvc.spec.resources.requests.storage 修改，Kubelet 与存储插件完成文件系统在线扩容，Pod 无需重建，业务无感知。 部分公有云早期 StorageClass 或本地盘 CSI 驱动未开启扩容，仍可通过 “新增大容量实例集 → 切换 → 删除老实例集” 完成“曲线”扩容。 步骤示例： 保留原 instance1（1 GiB），新增 instance2（10 GiB） spec: instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: resources: requests: storage: 1Gi - name: instance2 # 新实例集 replicas: 2 dataVolumeClaimSpec: resources: requests: storage: 10Gi 等待 instance2 副本同步追上主库 提交仅含 instance2 的清单，IVYO 将自动： 把 instance2 某一副本提升为新主 删除 instance1 所有 Pod & PVC 完成“数据迁移” 结果：业务未中断，磁盘已换成 10 GiB，老 PVC 被释放，费用停止计费。 反向操作即可“缩容”磁盘（先建小盘实例集 → 切换 → 删大盘）。 节点剩余资源不满足 requests → 扩容节点或降低 requests PVC 申请过大 / StorageClass 不存在 → 检查存储类及配额 确认 StorageClass： kubectl get sc -o custom-columns=NAME:.metadata.name,ALLOW Expansion:.allowVolumeExpansion ---- 若返回 false 或空值，请： 换用支持扩容的 StorageClass，或 使用上文“场景 B”实例集替换方案 管理 Ivory 集群中多个实例的诀窍之一是确保所有配置更改都能传播到每个实例。这正是 IVYO 的用武之地：当您为集群进行 Ivory 配置更改时，IVYO 会将其应用到所有 Ivory 实例。 例如，在上一步中，我们分别添加了 CPU 和内存限制为 2.0 和 4Gi。让我们调整一些 Ivory 设置以更好地利用我们的新资源。我们可以在 spec.patroni.dynamicConfiguration 部分中进行此操作。以下是一个更新后的示例清单，其中调整了几个设置： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB 特别是，我们在 spec 中添加了以下内容： patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB 使用以下命令将这些更新应用到您的 Ivory 集群： kubectl apply -k examples/kustomize/ivory IVYO 将应用这些设置，并在必要时重新启动每个 Ivory 实例。您可以使用 Ivory 的 SHOW 命令验证更改是否已生效，例如： SHOW work_mem; 应该会产生类似以下的结果： work_mem ---------- 2MB IVYO 中的所有连接都使用 TLS 加密组件之间的通信。IVYO 设置了一个 PKI 和证书颁发机构 (CA)，允许您创建可验证的端点。但是，您可能希望根据组织要求引入不同的 TLS 基础设施。好消息是：IVYO 允许您这样做！ IVYO 有几个不同的 TLS 端点可以自定义，包括 Ivory 集群的端点以及控制 Ivory 实例之间如何进行身份验证的端点。让我们看看如何通过定义以下内容来自定义 TLS： 一个 spec.customTLSSecret，用于标识集群并加密通信；以及 一个 spec.customReplicationTLSSecret，用于复制身份验证。 要自定义 Ivory 集群的 TLS，您需要在 Ivory 集群的命名空间中创建两个 Secret。其中一个 Secret 将是 customTLSSecret，另一个将是 customReplicationTLSSecret。这两个 Secret 都包含要使用的 TLS 密钥（tls.key）、TLS 证书（tls.crt）和 CA 证书（ca.crt）。 注意：如果提供了 spec.customTLSSecret，则必须也提供 spec.customReplicationTLSSecret，并且两者都必须包含相同的 ca.crt。 自定义 TLS 和自定义复制 TLS Secret 应包含以下字段（如果您无法控制 Secret 的 data 中的字段名称，请参见下面的解决方法）： data: ca.crt: <value> tls.crt: <value> tls.key: <value> 例如，如果您本地计算机上存储有名为 ca.crt、hippo.key 和 hippo.crt 的文件，您可以运行以下命令从这些文件创建 Secret： kubectl create secret generic -n ivory-operator hippo-cluster.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=hippo.key \\ --from-file=tls.crt=hippo.crt 创建 Secret 后，您可以在 ivorycluster.ivory-operator.ivorysql.org 自定义资源中指定自定义 TLS Secret。例如，如果您创建了 hippo-cluster.tls Secret 和 hippo-replication.tls Secret，您可以将它们添加到您的 Ivory 集群中： spec: customTLSSecret: name: hippo-cluster.tls customReplicationTLSSecret: name: hippo-replication.tls 如果您无法控制 Secret 中的键值对，您可以创建一个映射来告诉 Ivory Operator 哪个键保存了预期的值。它看起来类似于以下内容： spec: customTLSSecret: name: hippo.tls items: - key: <tls.crt key in the referenced hippo.tls Secret> path: tls.crt - key: <tls.key key in the referenced hippo.tls Secret> path: tls.key - key: <ca.crt key in the referenced hippo.tls Secret> path: ca.crt 例如，如果 hippo.tls Secret 中的 tls.crt 位于名为 hippo-tls.crt 的键中，tls.key 位于名为 hippo-tls.key 的键中，ca.crt 位于名为 hippo-ca.crt 的键中，那么您的映射将如下所示： spec: customTLSSecret: name: hippo.tls items: - key: hippo-tls.crt path: tls.crt - key: hippo-tls.key path: tls.key - key: hippo-ca.crt path: ca.crt 注意：尽管自定义 TLS 和自定义复制 TLS Secret 共享相同的 ca.crt，但它们不共享相同的 tls.crt： 您的 spec.customTLSSecret TLS 证书应具有与主服务名称匹配的通用名称 (CN) 设置。这是集群名称后缀为 -primary 的名称。例如，对于我们的 hippo 集群，这将是 hippo-primary。 您的 spec.customReplicationTLSSecret TLS 证书应具有与预设复制用户 _ivoryrepl 匹配的通用名称 (CN) 设置。 与其他更改一样，您可以使用 kubectl apply 推出 TLS 自定义设置。 有几种方法可以将您自己的自定义 Kubernetes 标签 添加到您的 Ivory 集群。 集群：您可以通过编辑自定义资源的 spec.metadata.labels 部分将标签应用于集群中的任何 IVYO 托管对象。 Ivory：您可以通过编辑 spec.instances.metadata.labels 将标签应用于 Ivory 实例集及其对象。 pgBackRest：您可以通过编辑 ivoryclusters.spec.backups.pgbackrest.metadata.labels 将标签应用于 pgBackRest 及其对象。 有几种方法可以将您自己的自定义 Kubernetes 注解 添加到您的 Ivory 集群。 集群：您可以通过编辑自定义资源的 spec.metadata.annotations 部分将注解应用于集群中的任何 IVYO 托管对象。 Ivory：您可以通过编辑 spec.instances.metadata.annotations 将注解应用于 Ivory 实例集及其对象。 pgBackRest：您可以通过编辑 spec.backups.pgbackrest.metadata.annotations 将注解应用于 pgBackRest 及其对象。 IVYO 允许您使用 pod 优先级类 通过设置 Ivory 集群上的 priorityClassName 字段来指示 pod 的相对重要性。这可以通过以下方式完成： 实例：优先级是按实例集定义的，并应用于该实例集中的所有 Pod，方法是编辑自定义资源的 spec.instances.priorityClassName 部分。 专用仓库主机：在规范的 repoHost 部分下定义的优先级通过编辑自定义资源的 spec.backups.pgbackrest.repoHost.priorityClassName 部分应用于专用仓库主机。 备份（手动和计划）：优先级在 spec.backups.pgbackrest.jobs.priorityClassName 部分下定义，并将该优先级应用于所有 pgBackRest 备份作业（手动和计划）。 还原（数据源或就地）：通过编辑自定义资源的 spec.dataSource.ivorycluster.priorityClassName 部分为“数据源”还原或就地还原定义优先级。 数据迁移：规范中第一个实例集（数组位置 0）定义的优先级用于 PGDATA 和 WAL 迁移作业。pgBackRest 仓库迁移作业将使用应用于 repoHost 的优先级类。 IvorySQL 通过将更改存储在其https://www.postgresql.org/docs/current/wal-intro.html[预写日志 (WAL)] 中来提交事务。由于访问和使用 WAL 文件的方式通常与数据文件不同，并且在高性能情况下，可能需要将 WAL 文件放在单独的存储卷上。使用 IVYO，可以通过在您的 ivorycluster 规范中为您所需的实例添加 walVolumeClaimSpec 块来实现，无论是在创建集群时还是之后的任何时间： spec: instances: - name: instance walVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 稍后可以通过从实例中移除 walVolumeClaimSpec 部分来移除此卷。请注意，在更改 WAL 目录时，会小心避免丢失任何 WAL 文件。只有在先前配置的卷上不再有任何 WAL 文件时，IVYO 才会删除 PVC。 IVYO 可以在集群创建和初始化过程中为您运行 SQL。IVYO 使用 psql 客户端运行 SQL，因此您可以使用元命令连接到不同的数据库、更改错误处理或设置和使用变量。其功能在 psql 文档 中有所描述。 Ivory 集群规范接受对包含您的 init SQL 文件的 ConfigMap 的引用。更新您的集群规范以包括 ConfigMap 名称 spec.databaseInitSQL.name 和您的 SQL 文件的数据键 spec.databaseInitSQL.key。例如，如果您使用以下命令创建 ConfigMap： kubectl -n ivory-operator create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql 您可以将以下部分添加到您的 ivorycluster 规范中： spec: databaseInitSQL: key: init.sql name: hippo-init-sql ConfigMap 必须与您的 Ivory 集群位于同一命名空间中。 在您将 ConfigMap 引用添加到您的规范后，使用 kubectl apply -k examples/kustomize/ivory 应用更改。IVYO 将创建您的 hippo 集群，并在集群启动后运行您的初始化 SQL。您可以通过检查 Ivory 集群上的 databaseInitSQL 状态来验证您的 SQL 是否已运行。在状态设置期间，您的 init SQL 不会再次运行。您可以使用 kubectl describe 命令检查集群状态： kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo 在某些情况下，由于 Kubernetes 处理 ivorycluster 状态的方式，IVYO 可能会多次运行您的 SQL 命令。请确保您在 init SQL 中定义的命令是幂等的。 现在 databaseInitSQL 已在您的集群状态中定义，请验证数据库对象是否已按预期创建。验证后，我们建议从您的规范中移除 spec.databaseInitSQL 字段。从规范中移除该字段也将从集群状态中移除 databaseInitSQL。 IVYO 使用 psql 交互式终端在您的数据库中执行 SQL 语句。语句使用标准输入和文件名标志传递（例如 psql -f -）。 SQL 语句以超级用户身份在默认维护数据库中执行。这意味着您可以完全控制创建数据库对象、扩展或运行您可能需要的任何 SQL 语句。 如果您正在创建用户或数据库，请参阅 用户/数据库管理 文档。通过规范的用户管理部分创建的数据库可以在您的初始化 sql 中引用。例如，如果定义了数据库 zoo： spec: users: - name: hippo databases: - \"zoo\" 您可以通过将以下 psql 元命令添加到您的 SQL 来连接到 zoo： \\c zoo create table t_zoo as select s, md5(random()::text) from generate_Series(1,5) s; 默认情况下，psql 会在每个 SQL 命令完成时提交它。要将多个命令组合成一个单独的 事务，请使用 BEGIN 和 COMMIT 命令。 BEGIN; create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; COMMIT; psql 的退出代码将决定何时设置 databaseInitSQL 状态。当 psql 返回 0 时，状态将被设置，并且不会再次运行 SQL。当 psql 返回错误退出代码时，状态将不会被设置。IVYO 将继续尝试执行 SQL，作为其协调循环的一部分，直到 psql 正常返回。如果 psql 以失败退出，您将需要编辑 ConfigMap 中的文件，以确保您的 SQL 语句将导致成功的 psql 返回。对 ConfigMap 进行实时更改的最简单方法是使用以下 kubectl edit 命令： kubectl -n <cluster-namespace> edit configmap hippo-init-sql 请务必将所有更改传回您的本地文件。另一个选项是在本地文件中进行更改，并使用 kubectl --dry-run 创建模板，并将输出通过管道传输到 kubectl apply： kubectl create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql --dry-run=client -o yaml | kubectl apply -f - 如果您编辑了 ConfigMap 但更改没有显示出来，您可能正在等待 IVYO 协调您的集群。一段时间后，IVYO 将自动协调集群，或者您可以通过对集群应用任何更改来触发协调（例如，使用 kubectl apply -k examples/kustomize/ivory）。 为了确保 psql 在您的 SQL 命令失败时返回失败退出代码，请在您的 SQL 文件中设置 ON_ERROR_STOP 变量： \\set ON_ERROR_STOP \\echo Any error will lead to exit code 3 create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; IVYO 内置了一些即用型便利功能，用于管理 Ivory 集群中的用户和数据库。然而，您可能有需要创建额外用户、调整用户权限或向集群添加额外数据库的需求。 有关 IVYO 中用户和数据库管理工作原理的详细信息，请参阅架构指南中的 用户管理 部分。 您可以通过在 ivorycluster 自定义资源中添加以下片段来创建新用户。让我们将其添加到我们的 hippo 数据库中： spec: users: - name: rhino 现在您可以应用更改，并看到新用户已创建。请注意以下事项： 该用户只能连接到默认的 ivory 数据库。 用户不会将任何连接凭据填充到 hippo-pguser-rhino Secret 中。 该用户是未特权的。 让我们创建一个名为 zoo 的新数据库，我们将允许 rhino 用户访问该数据库： spec: users: - name: rhino databases: - zoo 检查 hippo-pguser-rhino Secret。您现在应该看到 dbname 和 uri 字段已填充！ 我们可以通过使用 Ivory 提供的标准 角色属性 并将它们添加到 spec.users.options 来设置角色权限。假设我们希望 rhino 成为超级用户（在授予 Ivory 超级用户权限时要小心！）。您可以将以下内容添加到规范中： spec: users: - name: rhino databases: - zoo options: \"SUPERUSER\" 就这样：我们创建了一个名为 rhino 的 Ivory 用户，该用户具有超级用户权限，并且可以访问 rhino 数据库（尽管超级用户可以访问所有数据库！）。 假设您想从 rhino 中撤销超级用户权限。您可以通过以下方式执行此操作： spec: users: - name: rhino databases: - zoo options: \"NOSUPERUSER\" 如果您想添加多个权限，您可以在 options 中用空格分隔每个权限，例如： spec: users: - name: rhino databases: - zoo options: \"CREATEDB CREATEROLE\" 默认情况下，IVYO 不允许您访问 ivory 用户。但是，您可以通过执行以下操作来访问此帐户： spec: users: - name: ivory 这将创建一个模式为 <clusterName>-pguser-ivory 的 Secret，其中包含 ivory 帐户的凭据。对于我们的 hippo 集群，这将是 hippo-pguser-ivory。 IVYO 不会自动删除用户：将用户从规范中移除后，它将仍然存在于您的集群中。要删除用户及其所有对象，作为超级用户，您需要在用户拥有对象的每个数据库中运行 DROP OWNED，并在您的 Ivory 集群中运行 DROP ROLE。 例如，对于上面的 rhino 用户，您将运行以下命令： DROP OWNED BY rhino; DROP ROLE rhino; 请注意，您可能需要根据对象所有权结构运行 DROP OWNED BY rhino CASCADE; —— 请非常小心此命令！ IVYO 不会自动删除数据库：从规范中移除数据库的所有实例后，它将仍然存在于您的集群中。要完全删除数据库，您必须以 Ivory 超级用户身份运行 DROP DATABASE 命令。 例如，要删除 zoo 数据库，您将执行以下命令： DROP DATABASE zoo; 也许有人不小心删除了 users 表。也许你想把生产数据库克隆到降级环境。也许你想演练灾难恢复系统（这很重要！）。 无论哪种情况，了解如何使用 IVYO 执行“恢复”操作以便从特定时间点恢复数据，或出于其他目的克隆数据库都很重要。 我们来看看如何执行不同类型的恢复操作。首先，让我们了解自定义资源上的核心恢复属性。 IVYO 提供了从现有 ivorycluster 或远程云数据源（如 S3、GCS 等）恢复的能力。有关更多信息，请参阅 从 S3 / GCS / Azure Blob 存储中存储的备份克隆 部分。 请注意，您不能同时使用本地 ivorycluster 数据源和远程云数据源；如果同时填写了 dataSource.ivorycluster 和 dataSource.pgbackrest 字段，本地 ivorycluster 数据源将优先。 自定义资源上有几个重要属性需要了解，这些都是恢复过程中的关键。所有这些属性都分组在自定义资源的 spec.dataSource.ivorycluster 部分中。 请查看下表，了解每个属性在设置恢复操作时的工作原理。 spec.dataSource.ivorycluster.clusterName：您要从中恢复的集群的名称。这对应于另一个 ivorycluster 自定义资源的 metadata.name 属性。 spec.dataSource.ivorycluster.clusterNamespace：您要从中恢复的集群的命名空间。当集群存在于不同的命名空间时使用。 spec.dataSource.ivorycluster.repoName：用于恢复的 spec.dataSource.ivorycluster.clusterName 中的 pgBackRest 仓库的名称。可以是 repo1、repo2、repo3 或 repo4 之一。仓库必须存在于另一个集群中。 spec.dataSource.ivorycluster.options：IVYO 允许的任何额外 pgBackRest 恢复选项 或常规选项。例如，您可能希望设置 --process-max 以帮助提高大型数据库的性能；但您将无法设置 --target-action，因为该选项目前被禁止。（如果存在 --target，IVYO 总是将其设置为 promote，否则将其留空。） spec.dataSource.ivorycluster.resources：设置恢复作业的 资源限制和请求 可以确保其高效运行。 spec.dataSource.ivorycluster.affinity：自定义 Kubernetes 亲和性 规则约束恢复作业，使其仅在某些节点上运行。 spec.dataSource.ivorycluster.tolerations：自定义 Kubernetes 容忍度 允许恢复作业在 污点 节点上运行。 让我们通过一些示例来了解如何克隆和恢复我们的数据库。 让我们创建一个我们之前创建的 hippo 集群的克隆。我们知道我们的集群名为 hippo（基于其 metadata.name），并且我们只有一个名为 repo1 的备份仓库。 让我们称我们的新集群为 elephant。我们可以使用如下所示的清单创建 hippo 集群的克隆： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 请注意规范中的这一部分： spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 这部分告诉 IVYO 将 elephant 集群创建为 hippo 集群的独立副本。 以上就是克隆 Ivory 集群所需的全部操作！IVYO 将在新的持久卷声明 (PVC) 上创建数据副本，并致力于将集群初始化到规范。很简单！ 有人删除了用户表吗？您可能希望执行时间点恢复 (PITR) 以将数据库恢复到更改发生之前的状态。幸运的是，IVYO 可以帮助您做到这一点。 您可以使用为 IVYO 的灾难恢复功能提供支持的备份管理工具 pgBackRest 的 restore 命令来设置 PITR。您需要在 spec.dataSource.ivorycluster.options 上设置一些选项来执行 PITR。这些选项包括： --type=time：这告诉 pgBackRest 执行 PITR。 --target：执行 PITR 的目标位置。恢复目标的一个示例是 2021-06-09 14:15:11-04。此处指定的时区为 -04，即东部夏令时。有关其他时区选项，请参阅 pgBackRest 文档。 --set（可选）：选择从哪个备份开始 PITR。 开始前的一些快速说明： 要执行 PITR，您必须有一个在 PITR 时间之前完成的备份。换句话说，您不能对没有备份的时间执行 PITR！ 所有相关的 WAL 文件必须成功推送，以便恢复正确完成。 确保选择包含所需备份的正确仓库名称！ 考虑到这一点，让我们使用上面的 elephant 示例。假设我们要执行到 2021-06-09 14:15:11-04 的时间点恢复 (PITR)，我们可以使用以下清单： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 需要注意的部分是： spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" 请注意我们如何放入选项以指定进行 PITR 的位置。 使用上述清单，IVYO 将继续创建一个恢复其数据直到 2021-06-09 14:15:11-04 的新 Ivory 集群。此时，集群被提升，您可以从该特定时间点开始访问您的数据库！ 与上面描述的 PITR 恢复类似，您可能希望执行类似的回退到更改发生之前的状态，但不创建另一个 IvorySQL 集群。幸运的是，IVYO 也可以帮助您做到这一点。 您可以使用为 IVYO 的灾难恢复功能提供支持的备份管理工具 pgBackRest 的 restore 命令来设置 PITR。您需要在 spec.backups.pgbackrest.restore.options 上设置一些选项来执行 PITR。这些选项包括： --type=time：这告诉 pgBackRest 执行 PITR。 --target：执行 PITR 的目标位置。恢复目标的一个示例是 2021-06-09 14:15:11-04。 --set（可选）：选择从哪个备份开始 PITR。 开始前的一些快速说明： 要执行 PITR，您必须有一个在 PITR 时间之前完成的备份。换句话说，您不能对没有备份的时间执行 PITR！ 所有相关的 WAL 文件必须成功推送，以便恢复正确完成。 确保选择包含所需备份的正确仓库名称！ 要执行就地恢复，用户首先需要填写规范的恢复部分，如下所示： spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" 然后，要触发恢复，您需要使用以下命令注释 ivorycluster： kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-restore=id1 恢复完成后，可以禁用就地恢复： spec: backups: pgbackrest: restore: enabled: false 请注意我们如何放入选项以指定进行 PITR 的位置。 使用上述清单，IVYO 将继续重新创建您的 Ivory 集群，以恢复其数据直到 2021-06-09 14:15:11-04。此时，集群被提升，您可以从该特定时间点开始访问您的数据库！ 出于性能原因或为了将选定数据库移动到没有足够空间来恢复整个集群备份的计算机上，您可能需要从备份中恢复特定数据库。 pgBackRest 支持这种情况，但请务必确保这是您想要的。以这种方式恢复将从备份中恢复请求的数据库并使其可访问，但备份中的所有其他数据库在恢复后将无法访问。 例如，如果您的备份包含数据库 test1、test2 和 test3，并且您请求恢复 test2，则恢复完成后，test1 和 test3 数据库将无法访问。请查看 pgBackRest 文档中关于 恢复单个数据库的限制。 您可以使用类似于以下规范的规范从备份中恢复单个数据库： spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --db-include=hippo 其中 --db-include=hippo 将仅恢复 hippo 数据库的内容。 高级高可用性和灾难恢复策略涉及将您的数据库集群分布在数据中心之间，以帮助最大化正常运行时间。IVYO 提供了使用外部存储系统或 IvorySQL 流复制部署可以跨越多个 Kubernetes 集群的 ivorycluster 的方法。https://github.com/IvorySQL/ivory-operator/blob/master/docs/content/architecture/disaster-recovery.md[灾难恢复架构] 文档中提供了 IVYO 备用集群的高级概述。 本教程部分将描述如何创建三种不同类型的备用集群，一种使用外部存储系统，一种直接从主集群流式传输数据，一种利用外部存储和流式传输。这些示例集群可以在同一个 Kubernetes 集群中使用单个 IVYO 实例创建，也可以通过正确的存储和网络配置分布在不同的 Kubernetes 集群和 IVYO 实例中。 基于仓库的备用集群将从存储在外部存储中的 pgBackRest 仓库中恢复。主集群应使用基于云的 备份配置 创建。以下清单定义了一个 ivorycluster，其中 standby.enabled 设置为 true，并且 repoName 配置为指向主集群中配置的 s3 仓库： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" standby: enabled: true repoName: repo1 流式备用集群依赖于通过网络到主集群的经过身份验证的连接。主集群应可通过网络访问并允许 TLS 身份验证（默认启用 TLS）。在以下清单中，我们将 standby.enabled 设置为 true，并提供了指向主集群的 host 和 port。我们还定义了 customTLSSecret 和 customReplicationTLSSecret 以提供允许备用集群向主集群进行身份验证的证书。对于这种类型的备用集群，您必须使用 自定义 TLS： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: repos: - name: repo1 volume: volumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true host: \"192.0.2.2\" port: 5432 另一个选项是使用从主集群流式传输的外部 pgBackRest 仓库创建备用集群。通过此设置，如果流式复制落后，备用集群将继续从 pgBackRest 仓库恢复。在此清单中，我们启用了前两个示例中的设置： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true repoName: repo1 host: \"192.0.2.2\" port: 5432 在某些时候，您会希望提升备用集群以开始接受读取和写入。这具有将 WAL（事务归档）推送到 pgBackRest 仓库的净效应，因此我们需要确保我们不会意外创建脑裂场景。如果两个主实例尝试写入同一个仓库，则可能会发生脑裂。如果主集群仍处于活动状态，请确保在尝试提升备用集群之前 关闭 主集群。 一旦主集群处于非活动状态，我们可以通过移除或禁用其 spec.standby 部分来提升备用集群： spec: standby: enabled: false 此更改触发将备用领导者提升为 IvorySQL 主实例，并且集群开始接受写入。 您可以从存储在 AWS S3（或使用 S3 协议的存储系统）、GCS 或 Azure Blob 存储中的备份克隆 Ivory 集群，而无需活动的 Ivory 集群！方法与从现有 ivorycluster 克隆类似。如果您希望为人们提供数据集但将其压缩在更便宜的存储上，这很有用。 出于本示例的目的，假设您创建了一个名为 hippo 的 Ivory 集群，其备份存储在 S3 中，看起来类似于以下内容： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 manual: repoName: repo1 options: - --type=full repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" 确保 ivyo-s3-creds 中的凭据与您的 S3 凭据匹配。有关 使用 S3 部署 Ivory 集群进行备份 的更多详细信息，请参阅教程的 备份 部分。 为了从活跃集群创建新集群时获得最佳性能，请确保对前一个集群进行了最近的完整备份。上面的清单设置为进行完整备份。假设 hippo 是在 ivory-operator 命名空间中创建的，您可以使用以下命令触发完整备份： kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-backup=\"$( date '+%F_%H:%M:%S' )\" 等待备份完成。完成后，您可以删除 Ivory 集群。 现在，让我们将 hippo 备份中的数据克隆到一个名为 elephant 的新集群中。您可以使用类似于以下的清单： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/elephant/repo1 repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" 在此清单中需要注意以下几点。首先，请注意我们新的 ivorycluster 中的 spec.dataSource.pgbackrest 对象与旧的 ivorycluster 中的 spec.backups.pgbackrest 对象非常相似，但略有不同。主要区别是： 从基于云的数据源恢复时不需要镜像 从基于云的数据源恢复时，stanza 是必填字段 backups.pgbackrest 有一个 repos 字段，这是一个数组 dataSource.pgbackrest 有一个 repo 字段，这是一个单一对象 还要注意相似之处： 我们正在为两者重用密钥（因为新的恢复 pod 需要具有与原始备份 pod 相同的凭据） repo 对象是相同的 global 对象是相同的 这是因为 elephant ivorycluster 的新恢复 pod 将需要重用最初设置 hippo ivorycluster 时使用的配置和凭据。 在此示例中，我们正在创建一个新的集群，该集群也备份到同一个 S3 存储桶；只有 spec.backups.pgbackrest.global 字段已更改为指向不同的路径。这将确保新的 elephant 集群将预填充来自 hippo 备份的数据，但将备份到自己的文件夹，确保原始备份仓库得到适当保留。 部署此清单以创建 elephant Ivory 集群。观察它启动并运行： kubectl -n ivory-operator describe ivorycluster elephant 当它准备就绪时，您将看到预期实例的数量与就绪实例的数量相匹配，例如： Instances: Name: 00 Ready Replicas: 1 Replicas: 1 Updated Replicas: 1 前面的示例展示了如何使用现有的 S3 仓库预填充 ivorycluster，同时使用新的 S3 仓库进行备份。但是使用基于云的数据源的 ivorycluster 也可以使用本地仓库。 例如，假设一个名为 rhino 的 ivorycluster 旨在从原始的 hippo ivorycluster 预填充，清单将如下所示： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: rhino spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 虽然拥有 高可用性 和 灾难恢复 系统可以在您的 IvorySQL 集群出现问题时提供帮助，但监控可以帮助您预防问题的发生。此外，监控可以帮助您诊断和解决可能导致性能下降的问题，而不是停机。 让我们看看 IVYO 如何允许您在集群中启用监控。 让我们看看如何使用 Postgres Operator 示例 仓库中的 kustomize/ivory 示例将 IvorySQL Exporter sidecar 添加到您的集群中。 监控工具是使用自定义资源的 spec.monitoring 部分添加的。目前，唯一支持的监控工具是使用 pgMonitor 配置的 IvorySQL Exporter。 在 kustomize/ivory/ivory.yaml 文件中，将以下 YAML 添加到规范中： monitoring: pgmonitor: exporter: image: {{< param imagePostgresExporter >}} 保存您的更改并运行： kubectl apply -k kustomize/ivory IVYO 将检测到更改并将 Exporter sidecar 添加到集群中存在的所有 Ivory Pod 中。IVYO 还将完成允许 Exporter 连接到数据库并使用 IVYO 监控 堆栈收集指标的工作。 IVYO 允许您配置 exporter sidecar 以使用 TLS 加密。如果您通过 exporter 规范提供自定义 TLS Secret： monitoring: pgmonitor: exporter: customTLSSecret: name: hippo.tls 与 IVYO 可以配置的其他自定义 TLS Secret 一样，Secret 需要在与您的 PostgresCluster 相同的命名空间中创建。它还应该包含启用加密所需的 TLS 密钥 (tls.key) 和 TLS 证书 (tls.crt)。 data: tls.crt: <value> tls.key: <value> 为 exporter 配置 TLS 后，您将需要更新您的 Prometheus 部署以使用 TLS，并且与 exporter 的连接将被加密。查看 Prometheus 文档，了解有关为 Prometheus 配置 TLS 的更多信息。 在您的集群中启用 IvorySQL Exporter 后，请按照 IVYO 监控 中概述的步骤安装监控堆栈。这将允许您在 Kubernetes 中部署 pgMonitor 配置的 Prometheus、https://grafana.com/[Grafana] 和 Alertmanager 监控工具。这些工具将默认设置为连接到您的 Ivory Pod 上的 Exporter 容器。 虽然默认的 Kustomize 安装应该在大多数 Kubernetes 环境中工作，但可能需要根据您的特定需求进一步自定义项目。 例如，默认情况下，fsGroup 设置为 26，用于为组成 IVYO 监控堆栈的各种部署定义的 securityContext： securityContext: fsGroup: 26 在大多数 Kubernetes 环境中，此设置是必需的，以确保容器内的进程具有写入组成 IVYO 监控堆栈的每个 Pod 挂载的任何卷所需的权限。但是，在 OpenShift 环境中安装时（更具体地说，当使用 restricted 安全上下文约束时），应删除 fsGroup 设置，因为 OpenShift 将自动处理在 Pod 的 securityContext 中设置适当的 fsGroup。 此外，在同一部分中，可能还需要根据您的特定存储配置修改 supplmentalGroups 设置： securityContext: supplementalGroups : 65534 因此，应修改和/或修补（例如，使用额外的覆盖）kustomize/monitoring 下的以下文件，以确保 securityContext 为您的 Kubernetes 环境正确定义： deploy-alertmanager.yaml deploy-grafana.yaml deploy-prometheus.yaml 为了修改 IVYO 监控安装程序创建的各种存储资源（即 PersistentVolumeClaims）的配置，还可以修改 kustomize/monitoring/pvcs.yaml 文件。 此外，还可以通过修改以下配置资源来进一步自定义组成 IVYO 监控堆栈的各种组件（Grafana、Prometheus 和/或 AlertManager）的配置： alertmanager-config.yaml alertmanager-rules-config.yaml grafana-datasources.yaml prometheus-config.yaml 最后，请注意，可以通过修改 kustomize/monitoring/grafana-secret.yaml 文件中的 Grafana Secret 来更新 Grafana 的默认用户名和密码。 一旦 Kustomize 项目根据您的特定需求进行了修改，就可以使用 kubectl 和 Kustomize 安装 IVYO 监控： kubectl apply -k kustomize/monitoring 同样，一旦安装了 IVYO 监控，就可以使用 kubectl 和 Kustomize 卸载它： kubectl delete -k kustomize/monitoring 连接池有助于扩展和维护应用程序与数据库之间的整体可用性。IVYO 通过支持 PgBouncer 连接池和状态管理器来促进这一点。 让我们看看我们如何添加连接池并将其连接到我们的应用程序！ 让我们看看如何使用 Ivory Operator 仓库示例文件夹中的 kustomize/keycloak 示例添加连接池。 连接池是使用自定义资源的 spec.proxy 部分添加的。目前，唯一支持的连接池是 PgBouncer。 添加 PgBouncer 连接池的唯一必需属性是设置 spec.proxy.pgBouncer.image 属性。在 kustomize/keycloak/ivory.yaml 文件中，将以下 YAML 添加到规范中： proxy: pgBouncer: image: {{< param imageIvoryPGBouncer >}} （您也可以在 kustomize/examples/high-availability 示例中找到此示例）。 保存您的更改并运行： kubectl apply -k kustomize/keycloak IVYO 将检测到更改并创建一个新的 PgBouncer Deployment！ 设置起来相当容易，所以现在让我们看看如何将我们的应用程序连接到连接池。 当连接池部署到集群时，IVYO 会将附加信息添加到用户 Secret 中，以允许应用程序直接连接到连接池。回想一下，在此示例中，我们的用户 Secret 称为 keycloakdb-pguser-keycloakdb。描述用户 Secret： kubectl -n ivory-operator describe secrets keycloakdb-pguser-keycloakdb 您应该看到此 Secret 中包含几个新属性，允许您通过连接池连接到您的 Ivory 实例： pgbouncer-host：PgBouncer 连接池的主机名。这引用了 PgBouncer 连接池的 Service。 pgbouncer-port：PgBouncer 连接池正在侦听的端口。 pgbouncer-uri：一个 PostgreSQL 连接 URI，提供通过 PgBouncer 连接池登录到 Ivory 数据库的所有信息。 pgbouncer-jdbc-uri：一个 PostgreSQL JDBC 连接 URI，提供通过使用 JDBC 驱动程序的 PgBouncer 连接池登录到 Ivory 数据库的所有信息。请注意，默认情况下，连接字符串禁用 JDBC 管理预处理事务以实现 与 PgBouncer 一起使用的最佳方式。 打开 kustomize/keycloak/keycloak.yaml 中的文件。更新 DB_ADDR 和 DB_PORT 值如下： - name: DB_ADDR valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-port } } 这会更改 Keycloak 的配置，使其现在通过连接池连接。 应用更改： kubectl apply -k kustomize/keycloak Kubernetes 将检测到更改并开始部署新的 Keycloak Pod。完成后，Keycloak 现在将通过 PgBouncer 连接池连接到 Ivory！ IVYO 通过 TLS 部署每个集群和组件。这包括 PgBouncer 连接池。如果您使用自己的 自定义 TLS 设置，则需要在 spec.proxy.pgBouncer.customTLSSecret 中为 PgBouncer 提供 Secret 引用。 PgBouncer 的 TLS 证书应具有与 PgBouncer Service 名称匹配的通用名称 (CN)。这是集群的名称，后缀为 -pgbouncer。例如，对于我们的 hippo 集群，这将是 hippo-pgbouncer。对于 keycloakdb 示例，它将是 keycloakdb-pgBouncer。 要自定义 PgBouncer 的 TLS，您需要在您的 Ivory 集群的命名空间中创建一个 Secret，其中包含要使用的 TLS 密钥 (tls.key)、TLS 证书 (tls.crt) 和 CA 证书 (ca.crt)。Secret 应包含以下值： data: ca.crt: <value> tls.crt: <value> tls.key: <value> 例如，如果您本地计算机上存储有名为 ca.crt、keycloakdb-pgBouncer.key 和 keycloakdb-pgBouncer.crt 的文件，则可以运行以下命令： kubectl create secret generic -n ivory-operator keycloakdb-pgBouncer.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=keycloakdb-pgBouncer.key \\ --from-file=tls.crt=keycloakdb-pgBouncer.crt 您可以在您的 ivorycluster.ivory-operator.ivorysql.org 自定义资源中的 spec.proxy.pgBouncer.customTLSSecret.name 字段中指定自定义 TLS Secret，例如： spec: proxy: pgBouncer: customTLSSecret: name: keycloakdb-pgBouncer.tls PgBouncer 连接池是高度可定制的，从配置和 Kubernetes 部署角度来看都是如此。让我们探索一些您可以进行的自定义！ 可以通过 spec.proxy.pgBouncer.config 自定义 PgBouncer 配置。进行配置更改后，IVYO 会将它们推出到任何 PgBouncer 实例，并自动发出“重新加载”。 您可以通过以下几种方式自定义配置： spec.proxy.pgBouncer.config.global：接受键值对，这些更改全局应用于 PgBouncer。 spec.proxy.pgBouncer.config.databases：接受键值对，这些键值对代表 PgBouncer 数据库定义。 spec.proxy.pgBouncer.config.users：接受键值对，这些键值对代表 应用于特定用户的连接设置。 spec.proxy.pgBouncer.config.files：接受文件列表，这些文件挂载在 /etc/pgbouncer 目录中，并在使用 PgBouncer 的 include 指令 考虑任何其他选项之前加载。 例如，要将连接池模式设置为 transaction，您需要设置以下配置： spec: proxy: pgBouncer: config: global: pool_mode: transaction 有关 PgBouncer 配置 的参考，请参阅： https://www.pgbouncer.org/config.html 默认情况下，IVYO 部署一个 PgBouncer 实例。您可能希望运行多个 PgBouncer 实例以具有一定的冗余级别，尽管您仍然希望注意有多少连接将连接到您的 Ivory 数据库！ 您可以通过 spec.proxy.pgBouncer.replicas 属性管理部署的 PgBouncer 实例的数量。 您可以通过 spec.proxy.pgBouncer.resources 属性管理分配给 PgBouncer 实例的 CPU 和内存资源。spec.proxy.pgBouncer.resources 的布局应该很熟悉：它遵循设置 容器资源 的标准 Kubernetes 结构。 例如，假设我们想为 PgBouncer 实例设置一些 CPU 和内存限制。我们可以添加以下配置： spec: proxy: pgBouncer: resources: limits: cpu: 200m memory: 128Mi 由于 IVYO 使用 Deployment 部署 PgBouncer 实例，因此这些更改会使用滚动更新推出，以最大程度地减少应用程序与 Ivory 实例之间的中断！ 您可以通过 spec.proxy.pgBouncer.metadata.annotations 和 spec.proxy.pgBouncer.metadata.labels 属性分别为您的 PgBouncer 实例应用自定义注释和标签。请注意，对这两个属性中的任何一个的任何更改都将优先于您添加的任何其他自定义标签。 您可以通过 spec.proxy.pgBouncer.affinity 属性控制 pod 反亲和性、pod 亲和性和节点亲和性，特别是： spec.proxy.pgBouncer.affinity.nodeAffinity：控制 PgBouncer 实例的节点亲和性。 spec.proxy.pgBouncer.affinity.podAffinity：控制 PgBouncer 实例的 Pod 亲和性。 spec.proxy.pgBouncer.affinity.podAntiAffinity：控制 PgBouncer 实例的 Pod 反亲和性。 以上每个都遵循 设置亲和性的标准 Kubernetes 规范。 例如，要为 kustomize/keycloak 示例设置首选 Pod 反亲和性规则，您需要向配置中添加以下内容： spec: proxy: pgBouncer: affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: keycloakdb ivory-operator.ivorysql.org/role: pgbouncer topologyKey: kubernetes.io/hostname 您可以通过设置 容忍度 通过 spec.proxy.pgBouncer.tolerations 将 PgBouncer 实例部署到 具有污点的节点。此属性遵循 Kubernetes 标准容忍度布局。 例如，如果有一组具有 role=connection-poolers:NoSchedule 污点的节点，您希望将 PgBouncer 实例调度到这些节点，您可以应用以下配置： spec: proxy: pgBouncer: tolerations: - effect: NoSchedule key: role operator: Equal value: connection-poolers 请注意，设置容忍度并不一定意味着 PgBouncer 实例将被分配给具有这些污点的节点。容忍度充当密钥：它们允许您访问节点。如果您希望确保您的 PgBouncer 实例部署到特定节点，您需要将设置容忍度与节点亲和性相结合。 除了使用亲和性、反亲和性和容忍度之外，您还可以通过 spec.proxy.pgBouncer.topologySpreadConstraints 设置 拓扑分布约束。此属性遵循 Kubernetes 标准拓扑分布约束布局。 例如，由于我们的每个 pgBouncer Pod 都将设置标准的 ivory-operator.ivorysql.org/role: pgbouncer 标签，我们可以在确定 maxSkew 时使用此标签。在下面的示例中，由于我们有 3 个节点，maxSkew 为 1，并且我们将 whenUnsatisfiable 设置为 ScheduleAnyway，我们理想情况下应该在每个节点上看到 1 个 Pod，但如果其他约束阻止这种情况发生，我们的 Pod 可以分布得不那么均匀。 proxy: pgBouncer: replicas: 3 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/role: pgbouncer 如果您希望确保您的 PgBouncer 实例分布得更均匀（或根本不部署），您需要将 whenUnsatisfiable 更新为 DoNotSchedule。 有时您可能需要手动重启 IvorySQL。这可以通过向集群的 spec.metadata.annotations 部分添加或更新自定义注释来完成。IVYO 将检测到更改并执行 滚动重启。 例如，如果您在命名空间 ivory-operator 中有一个名为 hippo 的集群，您只需要使用以下命令修补 hippo ivorycluster： kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"metadata\":{\"annotations\":{\"restarted\":\"'\"$(date)\"'\"}}}}' 观察您的 hippo 集群：您将看到滚动更新已触发，重启已开始。 您可以通过将 spec.shutdown 属性设置为 true 来关闭 Ivory 集群。您可以通过编辑清单来执行此操作，或者在 hippo 集群的情况下，执行如下命令： kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"shutdown\": true}}' 这样做的结果是，此集群的所有 Kubernetes 工作负载都缩放为 0。您可以使用以下命令验证这一点： kubectl get deploy,sts,cronjob --selector=ivory-operator.ivorysql.org/cluster=hippo -n ivory-operator NAME READY AGE statefulset.apps/hippo-00-lwgx 0/0 1h NAME SCHEDULE SUSPEND ACTIVE cronjob.batch/hippo-repo1-full @daily True 0 要将已关闭的 Ivory 集群重新打开，您可以将 spec.shutdown 设置为 false。 您可以通过将 spec.paused 属性设置为 true 来暂停 Ivory 集群协调过程。您可以通过编辑清单来执行此操作，或者在 hippo 集群的情况下，执行如下命令： kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"paused\": true}}' 暂停集群将暂停对集群当前状态的任何更改，直到协调恢复。这允许您完全控制何时将 ivorycluster spec 的更改部署到 Ivory 集群。在暂停期间，除了“Progressing”条件外，不会更新任何状态。 要恢复 Ivory 集群的协调，您可以将 spec.paused 设置为 false 或从清单中删除该设置。 应尽可能频繁地使凭据失效并替换（轮换）它们，以最大限度地降低其被滥用的风险。与密码不同，每个 TLS 证书都有一个过期日期，因此替换它们是不可避免的。 实际上，IVYO 会在证书过期日期 之前 自动轮换其管理的客户端证书。将在其工作持续时间的 2/3 之后生成新的客户端证书；因此，例如，IVYO 创建的证书在 12 个月后过期，将在大约第 8 个月时被 IVYO 替换。这样做是为了让您不必担心遇到过期证书的问题或服务中断。 如果您想轮换单个客户端证书，您可以通过从其证书 Secret 中删除 tls.key 字段来重新生成现有集群的证书。 是时候轮换您的 IVYO 根证书了吗？您只需要删除 ivyo-root-cacert secret。IVYO 将无缝地重新生成并推出它，确保您的应用程序继续与 Ivory 集群通信，而无需更新任何配置或处理任何停机时间。 kubectl delete secret ivyo-root-cacert IVYO 仅更新包含生成的根证书的 secret。它不会触及自定义证书。 当您使用自己的 TLS 证书与 IVYO 时，您有责任适当地替换它们。方法如下。 IVYO 会自动检测并加载对 IvorySQL 服务器和复制 Secret 内容的更改，而不会停机。您或您的证书管理器只需要替换 spec.customTLSSecret 引用的 Secret 中的值。 如果您将 spec.customTLSSecret 更改为引用新的 Secret 或新的字段，IVYO 将执行 滚动重启。 更改 IvorySQL 证书颁发机构时，请确保同时更新 customReplicationTLSSecret。 有时您可能希望更改 HA 集群中的主节点。这可以通过使用 ivorycluster spec 的 patroni.switchover 部分来完成。它允许您在 ivoryclusters 中启用切换，将特定实例作为新的主节点，并在您的 ivorycluster 进入不良状态时运行故障转移。 让我们完成执行切换的过程！ 首先，您需要更新您的 spec 以准备您的集群以更改主节点。编辑您的 spec 以具有以下字段： spec: patroni: switchover: enabled: true 应用此更改后，IVYO 将寻找触发器以在您的集群中执行切换。您将通过将 ivory-operator.ivorysql.org/trigger-switchover 注释添加到您的自定义资源来触发切换。设置此注释的最佳方法是使用时间戳，这样您就知道何时启动了更改。 例如，对于我们的 hippo 集群，我们可以运行以下命令来触发切换： kubectl annotate -n ivory-operator ivorycluster hippo \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" 如果您想执行另一次切换，您可以重新运行注释命令并添加 --overwrite 标志： kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" IVYO 将检测到此注释并使用 Patroni API 请求更改当前主节点！ 随着 Patroni 的工作，您的数据库实例 Pod 上的角色将开始更改。新的主节点将具有 master 角色标签，旧的主节点将更新为 replica。 切换的状态将使用 status.patroni.switchover 字段进行跟踪。这将设置为您在触发器注释中定义的值。如果您使用时间戳作为注释，这是确定何时请求切换的另一种方法。 在实例 Pod 标签已更新且 status.patroni.switchover 已设置后，主节点已在您的集群上更改！ 更改主节点后，我们建议您通过将 spec.patroni.switchover.enabled 设置为 false 或完全从您的 spec 中删除该字段来禁用切换。如果该字段被删除，相应的状态也将从 ivorycluster 中删除。 切换主节点时，您可以选择的另一个选项是提供目标实例作为新的主节点。在执行切换时，此目标实例将用作候选节点。spec.patroni.switchover.targetInstance 字段接受您要切换到的实例的名称。 此名称可以在几个不同的地方找到；一个是 StatefulSet 的名称，另一个是数据库 Pod 上的 ivory-operator.ivorysql.org/instance 标签。以下命令可以帮助您确定谁是当前主节点以及用作 targetInstance 的名称： $ kubectl get pods -l ivory-operator.ivorysql.org/cluster=hippo \\ -L ivory-operator.ivorysql.org/instance \\ -L ivory-operator.ivorysql.org/role -n ivory-operator NAME READY STATUS RESTARTS AGE INSTANCE ROLE hippo-instance1-jdb5-0 3/3 Running 0 2m47s hippo-instance1-jdb5 master hippo-instance1-wm5p-0 3/3 Running 0 2m47s hippo-instance1-wm5p replica 在我们的示例集群中，hippo-instance1-jdb5 当前是主节点，这意味着我们希望在切换中定位 hippo-instance1-wm5p。现在您知道哪个实例当前是主节点以及如何找到您的 targetInstance，让我们更新您的集群 spec： spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p 应用此更改后，您将再次需要通过注释 ivorycluster 来触发切换（请参见上面的命令）。您可以通过检查 Pod 角色标签和 status.patroni.switchover 来验证切换是否已完成。 最后，当您的集群进入不健康状态时，我们可以选择进行故障转移。完成此操作所需的唯一 spec 更改是将 spec.patroni.switchover.type 字段更新为 Failover 类型。需要注意的是，执行故障转移时需要 targetInstance。基于上面的示例集群，假设 hippo-instance1-wm5p 仍然是副本，我们可以更新 spec： spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p type: Failover 应用此 spec 更改后，您的 ivorycluster 将准备好执行故障转移。同样，您需要通过注释 ivorycluster 来触发切换（请参见上面的命令），并验证 Pod 角色标签和 status.patroni.switchover 是否已相应更新。 切换过程中遇到的错误可能会使您的集群处于不良状态。如果您遇到问题，请在 operator 日志中找到问题，您可以更新 spec 以修复问题并应用更改。应用更改后，IVYO 将尝试再次执行切换。 总有一个时刻，您需要删除自己的集群。如果您一直在跟着示例操作，只需运行下面一条命令即可删除 Ivory 集群： kubectl delete -k examples/kustomize/ivory IVYO 会清理与该集群相关的所有对象。 关于数据保留：这取决于您 PVC 的 回收策略。如需了解 Kubernetes 如何管理数据保留，请参阅 Kubernetes 官方文档关于卷回收的说明。","title":"IvorySQL Operator部署IvorySQL","component":"ivorysql-doc","version":"master","name":"operator_deployment","url":"/ivorysql-doc/master/containerization/operator_deployment.html","titles":[{"text":"1. 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Do this. 1. Do that. 1. Done. 0. Do this. 1. Do that. 2. Done. 正确示范: 1. Do this. 2. Do that. 3. Done. 13、使用列表时，每一列表项的标识符（+、-、* 或数字）后只能空一格，后接列表内容。 正确示范 * Foo * Bar * Baz 1. Foo * Bar 1. Baz 14、列表（包括有序和无序列表）前后必须各空一行。 错误示范 Some text* Some* List 1. Some2. List Some text 正确示范 Some text * Some * List 1. Some 2. List Some text 15、代码块前后必须各空一行。 错误示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 正确示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 16、文档中禁止出现裸露的 URL。如果希望用户能直接点击并打开该 URL，则使用一对尖括号 (<URL>) 包裹该 URL。如果由于特殊情况必须使用裸露的 URL，不需要用户通过点击打开，则使用一对反引号 `URL` 包裹该 URL。 错误示范 For more information, see https://www.example.com/. 正确示范 For more information, see <https://www.example.com/>. 17、使用加粗、斜体等强调效果时，在强调标识符内禁止出现多余的空格。如不能出现 ** 加粗文本 **。 错误示范 Here is some ** bold ** text. Here is some * italic * text. Here is some more __ bold __ text. Here is some more _ italic _ text. 正确示范: Here is some **bold** text. Here is some *italic* text. Here is some more __bold__ text. Here is some more _italic_ text. 18、单个反引号包裹的代码块内禁止出现多余的空格。如不能出现 ` 示例文本`。 错误示范： some text some text 正确示范: some text 19、链接文本两边禁止出现多余的空格。如不能出现 某链接。 错误示范 [ a link ](https://www.example.com/) 正确示范: [a link](https://www.example.com/) 20、链接必须有链接路径。如不能出现空链接等情况。 错误示范 [an empty link]() [an empty fragment](#) 正确示范: [a valid link](https://example.com/) [a valid fragment](#fragment) 21、文档中的代码块统一使用三个反引号进行包裹，禁止使用缩进四格风格的代码块。 错误示范： Some text. # Indented code More text. 正确示范 ```ruby # Fenced code ​``` More text.","title":"asciidoc语法快速参考","component":"ivorysql-doc","version":"master","name":"asciidoc_syntax_reference","url":"/ivorysql-doc/master/contribution/asciidoc_syntax_reference.html","titles":[{"text":"1. 规范说明","hash":"规范说明","id":1},{"text":"2. 示例","hash":"示例","id":2}]},"36":{"id":36,"text":"IvorySQL 社区采用一套 闭环式开源协作流程，确保从问题提出到版本发布，每一个环节都能闭合反馈、持续改进。这一流程鼓励用户与开发者形成良性互动，让社区开发始终围绕实际需求持续演进。 整个协作闭环流程如下： 🐛 提问题（Issue） 用户或开发者在 GitHub 的 Issues 页面提交 Bug、功能建议或使用反馈。 💬 问题讨论（Discussion） 维护者与社区成员就问题展开讨论，确认问题性质与优先级，加入 ToDo List。 🛠️ 开发分支（Fork & Dev） 开发者认领 Issue，Fork 仓库并在本地开发测试，准备提交代码。 🚀 提交 Pull Request（PR） 将开发分支 Push 到 Fork 仓库后，向上游仓库发起合并请求。 🧐 代码评审（Review） 维护者或核心开发者对 PR 进行评审，提出修改建议并确保质量。 🔀 合并主分支（Merge） 审核通过后，PR 被合并至主分支，对应的 Issue 被关闭。 📦 版本发布（Release） 项目定期发布新版本（每季度小版本，每年大版本），包含最新的修复与功能。 🧪 用户测试（Test） 用户升级使用新版，反馈新问题，新的 Issue 随之产生，形成完整的反馈循环。 通过这套完整的闭环协作机制，IvorySQL 实现了问题响应 → 开发贡献 → 质量保障 → 发布反馈的全流程闭合，推动项目持续健康演进。 IvorySQL是一个由核心开发团队主导、社区共同维护的开源项目。我们欢迎用户、贡献者和维护者的加入，共同推动IvorySQL的发展。如果您希望看到您的代码或文档更改被添加到IvorySQL并出现在将来的版本中，本节的内容介绍是您需要知道的。 在参与贡献前，请确认您当前的参与身份，以便更高效地了解适合您的贡献方式： 用户 👉 欢迎提交问题反馈、功能建议，并参与社区讨论。 贡献者 👉 请先签署 CLA，然后选择您感兴趣的项目模块进行 Issue 认领和代码提交。 维护者 👉 请参考维护职责，包括社区规划、代码评审和协作机制建设。 无论您以何种身份加入，IvorySQL 社区都非常欢迎您的参与和支持！​IvorySQL社区欢迎并赞赏所有类型的贡献，期待您的加入！ 📢 请务必阅读并遵守我们的 IvorySQL社区行为准则。 无论您是要提交 Issue、参与讨论，还是贡献代码与文档，您都需要使用 GitHub 账号登录并与 IvorySQL 项目进行交互。 请参考https://docs.github.com/en/get-started/start-your-journey注册您的github账号，并熟悉Git工具和工作流。 IvorySQL源码托管在github: https://github.com/IvorySQL。 作为用户，您在使用 IvorySQL 过程中扮演着重要角色。我们鼓励您： 发现 Bug、性能缺陷或文档不准确？ 有新的功能建议或使用体验改进？ 如果您准备向社区上报 Bug 或者提交需求，请在 IvorySQL 社区对应的仓库上提交 Issue，并参考Issue 提交指南。 通过 邮件列表进行讨论 加入 GitHub讨论，补充信息或验证问题 在 微信、Discord等聊天群参与技术交流 我们欢迎代码、文档、测试等各类贡献。 在提交代码或文档贡献之前，为了确保代码合法合规，个人或企业贡献者需要签署贡献者许可协议(CLA)。签署CLA是IvorySQL社区接受贡献的必要条件，以确保您的贡献被合法分发。请根据下列链接下载CLA进行签署并将签署后的CLA发送至 cla@ivorysql.org。 个人贡献者 企业贡献者 未签署CLA的Pull Request将无法进入评审阶段。 我们将仓库划分为多个子项目，您可以从如下列表中找到感兴趣的项目及其代码仓库 代码仓库 描述 IvorySQL 负责社区IvorySQL数据库的开发和维护 Ivory-www 负责社区的官网开发和维护 ivory-operator 负责IvorySQL云原生数据库及周边工具开发和维护 docker_library 负责IvorySQL多架构的镜像构建开发和维护 ivory-cloud 负责IvorySQL云服务平台及周边生态开发和维护 Ivorysql_docs 负责社区的文档中心开发和维护 ivory-doc-builder 负责Ivorysql_docs的编译 Ivorysql_web 负责社区的文档中心网站维护 Ivorysql_wasm 负责IvorySQL在线易用体验网站的开发和维护 您可以将自己创建的Issue或者愿意处理的Issue分配给自己。 只需要在评论框内输入/assign，机器人就会将问题分配给您。 每个 Issue 下面可能已经有参与者的交流和讨论，如果您感兴趣，也可以在评论框中发表自己的意见参与 Issue 讨论。 对于提交一个PR应该保持一个功能，或者一个bug提交一次。禁止多个功能一次提交。 前往项目主页，点击Fork按钮，将IvorySQL项目Fork到您自己的GitHub账户中。 使用如下命令将项目克隆到本地进行开发： git clone https://github.com/$user/IvorySQL.git （将 $user 替换为你的 GitHub ID）。 git checkout -b feature/your-feature-name 在提交代码前，请确保通过回归测试 打开你 Fork 的仓库： https://github.com/$user/IvorySQL.git 点击 Compare & pull request 按钮填写PR信息 Fix test 功能描述 leave a comment 对该提交功能进行比较详细的描述 点击Create pull request 按钮即可提交。 维护者负责进行IvorySQL代码的管理，PR审查，主导版本发布与IvorySQL发展方向。 制定版本规划和 Roadmap 跟踪与评估社区需求 维护公开的 TODO 列表 参与 Pull Request 评审 审查安全问题，保障项目健康 优化协作机制（代码贡献指南、PR 模板等） 建立漏洞响应机制和行为守则 感谢每一位参与 IvorySQL 的开发者、文档编辑者、测试人员和使用者。正是有了你们的付出，IvorySQL 才能不断成长！我们欢迎所有人参与 IvorySQL 社区贡献，我们的目标是发展一个由贡献者组成的活跃、健康的社区。","title":"","component":"ivorysql-doc","version":"master","name":"community_contribution_guide","url":"/ivorysql-doc/master/contribution/community_contribution_guide.html","titles":[{"text":"1. 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Fork仓库","hash":"fork仓库","id":12},{"text":"2.3.4.2. 编码","hash":"编码","id":13},{"text":"2.3.4.3. 创建一个Pull Request并提交","hash":"创建一个pull-request并提交","id":14},{"text":"2.4. 维护者","hash":"维护者","id":15},{"text":"2.4.1. 社区规划","hash":"社区规划","id":16},{"text":"2.4.2. 代码管理","hash":"代码管理","id":17},{"text":"2.4.3. 流程与治理机制","hash":"流程与治理机制","id":18},{"text":"2.5. 致谢","hash":"致谢","id":19}]},"37":{"id":37,"text":"如果您提交的贡献是原创作品，那么您可以假设IvorySQL将作为整个IvorySQL版本的一部分发布给下游用户，该版本将遵循Apache许可证2.0版本。 如果您提交的内容不是原创作品，同样鼓励代码共享和尊重原作者的著作权，同样允许代码修改，再发布。请注意需要满足如下条件： 1、需要给代码的用户一份Apache许可证。 2、如果您修改了代码，需要在被修改的文件中说明。 3、在延伸的代码中（修改和有源代码衍生的代码中）需要带有原来代码中的协议、商标、专利声明和其他原来作者规定需要包含的说明。 4、如果再发布的产品中包含一个Notice文件，则在Notice文件中需要带有Apache许可证。您可以在Notice中增加自己的许可，但不可以表现为对Apache许可证构成更改。 最后，请记住，从非原始的工作中删除许可标头从来都不是一个好主意。即使您使用的文件部分最初在顶部有许可标头，您也应该保留它。与往常一样，如果您不太确定您的贡献所涉及的许可问题，请随时在开发人员邮件列表中联系我们。 您获得反馈和看到代码合并到项目中的机会在很大程度上取决于更改的粒度。如果您的想法发生了更大的变化，我们强烈建议您在花大量时间编写代码之前，先加入开发人员的邮件列表，并与我们分享您的建议。即使您的建议得到社区的验证，我们仍然建议您将实际工作作为一系列小型的、独立的提交来完成。这使得评审员的工作更加容易，并提高了反馈的及时性。 当谈到IvorySQL的C和C++部分时，我们尝试遵循PostgreSQL编码约定。除此之外： 对于C和Perl代码，如果需要，请运行pgindent。我们建议在查看更改时使用git diff—​color，这样您提交的代码中就不会出现任何虚假的空白问题。 所有贡献给IvorySQL的新功能都应该由与其一起贡献的回归测试覆盖。如果您不确定如何测试或记录您的工作，请在ivorysql-hackers邮件列表中提出问题，社区的开发人员将尽力帮助您。 至少，您应该始终运行make installcheck world，以确保您没有破坏任何东西。 如果您正在进行的更改涉及PostgreSQL和IvorySQL之间的通用功能，则可能会要求您将其转发到PostgreSQL。这不仅是为了我们不断减少两个项目之间的差异，而且是为了让与PostgreSQL相关的任何变化都能从对上游PostgreSQL社区更广泛的审查中受益。一般来说，将这两个代码库都放在手边是个好主意，这样您就可以确定您的更改是否需要前移。 一旦您准备好与IvorySQL核心团队和IvorySQL社区的其他成员共享您的工作，您应该将所有提交推送到从官方IvorySQL派生的分支的您自己的存储库中，并向我们发送请求。 假定提交的拉取请求通过验证检查，可供同行审查。同行审查是确保对IvorySQL的贡献具有高质量并与路线图和社区期望保持一致的过程。我们鼓励IvorySQL社区的每个成员审查请求并提供反馈。由于您不必成为核心团队成员就可以做到这一点，因此我们建议您向有兴趣成为IvorySQL长期贡献者的任何人提供一系列拉动式评论。 同行评审的一个结果可能是达成共识，即您需要以某些方式修改pull请求。GitHub允许您将其他提交推送到从中发送请求的分支中。这些额外的提交将对所有审阅者可见。 当同行评议收到参与者至少+1张+1和no-1张的选票时，同行评议会趋于一致。在这一点上，您应该期望核心团队成员之一将您的更改引入到项目中。 在补丁审查期间的任何时候，您都可能会因审查人员和核心团队成员的工作效率而遇到延迟。请耐心点，也不要气馁。如果您在几天内没有收到预期的反馈，请添加一条评论，要求更新pull请求本身，或者向邮件列表发送一封电子邮件。 有时，您会看到核心团队成员直接提交到存储库，而无需执行pull请求工作流。这仅适用于小的更改，我们使用的经验法则是：如果更改涉及任何可能导致测试失败的功能，那么它必须通过pull请求工作流。另一方面，如果更改发生在代码库的非功能部分（例如在注释块中修复打字错误），则核心团队成员可以决定直接提交到存储库。","title":"FAQ","component":"ivorysql-doc","version":"master","name":"faq","url":"/ivorysql-doc/master/contribution/faq.html","titles":[{"text":"1. IvorySQL贡献的许可","hash":"ivorysql贡献的许可","id":1},{"text":"2. 编码指南","hash":"编码指南","id":2},{"text":"3. 适用于上游PostgreSQL的更改","hash":"适用于上游postgresql的更改","id":3},{"text":"4. 补丁提交","hash":"补丁提交","id":4},{"text":"5. 补丁审查","hash":"补丁审查","id":5},{"text":"6. 直接提交到存储库","hash":"直接提交到存储库","id":6}]},"38":{"id":38,"text":"IvorySQL由一个核心开发团队维护，该团队拥有对GitHub上的IvorySQL主存储库的提交权限。同时，我们非常渴望从更广泛的IvorySQL社区中的成员那里获得贡献。如果您希望看到您的代码或文档更改被添加到IvorySQL并出现在将来的版本中，本节的内容介绍是您需要知道的。 ​IvorySQL社区欢迎并赞赏所有类型的贡献，期待您的加入！ 作为成员，贡献者和领导者，我们每个人都应阅读我们的 行为准则。我们承诺让每个人都能参与社区，成为一种无骚扰的体验，无论年龄、体型、有形或无形的残疾、种族、性特征、性别认同和表达、经验水平、教育、社会经济地位、国籍、个人外表、种族、宗教或性身份和取向如何。 ​ 我们承诺以有助于建立一个开放，热情，多样化，包容和健康社区的方式采取行动和互动。 团队是持续开放的团队，专注于IvorySQL项目的一部分。一个团队有其审查者、提交者和维护者，并拥有一个或多个存储库。团队级别的决策来自其维护者。IvorySQL开发人员的典型提升路径是从用户到审查者，然后是提交者和维护者。但获得更多的角色并不意味着你对其他社区成员拥有任何特权。IvorySQL社区中的每个人都是平等的，都有责任与其他贡献者进行建设性合作，建立一个友好的社区。这些角色是对您在IvorySQL开发中做出重大贡献的自然奖励，并为您在开发工作流中提供更多权利，以提高您的效率。同时，他们要求你承担一些额外的责任： ​ 团队荣誉：现在您已经是团队评审员/提交者/维护者的成员，意味着您代表着项目和您的团队成员。所以，请做一个好人来捍卫球队的声誉。 ​ 责任：提交者/维护者有权合并拉取请求，因此承担额外的责任来处理接受代码库或文档更改的后果。这包括在它导致问题时恢复或修复它，以及帮助发布经理解决发布前测试周期中发现的任何问题。 在贡献之前，我们需要了解下IvorySQL目前的版本以及文档的版本。目前，我们维护着5.0等版本，我们的版本紧跟PG的更新步伐，贡献之前请更新至最新版本。之后我们需要细心浏览一下贡献的样式风格，熟悉代码贡献风格、提Issue样式、拉取PR标题样式、代码注释样式、文档贡献样式、文章贡献样式，这可以帮助您尽快成为IvorySQL的贡献者奥~。 IvorySQL是在GitHub上开发的，任何希望对其作出贡献的人都必须拥有GitHub帐户，并熟悉Git工具和工作流。还建议您遵循开发人员的邮件列表，因为一些贡献可能会在那里产生更详细的讨论。 如果您有GitHub帐户，fork这个存储库至您的个人仓库中，这样您就可以拥有您的私人副本来开始hacking，并将其用作拉取请求的来源。 如果您提交的贡献是原创作品，那么您可以假设IvorySQL将作为整个IvorySQL版本的一部分发布给下游用户，该版本将遵循Apache许可证2.0版本。 ​ 如果您提交的内容不是原创作品，同样鼓励代码共享和尊重原作者的著作权，同样允许代码修改，再发布。\\*请注意需要满足如下条件\\*： （1）需要给代码的用户一份Apache许可证。 （2）如果您修改了代码，需要在被修改的文件中说明。 （3）在延伸的代码中（修改和有源代码衍生的代码中）需要带有原来代码中的协议、商标、专利声明和其他原来作者规定需要包含的说明。 （4）如果再发布的产品中包含一个Notice文件，则在Notice文件中需要带有Apache许可证。您可以在Notice中增加自己的许可，但不可以表现为对Apache许可证构成更改。 ​ 最后，请记住，从非原始的工作中删除许可标头从来都不是一个好主意。即使您使用的文件部分最初在顶部有许可标头，您也应该保留它。与往常一样，如果您不太确定您的贡献所涉及的许可问题，请随时在开发人员邮件列表中联系我们。 可以将自己修改的bug、新增的功能等代码上传至个人仓库，最后提交PR请求合并至官网：https://github.com/IvorySQL/IvorySQL 。 IvorySQL社区提供的是中英文文档。英文文档保存在…​文档存储库中，中文文档保存在i18n文档存储库中。您可以为任一文档存储库做出贡献。 GitHub: https://github.com/IvorySQL/IvorySQL ​码云：https://gitee.com/IvorySQL/ IvorySQL官网下载：https://github.com/IvorySQL/Ivory-www 邮件列表位于 官网下：https://lists.ivorysql.org/ 您可以在IvorySQL-WWW代码仓库自己提交至blog贡献，也可以联系我们IvorySQL小助理发送至邮箱ivorysql@highgo.com 。 您获得反馈和看到代码合并到项目中的机会在很大程度上取决于更改的粒度。如果您的想法发生了更大的变化，我们强烈建议您在花大量时间编写代码之前，先加入开发人员的邮件列表，并与我们分享您的建议。即使您的建议得到社区的验证，我们仍然建议您将实际工作作为一系列小型的、独立的提交来完成。这使得评审员的工作更加容易，并提高了反馈的及时性。 ​ 当谈到IvorySQL的C和C++部分时，我们尝试遵循PostgreSQL编码约定。除此之外： ​ 对于C和Perl代码，如果需要，请运行pgindent。我们建议在查看更改时使用git diff—​color，这样您提交的代码中就不会出现任何虚假的空白问题。 ​ 所有贡献给IvorySQL的新功能都应该有与其一起贡献的回归测试覆盖。如果您不确定如何测试或记录您的工作，请在ivorysql-hackers邮件列表中提出问题，社区的开发人员将尽力帮助您。 ​ 至少，您应该始终运行make install check world，以确保您没有破坏任何东西。 如果您正在进行的更改涉及PostgreSQL和IvorySQL之间的通用功能，则可能会要求您将其转发到PostgreSQL。这不仅是为了我们不断减少两个项目之间的差异，而且是为了让与PostgreSQL相关的任何变化都能从对上游PostgreSQL社区更广泛的审查中受益。一般来说，将这两个代码库都放在手边是个好主意，这样您就可以确定您的更改是否需要前移。 一旦您准备好与IvorySQL核心团队和IvorySQL社区的其他成员共享您的工作，您应该将所有提交推送到从官方IvorySQL派生的您自己的存储库中，并向我们发送请求。 假定提交的拉取请求通过验证检查，可供同行审查。同行审查是确保对IvorySQL的贡献具有高质量并与路线图和社区期望保持一致的过程。我们鼓励IvorySQL社区的每个成员审查请求并提供反馈。由于您不必成为核心团队成员就可以做到这一点，因此我们建议您向有兴趣成为IvorySQL长期贡献者的任何人提供一系列拉动式评论。 ​ 同行评审的一个结果可能是达成共识，即您需要以某些方式修改pull请求。GitHub允许您将其他提交推送到从中发送请求的分支中。这些额外的提交将对所有审阅者可见。 ​ 当同行评议收到参与者至少+1张+1和no-1张的选票时，同行评议会趋于一致。在这一点上，您应该期望核心团队成员之一将您的更改引入到项目中。 在补丁审查期间的任何时候，您都可能会因审查人员和核心团队成员的工作效率而遇到延迟。请耐心点，也不要气馁。如果您在几天内没有收到预期的反馈，请添加一条评论，要求更新pull请求本身，或者向邮件列表发送一封电子邮件。 有时，您会看到核心团队成员直接提交到存储库，而无需执行pull请求工作流。这仅适用于小的更改，我们使用的经验法则是：如果更改涉及任何可能导致测试失败的功能，那么它必须通过pull请求工作流。另一方面，如果更改发生在代码库的非功能部分（例如在注释块中修复打字错误），则核心团队成员可以决定直接提交到存储库。 1、进入 IvorySql官网：https://github.com/IvorySQL/IvorySQL ​2、点击New issue 1、bug report Title: 标题 ## Bug Report 对bug进行描述 \\### IvorySQL Version 在IvorySQL哪个版本发现的问题 \\### OS Version (uname -a) 系统版本 \\### Configuration options ( config.status --config ) 配置参数 \\### Current Behavior 当前的结果 \\### Expected behavior/code 期望的结果 \\### Step to reproduce 复现步骤 \\### Additional context that can be helpful for identifying the problem 有助于识别问题的其它信息 2、Enhancement Title: 标题 ## Enhancement 对于期望强化的功能作一个描述 3、Feature Request Title: 标题 ## Feature Request 描述你期望实现的一个功能 点击 submit new issue 按钮, 提交即可 1、打开ivorysql仓库 https://github.com/IvorySQL/IvorySQL 2、点击右上角fork按钮，等待fork完成 cd $working_dir # 将 $working_dir 替换为你想放置 repo 的目录。例如，`cd ~/Documents/GitHub` git clone git@github.com:$user/IvorySQL.git # 将 `$user` 替换为你的 GitHub ID cd $working_dir/IvorySQL git checkout -b new-branch-name 在新建的new-branch-name中修改代码。 Git add <file> Git commit -m “commit-message” Git push -u origin new-branch-name 1、打开你 Fork 的仓库： https://github.com/$user/IvorySQL（将 $user 替换为你的 GitHub ID）。 2、点击 Compare & pull request 按钮即可创建 PR。 对于提交一个PR应该保持一个功能，或者一个bug提交一次。禁止多个功能一次提交。 1、打开你 Fork 的仓库： https://github.com/$user/IvorySQL（将 $user 替换为你的 GitHub ID）。 ​ 2、点击 Compare & pull request 按钮 Fix test 功能描述 leave a comment 对该提交功能进行比较详细的描述 点击Create pull request 按钮即可提交。 （1）下载Markdown或者Typora文档编辑器。 （2）检查源仓库是否有更新，如果有更新请先更新并同步到自己的仓库。如有更新请参阅以下步骤，更新至最新版本： git remote git fetch upstream git merge upstream/main git push （3）熟悉样式风格（规范说明） IvorySQL社区提供双语文档。英文文档保存在IvorySQL/文档存储库（文档存储库）中，中文文档保存在 IvorySQL/文档-i18n存储库（文档-i18n 存储库）中。您可以为任何一方文档做出贡献，当然您也可以为两方同时做出贡献。 ​ 您可以从以下任何一项开始，以帮助改进IvorySQL网站（英文和 -i18n）上的 IvorySQL文档： ​ (1) 编写完善文档 ​ (2) 修复拼写错误或格式（标点符号、空格、缩进、代码块等） ​ (3) 修正或更新不当或过时的说明 ​ (4) 添加缺少的内容（句子、段落或新文档） ​ (5) 将文档更改从英文翻译成中文，或从中文翻译成英文。 ​ (6) 提交、回复和解决文档问题或文档-i18n问题 ​ (7) （高级）查看其他人创建的拉取请求 IvorySQL文档是用“markdown”编写的。为确保格式的质量和一致性，在修改更新文档时应遵循某些 Markdown 规则。 ​ Markdown规范 ​ 1、标题从一级开始递增使用，禁止跳级使用。例如：一级标题下面不能直接使用三级标题；二级标题下面不能直接使用四级标题。 ​ 2、标题必须统一使用 ATX 风格，即在标题前加 # 号来表示标题级别。 ​ 3、标题的引导符号 # 后必须空一格再接标题内容。 ​ 4、标题的引导符号“#”后只能空一格后再接标题内容，不能有多个空格。 ​ 5、标题必须出现在一行行首，即标题的 # 号前不能有任何空格。 ​ 6、标题末尾仅能出现中英文问号、反引号、中英文单双引号等符号。其余如冒号、逗号、句号、感叹号等符号均不能在标题末尾使用。 ​ 7、标题上面须空一行。 ​ 8、文档中不能连续出现内容重复的标题，如一级标题为 # IvorySQL 架构，紧接着的二级标题就不能是 ## IvorySQL 架构。如果不是连续的标题，则标题内容可重复。 ​ 9、文档中只能出现一个一级标题。 ​ 10、一般来说，除 TOC.md 文件可缩进 2 个空格外，其余所有 .md 文件每缩进一级，默认须缩进 4 个空格。 ​ 11、文档中（包括代码块内）禁止出现 Tab 制表符，如需缩进，必须统一用空格代替 ​ 12、禁止出现连续的空行。 ​ 13、块引用符号 > 后禁止出现多个空格，只能使用一个空格，后接引用内容。 ​ 14、使用有序列表时，必须从 1 开始，按顺序递增。 ​ 15、使用列表时，每一列表项的标识符（+、-、* 或数字）后只能空一格，后接列表内容。 ​ 16、列表（包括有序和无序列表）前后必须各空一行。 ​ 17、代码块前后必须各空一行。 ​ 18、文档中禁止出现裸露的 URL。如果希望用户能直接点击并打开该 URL，则使用一对尖括号 (<URL>) 包裹该 URL。如果由于特殊情况必须使用裸露的 URL，不需要用户通过点击打开，则使用一对反引号 (URL) 包裹该 URL。 ​ 19、使用加粗、斜体等强调效果时，在强调标识符内禁止出现多余的空格。如不能出现 加粗文本 。 ​ 20、单个反引号包裹的代码块内禁止出现多余的空格。如不能出现 ` 示例文本 `。 ​ 21、链接文本两边禁止出现多余的空格。如不能出现 [某链接](https://www.example.com/)。 ​ 22、链接必须有链接路径。如不能出现[空链接]()或[空链接](#)等情况。 1、标题从一级开始递增使用，禁止跳级使用。 # Heading 1 ### Heading 3 We skipped out a 2nd level heading in this document 2、标题必须统一使用 ATX 风格，即在标题前加 # 号来表示标题级别。 # Heading 1 ## Heading 2 ### Heading 3 #### Heading 4 ## Another Heading 2 ### Another Heading 3 3、标题的引导符号 # 后必须空一格再接标题内容。禁止#后多个空格，禁止#前出现空格 错误示范： # Heading 1 ## Heading 2 正确示范： # Heading 1 ## Heading 2 4、标题末尾仅能出现中英文问号、反引号、中英文单双引号等符号。 错误示范 # This is a heading. 正确示范 # This is a heading 5、标题上面空一行 错误示范 # Heading 1 Some text Some more text## Heading 2 正确示范 # Heading 1 Some text Some more text ## Heading 2 6、文档中不能连续出现内容重复的标题，如一级标题为 # IvorySQL 描述，紧接着的二级标题就不能是 ## IvorySQL 描述。如果不是连续的标题，则标题内容可重复。 错误示范 # Some text ## Some text 正确示范 # Some text ## Some more text 7、文档中只能出现一个一级标题。 错误示范 # Top level heading # Another top-level heading 正确示范 # Title ## Heading ## Another heading 8、一般来说，除 TOC.md 文件可缩进 2 个空格外，其余所有 .md 文件每缩进一级，默认须缩进 4 个空格。 错误示范 * List item * Nested list item indented by 3 spaces 正确示范: * List item * Nested list item indented by 4 spaces 9、文档中（包括代码块内）禁止出现 Tab 制表符，如需缩进，必须统一用空格代替 错误示范： Some text * hard tab character used to indent the list item 正确示范: Some text * Spaces used to indent the list item instead 10、禁止出现连续的空行 错误示范 Some text here Some more text here 正确示范: Some text here Some more text here 11、块引用符号 > 后禁止出现多个空格，只能使用一个空格，后接引用内容。 错误示范 > This is a blockquote with bad indentation> there should only be one. 正确示范 > This is a blockquote with correct> indentation. 12、使用有序列表时，必须从 1 开始，按顺序递增。 错误示范: 1. Do this. 1. Do that. 1. Done. 0. Do this. 1. Do that. 2. Done. 正确示范: 1. Do this. 2. Do that. 3. Done. 13、使用列表时，每一列表项的标识符（+、-、* 或数字）后只能空一格，后接列表内容。 正确示范 * Foo * Bar * Baz 1. Foo * Bar 1. Baz 14、列表（包括有序和无序列表）前后必须各空一行。 错误示范 Some text* Some* List 1. Some2. List Some text 正确示范 Some text * Some * List 1. Some 2. List Some text 15、代码块前后必须各空一行。 错误示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 正确示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 16、文档中禁止出现裸露的 URL。如果希望用户能直接点击并打开该 URL，则使用一对尖括号 (<URL>) 包裹该 URL。如果由于特殊情况必须使用裸露的 URL，不需要用户通过点击打开，则使用一对反引号 (URL) 包裹该 URL。 错误示范 For more information, see https://www.example.com/. 正确示范 For more information, see <https://www.example.com/>. 17、使用加粗、斜体等强调效果时，在强调标识符内禁止出现多余的空格。如不能出现 加粗文本 。 错误示范 Here is some ** bold ** text. Here is some * italic * text. Here is some more __ bold __ text. Here is some more _ italic _ text. 正确示范: Here is some **bold** text. Here is some *italic* text. Here is some more __bold__ text. Here is some more _italic_ text. 18、单个反引号包裹的代码块内禁止出现多余的空格。如不能出现 ` 示例文本 `。 错误示范： some text some text 正确示范: some text 19、链接文本两边禁止出现多余的空格。如不能出现 [ 某链接 ](https://www.example.com/)。 错误示范 [ a link ](https://www.example.com/) 正确示范: [a link](https://www.example.com/) 20、链接必须有链接路径。如不能出现[空链接]()或[空链接](#)等情况。 错误示范 [an empty link]() [an empty fragment](#) 正确示范: [a valid link](https://example.com/) [a valid fragment](#fragment) 21文档中的代码块统一使用三个反引号进行包裹，禁止使用缩进四格风格的代码块。 错误示范： Some text. # Indented code More text. 正确示范 ```ruby # Fenced code ​``` More text. 为了测试您所做的修改是否修改，您需要准备以下环境 Node.js 安装 Antora 安装 环境安装请参考 Antora docs。 安装成功后，在终端上显示如下即为成功安装。 通过阅读之前内容，相信您已经有了充足的准备，包括 fork 我们文档中心相关的三个仓库到个人仓库中，Antora 工具的安装准备等环境的搭建。 首先，您要知道网页对应的ui的位置，如下图： 中英文的网页ui模板基本一致，因此修改时应该尽量保证同时修改两个模板，将修改过后的ui再上传至个人Github上，完成了这些，就可以考虑在本地生成您修改过后的网页了。 文档中心是由 Antora 进行搭建的，在运行 Antora 之前，记得修改对应 playbook.yml 文件 完成上述流程之后，请在终端运行命令 antora antora-playbook.yml --stacktrace，然后耐心等待，当成功运行结束后，你就可以查看自己生成的网页了。 在检查之后，你就可以开始着手上传至我们的 ivorysql_web 仓库中，提交PR的流程和之前的流程相同，感谢您对社区的贡献_，我们会在审核过后，考虑是否更新网站。 1、下载 Markdown或者 Typora文档编辑器。 ​ 2、先下载博客源码到本地，检查源仓库(https://github.com/IvorySQL/Ivory-www)是否有更新， 如果有更新请先更新并同步到自己的仓库。请参阅以下步骤，更新至最新版本： # 拉取网站源码 git clone https://github.com/IvorySQL/Ivory-www.git # 同步更新仓库 git pull ​ 3、熟悉样式风格 ([_规范说明_2]) IvorySQL社区提供双语文档。英文博客保存在源码目录<u>Ivory-www/blog</u>中，中文博客保存在源码目录<u>Ivory-www/i18n/zh-CN/docusaurus-plugin-content-blog</u>中。您可以为任何一方博客做出贡献，当然您也可以为两方同时做出贡献。 在贡献之前，让我们快速浏览一下有关IvorySQL博客维护的信息。这可以帮助您尽快成功的提交blog成为贡献者。blog提交规范 ​ （1）将代码克隆到本地仓库 git clone https://github.com/IvorySQL/Ivory-www.git ​ （2）创建一个分支 git checkout -b <branch-name> ​ （3）在博客目录中创建自己文章的目录，目录名字规则参照 ([_规范说明_2])。 # 生成英文博客目录及文件 cd Ivory-www/blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md # 生成中文博客目录及文件 cd Ivory-www/i18n/zh-CN/docusaurus-plugin-content-blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md ​ （4）在index.md编写要发布的博文，将博客中需要的图片放到和index.md同级目录。 ​ （5）提交发布博客 git add <file-path> git commit -m \"<message>\" git push origin <branch-name>:<branch-name> （1）文件夹命名格式：年-月-日-名称 示例：2022-1-28-ivorysql-arrived ​ （2）文件属性统一为index.md ​ （3）图片属性统一为 .png形式，并将需要上传的图片提前放到要提交的文件夹中。 ​ 注意：图片名字唯一，不可重复奥~。 ​ 示例：po-one.png 博客是用markdown或者Typora来编写，您可以阅读 博客 | Docusaurus来了解博客的设计方式。 （1）文章头部部署包括以下信息 --- slug: IvorySQL title: 欢迎来到IvorySQL社区 authors: [official] authorTwitter: IvorySql tags: [IvorySQL, Welcome, Database, Join Us] --- ​ 提示：您可以将以上模板复制到您的文件夹中并进行编辑。 ​ 注意：1）slug、title、authors、tags后添加文字均空一格。 ​ 2）slug每篇名字唯一且不可重复，相同文章中英文版可以相同。 （2）文本格式 ​ 正文段落标题统一用h2/“二级标题”； ​ 正文使用默认字体字号。 （3）插入照片命名形式 ​ [Hello](Hello-banner.png) （4）插入超链接命名形式 ​ [名称]（链接） ​ [Github page](https://github.com/IvorySQL/) 下载源代码或发布的软件包。 IvorySQL的文档中心 采用开源工具 Antora 进行搭建，同样的，我们的文档中心也是开源的。文档中心由三部分组成，分别是 文档文件, 静态网页文件 以及生成网页所用到的 网页模板文件 。 我们的文档中心同样欢迎每一位愿意参与到开源工作中的小伙伴的加入，记得遵守我们的行为准则_。 由于我们的文档中心全部托管于Github上，这使得任何用户都可以将我们的文档仓库 fork 到个人仓库中，然后对其进行修改，之后提交PR，由我们的开源团队的人员审核过后就可以将修改更新到我们的文档中心中。 为了更加便捷地使您达到纠正文档错误的目的，首先需要您按照想要更新的大小来建立个人仓库，如下： 如果您想修改原有的内容或者添加新的页面，仅需要 fork 文档文件 到个人仓库中。 如果您想更深度地参与到文档中心的建设工作中来，除了文档文件外，还需要 fork 静态网页文件 和 网页模板文件 到个人仓库中。 本小节将会介绍发现网页内容不适宜之后，对网页内容进行修改的流程。 在有错误内容的网页的右上角，有一个 edit this page 的按钮，点击按钮。如图： 点击之后，就会跳转到我们存放当前页面源 .adoc 文件的编辑页面，请按照 Asciidoc 格式对内容进行修改。如图： 编辑完成后，如图： 确认更新后，如图： 接下来会由开源团队的相关人员负责审核您提交的内容，审核完成后您所提交的更新就会出现在对应页面上。 本小节会介绍如何在网站上添加新的页面组件，添加新页面涉及的修改主要包括以下几种： CN/modules/ROOT/pages/vX.X 目录下的 .adoc 文件添加 CN/modules/ROOT/nav.adoc 的修改，如果修改涉及到图片的修改或者添加，请修改 images 中的图片 EN/modules/ROOT/pages/vX.X 目录下的 .adoc 文件添加 EN/modules/ROOT/nav.adoc 的修改，如果修改涉及到图片的修改或者添加，请修改 images 中的图片 首先，您需要把您 fork 的仓库，clone到本地 git clone https://github.com/$username$/ivorysql_docs.git 然后，将要添加的 .adoc 文件放至正确目录下，切记中英文的都应该准备（中英文文件应该同名），并且各自放至正确目录下，同时，修改对应的 nav.adoc 文件（修改方式可以参照文件已有内容进行修改）。 上述修改完成后，先提交至个人仓库 git add . git commit -m \"$describe your change$\" git push 之后按照如下提交PR即可 如果您不满足于简单的在网页端进行提交或者仅修改网页内容，又或者您想要修改网页ui，本小节内容将会帮助到您。 在阅读本小节内容之前，您需要确认您的Github个人仓库是否已经 fork 了网页模板文件仓库和静态网页文件仓库，如果没有请参考[_如何贡献]。 部署网页这部分工作，暂时还是由开源团队的人员负责，我们重视每一次提交和每一个issue，所以请不要担心自己对于社区的贡献被埋没_ 本章节主要介绍了如何自己主动去更新我们的文档中心，但是我们明确地知道山高路远，时不我待。因此对于没有太多时间的小伙伴来说，可以发邮件到我们的 ivorysql-docs@ivorysql.org，我们会有专门的工作人员进行处理您的每一封来信，期待您的邮件。","title":"IvorySQL社区贡献指南","component":"ivorysql-doc","version":"master","name":"issue_submission_guide","url":"/ivorysql-doc/master/contribution/issue_submission_guide.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"1.1. 说明","hash":"说明","id":2},{"text":"1.2. 行为准则","hash":"行为准则","id":3},{"text":"1.3. 管理说明","hash":"管理说明","id":4},{"text":"2. 贡献者指南","hash":"贡献者指南","id":5},{"text":"2.1. 贡献前的准备","hash":"贡献前的准备","id":6},{"text":"2.1.1. 开始","hash":"开始","id":7},{"text":"2.1.2. 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Tip","hash":"tip","id":58}]},"39":{"id":39,"text":"IvorySQL适配认证如下CPU架构： 序号 架构名称 适配品牌 全平台介质包下载 1 x86_64 Intel、AMD、兆芯、海光 deb, rpm 2 aarch64 飞腾、鲲鹏 deb, rpm 3 mips64el 龙芯3000，龙芯4000 deb, rpm 4 loongarch64 龙芯5000 deb, rpm","title":"芯片架构适配","component":"ivorysql-doc","version":"master","name":"cpu_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html","titles":[]},"40":{"id":40,"text":"IvorySQL适配认证如下操作系统： 序号 操作系统名称 操作系统简介 证书查看 1 银河麒麟高级服务器操作系统 V11 银河麒麟高级服务器操作系统V11是麒麟软件依托多年技术研发积淀与丰富应用实践经验，为企业级关键业务量身打造的新一代服务器操作系统。产品以高可靠、高可用、高安全、高性能、高扩展为核心优势，在深度融合AI技术的基础上，更以自主创新突破构建起高速网络协议 MPTCP、热补丁管理、智能故障诊断、场景化优化及安全容器镜像仓库等关键技术体系，为党政机关信息化建设、重点行业数字化转型及国家重大工程实施，筑牢安全可信的支撑底座。 2 openKylin 2.0 SP1 OpenAtom openKylin （简称“openKylin”） 是由开放原子开源基金会孵化及运营的开源项目，由基础软硬件企业、非营利性组织、社团组织、高等院校、科研机构和个人开发者共同创立，旨在以“为世界提供与人工智能技术深度融合的开源操作系统”为社区愿景，在开源、自愿、平等、协作的基础上，共同打造全球领先的智能桌面开源操作系统根社区，推动Linux开源技术及其软硬件生态繁荣发展。 3 OpenAnolis OS (龙蜥操作系统) 23 龙蜥操作系统 Anolis OS 23 是龙蜥社区（OpenAnolis）基于开源生态发展合作倡议，面向上游原生社区独立选型，持续演进并保障兼容性和稳定性的操作系统。Anolis OS 23 基于 Linux Kernel 6.6 LTS 的企业级操作系统，依托 ANCK 6.6 内核深度优化，全面支持海光、飞腾、龙芯（LoongArch）、兆芯等国产芯片及通用 x86_64/ARM64 架构。其针对虚拟化、安全特性及性能优化进行专项增强，并通过分层架构设计与智能调优工具，实现软硬协同性能最大化。同时原生支持 AI 生态组件，提供安全的 AI 容器镜像，加速模型开发与推理部署。在开发工具链方面，集成 GCC 12.3+/LLVM 17、Python 3.11、Rust 等，支持多平台高效开发。桌面生态方面兼容 GNOME、DDE 桌面环境，并通过集成玲珑包管理器满足了多样化场景需求实现生态扩展。Anolis OS 23 支持各类常见应用和国产化应用，助力企业实现高效、安全、可靠的数字化转型。 4 deppin (深度操作系统) 20 深度操作系统是一个致力于为全球用户提供美观易用、安全可靠的Linux发行版。深度操作系统 20正式版（1002）采取统一的设计风格，从桌面环境和应用进行重新设计，带来焕然一新的视觉感受。底层仓库升级到Debian 10.5，系统安装采用双内核机制（Kernel 5.4、Kernel 5.7），全面提升系统稳定性和兼容性。全新设计的启动器菜单、指纹识别、系统安全增强等，系统部分预装应用升级到最新版本，只为给你更好体验。","title":"操作系统适配","component":"ivorysql-doc","version":"master","name":"os_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html","titles":[]},"41":{"id":41,"text":"IvorySQL 作为一款兼容 Oracle 且基于 PostgreSQL 的高级开源数据库，具备强大的扩展能力，支持丰富的生态系统插件。这些插件可以帮助用户在不同场景下增强数据库功能，包括地理信息处理、向量检索、全文搜索、数据定义提取和路径规划等。以下是当前 IvorySQL 官方兼容和支持的主要插件列表： 序号 插件名称 版本 功能描述 适用场景 1 postgis 3.5.4 为 IvorySQL 提供地理空间数据支持，包括空间索引、空间函数和地理对象存储 地理信息系统（GIS）、地图服务、位置数据分析 2 pgvector 0.8.1 支持向量相似性搜索，可用于存储和检索高维向量数据 AI 应用、图像检索、推荐系统、语义搜索 3 pgddl (DDL Extractor) 0.31 提取数据库中的 DDL（数据定义语言）语句，便于版本管理和迁移 数据库版本控制、CI/CD 集成、结构比对与同步 4 pg_cron​ 1.6.0 提供数据库内部的定时任务调度功能，支持定期执行SQL语句 数据清理、定期统计、自动化维护任务 5 pgsql-http​ 1.7.0 允许在SQL中发起HTTP请求，与外部Web服务进行交互 数据采集、API集成、微服务调用 6 plpgsql_check 2.8 提供PL/pgSQL代码的静态分析功能，可在开发阶段发现潜在错误 存储过程开发、代码质量检查、调试优化 7 pgroonga 4.0.4 提供​非英语语言全文搜索功能，满足高性能应用的需求 中日韩等语言的全文搜索功能 8 pgaudit 18.0 提供细粒度的审计功能，记录数据库操作日志，便于安全审计和合规性检查 数据库安全审计、合规性检查、审计报告生成 9 pgrouting 3.8.0 提供地理空间数据的路由计算功能，支持多种算法和数据格式 地理空间分析、路径规划、物流优化 10 system_stats 3.2 提供用于访问系统级统计信息的函数 系统监控 11 pg_ai_query 0.1.1 AI驱动的自然语言转SQL扩展，支持多种大语言模型 AI辅助查询、自然语言数据库交互 这些插件均经过 IvorySQL 团队的测试和适配，确保在 IvorySQL 环境下稳定运行。用户可以根据业务需求选择合适的插件，进一步提升数据库系统的能力和灵活性。 我们也将持续扩展和丰富 IvorySQL 的插件生态，欢迎社区开发者提交新的插件适配建议或代码贡献。如需了解更多每个插件的详细使用方法和最新兼容版本，请参阅各插件对应的文档章节。","title":"","component":"ivorysql-doc","version":"master","name":"ecosystem_overview","url":"/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html","titles":[{"text":"IvorySQL生态插件适配列表","hash":"ivorysql生态插件适配列表","id":1}]},"42":{"id":42,"text":"pg_ai_query 是一个用于 IvorySQL/PostgreSQL 的 AI 驱动自然语言转 SQL 扩展。它利用大语言模型（LLM）将用户的自然语言描述直接转换为可执行的 SQL 查询语句，支持 OpenAI、Anthropic Claude 和 Google Gemini 等多种 AI 模型。 项目地址：https://github.com/benodiwal/pg_ai_query/tree/main 开源协议：Apache-2.0 主要特性： 自然语言转 SQL：将文本语言描述转换为有效的 SQL 查询 多模型支持：支持 gpt-4o-mini、gpt-4o、gpt-5、claude-3-haiku-20240307、claude-sonnet-4-5-20250929、claude-4.5-opus 等大模型 安全保护：阻止访问 information_schema 和 pg_catalog 系统表 作用域限制：仅对用户表进行操作 可配置限制：内置行数限制强制执行 API 密钥安全：安全处理 API 凭证 下面以v0.1.1为例进行演示。 依赖要求 PostgreSQL 14+ with development headers CMake 3.16+ C++20 compatible compiler API key from OpenAI, Anthropic, or Google (Gemini) 安装依赖 sudo apt-get install libcurl4-openssl-dev 编译安装 IvorySQL 如需从源码编译 IvorySQL，可参考以下配置： ./configure \\ --prefix=$PWD/inst \\ --enable-cassert \\ --enable-debug \\ --enable-tap-tests \\ --enable-rpath \\ --enable-nls \\ --enable-injection-points \\ --with-tcl \\ --with-python \\ --with-gssapi \\ --with-pam \\ --with-ldap \\ --with-openssl \\ --with-libedit-preferred \\ --with-uuid=e2fs \\ --with-ossp-uuid \\ --with-libxml \\ --with-libxslt \\ --with-perl \\ --with-icu \\ --with-libnuma 编译安装 pg_ai_query git clone --recurse-submodules https://github.com/benodiwal/pg_ai_query.git cd pg_ai_query mkdir build && cd build export PATH=\"$HOME/works/repo/ivorysql/IvorySQL/inst/bin:$PATH\" cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/works/repo/ivorysql/IvorySQL/inst make && sudo make install 创建扩展 CREATE EXTENSION pg_ai_query; 在 home 目录下创建 ~/.pg_ai.config 配置文件： [general] log_level = \"INFO\" enable_logging = false [query] enforce_limit = true default_limit = 1000 [response] show_explanation = true show_warnings = true show_suggested_visualization = false use_formatted_response = false [anthropic] # Your Anthropic API key (if using Claude) api_key = \"******\" # Default model to use (options: claude-sonnet-4-5-20250929) default_model = \"claude-sonnet-4-5-20250929\" # Custom API endpoint (optional) - for Anthropic-compatible APIs api_endpoint = \"https://open.bigmodel.cn/api/anthropic\" [prompts] # Use file paths to read custom prompts system_prompt = /home/highgo/.pg_ai.prompts explain_system_prompt = /home/highgo/.pg_ai.explain.prompts 更多示例请参考：https://github.com/benodiwal/pg_ai_query/blob/main/docs/src/examples.md SELECT generate_query('找出所有的用户'); 输出示例： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135507cc16362d5d324ccd generate_query -------------------------------------------------------- SELECT * FROM public.users LIMIT 1000; + -- Explanation: -- Retrieves all columns and rows from the users table. + -- Warning: INFO: Applied LIMIT 1000 to prevent large result sets. Remove LIMIT if you need all data. + -- Note: Row limit was automatically applied to this query for safety (1 row) 执行查询： SELECT * FROM public.users LIMIT 1000; 输出： id | name | email | age | created_at | city ----+---------------+-------------------+-----+----------------------------+--------------- 1 | Alice Johnson | alice@example.com | 28 | 2026-02-04 15:47:55.208111 | New York 2 | Bob Smith | bob@example.com | 35 | 2026-02-04 15:47:55.208111 | San Francisco 3 | Carol Davis | carol@example.com | 31 | 2026-02-04 15:47:55.208111 | Chicago 4 | David Wilson | david@example.com | 27 | 2026-02-04 15:47:55.208111 | Seattle 5 | Eva Brown | eva@example.com | 33 | 2026-02-04 15:47:55.208111 | Boston (5 rows) 生成测试数据 SELECT generate_query('生成100条user数据，插入到users'); 输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_2026021114092101601c5650864a2d generate_query -------------------------------------------------------------------------------------------------------- INSERT INTO public.users (name, email, age, city, status) SELECT 'User_' || generate_series AS name, 'user' || generate_series || '@example.com' AS email, (18 + (generate_series % 50)) AS age, (ARRAY['Beijing','Shanghai','Guangzhou','Shenzhen','Hangzhou'])[1 + (generate_series % 5)] AS city, 'active' AS status FROM generate_series(1, 100); + -- Explanation: -- 生成100条模拟用户数据并插入到users表中。数据包括自动生成的姓名、唯一邮箱、随机年龄(18-67岁)、随机城市和默认状态。 + -- Warnings: -- 1. INFO: 依赖users表的id列有DEFAULT自增设置，未手动插入id。 -- 2. INFO: 使用generate_series函数生成序列数据，这是PostgreSQL/IvorySQL的特性。 -- 3. WARN: 确保在运行前users表为空或id序列不冲突，否则可能重复插入。 -- 4. WARN: 邮箱格式为简单模拟，实际环境中可能需要更复杂的逻辑或去重检查。 (1 row) 不区分大小写查询 SELECT generate_query('show users from beijing, beijing is non-Case insensitive'); 输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211142845878f5f1a5a2f44a7 generate_query ----------------------------------------- SELECT id, name, email, age, created_at, city, status FROM public.users WHERE LOWER(city) = LOWER('beijing') LIMIT 100; + -- Explanation: -- Selects all user details for users located in Beijing, performing a case-insensitive match on the city column. + -- Warnings: -- 1. INFO: Using LOWER() on both sides ensures case-insensitive matching but may prevent the database from using a standard index on the city column if one exists. -- 2. INFO: Row limit of 100 applied to prevent large result sets. + -- Note: Row limit was automatically applied to this query for safety (1 row) SELECT explain_query('SELECT * FROM orders WHERE user_id = 12'); 输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211175909d47a6871bcca4897 explain_query -------------------------------------------------------------------------------------------------------------- 1. 查询概述 + - 该查询旨在从 orders 表中检索 user_id 等于 12 的所有记录（SELECT *）。 - 这是一个典型的根据特定字段（user_id）筛选数据的查询。 + 2. 性能摘要 + - 总执行时间: 0.021 毫秒 - 规划时间: 0.430 毫秒 - 总成本: 18.12 - 返回行数: 0 行 (Actual Rows: 0) - 扫描行数: 0 行 (Rows Removed by Filter: 0) + 3. 执行计划分析 + - 关键步骤: 顺序扫描 - 数据库对 orders 表执行了全表扫描操作。 - 计划器预计会找到 3 行数据，但实际执行返回了 0 行。 - 过滤条件: orders.user_id = 12，这意味着数据库必须读取表中的每一行来检查这个条件。 + 4. 性能问题 + - 全表扫描风险: 虽然目前表的数据量很小（执行时间仅为 0.021ms），但使用了 Seq Scan（顺序扫描）意味着数据库没有使用索引。如果 orders 表随着时间推移增长到包含数百万行数据，这种查询方式将变得极其缓慢（高 I/O 消耗）。 - 缺失索引: 计划显示没有使用任何索引来定位 user_id = 12 的行，这表明在 user_id 列上可能缺少必要的 B-Tree 索引。 + 5. 优化建议 + - 主要建议: 在 user_id 列上创建索引以避免全表扫描。这将把查询从 O(N)（扫描所有行）转变为 O(log N)（索引查找）。 - SQL 优化示例: + CREATE INDEX idx_orders_user_id ON orders(user_id); + 6. 索引建议 + - 推荐索引: 在 orders 表的 user_id 列上创建 B-Tree 索引。 - 理由: 查询条件基于 user_id 的等值比较 (=)。创建索引后，IvorySQL (PostgreSQL) 将能够利用索引快速定位数据，显著减少查询时间和资源消耗，特别是在数据量大的情况下。 (1 row) 使用英语：虽然 AI 支持多种语言，但英语效果最佳 了解数据库结构：对数据库结构理解越深入，生成的查询越准确 迭代优化：从宽泛的开始，然后逐步添加细节以改进结果 明确指定：如果知道特定的表或列，请在提示中提及，这有助于 AI 生成精确的查询 当查询中引用的表不存在时，系统会返回错误信息： SELECT generate_query('列出所有的商品和价格'); 错误输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135642777cbc5c82ca4a85 ERROR: Query generation failed: Cannot generate query. Referenced table(s) for 'products' or 'goods' do not exist in the database. Available tables: public.orders, public.student_scores, public.users, sys.dual 在这种情况下，AI 会告知可用的表列表，帮助用户调整查询。","title":"pg_ai_query","component":"ivorysql-doc","version":"master","name":"pg_ai_query","url":"/ivorysql-doc/master/ecosystem_components/pg_ai_query.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 快速开始","hash":"快速开始","id":2},{"text":"2.1. 安装","hash":"安装","id":3},{"text":"2.2. 配置","hash":"配置","id":4},{"text":"3. 使用示例","hash":"使用示例","id":5},{"text":"3.1. 基本用法","hash":"基本用法","id":6},{"text":"3.2. generate_query 示例","hash":"generate_query-示例","id":7},{"text":"3.3. explain_query 示例","hash":"explain_query-示例","id":8},{"text":"4. 最佳实践","hash":"最佳实践","id":9},{"text":"4.1. 提示词（Prompt）编写建议","hash":"提示词prompt编写建议","id":10},{"text":"4.2. 错误处理示例","hash":"错误处理示例","id":11}]},"43":{"id":43,"text":"在 PostgreSQL 中运行周期性任务，例如执行 VACUUM或删除旧数据，是一种常见需求。实现这一点的简单方法是配置 cron或其他外部守护进程，使其定期连接到数据库并运行命令。然而，随着数据库越来越多地作为托管服务或独立容器运行，配置和运行一个单独的守护进程通常变得不切实际。此外，很难让您的 cron任务感知故障转移，或者跨集群节点调度任务。 pg_cron 是 PostgreSQL 的开源定时任务扩展，允许直接在数据库内部设置 cron 风格的任务调度，用于自动化数据维护任务(清理，聚合), 数据库健康检查，执行存储过程和自定义函数等操作。它将cron任务存储在表中，周期性任务会随着 PostgreSQL 服务器自动进行故障转移。详情可以参见 pg_cron文档。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 # 拉取pg_cron源码 git clone https://github.com/citusdata/pg_cron.git cd pg_cron # 将pg_config的路径设置到PATH环境变量里，eg： export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make make install # 共享预加载扩展 shared_preload_libraries = 'pg_cron' # 指定任务元数据存储库（默认当前库） cron.database_name = 'ivorysql' # 允许的最大并发任务数 cron.max_running_jobs = 5 pg_ctl restart -D ./data -l logfile psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension pg_cron; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'pg_cron'; name | default_version | installed_version | comment ---------+-----------------+-------------------+--------------------------- pg_cron | 1.6 | |Job scheduler for PostgreSQL (1 row) SELECT cron.schedule( 'nightly-data-cleanup', -- 任务名称（唯一标识） '0 3 * * *', -- cron表达式（每天UTC 3:00） $$DELETE FROM logs WHERE created_at < now() - interval '30 days'$$ -- 执行SQL ); cron表达式速查表： 示例 含义 '0 * * * *' 每小时整点执行 '*/15 * * * *' 每15分钟执行 '0 9 * * 1-5' 工作日早9点执行 '0 1 1 * *' 每月1日凌晨1点执行 pg_cron还允许使用 '$'表示月份的最后一天。 # 查看所有任务 SELECT * FROM cron.job; # 查看任务执行历史 SELECT * FROM cron.job_run_details ORDER BY start_time DESC LIMIT 10; # 删除任务 SELECT cron.unschedule('nightly-data-cleanup'); # 暂停任务（更新状态） UPDATE cron.job SET active = false WHERE jobname = 'delete-job-run-details';","title":"pg_cron","component":"ivorysql-doc","version":"master","name":"pg_cron","url":"/ivorysql-doc/master/ecosystem_components/pg_cron.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装配置","hash":"安装配置","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"2.2. 配置文件 (ivorysql.conf)","hash":"配置文件-ivorysql-conf","id":4},{"text":"2.3. 重启服务","hash":"重启服务","id":5},{"text":"2.4. 创建Extension并确认pg_cron版本","hash":"创建extension并确认pg_cron版本","id":6},{"text":"3. 核心功能使用","hash":"核心功能使用","id":7},{"text":"3.1. 创建定时任务","hash":"创建定时任务","id":8},{"text":"3.2. 任务管理","hash":"任务管理","id":9}]},"44":{"id":44,"text":"PgAudit 是一个审计扩展，可以为 DDL、DML、DCL 等关键操作生成可追溯的日志记录。通过审计日志，数据库管理员能够满足合规要求、及时发现异常行为，并在出现问题时快速定位责任主体与影响范围。 全面审计能力：记录 SELECT、INSERT、UPDATE、DELETE、DDL 命令以及权限变更等操作，帮助构建完整的操作轨迹。 灵活的审计维度：支持全局审计、角色审计与对象审计，可按用户、角色、模式或具体操作类型进行精细化配置。 平滑集成：复用 PostgreSQL 标准日志子系统，可与 syslog、logrotate 等工具联动，兼容现有日志采集与分析方案。 合规支撑：提供结构化审计日志，便于生成符合金融、政企等行业规范的审计报告。 安全增强：通过记录和分析数据库活动，及时发现未授权访问、异常 DML 或潜在数据泄露风险。 运维优化：辅助回放操作行为、定位性能瓶颈，支撑 SQL 优化与问题排查。 已安装的 IvorySQL 数据库。 编译工具链：gcc、make、tar 等。 数据库管理员权限，用于修改 ivorysql.conf 并重启数据库实例。 以 PgAudit 18.0 为例： wget https://github.com/pgaudit/pgaudit/archive/refs/tags/18.0.tar.gz tar -xf 18.0.tar.gz cd pgaudit-18.0 make install USE_PGXS=1 PG_CONFIG=$PGHOME/bin/pg_config 上述命令依赖环境变量 PGHOME 指向安装好的 IvorySQL 主目录。安装成功后，pgaudit.so 会被放置到 IvorySQL 的扩展目录中。 修改 ivorysql.conf，启用插件并设置常用参数： shared_preload_libraries = 'pgaudit' # 需实例重启生效 pgaudit.log = 'read, write, ddl' # 审计范围示例，可按需调整 重启数据库实例，使共享库配置生效。 CREATE EXTENSION IF NOT EXISTS pgaudit; SELECT name, default_version, installed_version, comment FROM pg_available_extensions WHERE name = 'pgaudit'; 若返回的 installed_version 与期望版本一致，说明扩展安装成功。 执行如下sql示例： CREATE TABLE audit_demo(id serial PRIMARY KEY, info text); INSERT INTO audit_demo(info) VALUES ('pgaudit test'); SELECT * FROM audit_demo; UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1; DELETE FROM audit_demo WHERE id = 1; 在数据库服务器上查看审计日志： tail -f $PGDATA/log/*.log | grep 'AUDIT:' 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE TABLE,TABLE,public.audit_demo,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE INDEX,INDEX,public.audit_demo_pkey,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,ALTER SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.117 CST [11451] LOG: AUDIT: SESSION,2,1,WRITE,INSERT,,,INSERT INTO audit_demo(info) VALUES ('pgaudit test'),<not logged> 2025-10-31 15:56:32.121 CST [11451] LOG: AUDIT: SESSION,3,1,READ,SELECT,,,SELECT * FROM audit_demo,<not logged> 2025-10-31 15:56:32.122 CST [11451] LOG: AUDIT: SESSION,4,1,WRITE,UPDATE,,,UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1,<not logged> 2025-10-31 15:56:32.127 CST [11451] LOG: AUDIT: SESSION,5,1,WRITE,DELETE,,,DELETE FROM audit_demo WHERE id = 1,<not logged> 若想记录参数的值，打开`pgaudit.log_parameter = 'on'`，效果如下： ivorysql=# SHOW pgaudit.log_parameter; pgaudit.log_parameter ----------------------- on (1 row)","title":"PgAudit","component":"ivorysql-doc","version":"master","name":"pgaudit","url":"/ivorysql-doc/master/ecosystem_components/pgaudit.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 功能特点","hash":"功能特点","id":2},{"text":"3. 安装部署","hash":"安装部署","id":3},{"text":"3.1. 环境准备","hash":"环境准备","id":4},{"text":"3.2. 编译安装 PgAudit","hash":"编译安装-pgaudit","id":5},{"text":"3.3. 注册扩展前的基础配置","hash":"注册扩展前的基础配置","id":6},{"text":"3.4. 创建扩展并验证","hash":"创建扩展并验证","id":7},{"text":"4. 使用","hash":"使用","id":8}]},"45":{"id":45,"text":"pgddl 是一个专为 PostgreSQL 数据库设计的 SQL 函数扩展，它能够直接从数据库系统目录中生成清晰、格式化的 SQL DDL (数据定义语言) 脚本，例如 CREATE TABLE 或 ALTER FUNCTION。它解决了 PostgreSQL 原生缺乏类似 SHOW CREATE TABLE 命令的问题，让用户无需借助外部工具（如 pg_dump）即可在纯 SQL 环境中轻松获取对象的创建语句。 该扩展通过一组简单的 SQL 函数提供了一套完整的解决方案，其优势包括：仅需使用 SQL 查询即可操作、支持通过 WHERE 子句灵活筛选对象、并能智能处理对象之间的依赖关系，生成包含 Drop 和 Create 步骤的完整脚本。这使得它特别适用于数据库变更管理、升级脚本编写和结构审计等场景。 需要注意的是，ddlx 仍在发展中，可能尚未覆盖所有 PostgreSQL 对象类型和高级选项。生成的脚本建议始终在非生产环境中先行检查和测试，以确保其正确性与安全性。 IvorySQL的安装包里已经集成了pgddl插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装pgddl即可使用。其它安装方式可以参考下面的源码安装步骤。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 从https://github.com/lacanoid/pgddl 下载pgddl v0.31代码。 cd pgddl # 设置PG_CONFIG环境变量值为pg_config路径，eg：/usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/path/to/pg_config make PG_CONFIG=/path/to/pg_config install psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension ddlx; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'ddlx'; name | default_version | installed_version | comment ------+-----------------+-------------------+------------------------- ddlx | 0.31 | 0.31 | DDL eXtractor functions (1 row) 关于pgddl的使用，请参阅 ddlx官方文档","title":"pgddl (DDL Extractor)","component":"ivorysql-doc","version":"master","name":"pgddl","url":"/ivorysql-doc/master/ecosystem_components/pgddl.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建Extension并确认ddlx版本","hash":"创建extension并确认ddlx版本","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"46":{"id":46,"text":"PostgreSQL 内置了全文搜索功能，但在处理​​大规模数据​​、​​复杂查询​​以及​​非英语语言（特别是中日韩等 CJK 语言）​​ 时，其功能和性能可能无法满足高性能应用的需求。 PGroonga 应运而生，它是一个 PostgreSQL 的扩展插件，将 ​​Groonga​​ 这款高性能的全功能全文搜索引擎与 PostgreSQL 数据库深度融合。Groonga 本身是一个优秀的开源搜索引擎，以其极致的速度和丰富的功能著称，尤其擅长处理多语言文本。PGroonga 的使命就是将 Groonga 的强大能力无缝地带入 PostgreSQL 的世界，为用户提供远超原生全文搜索的体验。 IvorySQL的安装包里已经集成了PGroonga插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装PGroonga，直接跳过安装步骤即可。 用户可从 PGroonga package安装 页面选择适合自己的方式进行PGroonga安装。 IvorySQL社区提供了源码安装步骤，下面以PGroonga v4.0.4为例进行演示。 环境 操作系统：Ubuntu 24.04 CPU架构：x86_64 IvorySQL: v5.0 编译PGroonga，有个配置选项：HAVE_MSGPACK=1，它是用于支持WAL，使用这个选项需要安装msgpack-c 1.4.1或者更高版本。 sudo apt install libmsgpack-dev 确保Groonga >= 14.0.0 sudo apt install groonga libgroonga-dev 验证Groonga安装成功 highgo@ubuntu:~/work/IvorySQL/inst$ groonga --version Groonga 15.1.7 [Linux,x86_64,utf8,match-escalation-threshold=0,nfkc,mecab,message-pack,mruby,onigmo,zlib,lz4,zstandard,epoll,apache-arrow,xxhash,blosc,h3,simdjson,llama.cpp] wget https://packages.groonga.org/source/pgroonga/pgroonga-4.0.4.tar.gz tar xvf pgroonga-4.0.4.tar.gz cd pgroonga-4.0.4 运行make前确保`pg_config`命令的路径在`PATH`环境变量里，例如IvorySQL安装的路径是`~/work/IvorySQL/inst`，则环境变量设置如下： export PGHOME=~/work/IvorySQL/inst export PGDATA=$PGHOME/data export PATH=$PGHOME/bin:$PATH 然后执行以下命令进行编译和安装： make HAVE_MSGPACK=1 make install psql 连接到数据库的pg模式下，执行命令： postgres=# CREATE extension pgroonga; CREATE EXTENSION postgres=# SELECT * FROM pg_available_extensions WHERE name = 'pgroonga'; name | default_version | installed_version | comment ----------+-----------------+-------------------+-------------------------------------------------------------------------------- pgroonga | 4.0.4 | 4.0.4 | Super fast and all languages supported full text search index based on Groonga (1 row) postgres=# select version(); version -------------------------------------------------------------------------------- PostgreSQL (IvorySQL 5.0) 18.0 on x86_64-linux, compiled by gcc-13.3.0, 64-bit (1 row) 关于PGroonga的使用，请参阅 PGroonga官方文档","title":"PGroonga","component":"ivorysql-doc","version":"master","name":"pgroonga","url":"/ivorysql-doc/master/ecosystem_components/pgroonga.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 安装依赖","hash":"安装依赖","id":3},{"text":"2.1.1. 安装 msgpack-c","hash":"安装-msgpack-c","id":4},{"text":"2.1.2. 安装 Groonga","hash":"安装-groonga","id":5},{"text":"2.2. 编译安装PGroonga","hash":"编译安装pgroonga","id":6},{"text":"2.2.1. 下载解压源码","hash":"下载解压源码","id":7},{"text":"2.2.2. 编译","hash":"编译","id":8},{"text":"3. 创建Extension并确认PGroonga版本","hash":"创建extension并确认pgroonga版本","id":9},{"text":"4. 使用","hash":"使用","id":10}]},"47":{"id":47,"text":"pgRouting 是一个基于 PostgreSQL/PostGIS 数据库构建的开源地理空间路由扩展库。它为数据库赋予了强大的网络分析功能，使其能够处理复杂的路径规划与图论计算问题，例如计算两点之间的最短路径、执行旅行推销员（TSP）分析或计算服务区范围等。它将路由算法直接嵌入到数据库中，从而避免了在应用层进行复杂的数据传输与计算。 该扩展的核心优势在于能够利用 PostgreSQL 强大的数据管理能力和 PostGIS 丰富的空间函数，直接在数据库内部对空间网络数据执行高效计算。这不仅简化了应用程序的开发流程，还通过减少数据移动大幅提升了大规模网络分析的性能。 pgRouting 广泛应用于物流配送、交通导航、网络分析、城市规划及供应链管理等多个领域。其开源特性吸引了全球开发者持续的贡献与完善，使其成为空间数据库领域进行路径分析和网络求解的首选工具之一。 环境中已经安装了IvorySQL5.0及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 安装依赖 对perl有依赖，perl一般在装IvorySQL时已经装上了，这里不用再装。 CMake版本要求 >= 3.12, Boost版本 >= 1.56 #安装依赖 sudo apt install cmake libboost-all-dev 编译安装 wget https://github.com/pgRouting/pgrouting/releases/download/v3.8.0/pgrouting-3.8.0.tar.gz tar xvf pgrouting-3.8.0.tar.gz cd pgrouting-3.8.0 mkdir build cd build cmake .. -DPOSTGRESQL_PG_CONFIG=/path/to/pg_config # eg: /usr/local/ivorysql/ivorysql-5/bin/pg_config make sudo make install psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension pgrouting; 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CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'http'; name | default_version | installed_version | comment -----------+-----------------+-------------------+------------------------------------------------------------------------- http | 1.7 | 1.7 | HTTP client for PostgreSQL, allows web page retrieval inside the database. 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Oracle兼容性","hash":"oracle兼容性","id":7},{"text":"4.1. 数据类型","hash":"数据类型","id":8},{"text":"4.2. 匿名块","hash":"匿名块","id":9},{"text":"4.3. 存储过程（PROCEDURE）","hash":"存储过程procedure","id":10},{"text":"4.4. 函数（FUNCTION）","hash":"函数function","id":11}]},"50":{"id":50,"text":"在 PostgreSQL 数据库开发过程中，编写存储过程和函数时往往难以在运行前发现代码中的语法错误、类型不匹配、未定义变量等潜在问题。传统方式需要等到函数实际执行时才能发现这些错误，这不仅增加了调试成本，也可能在生产环境中引发意外故障。 plpgsql_check 是一个专为 PostgreSQL 的 PL/pgSQL 语言设计的静态代码分析工具（Linter），它能够在不实际执行函数的情况下，对存储过程和函数的源代码进行深度检查。该工具可以提前发现语法错误、类型不匹配、未使用的变量、性能问题、安全隐患等多种代码质量问题，帮助开发者在开发阶段就确保代码的正确性和健壮性。详情可以参见 plpgsql_check官方文档。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 # 从https://github.com/okbob/plpgsql_check/releases/tag/v2.8.3 下载 2.8.3的源码包 plpgsql_check-2.8.3.tar.gz tar xvf plpgsql_check-2.8.3.tar.gz cd plpgsql_check-2.8.3 # 将pg_config的路径设置到PATH环境变量里，eg： export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make USE_PGXS=1 clean make USE_PGXS=1 all sudo make USE_PGXS=1 install psql 连接到数据库，执行如下命令： ivorysql=# CREATE EXTENSION plpgsql_check; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'plpgsql_check'; name | default_version | installed_version | comment ---------------+-----------------+-------------------+--------------------------------------------------- plpgsql_check | 2.8 | 2.8 | extended check for plpgsql functions (1 row) -- 创建一个示例函数 CREATE OR REPLACE FUNCTION test_function(p_id integer) RETURNS text AS $$ DECLARE v_name text; v_unused integer; -- 未使用的变量 BEGIN SELECT name INTO v_name FROM users WHERE id = p_id; RETURN v_naem; -- 拼写错误 END; $$ LANGUAGE plpgsql; -- 使用 plpgsql_check 检查函数 SELECT * FROM plpgsql_check_function('test_function(integer)'); 检查结果示例： plpgsql_check_function -------------------------------------------------------------- error:42601:7:assignment:target variable \"v_naem\" is undefined warning:00000:4:DECLARE:unused variable \"v_unused\" (2 rows) 更多详细使用方法和高级特性，请参阅 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tar xvf postgis-3.5.4.tar.gz $ cd postgis-3.5.4 $ ./configure --with-pgconfig=/path/to/pg_config eg: /usr/ivory-5/bin/pg_config，如果ivorysql安装目录在/usr/ivory-5. $ make $ sudo make install 如出现PGXS报错, 请根据环境中IvorySQL安装路径, 修改—​with-pgconfig的参数值。 psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension postgis; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'postgis'; name | default_version | installed_version | comment ---------+-----------------+-------------------+------------------------------------------------------------ postgis | 3.5.4 | 3.5.4 | PostGIS geometry and geography spatial types and functions (1 row) 关于PostGIS的使用，请参阅 PostGIS3.5官方文档","title":"PostGIS","component":"ivorysql-doc","version":"master","name":"postgis","url":"/ivorysql-doc/master/ecosystem_components/postgis.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建Extension并确认PostGIS版本","hash":"创建extension并确认postgis版本","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"52":{"id":52,"text":"system_stats 是一个 PostgreSQL 扩展，提供用于访问系统级统计信息的函数，可用于监控场景。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 # 从 https://github.com/EnterpriseDB/system_stats/releases/tag/v3.2 下载 3.2 的源码包 v3.2.zip unzip v3.2.zip cd system_stats-3.2 # 编译安装插件 make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config install psql 连接到数据库，执行如下命令： ivorysql=# CREATE EXTENSION system_stats; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'system_stats'; name | default_version | installed_version | comment --------------+-----------------+-------------------+----------------------------------------------- system_stats | 3.0 | 3.0 | EnterpriseDB system statistics for PostgreSQL (1 row) ivorysql=> select pg_sys_os_info() from dual; pg_sys_os_info -------------------------------------------------------------------------- (\"\"\"Ubuntu 24.04.1 LTS\"\" + \",\"Linux 6.14.0-29-generic\",Ubuntu,\"(none)\",5568,292,532,x86_64,,864122) (1 row) ivorysql=> select pg_sys_cpu_info() from dual; pg_sys_cpu_info ------------------------------------------------------------------------------------------------------------------------------------- (GenuineIntel,\"GenuineIntel model 154 family 6\",\"12th Gen Intel(R) Core(TM) i7-1260P\",,0,2,1,x86_64,2496000000,,,,48,32,1280,18432) (1 row) ivorysql=> select pg_sys_cpu_usage_info() from dual; pg_sys_cpu_usage_info ----------------------- (0,0,0,100,0,0,0,,,,) (1 row) ivorysql=> select pg_sys_memory_info() from dual; pg_sys_memory_info ----------------------------------------------------------------------------------- (4055482368,3911159808,144322560,4055887872,233725952,3822161920,2638602240,,,,,) (1 row) ivorysql=> select pg_sys_io_analysis_info() from dual; pg_sys_io_analysis_info --------------------------------------------------------------- (loop0,15,0,21504,0,1,0) (loop1,1362,0,27916288,0,680,0) (loop2,175,0,3354624,0,131,0) (loop3,141,0,1827840,0,83,0) (loop4,918,0,32752640,0,212,0) (loop5,156,0,2617344,0,67,0) (loop6,3707,0,131992576,0,828,0) (loop7,243,0,4321280,0,95,0) (fd0,0,0,0,0,0,0) (sda,639286,1580583,26547332096,240138948608,402702,2744653) (sda1,561,0,22966272,0,61,0) (sda2,636820,1580583,26491053056,240138948608,402316,2744653) (sr0,87,0,3219456,0,45,0) (sr1,0,0,0,0,0,0) (loop8,228,0,2491392,0,120,0) (loop10,2333,0,36571136,0,1628,0) (loop9,3730,0,66532352,0,3973,0) (loop11,87,0,709632,0,50,0) (loop12,156,0,1948672,0,52,0) (loop14,2579,0,97446912,0,2083,0) (loop13,30,0,102400,0,14,0) (loop15,82,0,1189888,0,117,0) (loop16,1357,0,28335104,0,500,0) (loop17,110,0,2187264,0,93,0) (loop18,129,0,2355200,0,28,0) (loop19,110,0,2202624,0,55,0) (26 rows) ivorysql=> select pg_sys_disk_info() from dual; pg_sys_disk_info ---------------------------------------------------------------------------------- (/,/dev/sda2,,,ext4,105086115840,50903396352,48797392896,6553600,619735,5933865) (1 row) ivorysql=> select pg_sys_load_avg_info() from dual; pg_sys_load_avg_info ---------------------- (0,0,0,) (1 row) ivorysql=> select pg_sys_process_info() from dual; pg_sys_process_info --------------------- (294,1,201,0,0) (1 row) ivorysql=> select pg_sys_network_info() from dual; pg_sys_network_info ----------------------------------------------------------------------------- (lo,127.0.0.1,1071575,5643,0,0,1071575,5643,0,0,0) (ens33,192.168.198.128,619877515,1490961,0,161,5540250106,4345449,0,0,1000) (2 rows) ivorysql=> select pg_sys_cpu_memory_by_process() from dual; pg_sys_cpu_memory_by_process ----------------------------------------------------------------------- (1,\"(systemd)\",864793,0,0.34,13840384) (2,\"(kthreadd)\",864793,0,0,0) (3,\"(pool_workqueue_release)\",864793,0,0,0) (4,\"(kworker/R-rcu_gp)\",864793,0,0,0) ......","title":"system_stats","component":"ivorysql-doc","version":"master","name":"system_stats","url":"/ivorysql-doc/master/ecosystem_components/system_stats.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建插件并确认版本","hash":"创建插件并确认版本","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"53":{"id":53,"text":"wal2json 是一个用于 PostgreSQL 逻辑解码的输出插件，这个插件为每个事务生成一个JSON对象。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/ivory-5 # 从 https://github.com/eulerto/wal2json/releases/tag/wal2json_2_6 下载 2.6 的源码包 wal2json_2_6.zip unzip wal2json_2_6.zip cd wal2json_2_6 # 编译安装插件 make PG_CONFIG=/usr/ivory-5/bin/pg_config make PG_CONFIG=/usr/ivory-5/bin/pg_config install 如果出现找不到xlocale.h的错误，需要手动修改 /usr/ivory-5/include/postgresql/server/pg_config.h 删除或者注释掉 #define HAVE_XLOCALE_H 1 这一行 修改 postgresql.conf 文件 启用wal_level为logical，并设置最大复制槽数和最大wal发送进程数 wal_level = logical max_replication_slots = 10 max_wal_senders = 10 pg_hba.conf不需要修改，有以下内容即可（仅限本地链接测试）： local replication all trust host replication all 127.0.0.1/32 trust host replication all ::1/128 trust 重启数据库后配置生效。 在第一个终端中执行命令： sudo -u ivorysql /usr/ivory-5/bin/bin/pg_recvlogical -d postgres --slot wal2json_slot --create-slot -P wal2json 启动监听，实时输出变更的JSON格式 sudo -u ivorysql /usr/ivory-5/bin/bin/pg_recvlogical -d postgres --slot wal2json_slot --start -o pretty-print=1 -f - 在第二个终端中连接数据库： /usr/ivory-5/bin/psql -d postgres -p 1521 执行下面的SQL语句： CREATE TABLE test_cdc (id int primary key, name varchar(50)); INSERT INTO test_cdc VALUES (1, 'test1'); UPDATE test_cdc SET name = 'test1_update' WHERE id = 1; DELETE FROM test_cdc WHERE id = 1; DROP TABLE test_cdc; 此时在第一个终端上可以看到下面的输出： { \"change\": [ ] } { \"change\": [ { \"kind\": \"insert\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1\"] } ] } { \"change\": [ { \"kind\": \"update\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1_update\"], \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ { \"kind\": \"delete\", \"schema\": \"public\", \"table\": \"test_cdc\", \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ ] }","title":"wal2json","component":"ivorysql-doc","version":"master","name":"wal2json","url":"/ivorysql-doc/master/ecosystem_components/wal2json.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"2.2. 修改数据库配置文件","hash":"修改数据库配置文件","id":4},{"text":"3. 使用","hash":"使用","id":5}]},"54":{"id":54,"text":"IvorySQL数据库要求周期性的清理维护。对于很多安装，让自动清理守护进程来执行清理已经足够，你也可能需要调整其中描述的自动清理参数来获得最佳结果。某些数据库管理员会希望使用手动管理的`VACUUM`命令来对后台进程的活动进行补充或者替换，这通常使用cron或任务计划程序脚本来执行。要正确地设置手动管理的清理。 IvorySQL的 VACUUM命令出于几个原因必须定期处理每一个表： 恢复或重用被已更新或已删除行所占用的磁盘空间。 更新被IvorySQL查询规划器使用的数据统计信息。 更新可见性映射，它可以加速只用索引的扫描。 保护老旧数据不会由于事务ID回卷或多事务ID回卷而丢失。 正如后续小节中解释的，每一个原因都将指示以不同的频率和范围执行`VACUUM`操作。 有两种`VACUUM`的变体：标准`VACUUM`和`VACUUM FULL`。VACUUM FULL`可以收回更多磁盘空间但是运行起来更慢。另外，标准形式的`VACUUM`可以和生产数据库操作并行运行（`SELECT、INSERT、UPDATE`和`DELETE`等命令将继续正常工作，但在清理期间你无法使用`ALTER TABLE`等命令来更新表的定义）。`VACUUM FULL`要求在其工作的表上得到一个`ACCESS EXCLUSIVE 锁，因此无法和对此表的其他使用并行。因此，通常管理员应该努力使用标准`VACUUM`并且避免`VACUUM FULL`。 `VACUUM`会产生大量I/O流量，这将导致其他活动会话性能变差。可以调整一些配置参数来后台清理活动造成的性能冲击. 在IvorySQL中，一次行的`UPDATE`或`DELETE`不会立即移除该行的旧版本。这种方法对于从多版本并发控制获益是必需的：当旧版本仍可能对其他事务可见时，它不能被删除。但是最后，任何事务都不会再对一个过时的或者被删除的行版本感兴趣。它所占用的空间必须被回收来用于新行，这样可避免磁盘空间需求的无限制增长。这通过运行`VACUUM`完成。 `VACUUM`的标准形式移除表和索引中的死亡行版本并将该空间标记为可在未来重用。不过，它将不会把该空间交还给操作系统，除非在特殊的情况中表尾部的一个或多个页面变成完全空闲并且能够很容易地得到一个排他表锁。相反，`VACUUM FULL`通过把死亡空间之外的内容写成一个完整的新版本表文件来主动紧缩表。这将最小化表的尺寸，但是要花较长的时间。它也需要额外的磁盘空间用于表的新副本，直到操作完成。 例行清理的一般目标是多做标准的`VACUUM`来避免需要`VACUUM FULL`。自动清理守护进程尝试这样工作，并且实际上永远不会发出`VACUUM FULL`。在这种方法中，其思想不是让表保持它们的最小尺寸，而是保持磁盘空间使用的稳定状态：每个表占用的空间等于其最小尺寸外加清理之间将使用的空间量。尽管`VACUUM FULL`可被用来把一个表收缩回它的最小尺寸并将该磁盘空间交还给操作系统，但是如果该表将在未来再次增长这样就没什么意义。因此，对于维护频繁被更新的表，适度运行标准`VACUUM`运行比少量运行`VACUUM FULL`要更好。 一些管理员更喜欢自己计划清理，例如在晚上负载低时做所有的工作。根据一个固定日程来做清理的难点在于，如果一个表有一次预期之外的更新活动尖峰，它可能膨胀得真正需要`VACUUM FULL`来回收空间。使用自动清理守护进程可以减轻这个问题，因为守护进程会根据更新活动动态规划清理操作。除非你的负载是完全可以预估的，完全禁用守护进程是不理智的。一种可能的折中方案是设置守护进程的参数，这样它将只对异常的大量更新活动做出反应，因而保证事情不会失控，而在负载正常时采用有计划的`VACUUM`来做批量工作。 对于那些不使用自动清理的用户，一种典型的方法是计划一个数据库范围的`VACUUM`，该操作每天在低使用量时段执行一次，并根据需要辅以在重度更新表上的更频繁的清理（一些有着极高更新率的安装会每几分钟清理一次它们的最繁忙的表）。如果你在一个集簇中有多个数据库，别忘记`VACUUM`每一个，你会用得上vacuumdb程序。 提示 当一个表因为大量更新或删除活动而包含大量死亡行版本时，纯粹的`VACUUM`可能不能令人满意。如果你有这样一个表并且你需要回收它占用的过量磁盘空间，你将需要使用`VACUUM FULL`，或者CLUSTER，或者ALTER TABLE的表重写变体之一。这些命令重写该表的一整个新拷贝并且为它构建新索引。所有这些选项都要求`ACCESS EXCLUSIVE` 锁。注意它们也临时使用大约等于该表尺寸的额外磁盘空间，因为直到新表和索引完成之前旧表和索引都不能被释放。 提示 如果你有一个表，它的整个内容会被周期性删除，考虑用TRUNCATE而不是先用`DELETE`再用`VACUUM`。`TRUNCATE`会立刻移除该表的整个内容，而不需要一次后续的`VACUUM`或`VACUUM FULL`来回收现在未被使用的磁盘空间。其缺点是会违背严格的 MVCC 语义。 IvorySQL查询规划器依赖于有关表内容的统计信息来为查询产生好的计划。这些统计信息由ANALYZE命令收集，它除了直接被调用之外还可以作为`VACUUM`的一个可选步骤被调用。拥有适度准确的统计信息很重要，否则差的计划可能降低数据库性能。 自动清理守护进程如果被启用，当一个表的内容被改变得足够多时，它将自动发出`ANALYZE`命令。不过，管理员可能更喜欢依靠手动的`ANALYZE`操作，特别是如果知道一个表上的更新活动将不会影响“感兴趣的”列的统计信息时。守护进程严格地按照一个被插入或更新行数的函数来计划`ANALYZE`，它不知道那是否将导致有意义的统计信息改变。 正如用于空间恢复的清理一样，频繁更新统计信息对重度更新的表更加有用。但即使对于一个重度更新的表，如果该数据的统计分布没有很大改变，也没有必要更新统计信息。一个简单的经验法则是考虑表中列的最大和最小值改变了多少。例如，一个包含行被更新时间的`timestamp`列将在行被增加和更新时有一直增加的最大值；这样一列将可能需要更频繁的统计更新，而一个包含一个网站上被访问页面 URL 的列则不需要。URL 列可以经常被更改，但是其值的统计分布的变化相对很慢。 可以在指定表上运行`ANALYZE`甚至在表的指定列上运行，因此如果你的应用需要，可以更加频繁地更新某些统计。但实际上，通常只分析整个数据库是最好的，因为它是一种很快的操作。`ANALYZE`对一个表的行使用一种统计的随机采样，而不是读取每一个单一行。 提示 尽管对每列的`ANALYZE`频度调整可能不是非常富有成效，你可能会发现值得为每列调整被`ANALYZE`收集统计信息的详细程度。经常在`WHERE`中被用到的列以及数据分布非常不规则的列可能需要比其他列更细粒度的数据直方图。见`ALTER TABLE SET STATISTICS`，或者使用default_statistics_target配置参数改变数据库范围的默认值。 提示 自动清理守护进程不会为外部表发出`ANALYZE`命令，因为无法确定一个合适的频度。如果你的查询需要外部表的统计信息来正确地进行规划，比较好的方式是按照一个合适的时间表在那些表上手工运行`ANALYZE`命令。 提示 autovacuum守护进程不会对分区表发出ANALYZE命令。继承性父表只有在父表本身发生变化时才会被分析—​对子表的变化不会触发对父表的自动分析。如果你的查询需要对父表进行统计以进行正确的规划，那么有必要定期对这些表运行手动ANALYZE以保持统计的最新性。 清理机制为每一个表维护着一个可见性映射，它被用来跟踪哪些页面只包含对所有活动事务（以及所有未来的事务，直到该页面被再次修改）可见的元组。这样做有两个目的。第一，清理本身可以在下一次运行时跳过这样的页面，因为其中没有什么需要被清除。 第二，这允许IvorySQL回答一些只用索引的查询，而不需要引用底层表。因为IvorySQL的索引不包含元组的可见性信息，一次普通的索引扫描会为每一个匹配的索引项获取堆元组，用来检查它是否能被当前事务所见。另一方面，一次*只用索引的扫描*会首先检查可见性映射。如果它了解到在该页面上的所有元组都是可见的，堆获取就可以被跳过。这对大数据集很有用，因为可见性映射可以防止磁盘访问。可见性映射比堆小很多，因此即使堆非常大，可见性映射也可以很容易地被缓存起来。 IvorySQL的 MVCC 事务语义依赖于能够比较事务 ID（XID）数字：如果一个行版本的插入 XID 大于当前事务的 XID，它就是“属于未来的”并且不应该对当前事务可见。但是因为事务 ID 的尺寸有限（32位），一个长时间（超过 40 亿个事务）运行的集簇会遭受到*事务 ID 回卷*问题：XID 计数器回卷到 0，并且本来属于过去的事务突然间就变成了属于未来 — 这意味着它们的输出变成不可见。简而言之，灾难性的数据丢失（实际上数据仍然在那里，但是如果你不能得到它也无济于事）。为了避免发生这种情况，有必要至少每 20 亿个事务就清理每个数据库中的每个表。 周期性的清理能够解决该问题的原因是，VACUUM会把行标记为 冻结，这表示它们是被一个在足够远的过去提交的事务所插入，这样从 MVCC 的角度来看，效果就是该插入事务对所有当前和未来事务来说当然都 是可见的。IvorySQL保留了一个特殊的 XID （FrozenTransactionId），这个 XID 并不遵循普通 XID 的比较规则 并且总是被认为比任何普通 XID 要老。普通 XID 使用模-232算 法来比较。这意味着对于每一个普通 XID都有 20 亿个 XID “更老”并且 有 20 亿个“更新”，另一种解释的方法是普通 XID 空间是没有端点的环。因此，一旦一个行版本创建时被分配了一个特定的普通 XID，该行版本将成为接下 来 20 亿个事务的“过去”（与我们谈论的具体哪个普通 XID 无关）。如果在 20 亿个事务之后该行版本仍然存在，它将突然变得好像在未来。要阻止这一切 发生，被冻结行版本会被看成其插入 XID 为`FrozenTransactionId`， 这样它们对所有普通事务来说都是“在过去”，而不管回卷问题。并且这样的行版本将一直有效直到被删除，不管它有多旧。 vacuum_freeze_min_age控制在其行版本被冻结前一个 XID 值应该有多老。如果被冻结的行将很快会被再次修改，增加这个设置可以避免不必要 的工作。但是减少这个设置会增加在表必须再次被清理之前能够流逝的事务数。 `VACUUM`通常会跳过不含有任何死亡行版本的页面，但是不会跳过那些含有带旧 XID 值的行版本的页面。要保证所有旧的行版本都已经被冻结，需要对整个表做一次扫描。vacuum_freeze_table_age控制`VACUUM`什么时候这样做：如果该表经过`vacuum_freeze_table_age`减去`vacuum_freeze_min_age`个事务还没有被完全扫描过，则会强制一次全表清扫。将这个参数设置为 0 将强制`VACUUM`总是扫描所有页面而实际上忽略可见性映射。 一个表能保持不被清理的最长时间是 20 亿个事务减去`VACUUM`上次扫描全表时的`vacuum_freeze_min_age`值。如果它超过该时间没有被清理，可能会导致数据丢失。要保证这不会发生，将在任何包含比autovacuum_freeze_max_age配置参数所指定的年龄更老的 XID 的未冻结行的表上调用自动清理（即使自动清理被禁用也会发生）。 这意味着如果一个表没有被清理，大约每`autovacuum_freeze_max_age`减去`vacuum_freeze_min_age`事务就会在该表上调用一次自动清理。对那些为了空间回收目的而被正常清理的表，这是无关紧要的。然而，对静态表（包括接收插入但没有更新或删除的表）就没有为空间回收而清理的需要，因此尝试在非常大的静态表上强制自动清理的间隔最大化会非常有用。显然我们可以通过增加`autovacuum_freeze_max_age`或减少`vacuum_freeze_min_age`来实现此目的。 vacuum_freeze_table_age的实际最大值是 0.95 * autovacuum_freeze_max_age，高于它的设置将被上限到最大值。一个高于`autovacuum_freeze_max_age`的值没有意义，因为不管怎样在那个点上都会触发一次防回卷自动清理，并且 0.95 的乘数为在防回卷自动清理发生之前运行一次手动`VACUUM`留出了一些空间。作为一种经验法则，`vacuum_freeze_table_age`应当被设置成一个低于`autovacuum_freeze_max_age`的值，留出一个足够的空间让一次被正常调度的`VACUUM`或一次被正常删除和更新活动触发的自动清理可以在这个窗口中被运行。将它设置得太接近可能导致防回卷自动清理，即使该表最近因为回收空间的目的被清理过，而较低的值将导致更频繁的全表扫描。 增加`autovacuum_freeze_max_age`（以及和它一起的`vacuum_freeze_table_age`）的唯一不足是数据库集簇的`pg_xact`和`pg_commit_ts`子目录将占据更多空间，因为它必须存储所有向后`autovacuum_freeze_max_age`范围内的所有事务的提交状态和（如果启用了`track_commit_timestamp`）时间戳。提交状态为每个事务使用两个二进制位，因此如果`autovacuum_freeze_max_age`被设置为它的最大允许值 20 亿，`pg_xact`将会增长到大约 0.5 吉字节，`pg_commit_ts`大约20GB。如果这对于你的总数据库尺寸是微小的，我们推荐设置`autovacuum_freeze_max_age`为它的最大允许值。否则，基于你想要允许`pg_xact`和`pg_commit_ts`使用的存储空间大小来设置它（默认情况下 2 亿个事务大约等于`pg_xact`的 50 MB存储空间，`pg_commit_ts`的2GB的存储空间）。 减小`vacuum_freeze_min_age`的一个不足之处是它可能导致`VACUUM`做无用的工作：如果该行在被替换成`FrozenXID`之后很快就被修改（导致该行获得一个新的 XID），那么冻结一个行版本就是浪费时间。因此该设置应该足够大，这样直到行不再可能被修改之前，它们都不会被冻结。 为了跟踪一个数据库中最老的未冻结 XID 的年龄，`VACUUM`在系统表`pg_class`和`pg_database`中存储 XID 的统计信息。特别地，一个表的`pg_class`行的`relfrozenxid`列包含被该表的上一次全表`VACUUM`所用的冻结截止 XID。该表中所有被有比这个截断 XID 老的普通 XID 的事务插入的行 都确保被冻结。相似地，一个数据库的`pg_database`行的`datfrozenxid`列是出现在该数据库中的未冻结 XID 的下界 — 它只是数据库中每一个表的`relfrozenxid`值的最小值。一种检查这些信息的方便方法是执行这样的查询： SELECT c.oid::regclass as table_name, greatest(age(c.relfrozenxid),age(t.relfrozenxid)) as age FROM pg_class c LEFT JOIN pg_class t ON c.reltoastrelid = t.oid WHERE c.relkind IN ('r', 'm'); SELECT datname, age(datfrozenxid) FROM pg_database; `age`列度量从该截断 XID 到当前事务 XID 的事务数。 `VACUUM`通常只扫描从上次清理后备修改过的页面，但是只有当全表被扫描时`relfrozenxid`才能被推进。当`relfrozenxid`比`vacuum_freeze_table_age`个事务还老时、当`VACUUM`的`FREEZE`选项被使用时或当所有页面正好要求清理来移除死亡行版本时，全表将被扫描。当`VACUUM`扫描全表时，在它被完成后，`age(relfrozenxid)`应该比被使用的`vacuum_freeze_min_age`设置略大（比在`VACUUM`开始后开始的事务数多）。如果在`autovacuum_freeze_max_age`被达到之前没有全表扫描`VACUUM`在该表上被发出，将很快为该表强制一次自动清理。 如果出于某种原因自动清理无法从一个表中清除旧的 XID，当数据库的最旧 XID 和回卷点之间达到 4 千万个事务时，系统将开始发出这样的警告消息： WARNING: database \"mydb\" must be vacuumed within 39985967 transactions HINT: To avoid a database shutdown, execute a database-wide VACUUM in that database. （如该示例所建议的，一次手动的`VACUUM`应该会修复该问题；但是注意该次`VACUUM`必须由一个超级用户来执行，否则它将无法处理系统目录并且因而不能推进数据库的`datfrozenxid`）。如果这些警告被忽略，一旦距离回卷点只剩下 3 百万个事务时，该系统将会关闭并且拒绝开始任何新的事务： ERROR: database is not accepting commands to avoid wraparound data loss in database \"mydb\" HINT: Stop the postmaster and vacuum that database in single-user mode. 这 3 百万个事务的安全余量是为了让管理员能通过手动执行所要求的`VACUUM`命令进行恢复而不丢失数据。但是，由于一旦系统进入到安全关闭模式，它将不会执行命令。做这个操作的唯一方法是停止服务器并且以单一用户启动服务器来执行`VACUUM`。单一用户模式中不会强制该关闭模式。 *Multixact ID*被用来支持被多个事务锁定的行。由于在一个元组头部 只有有限的空间可以用来存储锁信息，所以只要有多于一个事务并发地锁住一个行， 锁信息将使用一个“多个事务 ID”（或简称多事务 ID）来编码。任何特定 多事务 ID 中包括的事务 ID 的信息被独立地存储在`pg_multixact`子目 录中，并且只有多事务 ID 出现在元组头部的`xmax`域中。和事务 ID 类似，多事务 ID 也是用一个 32 位计数器实现，并且也采用了相似的存储，这些都要 求仔细的年龄管理、存储清除和回卷处理。在每个多事务中都有一个独立的存储区域 保存成员列表，它也使用一个 32 位计数器并且也应被管理。 在一次`VACUUM`表扫描（部分或者全部）期间，任何比 vacuum_multixact_freeze_min_age 要老的多事务 ID 会被替换为一个不同的值，该值可以是零值、 一个单一事务 ID 或者一个更新的多事务 ID。 对于每一个表，pg_class.relminmxid 存储了在该表任意元组中仍然存在的最老可能多事务 ID。如果这个值比 vacuum_multixact_freeze_table_age老， 将强制一次全表扫描。可以在 pg_class.relminmxid 上使用`mxid_age()`来找到它的年龄。 全表`VACUUM`扫描（不管是什么导致它们）将为表推进该值。 最后，当所有数据库中的所有表被扫描并且它们的最老多事务值被推进， 较老的多事务的磁盘存储可以被移除。 作为一种安全设备，对任何多事务年龄超过 autovacuum_multixact_freeze_max_age的表， 都将发生一次全表清理扫描。当多事务成员占用的存储超过 2GB 时，从那些具有最老多事务年龄的表开始，全表清理扫描也将逐步在所有表上进行。即使自动清理被 在名义上被禁用，也会发生这两种主动扫描。 IvorySQL有一个可选的但是被高度推荐的特性*autovacuum*，它的目的是自动执行`VACUUM`和`ANALYZE`命令。当它被启用时，自动清理会检查被大量插入、更新或删除元组的表。这些检查会利用统计信息收集功能，因此除非track_counts被设置为`true`，自动清理不能被使用。在默认配置下，自动清理是被启用的并且相关配置参数已被正确配置。 “自动清理后台进程”实际上由多个进程组成。有一个称为 自动清理启动器*的常驻后台进程， 它负责为所有数据库启动*自动清理工作者*进程。 启动器将把工作散布在一段时间上，它每隔 autovacuum_naptime秒尝试在每个数据库中启动一个工作者 （因此，如果安装中有N*个数据库，则每 autovacuum_naptime/*N秒将启动一个新的工作者）。 在同一时间只允许最多autovacuum_max_workers个工作者进程运行。如果有超过`autovacuum_max_workers` 个数据库需要被处理，下一个数据库将在第一个工作者结束后马上被处理。 每一个工作者进程将检查其数据库中的每一个表并且在需要时执行 VACUUM`和/或`ANALYZE。 可以设置log_autovacuum_min_duration来监控自动清理工作者的活动。 如果在一小段时间内多个大型表都变得可以被清理，所有的自动清理工作者可能都会被占用来在一段长的时间内清理这些表。这将会造成其他的表和数据库无法被清理，直到一个工作者变得可用。对于一个数据库中的工作者数量并没有限制，但是工作者确实会试图避免重复已经被其他工作者完成的工作。注意运行着的工作者的数量不会被计入max_connections或superuser_reserved_connections限制。 `relfrozenxid`值比autovacuum_freeze_max_age事务年龄更大的表总是会被清理（这页表示这些表的冻结最大年龄被通过表的存储参数修改过，参见后文）。否则，如果从上次`VACUUM`以来失效的元组数超过“清理阈值”，表也会被清理。清理阈值定义为： 清理阈值 = 清理基本阈值 + 清理缩放系数 * 元组数 其中清理基本阈值为autovacuum_vacuum_threshold， 清理缩放系数为autovacuum_vacuum_scale_factor， 元组数为`pg_class`.reltuples。 如果自上次清理以来插入的元组数量超过了定义的插入阈值，表也会被清理，该阈值定义为: 清理插入阈值 = 清理基础插入阈值 + 清理插入缩放系数 * 元组数 清理插入基础阈值为autovacuum_vacuum_insert_threshold，清理插入缩放系数为autovacuum_vacuum_insert_scale_factor。 这样的清理可以允许部分的表被标识为*all visible*，并且也可以允许元组被冻结，可以减小后续清理的工作需要。 对于可以接收`INSERT`操作但是不能或几乎不能`UPDATE`/DELETE`操作的表， 可能会从降低表的autovacuum_freeze_min_age中受益，因为这可能允许元组在早期清理中被冻结。 废弃元组的数量和插入元组的数量可从统计收集器中获得；它是一个半精确的计数，由每个`UPDATE、DELETE 和 INSERT 操作进行更新。 (它只是半精确的，因为一些信息可能会在重负载情况下丢失。) 如果表的`relfrozenxid`值大于`vacuum_freeze_table_age` 事务老的， 执行一个主动的清理来冻结旧的元组，并推进`relfrozenxid`;否则，只有上次清理以后修改过的页面被扫描。 对于分析，也使用了一个相似的阈值： 分析阈值 = 分析基本阈值 + 分析缩放系数 * 元组数 该阈值将与自从上次`ANALYZE`以来被插入、更新或删除的元组数进行比较。 临时表不能被自动清理访问。因此，临时表的清理和分析操作必须通过会话期间的SQL命令来执行。 默认的阈值和缩放系数都取自于`postgresql.conf`，但是可以为每一个表重写它们(和许多其他自动清理控制参数)， 详情参见Storage Parameters。 如果一个设置已经通过一个表的存储参数修改，那么在处理该表时使用该值，否则使用全局设置。 当多个工作者运行时，在所有运行着的工作者之间自动清理代价延迟参数是 “平衡的”，这样不管实际运行的工作者数量是多少， 对于系统的总体 I/O 影响总是相同的。不过，任何正在处理已经设置了每表 autovacuum_vacuum_cost_delay`或 `autovacuum_vacuum_cost_limit 存储参数的表的工作者不会被考虑在均衡算法中。 autovacuum工作进程通常不会阻止其他命令。如果某个进程尝试获取与autovacuum持有的`SHARE UPDATE EXCLUSIVE`锁冲突的锁，则锁获取将中断该autovacuum。有关冲突的锁定模式，但是，如果autovacuum正在运行以防止事务ID回卷（即在`pg_stat_activity`视图中的autovacuum查询名以`(to prevent wraparound)`结尾），则autovacuum不会被自动中断。 警告 定期运行需要获取与`SHARE UPDATE EXCLUSIVE`锁冲突的锁的命令（例如ANALYZE）可能会让autovacuum始终无法完成。 在某些情况下值得周期性地使用REINDEX命令或一系列独立重构步骤来重建索引。 已经完全变成空的B树索引页面被收回重用。但是，还是有一种低效的空间利用的可能性：如果一个页面上除少量索引键之外的全部键被删除，该页面仍然被分配。因此，在这种每个范围中大部分但不是全部键最终被删除的使用模式中，可以看到空间的使用是很差的。对于这样的使用模式，推荐使用定期重索引。 对于非B树索引可能的膨胀还没有很好地定量分析。在使用非B树索引时定期监控索引的物理尺寸是个好主意。 还有，对于B树索引，一个新建立的索引比更新了多次的索引访问起来要略快， 因为在新建立的索引上，逻辑上相邻的页面通常物理上也相邻（这样的考虑目前并不适用于非B树索引）。仅仅为了提高访问速度也值得定期重索引。 REINDEX在所有情况下都可以安全和容易地使用。 默认情况下，此命令需要一个`ACCESS EXCLUSIVE`锁，因此通常最好使用`CONCURRENTLY`选项执行它，该选项仅需要获取`SHARE UPDATE EXCLUSIVE`锁。 把数据库服务器的日志输出保存在一个地方是个好主意， 而不是仅仅通过`/dev/null`丢弃它们。 在进行问题诊断的时候，日志输出是非常宝贵的。不过，日志输出可能很庞大（特别是在比较高的调试级别上）， 因此你不会希望无休止地保存它们。你需要轮转日志文件， 这样在一段合理的时间后会开始新的日志文件并且移除旧的。 如果你简单地把`postgres`的stderr定向到一个文件中，你会得到日志输出，但是截断该日志文件的唯一方法是停止并重起服务器。这样做对于开发环境中使用的IvorySQL可能是可接受的，但是你肯定不想在生产环境上这么干。 一个更好的办法是把服务器的stderr输出发送到某种日志轮转程序里。我们有一个内建的日志轮转程序，你可以通过在 `postgresql.conf`里设置配置参数`logging_collector`为`true`的办法启用它。你也可以使用这种方法把日志数据捕捉成机器可读的CSV（逗号分隔值）格式。 另外，如果在你已经使用的其他服务器软件中有一个外部日志轮转程序，你可能更喜欢使用它。 比如，包含在Apache发布里的rotatelogs工具就可以用于IvorySQL。要做到这一点，方法之一是把服务器的stderr用管道重定向到要用的程序。 如果你用`pg_ctl`启动服务器，那么stderr已经重定向到stdout， 因此你只需要一个管道命令，比如： pg_ctl start | rotatelogs /var/log/pgsql_log 86400 您可以通过设置logrotate来收集由IvorySQL内置日志收集器生成的日志文件来组合这些方法。在这种情况下，日志收集器定义日志文件的名称和位置，而logrotate 则定期归档这些文件。启动日志轮转时，logrotate必须确保应用程序将进一步的输出发送到新文件。这通常是通过`postrotate`脚本完成的，该脚本向应用程序发送`SIGHUP`信号，使其重新打开日志文件。在IvorySQL中，您可以使用`logrotate`选项运行`pg_ctl`。服务器收到此命令后，服务器将切换到新的日志文件或重新打开现有文件，具体取决于日志记录配置。 注意 定期运行需要获取与`SHARE UPDATE EXCLUSIVE`锁冲突的锁的命令（例如ANALYZE）可能会让autovacuum始终无法完成使用静态日志文件名时，如果达到最大打开文件数限制或发生文件表溢出，则服务器可能无法重新打开日志文件。 在这种情况下，日志消息将发送到旧的日志文件，直到成功进行日志轮转为止。 如果将logrotate配置为压缩日志文件并将其删除，则服务器可能会丢失此时间范围内记录的消息。 为避免此问题，可以将日志收集器配置为动态分配日志文件名，并使用`prerotate`脚本忽略打开的日志文件。 另外一种生产级的管理日志输出的方法就是把它们发送给syslog，让syslog处理文件轮转。 要利用这个工具，我们需要设置`postgresql.conf`里的`log_destination`配置参数设置为`syslog`（记录`syslog`日志）。然后在你想强迫syslog守护进程开始写入一个新日志文件的时候， 你就可以发送一个 `SIGHUP`信号给它。 如果你想自动进行日志轮转，可以配置logrotate程序处理 来自syslog的日志文件。 不过，在很多系统上，syslog不是非常可靠，特别是在面对大量日志消息的情况下； 它可能在你最需要那些消息的时候截断或者丢弃它们。另外，在Linux，syslog会把每个消息刷写到磁盘上， 这将导致很差的性能（你可以在syslog配置文件里面的文件名开头使用一个“-”来禁用这种行为）。 请注意上面描述的所有解决方案关注的是在可配置的间隔上开始一个新的日志文件， 但它们并没有处理对旧的、不再需要的日志文件的删除。你可能还需要设置一个批处理任务来定期地删除旧日志文件。 另一种可能的方法是配置日志轮转程序，让它循环地覆盖旧的日志文件。 pgBadger 是一个外部项目，它可以进行日志文件的深度分析。check_postgres可在重要消息出现在日志文件中时向Nagios提供警告，也可以探测很多其他的特别情况。 共享磁盘故障转移避免了只使用一份数据库拷贝带来的同步开销。它使用一个由多个服务器共享的单一磁盘阵列。如果主数据库服务器失效，后备服务器则可以挂载并启动数据库，就好像它从一次数据库崩溃中恢复过来了。这是一种快速的故障转移，并且不存在数据丢失。 共享硬件功能在网络存储设备中很常见。也可以使用一个网络文件系统，但是要注意的是该文件系统应具有完全的POSIX行为。这种方法的一个重大限制是如果共享磁盘阵列失效或损坏，主要和后备服务器都会变得无法工作。另一个问题是在主要服务器运行时，后备服务器永远不能访问共享存储。 共享硬件功能的一种修改版本是文件系统复制，在其中对一个文件系统的所有改变会被镜像到位于另一台计算机上的一个文件系统。唯一的限制是该镜像过程必须能保证后备服务器有一份该文件系统的一致的拷贝 — 特别是对后备服务器的写入必须按照主控机上相同的顺序进行。DRBD是用于 Linux 的一种流行的文件系统复制方案。 温备和热备服务器能够通过读取一个预写式日志（WAL）记录的流来保持为当前状态。如果主服务器失效，后备服务器拥有主服务器的几乎所有数据，并且能够快速地被变成新的主数据库服务器。这可以是同步的或异步的，并且只能用于整个数据库服务器。 可以使用基于文件的日志传送、流复制或两者的组合来实现一个后备服务器。 逻辑复制允许数据库服务器发送数据更新流给另一台服务器。IvorySQL逻辑复制从WAL构建出逻辑数据更新流。逻辑复制允许您逐个表复制数据更改。此外，发布数据更新的服务器可以同时订阅其他服务器的更改，从而允许数据在多个方向流动。第三方扩展也能提供类似的功能。 基于触发器的复制通常会将修改数据的查询发送到指定的主服务器。它在逐个表的基础上工作，主服务器（通常）将数据更改异步发送到后备服务器。 主服务器运行时，后备服务器可以响应查询，并执行本地数据修改或写入操作。这种形式的复制通常用于减轻大数据分析型平台或者数据仓库查询负荷。 Slony-I是这种复制类型的一个例子。它使用表粒度，并且支持多个后备服务器。因为它会异步更新后备服务器（批量），在故障转移时可能会有数据丢失。 通过基于SQL的复制中间件，一个程序拦截每一个 SQL 查询并把它发送给一个或所有服务器。每一个服务器独立地操作。读写查询必须被发送给所有服务器，这样每一个服务器都能接收到任何修改。但只读查询可以只发送给一个服务器，这样允许读负载在服务器之间分布。 如果查询未经修改发送，则函数`random()`函数的随机值和`CURRENT_TIMESTAMP`函数的当前时间和序列值可能因不同服务器而异。 因为每个服务器独立运行，并且它发送 SQL 查询而没有真正的更改数据。如果这是不可接受的，那么中间件或应用程序必须从单一服务器源确定此类值，并将结果用于写入查询。 还必须注意确保所有服务器在提交或中止事务时都是相同的。这将涉及使用 两阶段提交PREPARE TRANSACTION和COMMIT PREPARED。 Pgpool-II和Continuent Tungsten就是这种复制的例子。 对于不会被定期连接或通讯链路较慢的服务器，如笔记本或远程服务器，保持服务器间的数据一致是一个挑战。通过使用异步的多主控机复制，每一个服务器独立工作并且定期与其他服务器通信来确定冲突的事务。这些冲突可以由用户或冲突解决规则来解决。Bucardo 是这种复制类型的一个例子。 在同步多主控机复制中，每一个服务器能够接受写请求，并且在每一个事务提交之前，被修改的数据会被从原始服务器传送给每一个其他服务器。繁重的写活动可能导致过多的锁定和提交延迟，进而导致很差的性能。读请求可以被发送给任意服务器。某些实现使用共享磁盘来减少通信负荷。同步多主控机复制主要对于读负载最好，尽管它的大优点是任意服务器都能接受写请求 — 没有必要在主服务器和后备服务器之间划分负载，并且因为数据修改被从一个服务器发送到另一个服务器，不会有非确定函数（如`random()`）的问题。 IvorySQL不提供这种复制类型，尽管在应用代码或中间件中可以使用 IvorySQL 的两阶段提交PREPARE TRANSACTION和COMMIT PREPARED来实现这种复制。 下表总结了上述多种方案的能力。 特性 共享磁盘 文件系统复制 预写式日志传送 逻辑复制 基于触发器的复制 SQL复制中间件 异步多主控机复制 同步多主控机复制 常用的示例 NAS DRBD 内建流复制 内建逻辑复制，pglogical Londiste，Slony pgpool-II Bucardo 通信方法 共享磁盘 磁盘块 WAL 逻辑解码 表行 SQL 表行 表行和行锁 不要求特殊硬件 • • • • • • • 允许多个主控机服务器 • • • • 无主服务器负载 • • • • 不等待多个服务器 • with sync off with sync off • • 主控机失效将永不丢失数据 • • with sync on with sync on • • 复制体接受只读查询 with hot • • • • • 每个表粒度 • • • • 不需要冲突解决 • • • • • • 有一些方案不适合上述的类别： 数据分区 数据分区将表分开成数据集。每个集合只能被一个服务器修改。例如，数据可以根据办公室划分，如伦敦和巴黎，每一个办公室有一个服务器。如果查询有必要组合伦敦和巴黎的数据，一个应用可以查询两个服务器，或者可以使用主/备复制来在每一台服务器上保持其他办公室数据的一个只读拷贝。 多服务器并行查询执行 上述的很多方案允许多个服务器来处理多个查询，但是没有一个允许一个单一查询使用多个服务器来更快完成。 这种方案允许多个服务器在一个单一查询上并发工作。 这通常通过把数据在服务器之间划分并且让每一个服务器执行该查询中属于它的部分，然后将结果返回给一个中心服务器，由它整合结果并发回给用户。 这也可以使用PL/Proxy工具集来实现这种方案。 创建主服务器和后备服务器通常是明智的，因此它们可以尽可能相似，至少从数据库服务器的角度来看是这样。特别地，与表空间相关的路径名将被未经修改地传递，因此如果该特性被使用，主、备服务器必须对表空间具有完全相同的挂载路径。记住如果CREATE TABLESPACE在主服务器上被执行，在命令被执行前，它所需要的任何新挂载点必须在主服务器和所有后备服务器上先创建好。硬件不需要完全相同，但是经验显示，在应用和系统的生命期内维护两个相同的系统比维护两个不相似的系统更容易。在任何情况下硬件架构必须相同 — 从一个 32 位系统传送到一个 64 位系统将不会工作。 通常，不能在两个运行着不同主版本IvorySQL的服务器之间传送日志。IvorySQL 全球开发组的策略是不在次版本升级中改变磁盘格式，因此在主服务器和后备服务器上运行不同次版本将会成功地工作。不过，在这方面并没有提供正式的支持，因此我们建议让主备服务器上运行的版本尽可能相同。当升级到一个新的次版本时，最安全的策略是先升级后备服务器 — 一个新的次版本发行更可能兼容从前一个次版本读取 WAL 文件。 服务器启动时，数据目录中存在 standby.signal 文件，服务器进入standby模式。 在后备模式中，服务器持续地应用从主控服务器接收到的 WAL。后备服务器可以从一个 WAL 归档restore_command或者通过一个 TCP 连接直接从主控机（流复制）读取 WAL。后备服务器将也尝试恢复在后备集簇的`pg_wal`目录中找到的 WAL。那通常在一次数据库重启后发生，那时后备机将在重启之前重播从主控机流过来的 WAL，但是你也可以在任何时候手动拷贝文件到`pg_wal`让它们被重播。 在启动时，后备机通过恢复归档位置所有可用的 WAL 来开始，这称为`restore_command`。一旦它到达那里可用的 WAL 的末尾并且`restore_command`失败，它会尝试恢复`pg_wal`目录中可用的任何 WAL。如果那也失败并且流复制已被配置，后备机会尝试连接到主服务器并且从在归档或`pg_wal`中找到的最后一个可用记录开始流式传送 WAL。如果那失败并且没有配置流复制，或者该连接后来断开，后备机会返回到步骤 1 并且尝试再次从归档里的文件恢复。这种尝试归档、`pg_wal`和流复制的循环会一直重复知道服务器停止或者一个触发器文件触发了故障转移。 当`pg_ctl promote`被运行，pg_promote()`被调用，或一个触发器文件被找到（`promote_trigger_file），后备模式会退出并且服务器会切换到普通操作。 在故障转移之前，在归档或`pg_wal`中立即可用的任何 WAL 将被恢复，但不会尝试连接到主控机。 在主服务器上设置连续归档到一个后备服务器可访问的归档目录。即使主服务器垮掉该归档位置也应当是后备服务器可访问的，即它应当位于后备服务器本身或者另一个可信赖的服务器，而不是位于主控服务器上。 如果你想要使用流复制，在主服务器上设置认证来允许来自后备服务器的复制连接。即创建一个角色并且在`pg_hba.conf`中提供一个或多个数据库域被设置为`replication`的项。还要保证在主服务器的配置文件中`max_wal_senders`被设置为足够大的值。如果要使用复制槽，请确保`max_replication_slots`也被设置得足够高。 要建立后备服务器，恢复从主服务器取得的基础备份。在后备服务器的集簇数据目录中创建一个文件standby.signal。将restore_command设置为一个从 WAL 归档中复制文件的简单命令。 如果你计划为了高可用性目的建立多个后备服务器，确认`recovery_target_timeline`被设置为`latest` (默认)来使得该后备服务器遵循发生在故障转移到另一个后备服务器之后发生的时间线改变。 注意 restore_command应该立即返回，如果必要该服务器将再次尝试该命令。 如果你想要使用流复制，在primary_conninfo中填入一个 libpq 连接字符串，其中包括主机名（或 IP 地址）和连接到主服务器所需的任何附加细节。如果主服务器需要一个口令用于认证，口令也应该被指定primary_conninfo中。 如果你正在为高性能目的建立后备服务器，像主服务器一样建立 WAL 归档、连接和认证，因为在故障转移后该后备服务器将作为一个主服务器工作。 如果你正在使用一个 WAL 归档，可以使用archive_cleanup_command参数来移除后备服务器不再需要的文件，这样可以最小化 WAL 归档的尺寸。pg_archivecleanup工具被特别设计为在典型单一后备配置下与`archive_cleanup_command`共同使用，见pg_archivecleanup。不过要注意，如果你正在为备份目的使用归档，有一些文件即使后备服务器不再需要你也需要保留它们，它们被用来从至少最后一个基础备份恢复。 配置的一个简单例子是： primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass options=''-c wal_sender_timeout=5000''' restore_command = 'cp /path/to/archive/%f %p' archive_cleanup_command = 'pg_archivecleanup /path/to/archive %r' 你可以有任意数量的后备服务器，但是如果你使用流复制，确保你在主服务器上将`max_wal_senders`设置得足够高，这样可以允许它们能同时连接。 流复制允许一台后备服务器比使用基于文件的日志传送更能保持为最新的状态。后备服务器连接到主服务器，主服务器则在 WAL 记录产生时即将它们以流式传送给后备服务器而不必等到 WAL 文件被填充。 默认情况下流复制是异步的，在这种情况下主服务器上提交一个事务与该变化在后备服务器上变得可见之间存在短暂的延迟。不过这种延迟比基于文件的日志传送方式中要小得多，在后备服务器的能力足以跟得上负载的前提下延迟通常低于一秒。在流复制中，不需要`archive_timeout`来缩减数据丢失窗口。 如果你使用的流复制没有基于文件的连续归档，该服务器可能在后备机收到 WAL 段之 前回收这些旧的 WAL 段。如果发生这种情况，后备机将需要重新从一个新的基础备 份初始化。通过设置`wal_keep_size`为一个足够高的值来确保旧 的 WAL 段不会被太早重用或者为后备机配置一个复制槽，可以避免发生这种情况。如 果设置了一个后备机可以访问的 WAL 归档，就不需要这些解决方案，因为该归档可以 为后备机保留足够的段，后备机总是可以使用该归档来追赶主控机。 要使用流复制，建立一个基于文件的日志传送后备服务器。将一个基于文件日志传送后备服务器转变成流复制后备服务器的步骤是把`recovery.conf`文件中的设置以指向主服务器。设置主服务器上的listen_addresses和认证选项（见`pg_hba.conf`），这样后备服务器可以连接到主服务器上的伪数据库`replication`。 在支持 keepalive 套接字选项的系统上，设置tcp_keepalives_idle、tcp_keepalives_interval和tcp_keepalives_count有助于主服务器迅速地注意到一个断开的连接。 设置来自后备服务器的并发连接的最大数目（详见max_wal_senders）。 当后备服务器被启动并且`primary_conninfo`被正确设置，后备服务器将在重放完归档中所有可用的 WAL 文件之后连接到主服务器。 如果连接被成功建立，你将在后备服务器中看到一个 walreceiver，并且在主服务器中有一个相应的 walsender 进程。 设置好用于复制的访问权限非常重要，这样只有受信的用户可以读取 WAL 流，因为很容易从 WAL 流中抽取出需要特权才能访问的信息。 后备服务器必须作为一个具有`REPLICATION`特权的账户或一个超级用户来向主服务器认证。 推荐为复制创建一个专用的具有`REPLICATION`和`LOGIN`特权的用户账户。 虽然`REPLICATION`特权给出了非常高的权限，但它不允许用户修改主系统上的任何数据，而`SUPERUSER`特权则可以。 复制的客户端认证由一个在*database*域中指定`replication`的`pg_hba.conf`记录控制。例如，如果后备服务器运行在主机 IP 192.168.1.100`并且用于复制的账户名为`foo，管理员可以在主服务器上向`pg_hba.conf`文件增加下列行： # 允许来自 192.168.1.100 的用户 \"foo\" 在提供了正确的口令时作为一个 # 复制后备机连接到主控机。 # # TYPE DATABASE USER ADDRESS METHOD host replication foo 192.168.1.100/32 md5 主服务器的主机名和端口号、连接用户名和口令在primary_conninfo中指定。在后备服务器上还可以在`~/.pgpass`文件中设置口令（在*database*域中指定`replication`）。例如，如果主服务器运行在主机 IP 192.168.1.50、端口`5432`上，并且口令为`foopass`，管理员可以在后备服务器的`postgresql.conf`文件中增加下列行： # 后备机要连接到的主控机运行在主机 192.168.1.50 上， # 端口号是 5432，连接所用的用户名是 \"foo\"，口令是 \"foopass\"。 primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' 流复制的一个重要健康指标是在主服务器上产生但还没有在后备服务器上应用的 WAL 记录数。你可以通过比较主服务器上的当前 WAL 写位置和后备服务器接收到的最后一个 WAL 位置来计算这个滞后量。这些位置分别可以用主服务器上的`pg_current_wal_lsn`和后备服务器上的`pg_last_wal_receive_lsn`来检索。后备服务器的最后 WAL 接收位置也被显示在 WAL 接收者进程的进程状态中，即使用`ps`命令显示的状态。 你可以通过pg_stat_replication视图检索 WAL 发送者进程的列表。 `pg_current_wal_lsn`与`sent_lsn`域之间的巨大差异表示主服务器承受着巨大的负载，而`sent_lsn`和后备服务器上`pg_last_wal_receive_lsn`之间的差异可能表示网络延迟或者后备服务器正承受着巨大的负载。 在一台热后备上，WAL接收者进程的状态可以通过pg_stat_wal_receiver`视图检索到。 `pg_last_wal_replay_lsn`和该视图的`flushed_lsn`的差别表示WAL的接收速度大于它被重放的速度。 复制槽提供了一种自动化的方法来确保主控机在所有的后备机收到 WAL 段 之前不会移除它们，并且主控机也不会移除可能导致恢复冲突的行，即使后备机断开也是如此。 作为复制槽的替代，也可以使用wal_keep_size阻止移除旧的 WAL 段，或者使用archive_command把段保存在一个归档中。 不过，这些方法常常会导致保留的 WAL 段比需要的更多，而复制槽只保留已知所需要的段。 另一方面，复制槽可以保留很多的WAL段以至于它们填满了分配给`pg_wal`的空间； max_slot_wal_keep_size限制复制槽所保留的WAL文件的大小。 类似地，hot_standby_feedback和 vacuum_defer_cleanup_age保护了相关行不被 vacuum 移除，但是前者在后备机断开期间无法提供保护，而后者则需要被设置为一个很高 的值已提供足够的保护。复制槽克服了这些缺点。 每个复制槽都有一个名字，名字可以包含小写字母、数字和下划线字符。 已有的复制槽和它们的状态可以在 pg_replication_slots视图中看到。 槽可以通过流复制协议 或者 SQL 函数创建并且移除。 你可以这样创建一个复制槽： postgres=# SELECT * FROM pg_create_physical_replication_slot('node_a_slot'); slot_name | lsn -------------+----- node_a_slot | postgres=# SELECT slot_name, slot_type, active FROM pg_replication_slots; slot_name | slot_type | active -------------+-----------+-------- node_a_slot | physical | f (1 row) 要配置后备机使用这个槽，在后备机中应该配置`primary_slot_name`。这里是一个简单的例子： primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' primary_slot_name = 'node_a_slot' 级联复制特性允许一台后备服务器接收复制连接并且把 WAL 记录流式传送给其他后备服务器，就像一个转发器一样。这可以被用来减小对于主控机的直接连接数并且使得站点间的带宽开销最小化。 一台同时扮演着接收者和发送者角色的后备服务器被称为一台级联后备服务器。“更直接”（通过更少的级联后备服务器）连接到主控机的后备服务器被称为上游服务器，而那些离得更远的后备服务器被称为下游服务器。级联复制并没有对下游服务器的数量或布置设定限制。 一台级联后备服务器不仅仅发送从主控机接收到的 WAL 记录，还要发送那些从归档中恢复的记录。因此即使某些上游连接中的复制连接被中断，只要还有新的 WAL 记录可用，下游的流复制都会继续下去。 级联复制目前是异步的。同步复制设置当前对级联复制无影响。 不管在什么样的级联布置中，热备反馈都会向上游传播。 如果一台上游后备服务器被提升为新的主控机，且下游服务器的`recovery_target_timeline`被设置成’latest'`(默认)，下游服务器将继续从新的主控机得到流。 要使用级联复制，需配置级联后备服务器以允许接收复制连接（即设置max_wal_senders和hot_standby，并且配置基于主机的认证）。你还将需要设置下游后备服务器中的`primary_conninfo`指向级联后备服务器。 PostgreSQL流复制默认是异步的。如果主服务器崩溃，则某些已被提交的事务可能还没有被复制到后备服务器，这会导致数据丢失。数据的丢失量与故障转移时的复制延迟成比例。 同步复制能够保证一个事务的所有修改都能被传送到一台或者多台同步后备服务器。这扩大了由一次事务提交所提供的标准持久化级别。在计算机科学理论中这种保护级别被称为 2-safe 复制。而当`synchronous_commit`被设置为`remote_write`时，则是 group-1-safe （group-safe 和 1-safe）。 在请求同步复制时，一个写事务的每次提交将一直等待，直到收到一个确认表明该提交在主服务器和后备服务器上都已经被写入到磁盘上的预写式日志中。数据会被丢失的唯一可能性是主服务器和后备服务器在同一时间都崩溃。这可以提供更高级别的持久性，尽管只有系统管理员要关系两台服务器的放置和管理。等待确认提高了用户对于修改不会丢失的信心，但是同时也不必要地增加了对请求事务的响应时间。最小等待时间是在主服务器和后备服务器之间的来回时间。 只读事务和事务回滚不需要等待后备服务器的回复。子事务提交也不需要等待后备服务器的响应，只有顶层提交才需要等待。长时间运行的动作（如数据载入或索引构建）不会等待最后的提交消息。所有两阶段提交动作要求提交等待，包括预备和提交。 同步后备可以是物理复制后备或者是逻辑复制订阅者。它还可以是任何其他物理或者逻辑WAL复制流的消费者，它懂得如何发送恰当的反馈消息。除内建的物理和逻辑复制系统之外，还包括`pg_receivewal`和`pg_recvlogical`之类的特殊程序，以及一些第三方复制系统和定制程序。同步复制支持的细节请查看相应的文档。 一旦流复制已经被配置，配置同步复制就只需要一个额外的配置步骤：synchronous_standby_names必须被设置为一个非空值。synchronous_commit`也必须被设置为`on，但由于这是默认值，通常不需要改变。这样的配置将导致每一次提交都等待确认消息，以保证后备服务器已经将提交记录写入到持久化存储中。`synchronous_commit`可以由个体用户设置，因此它可以在配置文件中配置、可以为特定用户或数据库配置或者由应用动态配置，这样可以在一种每事务基础上控制持久性保证。 在一个提交记录已经在主服务器上被写入到磁盘后，WAL 记录接着被发送到后备服务器。每次一批新的 WAL 数据被写入到磁盘后，后备服务器会发送回复消息，除非在后备服务器上`wal_receiver_status_interval`被设置为零。如果`synchronous_commit`被设置为`remote_apply`，当提交记录被重放时后备服务器会发送回应消息，这会让该事务变得可见。如果根据主服务器的`synchronous_standby_names`设置选中该后备服务器作为一个同步后备，将会根据来自该后备服务器和其他同步后备的回应消息来决定何时释放正在等待确认提交记录被收到的事务。这些参数允许管理员指定哪些后备服务器应该是同步后备。注意同步复制的配置主要在主控机上。命名的后备服务器必须直接连接到主控机，主控机对使用级联复制的下游后备服务器一无所知。 将`synchronous_commit`设置为`remote_write`将导致每次提交都等待后备服务器已经接收提交记录并将它写出到其自身所在的操作系统的确认，但并非等待数据都被刷出到后备服务器上的磁盘。这种设置提供了比`on`要弱一点的持久性保障：在一次操作系统崩溃事件中后备服务器可能丢失数据，尽管它不是一次IvorySQL崩溃。不过，在实际中它是一种有用的设置，因为它可以减少事务的响应时间。只有当主服务器和后备服务器都崩溃并且主服务器的数据库同时被损坏的情况下，数据丢失才会发生。 把`synchronous_commit`设置为`remote_apply`将导致每一次提交都会等待，直到当前的同步后备服务器报告说它们已经重放了该事务，这样就会使该事务对用户查询可见。在简单的情况下，这为带有因果一致性的负载均衡留出了余地。 如果请求一次快速关闭，用户将停止等待。不过，在使用异步复制时，在所有未解决的 WAL 记录被传输到当前连接的后备服务器之前，服务器将不会完全关闭。 同步复制支持一个或者更多个同步后备服务器，事务将会等待，直到所有同步后备服务器都确认收到了它们的数据为止。事务必须等待其回复的同步后备的数量由`synchronous_standby_names`指定。这个参数还指定一个后备服务器名称及方法（FIRST`和`ANY）的列表来从列出的后备中选取同步后备。 方法`FIRST`指定一种基于优先的同步复制并且让事务提交等待，直到它们的WAL记录被复制到基于优先级选中的所要求数量的同步后备上为止。在列表中出现较早的后备被给予较高的优先级，并且将被考虑为同步后备。其他在这个列表中位置靠后的后备服务器表示可能的同步后备。如果任何当前的同步后备由于任何原因断开连接，它将立刻被下一个最高优先级的后备所替代。 基于优先的多同步后备的`synchronous_standby_names`示例为： synchronous_standby_names = 'FIRST 2 (s1, s2, s3)' 在这个例子中，如果有四个后备服务器`s1`、s2、`s3`和`s4`在运行，两个后备服务器`s1`和`s2`将被选中为同步后备，因为它们出现在后备服务器名称列表的前部。`s3`是一个潜在的同步后备，当`s1`或`s2`中的任何一个失效， 它就会取而代之。`s4`则是一个异步后备因为它的名字不在列表中。 方法`ANY`指定一种基于规定数量的同步复制并且让事务提交等待，直到它们的WAL记录至少被复制到列表中所要求数量的同步后备上为止。 `synchronous_standby_names`的基于规定数量的多同步后备的例子： synchronous_standby_names = 'ANY 2 (s1, s2, s3)' 在这个例子中，如果有四台后备服务器`s1`、s2、`s3`以及`s4`正在运行，事务提交将会等待来自至少其中任意两台后备服务器的回复。`s4`是一台异步后备，因为它的名字不在该列表中。 后备服务器的同步状态可以使用`pg_stat_replication`视图查看。 同步复制通常要求仔细地规划和放置后备服务器来保证应用能令人满意地工作。等待并不利用系统资源，但是事务锁会持续保持直到传输被确认。其结果是，不小心使用同步复制将由于响应时间增加以及较高的争用率而降低数据库应用的性能。 IvorySQL允许应用开发者通过复制来指定所要求的持久性级别。这可以为整个系统指定，不过它也能够为特定的用户或连接指定，甚至还可以为单个事务指定。 例如，一个应用的载荷的组成可能是这样：10% 的改变是重要的客户详情，而 90% 的改变是不太重要的数据，即使它们丢失业务也比较容易容忍（例如用户间的聊天消息）。 通过在应用级别（在主服务器上）指定的同步复制选项，我们可以为大部分重要的改变提供同步复制，并且不会拖慢整体的载荷。应用级别选项是使高性能应用享受同步复制的一种重要和实用的工具。 你应该认为网络带宽必须比 WAL 数据的产生率高。 当`synchronous_commit`被设置为`on`、`remote_apply`或者`remote_write`时， `synchronous_standby_names`指定产生的事务提交要等待其回应的同步后备的数量和名称。如果任一同步后备崩溃，这类事务提交可能无法完成。 高可用的最佳方案是确保有所要求数量的同步后备。这可以通过使用`synchronous_standby_names`指定多个潜在后备服务器来实现。 在基于优先的同步复制中，出现在该列表前部的后备服务器将被用作同步后备。后面的后备服务器将在当前同步后备服务器失效时取而代之。 在基于规定数量的同步复制中，所有出现在该列表中的后备服务器都将被用作同步后备的候选。即使其中的一个失效，其他后备仍将继续担任候选同步后备的角色。 当一台后备服务器第一次附加到主服务器时，它将处于一种还没有正确同步的状态。这被描述为`追赶`模式。一旦后备服务器和主服务器之间的迟滞第一次变成零，我们就来到了实时的`流式`状态。在后备服务器被创建之后的很长一段时间内可能都是追赶模式。如果后备服务器被关闭，则追赶周期将被增加，增加量由后备服务器被关闭的时间长度决定。只有当后备服务器到达`流式`状态后，它才能成为一台同步后备。这种状态可以使用`pg_stat_replication`视图查看。 如果在提交正在等待确认时主服务器重启，那些正在等待的事务将在主数据库恢复时被标记为完全提交。没有办法确认所有后备服务器已经收到了在主服务器崩溃时所有还未处理的 WAL 数据。某些事务可能不会在后备服务器上显示为已提交，即使它们在主服务器上显示为已提交。我们提供的保证是：在 WAL 数据已经被所有后备服务器安全地收到之前，应用将不会收到一个事务成功提交的显式确认。 如果实在无法保持所要求数量的同步后备，那么应该减少`synchronous_standby_names`中指定的事务提交应该等待其回应的同步后备的数量（或者禁用），并且在主服务器上重载配置文件。 如果主服务器与剩下的后备服务器是隔离的，你应当故障转移到那些其他剩余后备服务器中的最佳候选者上。 如果在事务等待时你需要重建一台后备服务器，确保命令 pg_start_backup() 和 pg_stop_backup() 被运行在一个`synchronous_commit` = `off`的会话中，否则那些请求将永远等待后备服务器出现。 当在一个后备机上使用连续归档时，有两种不同的情景：WAL 归档在主服务器 和后备机之间共享，或者后备机有自己的 WAL 归档。当后备机拥有其自身的 WAL 归档时，将`archive_mode`设置为 always，后备机将在收到每个 WAL 段时调用归档命令， 不管它是从归档恢复还是使用流复制恢复。共享归档可以类似地处理，但是 `archive_command`必须测试要被归档的文件是否 已经存在，以及现有的文件是否有相同的内容。这要求 `archive_command`中有更多处理，因为它必须当心 不要覆盖具有不同内容的已有文件，但是如果完全相同的文件被归档两次时 应返回成功。并且如果两个服务器尝试同时归档同一个文件，所有这些都必须 在没有竞争情况的前提下完成。 如果`archive_mode`被设置为`on`，归档器在恢复或者后备模式中无法启用。 如果后备服务器被提升，它将在被提升后开始归档，但是它将不会归档任何不是它自身产生的 WAL或时间线历史文件。 要在归档中得到完整的一系列 WAL 文件，你必须确保所有 WAL 在到达后备机之前都被归档。 对于基于文件的日志传输来说天然就是这样，因为后备机只能恢复在归档中找到的文件，而启用了流复制时则不是这样。 当一台服务器不在恢复模式中时，在`on`和`always`模式之间没有差别。 如果主服务器失效，则后备服务器应该开始故障转移过程。 如果后备服务器失效，则不会有故障转移发生。如果后备服务器可以被重启（即使晚一点），由于可重启恢复的优势，那么恢复处理也能被立即重启。如果后备服务器不能被重启，则一个全新的后备服务器实例应该被创建。 如果主服务器失效并且后备服务器成为了新的主服务器，那么接下来旧的主服务器重启后，你必须有一种机制来通知旧的主服务器不再成为主服务器。有些时候这被称为STONITH（Shoot The Other Node In The Head，关闭其他节点），这对于避免出现两个系统都认为它们是主服务器的情况非常必要，那种情况将导致混乱并且最终导致数据丢失。 很多故障转移系统仅使用两个系统，主系统和后备系统，它们由某种心跳机制连接来持续验证两者之间的连接性和主系统的可用性。也可能会使用第三个系统（称为目击者服务器）来防止某些不当故障转移的情况，但是除非非常小心地建立它并且经过了严格地测试，额外的复杂度可能会使该工作得不偿失。 IvorySQL并不提供在主服务器上标识失败并且通知后备数据库服务器所需的系统软件。现在已有很多这样的工具并且很好地与成功的故障转移所需的操作系统功能整合在一起，例如 IP 地址迁移。 一旦发生到后备服务器的故障转移，就只有单一的一台服务器在操作。这被称为一种退化状态。之前的后备服务器现在是主服务器，但之前的主服务器处于关闭并且可能一直保持关闭。要回到正常的操作，一个后备服务器必须被重建，要么在之前的主系统起来时使用它重建，要么使用第三台（可能是全新的）服务器来重建。在大型集簇上，pg_rewind功能可以被用来加速这个过程。一旦完成，主服务器和后备服务器可以被认为是互换了角色。某些人选择使用第三台服务器来为新的主服务器提供备份，直到新的后备服务器被重建，不过显然这会使得系统配置和操作处理更复杂。 因此，从主服务器切换到后备服务器可以很快，但是要求一些时间来重新准备故障转移集群。从主服务器到后备服务器的常规切换是有用的，因为它允许每个系统有常规的关闭时间来进行维护。这也可以作为一种对故障转移机制的测试，以保证在你需要它时它真地能够工作。我们推荐写一些管理过程来做这些事情。 要触发一台日志传送后备服务器的故障转移，运行`pg_ctl promote`，调用 pg_promote()，或者创建一个触发器文件，其文件名和路径由`promote_trigger_file`设置指定。 如果你正在规划使用`pg_ctl promote`或调用`pg_promote()`以进行故障转移，`promote_trigger_file`就不是必要的。 如果你正在建立只用于从主服务器分流只读查询而不是高可用性目的的报告服务器，你不需要提升它。 术语热备用来描述处于归档恢复或后备模式中的服务器连接到服务器并运行只读查询的能力。这有助于复制目的以及以高精度恢复一个备份到一个期望的状态。术语热备也指服务器从恢复转移到正常操作而用户能继续运行查询并且保持其连接打开的能力。 在热备模式中运行查询与正常查询操作相似，尽管如下所述存在一些用法和管理上的区别。 当hot_standby参数在一台后备服务器上被设置为真时，一旦恢复将系统带到一个一致的状态它将开始接受连接。所有这些连接都被限制为只读，甚至临时表都不能被写入。 后备服务器上的数据需要一些时间从主服务器到达后备服务器，因此在主服务器和后备服务器之间将有一段可以度量的延迟。近乎同时在主服务器和后备服务器上运行相同的查询可能因此而返回不同的结果。我们说后备服务器上的数据与主服务器是*最终一致*的。一旦一个事务的提交记录在后备服务器上被重播，那个事务所作的修改将对后备服务器上所有新取得的快照可见。快照可以在每个查询或每个事务的开始时取得，这取决于当前的事务隔离级别。 在热备期间开始的事务可能发出下列命令： 查询访问: SELECT、COPY TO 游标命令: DECLARE、FETCH、CLOSE 设置: SHOW、SET、RESET 事务管理命令: BEGIN、END、ABORT、START TRANSACTION SAVEPOINT、RELEASE、ROLLBACK TO SAVEPOINT `EXCEPTION`块或其他内部子事务 LOCK TABLE，不过只在下列模式之一中明确发出： ACCESS SHARE、ROW SHARE 或 ROW EXCLUSIVE. 计划和资源: PREPARE、EXECUTE、 DEALLOCATE、DISCARD 插件和扩展: LOAD UNLISTEN 在热备期间开始的事务将不会被分配一个事务 ID 并且不能被写入到系统的预写式日志。因此，下列动作将产生错误消息： 数据操纵语言（DML）: INSERT、 UPDATE、DELETE、COPY FROM、 TRUNCATE。注意不允许在恢复期间导致一个触发器被执行的动作。这个限制甚至被应用到临时表，因为不分配事务 ID 表行就不能被读或写，而当前不可能在一个热备环境中分配事务 ID。 数据定义语言（DDL）: CREATE、 DROP、ALTER、COMMENT。这个限制甚至被应用到临时表，因为执行这些操作会要求更新系统目录表。 SELECT …​ FOR SHARE | UPDATE，因为不更新底层数据文件就无法取得行锁。 `SELECT`语句上的能产生 DML 命令的规则。 显式请求一个高于`ROW EXCLUSIVE MODE`的模式的`LOCK`。 默认短形式的`LOCK`，因为它请求`ACCESS EXCLUSIVE MODE`。 显式设置非只读状态的事务管理命令： BEGIN READ WRITE, START TRANSACTION READ WRITE SET TRANSACTION READ WRITE, SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE SET transaction_read_only = off 两阶段提交命令: PREPARE TRANSACTION、 COMMIT PREPARED、ROLLBACK PREPARED，因为即使只读事务也需要在准备阶段（两阶段提交中的第一个阶段）写 WAL。 序列更新 : nextval()、setval() LISTEN,NOTIFY 在正常操作中，“只读”事务被允许使用`LISTEN`和`NOTIFY`，因此热备会话在比普通只读会话更紧一点的限制下操作。这些限制中的某些可能会在一个未来的发行中被放松。 在热备期间，参数`transaction_read_only`总是为真并且不可以被改变。但是只要不尝试修改数据库，热备期间的连接工作起来更像其他数据库连接。如果发生故障转移或切换，该数据库将切换到正常处理模式。当服务器改变模式时会话将保持连接。一旦热备结束，它将可以发起读写事务（即使是一个在热备期间启动的会话）。 用户可以通过`SHOW in_hot_standby`来检查hot standby会话是否是活跃的 (在服务器版本 14 之前该参数`in_hot_standby`不存在。对于更早版本的服务器，可行的替代方法是 SHOW transaction_read_only。) 此外， 还有一些函数允许用户访问有关后备服务器的信息。 它们允许您编写程序来识别数据库当前的状态。用于监控恢复进度， 或者您可以编写复杂的程序将数据库恢复到特定状态。 主服务器和后备服务器在多方面都松散地连接在一起。主服务器上的动作将在后备服务器上产生效果。结果是在它们之间有潜在的负作用或冲突。最容易理解的冲突是性能：如果在主服务器上发生一次大数据量的载入，那么着将在后备服务器上产生一个相似的 WAL 记录流，因而后备服务器查询可能要竞争系统资源（例如 I/O）。 随着热备发生的还可能有其他类型的冲突。对于可能需要被取消的查询和（某些情况中）解决它们的已断开会话来说，这些冲突是硬冲突。用户可以用几种方式来处理这种冲突。冲突情况包括： 在主服务器上取得了访问排他锁（包括显式`LOCK`命令和多种DDL动作）与后备查询中的表访问冲突。 在主服务器上删除一个表空间与使用该表空间存储临时工作文件的后备查询冲突。 在主服务器上删除一个数据库与在后备服务器上连接到该数据库的会话冲突。 从 WAL 清除记录的应用与快照仍能“看见”任意要被移除的行的后备事务冲突。 从 WAL 清除记录的应用与在后备服务器上访问该目标页的查询冲突，不管要被移除的数据是否为可见。 在主服务器上，这些情况仅仅会导致等待；并且用户可以选择取消这些冲突动作中间的一个。但是，在后备服务器上则没有选择：已被 WAL 记录的动作已经在主服务器上发生，那么后备服务器不能在应用它时失败。此外，允许 WAL 应用无限等待是非常不可取的，因为后备服务器的状态将变得逐渐远远落后于主服务器的状态。因此，提供了一种机制来强制性地取消与要被应用的 WAL 记录冲突的后备查询。 该问题情形的一个例子是主服务器上的一位管理员在一个表上运行`DROP TABLE`，而该表正在后备服务器上被查询。如果`DROP TABLE`被应用在后备服务器上，很明显该后备查询不能继续。如果这种情况在主服务器上发生，`DROP TABLE`将等待直到其他查询结束。但是当`DROP TABLE`被运行在主服务器上，主服务器没有关于运行在后备服务器上查询的信息，因此它将不会等待任何这样的后备查询。WAL 改变记录在后备查询还在运行时来到后备服务器上，导致一个冲突。后备服务器必须要么延迟 WAL 记录的应用（还有它们之后的任何事情），要么取消冲突查询这样`DROP TABLE`可以被应用。 当一个冲突查询很短时，我们通常期望能延迟 WAL 应用一小会儿让它完成；但是在 WAL 应用中的一段长的延迟通常是不受欢迎的。因此取消机制有参数，max_standby_archive_delay和max_standby_streaming_delay，它们定义了在 WAL 应用中的最大允许延迟。当应用任何新收到的 WAL 数据花费了超过相关延迟设置值时，冲突查询将被取消。设立两个参数是为了对从一个归档读取 WAL 数据（即来自一个基础备份的初始恢复或者“追赶”一个已经落后很远的后备服务器）和通过流复制读取 WAL数据的两种情况指定不同的延迟值。 在一台后备服务器上这主要是为了该可用性而存在，最好把延迟参数设置得比较短，这样服务器不会由于后备查询导致的延迟落后主服务器太远。但是，如果该后备服务器是位了执行长时间运行的查询，则一个较高甚至无限的延迟值更好。但是记住一个长时间运行的查询延迟了 WAL 记录的应用，它可能导致后备服务器上的其他会话无法看到主服务器上最近的改变。 一旦`max_standby_archive_delay`或`max_standby_streaming_delay`指定的延迟被超越，冲突查询将被取消。这通常仅导致一个取消错误，尽管在重放一个`DROP DATABASE`的情况下整个冲突会话都将被中断。另外，如果冲突发生在一个被空闲事务持有的锁上，该冲突会话会被中断（这种行为可能在未来被改变）。 被取消的查询可能会立即被重试（当然是在开始一个新的事务后）。因为查询取消依赖于 WAL 记录被重放的本质，如果一个被取消的查询被再次执行，它可能会很好地成功完成。 记住延迟参数是从 WAL 数据被后备服务器收到后流逝的时间。因此，留给后备服务器上任何一个查询的宽限期从不会超过延迟参数，并且如果后备服务器已经由于等待之前的查询完成而落后或者因为过重的更新负载而无法跟上主服务器，宽限期可能会更少。 在后备查询和 WAL 重播之间发生冲突的最常见原因是“过早清除”。正常地，PostgreSQL允许在没有事务需要看到旧行版本时对它们进行清除，这样可以保证根据 MVCC 规则的正确的数据可见性。不过，这个规则只能被应用于执行在主控机上的事务。因此有可能主控机上的清除会移除对一个后备服务器事务还可见的行版本。 有经验的用户应当注意行版本清除和行版本冻结都可能与后备查询冲突。即便在一个没有被更新或被删除行的表上运行一次手工`VACUUM FREEZE`也可能导致冲突。 用户应当清楚，主服务器上被正常和重度更新的表将快速地导致后备服务器上长时间运行的查询被取消。在这样的情况下，`max_standby_archive_delay`或`max_standby_streaming_delay`的有限制设置可以被视作`statement_timeout`设置。 如果发现后备查询取消的数量不可接受，还是有补救的可能。第一种选项是设置参数 hot_standby_feedback，它阻止`VACUUM` 移除最近死亡的元组并且因此清除冲突不会产生。如果你这样做，你应当 注意这将使主服务器上的死亡元组清除被延迟，这可能会导致不希望发生 的表膨胀。不过，清除的情况不会比在主服务器上直接运行后备查询时更糟， 并且你仍然能够享受将执行分流到后备服务器的好处。如果后备服务器频繁地连接和 断开，你可能想要做些调整来处理无法提供`hot_standby_feedback` 反馈的时期。例如，考虑增加`max_standby_archive_delay`，这样 在断开连接的期间查询就不会快速地被 WAL 归档文件中的冲突取消。你也应该考虑 增加`max_standby_streaming_delay`来避免重新连接后新到达的流 WAL 项导致的快速取消。 另一个选项是增加主服务器上的vacuum_defer_cleanup_age，这样死亡行不会像平常那么快地被清理。这将允许在后备服务器上的查询能在被取消前有更多时间执行，并且不需要设置一个很高的`max_standby_streaming_delay`。但是，这种方法很难保证任何指定的执行时间窗口，因为`vacuum_defer_cleanup_age`是用主服务器上被执行的事务数来衡量的。 查询取消的数量和原因可以使用后备服务器上的`pg_stat_database_conflicts`系统视图查看。`pg_stat_database`系统视图也包含汇总信息。 当 WAL 重放由于冲突而需要比`deadlock_timeout`更长时间时，用户可以控制是否打印日志消息。由参数 log_recovery_conflict_waits控制。 如果`hot_standby`在`postgresql.conf`中被设置为`on`并且存在一个standby.signal文件，服务器将运行在热备模式。但是，可能需要一些时间来允许热备连接，因为在服务器完成足够的恢复来为查询提供一个一致的状态之前，它将不会接受连接。在此期间，尝试连接的客户端将被一个错误消息拒绝。要确认服务器已经可连接，要么循环地从应用尝试连接，要么在服务器日志中查找这些消息： LOG: entering standby mode ... then some time later ... LOG: consistent recovery state reached LOG: database system is ready to accept read only connections 在主服务器上，一旦创建一个检查点，一致性信息就被记录下来。当读取在特定时段（当在主服务器上`wal_level`没有被设置为`replica`或者`logical`的期间）产生的 WAL 时无法启用热备。在同时存在这些条件时，到达一个一致状态也会被延迟： 一个写事务有超过 64 个子事务 生存时间非常长的写事务 如果你正在运行基于文件的日志传送（“温备”），你可能需要等到下一个 WAL 文件到达，这可能和主服务器上的`archive_timeout`设置一样长。 设置几个参数可确定用于跟踪事务ID、锁和预备事务的共享内存大小。后备服务器上的设置必须大于或等于主服务器上的设置，以确保在恢复过程中不会耗尽共享内存。例如，如果主数据库正在执行预备事务，而备用数据库没有获取共享内存来跟踪预备事务，则备用数据库将无法继续恢复，直到配置更改。受影响的参数是： max_connections max_prepared_transactions max_locks_per_transaction max_wal_senders max_worker_processes 确保这不是问题的可靠方法是使备用数据库上的这些参数的值等于或大于主数据库上的值。因此，如果您想增加这些值，您应该先更改后备服务器上的设置，然后再更改主服务器上的设置。相反，如果要减小这些值，则应先更改主服务器上的设置，然后再更改后备服务器上的设置。请记住，当一个备用数据库被提升时，它会成为后续备用数据库所需参数设置的新基准。因此，最好在所有后备服务器上保持这些设置相同，这样在切换/故障转移期间就不会出现问题。 WAL 跟踪主节点上这些参数的变化。如果热备处理一个 WAL，表明主节点当前值大于备用数据库上的值，它将记录一个警告并中止恢复。例如: WARNING：由于参数设置不足，无法进行热备 详细信息：max_connections = 80 的设置低于主服务器上的设置，其值为 100。 LOG：恢复已暂停 详细信息：如果恢复未暂停，服务器将关闭。 提示：您可以在进行必要的配置更改后重新启动服务器。 此时，您应该更改备库设置并重新启动实例以继续恢复。如果备库不是热备，不兼容的参数更改将立即将其关闭而不会暂停。因为这样继续开机没有意义。 管理员为max_standby_archive_delay和max_standby_streaming_delay选择适当的设置很重要。最好的选择取决于业务的优先级。例如如果服务器主要的任务是作为高可用服务器，那么你将想要低延迟设置，甚至是零（尽管它是一个非常激进的设置）。如果后备服务器的任务是作为一个用于决策支持查询的额外服务器，那么将其最大延迟值设置为很多小时甚至 -1 （表示无限等待）可能都是可以接受的。 在主服务器上写出的事务状态 \"hint bits\" 是不被 WAL 记录的，因此后备服务器上的数据将可能重新写出该提示。这样，即使所有用户都是只读的，后备服务器仍将执行磁盘写操作；但数据值本身并没有发生改变。用户将仍写出大的排序临时文件并且重新生成 relcache 信息文件，这样在热备模式中数据库没有哪个部分是真正只读的。还要注意使用dblink模块写到远程数据库以及其他使用 PL 函数位于数据库之外的操作仍将可用，即使该事务是本地只读的。 在恢复模式期间，下列类型的管理命令是不被接受的： 数据定义语言（DDL）: e.g., CREATE INDEX 特权和所有权: GRANT, REVOKE, 维护命令: ANALYZE, VACUUM,CLUSTER, REINDEX 注意这些命令中的某些实际上在主服务器上的“只读”模式事务期间是被允许的。 结果是，你无法创建只存在于后备服务器上的额外索引以及统计信息。如果需要这些管理命令，它们应该在主服务器上被执行，并且最后那些改变将被传播到后备服务器。 `pg_cancel_backend()`和`pg_terminate_backend()`将在用户后端上工作，而不是执行恢复的 Startup 进程。`pg_stat_activity`不会为 Startup 进程显示一个项，也不会把恢复事务显示为活动。结果是在恢复期间`pg_prepared_xacts`总是为空。如果你希望解决不能确定的预备事务，查看主服务器上的`pg_prepared_xacts`并且发出命令来解决那里的事务或者在恢复结束后来解决它们。 和平常一样，pg_locks`将显示被后端持有的锁。`pg_locks`也会显示一个由 Startup 进程管理的虚拟事务，它拥有被恢复重播的事务所持有的所有`AccessExclusiveLocks。注意 Startup 进程不请求锁来做数据库更改，并且因此对于 Startup 进程除`AccessExclusiveLocks`之外的锁不显示在`pg_locks`中，它们仅被假定存在。 Nagios的插件check_pgsql将可以工作，因为它检查的简单信息是存在的。check_postgres监控脚本也将能工作，尽管某些被报告的值可能给出不同或者混乱的结果。例如，上一次清理时间将不会被维护，因为在后备服务器上不会发生清理。在主服务器上运行的清理仍会把它们的改变发送给后备服务器。 WAL 文件控制命令在恢复期间将不会工作，如`pg_start_backup`、`pg_switch_wal`等。 可动态载入的模块可以工作，包括`pg_stat_statements`。 咨询锁在恢复期间工作正常，包括死锁检测。注意咨询锁从来都不会被 WAL 记录，因此在主服务器或后备服务器上一个咨询锁不可能会与 WAL 重播发生冲突。也不可能会在主服务器上获得一个咨询锁并且在后备服务器上开始一个相似的咨询锁。咨询锁只与它们被取得的那个服务器相关。 基于触发器的复制系统（如Slony、Londiste和Bucardo）将根本不会运行在后备服务器上，然而只要改变不被发送到要被应用的后备服务器，它们将在主服务器上运行得很好。WAL 重播不是基于触发器的，因此你不能用后备服务器接替任何需要额外数据库写操作或依赖触发器使用的系统。 新的 OID 不能被分配，然而某些UUID生成器仍然能工作，只要它们不依赖于向数据库写新的状态。 当前，在只读事务期间不允许创建临时表，因此在某些情况中现有的脚本将不会正确运行。这个限制可能会在稍后的发行中被放松。这既是一个 SQL 标准符合问题也是一个技术问题。 只有在表空间为空时`DROP TABLESPACE`才能成功。某些后备服务器用户可能正在通过他们的`temp_tablespaces`参数使用该表空间。如果在该表空间中有临时文件，所有活动查询将被取消来保证临时文件被移除，这样该表空间可以被移除并且 WAL 重播可以继续。 在主服务器上运行`DROP DATABASE`或`ALTER DATABASE …​ SET TABLESPACE`将产生一个 WAL 项，它将导致所有连接到后备服务器上那个数据库的用户被强制地断开连接。这个动作会立即发生，不管`max_standby_streaming_delay`的设置是什么。注意`ALTER DATABASE …​ RENAME`不会断开用户，这在大部分情况中不会有提示，然而如果它依赖某种基于数据库名的方法，在某些情况中会导致程序混乱。 在普通（非恢复）模式中，如果你为具有登录能力的角色发出`DROP USER`或`DROP ROLE`，而该用户仍然连接着，则对已连接用户不会发生任何事情 - 他们保持连接。但是用户不能重新连接。这种行为也适用于恢复，因此在主服务器上的一次`DROP USER`不会使后备服务器上的用户断开。 在恢复期间统计收集器是活动的。所有扫描、读、阻塞、索引使用等将在后备服务器上被正常的记录。被重播的动作将不会重复它们在主服务器上的效果，因此重播一个插入将不会导致pg_stat_user_tables的 Inserts 列上的递增。在恢复的开始 stats 文件会被删除，因此来自主服务器和后备服务器的 stats 将不同；这被认为是一种特性而不是缺陷。 在恢复期间自动清理不是活动的。它将在恢复末尾正常启动。 检查点进程和后台写入进程在恢复期间是活动状态的。检查点进程将执行重启动点（与主服务器上的检查点相似），后台写入进程将执行正常的块清理活动。 这可以包括存储在后备服务器上的提示位信息的更新。在恢复期间，`CHECKPOINT`命令会被接受，然而它会执行一个重启点而不是一个新的检查点。 在主服务器上，可以使用参数wal_level和vacuum_defer_cleanup_age。在主服务器上设置max_standby_archive_delay和max_standby_streaming_delay不会产生效果。 在主服务器上，可以使用参数hot_standby、max_standby_archive_delay和max_standby_streaming_delay。只要服务器保持在后备模式vacuum_defer_cleanup_age就没有效果，然而当后备服务器变成主服务器时它将变得相关。 热备有一些限制。这些限制很可能在未来的发行中被修复： 在能够取得快照之前，需要正在运行的事务的完整知识。使用大量子事务（目前指超过 64 个）的事务将延迟只读连接的启动，直到最长的运行着的写事务完成。如果发生这种情况，说明消息将被发送到服务器日志。 主服务器上的每一个检查点将产生用于后备查询的可用启动点。如果后备服务器在主控机处于关闭状态时被关闭，就没有办法在主服务器启动之前重新进入热后备，因此它在 WAL 日志中产生一个进一步启动点。这种情况在它可能发生的大部分常见情况中不是一个问题。通常，如果主服务器被关闭并且不再可用，这可能是由于某种严重错误要求后备服务器被转变成为一个新的主服务器来操作。并且在主服务器被故意关闭的情况下，协调保证后备服务器平滑地过渡为新的主服务器也是一种标准过程。 在恢复尾声，由预备事务持有的`AccessExclusiveLocks`将要求两倍的正常锁表项。如果你计划运行大量并发的通常要求`AccessExclusiveLocks`的预备事务，或者你计划运行一个需要很多`AccessExclusiveLocks`的大型事务，我们建议你为`max_locks_per_transaction`选择一个更大的值，也许是主服务器上该参数值的两倍。如果你的`max_prepared_transactions`设置为 0，你根本不需要考虑这个问题。 可序列化事务隔离级别目前在热备中不可用。尝试在热备模式中将一个事务设置为可序列化隔离级别将产生一个错误。","title":"日常维护","component":"ivorysql-doc","version":"master","name":"daily_maintenance","url":"/ivorysql-doc/master/getting-started/daily_maintenance.html","titles":[{"text":"1. 日常清理","hash":"日常清理","id":1},{"text":"1.1. 清理的基础知识","hash":"清理的基础知识","id":2},{"text":"1.2. 恢复磁盘空间","hash":"恢复磁盘空间","id":3},{"text":"1.3. 更新规划器统计信息","hash":"更新规划器统计信息","id":4},{"text":"1.4. 更新可见性映射","hash":"更新可见性映射","id":5},{"text":"1.5. 防止事务ID回卷失败","hash":"防止事务id回卷失败","id":6},{"text":"1.6. 自动清理后台进程","hash":"自动清理后台进程","id":7},{"text":"2. 日常重建索引","hash":"日常重建索引","id":8},{"text":"3. 日志文件维护","hash":"日志文件维护","id":9},{"text":"4. 高可用、负载均衡和复制","hash":"高可用负载均衡和复制","id":10},{"text":"4.1. 不同方案的比较","hash":"不同方案的比较","id":11},{"text":"4.1.1. 共享磁盘故障转移","hash":"共享磁盘故障转移","id":12},{"text":"4.1.2. 文件系统（块设备）复制","hash":"文件系统块设备复制","id":13},{"text":"4.1.3. 预写式日志传送","hash":"预写式日志传送","id":14},{"text":"4.1.4. 逻辑复制","hash":"逻辑复制","id":15},{"text":"4.1.5. 基于触发器的主-备复制","hash":"基于触发器的主-备复制","id":16},{"text":"4.1.6. 基于SQL的复制中间件","hash":"基于sql的复制中间件","id":17},{"text":"4.1.7. 异步多主控机复制","hash":"异步多主控机复制","id":18},{"text":"4.1.8. 同步多主控机复制","hash":"同步多主控机复制","id":19},{"text":"4.2. 日志传送后备服务器","hash":"日志传送后备服务器","id":20},{"text":"4.2.1. 规划","hash":"规划","id":21},{"text":"4.2.2. 后备服务器操作","hash":"后备服务器操作","id":22},{"text":"4.2.3. 为后备服务器准备主控机","hash":"为后备服务器准备主控机","id":23},{"text":"4.2.4. 建立一个后备服务器","hash":"建立一个后备服务器","id":24},{"text":"4.2.5. 流复制","hash":"流复制","id":25},{"text":"4.2.5.1. 认证","hash":"认证","id":26},{"text":"4.2.5.2. 监控","hash":"监控","id":27},{"text":"4.2.6. 复制槽","hash":"复制槽","id":28},{"text":"4.2.6.1. 查询和操纵复制槽","hash":"查询和操纵复制槽","id":29},{"text":"4.2.6.2. 配置实例","hash":"配置实例","id":30},{"text":"4.2.7. 级联复制","hash":"级联复制","id":31},{"text":"4.2.8. 同步复制","hash":"同步复制","id":32},{"text":"4.2.8.1. 基本配置","hash":"基本配置","id":33},{"text":"4.2.8.2. 多个同步后备","hash":"多个同步后备","id":34},{"text":"4.2.8.3. 性能规划","hash":"性能规划","id":35},{"text":"4.2.8.4. 高可用性规划","hash":"高可用性规划","id":36},{"text":"4.2.9. 在后备机上连续归档","hash":"在后备机上连续归档","id":37},{"text":"4.3. 故障转移","hash":"故障转移","id":38},{"text":"4.4. 热备","hash":"热备","id":39},{"text":"4.4.1. 用户概览","hash":"用户概览","id":40},{"text":"4.4.2. 处理查询冲突","hash":"处理查询冲突","id":41},{"text":"4.4.3. 管理员概览","hash":"管理员概览","id":42},{"text":"4.4.4. 热备参数参考","hash":"热备参数参考","id":43},{"text":"4.4.5. 警告","hash":"警告","id":44}]},"55":{"id":55,"text":"在大部分 Unix 平台上，IvorySQL会修改由`ps`报告的命令标题，这样个体服务器进程可以被标识。一个显示样例 $ ps auxww | grep ^postgres postgres 15551 0.0 0.1 57536 7132 pts/0 S 18:02 0:00 postgres -i postgres 15554 0.0 0.0 57536 1184 ? Ss 18:02 0:00 postgres: background writer postgres 15555 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: checkpointer postgres 15556 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: walwriter postgres 15557 0.0 0.0 58504 2244 ? Ss 18:02 0:00 postgres: autovacuum launcher postgres 15558 0.0 0.0 17512 1068 ? Ss 18:02 0:00 postgres: stats collector postgres 15582 0.0 0.0 58772 3080 ? Ss 18:04 0:00 postgres: joe runbug 127.0.0.1 idle postgres 15606 0.0 0.0 58772 3052 ? Ss 18:07 0:00 postgres: tgl regression [local] SELECT waiting postgres 15610 0.0 0.0 58772 3056 ? Ss 18:07 0:00 postgres: tgl regression [local] idle in transaction （`ps`的调用方式随不同的平台而变，但是显示的细节都差不多。这个例子来自于一个最近的 Linux 系统）。列在这里的第一个进程是主服务器进程。为它显示的命令参数是当它被启动时使用的那些。接下来的五个进程是由主进程自动启动的后台工作者进程（如果你已经设置系统为不启动统计收集器，“统计收集器”进程将不会出现；同样“自动清理发动”进程也可以被禁用）。剩余的每一个进程都是一个处理一个客户端连接的服务器进程。每个这种进程都会把它的命令行显示设置为这种形式 postgres: user database host activity 在该客户端连接的生命期中，用户、数据库以及（客户端）主机项保持不变，但是活动指示器会改变。活动可以是`闲置`（即等待一个客户端命令）、在事务中闲置（在一个`BEGIN`块里等待客户端）或者一个命令类型名，例如`SELECT`。还有，如果服务器进程正在等待一个其它会话持有的锁， `等待中`会被追加到上述信息中。在上面的例子中，我们可以推断：进程 15606 正在等待进程 15610 完成其事务并且因此释放一些锁（进程 15610 必定是阻塞者，因为没有其他活动会话。在更复杂的情况中，可能需要查看pg_locks系统视图来决定谁阻塞了谁）。 如果配置了cluster_name，则集簇的名字 也将会显示在`ps`的输出中： $ psql -c 'SHOW cluster_name' cluster_name -------------- server1 (1 row) $ ps aux|grep server1 postgres 27093 0.0 0.0 30096 2752 ? Ss 11:34 0:00 postgres: server1: background writer ... 如果你已经关闭了update_process_title，那么活动指示器将不会被更新，进程标题仅在新进程被启动的时候设置一次。 在某些平台上这样做可以为每个命令节省可观的开销，但在其它平台上却不明显。 提示 Solaris需要特别的处理。你必需使用`/usr/ucb/ps`而不是`/bin/ps`。 你还必需使用两个`w`标志，而不是一个。另外，你对`postgres`命令的最初调用必须用一个比服务器进程提供的短的`ps`状态显示。如果你没有满足全部三个要求，每个服务器进程的`ps`输出将是原始的`postgres`命令行。 IvorySQL的 统计收集器 是一个支持收集和报告服务器活动信息的子系统。 目前，这个收集器可以对表和索引的访问计数，计数可以按磁盘块和个体行来进行。它还跟踪每个表中的总行数、每个表的清理和分析动作的信息。它也统计调用用户定义函数的次数以及在每次调用中花费的总时间。 IvorySQL也支持报告有关系统正在干什么的 动态信息，例如当前正在被其他服务器进程执行的命令以及系统中存在哪些其他连接。 这个功能是独立于收集器进程存在的。 因为统计收集给查询执行增加了一些负荷，系统可以被配置为收集或不收集信息。这由配置参数控制，它们通常在`postgresql.conf`中设置。 参数track_activities允许监控当前被任意服务器进程执行的命令。 参数track_counts控制是否收集关于表和索引访问的统计信息。 参数track_functions启用对用户定义函数使用的跟踪。 参数track_io_timing启用对块读写次数的监控。 通常这些参数被设置在`postgresql.conf`中，这样它们会应用于所有服务器进程，但是可以在单个会话中使用SET命令打开或关闭它们（为了阻止普通用户对管理员隐藏他们的活动，只有超级用户被允许使用`SET`来改变这些参数）。 统计收集器通过临时文件将收集到的信息传送给其他IvorySQL进程。这些文件被存储在名字由stats_temp_directory参数指定的目录中，默认是`pg_stat_tmp`。为了得到更好的性能，`stats_temp_directory`可以被指向一个基于 RAM 的文件系统来降低物理 I/O 需求。当服务器被干净地关闭时，一份统计数据的永久拷贝被存储在`pg_stat`子目录中，这样在服务器重启后统计信息能被保持。当在服务器启动时执行恢复时（例如立即关闭、服务器崩溃以及时间点恢复之后），所有统计计数器会被重置。 表 1 中列出了一些预定义视图 可以用来显示系统的当前状态。 表 2 中列出了另一些视图可以 显示统计收集的结果。你也可以使用底层统计函数来建立自定义的视图。 在使用统计信息监控收集到的数据时，你必须了解这些信息并非是实时更新的。每个独立的服务器进程只在进入闲置状态之前才向收集器传送新的统计计数；因此正在进行的查询或事务并不影响显示出来的总数。同样，收集器本身也最多每`PGSTAT_STAT_INTERVAL`毫秒（缺省为 500ms，除非在编译服务器的时候修改过）发送一 次新的报告。因此显示的信息总是落后于实际活动。但是由`track_activities`收集的当前查询信息总是最新的。 另一个重点是当一个服务器进程被要求显示任何这些统计信息时，它首先取得收集器进程最近发出的报告并且接着为所有统计视图和函数使用这个快照，直到它的当前事务的结尾。因此只要你继续当前事务，统计数据将会一直显示静态信息。相似地，当任何关于所有会话的当前查询的信息在一个事务中第一次被请求时，这样的信息将被收集。并且在整个事务期间将显示相同的信息。这是一种特性而非缺陷，因为它允许你在该统计信息上执行多个查询并且关联结果而不用担心那些数字会在你不知情的情况下改变。但是如果你希望用每个查询都看到新结果，要确保在任何事务块之外做那些查询。或者，你可以调用`pg_stat_clear_snapshot`()，那将丢弃当前事务的统计快照（如果有）。下一次对统计性信息的使用将导致获取一个新的快照。 一个事务也可以在视图`pg_stat_xact_all_tables`、pg_stat_xact_sys_tables、`pg_stat_xact_user_tables`和`pg_stat_xact_user_functions`中看到它自己的统计信息（还没有被传送给收集器）。这些数字并不像上面所述的那样行动，相反它们在事务期间持续被更新。 表 1中显示的动态统计视图中的一些信息是有安全限制的。 普通用户只能看到关于他们自己的会话的所有信息（属于他们是成员的角色的会话）。 在关于其他会话的行中，许多列将为空。 但是，请注意，一个会话的存在和它的一般属性，例如会话用户和数据库，对所有用户都是可见的。 超级用户和内置角色`pg_read_all_stats`的成员可以看到所有会话的所有信息。 表1.动态统计视图 视图名称 描述 pg_stat_activity 每个服务器进程一行，显示与那个进程的当前活动相关的信息，例如状态和当前查询。 pg_stat_replication 每一个 WAL 发送进程一行，显示有关到该发送进程连接的后备服务器的复制的统计信息。 pg_stat_wal_receiver 只有一行，显示来自 WAL 接收器所连接服务器的有关该接收器的统计信息。 pg_stat_subscription 每个订阅至少一行，显示有关该订阅的工作者的信息。 pg_stat_ssl 每个连接（常规的或者复制）一行，显示在这个连接上使用的SSL的信息。 pg_stat_gssapi 每个连接（常规和复制）有一行，显示关于GSSAPI验证和加密的信息。 pg_stat_progress_analyze 每个运行`ANALYZE`的后端(包括自动清理工作者进程)的行，显示当前进度。 pg_stat_progress_create_index 每个后台运行`CREATE INDEX`或`REINDEX`的后端都有一行，显示当前的进度。 pg_stat_progress_vacuum 每个运行着`VACUUM`的后端（包括autovacuum工作者进程）一行，显示当前的进度。 pg_stat_progress_cluster 每个运行着`CLUSTER`或`VACUUM FULL`的后端一行，显示当前进度。 pg_stat_progress_basebackup 每一个WAL发送者进程的行显示一个基础备份，显示当前进度。 表2.已收集统计信息的视图 视图名称 描述 pg_stat_archiver 只有一行，显示有关 WAL 归档进程活动的统计信息。 pg_stat_bgwriter 只有一行，显示有关后台写进程的活动的统计信息。 pg_stat_database 每个数据库一行，显示数据库范围的统计信息。 pg_stat_database_conflicts 每个数据库一行，显示数据库范围的统计信息， 这些信息的内容是关于由于与后备服务器的恢复过程 发生冲突而被取消的查询。 pg_stat_all_tables 当前数据库中每个表一行，显示有关访问指定表的统计信息。 pg_stat_sys_tables 和`pg_stat_all_tables`一样，但只显示系统表。 pg_stat_user_tables 和`pg_stat_all_tables`一样，但只显示用户表。 pg_stat_xact_all_tables 和`pg_stat_all_tables`相似，但计数动作只在当前事务内发生，用于生存和死亡行数量的列以及清理和分析动作在此视图中不出现。 pg_stat_xact_sys_tables 和`pg_stat_xact_all_tables`一样，但只显示系统表。 pg_stat_xact_user_tables 和`pg_stat_xact_all_tables`一样，但只显示用户表。 pg_stat_all_indexes 当前数据库中的每个索引一行，显示：表OID、索引OID、模式名、表名、索引名、 使用了该索引的索引扫描总数、索引扫描返回的索引记录数、使用该索引的简 单索引扫描抓取的活表(livetable)中数据行数。 当前数据库中的每个索引一行，显示与访问指定索引有关的统计信息。 pg_stat_sys_indexes 和`pg_stat_all_indexes`一样，但只显示系统表上的索引。 pg_stat_user_indexes 和`pg_stat_all_indexes`一样，但只显示用户表上的索引。 pg_statio_all_tables 当前数据库中每个表一行(包括TOAST表)，显示：表OID、模式名、表名、 从该表中读取的磁盘块总数、缓冲区命中次数、该表上所有索引的磁盘块读取总数、 该表上所有索引的缓冲区命中总数、在该表的辅助TOAST表(如果存在)上的磁盘块读取总数、 在该表的辅助TOAST表(如果存在)上的缓冲区命中总数、TOAST表的索引的磁盘块读 取总数、TOAST表的索引的缓冲区命中总数。 当前数据库中的每个表一行，显示有关在指定表上 I/O 的统计信息。 pg_statio_sys_tables 和`pg_statio_all_tables`一样，但只显示系统表。 pg_statio_user_tables 和`pg_statio_all_tables`一样，但只显示用户表。 pg_statio_all_indexes 当前数据库中每个索引一行，显示：表OID、索引OID、模式名、 表名、索引名、该索引的磁盘块读取总数、该索引的缓冲区命中总数。 当前数据库中的每个索引一行，显示与指定索引上的 I/O 有关的统计信息。 pg_statio_sys_indexes 和`pg_statio_all_indexes`一样，但只显示系统表上的索引。 pg_statio_user_indexes 和`pg_statio_all_indexes`一样，但只显示用户表上的索引。 pg_statio_all_sequences 当前数据库中每个序列对象一行，显示：序列OID、模式名、序列名、序列的磁盘读取总数、序列的缓冲区命中总数。 当前数据库中的每个序列一行，显示与指定序列上的 I/O 有关的统计信息。 pg_statio_sys_sequences 和`pg_statio_all_sequences`一样，但只显示系统序列（目前没有定义系统序列，因此这个视图总是为空）。 pg_statio_user_sequences 和`pg_statio_all_sequences`一样，但只显示用户序列。 pg_stat_user_functions 对于所有跟踪功能，函数的OID，模式，名称，数量 通话总时间，和自我的时间。自我时间是 在函数本身所花费的时间量，总时间包括 它调用函数所花费的时间。时间值以毫秒为单位。 每一个被跟踪的函数一行，显示与执行该函数有关的统计信息。 pg_stat_xact_user_functions 和`pg_stat_user_functions`相似，但是只统计在当前事务期间的调用 pg_stat_slru 每个SLRU一行, 显示操作的统计信息。 针对每个索引的统计信息对于判断哪个索引正被使用以及它们的效果特别有用。 `pg_statio_`系列视图主要用于判断缓冲区的效果。当实际磁盘读取数远小于缓冲区命中时，这个缓冲能满足大部分读请求而无需进行内核调用。但是，这些统计信息并没有给出所有的事情：由于IvorySQL处理磁盘 I/O 的方式，不在IvorySQL缓冲区中的数据库仍然驻留在内核的 I/O 缓存中，并且因此可以被再次读取而不需要物理磁盘读取。我们建议希望了解IvorySQL I/O 行为更多细节的用户将IvorySQL统计收集器和操作系统中允许观察内核处理 I/O 的工具一起使用。 `pg_stat_activity`视图每个服务器进程将有一行，显示与该进程当前活动相关的信息。 表3.pg_stat_activity 视图 列 类型 描述 datid oid 这个后端连接到的数据库的OID datname name 这个后端连接到的数据库的名称 pid integer 这个后端的进程 ID leader_pid integer 并行组组长的进程ID，如果该进程是并行查询工作者。如果该进程是一个并行组的组长或不参与并行查询，则为`NULL`。 usesysid oid 登录到这个后端的用户的 OID usename name 登录到这个后端的用户的 名称 application_name text 连接到这个后端的应用的名称 client_addr inet 连接到这个后端的客户端的 IP 地址。如果这个字段为空，它表示客户端通过服务器机器上的一个 Unix 套接字连接或者这是一个内部进程，如自动清理。 client_hostname text 已连接的客户端的主机名，由 client_addr 的反向 DNS 查找报告。 这个字段将只对 IP 连接非空，并且只有log_hostname被启用时才会非空。 client_port integer 客户端用于与此后端通信的TCP端口号，如果使用Unix套接字，则为`-1`。如果该字段为空，它表示这是一个内部服务器进程。 backend_start timestamp with time zone 这个进程被启动的时间。对客户端后端来说，这就是客户端连接到服务器的时间。 xact_start timestamp with time zone 这个进程的当前事务被启动的时间，如果没有活动事务则为空。 如果当前查询是它的第一个事务，这一列等于 query_start 列。 query_start timestamp with time zone 当前活动查询被开始的时间，如果 state 不是 active，则为上一个查询开始的时间 state_change timestamp with time zone state 上一次被改变的时间 wait_event_type text 后端等待的事件类型，如果有的话;否则NULL。 wait_event text 如果后端当前正在等待，则等待事件名称，否则为NULL。 state text 这个后端的当前总体状态。可能的值为：active: 后端正在执行一个查询。idle: 后端正在等待一个新的客户端命令。idle in transaction: 后端在一个事务中，但是当前没有正在执行一个查询。idle in transaction (aborted): 这个状态与 idle in transaction 相似，除了在该事务中的一个语句导致了一个错误。fastpath function call: 后端正在执行一个 fast-path 函数。disabled: 如果在这个后端中track_activities被禁用，则报告这个状态。 backend_xid xid 这个后端的顶层事务标识符，如果存在。 backend_xmin xid 当前后端的 xmin 范围。 query text 这个后端最近查询的文本。如果 state 为 active，这个字段显示当前正在执行的查询。 在所有其他状态下，它显示上一个被执行的查询。默认情况下，查询文本会被截断至1024个字节，这个值可以通过参数track_activity_query_size更改。 backend_type text 当前后端的类型。可能的类型为 autovacuum launcher, autovacuum worker, logical replication launcher, logical replication worker, parallel worker, background writer, client backend, checkpointer, startup, walreceiver, walsender and walwriter. 除此以外，由扩展注册的后台Worker可能有额外的类型。 wait_event`和`state`列是独立的。如果一个后端处于`active`状态，它可能是也可能不是某个事件上的`waiting。如果状态是`active`并且`wait_event`为非空，它意味着一个查询正在被执行，但是它被阻塞在系统中某处。 表4.等待事件类型 等待事件类型 描述 Activity 服务器进程空闲。此事件类型表示在其主处理循环中等待活动的进程。 `wait_event`将识别特定的等待点。 BufferPin 服务器进程正在等待对数据缓冲的独占访问。 如果另一个进程持有一个打开的游标，该游标最后一次从相关缓冲区读取数据，则缓冲区销等待可能是漫长的。 Client 服务器进程正在等待连接到用户应用程序的套接字上的活动。 因此，服务器预计发生一些独立于其内部进程的事情。`wait_event`将识别特定的等待点。 Extension 服务器进程正在等待扩展模块定义的某个条件。 IO 服务器进程正在等待一个I/O操作完成。`wait_event`将识别特定的等待点。 IPC 服务器进程正在等待与另一个服务器进程进行交互。`wait_event`将识别特定的等待点。 Lock 服务器进程正在等待一个重量级锁。重量级锁，也称为锁管理器锁或简单锁，主要保护表等SQL可见对象。 然而，它们也用于确保某些内部操作的互斥，例如关系扩展。`wait_event`将识别等待的锁的类型。 LWLock 服务器进程正在等待一个轻量级锁。大多数这样的锁保护共享内存中的特定数据结构。 `wait_event`将包含标识轻量级锁用途的名称。 (有些锁有特定的名称；其他锁是一组锁的一部分，每个锁具有类似的目的。) Timeout 服务器进程正在等待超时过期。`wait_event`将识别特定的等待点。 表5.`Activity`类型的等待事件 Activity 等待事件 描述 ArchiverMain 在归档进程的主循环中等待。 AutoVacuumMain 在自动清理启动过程的主循环中等待。 BgWriterHibernate 在后台写进程中等待，休眠状态。 BgWriterMain 在后台写进程主循环中等待。 CheckpointerMain 在校验指针进程的主循环中等待。 LogicalApplyMain 在逻辑复制应用进程的主循环中等待。 LogicalLauncherMain 在逻辑复制启动器进程的主循环中等待。 PgStatMain 在统计收集器进程的主循环中等待。 RecoveryWalStream 流恢复期间，在启动进程主循环等待WAL到达。 SysLoggerMain 在syslogger进程的主循环中等待。 WalReceiverMain 在WAL接收器进程的主循环中等待。 WalSenderMain 在WAL发送者进程的主循环中等待。 WalWriterMain 在WAL写入进程的主循环中等待。 表6.`BufferPin`类型的等待事件 BufferPin 等待事件 描述 BufferPin 等待获得缓冲区上的独占销。 表7.`Client`类型的等待事件 Client 等待事件 描述 ClientRead 等待从客户端读取数据。 ClientWrite 等待写入数据到客户端。 GSSOpenServer 在建立GSSAPI会话时等待从客户端读取数据。 LibPQWalReceiverConnect 在WAL接收器等待与远程服务器建立连接。 LibPQWalReceiverReceive 在WAL接收器中等待从远程服务器接收数据。 SSLOpenServer 在尝试连接时等待SSL。 WalReceiverWaitStart 等待启动进程发送用于流复制的初始数据。 WalSenderWaitForWAL 在WAL发送器进程中等待WAL被刷新。 WalSenderWriteData 在WAL发送器进程中处理WAL接收器的回复时，等待任何活动。 表8.`Extension`类型的等待事件 Extension 等待事件 描述 Extension 在扩展中等待。 表9.`IO`类型的等待事件 IO 等待事件 描述 BufFileRead 等待从缓冲文件中读取。 BufFileWrite 等待对缓冲文件的写入。 ControlFileRead 等待读取`pg_control`文件。 ControlFileSync 等待`pg_control`文件到达持久存储。 ControlFileSyncUpdate 等待更新`pg_control`文件以达到持久存储。 ControlFileWrite 等待写入`pg_control`文件。 ControlFileWriteUpdate 等待写入更新`pg_control`文件。 CopyFileRead 在文件复制操作期间等待读取。 CopyFileWrite 在文件拷贝操作期间等待写入。 DSMFillZeroWrite 等待用零填充动态共享内存备份(backing)文件。 DataFileExtend 等待关系数据文件被扩展。 DataFileFlush 等待关系数据文件达到持久存储。 DataFileImmediateSync 等待关系数据文件到持久存储的立即同步。 DataFilePrefetch 等待关系数据文件的异步预取。 DataFileRead 等待对关系数据文件的读取。 DataFileSync 等待对关系数据文件的更改达到持久存储。 DataFileTruncate 等待关系数据文件被截断。 DataFileWrite 等待对关系数据文件的写入。 LockFileAddToDataDirRead 在向数据目录锁文件中添加一行时等待读取。 LockFileAddToDataDirSync 等待数据到达持久存储，同时向数据目录锁文件添加一行。 LockFileAddToDataDirWrite 在向数据目录锁文件中添加一行时等待写操作。 LockFileCreateRead 创建数据目录锁文件时等待读取。 LockFileCreateSync 在创建数据目录锁文件时等待数据到达持久存储。 LockFileCreateWrite 在创建数据目录锁文件时等待写操作。 LockFileReCheckDataDirRead 在重新检查数据目录锁文件期间等待读取。 LogicalRewriteCheckpointSync 等待逻辑重写映射到在检查点到达持久存储。 LogicalRewriteMappingSync 在逻辑重写期间等待映射数据到达持久存储 LogicalRewriteMappingWrite 在逻辑重写期间等待映射数据的写入。 LogicalRewriteSync 等待逻辑重写映射到达持久存储。 LogicalRewriteTruncate 等待在逻辑重写期间截断映射数据。 LogicalRewriteWrite 等待逻辑重写映射的写入。 RelationMapRead 等待关系映射文件的读取。 RelationMapSync 等待关系映射文件到达持久存储。 RelationMapWrite 等待对关系映射文件的写入。 ReorderBufferRead 在重新排序缓冲区管理期间等待读取。 ReorderBufferWrite 在重新排序缓冲区管理期间等待写操作。 ReorderLogicalMappingRead 在重新排序缓冲区管理期间等待读取逻辑映射。 ReplicationSlotRead 等待从复制槽位控制文件读取。 ReplicationSlotRestoreSync 等待复制槽控制文件到达持久存储，同时将其恢复到内存中。 ReplicationSlotSync 等待复制槽控制文件到达持久存储。 ReplicationSlotWrite 等待对复制槽控制文件的写入。 SLRUFlushSync 在检查点或数据库关闭期间等待SLRU数据到达持久存储。 SLRURead 等待读取SLRU页面。 SLRUSync 在写页面后等待SLRU数据到达持久存储。 SLRUWrite 等待SLRU页面的写入。 SnapbuildRead 等待读取序列化的历史目录快照。 SnapbuildSync 等待序列化历史目录快照到达持久存储。 SnapbuildWrite 等待串行历史目录快照的写入。 TimelineHistoryFileSync 等待通过流复制接收的时间线历史文件到达持久存储。 TimelineHistoryFileWrite 等待通过流复制接收的时间线历史文件的写入。 TimelineHistoryRead 等待读取时间线历史文件。 TimelineHistorySync 等待新创建的时间线历史文件到达持久存储。 TimelineHistoryWrite 等待写入新创建的时间线历史文件。 TwophaseFileRead 等待读取两阶段状态文件。 TwophaseFileSync 等待两阶段状态文件到达持久存储。 TwophaseFileWrite 等待对两阶段状态文件的写入。 WALBootstrapSync 在引导过程中等待WAL达到持久存储。 WALBootstrapWrite 在引导过程中等待WAL页面的写入。 WALCopyRead 通过复制一个已有WAL段来创建一个新的WAL段时等待读取。 WALCopySync 等待通过复制一个已有WAL段到持久存储来创建一个新的WAL段。 WALCopyWrite 通过复制一个已有WAL段来创建一个新的WAL段时等待写入。 WALInitSync 等待一个新初始化的WAL文件到持久存储。 WALInitWrite 在初始化一个新的WAL文件时等待写入。 WALRead 等待WAL文件的读取。 WALSenderTimelineHistoryRead 在walsender时间线命令期间等待从时间线历史文件读取。 WALSync 等待WAL文件到达持久存储。 WALSyncMethodAssign 等待数据到达持久存储，同时分配一个新的WAL同步方法。 WALWrite 等待写入WAL文件。 表10.`IPC`类型的等待事件 IPC 等待事件 描述 BackupWaitWalArchive 等待备份所需的WAL文件成功存档。 BgWorkerShutdown 等待后台工作者关闭。 BgWorkerStartup 等待后台工作者启动。 BtreePage 正等待继续并行B-树扫描所需的页号变得可用。 CheckpointDone 等待检查点完成。 CheckpointStart 等待检查点开始。 ExecuteGather 在执行`Gather` 计划节点时，等待子进程的活动。 HashBatchAllocate 等待一个选定的并行哈希参与者分配哈希表。 HashBatchElect 等待选择一个并行哈希参与者来分配哈希表。 HashBatchLoad 等待其他并行哈希参与者完成哈希表的加载。 HashBuildAllocate 等待一个选定的并行哈希参与者分配初始哈希表。 HashBuildElect 等待选择一个并行哈希参与者来分配初始哈希表。 HashBuildHashInner 等待其他并行哈希参与者完成内部关系的散列。 HashBuildHashOuter 等待其他Parallel 哈希参与者完成对外部关系的分区。 HashGrowBatchesAllocate 等待选定的并行哈希参与者分配更多批处理。 HashGrowBatchesDecide 等待选择一个并行哈希参与者来决定未来的批处理增长。 HashGrowBatchesElect 等待选择一个Parallel 哈希参与者来分配更多批处理。 HashGrowBatchesFinish 等待一个选定的并行哈希参与者决定未来的批量增长。 HashGrowBatchesRepartition 等待一个选定的并行哈希参与者决定未来的批处理增长。 HashGrowBucketsAllocate 等待选定的并行哈希参与者完成更多bucket的分配。 HashGrowBucketsElect 等待选择一个并行哈希参与者来分配更多的buckets。 HashGrowBucketsReinsert 等待其他Parallel 哈希参与者完成将元组插入到新buckets中。 LogicalSyncData 等待逻辑复制远程服务器发送用于初始表同步的数据。 LogicalSyncStateChange 等待逻辑复制远程服务器更改状态。 MessageQueueInternal 等待另一个进程附加到共享消息队列。 MessageQueuePutMessage 等待将协议消息写入共享消息队列。 MessageQueueReceive 等待从共享消息队列接收字节。 MessageQueueSend 等待将字节发送到共享消息队列。 ParallelBitmapScan 等待并行位图扫描被初始化。 ParallelCreateIndexScan 等待并行`CREATE INDEX` 工作者完成堆扫描。 ParallelFinish 等待并行工作人员完成计算。 ProcArrayGroupUpdate 等待组领导在并行操作结束时清除事务ID。 ProcSignalBarrier 等待屏障事件被所有后端处理。 Promote 等待备用系统提升。 RecoveryConflictSnapshot 等待vacuum清理的恢复冲突解决。 RecoveryConflictTablespace 等待恢复冲突解决删除表空间。 RecoveryPause 等待恢复继续进行。 ReplicationOriginDrop 等待复制源变为非活动状态，以便可以删除它。 ReplicationSlotDrop 等待复制槽变为非活动状态，以便可以删除它。 SafeSnapshot 等待获取`READ ONLY DEFERRABLE`事务的有效快照。 SyncRep 在同步复制期间等待远程服务器的确认。 XactGroupUpdate 等待分组组长在并行操作结束时更新事务状态。 表11.`Lock`类型的等待事件 Lock 等待事件 描述 advisory 等待获得一个建议用户锁。 extend 等待扩展一个关系。 frozenid 等待升级 pg_database.datfrozenxid 和 pg_database.datminmxid. object 等待获取非关系数据库对象上的锁。 page 等待获取一个关系页面上的锁。 relation 等待获得一个关系的锁。 spectoken 等待获取推测的插入锁。 transactionid 等待事务完成。 tuple 等待获取元组上的锁。 userlock 等待获取用户锁。 virtualxid 等待获取虚拟事务ID锁。 表12.`LWLock`类型的等待事件 LWLock 等待事件 描述 AddinShmemInit 等待管理共享内存中的扩展空间分配。 AutoFile 等待更新`postgresql.auto.conf`文件。 Autovacuum 等待读取或更新自动清理工作者的当前状态。 AutovacuumSchedule 等待确保选择为自动清理的表仍然需要清理。 BackgroundWorker 等待读取或更新后台工作者状态。 BtreeVacuum 等待读取或更新b-树索引的清理相关信息。 BufferContent 等待访问内存中的数据页。 BufferIO 等待数据页上的I/O。 BufferMapping 等待将数据块与缓冲池中的缓冲区关联。 Checkpoint 等待开始一个检查点。 CheckpointerComm 等待管理fsync请求。 CommitTs 等待读取或更新事务提交时间戳的最后一个值集。 CommitTsBuffer 在提交时间戳SLRU缓冲区上等待I/O。 CommitTsSLRU 等待访问提交时间戳SLRU缓存。 ControlFile 等待读取或更新`pg_control`文件或创建一个新的WAL文件。 DynamicSharedMemoryControl 等待读取或更新动态共享内存分配信息。 LockFastPath 等待读取或更新进程的快速路径锁信息。 LockManager 等待读取或更新关于“heavyweight”锁。 LogicalRepWorker 等待读取或更新逻辑复制工作器的状态。 MultiXactGen 等待读取或更新共享的multixact状态。 MultiXactMemberBuffer 在multixact成员SLRU缓冲区上等待I/O。 MultiXactMemberSLRU 等待访问multixact成员SLRU缓存。 MultiXactOffsetBuffer 在multixact 偏移 SLRU缓冲区上等待I/O。 MultiXactOffsetSLRU 等待访问multixact 偏移 SLRU缓存。 MultiXactTruncation 等待读取或截断multixact信息。 NotifyBuffer 在`NOTIFY` 消息 SLRU缓冲区上等待I/O。 NotifyQueue 等待读取或更新`NOTIFY` 消息。 NotifyQueueTail 等待`NOTIFY`消息存储上的更新限制。 NotifySLRU 等待访问`NOTIFY`消息SLRU缓存。 OidGen 等待分配一个新的OID。 OldSnapshotTimeMap 等待读取或更新旧的快照控制信息。 ParallelAppend 在并行附加计划执行期间等待选择下一个子计划。 ParallelHashJoin 在并行哈希连接计划执行期间等待同步工作器。 ParallelQueryDSA 等待并行查询动态共享内存分配。 PerSessionDSA 等待并行查询动态共享内存分配。 PerSessionRecordType 等待访问有关复合类型的并行查询信息。 PerSessionRecordTypmod 等待访问有关标识匿名记录类型的类型修饰符的并行查询信息。 PerXactPredicateList 在并行查询期间等待访问当前可序列化事务持有的谓词锁列表。 PredicateLockManager 等待访问可序列化事务使用的谓词锁信息。 ProcArray 等待访问每个进程共享的数据结构(通常情况，是获取快照或报告会话的事务ID)。 RelationMapping 等待读取或更新`pg_filenode.map`文件(用于跟踪某些系统目录的文件节点分配)。 RelCacheInit 等待读取或更新`pg_internal.init`关系缓存初始化文件。 ReplicationOrigin 等待创建、删除或使用复制源。 ReplicationOriginState 等待读取或更新一个复制源的进度。 ReplicationSlotAllocation 等待分配或释放复制槽。 ReplicationSlotControl 等待读取或更新复制槽状态。 ReplicationSlotIO 在复制槽位上等待I/O。 SerialBuffer 在可串行事务冲突的SLRU缓冲区上等待I/O。 SerializableFinishedList 等待访问已完成的可序列化事务列表。 SerializablePredicateList 等待访问可序列化事务持有的谓词锁列表。 SerializableXactHash 等待读取或更新关于可序列化事务的信息。 SerialSLRU 等待访问可序列化事务冲突SLRU缓存。 SharedTidBitmap 在并行位图索引扫描期间等待访问共享的TID位图。 SharedTupleStore 在并行查询期间等待访问共享元组存储。 ShmemIndex 等待在共享内存中找到或分配空间。 SInvalRead 等待从共享目录失效队列中检索消息。 SInvalWrite 等待向共享编目失效队列添加消息。 SubtransBuffer 在子事务SLRU缓冲区上等待I/O。 SubtransSLRU 等待访问子事务SLRU缓存。 SyncRep 等待读取或更新有关同步复制状态的信息。 SyncScan 等待选择同步表扫描的起始位置。 TablespaceCreate 等待创建或删除表空间。 TwoPhaseState 等待读取或更新已准备事务的状态。 WALBufMapping 等待在WAL缓冲区中替换一个页面。 WALInsert 等待将WAL数据插入内存缓冲区。 WALWrite 等待WAL缓冲区写入磁盘。 WrapLimitsVacuum 等待更新事务id和multixact消费的限制。 XactBuffer 在事务状态的SLRU缓冲区上等待I/O。 XactSLRU 等待访问事务状态的SLRU缓存。 XactTruncation 等待执行`pg_xact_status`或更新它可用的最早的事务ID。 XidGen 等待分配新的事务ID。 表13.`Timeout`类型的等待事件 Timeout 等待事件 描述 BaseBackupThrottle 当有限流活动时在基础备份期间等待。 PgSleep 由于调用`pg_sleep`或同类函数而等待。 RecoveryApplyDelay 由于延迟设置，在恢复期间等待应用WAL。 RecoveryRetrieveRetryInterval 当WAL数据无法从任何来源(pg_wal，存档或流)获得时，在恢复期间等待。 VacuumDelay 在一个基于代价的清理延迟点。 下面的例子展示了如何查看等待事件： SELECT pid, wait_event_type, wait_event FROM pg_stat_activity WHERE wait_event is NOT NULL; pid | wait_event_type | wait_event ------+-----------------+------------ 2540 | Lock | relation 6644 | LWLock | ProcArray (2 rows) `pg_stat_replication`视图将在每个WAL发送方进程中包含一行，显示关于复制到发送方连接的后备服务器的统计信息。 只有直接连接的备用设备被列出;没有关于下游后备服务器的信息。 表14.pg_stat_replication 视图 列 类型 描述 pid integer 一个 WAL 发送进程的进程 ID usesysid oid 登录到这个 WAL 发送进程的用户的 OID usename name 登录到这个 WAL 发送进程的用户的名称 application_name text 连接到这个 WAL 发送进程的应用的名称 client_addr inet 连接到这个 WAL 发送进程的客户端的 IP 地址。 如果这个域为空，它表示该客户端通过服务器机器上的一个Unix 套接字连接。 client_hostname text 连接上的客户端的主机名，由一次对`client_addr`的逆向 DNS 查找报告。 这个域将只对 IP 连接非空，并且只有在 log_hostname被启用时非空。 client_port integer 客户端用来与这个 WAL 发送进程通讯的 TCP 端口号，如果使用 Unix 套接字则为`-1` backend_start timestamp with time zone 这个进程开始的时间，即客户端是何时连接到这个WAL 发送进程的。 backend_xmin xid 由hot_standby_feedback报告的这个后备机的`xmin`水平线。 state text 当前的 WAL 发送进程状态。 可能的值是：startup: 这个WAL发送器正在启动。catchup: 这个WAL发送者连接的后备服务器正在赶上主服务器。streaming: 在其连接的后备服务器赶上主服务器之后，这个WAL发送方正在流化变化。backup: 这个WAL发送器正在发送一个备份。stopping: 这个WAL发送器正在停止。 sent_lsn pg_lsn 在这个连接上发送的最后一个预写式日志的位置 write_lsn pg_lsn 被这个后备服务器写入到磁盘的最后一个预写式日志的位置 flush_lsn pg_lsn 被这个后备服务器刷入到磁盘的最后一个预写式日志的位置 replay_lsn pg_lsn 被重放到这个后备服务器上的数据库中的最后一个预写式日志的位置 write_lag interval 从本地刷新近期的WAL与接收到此后备服务器已写入WAL的通知(但尚未刷新或应用它)之间的时间经过。 如果将此服务器配置为同步后备服务器，则可以使用此参数来衡量在提交时`synchronous_commit`级别`remote_write`所导致的延迟。 flush_lag interval 在本地刷写近期的WAL与接收到后备服务器已经写入并且刷写它（但还没有应用）的通知之间流逝的时间。 如果这台服务器被配置为一个同步后备，这可以用来计量在提交时`synchronous_commit`的级别`on`所导致的延迟。 replay_lag interval 在本地刷写近期的WAL与接收到后备服务器已经写入它、刷写它并且应用它的通知之间流逝的时间。 如果这台服务器被配置为一个同步后备，这可以用来计量在提交时`synchronous_commit`的级别`remote_apply`所导致的延迟。 sync_priority integer 在基于优先的同步复制中，这台后备服务器被选为同步后备的优先级。在基于规定数量的同步复制中，这个值没有效果。 sync_state text 这一台后备服务器的同步状态。 可能的值是：async: 这台后备服务器是异步的。potential: 这台后备服务器现在是异步的，但可能在当前的同步后备失效时变成同步的。sync: 这台后备服务器是同步的。quorum: 这台后备服务器被当做规定数量后备服务器的候选。 reply_time 带时区的时间戳 从后备服务器收到的最后一条回复信息的发送时间 `pg_stat_replication`视图中报告的滞后时间近期的WAL被写入、刷写并且重放以及发送器知道这一切所花的时间的度量。如果远程服务器被配置为一台同步后备，这些时间表示由每一种同步提交级别所带来（或者是可能带来）的提交延迟。对于一台异步后备，`replay_lag`列是最近的事务变得对查询可见的延迟时间的近似值。如果后备服务器已经完全追上了发送服务器并且没有WAL活动，在短时间内将继续显示最近测到的滞后时间，再然后就会显示为NULL。 对于物理复制会自动测量滞后时间。逻辑解码插件可能会选择性地发出跟踪消息，如果它们没有这样做，跟踪机制将把滞后显示为NULL。 注意 报告的滞后时间并非按照当前的重放速率该后备还有多久才能追上发送服务器的预测。在新的WAL被生成期间，这样一种系统将显示类似的时间，但是当发送器变为闲置时会显示不同的值。特别是当后备服务器完全追上时，`pg_stat_replication`显示的是写入、刷写及重放最近报告的WAL位置所花的时间而不是一些用户可能预期的零。这种做法与为近期的写事务测量同步提交和事务可见性延迟的目的一致。为了降低用户预期一种不同的滞后模型带来的混淆，在一个完全重放完的闲置系统上，lag列会在一段比较短的时间后回复成NULL。监控系统应该选择将这种情况表示为缺失数据、零或者继续显示最近的已知值。 `pg_stat_wal_receiver`事务只包含一行，它显示了从 WAL 接收器所连接的服务器得到的有关该接收器的统计信息。 表15.pg_stat_wal_receiver 视图 列 类型 描述 pid integer WAL接收器进程的进程ID status text WAL接收进程的活动状态 receive_start_lsn pg_lsn WAL接收器启动时使用的第一个写前日志位置 receive_start_tli integer WAL接收器启动时使用的第一个时间线数字 written_lsn pg_lsn 已经接收并写入磁盘的最后一个预写式日志位置，但没有刷入。这不能用于数据完整性检查。 flushed_lsn pg_lsn 已经接收并刷入到磁盘的最后一个预写式日志位置，该字段的初始值是启动WAL接收器时使用的第一个日志位置 received_tli integer 接收并刷入到磁盘的最后一个预写式日志位置的时间线数字，该字段的初始值为启动WAL接收器时使用的第一个日志位置的时间线数字 last_msg_send_time timestamp with time zone 从源头WAL发送器收到的最后一条信息的发送时间 last_msg_receipt_time timestamp with time zone 从源头WAL发送器收到的最后一条信息的接收时间 latest_end_lsn pg_lsn 向源头WAL发送器报告的最后的预写式日志位置 latest_end_time timestamp with time zone 向源头WAL发送方报告的最后一次写前日志位置的时间 slot_name text 这个WAL接收器使用的复制槽的名称 sender_host text 这个WAL接收器连接到的IvorySQL实例的主机。这可以是主机名、IP地址，或者目录路径，如果连接是通过Unix套接字进行的。(路径的情况可以区分，因为它总是以/开头的绝对路径。) sender_port integer 这个WAL接收器连接的IvorySQL实例的端口号。 conninfo text 这个WAL接收器使用的连接字符串，对安全敏感的字段进行了模糊处理。 每一个订阅的主工作者都在`pg_stat_subscription`视图中有一行（如果工作者没有运行则PID为空），处理被订阅表的初始数据拷贝操作的工作者还会有额外的行。 表16.pg_stat_subscription 视图 列 类型 描述 subid oid 订阅的OID subname name 订阅的名称 pid integer 订阅工作者进程的进程ID relid oid 工作器正在同步的关系的OID;Null用于主应用工作器 received_lsn pg_lsn 接收到的最后一个预写式日志位置，该字段的初始值为0 last_msg_send_time timestamp with time zone 从WAL发送器收到的最后一条信息的发送时间 last_msg_receipt_time timestamp with time zone 从WAL发送器收到的最后一条信息的接收时间 latest_end_lsn pg_lsn 向WAL发送器报告的最后预写式日志位置 latest_end_time timestamp with time zone 向WAL发送器报告的最后一次预写式日志位置的时间 `pg_stat_ssl`视图将为每一个后端或者 WAL 发送进程包含一行，用来显示这个连接上的 SSL 使用情况。 可以把它与`pg_stat_activity`或者`pg_stat_replication`通过`pid`列连接来得到更多有关该连接的细节。 表17.pg_stat_ssl 视图 列 类型 描述 pid integer 后端或WAL发送器进程ID ssl boolean 如果在此连接上使用SSL，则为真 version text 使用SSL的版本，如果此连接上没有使用SSL则为NULL cipher text 正在使用的SSL密码的名称，如果此连接上没有使用SSL则为NULL bits integer 使用的加密算法中的位数，如果此连接上没有使用SSL则为NULL compression boolean 如果使用SSL压缩则为真，否则为假，如果此连接未使用SSL则为NULL client_dn text 区别名称(DN，Distinguished Name)字段与使用的客户端证书，如果没有提供客户端证书或在此连接上没有使用SSL，则为NULL。 如果DN字段长于NAMEDATALEN(标准构建中为64个字符)，则该字段将被截断。 client_serial numeric 客户端证书的序列号，如果没有提供客户端证书或在此连接上没有使用SSL，则为NULL。 证书序列号和证书颁发者的组合唯一标识一个证书(除非颁发者错误地重用序列号)。 issuer_dn text 客户端证书颁发者的区别名称(DN，Distinguished Name)，如果没有提供客户端证书或在此连接上没有使用SSL，则为NULL。该字段像client_dn一样被截断。 `pg_stat_gssapi`视图将包含每一个后端一个行，显示该连接上的GSSAPI使用情况。 它可以加入到`pg_stat_activity`或`pg_stat_replication`上的`pid`列，获取更多关于连接的详细信息。 表18.pg_stat_gssapi 视图 列 类型 描述 pid integer 后端进程ID gss_authenticated boolean 如果此连接使用了GSSAPI身份验证，则为True principal text 用于验证此连接的主体，如果未使用GSSAPI对此连接进行身份验证，则为NULL。 如果主体长度超过`NAMEDATALEN`(标准构建中为64个字符)，则该字段被截断。 encrypted boolean 如果在此连接上使用了GSSAPI加密，则为真 `pg_stat_archiver`视图总是有一行，其中包含关于集群的存档进程的数据。 表19.pg_stat_archiver 视图 列 类型 描述 archived_count bigint 已成功存档的WAL文件数 last_archived_wal text 最后一个成功存档的WAL文件的名称 last_archived_time timestamp with time zone 最后一次成功存档操作的时间 failed_count bigint 记录WAL文件归档失败次数 last_failed_wal text 最后一次失败的存档操作的WAL文件的名称 last_failed_time timestamp with time zone 上次存档操作失败的时间 stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 `pg_stat_bgwriter`视图始终只有一行，其中包含集群的全局数据。 表20.pg_stat_bgwriter 视图 列 类型 描述 checkpoints_timed bigint 已执行的预定检查点数 checkpoints_req bigint 请求已执行的检查点数 checkpoint_write_time double precision 检查点处理中将文件写入磁盘的部分所花费的总时间，以毫秒为单位 checkpoint_sync_time double precision 检查点处理中将文件同步到磁盘的部分所花费的总时间，以毫秒为单位 buffers_checkpoint bigint 检查点期间写入的缓冲区数 buffers_clean bigint 后台写入器写入的缓冲区数 maxwritten_clean bigint 后台写入器因为写入太多缓冲区而停止清理扫描的次数 buffers_backend bigint 后端直接写入的缓冲区数 buffers_backend_fsync bigint 后端必须执行自己的 fsync 调用的次数（通常后台写入器处理这些，即使后端执行自己的写入） buffers_alloc bigint 分配的缓冲区数 stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 `pg_stat_database`视图将包含一行用于集群中的每个数据库，加一行用于共享对象，显示数据库范围的统计信息。 表21. pg_stat_database 视图 列 类型 描述 datid oid 该数据库的 OID，属于共享关系的对象为 0 datname name 这个数据库的名称，或者共享对象为 NULL numbackends integer 当前连接到此数据库的后端数，对于共享对象则为 NULL。这是该视图中唯一返回反映当前状态的值的列；所有其他列返回自上次重置以来累积的值 xact_commit bigint 此数据库中已提交的事务数 xact_rollback bigint 该数据库中已回滚的事务数 blks_read bigint 在该数据库中读取的磁盘块数 blks_hit bigint 在缓存中发现磁盘块的次数，因此读取不是必需的（这只包括在 IvorySQL 缓存中，而不是在操作系统的文件系统缓存中） tup_returned bigint 这个数据库中查询返回的行数 tup_fetched bigint 这个数据库中查询获取的行数 tup_inserted bigint 查询在该数据库中插入的行数 tup_updated bigint 这个数据库中查询更新的行数 tup_deleted bigint 这个数据库中被查询删除的行数 conflicts bigint 由于与此数据库中的恢复冲突而取消的查询数（冲突只发生在后备服务器上） temp_files bigint 这个数据库中查询创建的临时文件的数量。所有临时文件都将被计数，而不顾及临时文件为什么被创建（例如，排序或散列），也不考虑 log_temp_files 设置 temp_bytes bigint 这个数据库中的查询写入临时文件的数据总量。所有临时文件都将被计数，而不考虑临时文件为什么被创建，也不考虑 log_temp_files 设置 deadlocks bigint 在此数据库中检测到的死锁数 checksum_failures bigint 在此数据库（或共享对象）中检测到的数据页校验码失败数，如果没有启用数据校验码则为 NULL checksum_last_failure timestamp with time zone 在此数据库（或共享对象）中检测到最后一个数据页校验码失败的时间，如果没有启用数据校验码则为 NULL blk_read_time double precision 在这个数据库中通过后端读取数据文件块所花费的时间，以毫秒为单位（如果启用了 track_io_timing，否则为零） blk_write_time double precision 在这个数据库中通过后端写数据文件块所花费的时间，以毫秒为单位（如果启用了 track_io_timing，否则为零） stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 `pg_stat_database_conflicts`视图为每一个数据库包含一行， 用来显示数据库范围内由于与后备服务器上的恢复过程冲突而被取消的查询的统计信息。 这个视图将只包含后备服务器上的信息，因为冲突会不发生在主服务器上。 表22.pg_stat_database_conflicts 视图 列 类型 描述 datid oid 数据库的OID datname name 数据库的名称 confl_tablespace bigint 这个数据库中由于删除表空间而取消的查询的数量 confl_lock bigint 此数据库中由于锁定超时而被取消的查询数 confl_snapshot bigint 此数据库中由于旧快照而取消的查询数 confl_bufferpin bigint 此数据库中由于固定缓冲区而被取消的查询数 confl_deadlock bigint 此数据库中由于死锁而被取消的查询数 `pg_stat_all_tables`视图将为当前数据库中的每一个表（包括 TOAST 表）包含一行，该行显示与对该表的访问相关的统计信息。 `pg_stat_user_tables`和`pg_stat_sys_tables`视图包含相同的信息，但是被过滤得分别只显示用户和系统表。 表23.pg_stat_all_tables 视图 列 类型 描述 relid oid 表的OID schemaname name 该表所在的模式的名称 relname name 这个表的名称 seq_scan bigint 在此表上启动的顺序扫描数 seq_tup_read bigint 连续扫描获取的实时行数 idx_scan bigint 对这个表发起的索引扫描数 idx_tup_fetch bigint 索引扫描获取的实时行数 n_tup_ins bigint 插入的行数 n_tup_upd bigint 更新的行数（包括HOT更新的行） n_tup_del bigint 删除的行数 n_tup_hot_upd bigint HOT更新的行数（即不需要单独的索引更新） n_live_tup bigint 活的行的估计数量 n_dead_tup bigint 僵死行的估计数量 n_mod_since_analyze bigint 自上次分析此表以来修改的行的估计数量 n_ins_since_vacuum bigint 自上次清空此表以来插入的行的估计数量 last_vacuum timestamp with time zone 最后一次手动清理这个表（不包括VACUUM FULL） last_autovacuum timestamp with time zone 这个表最后一次被自动清理守护进程清理的时间 last_analyze timestamp with time zone 上一次手动分析这个表 last_autoanalyze timestamp with time zone 自动清理守护进程最后一次分析这个表 vacuum_count bigint 这个表被手动清理的次数（VACUUM FULL不计数） autovacuum_count bigint 这个表被autovacuum守护进程清理的次数 analyze_count bigint 手动分析这个表的次数 autoanalyze_count bigint 这个表被autovacuum守护进程分析的次数 `pg_stat_all_indexes`视图将为当前数据库中的每个索引包含一行，该行显示关于对该索引访问的统计信息。`pg_stat_user_indexes`和`pg_stat_sys_indexes`视图包含相同的信息，但是被过滤得只分别显示用户和系统索引。 表24.pg_stat_all_indexes 视图 列 类型 描述 relid oid 这个索引所在表的OID indexrelid oid 这个索引的OID schemaname name 这个索引所在的模式名称 relname name 这个索引所在表的名称 indexrelname name 这个索引的名称 idx_scan bigint 在这个索引上开启的索引扫描的数量 idx_tup_read bigint 扫描此索引返回的索引项数 idx_tup_fetch bigint 使用此索引进行简单索引扫描获取的活动表行数 索引可以被简单索引扫描、“位图”索引扫描以及优化器使用。在一次位图扫描中，多个索引的输出可以被通过 AND 或 OR 规则组合，因此当使用一次位图扫描时难以将取得的个体堆行与特定的索引关联起来。因此，一次位图扫描会增加它使用的索引的`pg_stat_all_indexes`.idx_tup_read`计数，并且为每个表增加`pg_stat_all_tables.idx_tup_fetch`计数，但是它不影响`pg_stat_all_indexes.idx_tup_fetch。如果所提供的常量值不在优化器统计信息记录的范围之内，优化器也会访问索引来检查，因为优化器统计信息可能已经“不新鲜”了。 注意 即使不用位图扫描，`idx_tup_read`和`idx_tup_fetch`计数也可能不同，因为`idx_tup_read`统计从该索引取得的索引项而`idx_tup_fetch`统计从表取得的活着的行。如果使用该索引取得了任何死亡行或还未提交的行，或者如果通过一次只用索引扫描的方式避免了任何堆获取，后者将较小。 `pg_statio_all_tables`视图将为当前数据库中的每个表（包括 TOAST 表）包含一行，该行显示指定表上有关 I/O 的统计信息。`pg_statio_user_tables`和`pg_statio_sys_tables`视图包含相同的信息，但是被过滤得分别只显示用户表和系统表。 表25.pg_statio_all_tables 视图 列 类型 描述 relid oid 表的OID schemaname name 该表所在的模式名 relname name 这个表的名称 heap_blks_read bigint 从该表中读取的磁盘块的数量 heap_blks_hit bigint 该表中的缓冲区命中数 idx_blks_read bigint 从这个表上所有索引读取的磁盘块数 idx_blks_hit bigint 这个表上所有索引中的缓冲区命中数 toast_blks_read bigint 从这个表的TOAST表中读取的磁盘块的数量（如果有的话） toast_blks_hit bigint 这个表的TOAST表中的缓冲区命中数（如果有的话） tidx_blks_read bigint 从这个表的TOAST表索引中读取的磁盘块的数量（如果有的话） tidx_blks_hit bigint 这个表的TOAST表索引中的缓冲区命中数（如果有的话） `pg_statio_all_indexes`视图将为当前数据库中的每个索引包含一行，该行显示指定索引上有关 I/O 的统计信息。 `pg_statio_user_indexes`和`pg_statio_sys_indexes`视图包含相同的信息，但是被过滤得分别只显示用户索引和系统索引。 表26.pg_statio_all_indexes 视图 列 类型 描述 relid oid 这个索引所在表的OID indexrelid oid 这个索引的OID schemaname name 索引所在的模式名称 relname name 这个索引所在表的名称 indexrelname name 这个索引的名称 idx_blks_read bigint 从这个索引中读取的磁盘块数量 idx_blks_hit bigint 这个索引中的缓冲区命中数 `pg_statio_all_sequences`视图将为当前数据库中的每个序列包含一行，该行显示在指定序列上有关 I/O 的统计信息。 表27.pg_statio_all_sequences 视图 列 类型 描述 relid oid 序列的OID schemaname name 此序列所在的模式的名称 relname name 此序列的名称 blks_read bigint 从这个序列中读取的磁盘块的数量 blks_hit bigint 在此序列中的缓冲区命中数 `pg_stat_user_functions`视图将为每一个被追踪的函数包含一行，该行显示有关该函数执行的统计信息。 track_functions参数控制到底哪些函数被跟踪。 表28.pg_stat_user_functions 视图 列 类型 描述 funcid oid 函数的OID schemaname name 这个函数所在的模式的名称 funcname name 这个函数的名称 calls bigint 这个函数已经被调用的次数 total_time double precision 在这个函数以及它所调用的其他函数中花费的总时间，以毫秒计 self_time double precision 在这个函数本身花费的总时间，不包括被它调用的其他函数，以毫秒计 IvorySQL通过*SLRU*(simple least-recently-used，简单的最近-最少-使用)缓存访问某些磁盘上的信息。 `pg_stat_slru`视图将为每个被跟踪的SLRU缓存包含一行，显示关于访问缓存页面的统计信息。 表29.pg_stat_slru 视图 列 类型 描述 name text SLRU的名称 blks_zeroed bigint 初始化期间被置零的块数 blks_hit bigint 已经在SLRU中的磁盘块被发现的次数，因此不需要读取（这只包括SLRU中的命中，而不是操作系统的文件系统缓存） blks_read bigint 为这个SLRU读取的磁盘块数 blks_written bigint 为这个SLRU写入的磁盘块数 blks_exists bigint 为这个SLRU检查是否存在的块数 flushes bigint 此SLRU的脏数据刷新数 truncates bigint 这个SLRU的截断数 stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 其他查看统计信息的方法是直接使用查询，这些查询使用上述标准视图用到的底层统计信息访问函数。 如要了解如函数名等细节，可参考标准视图的定义（例如，在psql中你可以发出`\\d+ pg_stat_activity`）。 针对每一个数据库统计信息的访问函数把一个数据库 OID 作为参数来标识要报告哪个数据库。而针对每个表和每个索引的函数要求表或索引 OID。 针对每个函数统计信息的函数用一个函数 OID。注意只有在当前数据库中的表、索引和函数才能被这些函数看到。 更多统计集合的函数列在 表 30 中。 表30.Additional Statistics Functions 函数 描述 pg_backend_pid () → integer 返回附加到当前会话的服务器进程的进程ID。 pg_stat_get_activity ( integer ) → setof record 使用指定的进程ID返回有关后端信息的记录，如果指定了NULL，则返回系统中每个活动后端的一条记录。返回的字段是pg_stat_activity视图中字段的子集。 pg_stat_get_snapshot_timestamp () → timestamp with time zone 返回当前统计快照的时间戳。 pg_stat_clear_snapshot () → void 丢弃当前的统计快照。 pg_stat_reset () → void 将当前数据库的所有统计计数器重置为零。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_shared ( text ) → void 根据参数的不同，将一些集群范围的统计计数器重置为零。参数可以是bgwriter来重置pg_stat_bgwriter视图中显示的所有计数器，或者archiver来重置pg_stat_archiver视图中显示的所有计数器。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_single_table_counters ( oid ) → void 将当前数据库中单个表或索引的统计信息重置为零。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_single_function_counters ( oid ) → void 将当前数据库中单个函数的统计信息重置为零。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_slru ( text ) → void 将单个SLRU缓存或集群中所有SLRU的统计信息重置为零。如果该参数为NULL，则所有SLRU缓存的pg_stat_slru视图中显示的计数器将被重置。参数可以是CommitTs、MultiXactMember、MultiXactOffset、Notify、Serial、Subtrans或Xact中的一个，以便只重置该条目的计数器。如果参数是other（或实际上，任何无法识别的名称），那么所有其他SLRU缓存的计数器，如扩展定义的缓存，将被重置。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 `pg_stat_get_activity`是`pg_stat_activity`视图的底层函数， 它返回一个行集合，其中包含有关每个后端进程所有可用的信息。有时只获得该信息的一个子集可能会更方便。 在那些情况中，可以使用一组更老的针对每个后端的统计访问函数，这些显示在 表 31中。 这些访问函数使用一个后端 ID 号，范围从 1 到当前活动后端数目。 函数`pg_stat_get_backend_idset`提供了一种方便的方法为每个活动后端产生一行来调用这些函数。 例如，要显示PID以及所有后端当前的查询： SELECT pg_stat_get_backend_pid(s.backendid) AS pid, pg_stat_get_backend_activity(s.backendid) AS query FROM (SELECT pg_stat_get_backend_idset() AS backendid) AS s; 表31.Per-Backend Statistics Functions 函数 描述 pg_stat_get_backend_idset () → setof integer 返回当前活动后端ID号的集合(从1到活动后端数)。 pg_stat_get_backend_activity ( integer ) → text 返回此后端最近查询的文本。 pg_stat_get_backend_activity_start ( integer ) → timestamp with time zone 返回后端最近一次查询开始的时间。 pg_stat_get_backend_client_addr ( integer ) → inet 返回连接到此后端的客户端的IP地址。 pg_stat_get_backend_client_port ( integer ) → integer 返回客户端用于通信的TCP端口号。 pg_stat_get_backend_dbid ( integer ) → oid 返回此后端连接的数据库的OID。 pg_stat_get_backend_pid ( integer ) → integer 返回此后端进程ID。 pg_stat_get_backend_start ( integer ) → timestamp with time zone 返回该进程开始的时间。 pg_stat_get_backend_userid ( integer ) → oid 返回登录到此后端的用户的OID。 pg_stat_get_backend_wait_event_type ( integer ) → text 如果后端当前正在等待，返回等待事件类型名称，否则返回NULL。 pg_stat_get_backend_wait_event ( integer ) → text 如果后端当前正在等待，则返回等待事件名称，否则为NULL。 pg_stat_get_backend_xact_start ( integer ) → timestamp with time zone 返回后端当前事务开始的时间。 监控数据库活动的另外一个有用的工具是`pg_locks`系统表。这样就允许数据库管理员查看在锁管理器里面未解决的锁的信息。例如，这个功能可以被用于： 查看当前所有未解决的锁、在一个特定数据库中的关系上所有的锁、在一个特定关系上所有的锁，或者由一个特定IvorySQL会话持有的所有的锁。 判断当前数据库中带有最多未授予锁的关系（它很可能是数据库客户端的竞争源）。 判断锁竞争给数据库总体性能带来的影响，以及锁竞争随着整个数据库流量的变化范围。 IvorySQL具有在命令执行过程中报告某些命令进度的能力。 目前，支持进度报告的命令只有`ANALYZE`,CLUSTER,CREATE INDEX, VACUUM, 和 BASE_BACKUP例如 pg_basebackup发出的进行基础备份的复制命令。未来可能还会扩展。 每当`ANALYZE`运行时，`pg_stat_progress_analyze`视图将包含当前运行该命令的每个后端的一行。 下面的表描述了将要报告的信息，并提供了关于如何解释它们的信息。 表32.pg_stat_progress_analyze 视图 列 类型 描述 pid integer 后端的进程ID。 datid oid 后端连接到的数据库的OID。 datname name 后端连接到的数据库的名称。 relid oid 被分析的表的OID。 phase text 当前处理阶段。参见 表 33。 sample_blks_total bigint 将被采样的堆块的总数。 sample_blks_scanned bigint 扫描的堆块数量。 ext_stats_total bigint 扩展统计信息的数量。 ext_stats_computed bigint 已经计算的扩展统计的数量。此计数器仅在 computing extended statistics 阶段增进。 child_tables_total bigint 子表的数量。 child_tables_done bigint 扫描的子表数。此计数器只有在 acquiring inherited sample rows 阶段才会增进。 current_child_table_relid oid 当前正在扫描的子表的 OID。此字段仅在 acquiring inherited sample rows 时有效。 表33.ANALYZE phases 阶段 描述 initializing 命令正在准备开始扫描堆。这个阶段预计会非常短暂。 acquiring sample rows 该命令当前正在扫描`relid`给出的表以获得示例行。 acquiring inherited sample rows 该命令当前正在扫描子表以获得示例行。列`child_tables_total`,child_tables_done, 和`current_child_table_relid`包含此阶段的进度信息。 computing statistics 该命令从表扫描期间获得的样例行计算统计信息。 computing extended statistics 该命令从表扫描期间获得的样例行计算扩展统计信息。 finalizing analyze 该命令在更新`pg_class`。当此阶段完成时，ANALYZE 将结束。 注意 当在分区表上运行`ANALYZE`时，它的所有分区也会被递归分析，如在ANALYZE中曾提到过。 在这种情况下，首先报告父表的`ANALYZE`进度，收集它的继承统计信息，然后是每个分区的(继承统计信息)。 每当运行`CREATE INDEX`或`REINDEX`时，`pg_stat_progress_create_index`视图将包含当前正在创建索引的每个后端的一行。 下面的表描述了将要报告的信息，并提供了关于如何解释它的信息。 表34.pg_stat_progress_create_index 视图 列 类型 描述 pid integer 后端的进程ID。 datid oid 后端连接到的数据库的OID。 datname name 后端连接到的数据库的名称。 relid oid 正在创建索引的表的OID。 index_relid oid 正在创建或重建索引的OID。在非并发 CREATE INDEX 的时候，此为 0。 command text 在运行的命令：CREATE INDEX、CREATE INDEX CONCURRENTLY、REINDEX 或 REINDEX CONCURRENTLY。 phase text 索引创建的当前处理阶段。参见 [表 35](http://www.postgresql.org/docs/17/progress-reporting.html#CREATE-INDEX-PHASES)。 lockers_total bigint 在适用的情况下，需要等待的储物柜总数。 lockers_done bigint 已经等待的储物柜数量。 current_locker_pid bigint 目前正在等待的储物柜的进程ID。 blocks_total bigint 本阶段要处理的区块总数。 blocks_done bigint 当前阶段已经处理的区块数量。 tuples_total bigint 当前阶段要处理的元组总数。 tuples_done bigint 在当前阶段已经处理的元组数量。 partitions_total bigint 在分区表上创建索引时，该列被设置为要在其上创建索引的分区总数。 partitions_done bigint 当在分区表上创建索引时，该列被设置为在其上完成索引的分区数。 表35.CREATE INDEX 的阶段 阶段 描述 初始化 `CREATE INDEX`或`REINDEX`正在准备创建索引。 这个阶段预计会非常短暂。 构建前等待读写器 CREATE INDEX CONCURRENTLY`或`REINDEX CONCURRENTLY`正在等待有可能看到表的写锁的事务完成。 当不在并发模式时，这个阶段会被跳过。`lockers_total、 lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 新建索引 索引是由访问方法专用代码建立的。 在这一阶段，支持进度报告的访问方法填写自己的进度数据，子阶段在这一栏中表示。 通常情况下，blocks_total`和`blocks_done`将包含进度数据，也可能包含`tuples_total`和`tuples_done。 在验证前等待读写器 CREATE INDEX CONCURRENTLY`或`REINDEX CONCURRENTLY`正在等待有可能写入表的事务完成写锁的事务。当不在并发模式时，这个阶段会被跳过。`lockers_total、 lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 索引验证：扫描索引 CREATE INDEX CONCURRENTLY`正在扫描索引，搜索需要验证的图元组。如果不是在并发模式下，这个阶段会被跳过。列 `blocks_total（设置为索引的总大小）和 `blocks_done`包含了这个阶段的进度信息。 指数验证：排序元组 `CREATE INDEX CONCURRENTLY`正在对索引扫描阶段的输出进行排序。 索引验证：扫描表 `CREATE INDEX CONCURRENTLY`正在扫描表，以验证前两个阶段收集的索引图元。当不在并发模式时，这个阶段被跳过。`blocks_total`列（设置为表的总大小）和`blocks_done`列包含这个阶段的进度信息。 等待旧照 CREATE INDEX CONCURRENTLY`或`REINDEX CONCURRENTLY`正在等待可能看到表的事务释放快照。 当不处于并发模式时，这个阶段会被跳过。 `lockers_total、lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 标记 dead之前等待readers REINDEX CONCURRENTLY`等待表上有读锁的事务完成后，再将旧索引标记为死索引。当不在并发模式时，这个阶段被跳过。`lockers_total、lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 在 dropping之前等待readers REINDEX CONCURRENTLY`等待表上有读锁的事务完成后，再丢弃旧索引。当不在并发模式时，这个阶段被跳过。列 `lockers_total、lockers_done 和 `current_locker_pid`包含了这个阶段的进度信息。 只要`VACUUM`正在运行，每一个当前正在清理的后端（包括autovacuum工作者进程）在`pg_stat_progress_vacuum`视图中都会有一行。下面的表描述了将被报告的信息并且提供了如何解释它们的信息。`VACUUM FULL`命令的进度是通过`pg_stat_progress_cluster`报告的，因为`VACUUM FULL`和`CLUSTER`都是重写表，而普通的`VACUUM`只是原地修改表。 表36.pg_stat_progress_vacuum 视图 列 类型 描述 pid integer 后端的进程ID。 datid oid 这个后端连接的数据库的OID。 datname name 这个后端连接的数据库的名称。 relid oid 被vacuum的表的OID。 phase text vacuum的当前处理阶段。 heap_blks_total bigint 该表中堆块的总数。这个数字在扫描开始时报告，之后增加的块将不会（并且不需要）被这个VACUUM访问。 heap_blks_scanned bigint 被扫描的堆块数量。由于visibility map被用来优化扫描，一些块将被跳过而不做检查，被跳过的块会被包括在这个总数中，因此当清理完成时这个数字最终将会等于heap_blks_total。仅当处于扫描堆阶段时这个计数器才会前进。 heap_blks_vacuumed bigint 被清理的堆块数量。除非表没有索引，这个计数器仅在处于清理堆阶段时才会前进。不包含死亡元组的块会被跳过，因此这个计数器可能有时会向前跳跃一个比较大的增量。 index_vacuum_count bigint 已完成的索引清理周期数。 max_dead_tuples bigint 在需要执行一个索引清理周期之前我们可以存储的死亡元组数，取决于maintenance_work_mem。 num_dead_tuples bigint 从上一个索引清理周期以来收集的死亡元组数。 表37.VACUUM的阶段 阶段 描述 初始化 `VACUUM`正在准备开始扫描堆。这个阶段应该很简短。 扫描堆 `VACUUM`正在扫描堆。如果需要，它将会对每个页面进行修建以及碎片整理，并且可能会执行冻结动作。`heap_blks_scanned`列可以用来监控扫描的进度。 清理索引 `VACUUM`当前正在清理索引。如果一个表拥有索引，那么每次清理时这个阶段会在堆扫描完成后至少发生一次。如果maintenance_work_mem不足以存放找到的死亡元组，则每次清理时会多次清理索引。 清理堆 VACUUM`当前正在清理堆。清理堆与扫描堆不是同一个概念，清理堆发生在每一次清理索引的实例之后。如果`heap_blks_scanned`小于`heap_blks_total，系统将在这个阶段完成之后回去扫描堆；否则，系统将在这个阶段完成后开始清理索引。 清除索引 `VACUUM`当前正在清除索引。这个阶段发生在堆被完全扫描并且对堆和索引的所有清理都已经完成以后。 截断堆 `VACUUM`正在截断堆，以便把关系尾部的空页面返还给操作系统。这个阶段发生在清除完索引之后。 执行最后的清除 `VACUUM`在执行最终的清除。在这个阶段中，`VACUUM`将清理空闲空间映射、更新`pg_class`中的统计信息并且将统计信息报告给统计收集器。当这个阶段完成时，`VACUUM`也就结束了。 每当`CLUSTER`或`VACUUM FULL`运行时，`pg_stat_progress_cluster`视图将包含当前正在运行的每一个后台的记录。下面的表格描述了将被报告的信息，并提供了关于如何解释这些信息的信息。 表38.pg_stat_progress_cluster 视图 列 类型 描述 pid integer 后台的进程ID。 datid oid 该后端连接的数据库的OID。 datname name 与此后端连接的数据库的名称。 relid oid 被集群的表的OID。 command text 正在运行的命令。CLUSTER`或`VACUUM FULL。 phase text 当前处理阶段。 cluster_index_relid oid 如果正在使用索引对表进行扫描，这就是正在使用的索引的OID；否则为0。 heap_tuples_scanned bigint 扫描的堆元组数。这个计数器只有在阶段为`seq scanning heap`、`index scanning heap`或`writing new heap`时才会增进。 heap_tuples_written bigint 写入的堆元组的数量。这个计数器只有在阶段为`seq scanning heap`、`index scanning heap`或`writing new heap`时才会前进。 heap_blks_total bigint 表中的堆块总数。这个数字是在`seq scanning heap`的开始时报告的。 heap_blks_scanned bigint 扫描的堆块数量。这个计数器只有在阶段为`seq scanning heap`时才会增进。 index_rebuild_count bigint 重建的索引数。该计数器仅在`重建索引`阶段时才会增进。 表39.CLUSTER 和 VACUUM FULL 阶段 阶段 描述 初始化 该命令准备开始扫描堆栈。 这个阶段预计会非常短暂。 seq扫描堆 该命令目前采用顺序扫描的方式对表进行扫描。 索引扫描堆 `CLUSTER`目前正在使用索引扫描表。 元组排序 `CLUSTER`目前正在对元组进行排序。 新写入堆 `CLUSTER`目前正在编写新的堆。 交换关系文件 目前，该命令正在将新建立的文件调换到位。 重建索引 该命令目前正在重建一个索引。 清理 该命令正在执行最后的清理工作。 当此阶段完成后，`CLUSTER`或`VACUUM FULL`将结束。 每当像pg_basebackup这样的应用程序进行基本备份时， `pg_stat_progress_basebackup`视图将包含当前运行`BASE_BACKUP`复制命令和流备份的每个WAL发送进程的一行。 下面的表描述了将要报告的信息，并提供了关于如何解释它的信息。 表40.pg_stat_progress_basebackup 视图 列 类型 描述 pid integer WAL发送方进程ID。 phase text 目前的处理阶段。 backup_total bigint 将被流输送的数据总量。这是在streaming database files阶段开始时的估计和报告。注意，这只是一个近似值，因为在streaming database files阶段，数据库可能会改变，而WAL日志可能会在稍后的备份中包含。一旦流数据量超过了估计的总大小，该值始终与backup_streamed相同。如果在pg_basebackup中禁用估算（也就是说，指定了—​no-estimate-size选项），这为NULL。 backup_streamed bigint 数据流的总量。这个计数器只在streaming database files阶段或transferring wal files时增进。 tablespaces_total bigint 要流输送的表空间总数。 tablespaces_streamed bigint 流输送的表空间数。此计数器仅在streaming database files阶段增进。 表41.基础备份阶段 阶段 描述 initializing WAL发送器进程正在准备开始备份。这个阶段预计会非常短暂。 waiting for checkpoint to finish WAL发送器进程目前正在执行`pg_start_backup`以准备进行基础备份，并等待启动备份检查点完成。 estimating backup size WAL发送程序目前正在估计将作为基础备份流传输的数据库文件的总量。 streaming database files WAL发送器当前正在流数据库文件作为基础备份。 waiting for wal archiving to finish WAL发送方进程目前正在执行`pg_stop_backup`以完成备份，并等待基础备份所需的所有WAL文件成功存档。 如果在pg_basebackup中指定了`--wal-method=none`或`--wal-method=stream`，则备份将在此阶段完成后结束。 transferring wal files WAL发送器进程正在传输备份过程中产生的所有WAL日志。 如果pg_basebackup中指定了`--wal-method=fetch`， 则该阶段发生在`waiting for wal archiving to finish`阶段之后。当此阶段完成时备份将结束。 IvorySQL提供了功能来支持数据库服务器的动态追踪。这样就允许在代码中的特定点上调用外部工具来追踪执行过程。 一些探针或追踪点已经被插入在源代码中。这些探针的目的是被数据库开发者和管理员使用。默认情况下，探针不被编译到IvorySQL中；用户需要显式地告诉配置脚本使得探针可用。 目前，DTrace已被支持，它在 Solaris、macOS、FreeBSD、NetBSD 和 Oracle Linux 上可用。 Linux 的SystemTap项目提供了一种可用的 DTrace 等价物。支持其他动态追踪工具在理论上可以通过改变`src/include/utils/probes.h`中的宏定义实现。 默认情况下，探针是不可用的，因此你将需要显式地告诉配置脚本让探针在IvorySQL中可用。要包括 DTrace 支持，在配置时指定`--enable-dtrace`。 如表 42所示，源代码中提供了一些标准探针。表 43显示了在探针中使用的类型。当然，可以增加更多探针来增强IvorySQL的可观测性。 表42.内建 DTrace 探针 名称 参数 描述 transaction-start (LocalTransactionId) 在一个新事务开始时触发的探针。arg0 是事务 ID。 transaction-commit (LocalTransactionId) 在一个事务成功完成时触发的探针。arg0 是事务 ID。 transaction-abort (LocalTransactionId) 当一个事务失败完成时触发的探针。arg0 是事务 ID。 query-start (const char *) 当一个查询的处理被开始时触发的探针。arg0 是查询字符串。 query-done (const char *) 当一个查询的处理完成时触发的探针。arg0 是查询字符串。 query-parse-start (const char *) 当一个查询的解析被开始时触发的探针。arg0 是查询字符串。 query-parse-done (const char *) 当一个查询的解析完成时触发的探针。arg0 是查询字符串。 query-rewrite-start (const char *) 当一个查询的重写被开始时触发的探针。arg0 是查询字符串。 query-rewrite-done (const char *) 当一个查询的重写完成时触发的探针。arg0 是查询字符串。 query-plan-start () 当一个查询的规划被开始时触发的探针。 query-plan-done () 当一个查询的规划完成时触发的探针。 query-execute-start () 当一个查询的执行被开始时触发的探针。 query-execute-done () 当一个查询的执行完成时触发的探针。 statement-status (const char *) 任何时候当服务器进程更新它的`pg_stat_activity`.`status`时触发的探针。arg0 是新的状态字符串。 checkpoint-start (int) 当一个检查点被开始时触发的探针。arg0 传递位标志来区分不同的检查点类型，例如关闭（shutdown）、立即（immediate）或强制（force）。 checkpoint-done (int, int, int, int, int) 当一个检查点完成时触发的探针（检查点处理过程中序列中列出的下一个触发的探针）。arg0 是要写的缓冲区数量。arg1 是缓冲区的总数。arg2、arg3 和 arg4 分别包含了增加、删除和循环回收的 WAL 文件的数量。 clog-checkpoint-start (bool) 当一个检查点的 CLOG 部分被开始时触发的探针。arg0 为真表示正常检查点，为假表示关闭检查点。 clog-checkpoint-done (bool) 当一个检查点的 CLOG 部分完成时触发的探针。arg0 的含义与`clog-checkpoint-start`中相同。 subtrans-checkpoint-start (bool) 当一个检查点的 SUBTRANS 部分被开始时触发的探针。arg0 为真表示正常检查点，为假表示关闭检查点。 subtrans-checkpoint-done (bool) 当一个检查点的 SUBTRANS 部分完成时触发的探针。arg0 的含义与`subtrans-checkpoint-start`中相同。 multixact-checkpoint-start (bool) 当一个检查点的 MultiXact 部分被开始时触发的探针。arg0 为真表示正常检查点，为假表示关闭检查点。 multixact-checkpoint-done (bool) 当一个检查点的 MultiXact 部分完成时触发的探针。arg0 的含义与`multixact-checkpoint-start`中相同。 buffer-checkpoint-start (int) 当一个检查点的写缓冲区部分被开始时触发的探针。arg0 传递位标志来区分不同的检查点类型，例如关闭（shutdown）、立即（immediate）或强制（force）。 buffer-sync-start (int, int) 当我们在检查点期间开始写脏缓冲区时（在标识哪些缓冲区必须被写之后）触发的探针。arg0 是缓冲区总数，arg1 是当前为脏并且需要被写的缓冲区数量。 buffer-sync-written (int) 在检查点期间当每个缓冲区被写完之后触发的探针。arg0 是缓冲区的 ID。 buffer-sync-done (int, int, int) 当所有脏缓冲区被写之后触发的探针。arg0 是缓冲区总数。arg1 是检查点进程实际写的缓冲区数量。arg2 是期望写的数目（`buffer-sync-start`的 arg1）；arg1 和 arg2 的任何的不同反映在该检查点期间有其他进程刷写了缓冲区。 buffer-checkpoint-sync-start () 在脏缓冲区被写入到内核之后并且在开始发出 fsync 请求之前触发的探针。 buffer-checkpoint-done () 当同步缓冲区到磁盘完成时触发的探针。 twophase-checkpoint-start () 当一个检查点的两阶段部分被开始时触发的探针。 twophase-checkpoint-done () 当一个检查点的两阶段部分完成时触发的探针。 buffer-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool) 当一次缓冲区读被开始时触发的探针。arg0 和 arg1 包含该页的分叉号和块号（如果这是一次关系扩展请求，arg1 为 -1）。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是 InvalidBackendId（-1）。arg6 为真表示一次关系扩展请求，为假表示正常读。 buffer-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool, bool) 当一次缓冲区读完成时触发的探针。arg0 和 arg1 包含该页的分叉号和块号（如果这是一次关系扩展请求，arg1 现在包含新增加块的块号）。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是 InvalidBackendId（-1）。arg6 为真表示一次关系扩展请求，为假表示正常读。arg7 为真表示在池中找到该缓冲区，为假表示没有找到。 buffer-flush-start (ForkNumber, BlockNumber, Oid, Oid, Oid) 在发出对一个共享缓冲区的任意写请求之前触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。 buffer-flush-done (ForkNumber, BlockNumber, Oid, Oid, Oid) 当一个写请求完成时触发的探针（注意这只反映传递数据给内核的时间，它通常并没有实际地被写入到磁盘）。参数和`buffer-flush-start`的相同。 buffer-write-dirty-start (ForkNumber, BlockNumber, Oid, Oid, Oid) 当一个服务器进程开始写一个脏缓冲区时触发的探针（如果这经常发生，表示shared_buffers太小，或需要调整后台写入器的控制参数）。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。 buffer-write-dirty-done (ForkNumber, BlockNumber, Oid, Oid, Oid) 当一次脏缓冲区写完成时触发的探针。参数与`buffer-write-dirty-start`相同。 wal-buffer-write-dirty-start () 当一个服务器进程因为没有可用 WAL 缓冲区空间开始写一个脏 WAL 缓冲区时触发的探针（如果这经常发生，表示wal_buffers太小）。 wal-buffer-write-dirty-done () 当一次脏 WAL 缓冲区完成时触发的探针。 wal-insert (unsigned char, unsigned char) 当一个 WAL 记录被插入时触发的探针。arg0 是该记录的资源管理者（rmid）。arg1 包含 info 标志。 wal-switch () 当请求一次 WAL 段切换时触发的探针。 smgr-md-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) 当开始从一个关系读取一块时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。 smgr-md-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) 当一次块读取完成时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。arg6 是实际读取的字节数，而 arg7 是请求读取的字节数（如果两者不同就意味着麻烦）。 smgr-md-write-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) 当开始向一个关系中写入一个块时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。 smgr-md-write-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) 当一个块写操作完成时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3和arg4 包含表空间、数据库和关系 OID来标识该关系。对于一个本地缓冲区，arg5 是创建临时关系的后端 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。arg6 是实际写的字节数，而 arg7 是要求写的字节数（如果这两者不同，则意味着麻烦）。 sort-start (int, bool, int, int, bool, int) 当一次排序操作开始时触发的探针。arg0 指示是堆排序、索引排序或数据排序。arg1 为真表示唯一值强制。arg2 是键列的数目。arg3 是允许使用的工作内存数（以千字节计）。如果要求随机访问排序结果，那么 arg4 为真。arg5为`0`时表示串行，为`1`时表示并行工作者，为`2`时表示并行领袖。 sort-done (bool, long) 当一次排序完成时触发的探针。arg0 为真表示外排序，为假表示内排序。arg1 是用于一次外排序的磁盘块的数目，或用于一次内排序的以千字节计的内存。 lwlock-acquire (char *, LWLockMode) 当成功获得一个 LWLock 时触发的探针。 arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lwlock-release (char *) 当一个 LWLock 被释放时（但是注意还没有唤醒任何一个被释放的等待者）触发的探针。 arg0 是该 LWLock 所在的切片（Tranche）。 lwlock-wait-start (char *, LWLockMode) 当一个 LWLock不是当即可用并且一个服务器进程因此开始等待该锁变为可用时触发的探针。 arg0 是该 LWLock 所在的切片（Tranche）。 arg1 请求的锁模式，是排他或共享。 lwlock-wait-done (char *, LWLockMode) 当一个进程从对一个 LWLock 的等待中被释放时（它实际还没有得到该锁）时触发的探针。arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lwlock-condacquire (char *, LWLockMode) 当调用者指定无需等待而成功获得一个 LWLock 时触发的探针。arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lwlock-condacquire-fail (char *, LWLockMode) 当调用者指定无需等待而没有成功获得一个 LWLock 时触发的探针。arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lock-wait-start (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) 当一个重量级锁（lmgr锁）的请求由于锁不可用开始等待时触发的探针。arg0 到 arg3 是标识被锁定对象的标签域。arg4 指示被锁对象的类型。arg5 表示被请求的锁类型。 lock-wait-done (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) 当一个重量级锁（lmgr 锁）的请求结束等待时（即已经得到锁）触发的探针。参数与`lock-wait-start`一样。 deadlock-found () 当死锁检测器发现死锁时触发的探针。 表43.定义用在探针参数中的类型 类型 定义 LocalTransactionId unsigned int LWLockMode int LOCKMODE int BlockNumber unsigned int Oid unsigned int ForkNumber int bool unsigned char 下面的例子展示了一个分析系统中事务计数的 DTrace 脚本，可以用来代替一次性能测试之前和之后的`pg_stat_database`快照： #!/usr/sbin/dtrace -qs postgresql$1:::transaction-start { @start[\"Start\"] = count(); self->ts = timestamp; } postgresql$1:::transaction-abort { @abort[\"Abort\"] = count(); } postgresql$1:::transaction-commit /self->ts/ { @commit[\"Commit\"] = count(); @time[\"Total time (ns)\"] = sum(timestamp - self->ts); self->ts=0; } 当被执行时，该例子 D 脚本给出这样的输出： # ./txn_count.d `pgrep -n postgres` or ./txn_count.d <PID> ^C Start 71 Commit 70 Total time (ns) 2312105013 注意 SystemTap 为追踪脚本使用一个不同于 DTrace 的标记，但是底层的探针是兼容的。值得注意的是，在这样写的时候，SystemTap 脚本必须使用双下划线代替连字符来引用探针名。在未来的 SystemTap 发行中这很可能会被修复。你应该记住，DTrace 脚本需要细心地编写和调试，否则被收集的追踪信息可能会毫无意义。在大部分发现问题的情况中，它就是发生问题的部件，而不是底层系统。当讨论使用动态追踪发现的信息时，一定要附上使用的脚本以便其也被检查和讨论。 开发者可以在代码中任意位置定义新的探针，当然这要重新编译之后才能生效。下面是插入新探针的步骤： 决定探针名称以及探针可用的数据 把该探针定义加入到`src/backend/utils/probes.d` 如果`pg_trace.h`还不存在于包含该探针点的模块中，包括它，并且在源代码中期望的位置插入`TRACE_POSTGRESQL`探针宏 重新编译并验证新探针是可用的 例子：. 这里是一个如何增加一个探针来用事务 ID 追踪所有新事务的例子。 决定探针将被命名为`transaction-start`并且需要一个`LocalTransactionId`类型的参数 将该探针定义加入到`src/backend/utils/probes.d`： ``` probe transaction__start(LocalTransactionId); ``` 注意探针名字中双下划线的使用。在一个使用探针的 DTrace 脚本中，双下划线需要被替换为一个连字符，因此 ，对用户而言`transaction-start`是文档名。 在编译时，transaction__start`被转换成一个宏调用`TRACE_POSTGRESQL_TRANSACTION_START（注意这里是单下划线），可以通过包括头文件`pg_trace.h`获得。将宏调用加入到源代码中的合适位置。在这种情况下，看起来类似： ``` TRACE_POSTGRESQL_TRANSACTION_START(vxid.localTransactionId); ``` 在重新编译和运行新的二进制文件之后，通过运行下面的 DTrace 命令来检查新增的探针是否可用。你应该看到类似下面的输出： ``` # dtrace -ln transaction-start ID PROVIDER MODULE FUNCTION NAME 18705 postgresql49878 postgres StartTransactionCommand transaction-start 18755 postgresql49877 postgres StartTransactionCommand transaction-start 18805 postgresql49876 postgres StartTransactionCommand transaction-start 18855 postgresql49875 postgres StartTransactionCommand transaction-start 18986 postgresql49873 postgres StartTransactionCommand transaction-start ``` 向C代码中添加追踪宏时，有一些事情需要注意： 需要小心的是，为探针参数指定的数据类型要匹配宏中使用的变量的数据类型，否则会发生编译错误。 在大多数平台上，如果用`--enable-dtrace`编译了IvorySQL，无论何时当控制经过一个追踪宏时，都会评估该宏的参数，即使没有进行追踪也会这样做。通常不需要担心你是否只在报告一些局部变量的值。但是要注意不要将开销大的函数调用放入参数中。如果你需要这样做，考虑通过检查追踪是否真的被启用来保护该宏： ``` if (TRACE_POSTGRESQL_TRANSACTION_START_ENABLED()) TRACE_POSTGRESQL_TRANSACTION_START(some_function(...)); ``` 每个追踪宏有一个对应的`ENABLED`宏。 每个表都有一个主要的堆磁盘文件，大多数数据都存储在其中。如果一个表有着可能会很宽（尺寸大）的列， 则另外还有一个TOAST文件与这个表相关联， 它用于存储因为太宽而不能存储在主表里面的值。如果有这个附属文件，那么TOAST表上会有一个可用的索引。 当然，同时还可能有索引和基表关联。每个表和索引都存放在单独的磁盘文件里 — 如果文件超过 1G 字节，甚至可能多于一个文件。 你可以以三种方式监视磁盘空间：使用oid2name模块或者人工观察系统目录。SQL函数是最容易使用的方法，同时也是我们通常推荐的方法。本节剩余的部分将展示如何通过观察系统目录来监视磁盘空间。 在一个最近清理过或者分析过的数据库上使用psql，你可以发出查询来查看任意表的磁盘用量： SELECT pg_relation_filepath(oid), relpages FROM pg_class WHERE relname = 'customer'; pg_relation_filepath | relpages ----------------------+---------- base/16384/16806 | 60 (1 row) 每个页通常都是 8K 字节（记住，relpages`只会由`VACUUM、`ANALYZE`和少数几个 DDL 命令如`CREATE INDEX`所更新）。如果你想直接检查表的磁盘文件，那么文件路径名应该有用。 要显示TOAST表使用的空间，我们可以使用一个类似下面这样的查询： SELECT relname, relpages FROM pg_class, (SELECT reltoastrelid FROM pg_class WHERE relname = 'customer') AS ss WHERE oid = ss.reltoastrelid OR oid = (SELECT indexrelid FROM pg_index WHERE indrelid = ss.reltoastrelid) ORDER BY relname; relname | relpages ----------------------+---------- pg_toast_16806 | 0 pg_toast_16806_index | 1 你也可以很容易地显示索引的尺寸： SELECT c2.relname, c2.relpages FROM pg_class c, pg_class c2, pg_index i WHERE c.relname = 'customer' AND c.oid = i.indrelid AND c2.oid = i.indexrelid ORDER BY c2.relname; relname | relpages -------------------+---------- customer_id_index | 26 我们很容易用下面的信息找出最大的表和索引： SELECT relname, relpages FROM pg_class ORDER BY relpages DESC; relname | relpages ----------------------+---------- bigtable | 3290 customer | 3144 一个数据库管理员最重要的磁盘监控任务就是确保磁盘不会写满。一个写满了的数据磁盘可能不会导致数据的崩溃，但它肯定会让系统变得不可用。如果保存 WAL 文件的磁盘变满，会发生数据库服务器致命错误并且可能发生关闭。 如果你不能通过删除一些其他的东西来释放一些磁盘空间，那么你可以通过使用表空间把一些数据库文件移动到其他文件系统上去。 提示 有些文件系统在快满的时候性能会急剧恶化，因此不要等到磁盘完全满的时候才采取行动。 如果你的系统支持每用户的磁盘份额，那么数据库将自然地受制于用户所处的服务器给他的份额限制。超过份额的负面影响和完全用光磁盘是完全一样的。","title":"日常监控","component":"ivorysql-doc","version":"master","name":"daily_monitoring","url":"/ivorysql-doc/master/getting-started/daily_monitoring.html","titles":[{"text":"1. 监控数据活动","hash":"监控数据活动","id":1},{"text":"1.1. 标准Unix工具","hash":"标准unix工具","id":2},{"text":"1.2. 统计收集器","hash":"统计收集器","id":3},{"text":"1.2.1. 统计收集配置","hash":"统计收集配置","id":4},{"text":"1.2.2. 查看统计信息","hash":"查看统计信息","id":5},{"text":"1.3. pg_stat_activity","hash":"pg_stat_activity","id":6},{"text":"1.3.1. pg_stat_replication","hash":"pg_stat_replication","id":7},{"text":"1.3.2. pg_stat_wal_receiver","hash":"pg_stat_wal_receiver","id":8},{"text":"1.3.3. pg_stat_subscription","hash":"pg_stat_subscription","id":9},{"text":"1.3.4. pg_stat_ssl","hash":"pg_stat_ssl","id":10},{"text":"1.3.5. pg_stat_gssapi","hash":"pg_stat_gssapi","id":11},{"text":"1.3.6. pg_stat_archiver","hash":"pg_stat_archiver","id":12},{"text":"1.3.7. pg_stat_bgwriter","hash":"pg_stat_bgwriter","id":13},{"text":"1.3.8. pg_stat_database","hash":"pg_stat_database","id":14},{"text":"1.3.9. pg_stat_database_conflicts","hash":"pg_stat_database_conflicts","id":15},{"text":"1.3.10. pg_stat_all_tables","hash":"pg_stat_all_tables","id":16},{"text":"1.3.11. pg_stat_all_indexes","hash":"pg_stat_all_indexes","id":17},{"text":"1.3.12. pg_statio_all_tables","hash":"pg_statio_all_tables","id":18},{"text":"1.3.13. pg_statio_all_indexes","hash":"pg_statio_all_indexes","id":19},{"text":"1.3.14. pg_statio_all_sequences","hash":"pg_statio_all_sequences","id":20},{"text":"1.3.15. pg_stat_user_functions","hash":"pg_stat_user_functions","id":21},{"text":"1.3.16. pg_stat_slru","hash":"pg_stat_slru","id":22},{"text":"1.3.17. Statistics Functions","hash":"statistics-functions","id":23},{"text":"2. 查看锁","hash":"查看锁","id":24},{"text":"3. Progress Reporting","hash":"progress-reporting","id":25},{"text":"3.1. ANALYZE Progress Reporting","hash":"analyze-progress-reporting","id":26},{"text":"3.2. CREATE INDEX Progress Reporting","hash":"create-index-progress-reporting","id":27},{"text":"3.3. VACUUM进度报告","hash":"vacuum进度报告","id":28},{"text":"3.4. CLUSTER进度报告","hash":"cluster进度报告","id":29},{"text":"3.5. 基础备份进度报告","hash":"基础备份进度报告","id":30},{"text":"4. 动态追踪","hash":"动态追踪","id":31},{"text":"4.1. 动态追踪的编译","hash":"动态追踪的编译","id":32},{"text":"4.2. 内建探针","hash":"内建探针","id":33},{"text":"4.3. 使用探针","hash":"使用探针","id":34},{"text":"4.4. 定义新探针","hash":"定义新探针","id":35},{"text":"5. 监控磁盘的使用","hash":"监控磁盘的使用","id":36},{"text":"5.1. 判断磁盘用量","hash":"判断磁盘用量","id":37},{"text":"5.2. 磁盘满失败","hash":"磁盘满失败","id":38}]},"56":{"id":56,"text":"硬件要求 配置参数 最低配置 推荐配置 CPU 4核 16核 内存 4GB 64GB 存储 800MB，机械硬盘 5GB以上，SSD或NvMe 网络 千兆网络 万兆网络 软件要求 IvorySQL数据库目前支持的操作系统包括但不限于CentOS 8.X、CentOS Stream 9以及Ubuntu系统。 快速开始示例所使用的操作系统为CentOS Stream 9。 安装前准备 安装前请先创建一个用户，并赋予其root权限，安装和使用均以该用户执行。这里以ivorysql用户为例。 如何创建sudo用户 下载安装 创建或编辑IvorySQL yum源配置文件/etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 保存退出后，安装IvorySQL5 $ sudo dnf install -y ivorysql5-5.0 正确安装后，数据库将被安装在/usr/ivory-5/路径下的IvorySQL-version(如:IvorySQL-5.0)文件夹内 执行以下命令为ivorysql用户赋权： $ sudo chown -R ivorysql:ivorysql /usr/ivory-5 配置环境变量 将以下配置写入~/.bash_profile文件并使用source命令该文件使环境变量生效： PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile 数据库初始化 $ initdb -D /usr/ivory-5/data 其中-D参数用来指定数据库的数据目录。更多参数使用方法，请使用initdb --help命令获取。 启动数据库服务 $ pg_ctl -D /usr/ivory-5/data -l ivory.log start 其中-D参数用来指定数据库的数据目录，如果[配置环境变量] 配置了PGDATA，则该参数可以省略。-l参数用来指定日志文件。更多参数使用方法，请使用pg_ctl --help命令获取。 查看确认数据库启动成功： $ ps -ef | grep postgres ivorysql 3214 1 0 20:35 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 3215 3214 0 20:35 ? 00:00:00 postgres: checkpointer ivorysql 3216 3214 0 20:35 ? 00:00:00 postgres: background writer ivorysql 3218 3214 0 20:35 ? 00:00:00 postgres: walwriter ivorysql 3219 3214 0 20:35 ? 00:00:00 postgres: autovacuum launcher ivorysql 3220 3214 0 20:35 ? 00:00:00 postgres: logical replication launcher ivorysql 3238 1551 0 20:35 pts/0 00:00:00 grep --color=auto postgres 从Docker Hub上获取IvorySQL镜像 $ docker pull ivorysql/ivorysql:5.0-ubi8 运行IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 查看IvorySQL容器运行是否成功 $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql psql连接数据库： $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# 其中-d参数用来指定想要连接到的数据库名称。IvorySQL默认使用ivorysql数据库，但较低版本的IvorySQL首次使用时需用户先连接postgres数据库，然后自己创建ivorysql数据库。较高版本的IvorySQL则已为用户创建好ivorysql数据库，可以直接连接。 更多参数使用方法，请使用psql --help命令获取。 Docker运行IvorySQL时，需要添加额外参数，如 psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 现在可以开始使用IvorySQL啦！就是这么简单！ 想要获得更多安装方式，请参考安装指南","title":"快速开始","component":"ivorysql-doc","version":"master","name":"quick_start","url":"/ivorysql-doc/master/getting-started/quick_start.html","titles":[{"text":"1. 环境要求","hash":"环境要求","id":1},{"text":"2. 快速安装","hash":"快速安装","id":2},{"text":"2.1. 从yum源安装IvorySQL数据库","hash":"从yum源安装ivorysql数据库","id":3},{"text":"2.2. docker方式运行","hash":"docker方式运行","id":4},{"text":"3. 数据库连接","hash":"数据库连接","id":5}]},"57":{"id":57,"text":"IvorySQL提供兼容Oracle内置函数 SYS_CONTEXT('namespace', 'parameter' [, length ]) , 返回当前时刻与给定上下文关联参数的值，可以在SQL和PLSQL语言中使用。 提供以下命名空间： USERENV​ - 用于描述当前会话。 SYS_SESSION_ROLES​ - 指示某个特定角色在当前会话中是否已启用。 SYS_CONTEXT 的实现原理是通过动态查询系统表与postgres的内置函数，确保结果的实时性， 使用 SECURITY INVOKER 确保函数以调用者的权限执行，避免权限提升问题。 该方法的实现逻辑如下： CREATE OR REPLACE FUNCTION sys.sys_context(a varchar2, b varchar2) RETURNS varchar2 AS $$ DECLARE res varchar2; BEGIN IF upper(a) = 'USERENV' THEN CASE upper(b) WHEN 'CURRENT_SCHEMA' THEN SELECT current_schema() INTO res; WHEN 'LANG' THEN SELECT sys.get_lang() INTO res; ... ELSE RAISE EXCEPTION 'invalid USERENV parameter: %', b; END CASE; ELSIF upper(a) = 'SYS_SESSION_ROLES' THEN CASE upper(b) WHEN 'LOGIN' THEN SELECT CASE WHEN rolcanlogin = 't' THEN 'TRUE' ELSE 'FALSE' END INTO res FROM pg_roles WHERE oid = current_user::regrole::oid; ... ELSE RAISE EXCEPTION 'invalid SYS_SESSION_ROLES parameter: %', b; END CASE; ELSE SELECT current_setting(a||'.'||b, true) INTO res; END IF; RETURN res; END; $$ LANGUAGE plisql SECURITY INVOKER; 参数名称 返回值 CURRENT_SCHEMA 当前活动默认模式的名称。在会话期间，可以通过 ALTER SESSION SET CURRENT_SCHEMA 语句更改该值。该值也可能在会话期间发生变化，以反映任何活动定义者权限对象的所有者。如果直接在视图定义的正文中使用，那么将返回在执行使用视图的游标时使用的默认模式。 CURRENT_SCHEMAID 当前活动的默认模式的标识符。 SESSION_USER 当前session的用户 会话用户（登录用户）的名称。在数据库会话期间，随着 Real Application Security 会话的附加或分离，该名称可能会发生变化。对于企业用户，返回模式。对于其他用户，返回数据库用户名。如果数据库会话当前附加了 Real Application Security 会话，则返回用户 XS$NULL。 SESSION_USERID 当前session的用户ID。 PROXY_USER 代表 SESSION_USER 打开当前会话的数据库用户名称。 PROXY_USERID 代表 SESSION_USER 打开当前会话的数据库用户的标识符。 CURRENT_USER 当前会话中执行操作的用户。 CURRENT_USERID 当前会话中执行操作的用户标识符。 CURRENT_EDITION_NAME 当前版本的名字。 CLIENT_PROGRAM_NAME 客户端程序的名称。 参数名称 返回值 IP_ADDRESS 客户端的IP地址。 HOST 客户端主机的名称。 ISDBA 如果当前用户是数据库管理员，则返回TRUE。 LANG 语言的缩写名称，比现有的 “LANGUAGE ”参数更简短。 LANGUAGE 当前会话的使用的语言和地区，以及数据库字符集，格式为：language_territory.characterset。 NLS_DATE_FORMAT 当前session的日期格式。 PLATFORM_SLASH 在用户的平台上用作文件路径分隔符的斜线字符（Unix或Linux是’/’，Windows是’\\’）。 DB_NAME 当前连接的CDB的名称。 SID 当前会话的系统标识符。 SESSIONID 审计会话标识符。不能在分布式 SQL 语句中使用此属性。 CLIENT_INFO 返回最多 64 字节的用户会话信息，应用程序可使用 DBMS_APPLICATION_INFO 包存储这些信息。 ENTRYID 当前审计条目编号。审计条目编号序列在细粒度审计记录和常规审计记录之间共享。不能在分布式 SQL 语句中使用此属性。只有通过标准或细粒度审计的审计处理程序才能看到正确的审计条目标识符。 TERMINAL 当前会话客户端的操作系统标识符。在分布式 SQL 语句中，该属性返回本地会话的标识符。在分布式环境中，仅支持远程 SELECT 语句，不支持远程 INSERT、UPDATE 或 DELETE 操作。(此参数的返回长度可能因操作系统而异)。 参数名称 返回值 DBA 如果当前用户是数据库管理员，则返回TRUE。 LOGIN 如果当前用户是登录角色，则返回TRUE。 CREATEROLE 如果当前session的用户具有创建角色的权限，则返回TRUE。 CREATEDB 如果当前session的用户具有创建数据库的权限，则返回TRUE。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"sys_context","url":"/ivorysql-doc/master/oracle_builtin_functions/sys_context.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"2. 命名空间USERENV支持的参数","hash":"命名空间userenv支持的参数","id":2},{"text":"3. 命名空间SYS_SESSION_ROLES支持的参数","hash":"命名空间sys_session_roles支持的参数","id":3}]},"58":{"id":58,"text":"IvorySQL提供兼容Oracle内置函数 USERENV('parameter') ,用于返回当前会话的相关信息。 这是一个遗留函数，建议使用 SYS_CONTEXT 函数及其内置的 USERENV 命名空间来获取当前功能。 USERENV 通过Bison规则解析函数调用，检查参数合法性并映射到对应的具体SQL函数。 解析规则在 ora_gram.y 中实现，逻辑如下： USERENV '(' Sconst ')' { char *normalized_param = downcase_identifier($3, strlen($3), true, true); #define CHECK_AND_CALL(param, func_name) \\ if (strcmp(normalized_param, param) == 0) \\ $$ = (Node *) makeFuncCall(OracleSystemFuncName(func_name), NIL, COERCE_EXPLICIT_CALL, @1); CHECK_AND_CALL(\"client_info\", \"get_client_info\") else CHECK_AND_CALL(\"entryid\", \"get_entryid\") else CHECK_AND_CALL(\"terminal\", \"get_terminal\") else CHECK_AND_CALL(\"isdba\", \"get_isdba\") else CHECK_AND_CALL(\"lang\", \"get_lang\") else CHECK_AND_CALL(\"language\", \"get_language\") else CHECK_AND_CALL(\"sessionid\", \"get_sessionid\") else CHECK_AND_CALL(\"sid\", \"get_sid\") else ereport(ERROR, (errcode(ERRCODE_FEATURE_NOT_SUPPORTED), errmsg(\"invalid USERENV parameter: \\\"%s\\\".\", $3))); #undef CHECK_AND_CALL } 具体功能则是在 builtin_functions—​1.0.sql 中实现。例如： CREATE OR REPLACE FUNCTION sys.get_language() RETURNS varchar2 AS $$ SELECT (regexp_split_to_array(current_setting('lc_monetary'), '\\.'))[1]||'.'||pg_client_encoding(); $$ LANGUAGE sql STRICT; 参数名称 返回值 sid 当前session的用户ID。 sessionid 返回审计会话标识符。 language 返回数据库当前会话的语言、地域和字符集。 lang 返回ISO缩写语言名称。 isdba 如果用户已经被认证为管理员;或者是通过操作系统或口令文件具有管理员特权的，返回“TRUE\"，否则返回\"FALSE\"。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"userenv","url":"/ivorysql-doc/master/oracle_builtin_functions/userenv.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"2. USERENV支持的参数","hash":"userenv支持的参数","id":2}]},"59":{"id":59,"text":"本文档是PLSQL匿名块（anonymous block）兼容Oracle语法功能的设计文档，目的是可以在IvorySQL中兼容Oracle的匿名块语句。 匿名块是能够动态地创建和执行过程代码的PLSQL结构，而不需要以持久化的方式将代码作为数据库对象储存在系统目录中。本次实现中IvorySQL主要兼容的是PLSQL匿名块的语法格式，我们主要处理的部分包括客户端工具psql、主服务器和PSQL端支持。 declare i integer := 10; begin raise notice '%', i; end; / NOTICE: 10 DECLARE grade CHAR(1); BEGIN grade := 'B'; CASE grade WHEN 'A' THEN raise notice 'Excellent'; WHEN 'B' THEN raise notice 'Very Good'; END CASE; EXCEPTION WHEN CASE_NOT_FOUND THEN raise notice 'No such grade'; END; / NOTICE: Very Good","title":"兼容Oracle匿名块","component":"ivorysql-doc","version":"master","name":"anonymous_block","url":"/ivorysql-doc/master/oracle_compatibility/anonymous_block.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"60":{"id":60,"text":"char varchar varchar2 number binary_float binary_double date timestamp timestamp with time zone timestamp with local time zone interval year to month interval day to second raw long sysdate systimestamp add_months last_day next_day months_between current_date current_timestamp new_time tz_offset trunc instr instrb substr substrb trim ltrim rtrim length lengthb rawtohex replace regexp_replace regexp_substr regexp_instr regexp_like to_number to_char to_date to_timestamp to_timestamp_tz to_yminterval to_dsinterval numtodsinterval numtoyminterval localtimestamp from_tz sys_extract_utc sessiontimezone hextoraw uid USERENV asciistr to_multi_byte to_single_byte compose decompose sys_context 功能：查看对应的日期与时间，测试用例如下： 查询当前系统的日期： select sysdate() from dual; sysdate ------------ 2023-07-06 (1 row) 查询往前推1天的日期： select sysdate()-1 from dual; ?column? ------------ 2023-07-05 (1 row) 功能：返回本机数据库上当前系统日期和时间（包括微秒和时区），测试用例如下： 查询当前日期的日期和时间： select systimestamp() from dual; systimestamp ----------------------------------- 2023-07-06 10:18:31.674322 +08:00 (1 row) 功能：函数将一个月数(n)添加一个日期，并返回相隔n月的同一天，支持参数：date, number；测试用例如下： 查询当前日期（七月六日）的下个月的同一天： select add_months(sysdate(),1) from dual; add_months ------------ 2023-08-06 (1 row) 查询当前日期的上个月的同一天： select add_months(sysdate(),-1) from dual; add_months ------------ 2023-06-06 (1 row) 功能：返回指定日期所在月份的最后一天，支持参数：date,测试用例如下： 查询当天所在月份的最后一天： select last_day(sysdate())from dual; last_day ------------ 2023-07-31 (1 row) 查询某一天所在月份的最后一天： select last_day(to_date('2019-09-01'))from dual; last_day ------------ 2019-09-30 (1 row) 功能：返回指定日期的下一个日期。支持参数:date, integer /date ,text, 说明：当函数中第二个参数传的星期数比现有星期数小时，会返回下一个星期的日期；当函数中第二个参数所传的日期比现有星期数大，会返回本周相应星期日期。测试用例如下： 查询当前日期的下一天： select next_day(sysdate(),1) from dual; next_day ------------ 2023-07-07 (1 row) 查询当前日期的下个星期五： select next_day(sysdate(),'FRIDAY') from dual; next_day ------------ 2023-07-07 (1 row) 功能：返回日期类型的date1和date2之间相差的月份，支持参数：date,date，说明：如果date1晚于date2，返回正数；如果date1早于date2返回负数；如果date1和date2是某月里的同一天，返回结果为整数；如果不是同一天，会在每月31天的基础上返回带有小数部分的结果。测试用例如下： 查询不同月份同一天之间相差的月份： select months_between(to_date('2023-07-06'),to_date('2023-08-06')) from dual; months_between ---------------- -1 (1 row) 查询不同月份不同日期之间相差的月份： select months_between(to_date('2023-07-06'),to_date('2023-08-05')) from dual; months_between -------------------- -0.967741935483871 (1 row) 功能：返回当前时区的当前日期，测试用例如下： 查询当前时区的当前日期： select current_date from dual; current_date -------------- 2023-07-06 (1 row) 功能：返回当前时区的当前日期与当前时间，包含当前时区信息。支持参数：integer, 说明：返回的时间可调整精度。测试用例如下： 查询当前时区的当前日期与时间： select current_timestamp from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:01.440600 +08:00 (1 row) 查询当前时区的当前日期与时间(精度调整为前三位小数)： select current_timestamp(3) from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:14.182000 +08:00 (1 row) 功能：返回某个时间在某时区所对应的在另一个时区的日期，支持参数：date, text, text ,测试用例如下： 返回当前日期在另一个时区对应的日期： select sysdate() bj_time,new_time(sysdate(),'PDT','GMT')los_angles from dual; bj_time | los_angles ------------+------------ 2023-07-06 | 2023-07-06 (1 row) 功能：返回给定时区与标准时区的偏移量，支持参数：text,测试用例如下： 返回给定时区与标准时区偏移量： select tz_offset('US/Eastern') from dual; tz_offset ----------- -04:00 (1 row) 功能：可以截取日期，得到想要的数值，如年，月，日，时，分，支持参数：date/date,text,测试用例如下： 截取当前日期： select trunc(sysdate()) from dual; trunc ------------ 2023-07-06 (1 row) 截取年，返回值只有年是正确的，月和日不是准确值： select trunc(sysdate(),'yyyy') from dual; trunc ------------ 2023-01-01 (1 row) 截取月，返回值只有月是正确的，年和日不是准确值： select trunc(sysdate(),'mm') from dual; trunc ------------ 2023-07-01 (1 row) 功能：字符串查找函数，用于判断源字符串中是否包含目标字符串，并返回匹配位置。支持参数形式如下： instr(string, str) instr(string, str, start_position, nth_appearance) 以下为测试用例： 默认情况下返回第一次匹配的位置： SELECT INSTR('database administration', 'data') FROM DUAL; instr ------- 1 (1 row) 指定起始位置和匹配序号： SELECT INSTR('database administration', 'i', 1, 2) FROM DUAL; instr ------- 15 (1 row) 支持从尾部开始的反向搜索： SELECT INSTR('mississippi river', 's', -5, 2) FROM DUAL; instr ------- 6 (1 row) 当未匹配到目标字符串时返回0： SELECT INSTR('database administration', 'z') FROM DUAL; instr ------- 0 (1 row) instr 还能像 like 一样用于模糊查询： select * from tableName where instr(name,'helloworld')>0; 函数实现说明： 查找一个字符串在另一个字符串中的位置，采用经典的暴力匹配算法。 复用了 text_instring 函数。 通过 isByte 参数判断当前数据库编码是否为多字节，并区分单字节与多字节处理逻辑。 根据 position 的正负确定正向或反向搜索，并设置循环的起止位置和步长。 在循环中逐字节移动并比较，与模式字符串匹配成功即返回相应位置。 功能：字符串查找函数，返回字符串的位置，支持参数： varchar2, text, number DEFAULT 1, number DEFAULT 1,以下为测试用例： 返回CORPORATE FLOOR中默认第一次出现OR时字符串的位置： SELECT INSTRB('CORPORATE FLOOR','OR') \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 2 (1 row) 返回corporate floor中从第五个字符开始查询，第二次出现or时字符串的位置： SELECT INSTRB('CORPORATE FLOOR','OR',5,2) \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 14 (1 row) 功能：截取字符串函数，以字符为单位截取，支持参数：text, integer, 测试用例如下： 截取’今天天气很好’中从第五个字符开始，往后的字符串： SELECT SUBSTR('今天天气很好',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- 很好 (1 row) 功能：截取字符串函数，以字节为单位截取，支持参数：varchar2, number/varchar2, number，number,测试用例如下： 截取’今天天气很好’中从第五个字节开始，往后的字符串： SELECT SUBSTRB('今天天气很好',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- 天气很好 (1 row) 截取’今天天气很好’中从第五个字节开始，第八个字节结束的字符串： SELECT SUBSTRB('今天天气很好',5，8) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- 天气 (1 row) 功能：去除指定字符串的左右空格或对应数据，支持参数：varchar2 /varchar2，varchar2，测试用例如下： 去除' aaa bbb ccc '的左右空格： select trim(' aaa bbb ccc ')trim from dual; trim ------------- aaa bbb ccc (1 row) 去除’aaa bbb ccc’中的aaa： select trim('aaa bbb ccc','aaa')trim from dual; trim ---------- bbb ccc (1 row) 功能：去除指定字符串的左侧空格或对应数据，支持参数：varchar2 /varchar2,varchar2,测试用例如下： 去除' abcdefg '的左侧空格： select ltrim(' abcdefg ')ltrim from dual; ltrim ------------ abcdefg (1 row) 从’abcdefg’左侧开始遍历，一旦存在某字符出现在’fegab’中就去除，不存在则返回结果： select ltrim('abcdefg','fegab')ltrim from dual; ltrim ------- cdefg (1 row) 功能：去除指定字符串的右侧空格，测试用例如下： 去除' abcdefg '的右侧空格： select rtrim(' abcdefg ')rtrim from dual; rtrim ---------------- abcdefg (1 row) 从’abcdefg’右侧开始遍历，一旦存在某字符出现在’fegab’中就去除，不存在则返回结果： select rtrim('abcdefg','fegab')rtrim from dual; rtrim ------- abcd (1 row) 功能：求取指定字符串字符的长度，支持参数：char/integer/varchar2测试用例如下： 查询223的字符长度： select length(223) from dual; length -------- 3 (1 row) 查询'223’的字符长度： select length('223') from dual; length -------- 3 (1 row) 查询’ivorysql数据库’的字符长度： select length('ivorysql数据库') from dual; length -------- 11 (1 row) 功能：求取指定字符串字节的长度，支持参数：char/bytea/varchar2测试用例如下： 查询’ivorysql’的字节长度: select lengthb('ivorysq'::char) from dual; lengthb --------- 1 (1 row) 查询'0x2C’的字节长度: select lengthb('0x2C'::bytea) from dual; lengthb --------- 4 (1 row) 查询’ivorysql数据库’的字节长度： select lengthb('ivorysql数据库'::varchar2) from dual; lengthb --------- 17 (1 row) 功能：替换指定字符串中的字符或删除字符，支持参数：text, text, text/varchar2, varchar2, varchar2 DEFAULT NULL::varchar2, 测试用例如下： 替换’jack and jue’中的’j’为’bl'： select replace('jack and jue','j','bl') from dual; replace ---------------- black and blue (1 row) 删除’jack and jue’中的’j': select replace('jack and jue','j') from dual; replace ------------ ack and ue (1 row) 此函数为`replace` 函数的扩展。功能：用于通过正则表达式来进行匹配替换。支持参数：text, text, text /text, text, text, integer/varchar2, varchar2/varchar2, varchar2 varchar2, 测试用例如下： 将匹配到的数字替换为*#： select regexp_replace('01234abcd56789','[0-9]','*#')from dual; regexp_replace -------------------------- *#*#*#*#*#abcd*#*#*#*#*# (1 row) 从第二个数开始将匹配到的数字替换为*#： select regexp_replace('01234abcd56789','[0-9]','*#',2)from dual; regexp_replace ------------------------- 0*#*#*#*#abcd*#*#*#*#*# 删除'01234abcd56789’中的'01'： select regexp_replace('01234abcd56789','01')from dual; regexp_replace ---------------- 234abcd56789 (1 row) 用’xxx’替换01234abcd56789’中的012： select regexp_replace('01234abcd56789','012','xxx')from dual; regexp_replace ---------------- xxx34abcd56789 (1 row) 功能：拾取符合正则表达式描述的字符子串，支持参数：text, text,integer /text, text, integer, integer/ text, text, integer, integer, text /varchar2 ,varchar2,测试用例如下： 查询'012ab34’中从第一个数开始的012字串： select regexp_substr('012ab34', '012',1) from dual; regexp_substr --------------- 012 (1 row) 查询'012ab34’中从第一个数第一组开始的012字串： select regexp_substr('012ab34', '012',1,1) from dual; regexp_substr --------------- 012 (1 row) 查询'012a012Ab34’中从第一个数第一组开始不区分大小写的012字串： select regexp_substr('012a012Ab34', '012A',1,1,'i') from dual; regexp_substr --------------- 012a (1 row) 查询'012a012Ab34’中从第一个数第一组开始区分大小写的012字串： select regexp_substr('012a012Ab34', '012A',1,1,'c') from dual; regexp_substr --------------- 012A (1 row) 查询’数据库’中 '数据’子串： select regexp_substr('数据库', '数据') from dual; regexp_substr --------------- 数据 (1 row) 功能：用于标定符合正则表达式的字符子串的开始位置，支持参数：text, text,integer /text, text, integer, integer/ text, text, integer, integer, text/text, text, integer, integer, text, integer/ varchar2, varchar2,测试用例如下： 查询’abcaBcabc’中从第一个字符开始，出现abc子串的位置： SELECT regexp_instr('abcaBcabc', 'abc', 1); regexp_instr -------------- 1 (1 row) 查询’abcaBcabc’中从第一个字符开始，第三次出现abc子串的位置： SELECT regexp_instr('abcaBcabc', 'abc', 1, 3); regexp_instr -------------- 7 (1 row) 查询’abcabcabc’中从第一个字符开始，第二次出现abc子串后发生的位置： SELECT regexp_instr('abcaBcabc', 'abc', 1, 2,1); regexp_instr -------------- 7 (1 row) 查询’abcaBcabc’中从第一个字符开始，第一次出现abc子串后发生的位置（区分大小写）： SELECT regexp_instr('abcaBcabc', 'abc',1,2,1,'c'); regexp_instr -------------- 7 (1 row) 查询’数据库’中’库’出现的位置： SELECT regexp_instr('数据库', '库'); regexp_instr -------------- 3 (1 row) 功能：与like类似，用于模糊查询。支持参数：varchar2, varchar2 /varchar2, varchar2 varchar2, 首先创建一个regexp_like表用于测试用例查询： create table t_regexp_like ( id varchar(4), value varchar(10) ); insert into t_regexp_like values ('1','1234560'); insert into t_regexp_like values ('2','1234560'); insert into t_regexp_like values ('3','1b3b560'); insert into t_regexp_like values ('4','abc'); insert into t_regexp_like values ('5','abcde'); insert into t_regexp_like values ('6','ADREasx'); insert into t_regexp_like values ('7','123 45'); insert into t_regexp_like values ('8','adc de'); insert into t_regexp_like values ('9','adc,.de'); insert into t_regexp_like values ('10','abcbvbnb'); insert into t_regexp_like values ('11','11114560'); 测试用例如下： 查询t_regexp_like表中带有abc的列： select * from t_regexp_like where regexp_like(value,'abc'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) 查询t_regexp_like表中带有ABC的列(不区分大小写)： select * from t_regexp_like where regexp_like(value,'ABC','i'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) 功能：是将一些处理过的按一定格式编排过的字符串变回数值型的格式，支持参数：text/text,text测试用例如下： 将字符串'-34,338,492’转换为数值型格式： SELECT to_number('34,338,492', '99,999,999') from dual; to_number ----------- -34338492 (1 row) 将字符串'5.01-'转换为数值型格式： SELECT to_number('5.01-', '9.99S'); to_number ----------- -5.01 (1 row) 功能：将数字或日期转换为字符类型，支持参数：date/date,text/timestamp/timestamp,text测试用例如下： 将当前系统日期转换为字符格式： select to_char(sysdate()) from dual; to_char ------------ 2023-07-10 (1 row) 将当前系统日期转换为月份/日期/年字符格式： select to_char(sysdate(),'mm/dd/yyyy') from dual; to_char ------------ 07/10/2023 (1 row) 将当前日期的timestamp格式转换为字符格式： SELECT to_char(sysdate()::timestamp); to_char ---------------------------- 2023-07-10 09:46:44.000000 将当前日期的timestamp格式转换为月份/日期/年字符格式： SELECT to_char(sysdate()::timestamp,'MM-YYYY-DD'); to_char ------------ 07-2023-10 (1 row) 功能：将字符类型转换为日期类型，支持参数：text/text,text测试用例如下： 将'2023/07/06’转换为日期类型： select to_date('20230706') from dual; to_date ------------ 2023-07-06 (1 row) 将'-44-02-01’转换为日期类型： SELECT to_date('-44,0201','YYYY-MM-DD'); to_date ------------ 0044-02-01 (1 row) 功能：可以存储年、月、日、小时、分钟、秒，同时还可以存储秒的小数部分。支持参数：text/text,text测试用例如下： 查询'2018-11-02 12:34:56.025’以日期形式输出： SELECT to_timestamp('20181102.12.34.56.025'); to_timestamp ---------------------------- 2018-11-02 12:34:56.025000 (1 row) 查询’2011,12,18 11:38’以日期形式输出： SELECT to_timestamp('2011,12,18 11:38 ', 'YYYY-MM-DD HH24:MI:SS'); to_timestamp ---------------------------- 2011-12-18 11:38:00.000000 (1 row) 功能：根据时间查询，时间字符串有T,Z并有毫秒，时区。测试用例如下： 查询'2016-10-9 14:10:10.123000’以日期形式输出： SELECT to_timestamp_tz('2016-10-9 14:10:10.123000') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-10-09 14:10:10.123000 +08:00 (1 row) 查询'10-9-2016 14:10:10.123000 +8:30’以日期形式输出： SELECT to_timestamp_tz('10-9-2016 14:10:10.123000 +8:30', 'DD-MM-YYYY HH24:MI:SS.FF TZH:TZM') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-09-10 13:40:10.123000 +08:00 (1 row) 功能：将一个字符串类型转化为年和月的时间差类型，支持参数:text, 测试用例如下： 查询'20110101’以后两个年零八个月后的日期： select to_date('20110101','yyyymmdd')+to_yminterval('02-08') from dual; ?column? ------------ 2013-09-01 (1 row) 功能：将一个日期加上一定的小时或者天数变成另外一个日期,支持参数:text,测试用例如下： 查询当前系统时间加上9个半小时后的日期（当前为2023-07-06，18：00）： select sysdate()+to_dsinterval('0 09:30:00')as newdate from dual; newdate ------------ 2023-07-07 (1 row) 功能：将数字转换成时间间隔类型的数据。支持参数:double precision, text测试用例如下： 转换100.00个小时为时间间隔类型数据： SELECT NUMTODSINTERVAL(100.00, 'hour'); numtodsinterval ------------------------------- +000000004 04:00:00.000000000 (1 row) 转换100分钟为时间间隔类型数据： SELECT NUMTODSINTERVAL(100, 'minute'); numtodsinterval ------------------------------- +000000000 01:40:00.000000000 (1 row) 功能：将数字转换成日期间隔类型的数据。 支持参数：double precision,text,测试用例如下： 转换1.00，year为日期间隔： SELECT NUMTOYMINTERVAL(1.00,'year'); numtoyminterval ----------------- +000000001-00 (1 row) 转换1，mouth为日期间隔： SELECT NUMTOYMINTERVAL(1,'month'); numtoyminterval ----------------- +000000000-01 (1 row) 功能：返回会话中的日期和时间，支持参数：integer, 函数中增加参数为精度，测试用例如下： 返回当前会话中的日期和时间： select localtimestamp from dual; localtimestamp ---------------------------- 2023-07-07 09:18:15.896472 (1 row) 返回当前会话中的日期和时间（精度为1）： select localtimestamp(1) from dual; localtimestamp ---------------------------- 2023-07-07 09:18:16.100000 (1 row) 功能：将时间从一个时区转换为另一个时区，支持参数;timestamp, text ,测试用例如下： 将'2000-03-28 08:00:00', '3:00’转换为当前时区： SELECT FROM_TZ(TIMESTAMP '2000-03-28 08:00:00', '3:00') FROM DUAL; from_tz ----------------------------------- 2000-03-28 13:00:00.000000 +08:00 (1 row) 功能：将一个timestamptz转换为UTC时区时间。支持参数:timestamp with time zone 测试用例如下： 查询转换timestamp '2000-03-28 11:30:00.00 -8:00’为UTC时区后的时间： select sys_extract_utc(timestamp '2000-03-28 11:30:00.00 -8:00') from dual; sys_extract_utc ---------------------------- 2000-03-28 19:30:00.000000 (1 row) 功能：查看时区详细信息，测试用例如下： 查看当前时区的详细信息： select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Shanghai (1 row) 修改timezone后，查看时区详细信息： set timezone = 'Asia/Hong_Kong'; SET select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Hong_Kong (1 row) 功能：将字符串表示的二进制数值转换为一个raw数值。支持参数：text,测试用例如下： 将字符串’abcdef’转换为raw数值： select hextoraw('abcdef')from dual; hextoraw ---------- \\xabcdef (1 row) 功能：获取数据库的实例名。测试用例如下： 获取当前数据库的实例名： select uid() from dual; uid ----- 10 (1 row) 功能：返回当前用户环境的信息，测试用例如下： 查看当前用户是否是dba，如果是返回true： select userenv('isdba')from dual; get_isdba ----------- TRUE (1 row) 查看会话标志： select userenv('sessionid')from dual; get_sessionid --------------- 1 (1 row) 功能：传入字符串，返回对应的ASCII字符，测试用例如下： 只有ASCII字符： select asciistr('Hello, World!') from dual; asciistr --------------- Hello, World! (1 row) 非ASCII字符： select asciistr('你好') from dual; asciistr ------------ \\4F60\\597D 同时包含ASCII字符和非ASCII字符： select asciistr('ABÄCDE') from dual; asciistr ------------ AB\\00C4CDE (1 row) 功能：将字符串中的半角字符转换为全角字符： 输入半角字符，转换为全角字符： select to_multi_byte('1.2'::text) ; to_multi_byte --------------- １．２ 功能：将字符串中的半角字符转换为全角字符 输入全角字符，转换为半角字符： select to_single_byte('１．２'); to_single_byte ---------------- 1.2 功能：将基本字符和组合标记组合一个复合Unicode字符: 输入基本字符a和组合标记768, 返回法语à select compose('a'||chr(768)) from dual; compose --------- à (1 row) 功能：将复合Unicode字符（如带有重音或特殊符号的字符）分解为其基本字符和组合标记 输入法语é,返回基本字符e和组合标记301： select asciistr(decompose('é')) from dual; asciistr ---------- e\\0301 功能：返回当前时刻与给定上下文关联的参数的值，可以在SQL和PLSQL语言中使用。 ivorysql=# select sys_context('USERENV','DB_NAME'); sys_context ------------- ivorysql (1 row)","title":"内置数据类型与内置函数","component":"ivorysql-doc","version":"master","name":"builtin_types_functions","url":"/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html","titles":[{"text":"1. 内置数据类型","hash":"内置数据类型","id":1},{"text":"2. 内置函数类型","hash":"内置函数类型","id":2},{"text":"3. 内置函数说明","hash":"内置函数说明","id":3},{"text":"3.1. sysdate 函数","hash":"sysdate-函数","id":4},{"text":"3.2. systimestamp 函数","hash":"systimestamp-函数","id":5},{"text":"3.3. add_months 函数","hash":"add_months-函数","id":6},{"text":"3.4. last_day 函数","hash":"last_day-函数","id":7},{"text":"3.5. next_day 函数","hash":"next_day-函数","id":8},{"text":"3.6. months_between 函数","hash":"months_between-函数","id":9},{"text":"3.7. current_date 函数","hash":"current_date-函数","id":10},{"text":"3.8. current_timestamp 函数","hash":"current_timestamp-函数","id":11},{"text":"3.9. new_time 函数","hash":"new_time-函数","id":12},{"text":"3.10. tz_offset 函数","hash":"tz_offset-函数","id":13},{"text":"3.11. trunc 函数","hash":"trunc-函数","id":14},{"text":"3.12. instr 函数","hash":"instr-函数","id":15},{"text":"3.13. instrb 函数","hash":"instrb-函数","id":16},{"text":"3.14. substr 函数","hash":"substr-函数","id":17},{"text":"3.15. substrb 函数","hash":"substrb-函数","id":18},{"text":"3.16. trim 函数","hash":"trim-函数","id":19},{"text":"3.17. ltrim 函数","hash":"ltrim-函数","id":20},{"text":"3.18. rtrim 函数","hash":"rtrim-函数","id":21},{"text":"3.19. length 函数","hash":"length-函数","id":22},{"text":"3.20. lengthb 函数","hash":"lengthb-函数","id":23},{"text":"3.21. replace 函数","hash":"replace-函数","id":24},{"text":"3.22. regexp_replace 函数","hash":"regexp_replace-函数","id":25},{"text":"3.23. regexp_substr 函数","hash":"regexp_substr-函数","id":26},{"text":"3.24. regexp_instr 函数","hash":"regexp_instr-函数","id":27},{"text":"3.25. regexp_like 函数","hash":"regexp_like-函数","id":28},{"text":"3.26. to_number 函数","hash":"to_number-函数","id":29},{"text":"3.27. to_char 函数","hash":"to_char-函数","id":30},{"text":"3.28. to_date 函数","hash":"to_date-函数","id":31},{"text":"3.29. to_timestamp 函数","hash":"to_timestamp-函数","id":32},{"text":"3.30. to_timestamp_tz 函数","hash":"to_timestamp_tz-函数","id":33},{"text":"3.31. to_yminterval 函数","hash":"to_yminterval-函数","id":34},{"text":"3.32. to_dsinterval 函数","hash":"to_dsinterval-函数","id":35},{"text":"3.33. numtodsinterval 函数","hash":"numtodsinterval-函数","id":36},{"text":"3.34. numtoyminterval 函数","hash":"numtoyminterval-函数","id":37},{"text":"3.35. localtimestamp 函数","hash":"localtimestamp-函数","id":38},{"text":"3.36. from_tz 函数","hash":"from_tz-函数","id":39},{"text":"3.37. sys_extract_utc 函数","hash":"sys_extract_utc-函数","id":40},{"text":"3.38. sessiontimezone 函数","hash":"sessiontimezone-函数","id":41},{"text":"3.39. hextoraw 函数","hash":"hextoraw-函数","id":42},{"text":"3.40. uid 函数","hash":"uid-函数","id":43},{"text":"3.41. USERENV 函数","hash":"userenv-函数","id":44},{"text":"3.42. ASCIISTR 函数","hash":"asciistr-函数","id":45},{"text":"3.43. TO_MULTI_BYTE 函数","hash":"to_multi_byte-函数","id":46},{"text":"3.44. TO_SINGLE_BYTE 函数","hash":"to_single_byte-函数","id":47},{"text":"3.45. COMPOSE 函数","hash":"compose-函数","id":48},{"text":"3.46. DECOMPOSE 函数","hash":"decompose-函数","id":49},{"text":"3.47. SYS_CONTEXT 函数","hash":"sys_context-函数","id":50}]},"61":{"id":61,"text":"IvorySQL中的CALL语句支持调用单独的函数存储过程，也可以是在包或对象类型中的函数和存储过程。CALL调用函数增加INTO子句语法，插入的目标是一个host variable。 CALL支持调用单独的或定义在包中函数和存储过程。 CALL调用函数时，增加INTO子句语法，插入的目标是一个host variable。 CALL调用无参或全取默认值的函数/存储过程不能省略空括号。 CALL在调用函数和存储过程时的参数或INTO子句中支持引用绑定变量。 CALL语句中OUT参数对应的绑定变量支持对精度和数据类型的验证。 输出绑定变量不允许被重复绑定。 -- 创建函数 ivorysql=# create or replace function f_defs(a number default 1314) ivorysql-# return number ivorysql-# is ivorysql-# begin ivorysql-# raise notice '%', a; ivorysql-# return a; ivorysql-# end; ivorysql-# / CREATE FUNCTION -- 声明绑定变量 ivorysql=# variable x number -- 调用函数并获取返回值 ivorysql=# call f_defs() into :x; NOTICE: 1314 Call completed. ivorysql=# print x X ------ 1314 ivorysql=# create table tb1(c1 int); CREATE TABLE -- 创建包规范 ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# procedure test_p ; ivorysql-# end; ivorysql-# / CREATE PACKAGE -- 创建包体 ivorysql=# create or replace package body pkg is ivorysql-# procedure test_p is ivorysql-# begin ivorysql-# insert into tb1 values(1); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 2; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY -- 调用包中的过程 ivorysql=# call pkg.test_p(); CALL ivorysql=# select * from tb1; c1 ----- 1 (1 row) -- 创建带默认参数函数 ivorysql=# CREATE OR REPLACE FUNCTION default_arg_func(p_num NUMBER DEFAULT 100) RETURN NUMBER AS ivorysql-# BEGIN ivorysql-# RETURN p_num + 5; ivorysql-# END; ivorysql-# / CREATE FUNCTION -- 正确调用默认参数函数（必须带括号） ivorysql=# VARIABLE default_result NUMBER; ivorysql=# CALL default_arg_func() INTO :default_result; -- 使用默认值100 Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 105 -- 带参数调用 ivorysql=# CALL default_arg_func(200) INTO :default_result; Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 205 -- 设置输入绑定变量 ivorysql=# VARIABLE input_num NUMBER = 7; -- 使用绑定变量作为参数调用函数 ivorysql=# VARIABLE func_result NUMBER; ivorysql=# CALL stand_alone_func(:input_num) INTO :func_result; Call completed. ivorysql=# PRINT func_result; FUNC_RESULT ------------- 14 -- 创建带OUT参数的过程 ivorysql=# CREATE OR REPLACE PROCEDURE out_param_proc( ivorysql(# p_in IN VARCHAR2, ivorysql(# p_out OUT VARCHAR2, ivorysql(# p_num_out OUT NUMBER ivorysql(# ) AS ivorysql-# BEGIN ivorysql-# p_out := p_in || ' processed'; ivorysql-# p_num_out := LENGTH(p_in); ivorysql-# END; ivorysql-# / CREATE PROCEDURE -- 测试OUT参数类型匹配 ivorysql=# VARIABLE out_var VARCHAR2(50); ivorysql=# VARIABLE num_var NUMBER; ivorysql=# CALL out_param_proc('Test input', :out_var, :num_var); Call completed. ivorysql=# PRINT out_var; OUT_VAR ---------------------- Test input processed ivorysql=# PRINT num_var; NUM_VAR --------- 10 -- 测试OUT参数精度不足（会截断） ivorysql=# VARIABLE short_out VARCHAR2(5); ivorysql=# CALL out_param_proc('Long input string', :short_out, :num_var); Call completed. ivorysql=# PRINT short_out; SHORT_OUT ----------- Long -- 测试类型不匹配（会报错） ivorysql=# VARIABLE wrong_type NUMBER; ivorysql=# CALL out_param_proc('Test', :wrong_type, :num_var); ERROR: invalid input syntax for type numeric: \"Test processed\" -- 准备绑定变量 ivorysql=# VARIABLE dup_var VARCHAR2(100); -- 尝试重复绑定（会报错） ivorysql=# CALL out_param_func('Test', :dup_var) INTO :dup_var; ERROR: output parameter cannot be a duplicate bind -- 正确做法：使用不同的绑定变量 ivorysql=# VARIABLE out1 VARCHAR2(100); ivorysql=# CALL out_param_proc('Correct usage', :out1, :num_var); Call completed. ivorysql=# PRINT out1; OUT1 ------------------------- Correct usage processed","title":"兼容Oracle的CALL INTO","component":"ivorysql-doc","version":"master","name":"compat_call_into","url":"/ivorysql-doc/master/oracle_compatibility/compat_call_into.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3},{"text":"3.1. call into调用函数，并插入host variable","hash":"call-into调用函数并插入host-variable","id":4},{"text":"3.2. call调用包中的存储过程","hash":"call调用包中的存储过程","id":5},{"text":"3.3. 无参和默认参数调用","hash":"无参和默认参数调用","id":6},{"text":"3.4. 在参数或INTO子句中引用绑定变量","hash":"在参数或into子句中引用绑定变量","id":7},{"text":"3.5. CALL语句中OUT参数支持精度和数据类型验证","hash":"call语句中out参数支持精度和数据类型验证","id":8},{"text":"3.6. 输出绑定变量不允许重复绑定","hash":"输出绑定变量不允许重复绑定","id":9}]},"62":{"id":62,"text":"为了满足PG和Oracle的引用标识符大小写兼容，ivorysql设计了三种引用标识符的大小写转换模式。通过guc参数“identifier_case_switch”选择转换模式; 如果 guc参数“identifier_case_switch”值为“normal” 1). 保持双引号所引用的标识符中的字母大小写不变。 如果 guc参数“identifier_case_switch”值为“interchange”： 1). 如果双引号所引用的标识符中的字母全部为大写，则将大写转换为小写。 2). 如果双引号所引用的标识符中的字母全部为小写，则将小写转换为大写。 3). 如果用双引号引起来的标识符中的字母是大小写混合的，则保持标识符不变。 如果 guc参数“identifier_case_switch”值为“lowercase” 1). 如果双引号所引用的标识符中的字母全部为大写，则将大写转换为小写。 2). 如果用双引号引起来的标识符中的字母是大小写混合的，则保持标识符不变。 在initdb程序中加入 -C选项设置大小写转换模式，-C对应的值为： \"normal\" ------ \"0\"同义 \"interchange\" ------ \"1\"同义 \"lowercase\" ------ \"2\"同义 在初始化数据库集簇的过程中，将大小写转换模式保存到data目录的global/pg_control文件中。 normal ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = normal; SET ivorysql=# CREATE TABLE \"NORMAL_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Normal_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"normal_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"NORMAL_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Normal_1\"; ERROR: relation \"Normal_1\" does not exist LINE 1: select * from \"Normal_1\"; ivorysql=# select * from \"normal_1\"; ERROR: relation \"normal\" does not exist LINE 1: select * from \"normal\"; ivorysql=# select * from NORMAL_1; ERROR: relation \"normal_1\" does not exist LINE 1: select * from NORMAL_1; ivorysql=# select * from \"Normal_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_2\"; ERROR: relation \"NORMAL_2\" does not exist LINE 1: select * from \"NORMAL_2\"; ivorysql=# select * from \"normal_2\"; ERROR: relation \"normal_2\" does not exist LINE 1: select * from \"normal_2\"; ivorysql=# select * from Normal_2; ERROR: relation \"normal_2\" does not exist LINE 1: select * from Normal_2; ivorysql=# select * from \"normal_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_3\"; ERROR: relation \"NORMAL_3\" does not exist LINE 1: select * from \"NORMAL_3\"; ivorysql=# select * from \"Normal_3\"; ERROR: relation \"Normal_3\" does not exist LINE 1: select * from \"Normal_3\"; ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE interchange ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = interchange; SET ivorysql=# CREATE TABLE \"INTER_CHANGE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Inter_Change_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"inter_change_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"INTER_CHANGE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_1\"; ERROR: relation \"Inter_Change_1\" does not exist LINE 1: select * from \"Inter_Change_1\"; ivorysql=# select * from \"inter_change_1\"; ERROR: relation \"INTER_CHANGE_1\" does not exist LINE 1: select * from \"inter_change_1\"; ivorysql=# select * from INTER_CHANGE_1; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_2\"; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from \"INTER_CHANGE_2\"; ivorysql=# select * from \"inter_change_2\"; ERROR: relation \"INTER_CHANGE_2\" does not exist LINE 1: select * from \"inter_change_2\"; ivorysql=# select * from Inter_Change_2; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from Inter_Change_2; ivorysql=# select * from \"inter_change_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_3\"; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# select * from \"Inter_Change_3\"; ERROR: relation \"Inter_Change_3\" does not exist LINE 1: select * from \"Inter_Change_3\"; ivorysql=# select * from inter_change_3; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# drop table \"INTER_CHANGE_1\"; DROP TABLE ivorysql=# drop table \"Inter_Change_2\"; DROP TABLE ivorysql=# drop table \"inter_change_3\"; DROP TABLE lowercase ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = lowercase; SET ivorysql=# CREATE TABLE \"LOWER_CASE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Lower_Case_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"lower_case_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"LOWER_CASE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_1\"; ERROR: relation \"Lower_Case_1\" does not exist LINE 1: select * from \"Lower_Case_1\"; ivorysql=# select * from \"lower_case_1\"; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from LOWER_CASE_1; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from \"Lower_Case_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"LOWER_CASE_2\"; ivorysql=# select * from \"lower_case_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"lower_case_2\"; ivorysql=# select * from Lower_Case_2; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from Lower_Case_2; ivorysql=# select * from \"lower_case_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_3\"; ERROR: relation \"Lower_Case_3\" does not exist LINE 1: select * from \"Lower_Case_3\"; ivorysql=# select * from LOWER_CASE_3; c1 | c2 ----+---- (0 行记录) ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE","title":"引用标识符的大小写转换设计","component":"ivorysql-doc","version":"master","name":"compat_case_conversion","url":"/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"2.1. 大小写转换的三种模式（默认为interchange）","hash":"大小写转换的三种模式默认为interchange","id":3},{"text":"2.2. 初始化数据库集簇时","hash":"初始化数据库集簇时","id":4},{"text":"2.3. 经典用例","hash":"经典用例","id":5}]},"63":{"id":63,"text":"在 IvorySQL 的 Oracle 兼容模式下,支持将空字符串转换成NULL进行存储,提供与Oracle数据库一致的行为。 Oracle兼容模式下支持将空字符串转换成NULL进行存储。 通过参数 ivorysql.enable_emptystring_to_null 控制该特性,默认值为 on。 当该参数开启时,插入空字符串会自动转换为NULL值存储。 可以通过 IS NULL 条件查询到转换后的NULL值。 -- 创建测试表 ivorysql=# create table abc (id int); CREATE TABLE -- 查看空字符串转NULL参数状态 ivorysql=# show ivorysql.enable_emptystring_to_null; ivorysql.enable_emptystring_to_NULL ------------------------------------- on (1 row) -- 插入空字符串 ivorysql=# insert into abc values(''); INSERT 0 1 -- 查询表数据,显示为NULL ivorysql=# select * from abc; id \\---- (1 row) -- 使用IS NULL条件查询 ivorysql=# select * from abc where id is null; id \\---- (1 row)","title":"兼容Oracle 空字符串转NULL","component":"ivorysql-doc","version":"master","name":"compat_empty_string_to_null","url":"/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"64":{"id":64,"text":"本文档解释 IvorySQL 中 Force View 的用途，帮助用户在依赖对象尚未就绪时仍可创建强制视图占位并保持与 Oracle 行为一致。 Force View 可支持在依赖表尚未准备好的情况下进行视图迁移，在依赖满足后通过自动或显式编译快速恢复为普通视图。 CREATE [OR REPLACE] FORCE VIEW：即使查询引用了不存在的表或函数，也会创建视图占位对象并保存原始 SQL，提示“View created with compilation errors”。 自动编译：访问 Force View 时会尝试重新编译；成功后变为普通视图，失败则报错 view \"<schema>.<name>\" has errors。 依赖失效回退：当普通视图因依赖对象被删除或结构变更而失效时，系统会自动将其转化为 Force View，保留最后一次有效定义以便后续恢复。 -- 引用不存在的基础表，验证可成功创建占位 CREATE FORCE VIEW fv_customer AS SELECT c_id, c_name FROM missing_customer; -- 期望输出：WARNING: View created with compilation errors -- 先补齐依赖对象 CREATE TABLE missing_customer( c_id int primary key, c_name text ); INSERT INTO missing_customer VALUES (1, 'Alice'); -- 访问 Force View 时自动尝试编译 SELECT * FROM fv_customer; -- 成功时视图转换为普通视图并返回数据 -- 再次让视图失效：重建定义为 Force View CREATE OR REPLACE FORCE VIEW fv_customer AS SELECT c_id, upper(c_name) AS c_name FROM missing_customer_v2; -- 显式编译：依赖仍缺少，保持 Force View 状态并输出 WARNING ALTER VIEW fv_customer COMPILE; -- 期望输出：WARNING: View altered with compilation errors -- 补齐依赖后再次编译 CREATE TABLE missing_customer_v2( c_id int, c_name text ); ALTER VIEW fv_customer COMPILE; -- 期望输出：ALTER VIEW 成功，视图恢复为普通视图","title":"Force View","component":"ivorysql-doc","version":"master","name":"compat_force_view","url":"/ivorysql-doc/master/oracle_compatibility/compat_force_view.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3},{"text":"3.1. 创建缺失依赖的 Force View","hash":"创建缺失依赖的-force-view","id":4},{"text":"3.2. 自动编译并恢复为普通视图","hash":"自动编译并恢复为普通视图","id":5},{"text":"3.3. 显式编译与失败回退","hash":"显式编译与失败回退","id":6}]},"65":{"id":65,"text":"PostgreSQL支持函数(FUNCTION)和存储过程(PROCEDURE)，但是在语法上和Oracle有差异，为了让Oracle的PLSQL语句可以在IvorySQL上执行，也就是“语法兼容”，IvorySQL采取这样的解决方案：如果Oracle的子句在IvorySQL中存在相同功能的子句，则直接映射为对应的IvorySQL子句，否则只实现其语法，不实现其功能。 具体的Oracle兼容支持包括以下几个方面： CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer AS BEGIN RETURN 1; END; / CREATE or replace NONEDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / configure --help --with-max-funarg=MAXFUNARG CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE OR REPLACE FUNCTION ora_func (num1 IN int, num2 IN int) RETURN int AS num3 int :=10; num4 int :=10; num5 int; BEGIN num3 := num1 + num2; num4 := num1 * num2; num5 := num3 * num4; RETURN num5; END; / CREATE FUNCTION select ora_func(5,9)from dual; ora_func ---------- 630 (1 row) CREATE OR REPLACE FUNCTION test_nocopy(a IN int, b OUT NOCOPY int, c IN OUT NOCOPY int) RETURN int IS BEGIN b := a; c := a; return 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID CURRENT_USER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON () IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING pg_catalog.int4 IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED TABLE POLYMORPHIC USING pg_catalog.int4 SQL_MACRO IS BEGIN RETURN 1; END; / alter function public.test_func noneditionable; alter function test_func compile; alter function test_func compile debug; alter function test_func compile debug sd = mv; alter function test_func compile debug reuse settings; CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE NONEDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = NONE IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID DEFINER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS p integer := 20; begin raise notice '%', p; end; / alter procedure test_proc editionable; alter procedure public.test_proc noneditionable; alter procedure test_proc compile; alter procedure test_proc compile debug; alter procedure test_proc compile debug sd = mv; alter procedure test_proc compile debug reuse settings; create or replace function f_noparentheses return int is begin return 11; end; / select f_noparentheses from dual; create or replace procedure protest as begin raise notice 'protest'; end; / CALL protest(); 相关视图位于文件 contrib/ivorysql_ora/src/sysview/sysview--1.0.sql 中，包括DBA_PROCEDURES，ALL_PROCEDURES，USER_PROCEDURES，DBA_SOURCE，ALL_SOURCE，USER_SOURCE，DBA_ARGUMENTS，ALL_ARGUMENTS，USER_ARGUMENTS等。","title":"PL/iSQL函数与存储过程","component":"ivorysql-doc","version":"master","name":"compat_function_procedure","url":"/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 兼容支持","hash":"兼容支持","id":2},{"text":"2.1. CREATE FUNCTION 语法支持 EDITIONABLE/NONEDITIONABLE","hash":"create-function-语法支持-editionablenoneditionable","id":3},{"text":"2.2. CREATE FUNCTION 语法支持 RETURN/IS 关键字，无需指定语言","hash":"create-function-语法支持-returnis-关键字无需指定语言","id":4},{"text":"2.3. CREATE FUNCTION 语法函数没有参数，函数名后面不带()","hash":"create-function-语法函数没有参数函数名后面不带","id":5},{"text":"2.4. CREATE FUNCTION 参数个数增加，最大值可在configure时指定","hash":"create-function-参数个数增加最大值可在configure时指定","id":6},{"text":"2.5. CREATE FUNCTION 语法中 END; 在psql中以/结束","hash":"create-function-语法中-end-在psql中以结束","id":7},{"text":"2.6. CREATE FUNCTION 语法变量声明前面没有 DECLARE 关键字","hash":"create-function-语法变量声明前面没有-declare-关键字","id":8},{"text":"2.7. CREATE FUNCTION 语法支持 OUT 参数 NOCOPY 功能","hash":"create-function-语法支持-out-参数-nocopy-功能","id":9},{"text":"2.8. CREATE FUNCTION 语法支持 sharing 子句","hash":"create-function-语法支持-sharing-子句","id":10},{"text":"2.9. CREATE FUNCTION 语法支持 invoker_rights (AUTHID) 子句，默认权限改成 DR（DEFINER）","hash":"create-function-语法支持-invoker_rights-authid-子句默认权限改成-drdefiner","id":11},{"text":"2.10. CREATE FUNCTION 语法支持 ACCESSIBLE BY","hash":"create-function-语法支持-accessible-by","id":12},{"text":"2.11. CREATE FUNCTION 语法支持 DEFAULT COLLATION","hash":"create-function-语法支持-default-collation","id":13},{"text":"2.12. CREATE FUNCTION 语法支持 deterministic 子句，功能与 IvorySQL 的 IMMUTABLE 相同","hash":"create-function-语法支持-deterministic-子句功能与-ivorysql-的-immutable-相同","id":14},{"text":"2.13. CREATE FUNCTION 语法支持 parallel_enable 子句，功能与 IvorySQL 的 PARALLEL SAFE 相同","hash":"create-function-语法支持-parallel_enable-子句功能与-ivorysql-的-parallel-safe-相同","id":15},{"text":"2.14. CREATE FUNCTION 语法支持 result_cache 子句","hash":"create-function-语法支持-result_cache-子句","id":16},{"text":"2.15. CREATE FUNCTION 语法支持 aggregate 子句","hash":"create-function-语法支持-aggregate-子句","id":17},{"text":"2.16. CREATE FUNCTION 语法支持 pipelined 子句","hash":"create-function-语法支持-pipelined-子句","id":18},{"text":"2.17. CREATE FUNCTION 语法支持 sql_macro 子句","hash":"create-function-语法支持-sql_macro-子句","id":19},{"text":"2.18. ALTER FUNCTION 语法兼容","hash":"alter-function-语法兼容","id":20},{"text":"2.19. CREATE PROCEDURE 语法支持 EDITIONABLE / NONEDITIONABLE","hash":"create-procedure-语法支持-editionable-noneditionable","id":21},{"text":"2.20. CREATE PROCEDURE 语法函数没有参数，函数名后面不带()","hash":"create-procedure-语法函数没有参数函数名后面不带","id":22},{"text":"2.21. CREATE PROCEDURE 语法中 END; 在psql中以 / 结束","hash":"create-procedure-语法中-end-在psql中以-结束","id":23},{"text":"2.22. CREATE PROCEDURE 语法支持 sharing 子句","hash":"create-procedure-语法支持-sharing-子句","id":24},{"text":"2.23. CREATE PROCEDURE 语法支持 DEFAULT COLLATION 子句","hash":"create-procedure-语法支持-default-collation-子句","id":25},{"text":"2.24. CREATE PROCEDURE 语法支持 invoker_rights (AUTHID) 子句","hash":"create-procedure-语法支持-invoker_rights-authid-子句","id":26},{"text":"2.25. CREATE PROCEDURE 语法支持 ACCESSIBLE BY 子句","hash":"create-procedure-语法支持-accessible-by-子句","id":27},{"text":"2.26. ALTER PROCEDURE 语法兼容","hash":"alter-procedure-语法兼容","id":28},{"text":"2.27. 存储过程和函数没有参数，SELECT调用支持不带 ()","hash":"存储过程和函数没有参数select调用支持不带","id":29},{"text":"2.28. 函数和存储过程相关的视图","hash":"函数和存储过程相关的视图","id":30},{"text":"2.29. 支持—​和/**/两种注释方法","hash":"支持和两种注释方法","id":31},{"text":"2.30. pg_dump备份SQL脚本文件的备份格式时，在函数的定义最后增加一个斜线 /","hash":"pg_dump备份sql脚本文件的备份格式时在函数的定义最后增加一个斜线","id":32}]},"66":{"id":66,"text":"本文档意在为使用 like 模糊查询的人员提供一个深入了解兼容Oracle 的模糊查询like实现的过程，是like兼容的实现文档。 数据库名称 like模糊查询 oracle oracle的字符串类型是varchar2，支持对数字、日期、字符串字段类型的列用Like关键字配合通配符来实现模糊查询 IvorySQL IvorySQL的字符串基本类型是text，所以like是以text为基础上，其他IvorySQL的类型能隐式转换成text，这样不用创建operator就能自动转换 create table t_ora_like (id int ,str1 varchar(8), date1 timestamp with time zone, date2 time with time zone, num int, str2 varchar(8)); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123456,'test1','2022-09-26 16:39:20','2022-09-26 16:39:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123457,'test2','2022-09-26 16:40:20','2022-09-26 16:40:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (223456,'test3','2022-09-26 16:41:20','2022-09-26 16:41:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123458,'test4','2022-09-26 16:42:20','2022-09-26 16:42:20'); select * from t_ora_like where str1 like 'test%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date1 like '2022%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date2 like '16%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where id like '123%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (3 rows) select * from t_ora_like where id like null; id | str1 | date1 | date2 | num | str2 ----+------+-------+-------+-----+------ (0 rows)","title":"兼容Oracle like","component":"ivorysql-doc","version":"master","name":"compat_like_operator","url":"/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"67":{"id":67,"text":"在 IvorySQL 中使用 Oracle 风格嵌套子函数。 支持在匿名块、函数或过程内部声明与调用子函数/子过程，作用域限定在父块内。 子函数可读取及更新父级变量，也可定义自身局部变量；父级无法直接访问子函数内部状态。 支持重载解析机制，按参数个数、类型或命名区分同名子程序。 DO $$ DECLARE v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; RETURN p_value * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; END; $$ LANGUAGE plisql; DO $$ DECLARE v_base_multiplier integer := 20; v_audit_counter integer := 0; v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; v_audit_counter := v_audit_counter + 1; RETURN v_base_multiplier * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; RAISE NOTICE 'v_audit_counter=%', v_audit_counter; END; $$ LANGUAGE plisql; -- Polymorphic nested function specializing on argument type DO $$ DECLARE v_last_notice text := 'none'; FUNCTION describe_value(p_input anyelement) RETURN text IS BEGIN v_last_notice := format('polymorphic dispatch with %s', pg_typeof(p_input)); RETURN v_last_notice; END; FUNCTION describe_value(p_input anyarray, p_element anyelement) RETURN text IS BEGIN v_last_notice := format('array dispatch with %s', pg_typeof(p_input)::text); RETURN v_last_notice; END; BEGIN RAISE NOTICE '%', describe_value(100); RAISE NOTICE '%', describe_value('IvorySQL'::text); -- explicit cast avoids ambiguous literal RAISE NOTICE '%', describe_value(ARRAY[1,2,3], NULL::int); -- extra arg guides anyarray resolution RAISE NOTICE 'last notice=%', v_last_notice; END; $$ LANGUAGE plisql;","title":"兼容Oracle 嵌套子函数","component":"ivorysql-doc","version":"master","name":"compat_nested_function","url":"/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"68":{"id":68,"text":"NLS 是 National Language Support（国家语言支持）的缩写，指的是 Oracle 提供的本地化支持功能。 IvorySQL提供兼容Oracle的NLS参数功能。 包含如下参数： 参数名称 功能描述 ivorysql.datetime_ignore_nls_mask 表示日期格式是否忽略NLS参数影响，默认为0。 nls_length_semantics 兼容Oracle的同名参数，表示char/varchar/varchar2的类型修饰符的大小单位是字节还是字符。 nls_date_format 表示默认的日期格式，可以通过show命令查看，默认为‘YYYY-MM-DD’。 nls_timestamp_format 兼容Oracle的同名参数，控制带时间的日期格式。 nls_timestamp_tz_format 兼容Oracle的同名参数，控制带时区的日期时间格式。 nls_territory 兼容Oracle的同名参数，指定数据库的默认区域。 nls_iso_currency 兼容Oracle的同名参数，指定国家和区域对应的唯一货币符。 nls_currency 兼容Oracle的同名参数，指定显示本地货币的符号，对应数字字符串格式中占位符L。 示例： ivorysql=# set ivorysql.datetime_ignore_nls_mask = 0; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ERROR: datetime format picture ends before converting entire input string LINE 1: select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ^ ivorysql=# set ivorysql.datetime_ignore_nls_mask = 2; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) 示例： ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) ivorysql=# set nls_timestamp_format=\"pg\"; SET ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; ERROR: date format not recognized LINE 1: select '2025-10-15 11:00:00.102030 '::oratimestamp ; ^ IvorySQL使用nls_length_semantics参数的值来决定长度类型，有byte和char两种值，默认为byte。 示例： vorysql=# alter session set nls_length_semantics = char; SET ivorysql=# create table character_tb(char_c char(6), char_b varchar2(6 byte), char_v varchar(6)); CREATE TABLE ivorysql=# insert into character_tb values('中文测试数据', '123456', '成功'); INSERT 0 1 ivorysql=# select * from character_tb ; char_c | char_b | char_v --------------+--------+-------- 中文测试数据 | 123456 | 成功 (1 row) ivorysql=# select length(char_b), length(char_c), length(char_v) from character_tb; length | length | length --------+--------+-------- 6 | 6 | 2 (1 row) 示例： ivorysql=# show ivorysql.identifier_case_switch; ivorysql.identifier_case_switch --------------------------------- interchange (1 row) ivorysql=# set nls_currency to \"CHINA\"; SET ivorysql=# show nls_currency; nls_currency -------------- CHINA (1 row) ivorysql=# set nls_currency to \"China\"; SET ivorysql=# show nls_currency; nls_currency -------------- China (1 row) nls_currency和nls_iso_currency的行为，受兼容oracle大小写特性 identifier_case_switch 影响","title":"NLS 参数","component":"ivorysql-doc","version":"master","name":"compat_nls_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. NLS 日期掩码设置","hash":"nls-日期掩码设置","id":3},{"text":"2.2. 禁用NLS日期/时间戳参数","hash":"禁用nls日期时间戳参数","id":4},{"text":"2.3. 设置nls_length_semantics","hash":"设置nls_length_semantics","id":5},{"text":"2.4. 设置NLS货币符号","hash":"设置nls货币符号","id":6}]},"69":{"id":69,"text":"IvorySQL提供了兼容Oracle的out参数功能，包括含有out参数的函数与过程、匿名块支持out参数、libpq支持out参数。 本文档旨在为使用人员介绍out参数的功能。 IvorySQL提供了兼容Oracle的out参数功能，包括如下内容。 创建语法如下： CREATE [ OR REPLACE ] FUNCTION name ( [ [ argmode ] [ argname ] argtype [ { DEFAULT | = } default_expr ] [, ...] ] ) [ RETURNS rettype | RETURNS TABLE ( column_name column_type [, ...] ) ] { LANGUAGE lang_name | TRANSFORM { FOR TYPE type_name } [, ... ] | WINDOW | { IMMUTABLE | STABLE | VOLATILE } | [ NOT ] LEAKPROOF | { CALLED ON NULL INPUT | RETURNS NULL ON NULL INPUT | STRICT } | { [ EXTERNAL ] SECURITY INVOKER | [ EXTERNAL ] SECURITY DEFINER } | PARALLEL { UNSAFE | RESTRICTED | SAFE } | COST execution_cost | ROWS result_rows | SUPPORT support_function | SET configuration_parameter { TO value | = value | FROM CURRENT } | AS 'definition' | AS 'obj_file', 'link_symbol' | sql_body } ... 其中argmode可以是IN，OUT，INOUT（IN OUT），VARIADIC。如果不指定默认是IN。argmode也可以写在argname的后面。 不同于原生PG，兼容Oracle的out参数和返回值数据类型没有关联。IN OUT模式和OUT模式都不能有默认值。如果有out参数，则函数体中必须有RETURN语句。 支持冒号占位符形式的绑定变量，例如: 1，:name。 新增DO+USING语法： DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, …​] 支持在libpq中按位置和按参数名字绑定变量，提供系统函数get_parameter_descr，该函数根据SQL语句，返回变量名字与位置的关系。 libpq接口端提供准备、绑定、执行函数，这些函数与OCI相应函数类似。 使用流程为： 使用IvyHandleAlloc分配语句句柄和错误句柄。 调用IvyStmtPrepare准备语句。 调用IvyBindByPos或IvyBindByName 绑定参数。 调用IvyStmtExecute 执行，可重复执行。 调用IvyFreeHandle 释放语句句柄和错误句柄。 另外还实现了Ivyconnectdb，Ivystatus，Ivyexec，IvyresultStatus，IvyCreatePreparedStatement，IvybindOutParameterByPos，IvyexecPreparedStatement，IvyexecPreparedStatement2，Ivynfields，Ivyntuples，Ivyclear等一系列接口函数。 在源代码中 src/interfaces/libpq/ivytest 目录里可以找到示例程序。 out参数和返回值数据类型没有关联 ``` ivorysql=# create or replace function test_return_out(id integer,price out integer,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000; ivorysql-# name := 'test a char out'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / CREATE FUNCTION ``` IN OUT模式和OUT模式都不能有默认值 ``` ivorysql=# create or replace function test_return_inout(id integer,price in out integer default 100,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000 + price; ivorysql-# name := 'this is a test'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / ERROR: IN OUT formal parameters may have no default expressions ``` 如果有out参数，并且函数返回类型不是void，则函数体中必须有RETURN语句 ``` --if function's return type is non-void, the function body must has RETURN statement --if there is no RETURN statement, the function can be created, but when it is called, --an error is raised ivorysql=# create or replace function f2(id integer,price out integer) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 2; ivorysql-# end; ivorysql-# / CREATE FUNCTION ivorysql=# declare ivorysql-# a varchar(20); ivorysql-# b int; ivorysql-# begin ivorysql-# a := f2(1, b); ivorysql-# end; ivorysql-# / ERROR: Function returned without value CONTEXT: PL/iSQL function f2(pg_catalog.int4,pg_catalog.int4) line 0 at RETURN PL/iSQL function inline_code_block line 5 at assignment ``` 支持冒号占位符形式的绑定变量，新增DO+USING语法 ``` ivorysql=# do $$ ivorysql$# declare ivorysql$# a int; ivorysql$# begin ivorysql$# :x := 1; ivorysql$# :y := 2; ivorysql$# end; $$ using out, out; $1 | $2 ----+---- 1 | 2 (1 row) ``` 系统函数 get_parameter_descr ``` ivorysql=# select * from get_parameter_description('insert into t values(:x,:y)'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows) ```","title":"out参数","component":"ivorysql-doc","version":"master","name":"compat_out_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. 含有out参数的函数","hash":"含有out参数的函数","id":3},{"text":"2.2. 匿名块支持out参数","hash":"匿名块支持out参数","id":4},{"text":"2.3. libpq中调用含out参数的函数","hash":"libpq中调用含out参数的函数","id":5},{"text":"3. 测试用例","hash":"测试用例","id":6},{"text":"3.1. 含有out参数的函数","hash":"含有out参数的函数-2","id":7},{"text":"3.2. 匿名块支持out参数","hash":"匿名块支持out参数-2","id":8}]},"70":{"id":70,"text":"IvorySQL提供了兼容Oracle RowID的功能。RowID是一种伪列，在创建表时由数据库自动生成，对于数据库中的每一行，RowID 伪列返回该行的地址。 RowID 应当具备以下特性： 1. 逻辑地标识每一行，且值唯一 2. 可以通过ROWID快速查询和修改表的其他列，自身不能被插入和修改 3. 用户可以控制是否开启此功能 IvorySQL提供了多种方式来开启RowID功能。 在IvorySQL 的兼容Oracle 模式下，可以通过 set ivorysql.default_with_rowids to on 来开启RowID，这个参数默认值为off。打开后创建的表，默认就带有了RowID列，可以通过 \\d+ 表名 来查看。 ivorysql=# show ivorysql.default_with_rowids; ivorysql.default_with_rowids ------------------------------ off (1 row) ivorysql=# create table t(a int); CREATE TABLE ivorysql=# \\d+ t Table \"public.t\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap 用户可以选择在需要的表上带有这个选项，没有WITH ROWID选项，将会创建一个普通的表。 ivorysql=# create table t2(a int) with rowid; CREATE TABLE ivorysql=# \\d+ t2 Table \"public.t2\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Indexes: \"t2_16432_rowid_idx\" btree (rowid) Access method: heap Has ROWID: yes 这种方式允许当一个普通表需要使用 ROWID 时，通过ATLER 命令去添加ROWID 列。 也可以通过ALTER TABLE … SET WITHOUT ROWID命令去除RowID。 ivorysql=# create table t3(a int); CREATE TABLE ivorysql=# alter table t3 set with rowid; ALTER TABLE ivorysql=# \\d+ t3; Table \"public.t3\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap Has ROWID: yes","title":"RowID","component":"ivorysql-doc","version":"master","name":"compat_rowid","url":"/ivorysql-doc/master/oracle_compatibility/compat_rowid.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能开启","hash":"功能开启","id":2},{"text":"2.1. 通过GUC参数开启","hash":"通过guc参数开启","id":3},{"text":"2.2. 通过建表语句中增加 WITH ROWID 选项开启","hash":"通过建表语句中增加-with-rowid-选项开启","id":4},{"text":"2.3. 通过对现有表ALTER TABLE … SET WITH ROWID开启","hash":"通过对现有表alter-table-set-with-rowid开启","id":5}]},"71":{"id":71,"text":"IvorySQL的sys_guid() 是一个强大的随机数产生函数，它产生并返回一个由16个字节组成的数据库级别唯一的标识符(原始值)。 ivorysql=# select sys_guid() from dual; sys_guid ------------------------------------ \\x3ed9426c8a093442a38bea09a74f44a1 (1 row) create table student ( student_id raw(16) default sys_guid() primary key, student_name varchar2(100) not null ); insert into student(student_name) values ('Steven');","title":"sys_guid() 函数","component":"ivorysql-doc","version":"master","name":"compat_sys_guid","url":"/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 使用示例","hash":"使用示例","id":2},{"text":"3. sys_guid函数在建表时可以作为主键","hash":"sys_guid函数在建表时可以作为主键","id":3},{"text":"4. 新增数据时自动填充主键","hash":"新增数据时自动填充主键","id":4}]},"72":{"id":72,"text":"IvorySQL提供了兼容Oracle的plsql数据类型功能，包括%TYPE、%ROWTYPE。 本文档旨在为使用人员介绍%TYPE、%ROWTYPE的功能。 IvorySQL提供了兼容Oracle的%TYPE、%ROWTYPE功能，包括如下内容。 创建表以及函数。 CREATE TABLE t1(id int, name varchar(20)); --function's parameter datatype is tablename.columnname%TYPE CREATE OR REPLACE FUNCTION fun1(v t1.id%TYPE) RETURN varchar AS BEGIN RETURN v; END; / 函数状态是valid，并且执行函数能够成功。 SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) SELECT fun1(1) FROM dual; fun1 ------ 1 (1 row) 修改引用的先前定义的声明，函数状态变为invalid，但是函数能够执行成功。 ALTER TABLE t1 ALTER COLUMN id TYPE varchar(20); SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --n prostatus ----------- n (1 row) --after changing the column id type from int to varchar, call the function again SELECT fun1('a') FROM dual; --successfully fun1 ------ a (1 row) 重新编译函数，状态重新变成valid。 ALTER FUNCTION fun1 COMPILE; SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) 示例： --the following testcase will fail DECLARE name VARCHAR(25) NOT NULL := 'Niu'; surname name%TYPE ; BEGIN raise notice 'name=%' ,name; raise notice 'surname=%' ,surname; END; / ERROR: variable \"surname\" must have a default value, since it's declared NOT NULL LINE 3: surname name%TYPE ; ^ 示例： CREATE TABLE employees(first_name varchar(20) not null, last_name varchar(20) not null, phone_number varchar(50)); INSERT INTO employees VALUES ('Steven','Niu','1-650-555-1234'); CREATE OR REPLACE PROCEDURE p0(v employees%ROWTYPE) AS BEGIN raise notice 'v.first_name = %, v.last_name = %, v.phone_number = %', v.first_name, v.last_name, v.phone_number; END; / DECLARE a employees%ROWTYPE; BEGIN select * into a from employees ; raise notice 'a=%', a; call p0(a); END; / NOTICE: a=(Steven,Niu,1-650-555-1234) NOTICE: v.first_name = Steven, v.last_name = Niu, v.phone_number = 1-650-555-1234 \\df p0 List of functions Schema | Name | Result data type | Argument data types | Type --------+------+------------------+------------------------+------ public | p0 | | IN v employees%ROWTYPE | proc (1 row) INSERT语句增强支持把一个%ROWTYPE声明的变量插入表中。 语法为： INSERT INTO table_name VALUES row_variable ; 示例： CREATE TABLE t1(id int, name varchar(20)); DECLARE v1 t1%ROWTYPE; BEGIN FOR i IN 1 .. 5 LOOP v1.id := i; v1.name := 'a' || i; INSERT INTO t1 VALUES v1; END LOOP; END; / SELECT * FROM t1; id | name ----+------ 1 | a1 2 | a2 3 | a3 4 | a4 5 | a5 (5 rows) 示例： CREATE TABLE t1(id int, name varchar(20)); DELETE FROM t1; DECLARE v1 t1%ROWTYPE; v2 t1%ROWTYPE; BEGIN v1.id := 11; v1.name := 'abc'; INSERT INTO t1 VALUES v1; v2.id := 22; v2.name := 'new'; UPDATE t1 SET ROW = v2; END; / SELECT * FROM t1; id | name ----+------ 22 | new (1 row)","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"compat_type_rowtype","url":"/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. 引用发生改变，%TYPE或%ROWTYPE声明的变量也相应改变。","hash":"引用发生改变type或rowtype声明的变量也相应改变","id":3},{"text":"2.2. %TYPE声明的变量继承引用变量的约束","hash":"type声明的变量继承引用变量的约束","id":4},{"text":"2.3. 表名%ROWTYPE或视图名%ROWTYPE作为函数或存储过程的参数类型和函数返回值类型","hash":"表名rowtype或视图名rowtype作为函数或存储过程的参数类型和函数返回值类型","id":5},{"text":"2.4. INSERT语句增强","hash":"insert语句增强","id":6},{"text":"2.5. UPDATE语句增强","hash":"update语句增强","id":7}]},"73":{"id":73,"text":"本功能的引入是为了兼容Oracle的不可见列功能，将使数据库设计更加简洁，数据管理的灵活性得到提高，用户可以更好地控制列的可见性。 在应用迁移过程中，不可见列也很有用。使新列不可见意味着它们不会被任何现有应用程序看到，但仍然可以被任何新应用程序引用，从而使应用程序的在线迁移更加简单。 不可见列功能使用户能够在执行诸如SELECT * 等操作时隐藏特定列。可以使用不可见列对表进行更改，而不会干扰使用该表的应用程序。对表的任何通用访问都不会显示不可见的列，必须通过列名显式引用不可见列，查询和其他操作才能访问不可见列。 在Oracle模式下，psql的扩展描述命令（\\d+）增加了对不可见列的支持。 ivorysql=# CREATE TABLE mytable (a INT, b INT INVISIBLE, c INT); CREATE TABLE 只有在INSERT语句中显式指定不可见列时，才能向不可见列插入值。向包含不可见列的表插入值时不能省略列列表，否则会报错。 ivorysql=# INSERT INTO mytable (a, b, c) VALUES (1,2,3); INSERT 0 1 ivorysql=# INSERT INTO mytable VALUES (4,5,6); ERROR: INSERT has more expressions than target columns LINE 1: INSERT INTO mytable VALUES (4,5,6); 使用SELECT语句查询时，如果要显示不可见列信息，必须显式指定该不可见列，否则将不显示该不可见列。 ivorysql=# select * from mytable; a | c ---+--- 1 | 3 (1 row) ivorysql=# select a,b,c from mytable; a | b | c ---+---+--- 1 | 2 | 3 (1 row) ivorysql=# \\d+ mytable Table \"public.mytable\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | b | pg_catalog.int4 | | | | invisible | plain | | | c | pg_catalog.int4 | | | | | plain | | | Access method: heap 目前不支持modify column，将在未来版本实现； 在Oracle中，不可见列和列顺序有特别的注意事项。当一个表包含一个或多个不可见列时，这些不可见列不会包含在表的列顺序中。目前我们尚未对这块做处理。","title":"不可见列","component":"ivorysql-doc","version":"master","name":"invisible_column","url":"/ivorysql-doc/master/oracle_compatibility/invisible_column.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3},{"text":"3.1. 创建包含不可见列的表","hash":"创建包含不可见列的表","id":4},{"text":"3.2. 向不可见列插入值","hash":"向不可见列插入值","id":5},{"text":"3.3. 显示不可见列的输出","hash":"显示不可见列的输出","id":6},{"text":"3.4. 在Oracle模式下psql扩展描述命令（\\d+）对不可见列的支持","hash":"在oracle模式下psql扩展描述命令d对不可见列的支持","id":7},{"text":"4. 限制","hash":"限制","id":8}]},"74":{"id":74,"text":"IvorySQL提供了兼容Oracle自定义包的功能。包（package）是一个封装的相关程序对象集合，这些对象存储在数据库中。程序对象包括过程、函数、变量、常量、游标和异常。 本文档旨在为使用人员提供一个深入了解自定义包实现的过程。 IvorySQL提供了兼容Oracle自定义包的功能，包括包和包体的创建、修改和描述，在PostgreSQL交互终端（psql）中增加了与包相关的命令，新增了 \\dk 命令。 使用CREATE OR REPLACE PACKAGE语句创建或替换一个包的规范。包是相关的存储过程、函数、和其他程序对象的集合，在数据库中作为一个单元存储。包规范声明这些对象，在包体中定义这些对象。 在自己的模式下创建或替换一个包规范，必须有CREATE PROCEDURE系统权限，如果在其他用户的模式下创建或替换一个包，则需要有CREATE ANY PROCEDURE系统权限。 使用CREATE OR REPLACE PACKAGE BODY语句创建或替换一个包体。创建包体需要有与创建包规范一样的权限，要求包体与包规范在同一个模式下，且包规范必须存在，该语句定义包规范中声明的对象。 当包规范中有cursor或子过程时，那么必须拥有一个包体去定义它，否则包体是可选的。 使用ALTER PACKAGE语句来改变包的属性。执行ALTER PACKAGE语句的权限要求：必须是包的所有者，或者具有ALTER ANY PROCEDURE权限才能修改其他用户的包。 DROP PACKAGE语句删除数据库中的一个存储包。这个语句会同时删除包体和包规范。DROP PACKAGE BODY则只删除包体。 不能使用此语句删除包中的一个单独对象。权限要求：包必须在用户模式下，或用户有DROP ANY PROCEDURE系统权限。 DISCARD PACKAGE功能是为了兼容PostgreSQL的DISCARD功能而做的。 psql中支持 \\dk[+] 命令查看包与包体的定义信息。 命令 描述 \\dk[+] 列出当前能看到的包信息 \\dk[+] xxx 列出xxx包的包规范和包体内容 ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# var2 integer; ivorysql-# function test_f(id integer) return integer; ivorysql-# procedure test_p(id integer); ivorysql-# end; ivorysql-# / CREATE PACKAGE ivorysql=# create or replace package body pkg is ivorysql-# var3 integer; ivorysql-# function test_f(id integer) return integer is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg test_f'); ivorysql-# return id; ivorysql-# end; ivorysql-# procedure test_p(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg proc'); ivorysql-# end; ivorysql-# --privite function ivorysql-# function test_piv1(id integer) return integer is ivorysql-# begin ivorysql-# return id; ivorysql-# end; ivorysql-# --privite procedure ivorysql-# procedure test_piv2(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('privite proc'); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 1; ivorysql-# var2 := 2; ivorysql-# var3 := 4; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY ivorysql=# alter package pkg noneditionable; ALTER PACKAGE ivorysql=# Drop package pkg; DROP PACKAGE ivorysql=# Drop package body pkg; DROP PACKAGE BODY ivorysql=# discard package; DISCARD PACKAGES ivorysql=# \\dk List of packages Schema | Name | Owner --------+----------+---------- public | pkg | ivorysql public | test_pkg | ivorysql (2 rows) ivorysql=# \\dk pkg List of packages Schema | Name | Owner --------+------+---------- public | pkg | ivorysql (1 row) ivorysql=# \\dk pkg1 Did not find any package named \"pkg1\". ivorysql=# \\dk+ List of packages Schema | Name | Owner | Security | Editionable | Use Collation | Specification | Package Body --------+----------+----------+----------+-------------+---------------+-----------------------------------------------+----------------------------------------------------- --------- public | pkg | ivorysql | definer | Editionable | default | var1 integer; +| | | | | | | var2 integer; +| | | | | | | function test_f(id integer) return integer; +| | | | | | | procedure test_p(id integer); +| | | | | | | end | public | test_pkg | ivorysql | definer | Editionable | default | var1 integer; +| FUNCTION test_f(id integer) RETURN integer IS + | | | | | | FUNCTION test_f(id integer) RETURN integer;+| BEGIN + | | | | | | end | dbms_output.put_line('invoke function test_pkg.t est_f');+ | | | | | | | RETURN 23; + | | | | | | | end; + | | | | | | | BEGIN + | | | | | | | var1 := 23; + | | | | | | | end (2 rows)","title":"包","component":"ivorysql-doc","version":"master","name":"package","url":"/ivorysql-doc/master/oracle_compatibility/package.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. 创建包规范","hash":"创建包规范","id":3},{"text":"2.2. 创建包体","hash":"创建包体","id":4},{"text":"2.3. 更新包","hash":"更新包","id":5},{"text":"2.4. 删除包和包体","hash":"删除包和包体","id":6},{"text":"2.5. 丢弃包","hash":"丢弃包","id":7},{"text":"2.6. 在psql中使用 \\dk[+] 查看包与包体定义","hash":"在psql中使用-dk-查看包与包体定义","id":8},{"text":"3. 测试用例","hash":"测试用例","id":9},{"text":"3.1. 创建包规范","hash":"创建包规范-2","id":10},{"text":"3.2. 创建包体","hash":"创建包体-2","id":11},{"text":"3.3. 更新包","hash":"更新包-2","id":12},{"text":"3.4. 删除包和包体","hash":"删除包和包体-2","id":13},{"text":"3.5. 丢弃包","hash":"丢弃包-2","id":14},{"text":"3.6. 在psql中使用 \\dk[+] 查看包与包体定义","hash":"在psql中使用-dk-查看包与包体定义-2","id":15}]},"75":{"id":75,"text":"为了将Oracle端口、IP与PG的端口IP进行区分。现需要增加对ORAPORT和ORAHOST的处理; 新增ivoryhost：需要在连接时新增参数ivoryhost即可指定，其功能类似 host; 新增ivoryport：相较于host，port的功能相对复杂一些。其中涉及到可以在configure阶段配置，连接阶段指定端口; ./configure --with-oraport=5555 ./initdb .... ./pg_ctl -D ../data start ./pg_ctl -o \"-p 5433 -o 1522\" -D ../data","title":"新增Oracle兼容模式的端口与IP","component":"ivorysql-doc","version":"master","name":"port_ip","url":"/ivorysql-doc/master/oracle_compatibility/port_ip.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"3. 测试用例：","hash":"测试用例","id":3}]},"76":{"id":76,"text":"本文档是IvorySQL兼容Oracle sequence文档，目的是可以在IvorySQL中使用Oracle的sequence语句。 序列是一个数据库对象，与表和视图类似，它表示可以由全局数据库命名空间中的任何表和视图使用的整数序列。可以使用NEXTVAL和CURRVAL访问序列值。序列可以是升序或降序 ivorysql=# CREATE sequence seq; CREATE SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 1 (1 row) ivorysql=# ALTER sequence seq restart start with 10; ALTER SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 10 (1 row) ivorysql=# DROP SEQUENCE seq; DROP SEQUENCE","title":"兼容Oracle sequence","component":"ivorysql-doc","version":"master","name":"sequence","url":"/ivorysql-doc/master/oracle_compatibility/sequence.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"77":{"id":77,"text":"在Oracle中，常会出现带有XML函数的SQL代码，IvorySQL在PostgreSQL的基础上，实现与Oracle XML函数的高度兼容，确保了从Oracle迁移到IvorySQL后的数据格式和结构的一致性。这种兼容性意味着用户无需对现有的XML处理逻辑进行大规模修改，从而保证了数据的完整性和准确性。此外，IvorySQL的这种跨平台的兼容特性，也降低了因格式差异带来的额外用户维护和升级成本，使得数据处理和管理更加高效、可靠和灵活。 XML（eXtended Markup Language可扩展标记语言）是一种基于文本的，用于结构化任何可标记文档的格式语言。它是一种轻便的，可扩展的，标准的且简单易懂的保存数据的语言。 IvorySQL在实现与Oracle 12c中11个常用XML SQL函数的兼容性时，与PostgreSQL保持了一致，底层处理函数同样采用了libxml2库提供的接口。这些XML函数作为ivorysql_ora插件的一个子插件提供，确保了与PostgreSQL数据库在XML处理方面的兼容性和一致性。 由于Oracle的xml函数要求某些参数类型是 XMLType，比如下面这个existsnode()函数： 原型： EXISTSNODE(XMLType_instance, XPath_string [, namespace_string ]) 示例： SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a') from dual; 因此为了兼容，添加了一个 XMLType 数据类型，作用是把用户提供的字符串转换成内部的xmltype类型，让用户的SQL语句从Oracle迁移到IvorySQL不用做改动 另外，为了避免与PostgreSQL中已有的同名关键字“extract”产生混淆，IvorySQL将原有的同名关键字重命名为“PGEXTRACT”，以确保函数调用的清晰性和准确性。 在实现这11个Oracle兼容的XML函数时，IvorySQL采用了两种不同的实现方式。其中，除了UPDATEXML函数外，其他10个函数均通过SQL函数的方式进行实现。由于UPDATEXML函数的参数数量是不确定的，因此采用了表达式方式来实现，这要求为其编写专门的语法分析代码和执行器代码，以确保其功能的正确性和灵活性。 序号 函数名 功能简介 1 extract(XML) 该函数用于返回XML节点路径下的相应内容, 其中参数XMLType_instance用于指定XMLType实例，Xpath_string用于指定XML节点路径 2 extractvalue 该函数提供对XML内容的检索功能，extractvalue只能返回一个节点的一个值。 3 existsnode 该函数用于检查XML内容是否符合指定的路径表达式。 4 deletexml 该函数用于删除指定路径的XML节点 5 appendchildxml 该函数用于在XML对象中插入子节点。 它接受一个XML对象和一个XML片段作为参数，并将XML片段作为子节点插入到XML对象中 6 updatexml 该函数用于更新特定XML相应的节点路径的内容 7 insertxmlbefore 该函数用于在XML中的指定路径前插入子节点 8 insertxmlafter 该函数用于在XML中的指定路径后插入子节点 9 insertchildxml 该函数用于向指定的XML路径中插入子节点 10 insertchildxmlbefore 该函数用于向指定的XML路径之前插入子节点 11 insertchildxmlafter 该函数用于向指定的XML路径之后插入子节点 12 xmlisvalid 该函数用于检查XML格式是否正确 ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# create table inaf(a int, b xmltype); CREATE TABLE ivorysql=# insert into inaf values(1,xmltype('<a><b>100</b></a>')); INSERT 0 1 ivorysql=# insert into inaf values(2, ''); INSERT 0 1 ivorysql=# select * from inaf; a | b ---+------------------- 1 | <a><b>100</b></a> 2 | (2 rows) ivorysql=# create table xmltest(id int, data XMLType); CREATE TABLE ivorysql=# insert into xmltest values(1, '<value>one</value>'); INSERT 0 1 ivorysql=# insert into xmltest values(2, '<value>two</value>'); INSERT 0 1 ivorysql=# select * from xmltest; id | data ----+-------------------- 1 | <value>one</value> 2 | <value>two</value> (2 rows) ivorysql=# create table xmlnstest(id int, data xmltype); CREATE TABLE ivorysql=# INSERT INTO xmlnstest VALUES(1, xmltype('<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:typ=\"http://www.def.com\" xmlns:web=\"http://www.abc.com\"><soapenv:Body><web:BBB><typ:EEE>41</typ:EEE><typ:FFF>42</typ:FFF></web:BBB></soapenv:Body></soapenv:Envelope>')); INSERT 0 1 ivorysql# SELECT extract(XMLType('<AA><ID>1</ID></AA>'), '/AA/ID') from dual; extract ------------ <ID>1</ID> (1 row) ivorysql# SELECT extractvalue(XMLType('<a><b>100</b></a>'),'/a/b') from dual; extractvalue -------------- 100 (1 row) ivorysql=# SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a/b') from dual; existsnode ------------ 1 (1 row) ivorysql=# SELECT deletexml(XMLType('<test><value>oldnode</value><value>oldnode</value></test>'), '/test/value') from dual; deletexml ----------- <test/> (1 row) ivorysql=# SELECT appendchildxml(XMLType('<test><value></value><value></value></test>'), '/test/value', XMLTYPE('<name>newnode</name>')) from dual; appendchildxml -------------------------- <test> + <value> + <name>newnode</name>+ </value> + <value> + <name>newnode</name>+ </value> + </test> (1 row) ivorysql=# SELECT updatexml(xmltype('<value>one</value>'), '/value', xmltype('<newvalue>1111</newvalue>')) FROM dual; updatexml --------------------------- <newvalue>1111</newvalue> (1 row) ivorysql=# SELECT insertxmlbefore(XMLType('<a>222<b>100</b><b>200</b></a>'), '/a/b', XMLTYPE('<c>88</c>')) from dual; insertxmlbefore -------------------------------------------------- <a>222<c>88</c><b>100</b><c>88</c><b>200</b></a> (1 row) ivorysql=# SELECT insertxmlafter(XMLType('<a><b>100</b></a>'),'/a/b',XMLType('<c>88</c>')) from dual; insertxmlafter ---------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT insertchildxml(XMLType('<a>one<b></b>three<b></b></a>'), '//b', 'name', XMLTYPE('<name>newnode</name>')) from dual; insertchildxml ----------------------------------------------------------------------- <a>one<b><name>newnode</name></b>three<b><name>newnode</name></b></a> (1 row) ivorysql=# SELECT insertchildxmlbefore(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlbefore ---------------------- <a> + <c>88</c> + <b>100</b> + </a> (1 row) ivorysql=# SELECT insertchildxmlafter(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlafter --------------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a>')); xmlisvalid ------------ f (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a/>')); xmlisvalid ------------ t (1 row)","title":"XML","component":"ivorysql-doc","version":"master","name":"xml_functions","url":"/ivorysql-doc/master/oracle_compatibility/xml_functions.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现原理","hash":"实现原理","id":2},{"text":"3. 兼容函数如下","hash":"兼容函数如下","id":3},{"text":"4. XML函数使用示例","hash":"xml函数使用示例","id":4},{"text":"4.1. 准备表与数据","hash":"准备表与数据","id":5},{"text":"4.2. extract(XML)","hash":"extract(XML)","id":6},{"text":"4.3. extractvalue","hash":"extractvalue","id":7},{"text":"4.4. existsnode","hash":"existsnode","id":8},{"text":"4.5. deletexml","hash":"deletexml","id":9},{"text":"4.6. appendchildxml","hash":"appendchildxml","id":10},{"text":"4.7. updatexml","hash":"updatexml","id":11},{"text":"4.8. insertxmlbefore","hash":"insertxmlbefore","id":12},{"text":"4.9. insertxmlafter","hash":"insertxmlafter","id":13},{"text":"4.10. insertchildxml","hash":"insertchildxml","id":14},{"text":"4.11. insertchildxmlbefore","hash":"insertchildxmlbefore","id":15},{"text":"4.12. insertchildxmlafter","hash":"insertchildxmlafter","id":16},{"text":"4.13. xmlisvalid","hash":"xmlisvalid","id":17}]},"78":{"id":78,"text":"函数名称 current_catalog 参数数据类型 无输入参数 函数返回值数据类型 Name 函数含义 获得当前的database 名称。 注意：在SQL标准中，databases被称之为catalogs。 使用举例 postgres=# select current_catalog; current_catalog ----------------- postgres (1 row) postgres=# 函数名称 current_database() 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得当前的database 名称。 使用举例 postgres=# select current_database(); current_database ------------------ postgres (1 row) postgres=# 函数名称 current_query() 参数数据类型 无输入参数 函数返回值数据类型 text 函数含义 获得client端提交的当前执行的SQL语句的文本 使用举例 postgres=# BEGIN; BEGIN postgres=*# select 1,current_query(); ?column? | current_query ----------+--------------------------- 1 | select 1,current_query(); (1 row) postgres=*# select 2,current_query(); ?column? | current_query ----------+--------------------------- 2 | select 2,current_query(); (1 row) postgres=*# ROLLBACK; ROLLBACK postgres=# 函数名称 current_role 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 与current_user作用相同，获得当前执行上下文的username 使用举例 postgres=# select current_role; current_role -------------- pg131 (1 row) postgres=# 函数名称 current_schema 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得search path中的第一个schema名称（当search path为空时，返回空值）。当表或者其他对象建立不带schema名称时，本函数返回的schema就是该表或者该对象的schema 使用举例 postgres=# select current_schema; current_schema ---------------- public (1 row) postgres=# 函数名称 current_schema() 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 作用与current_schema函数相同：获得search path中的第一个schema名称（当search path为空时，返回空值）。当表或者其他对象建立不带schema名称时，本函数返回的schema就是该表或者该对象的schema 使用举例 postgres=# select current_schema(); current_schema ---------------- public (1 row) postgres=# 函数名称 current_schemas(include_implicit boolean) 参数数据类型 bool 函数返回值数据类型 name 函数含义 以优先级顺序返回有效search_path中当前所有模式名称，如果入参为true，那么在函数返回结果中会包括隐式搜索的系统模式pg_catalog 使用举例 postgres=# select current_schemas(false); current_schemas ----------------- {public} (1 row) postgres=# select current_schemas(true); current_schemas --------------------- {pg_catalog,public} (1 row) postgres=# 函数名称 current_user 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得当前执行上下文的username 使用举例 postgres=# select current_user; current_user -------------- pg131 (1 row) postgres=# 函数名称 inet_client_addr() 参数数据类型 无输入参数 函数返回值数据类型 inet 函数含义 获得当前client的ip地址。如果返回值为空，表示当前连接是经过Unix-domain socket的 使用举例 postgres=# select inet_client_addr(); inet_client_addr ------------------ (1 row) postgres=# 函数名称 inet_client_port() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 获得当前client的端口号。如果返回值为空，表示当前连接是经过Unix-domain socket的 使用举例 postgres=# select inet_client_port(); inet_client_port ------------------ (1 row) postgres=# 函数名称 inet_server_addr() 参数数据类型 无输入参数 函数返回值数据类型 inet 函数含义 获得当前连接的pg数据库服务器的ip地址。如果返回值为空，表示当前连接是经过Unix-domain socket的。 使用举例 postgres=# select inet_server_port(); inet_server_port ------------------ (1 row) postgres=# 函数名称 inet_server_port() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 获得当前连接的pg数据库服务器的端口号。如果返回值为空，表示当前连接是经过Unix-domain socket的。 使用举例 postgres=# select inet_server_port(); inet_server_port ------------------ (1 row) postgres=# 函数名称 pg_backend_pid() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 获得附加到当前session的backend进程的进程ID 使用举例 postgres=# select pg_backend_pid(); pg_backend_pid ---------------- 4068 (1 row) postgres=# 函数名称 pg_blocking_pids(integer) 参数数据类型 Integer 函数返回值数据类型 Integer[] 函数含义 返回入参pid的blocking pid(即:阻塞者pid)若是本函数返回空值，表示不存在阻塞者若是本函数返回值是零，那么表示阻塞者是prepared transacion，见下面的输出： postgres=# select pg_blocking_pids(12947); pg_blocking_pids ------------------ {0} (1 row) postgres=# 频繁调用本函数会对数据库性能有影响，因为本函数在短时间内对lock manager’s shared state进行排他访问(exclusive accesss) 使用举例 Session1： postgres=# SELECT pg_backend_pid(); pg_backend_pid ---------------- 32262 (1 row) postgres=# CREATE TABLE tbl_students(rno int, name character varying(10)); CREATE TABLE postgres=# BEGIN TRANSACTION; BEGIN postgres=*# LOCK tbl_students IN ACCESS EXCLUSIVE MODE; LOCK TABLE postgres=*# session2： postgres=# SELECT pg_backend_pid(); pg_backend_pid ---------------- 32439 (1 row) postgres=# INSERT INTO tbl_students VALUES (1,'Anvesh'); session3: postgres=# SELECT pg_blocking_pids(32439); pg_blocking_pids ------------------ {32262} (1 row) postgres=# 函数名称 pg_conf_load_time() 参数数据类型 无输入参数 函数返回值数据类型 timestamp with time zone 函数含义 返回PG配置参数文件最后一次被reload的时间 使用举例 postgres=# select pg_conf_load_time(); pg_conf_load_time ------------------------------- 2021-03-22 12:46:18.962643+08 (1 row) postgres=# 函数名称 pg_current_logfile([text]); 参数数据类型 无输入参数或者text型输入参数 (csvlog、stderr) 函数返回值数据类型 Text 函数含义 返回正在被logging collector进程使用的log file的path(是log_directory的值)和文件名。当logging collector是被禁用时，本函数返回结果是null。当有不同格式的多种log files存在时，不带任何参数的pg_current_logfile()函数返回有序列表(stderr、csvlog)中找到的第一种格式的文件path和文件名。当没有这两个格式的文件时，本函数返回NULL 使用举例 postgres=# select pg_current_logfile(); pg_current_logfile -------------------------------------- log/postgresql-2021-03-22_124618.log (1 row) postgres=# select pg_current_logfile('stderr'); pg_current_logfile -------------------------------------- log/postgresql-2021-03-22_124618.log (1 row) postgres=# select pg_current_logfile('csvlog'); pg_current_logfile -------------------- (1 row) postgres=# show log_destination ; log_destination ----------------- stderr (1 row) postgres=# 函数名称 pg_my_temp_schema() 参数数据类型 无输入参数 函数返回值数据类型 oid 函数含义 返回当前会话的临时schema的OID，如果没有临时schema，本函数返回0（即：数字零） 使用举例 postgres=# select pg_my_temp_schema(); pg_my_temp_schema ------------------- 0 (1 row) postgres=# 函数名称 pg_is_other_temp_schema(oid) 参数数据类型 oid 函数返回值数据类型 boolean 函数含义 当入参的oid是其他session中临时schema的oid时，本函数返回true。 使用举例 postgres=# select pg_is_other_temp_schema(16426); pg_is_other_temp_schema ------------------------- f (1 row) postgres=# 函数名称 pg_jit_available() 参数数据类型 无输入参数 函数返回值数据类型 boolean 函数含义 如果jit编译器扩展是可用的并且jit配置参数设置为on的话，本函数返回true 使用举例 postgres=# select pg_jit_available(); pg_jit_available ------------------ f (1 row) postgres=# select * from pg_config where name='CONFIGURE' AND SETTING LIKE '%--with-llvm%'; name | setting ------+--------- (0 rows) postgres=# show jit; jit ----- on (1 row) postgres=# 函数名称 pg_listening_channels() 参数数据类型 无输入参数 函数返回值数据类型 setof text 函数含义 当前session正在监听的异步通知通道的名称 使用举例 postgres=# select pg_listening_channels(); pg_listening_channels ----------------------- (0 rows) postgres=# 函数名称 pg_notification_queue_usage() 参数数据类型 无输入参数 函数返回值数据类型 double precision 函数含义 返回被正在等待处理的通知占据的异步通知队列最大大小的比例。 使用举例 postgres=# select pg_notification_queue_usage(); pg_notification_queue_usage ----------------------------- 0 (1 row) postgres=# 函数名称 pg_postmaster_start_time() 参数数据类型 无输入参数 函数返回值数据类型 timestamp with time zone 函数含义 返回PG intance 启动的时间点 使用举例 postgres=# select pg_postmaster_start_time(); pg_postmaster_start_time ------------------------------- 2021-03-23 14:01:33.352011+08 (1 row) postgres=# 函数名称 pg_safe_snapshot_blocking_pids(integer) 参数数据类型 Integer 函数返回值数据类型 integer[] 函数含义 返回阻止指定服务器进程ID获取安全快照的进程ID若是没有阻塞，则返回空array。一个运行SERIALIZABLE事务的session阻塞了一个SERIALIZABLE READ ONLY DEFERRABLE事务，防止后者获得snapshot，直到后者确认可以安全的避免获取任何谓词锁。频繁调用本函数会对数据库性能有影响，因为它在短时间内需要访问谓词锁管理器的共享状态(predicate lock manager’s shared state) 使用举例 postgres=# select pg_safe_snapshot_blocking_pids(1234); pg_safe_snapshot_blocking_pids -------------------------------- {} (1 row) postgres=# 函数名称 pg_trigger_depth() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 返回PostgreSQL触发器的当前嵌套级别(如果没有从触发器内部直接或间接调用，则为返回值为0) 使用举例 postgres=# select pg_trigger_depth(); pg_trigger_depth ------------------ 0 (1 row) postgres=# 函数名称 user 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得当前执行上下文的username，等同于current_user函数 使用举例 postgres=# select user; user ------- pg131 (1 row) postgres=# 函数名称 Version() 参数数据类型 无输入参数 函数返回值数据类型 text 函数含义 返回带有数据库版本号的字符串，您也可以查询server_version配置参数获得数据库版本号。对于机器可读的版本，请使用server_version_num配置参数。在软件开发过程中应该使用server_version_num配置参数或者PqserverVersion，而不是去解析文本的版本号(见下面的使用举例) 使用举例 postgres=# select version(); version --------------------------------------------------------------------------------------------------------- PostgreSQL 13.1 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44), 64-bit (1 row) postgres=# 函数名称 has_any_column_privilege(user, table, privilege) 参数数据类型 user, table, privilege或者table, privilege 函数返回值数据类型 boolean 函数含义 确定一个user是否对当前PG database中的某个table任一列有列权限确定当前user是否对当前PG database中的某个table任一列有列权限权限类型是SELECT, INSERT, UPDATE,REFERENCES的组合。 使用举例 [pg131@VM-0-8-centos ~]$ psql -d cwbase3 -U lc0039999 psql (13.1) Type \"help\" for help. cwbase3=> \\d List of relations Schema | Name | Type | Owner --------+-------+-------+----------- public | test1 | table | lc0039999 (1 row) cwbase3=> select has_any_column_privilege('lc0039999','test1','select'); has_any_column_privilege -------------------------- t (1 row) cwbase3=> cwbase3=> select has_any_column_privilege('test1','select,insert'); has_any_column_privilege -------------------------- t (1 row) cwbase3=> cwbase3=> select has_any_column_privilege('test1','select'); has_any_column_privilege -------------------------- t (1 row) cwbase3=> 函数名称 has_column_privilege(user, table, column, privilege); 参数数据类型 user, table, column, privilege或者table, column, privilege 函数返回值数据类型 boolean 函数含义 特定user对特定表的某一列是否具有某种权限当前user对特定表的某一列是否具有某种权限权限类型是SELECT, INSERT, UPDATE,REFERENCES的组合。 使用举例 cwbase3=> \\d test1 Table \"public.test1\" Column | Type | Collation | Nullable | Default --------+---------+-----------+----------+--------- id | integer | | | cwbase3=> cwbase3=> select has_column_privilege('lc0039999','test1','id','select'); has_column_privilege ---------------------- t (1 row) cwbase3=> cwbase3=> select has_column_privilege('test1','id','select'); has_column_privilege ---------------------- t (1 row) cwbase3=> 函数名称 has_database_privilege 参数数据类型 user, database, privilege或者database, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定database有某种权限当前user是否对特定database有某种权限权限类型是CREATE, CONNECT, TEMPORARY, or TEMP的组合。 使用举例 postgres=# select has_database_privilege('lc0039999','cwbase3','create,connect,temp'); has_database_privilege ------------------------ t (1 row) postgres=# select has_database_privilege('lc0019999','cwbase3','create,connect,temp'); has_database_privilege ------------------------ t (1 row) postgres=# 函数名称 has_foreign_data_wrapper_privilege 参数数据类型 user, fdw, privilege或者fdw, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定fdw有usage权限当前user是否对特定fdw有usage权限此处的权限只能指定usage 使用举例 postgres=# select has_foreign_data_wrapper_privilege('lc0019999','file_fdw','usage'); has_foreign_data_wrapper_privilege ------------------------------------ f (1 row) postgres=# postgres=# select has_foreign_data_wrapper_privilege('file_fdw','usage'); has_foreign_data_wrapper_privilege ------------------------------------ t (1 row) postgres=# 函数名称 has_function_privilege 参数数据类型 user, function, privilege或者function, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定function有EXECUTE权限当前user是否对特定function有EXECUTE权限此处的权限只能是EXECUTE 使用举例 postgres=# select has_function_privilege('lc0039999','version()','execute'); has_function_privilege ------------------------ t (1 row) postgres=# postgres=# select has_function_privilege('version()','execute'); has_function_privilege ------------------------ t (1 row) postgres=# 函数名称 has_language_privilege 参数数据类型 user, language, privilege或者language, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定procedural language有usage权限当前user是否对特定procedural language有usage权限此处的权限必须是usage 使用举例 postgres=# select has_language_privilege('lc0019999','sql','usage'); has_language_privilege ------------------------ t (1 row) postgres=# select has_language_privilege('plpgsql','usage'); has_language_privilege ------------------------ t (1 row) postgres=# 注意：此处的language可以从pg_language系统表查询到。 函数名称 has_schema_privilege 参数数据类型 user, schema, privilege或者schema, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定schema有usage或者create权限当前user是否对特定schema有usage或者create权限此处的权限必须是create或者usage的组合 使用举例 postgres=# \\dnS List of schemas Name | Owner --------------------+------- information_schema | pg131 pg_catalog | pg131 pg_toast | pg131 public | pg131 s_abc | pg131 (5 rows) postgres=# select has_schema_privilege('lc0019999','s_abc','usage'); has_schema_privilege ---------------------- f (1 row) postgres=# select has_schema_privilege('lc0019999','s_abc','usage,create'); has_schema_privilege ---------------------- f (1 row) postgres=# 函数名称 has_sequence_privilege 参数数据类型 user, sequence, privilege或者sequence, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定sequence有特定权限当前user是否对特定sequence有特定权限此处的权限是USAGE, SELECT, UPDATE的组合。 使用举例 postgres=# \\ds+ List of relations Schema | Name | Type | Owner | Persistence | Size | Description --------+--------------+----------+-------+-------------+------------+------------- public | gen_y_c1_seq | sequence | pg131 | permanent | 8192 bytes | (1 row) postgres=# select has_sequence_privilege('pg131','gen_y_c1_seq','usage'); has_sequence_privilege ------------------------ t (1 row) postgres=# select has_sequence_privilege('lc0039999','gen_y_c1_seq','usage'); has_sequence_privilege ------------------------ f (1 row) postgres=# 函数名称 has_server_privilege 参数数据类型 user, server, privilege或者server, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定foreign server有usage权限特定user是否对特定foreign server有usage权限此处的权限必须是usage 使用举例 postgres=# select has_server_privilege('s1','usage'); has_server_privilege ---------------------- t (1 row) postgres=# select has_server_privilege('s1','USAGE'); has_server_privilege ---------------------- t (1 row) postgres=# select has_server_privilege('pg123','s1','USAGE'); has_server_privilege ---------------------- t (1 row) postgres=# 函数名称 has_table_privilege 参数数据类型 user, table, privilege或者table, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定table有特定权限当前user是否对特定table有特定权限此处的权限是指如下的组合： SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, TRIGGER，还可以带有WITH GRANT OPTION用来测试privilege是否带有grant option。当权限字符串有多个值(比如’insert, select')时,只要有任何一个权限是满足的，本函数会返回t有关对权限字符串的说明：权限字符串不区分大小写权限字符串的前后可以有一个或者多个空格（权限名字内部不能有空格） 使用举例 postgres=# select has_table_privilege('t1',' select, insert '); has_table_privilege --------------------- t (1 row) postgres=# [pg131@VM-0-8-centos ~]$ psql -d cwbase3 -U lc0039999 psql (13.1) Type \"help\" for help. cwbase3=> cwbase3=> grant select on test1 to lc0019999; GRANT cwbase3=> exit [pg131@VM-0-8-centos ~]$ psql -d cwbase3 -U lc0019999 psql (13.1) Type \"help\" for help. cwbase3=> select * from test1; id ---- 1 2 3 4 5 (6 rows) cwbase3=> select has_table_privilege('test1','insert, select '); has_table_privilege --------------------- t (1 row) cwbase3=> select has_table_privilege('test1','insert'); has_table_privilege --------------------- f (1 row) cwbase3=> select has_table_privilege('test1',' insert '); has_table_privilege --------------------- f (1 row) cwbase3=> insert into test1 values(6); ERROR: permission denied for table test1 cwbase3=> 函数名称 has_tablespace_privilege 参数数据类型 user, tablespace, privilege或者tablespace, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定tablesapce有特定权限当前user是否对特定tablespace有特定权限此处的权限必须是create 使用举例 postgres=# select has_tablespace_privilege('ts1','create '); has_tablespace_privilege -------------------------- t (1 row) postgres=# select has_tablespace_privilege('lc0019999','ts1','create '); has_tablespace_privilege -------------------------- f (1 row) postgres=# 函数名称 has_type_privilege 参数数据类型 user, type, privilege或者type, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定type有特定权限当前user是否对特定type有特定权限特定type可以使用type的name，也可以使用oid::regtype的形式此处的权限必须是USAGE 使用举例 postgres=# select has_type_privilege('bytea','usage'); has_type_privilege -------------------- t (1 row) postgres=# select has_type_privilege('lc0019999','bytea','usage'); has_type_privilege -------------------- t (1 row) postgres=# select has_type_privilege('lc0019999','17:regtype','usage'); ERROR: syntax error at or near \"17\" CONTEXT: invalid type name \"17:regtype\" postgres=# select has_type_privilege('lc0019999',17::regtype,'usage'); has_type_privilege -------------------- t (1 row) postgres=# select has_type_privilege('lc0019999','17::regtype','usage'); ERROR: syntax error at or near \"17\" CONTEXT: invalid type name \"17::regtype\" postgres=# 函数名称 pg_has_role 参数数据类型 user, role, privilege或者role, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定role group有特定权限当前user是否对特定role group有特定权限此处的权限必须是member或者usage当权限是member时，表示特定user是否是特定role group的member当权限是usage时，表示在不执行set role时，特定user是否有特定role的权限 使用举例 postgres=# create role editors; CREATE ROLE postgres=# create user maxwell; CREATE ROLE postgres=# create user ernest; CREATE ROLE postgres=# grant authors to editors; --editors can do what authors can do GRANT ROLE postgres=# grant editors to maxwell; --maxwell is an editor GRANT ROLE postgres=# grant authors to ernest; --ernest is an author GRANT ROLE postgres=# select pg_has_role('maxwll','editors','member'); ERROR: role \"maxwll\" does not exist postgres=# select pg_has_role('maxwell','editors','member'); pg_has_role ------------- t (1 row) postgres=# select pg_has_role('maxwell','editors','usage'); pg_has_role ------------- t (1 row) postgres=# select pg_has_role('maxwell','ernest','usage'); pg_has_role ------------- f (1 row) postgres=# select pg_has_role('maxwell','ernest','member'); pg_has_role ------------- f (1 row) postgres=# 函数名称 row_security_active 参数数据类型 Table，可以是table name，也可以是table的oid 函数返回值数据类型 boolean 函数含义 在current_user的上下文中，特定table是否已开启row security 使用举例 postgres=# select oid from pg_class where relname='test'; oid ------- 16860 (1 row) postgres=# select row_security_active('test'); row_security_active --------------------- f (1 row) postgres=# select row_security_active(16860); row_security_active --------------------- f (1 row) postgres=# 函数名称 acldefault 参数数据类型 type, ownerId 函数返回值数据类型 aclitem[] 函数含义 返回属于某个user oid对象的默认访问权限。当一个对象的ACL条目为null时这些访问权限是该对象的默认权限Type参数的取值如下：'c' for COLUMN’r' for TABLE 以及类似对象’s' for SEQUENCE’d' for DATABASE’f' for FUNCTION 或者PROCEDURE’l' for LANGUAGE’L' for LARGE OBJECT’n' for SCHEMA’t' for TABLESPACE’F' for FOREIGN DATA WRAPPER’S' for FOREIGN SERVER’T' for TYPE or DOMAIN 使用举例 postgres=# select acldefault('f',16877); acldefault -------------------------------------- {=X/lc0039999,lc0039999=X/lc0039999} (1 row) postgres=# 函数名称 aclexplode 参数数据类型 aclitem[] 函数返回值数据类型 setof record 函数含义 解析 acl 权限 使用举例 postgres=# select aclexplode('{=X/lc0039999,lc0039999=X/lc0039999}'); aclexplode ------------------------- (16877,0,EXECUTE,f) (16877,16877,EXECUTE,f) (2 rows) postgres=# postgres=# select \\* from aclexplode('{=X/lc0039999,lc0039999=X/lc0039999}'); grantor | grantee | privilege_type | is_grantable ---------+---------+----------------+-------------- 16877 | 0 | EXECUTE | f 16877 | 16877 | EXECUTE | f (2 rows) postgres=# 函数名称 makeaclitem 参数数据类型 grantee, grantor, privilege, grantable 函数返回值数据类型 aclitem 函数含义 构建aclitem 使用举例 postgres=# select \\* from makeaclitem(16877,16877,'EXECUTE',false); makeaclitem ----------------------- lc0039999=X/lc0039999 (1 row) postgres=# 函数名称 pg_collation_is_visible 参数数据类型 collation_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定collation是否可见。 使用举例 postgres=# select * from pg_collation where collname = 'zh_CN'; oid | collname | collnamespace | collowner | collprovider | collisdeterministic | collencoding | collcollate | collctype | collversion -------+----------+---------------+-----------+--------------+---------------------+--------------+-------------+------------+------------- 13086 | zh_CN | 11 | 10 | c | t | 2 | zh_CN | zh_CN | 2.17 13243 | zh_CN | 11 | 10 | c | t | 6 | zh_CN.utf8 | zh_CN.utf8 | 2.17 (2 rows) postgres=# select pg_collation_is_visible(13086); pg_collation_is_visible ------------------------- f (1 row) postgres=# select pg_collation_is_visible(13243); pg_collation_is_visible ------------------------- t (1 row) postgres=# postgres=# select pg_collation_is_visible('\"zh_CN\"'::regcollation); pg_collation_is_visible ------------------------- t (1 row) postgres=# 函数名称 pg_conversion_is_visible 参数数据类型 conversion_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定conversion是否可见 使用举例 postgres=# select * from pg_conversion where conname like '%cn%'; oid | conname | connamespace | conowner | conforencoding | contoencoding | conproc | condefault ------+----------------+--------------+----------+----------------+---------------+----------------+------------ 4422 | euc_cn_to_mic | 11 | 10 | 2 | 7 | euc_cn_to_mic | t 4423 | mic_to_euc_cn | 11 | 10 | 7 | 2 | mic_to_euc_cn | t 4480 | euc_cn_to_utf8 | 11 | 10 | 2 | 6 | euc_cn_to_utf8 | t 4481 | utf8_to_euc_cn | 11 | 10 | 6 | 2 | utf8_to_euc_cn | t (4 rows) postgres=# select pg_conversion_is_visible(4480); pg_conversion_is_visible -------------------------- t (1 row) postgres=# 函数名称 pg_function_is_visible 参数数据类型 function_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定function、procedures、aggregates是否可见。对于函数，如果路径前面没有相同名称和参数数据类型的对象，则搜索路径中的对象可见 使用举例 postgres=# select oid from pg_proc where proname='version'; oid ----- 89 (1 row) postgres=# select pg_function_is_visible(89); pg_function_is_visible ------------------------ t (1 row) postgres=# postgres=# select pg_function_is_visible('version'::regproc); pg_function_is_visible ------------------------ t (1 row) postgres=# select pg_function_is_visible('version()'::regprocedure); pg_function_is_visible ------------------------ t (1 row) postgres=# 函数名称 pg_opclass_is_visible 参数数据类型 opclass_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator class是否可见考虑因素是operator class name和相关的索引访问方法。 使用举例 postgres=# select oid from pg_opclass where opcname='array_ops'; oid ------- 10000 10001 10063 (3 rows) postgres=# select pg_opclass_is_visible(10000); pg_opclass_is_visible ----------------------- t (1 row) postgres=# 函数名称 pg_operator_is_visible 参数数据类型 operator_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator 是否可见对于运算符，如果路径前面没有相同名称和参数数据类型的对象，则搜索路径中的对象可见 使用举例 postgres=# select oprname from pg_operator where oid=15; oprname --------- = (1 row) postgres=# select pg_operator_is_visible(15); pg_operator_is_visible ------------------------ t (1 row) postgres=# postgres=# select pg_operator_is_visible('=(integer,integer)'::regoperator); pg_operator_is_visible ------------------------ t (1 row) postgres=# 函数名称 pg_opfamily_is_visible 参数数据类型 opclass_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator family是否可见 使用举例 postgres=# select * from pg_opfamily where oid='397'; oid | opfmethod | opfname | opfnamespace | opfowner -----+-----------+-----------+--------------+---------- 397 | 403 | array_ops | 11 | 10 (1 row) postgres=# select pg_opfamily_is_visible(397); pg_opfamily_is_visible ------------------------ t (1 row) postgres=# 函数名称 pg_statistics_obj_is_visible 参数数据类型 stat_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator class是否可见 使用举例 函数名称 pg_table_is_visible 参数数据类型 table_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定的table、views、materialized views、 indexes、 sequences、foreign tables是否可见 使用举例 postgres=# select oid from pg_class where relname='data1'; oid ------- 16384 (1 row) postgres=# select pg_table_is_visible(16384); pg_table_is_visible --------------------- t (1 row) postgres=# postgres=# SELECT pg_table_is_visible('public.data1'::regclass); pg_table_is_visible --------------------- t (1 row) postgres=# 函数名称 pg_ts_config_is_visible 参数数据类型 config_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search configuration是否可见 使用举例 postgres=# select oid from pg_ts_config where cfgname='greek'; oid ------- 13861 (1 row) postgres=# select pg_ts_config_is_visible(13861); pg_ts_config_is_visible ------------------------- t (1 row) postgres=# postgres=# select pg_ts_config_is_visible('greek'::regconfig); pg_ts_config_is_visible ------------------------- t (1 row) postgres=# 函数名称 pg_ts_dict_is_visible 参数数据类型 dict_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search dictionary是否可见 使用举例 postgres=# select oid,dictname,dictinitoption from pg_ts_dict where dictname='greek_stem'; oid | dictname | dictinitoption -------+------------+-------------------- 13860 | greek_stem | language = 'greek' (1 row) postgres=# select pg_ts_dict_is_visible(13860); pg_ts_dict_is_visible ----------------------- t (1 row) postgres=# postgres=# select pg_ts_dict_is_visible('greek_stem'::regdictionary); pg_ts_dict_is_visible ----------------------- t (1 row) postgres=# 函数名称 pg_ts_parser_is_visible 参数数据类型 parser_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search parser是否可见 使用举例 postgres=# select * from pg_ts_parser ; oid | prsname | prsnamespace | prsstart | prstoken | prsend | prsheadline | prslextype ------+---------+--------------+------------+----------------+----------+---------------+-------------- 3722 | default | 11 | prsd_start | prsd_nexttoken | prsd_end | prsd_headline | prsd_lextype (1 row) postgres=# select pg_ts_parser_is_visible(3722); pg_ts_parser_is_visible ------------------------- t (1 row) postgres=# 函数名称 pg_ts_template_is_visible 参数数据类型 template_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search template是否可见 使用举例 postgres=# select * from pg_ts_template ; oid | tmplname | tmplnamespace | tmplinit | tmpllexize -------+-----------+---------------+----------------+------------------ 3727 | simple | 11 | dsimple_init | dsimple_lexize 3730 | synonym | 11 | dsynonym_init | dsynonym_lexize 3733 | ispell | 11 | dispell_init | dispell_lexize 3742 | thesaurus | 11 | thesaurus_init | thesaurus_lexize 13845 | snowball | 11 | dsnowball_init | dsnowball_lexize (5 rows) postgres=# select pg_ts_temp postgres=# select pg_ts_template_is_visible(3730); pg_ts_template_is_visible --------------------------- t (1 row) postgres=# 函数名称 pg_type_is_visible 参数数据类型 type_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定type、domain是否可见 使用举例 postgres=# select oid,typname from pg_type where typname='bool'; oid | typname -----+--------- 16 | bool (1 row) postgres=# select pg_type_is_visible(16); pg_type_is_visible -------------------- t (1 row) postgres=# postgres=# SELECT pg_type_is_visible('pg_catalog.bool'::regtype); pg_type_is_visible -------------------- t (1 row) postgres=# 函数名称 format_type 参数数据类型 type_oid, typemod 函数返回值数据类型 Text 函数含义 得到一个data type的SQL Name 使用举例 postgres=# select * from pg_type where typname='bool'; -[ RECORD 1 ]--+--------- oid | 16 typname | bool typnamespace | 11 typowner | 10 typlen | 1 typbyval | t typtype | b typcategory | B typispreferred | t typisdefined | t typdelim | , typrelid | 0 typelem | 0 typarray | 1000 typinput | boolin typoutput | boolout typreceive | boolrecv typsend | boolsend typmodin | - typmodout | - typanalyze | - typalign | c typstorage | p typnotnull | f typbasetype | 0 typtypmod | -1 typndims | 0 typcollation | 0 typdefaultbin | typdefault | typacl | postgres=# postgres=# select format_type(16,0); format_type ------------- boolean (1 row) postgres=# 函数名称 pg_get_constraintdef 参数数据类型 constraint_oid或者constraint_oid, pretty_bool 函数返回值数据类型 text 函数含义 得到一个constraint的定义 使用举例 postgres=# select pg_get_constraintdef(16918); pg_get_constraintdef ---------------------- PRIMARY KEY (c1) (1 row) postgres=# 函数名称 pg_get_expr 参数数据类型 pg_node_tree, relation_oid,或者 pg_node_tree, relation_oid, pretty_bool 函数返回值数据类型 Text 函数含义 反编译指定expression的内部格式，假设：其中的任何变量引用第二个参数指示的relation 使用举例 postgres=# CREATE TABLE tab1 (a int, b int) PARTITION BY RANGE(a); CREATE TABLE postgres=# CREATE TABLE tab1_p1 PARTITION OF tab1 FOR VALUES FROM (0) TO (100); CREATE TABLE postgres=# CREATE TABLE tab1_p2 PARTITION OF tab1 FOR VALUES FROM (100) TO (200); CREATE TABLE postgres=# select relname, pg_get_expr(relpartbound, oid) from pg_class where relispartition and relname ~ 'tab1' order by relname; relname | pg_get_expr ---------+-------------------------------- tab1_p1 | FOR VALUES FROM (0) TO (100) tab1_p2 | FOR VALUES FROM (100) TO (200) (2 rows) postgres=# 函数名称 pg_get_functiondef 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得function或者procedure的定义 使用举例 postgres=# select pg_get_functiondef(89); pg_get_functiondef ------------------------------------------------- CREATE OR REPLACE FUNCTION pg_catalog.version()+ RETURNS text + LANGUAGE internal + STABLE PARALLEL SAFE STRICT + AS $function$pgsql_version$function$ (1 row) postgres=# 函数名称 pg_get_function_arguments 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得function或者procedure的传入参数清单 使用举例 postgres=# select oid from pg_proc where proname='has_database_privilege'; -[ RECORD 1 ] oid | 2250 -[ RECORD 2 ] oid | 2251 -[ RECORD 3 ] oid | 2252 -[ RECORD 4 ] oid | 2253 -[ RECORD 5 ] oid | 2254 -[ RECORD 6 ] oid | 2255 postgres=# select pg_get_function_arguments(2250); -[ RECORD 1 ]-------------+----------------- pg_get_function_arguments | name, text, text postgres=# \\x Expanded display is off. postgres=# select pg_get_function_arguments(2250); pg_get_function_arguments --------------------------- name, text, text (1 row) postgres=# 函数名称 pg_get_function_identity_arguments 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得参数清单以标识出function或者procedure 使用举例 postgres=# select pg_get_function_identity_arguments(2250); pg_get_function_identity_arguments ------------------------------------ name, text, text (1 row) postgres=# 函数名称 pg_get_function_result 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得function，若是procedure，返回null 使用举例 postgres=# select pg_get_function_result(89); pg_get_function_result ------------------------ text (1 row) postgres=# 函数名称 pg_get_indexdef 参数数据类型 index_oid或者index_oid, column_no, pretty_bool 函数返回值数据类型 text 函数含义 获得索引的定义语句获得某个索引上第n个索引列的名称 使用举例 postgres=# select oid from pg_class where relname='idx_2'; oid ------- 16427 (1 row) postgres=# select pg_get_indexdef(16427); pg_get_indexdef --------------------------------------------------------- CREATE INDEX idx_2 ON public.data1 USING btree (c1, c3) (1 row) postgres=# postgres=# select pg_get_indexdef(16427,2,true); pg_get_indexdef ----------------- c3 (1 row) postgres=# select pg_get_indexdef(16427,1,true); pg_get_indexdef ----------------- c1 (1 row) postgres=# 函数名称 pg_get_keywords 参数数据类型 无输入参数 函数返回值数据类型 setof record 函数含义 返回PostgreSQL数据库中的SQL关键字清单返回结果中有3列(以逗号分隔)，第一列是关键字名称，第二列catcode是category code U for unreserved C for column name T for type or function name R for reserved第三列catdesc是描述 使用举例 postgres=# select pg_get_keywords(); pg_get_keywords ------------------------------------------------------------------ (abort,U,unreserved) (absolute,U,unreserved) (access,U,unreserved) (action,U,unreserved) (add,U,unreserved) (admin,U,unreserved) (after,U,unreserved) (aggregate,U,unreserved) (all,R,reserved) (also,U,unreserved) (alter,U,unreserved) (always,U,unreserved) (analyse,R,reserved) (analyze,R,reserved) (and,R,reserved) 限于篇幅，本函数的输出仅摘录到此处 函数名称 pg_get_ruledef 参数数据类型 rule_oid或者rule_oid, pretty_bool 函数返回值数据类型 Text 函数含义 获得rule的create rule语句 使用举例 postgres=# select oid ,rulename from pg_rewrite where rulename='pg_settings_u'; oid | rulename -------+--------------- 12168 | pg_settings_u (1 row) postgres=# select pg_get_ruledef(12168); pg_get_ruledef --------------------------------------------------------------------------------------------------- CREATE RULE pg_settings_u AS + ON UPDATE TO pg_catalog.pg_settings + WHERE (new.name = old.name) DO SELECT set_config(old.name, new.setting, false) AS set_config; (1 row) postgres=# 函数名称 pg_get_serial_sequence 参数数据类型 table_name, column_name 函数返回值数据类型 text 函数含义 获得自增列或者标识列使用的sequence的name 使用举例 postgres=# select pg_get_serial_sequence('gen_y','c1'); pg_get_serial_sequence ------------------------ public.gen_y_c1_seq (1 row) postgres=# 函数名称 pg_get_statisticsobjdef 参数数据类型 statobj_oid 函数返回值数据类型 text 函数含义 获得extended statistics object的CREATE STATISTICS语句。 使用举例 函数名称 pg_get_triggerdef 参数数据类型 trigger_oid 或者trigger_oid, pretty_bool 函数返回值数据类型 text 函数含义 获得trigger的create trigger语句 使用举例 postgres=# select pg_get_triggerdef(16992); pg_get_triggerdef ------------------------------------------------------------------------------------------------------------ CREATE TRIGGER example_trigger AFTER INSERT ON public.company FOR EACH ROW EXECUTE FUNCTION auditlogfunc() (1 row) postgres=# 函数名称 pg_get_userbyid 参数数据类型 role_oid 函数返回值数据类型 name 函数含义 获得特定role oid的role name 使用举例 postgres=# select pg_get_userbyid(16426); pg_get_userbyid ----------------- lc0029999 (1 row) postgres=# 函数名称 pg_get_viewdef 参数数据类型 view_name 或者 view_name, pretty_bool或者 view_oid或者 view_oid, pretty_bool或者view_oid, wrap_column_int 函数返回值数据类型 text 函数含义 获得view的定义 使用举例 postgres=# select pg_get_viewdef('company_view'); pg_get_viewdef --------------------- SELECT company.id,+ company.name, + company.age + FROM company; (1 row) postgres=# 函数名称 pg_index_column_has_property 参数数据类型 index regclass, column integer, property text 函数返回值数据类型 boolean 函数含义 测试索引列是否有特定的索引列属性。索引列属性见下： asc 索引列升序 desc 索引列降序 nulls_first null值在前 nulls_last null值在后 orderable 列是否具有任何定义的排序顺序？ distance_orderable 列是否可以被\"distance\" operator 顺序扫描到, 比如 ORDER BY col <→ constant ? returnable index-only扫描是否可以返回列值 search_array 列是否原生支持 col = ANY(array)搜索 search_nulls 列是否支持IS NULL 和IS NOT NULL 搜索 注意：extension的访问方法可以为它们的indexes定义额外的属性名称。 本函数在如下情况下会返回NULL：属性名称未知(not known)或者不适用于特定的对象Oid或者column number不能标识出一个有效的对象。 使用举例 postgres=# select pg_index_column_has_property('idx_2'::regclass,1,'desc'); pg_index_column_has_property ------------------------------ f (1 row) postgres=# select pg_index_column_has_property('idx_2'::regclass,1,'asc'); pg_index_column_has_property ------------------------------ t (1 row) postgres=# 函数名称 pg_index_has_property 参数数据类型 index regclass, property text 函数返回值数据类型 boolean 函数含义 测试一个index是否具有特定的索引属性。如下是索引属性的清单 clusterable 在cluster命令中是否可以用到index index_scan index 是否支持 plain (non-bitmap) scans? bitmap_scan index 是否支持 bitmap scans? backward_scan Can the scan direction be changed in mid-scan (to support FETCH BACKWARD on a cursor without needing materialization)? 注意：extension的访问方法可以为它们的indexes定义额外的属性名称。 本函数在如下情况下会返回NULL：属性名称未知(not known)或者不适用于特定的对象oid不能标识出一个有效的对象。 使用举例 postgres=# select pg_index_has_property('idx_2'::regclass,'index_scan'); pg_index_has_property ----------------------- t (1 row) postgres=# 函数名称 pg_indexam_has_property 参数数据类型 am oid, property text 函数返回值数据类型 boolean 函数含义 测试一个index访问方法是否具有特定的属性。如下是索引访问方法属性清单： can_order 访问方法是否支持 ASC, DESC 以及CREATE INDEX的related keywords can_unique 访问方法是否支持unique indexes? can_multi_col 访问方法是否支持多列indexes? can_exclude 访问方法是否支持exclusion constraints? can_include 访问方法是否支持CREATE INDEX的 INCLUDE 子句 本函数在如下情况下会返回NULL：属性名称未知(not known)或者不适用于特定的对象 oid不能标识出一个有效的对象。 使用举例 postgres=# select * from pg_am; oid | amname | amhandler | amtype ------+--------+----------------------+-------- 2 | heap | heap_tableam_handler | t 403 | btree | bthandler | i 405 | hash | hashhandler | i 783 | gist | gisthandler | i 2742 | gin | ginhandler | i 4000 | spgist | spghandler | i 3580 | brin | brinhandler | i (7 rows) postgres=# postgres=# select pg_indexam_has_property(4000,'can_multi_col'); pg_indexam_has_property ------------------------- f (1 row) postgres=# postgres=# select pg_indexam_has_property(403,'can_unique'); pg_indexam_has_property ------------------------- t (1 row) postgres=# 函数名称 pg_options_to_table 参数数据类型 options_array text[] 函数返回值数据类型 setof record ( option_name text, option_value text ) 函数含义 返回pg_class.reloptions或者pg_attribute.attoptions表示的存储选项 使用举例 postgres=# select pg_options_to_table(reloptions) from pg_class where oid='16384'; pg_options_to_table --------------------------------------------- (autovacuum_vacuum_insert_threshold,10000) (autovacuum_vacuum_insert_scale_factor,0.1) (2 rows) postgres=# 函数名称 pg_tablespace_databases 参数数据类型 tablespace oid 函数返回值数据类型 setof oid 函数含义 返回特定tablespace中保存的object的database的oid，如果本函数返回任何值，那么就表示tablespace不为空并且不能被drop掉。若想查出在该表空间内有哪些对象，你需要连接到本函数返回的database中，并查询pg_class获得这些对象。 使用举例 postgres=# select pg_tablespace_databases(1663); pg_tablespace_databases ------------------------- 16424 1 16878 14174 14173 (5 rows) postgres=# 函数名称 pg_tablespace_location 参数数据类型 tablespace oid 函数返回值数据类型 Text 函数含义 返回指定tablespace的文件系统路径 使用举例 postgres=# select pg_tablespace_location(16391); pg_tablespace_location ------------------------ /home/pg131/ts1 (1 row) postgres=# 函数名称 pg_typeof 参数数据类型 any 函数返回值数据类型 regtype 函数含义 指定值的数据类型的oid 使用举例 postgres=# SELECT pg_typeof(33.339999); -[ RECORD 1 ]------ pg_typeof | numeric postgres=# SELECT typlen FROM pg_type WHERE oid = pg_typeof(33.339999); -[ RECORD 1 ] typlen | -1 postgres=# 函数名称 COLLATION FOR 参数数据类型 \"any\" 函数返回值数据类型 text 函数含义 返回指定值的collation的name，本函数的返回值是被双引号引起来的，如果入参表达式不能派生出collation，则返回null如果传入的参数不是可以collatable的data type，则会抛出错误 使用举例 postgres=# select collation for(datname) from pg_database; pg_collation_for ------------------ \"C\" \"C\" \"C\" \"C\" \"C\" (5 rows) postgres=# 函数名称 to_regclass 参数数据类型 text 函数返回值数据类型 Regclass 函数含义 将文本形式的relation name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL 使用举例 postgres=# select to_regclass('public.data1'); -[ RECORD 1 ]------ to_regclass | data1 postgres=# \\x Expanded display is off. postgres=# select oid, relname from pg_class where oid=to_regclass('public.data1'); oid | relname -------+--------- 16384 | data1 (1 row) postgres=# 函数名称 to_regcollation 参数数据类型 Text 函数返回值数据类型 regcollation 函数含义 将文本形式的collation name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL 使用举例 postgres=# select to_regcollation('\"POSIX\"'); to_regcollation ----------------- \"POSIX\" (1 行记录) postgres=# select * from pg_collation where oid=to_regcollation('\"POSIX\"'); oid | collname | collnamespace | collowner | collprovider | collisdeterministic | collencoding | collcollate | collctype | collversion -----+----------+---------------+-----------+--------------+---------------------+--------------+-------------+-----------+------------- 951 | POSIX | 11 | 10 | c | t | -1 | POSIX | POSIX | (1 行记录) postgres=# select oid,collname from pg_collation where oid=to_regcollation('\"POSIX\"'); oid | collname -----+---------- 951 | POSIX (1 行记录) postgres=# 函数名称 to_regnamespace 参数数据类型 text 函数返回值数据类型 regnamespace 函数含义 将文本形式的schema name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL 使用举例 postgres=# select to_regnamespace('public'); to_regnamespace ----------------- public (1 行记录) postgres=# 函数名称 to_regoper 参数数据类型 text 函数返回值数据类型 regoper 函数含义 将文本形式的operator name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name或者含义不明确，本函数会返回NULL 使用举例 postgres=# select oid, oprname from pg_operator where oid=to_regoper('!'); oid | oprname -----+--------- 388 | ! (1 row) postgres=# select to_regoper('!'); to_regoper ------------ ! (1 row) postgres=# 函数名称 to_regoperator 参数数据类型 text 函数返回值数据类型 regoperator 函数含义 将文本形式的operator name(带参数类型)转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL. 使用举例 postgres=# select oid, oprname from pg_operator where oid=to_regoperator('+(integer,integer)'); oid | oprname -----+--------- 551 | + (1 row) postgres=# select to_regoperator('+(int4,int4)'); to_regoperator -------------------- +(integer,integer) (1 row) postgres=# 函数名称 to_regproc 参数数据类型 text 函数返回值数据类型 regproc 函数含义 将文本的function或者procedure转换为其oid。如果本函数未找到输入参数指定的name或者含义不明确，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select to_regproc('pg_stat_statements'); to_regproc -------------------- pg_stat_statements (1 row) postgres=# select oid,proname from pg_proc where oid=to_regproc('pg_stat_statements'); oid | proname -------+-------------------- 16401 | pg_stat_statements (1 row) postgres=# 函数名称 to_regprocedure 参数数据类型 text 函数返回值数据类型 regprocedure 函数含义 将文本的function或者procedure(带参数)转换为其oid。如果本函数未找到输入参数指定的name，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select to_regprocedure('pg_get_viewdef(text)'); to_regprocedure ---------------------- pg_get_viewdef(text) (1 row) postgres=# select oid, proname from pg_proc where oid= to_regprocedure('pg_get_viewdef(text)'); oid | proname ------+---------------- 1640 | pg_get_viewdef (1 row) postgres=# 函数名称 to_regrole 参数数据类型 text 函数返回值数据类型 regrole 函数含义 将文本的role name转换为其oid。如果本函数未找到输入参数指定的name，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select oid,rolname from pg_roles where oid=to_regrole('lc0039999'); oid | rolname -------+----------- 16877 | lc0039999 (1 row) postgres=# select to_regrole('lc0039999'); to_regrole ------------ lc0039999 (1 row) postgres=# 函数名称 to_regtype 参数数据类型 text 函数返回值数据类型 regtype 函数含义 将文本的type name转换为其oid。如果本函数未找到输入参数指定的name，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select to_regtype('int4'); to_regtype ------------ integer (1 row) postgres=# select oid, typname from pg_type where oid=to_regtype('int4'); oid | typname -----+--------- 23 | int4 (1 row) postgres=# 函数名称 pg_describe_object 参数数据类型 classid oid, objid oid, objsubid integer 函数返回值数据类型 text 函数含义 用于描述pg_depend系统表中保存的依赖对象。 使用举例 postgres=# select oid,relname from pg_class where oid in (select distinct classid from pg_depend) order by oid; oid | relname ------+------------------------- 1247 | pg_type 1255 | pg_proc 1259 | pg_class 2328 | pg_foreign_data_wrapper 2602 | pg_amop 2603 | pg_amproc 2606 | pg_constraint 2612 | pg_language 2616 | pg_opclass 2617 | pg_operator 2618 | pg_rewrite 2620 | pg_trigger 2753 | pg_opfamily 3079 | pg_extension 3600 | pg_ts_dict 3602 | pg_ts_config 3764 | pg_ts_template (17 rows) postgres=# select * from pg_foreign_data_wrapper ; oid | fdwname | fdwowner | fdwhandler | fdwvalidator | fdwacl | fdwoptions -------+----------+----------+------------+--------------+--------+------------ 16954 | file_fdw | 10 | 16952 | 16953 | | (1 row) postgres=# select pg_describe_object(2328,16954,0); pg_describe_object ------------------------------- foreign-data wrapper file_fdw (1 row) postgres=# 函数名称 pg_identify_object 参数数据类型 classid oid, objid oid, objsubid integer 函数返回值数据类型 record ( type text, schema text, name text, identity text ) 函数含义 返回一个数据库对象的详细信息 使用举例 postgres=# select pg_identify_object(2328,16954,0); pg_identify_object --------------------------------------------- (\"foreign-data wrapper\",,file_fdw,file_fdw) (1 row) postgres=# 函数名称 pg_identify_object_as_address 参数数据类型 classid oid, objid oid, objsubid integer 函数返回值数据类型 record ( type text, object_names text[], object_args text[] ) 函数含义 返回一个数据库对象的足够信息以标识出数据库对象 使用举例 postgres=# select pg_identify_object_as_address(2328,16954,0); pg_identify_object_as_address ---------------------------------------- (\"foreign-data wrapper\",{file_fdw},{}) (1 row) postgres=# 函数名称 pg_get_object_address 参数数据类型 type text, object_names text[], object_args text[] 函数返回值数据类型 record ( classid oid, objid oid, objsubid integer ) 函数含义 本函数是pg_identify_object_as_address的反函数 使用举例 postgres=# select pg_get_object_address('foreign-data wrapper','{file_fdw}','{}'); pg_get_object_address ----------------------- (2328,16954,0) (1 row) postgres=# 函数名称 col_description 参数数据类型 table oid, column integer 函数返回值数据类型 text 函数含义 获取某个表的某个列的comment 使用举例 postgres=# create table t_comment(c1 int ); CREATE TABLE postgres=# comment on column t_comment.c1 is 'this is c1 comment!'; COMMENT postgres=# select oid from pg_class where oid=to_regclass('t_comment') postgres-# ; oid ------- 17001 (1 row) postgres=# select col_description(17001,1); col_description --------------------- this is c1 comment! (1 row) postgres=# postgres=# select col_description(to_regclass('t_comment'),1); col_description --------------------- this is c1 comment! (1 row) postgres=# 函数名称 obj_description 参数数据类型 object oid, catalog name 或者object oid 函数返回值数据类型 text 函数含义 返回特定数据库对象的注释信息 使用举例 postgres=# comment on table t_comment is ' table-level test comment'; COMMENT postgres=# \\dt+ data1 List of relations Schema | Name | Type | Owner | Persistence | Size | Description --------+-------+-------+-------+-------------+-------+------------- public | data1 | table | pg131 | permanent | 42 MB | (1 row) postgres=# \\dt+ t_comment; List of relations Schema | Name | Type | Owner | Persistence | Size | Description --------+-----------+-------+-------+-------------+---------+--------------------------- public | t_comment | table | pg131 | permanent | 0 bytes | table-level test comment (1 row) postgres=# select obj_description(17001); obj_description --------------------------- table-level test comment (1 row) postgres=# 函数名称 shobj_description 参数数据类型 object oid, catalog name 函数返回值数据类型 text 函数含义 返回特定共享数据库对象(如databases, roles, and tablespaces)的注释信息. 使用举例 postgres=# select shobj_description(14174,'pg_database'); shobj_description -------------------------------------------- default administrative connection database (1 row) postgres=# 函数名称 pg_current_xact_id 参数数据类型 无输入参数 函数返回值数据类型 xid8 函数含义 返回当前的事务id，若是没有当前事务id，系统会自动指派一个新的事务id 使用举例 postgres=# select pg_current_xact_id(); pg_current_xact_id -------------------- 720 (1 row) postgres=# 函数名称 pg_current_xact_id_if_assigned() 参数数据类型 无输入参数 函数返回值数据类型 xid8 函数含义 返回当前的current transaction’s ID，若是没有transaction’s ID被指派，本函数返回NULL。如果事务可能是只读的，最好使用本函数，以避免不必要的XID消耗。 使用举例 postgres=*# select pg_current_xact_id_if_assigned(); pg_current_xact_id_if_assigned -------------------------------- (1 row) postgres=*# update data1 set c2='data1_1' where c1=1; UPDATE 1 postgres=*# select pg_current_xact_id_if_assigned(); pg_current_xact_id_if_assigned -------------------------------- 722 (1 row) postgres=*# 函数名称 pg_xact_status 参数数据类型 xid8 函数返回值数据类型 text 函数含义 返回近期事务的提交状态。本函数的执行结果是下面三个中的一个:in progress、commited、aborted。当事务足够老而没有在PostgreSQL中保存下来时，commit status会被丢弃，此时本函数返回结果为NULL。请注意，针对prepared transactions，本函数的返回结果为in progress,应用程序必须检查pg_prepared_xacts以确定一个transaction ID是否属于prepared transaction 使用举例 postgres=# select pg_xact_status(pg_current_xact_id()); pg_xact_status ---------------- in progress (1 row) postgres=# postgres=# select pg_xact_status(722::text::xid8); pg_xact_status ---------------- committed (1 row) postgres=# 函数名称 pg_current_snapshot 参数数据类型 无输入参数 函数返回值数据类型 pg_snapshot 函数含义 返回当前的snapshot,这是一种显示哪个transaction id是正处于in-progress状态的数据结构。注意：有关pg_snapshot数据类型： pg_snapshot数据类型用于存储在一个特定时间点上的transaction ID visibility信息。 pg_snapshot数据类型的表示方法是xmin:xmax:xip_list这个xmin:xmax:xip_list就是pg_snapshot数据类型组成。见下： xmin:处于active状态的最小的transaction id，所有小于xmin的transaction id要么是committed (visible),要么是rolled back(dead) xmax:大于或者等于本xmax值的transaction id是在snapshot结束时未完成的，因此，xmax是不可见的。 xip_list:在生成snapshot时，处于in progress状态的transactions。在xmin ⇐ X < xmax范围内并且不在本xip_list中的transaction ID 表示在snapshot生成时是已经完成的transaction ID,因此，根据commit status，要么是visible的要么是dead，本列表中不包括subtransactions的transaction IDs 使用举例 postgres=# select pg_current_snapshot(); pg_current_snapshot --------------------- 727:727: (1 row) postgres=# 函数名称 pg_snapshot_xip 参数数据类型 pg_snapshot 函数返回值数据类型 setof xid8 函数含义 返回snapshot中处于in-progress状态的transaction id 使用举例 postgres=# select pg_snapshot_xip('10:20:10,14,15'); pg_snapshot_xip ----------------- 10 14 15 (3 rows) postgres=# 函数名称 pg_snapshot_xmax 参数数据类型 pg_snapshot 函数返回值数据类型 xid8 函数含义 返回snapshot的xmax 使用举例 postgres=# select pg_snapshot_xmax('10:20:10,14,15'); pg_snapshot_xmax ------------------ 20 (1 row) postgres=# 函数名称 pg_snapshot_xmin 参数数据类型 pg_snapshot 函数返回值数据类型 xid8 函数含义 返回snapshot的xmin 使用举例 postgres=# select pg_snapshot_xmin('10:20:10,14,15'); pg_snapshot_xmin ------------------ 10 (1 row) postgres=# 函数名称 pg_visible_in_snapshot 参数数据类型 xid8, pg_snapshot 函数返回值数据类型 bool 函数含义 给定的事务ID是否对snapshot可见，即：给定的事务ID是否在生成snapshot之前完成。本函数对subtransaction ID不能给出正确的结果。 使用举例 postgres=# select pg_visible_in_snapshot(722::text::xid8,'10:20:10,14,15'); pg_visible_in_snapshot ------------------------ f (1 row) postgres=# select pg_visible_in_snapshot(12::text::xid8,'10:20:10,14,15'); pg_visible_in_snapshot ------------------------ t (1 row) postgres=# 函数名称 txid_current 参数数据类型 无输入参数 函数返回值数据类型 bigint 函数含义 返回当前的事务id，若是没有当前事务id，系统会自动指派一个新的事务id。本函数（返回值是bigint数据类型）的作用与pg_current_xact_id(返回值是xid8数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_current(); txid_current -------------- 735 (1 row) postgres=# 函数名称 txid_current_if_assigned() 参数数据类型 无输入参数 函数返回值数据类型 bigint 函数含义 本函数（注意：返回值是bigint数据类型）的作用与pg_current_xact_id_if_assigned(注意：返回值是xid8数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 [pg131@VM-0-8-centos ~]$ psql -d postgres psql (13.1) Type \"help\" for help. postgres=# select txid_current_if_assigned(); txid_current_if_assigned -------------------------- (1 row) postgres=# begin; BEGIN postgres=*# select txid_current_if_assigned(); txid_current_if_assigned -------------------------- (1 row) postgres=*# select txid_current(); txid_current -------------- 738 (1 row) postgres=*# select txid_current_if_assigned(); txid_current_if_assigned -------------------------- 738 (1 row) 函数名称 txid_current_snapshot() 参数数据类型 无输入参数 函数返回值数据类型 txid_snapshot 函数含义 返回当前的snapshot,这是一种显示哪个transaction id是正处于in-progress状态的数据结构。本函数（注意：返回值是txid_snapshot数据类型）的作用与pg_current_snapshot (注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_current_snapshot(); txid_current_snapshot ----------------------- 741:741: (1 row) postgres=# \\gdesc Column | Type -----------------------+--------------- txid_current_snapshot | txid_snapshot (1 row) postgres=# 函数名称 txid_snapshot_xip(txid_snapshot) 参数数据类型 txid_snapshot 函数返回值数据类型 setof bigint 函数含义 返回snapshot中处于in-progress状态的transaction id。本函数（注意：返回值是txid_snapshot数据类型）的作用与pg_snapshot_xip (注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_snapshot_xip('10:20:10,14,15'); txid_snapshot_xip ------------------- 10 14 15 (3 rows) postgres=# \\gdesc Column | Type -------------------+-------- txid_snapshot_xip | bigint (1 row) 函数名称 txid_snapshot_xmax 参数数据类型 txid_snapshot 函数返回值数据类型 bigint 函数含义 返回snapshot的xmax。本函数（注意：返回值是bigint数据类型）的作用与pg_snapshot_xmax (注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_snapshot_xmax('10:20:10,14,15'); txid_snapshot_xmax -------------------- 20 (1 row) postgres=# \\gdesc Column | Type --------------------+-------- txid_snapshot_xmax | bigint (1 row) postgres=# 函数名称 txid_snapshot_xmin 参数数据类型 txid_snapshot 函数返回值数据类型 bigint 函数含义 返回snapshot的xmin。本函数（注意：返回值是bigint数据类型）的作用与pg_snapshot_xmin(注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_snapshot_xmin('10:20:10,14,15'); txid_snapshot_xmin -------------------- 10 (1 row) postgres=# \\gdesc Column | Type --------------------+-------- txid_snapshot_xmin | bigint (1 row) postgres=# 函数名称 txid_visible_in_snapshot 参数数据类型 bigint, txid_snapshot 函数返回值数据类型 boolean 函数含义 给定的事务ID是否对snapshot可见，即：给定的事务ID是否在生成snapshot之前完成。本函数对subtransaction ID不能给出正确的结果。 使用举例 postgres=# select txid_visible_in_snapshot(722,'10:20:10,14,15'); txid_visible_in_snapshot -------------------------- f (1 row) postgres=# select txid_visible_in_snapshot(12,'10:20:10,14,15'); txid_visible_in_snapshot -------------------------- t (1 row) postgres=# 函数名称 txid_status 参数数据类型 bigint 函数返回值数据类型 text 函数含义 返回近期事务的提交状态。本函数的执行结果是下面三个中的一个:in progress、commited、aborted。当事务足够老而没有在PostgreSQL中保存下来时，commit status会被丢弃，此时本函数返回结果为NULL。请注意，针对prepared transactions，本函数的返回结果为in progress,应用程序必须检查pg_prepared_xacts以确定一个transaction ID是否属于prepared transaction. 使用举例 postgres=# select txid_status(txid_current()); txid_status ------------- in progress (1 row) postgres=# select txid_current(); txid_current -------------- 742 (1 row) postgres=# select txid_status(722); txid_status ------------- committed (1 row) postgres=# 函数名称 pg_xact_commit_timestamp 参数数据类型 xid 函数返回值数据类型 timestamp with time zone 函数含义 返回一个事务的提交时间。本函数能正常使用的前提就是配置参数track_commit_timestamp设置为on，否会有如下提示： postgres=# select pg_xact_commit_timestamp(722::text::xid); ERROR: could not get commit timestamp data HINT: Make sure the configuration parameter \"track_commit_timestamp\" is set. postgres=# 使用举例 postgres=# select txid_current(); txid_current -------------- 743 (1 row) postgres=# select pg_xact_commit_timestamp(743::text::xid); pg_xact_commit_timestamp ------------------------------ 2021-04-29 16:52:37.19133+08 (1 row) postgres=# 函数名称 pg_last_committed_xact 参数数据类型 无输入数据类型 函数返回值数据类型 record ( xid xid, timestamp timestamp with time zone ) 函数含义 返回最后一个提交事务的transaction id和commit timestamp 使用举例 postgres=# select pg_last_committed_xact(); pg_last_committed_xact -------------------------------------- (743,\"2021-04-29 16:52:37.19133+08\") (1 row) postgres=# 函数名称 pg_control_checkpoint 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回当前checkpoint状态的相关信息 使用举例 函数名称 pg_control_system 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回当前控制文件状态的相关信息。 使用举例 postgres=# select pg_control_system(); pg_control_system --------------------------------------------------------------- (1300,202007201,6908228805274066467,\"2021-04-29 16:57:56+08\") (1 row) postgres=# 函数名称 pg_control_init 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回cluster初始化时的相关信息 使用举例 postgres=# select pg_control_init(); pg_control_init --------------------------------------------------- (8,8192,131072,8192,16777216,64,32,1996,2048,t,0) (1 row) postgres=# 函数名称 pg_control_recovery 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回恢复状态的信息。 使用举例 postgres=# select pg_control_recovery(); pg_control_recovery --------------------- (0/0,0,0/0,0/0,f) (1 row) postgres=# 函数名称 gen_random_uuid 参数数据类型 无输入参数 函数返回值数据类型 uuid 函数含义 本函数返回version 4版本的uuid 使用举例 postgres=# select gen_random_uuid(); gen_random_uuid -------------------------------------- 39158a63-f6d0-4883-8765-8b8e9c9cae62 (1 row) postgres=# 函数名称 array_to_tsvector 参数数据类型 text[] 函数返回值数据类型 tsvector 函数含义 将array of lexemes转换为tsvector 使用举例 postgres=# select array_to_tsvector('{fat,cat,rat}'::text[]); array_to_tsvector ------------------- 'cat' 'fat' 'rat' (1 row) postgres=# 函数名称 get_current_ts_config 参数数据类型 无输入参数 函数返回值数据类型 regconfig 函数含义 返回当前默认的text search配置的oid（参见配置参数default_text_search_config） 使用举例 postgres=# select get_current_ts_config(); get_current_ts_config ----------------------- english (1 row) postgres=# 函数名称 length 参数数据类型 tsvector 函数返回值数据类型 integer 函数含义 返回lexemes的数量 使用举例 postgres=# select length('fat:2,4 cat:3 rat:5A'::tsvector); length -------- 3 (1 row) postgres=# 函数名称 numnode 参数数据类型 tsquery 函数返回值数据类型 integer 函数含义 返回lexemes加operator的数量 使用举例 postgres=# select numnode('(fat & rat) | cat'::tsquery) ; numnode --------- 5 (1 row) postgres=# 函数名称 plainto_tsquery 参数数据类型 [config regconfig,] query text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery。其中，字符串中的任何标点都将被忽略 使用举例 postgres=# select plainto_tsquery('english', 'The Fat Rats'); plainto_tsquery ----------------- 'fat' & 'rat' (1 row) postgres=# 函数名称 phraseto_tsquery 参数数据类型 [config regconfig,] query text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery。其中，字符串中的任何标点都将被忽略 使用举例 postgres=# select phraseto_tsquery('english', 'The Fat Rats') ; phraseto_tsquery ------------------ 'fat' <-> 'rat' (1 row) postgres=# 函数名称 websearch_to_tsquery 参数数据类型 [ config regconfig, ] query text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery。引用的单词序列被转换成短语测试。\"or\"一词被理解为产生or运算符，破折号产生NOT运算符，忽略其他标点符号 使用举例 postgres=# select websearch_to_tsquery('english', '\"fat rat\" or cat dog'); websearch_to_tsquery --------------------------------- 'fat' <-> 'rat' | 'cat' & 'dog' (1 row) postgres=# 函数名称 querytree 参数数据类型 tsquery 函数返回值数据类型 text 函数含义 返回一个tsquery中的可索引部分。当返回结果为空或者T时，表示这是一个不可索引的query。 使用举例 postgres=# select querytree('foo & ! bar'::tsquery); querytree ----------- 'foo' (1 row) postgres=# 函数名称 setweight 参数数据类型 vector tsvector, weight \"char\"或者vector tsvector, weight \"char\", lexemes text[] 函数返回值数据类型 tsvector 函数含义 为vector中的每个元素指定权重或者为在lexemes之内的vector中的每个元素指定权重 使用举例 postgres=# select setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A'); setweight ------------------------------- 'cat':3A 'fat':2A,4A 'rat':5A (1 row) postgres=# postgres=# select setweight('fat:2,4 cat:3 rat:5,6B'::tsvector, 'A', '{cat,rat}'); setweight -------------------------------- 'cat':3A 'fat':2,4 'rat':5A,6A (1 row) postgres=# 函数名称 strip 参数数据类型 tsvector 函数返回值数据类型 tsvector 函数含义 从tsvector中抹掉position和weight 使用举例 postgres=# select strip('fat:2,4 cat:3 rat:5A'::tsvector); strip ------------------- 'cat' 'fat' 'rat' (1 row) postgres=# 函数名称 to_tsquery 参数数据类型 regconfig, text 或者text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery，本函数的返回结果必须由有效的tsquery operator进行连接。 使用举例 postgres=# select to_tsquery('english', 'The & Fat & Rats'); to_tsquery --------------- 'fat' & 'rat' (1 row) postgres=# select to_tsquery( 'The & Fat & Rats'); to_tsquery --------------- 'fat' & 'rat' (1 row) postgres=# 函数名称 to_tsvector 参数数据类型 regconfig,text 函数返回值数据类型 tsvector 函数含义 将text转换为tsvector ，结果中包括位置信息 使用举例 postgres=# select to_tsvector('english', 'The Fat Rats'); to_tsvector ----------------- 'fat':2 'rat':3 (1 row) postgres=# 函数名称 to_tsvector 参数数据类型 regconfig,json或者regconfig,jsonb 函数返回值数据类型 tsvector 函数含义 将json文档中的每个字符串值转换为tsvector，结果值是按照文档顺序进行连接后输出的，当输入参数数据类型为jsonb时，json对象的字段的文档顺序是与实现相关的 使用举例 postgres=# select to_tsvector('english', '{\"aa\": \"The Fat Rats\", \"b\": \"dog\"}'::json); to_tsvector ------------------------- 'dog':5 'fat':2 'rat':3 (1 row) postgres=# postgres=# select to_tsvector('english', '{\"aa\": \"The Fat Rats\", \"b\": \"dog\"}'::jsonb); to_tsvector ------------------------- 'dog':1 'fat':4 'rat':5 (1 row) postgres=# 函数名称 json_to_tsvector 参数数据类型 [config regconfig,] json, jsonb 函数返回值数据类型 tsvector 函数含义 将json文档中满足filter条件的每个item转换为一个tsvector。结果值是按照文档顺序进行连接后输出的，当输入参数数据类型为jsonb时，json对象的字段的文档顺序是与实现相关的。Filter条件必须是一个包括另个或者多个关键字的json 数组：string代表包括所有字符串值numeric代表包括所有数字值boolean代表包括所有boolean值key代表包括所有keyall代表包括上述所有。作为一个简单的例子，filter条件不可以是一个简单的并且是上述关键字之一的json值。 使用举例 postgres=# select json_to_tsvector('english', '{\"a\": \"The Fat Rats\", \"b\": 123}'::json, '[\"string\", \"numeric\"]'); json_to_tsvector ------------------------- '123':5 'fat':2 'rat':3 (1 row) postgres=# 函数名称 jsonb_to_tsvector 参数数据类型 [config regconfig,] jsonb,jsonb 函数返回值数据类型 tsvector 函数含义 将json文档中满足filter条件的每个item转换为一个tsvector。结果值是按照文档顺序进行连接后输出的，当输入参数数据类型为jsonb时，json对象的字段的文档顺序是与实现相关的。Filter条件必须是一个包括另个或者多个关键字的json 数组：string代表包括所有字符串值numeric代表包括所有数字值boolean代表包括所有boolean值key代表包括所有keyall代表包括上述所有。作为一个简单的例子，filter条件不可以是一个简单的并且是上述关键字之一的json值。 使用举例 postgres=# select jsonb_to_tsvector('english', '{\"a\": \"The Fat Rats\", \"b\": 123}'::jsonb, '[\"string\", \"numeric\"]'); jsonb_to_tsvector ------------------------- '123':5 'fat':2 'rat':3 (1 row) postgres=# 函数名称 ts_delete 参数数据类型 tsvector,text 函数返回值数据类型 tsvector 函数含义 从vector中删除掉指定的lexeme 使用举例 postgres=# select ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, 'fat'); ts_delete ------------------ 'cat':3 'rat':5A (1 row) postgres=# 函数名称 ts_delete 参数数据类型 tsvector,text[] 函数返回值数据类型 tsvector 函数含义 从vector中删除掉指定的lexeme 使用举例 postgres=# select ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, ARRAY['fat','rat']); ts_delete ----------- 'cat':3 (1 row) postgres=# 函数名称 ts_filter 参数数据类型 tsvector, \"char\"[] 函数返回值数据类型 tsvector 函数含义 从vector中筛选出带有指定weights的元素 使用举例 postgres=# select ts_filter('fat:2,4 cat:3b,7c rat:5A'::tsvector, '{a,b}'); ts_filter ------------------- 'cat':3B 'rat':5A (1 row) postgres=# 函数名称 ts_headline 参数数据类型 [config regconfig,] document text, query tsquery [, options text] 函数返回值数据类型 text 函数含义 以简写的形式显示在document匹配到的query 使用举例 postgres=# select ts_headline('The fat cat ate the rat.', 'cat'); ts_headline --------------------------------- The fat <b>cat</b> ate the rat. (1 row) postgres=# 函数名称 ts_headline 参数数据类型 [config regconfig,] document json, query tsquery [, options text] 函数返回值数据类型 json 函数含义 以缩写的形式显示在json document匹配到的query 使用举例 postgres=# select ts_headline('{\"cat\":\"raining cats and dogs\"}'::json, 'cat'); ts_headline ---------------------------------------- {\"cat\":\"raining <b>cats</b> and dogs\"} (1 row) 函数名称 ts_headline 参数数据类型 [config regconfig,] document jsonb, query tsquery [, options text] 函数返回值数据类型 jsonb 函数含义 以缩写的形式显示在json document匹配到的query 使用举例 postgres=# select ts_headline('{\"cat\":\"raining cats and dogs\"}'::jsonb, 'cat'); ts_headline ----------------------------------------- {\"cat\": \"raining <b>cats</b> and dogs\"} (1 row) postgres=# 函数名称 ts_rank 参数数据类型 [weights real[], ] vector tsvector, query tsquery [, normalization integer] 函数返回值数据类型 real 函数含义 计算query与vector的匹配程度 使用举例 postgres=# SELECT ts_rank(to_tsvector('The quick brown fox jumps over the lazy dog.'),to_tsquery('fox & dog')); ts_rank ------------ 0.09148999 (1 row) postgres=# 函数名称 ts_rank_cd 参数数据类型 [weights real[], ] vector tsvector, query tsquery [, normalization integer] 函数返回值数据类型 real 函数含义 使用覆盖密度算法来计算query与vector的匹配程度 使用举例 postgres=# SELECT ts_rank_cd('{0.1, 0.2, 0.4, 1.0}', to_tsvector('PostgreSQL full text search is a wonderful method'),to_tsquery('wonderful')); ts_rank_cd ------------ 0.1 (1 row) postgres=# 函数名称 ts_rewrite 参数数据类型 query tsquery, target tsquery, substitute tsquery 函数返回值数据类型 tsquery 函数含义 用substitute替换掉query中的target 使用举例 postgres=# SELECT ts_rewrite('c & b'::tsquery, 'b'::tsquery, 'bad|bat'::tsquery); ts_rewrite ------------------------- ( 'bad' | 'bat' ) & 'c' (1 row) postgres=# 函数名称 ts_rewrite 参数数据类型 query tsquery, select text 函数返回值数据类型 tsquery 函数含义 使用举例 postgres=# CREATE TABLE test (col1 tsquery PRIMARY KEY, COL2 tsquery); CREATE TABLE postgres=# INSERT INTO test values ('p','q'); INSERT 0 1 postgres=# SELECT ts_rewrite('r & q'::tsquery, 'SELECT col1,col2 FROM test'); ts_rewrite ------------ 'r' & 'q' (1 row) postgres=# 函数名称 tsquery_phrase 参数数据类型 query1 tsquery, query2 tsquery 函数返回值数据类型 tsquery 函数含义 构造一个phrase query用于在连续的词位匹配query1和query2(同<→操作符) 使用举例 postgres=# select tsquery_phrase(to_tsquery('fat'), to_tsquery('cat')); tsquery_phrase ----------------- 'fat' <-> 'cat' (1 row) postgres=# 函数名称 tsquery_phrase 参数数据类型 query1 tsquery, query2 tsquery, distance integer 函数返回值数据类型 tsquery 函数含义 构造一个词组查询，该词组搜索匹配query1且query2完全distance分开词素的匹配项 使用举例 postgres=# select tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10); tsquery_phrase ------------------ 'fat' <10> 'cat' (1 row) postgres=# 函数名称 tsvector_to_array 参数数据类型 tsvector 函数返回值数据类型 text[] 函数含义 将tsvector转换为词素数组 使用举例 postgres=# select tsvector_to_array('fat:2,4 cat:3 rat:5A'::tsvector) ; tsvector_to_array ------------------- {cat,fat,rat} (1 row) postgres=# 函数名称 unnest 参数数据类型 tsvector 函数返回值数据类型 setof record ( lexeme text, positions smallint[], weights text ) 函数含义 将tsvector扩展为一行一个词素 使用举例 postgres=# select * from unnest('cat:3 fat:2,4 rat:5A'::tsvector); lexeme | positions | weights --------+-----------+--------- cat | {3} | {D} fat | {2,4} | {D,D} rat | {5} | {A} (3 rows) postgres=# 函数名称 abbrev 参数数据类型 inet 函数返回值数据类型 text 函数含义 将inet转换为text的缩写显示格式 使用举例 postgres=# select abbrev(inet '10.1.1.3'); abbrev ---------- 10.1.1.3 (1 row) postgres=# 函数名称 abbrev 参数数据类型 cidr 函数返回值数据类型 text 函数含义 将cidr转换为text的缩写显示格式，返回结果中去掉了netmask中右边的零 使用举例 postgres=# select abbrev(cidr '10.1.0.0/16'); abbrev --------- 10.1/16 (1 row) postgres=# 函数名称 broadcast 参数数据类型 inet 函数返回值数据类型 inet 函数含义 计算一个ip地址的广播地址 使用举例 postgres=# select broadcast(inet '192.168.1.5/24'); broadcast ------------------ 192.168.1.255/24 (1 row) postgres=# 函数名称 family 参数数据类型 inet 函数返回值数据类型 integer 函数含义 返回一个地址是IPV4（返回4）还是IPV6（返回6） 使用举例 postgres=# select family('fe80::5054:ff:fe99:540c') ; family -------- 6 (1 row) postgres=# select family(inet '172.1.0.23') ; family -------- 4 (1 row) postgres=# 函数名称 host 参数数据类型 inet 函数返回值数据类型 text 函数含义 返回IP地址的text形式，且忽略掉netmask 使用举例 postgres=# select host(inet '192.168.1.0/24'); host ------------- 192.168.1.0 (1 row) postgres=# 函数名称 hostmask 参数数据类型 Inet 函数返回值数据类型 Inet 函数含义 返回ip地址的host mask 使用举例 postgres=# select hostmask(inet '192.168.23.20/30') ; hostmask ---------- 0.0.0.3 (1 row) postgres=# 函数名称 inet_merge 参数数据类型 inet, inet 函数返回值数据类型 cidr 函数含义 返回包含两个给定网络的最小网络 使用举例 postgres=# select inet_merge(inet '192.168.1.5/24', inet '192.168.2.5/24'); inet_merge ---------------- 192.168.0.0/22 (1 row) postgres=# 函数名称 inet_same_family 参数数据类型 inet，inet 函数返回值数据类型 bool 函数含义 返回两个ip地址是否属于同一个family 使用举例 postgres=# select inet_same_family(inet '192.168.1.5/24', inet '::1'); inet_same_family ------------------ f (1 row) postgres=# 函数名称 masklen 参数数据类型 inet 函数返回值数据类型 integer 函数含义 返回以位为单位的网络掩码长度 使用举例 postgres=# select masklen(inet '192.168.1.5/24'); masklen --------- 24 (1 row) postgres=# 函数名称 netmask 参数数据类型 inet 函数返回值数据类型 inet 函数含义 返回网络地址的网络掩码 使用举例 postgres=# select netmask(inet '192.168.1.5/24'); netmask --------------- 255.255.255.0 (1 row) postgres=# 函数名称 network 参数数据类型 inet 函数返回值数据类型 inet 函数含义 返回地址的网络部分，将网络掩码右边的内容清零 使用举例 postgres=# select network(inet '192.168.1.5/24') ; network ---------------- 192.168.1.0/24 (1 row) postgres=# 函数名称 set_masklen 参数数据类型 inet, integer 函数返回值数据类型 inet 函数含义 为inet设置netmask长度，网络地址部分保持不变 使用举例 postgres=# select set_masklen(inet '192.168.1.5/24', 16); set_masklen ---------------- 192.168.1.5/16 (1 row) postgres=# 函数名称 set_masklen 参数数据类型 cidr, integer 函数返回值数据类型 cidr 函数含义 为cidr设置netmask长度，网络地址右边的设置为零 使用举例 postgres=# select set_masklen(cidr '192.168.1.0/24', 16); set_masklen ---------------- 192.168.0.0/16 (1 row) postgres=# 函数名称 text 参数数据类型 inet 函数返回值数据类型 text 函数含义 返回ip地址及其掩码的text形式 使用举例 postgres=# select text(inet '192.168.1.5'); text ---------------- 192.168.1.5/32 (1 row) postgres=# 函数名称 trunc 参数数据类型 macaddr 函数返回值数据类型 macaddr 函数含义 将mac地址的最后三个字节清零，前缀可以与特定的网卡制造商关联 使用举例 postgres=# select trunc(macaddr '12:34:56:78:90:ab') ; trunc ------------------- 12:34:56:00:00:00 (1 row) postgres=# 函数名称 trunc 参数数据类型 macaddr8 函数返回值数据类型 macaddr8 函数含义 将mac地址的最后五个字节清零，前缀可以与特定的网卡制造商关联 使用举例 postgres=# select trunc(macaddr8 '12:34:56:78:90:ab:cd:ef') ; trunc ------------------------- 12:34:56:00:00:00:00:00 (1 row) postgres=# 函数名称 macaddr8_set7bit 参数数据类型 macaddr8 函数返回值数据类型 macaddr8 函数含义 将地址的第7位设置为1，从而创建被称为修改后的EUI-64，以包含在IPv6地址中。 使用举例 postgres=# select macaddr8_set7bit(macaddr8 '00:34:56:ab:cd:ef'); macaddr8_set7bit ------------------------- 02:34:56:ff:fe:ab:cd:ef (1 row) postgres=# 函数名称 area 参数数据类型 geometric_type 函数返回值数据类型 double precision 函数含义 计算面积，针对box, path, circle适用，path必须是能closed，否则会返回NULL 使用举例 postgres=# select area(box '(2,2),(0,0)'); area ------ 4 (1 row) postgres=# 函数名称 center 参数数据类型 geometric_type 函数返回值数据类型 point 函数含义 计算中点，针对box, circle适用 使用举例 postgres=# select center(box '(1,2),(0,0)') ; center --------- (0.5,1) (1 row) postgres=# 函数名称 diagonal 参数数据类型 box 函数返回值数据类型 lseg 函数含义 获取box的对角线作为一个line segment，作用与lseg(box)一样 使用举例 postgres=# select diagonal(box '(1,2),(0,0)'); diagonal --------------- [(1,2),(0,0)] (1 row) postgres=# 函数名称 diameter 参数数据类型 circle 函数返回值数据类型 double precision 函数含义 计算circle的直径 使用举例 postgres=# select diameter(circle '<(0,0),2>') ; diameter ---------- 4 (1 row) postgres=# 函数名称 height 参数数据类型 box 函数返回值数据类型 double precision 函数含义 计算box的垂直大小 使用举例 postgres=# select height(box '(1,2),(0,0)'); height -------- 2 (1 row) postgres=# 函数名称 isclosed 参数数据类型 path 函数返回值数据类型 bool 函数含义 path是否是closed的 使用举例 postgres=# select isclosed(path '((0,0),(1,1),(2,0))'); isclosed ---------- t (1 row) postgres=# 函数名称 isopen 参数数据类型 path 函数返回值数据类型 bool 函数含义 Path是否是open的 使用举例 postgres=# select isopen(path '[(0,0),(1,1),(2,0)]'); isopen -------- t (1 row) postgres=# 函数名称 length 参数数据类型 geometric_type 函数返回值数据类型 double precision 函数含义 计算总长度，对lseg以及path适用 使用举例 postgres=# select length(path '((-1,0),(1,0))') ; length -------- 4 (1 row) postgres=# 函数名称 npoints 参数数据类型 geometric_type 函数返回值数据类型 integer 函数含义 返回point的数量，对path以及polygon适用 使用举例 postgres=# select npoints(path '[(0,0),(1,1),(2,0)]'); npoints --------- 3 (1 row) postgres=# 函数名称 pclose 参数数据类型 path 函数返回值数据类型 path 函数含义 将patch转换为closed的形式 使用举例 postgres=# select pclose(path '[(0,0),(1,1),(2,0)]'); pclose --------------------- ((0,0),(1,1),(2,0)) (1 row) postgres=# 函数名称 popen 参数数据类型 path 函数返回值数据类型 path 函数含义 将patch转换为open的形式 使用举例 postgres=# select popen(path '((0,0),(1,1),(2,0))'); popen --------------------- [(0,0),(1,1),(2,0)] (1 row) postgres=# 函数名称 radius 参数数据类型 circle 函数返回值数据类型 double precision 函数含义 求一个圆形的半径 使用举例 postgres=# select radius(circle '<(0,0),2>'); radius -------- 2 (1 row) postgres=# 函数名称 slope 参数数据类型 point,point 函数返回值数据类型 double precision 函数含义 计算通过两个点的直线的斜率 使用举例 postgres=# select slope(point '(0,0)', point '(2,1)'); slope ------- 0.5 (1 row) postgres=# 函数名称 width 参数数据类型 double precision 函数返回值数据类型 box 函数含义 计算box的horizontal size 使用举例 postgres=# select width(box '(1,2),(0,0)') ; 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track_commit_timestamp 参数值数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 记录transaction的提交时间。注意：只有当这个参数是on时，函数pg_xact_commit_timestamp(xid)、pg_last_committed_xact()才能获得已提交事务的时间值。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 transform_null_equals 参数值数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当本参数值为on时，PG会将expr=NULL(或者NULL=expr)视为expr IS NULL.本参数仅仅影响=NULL这个形式，不会影响其他形式（如in） 是否可session级修改 可以，superuser和普通用户均可以在session级修改 修改后何时生效 Reload即可生效Session级修改时会立即在session级生效 参数名称 max_files_per_process 参数值数据类型 integer 默认值 1000 取值范围 64到2147483647 参数单位 参数含义 本参数用于设置每个server subprocess允许同时打开的files的最大数量。如果遇到 \"Too many open files\"的提示，请尝试减少本参数值。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 cluster_name 参数值数据类型 string 默认值 空值 取值范围 参数单位 参数含义 本参数用于设置PGCluster的名字，可以用操作系统命令ps -ef| grep postgres查看该名字。该名字会出现在所有server process的开头位置，另外，对于standby连接来说，该名字还是默认的application name. 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 update_process_title 参数值数据类型 Bool 默认值 on（除windows之外的平台）；off（Windows平台） 取值范围 on和off 参数单位 参数含义 每当服务器接收到一个新的 SQL 命令时都更新process title 是否可session级修改 是，此时仅限于superuser可以在session级修改 修改后何时生效 Reload即可生效Session级修改时会立即在session级生效 参数名称 synchronous_standby_names 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为primary角色库参数。本参数值区分大小写。本参数指定了支持同步复制的standby server清单。standby server的name是standby连接信息中的application_name,在物理复制standby中，standby server的name是在primary_conninfo中设置的，如果cluster_name配置参数的配置过了，那么standby server的name默认是cluster_name参数值，否则是walreceiver对于逻辑复制，可以在subscription中设置连接信息，默认值是subscription name本参数使用如下语法指定了一系列的standby servers的列表：[FIRST] num_sync ( standby_name [, …​] )ANY num_sync ( standby_name [, …​] )standby_name [, …​] num_sync代表同步standby的个数举例，FIRST 3 (s1, s2, s3, s4)的含义:s1, s2, s3这三个standby是同步standbys4这个standby是潜在的同步standby(potential synchronous standbys)如果当前同步standby不论什么原因断开，它的位置会立即被next-highest-priority standby取代。关键字FIRST是可选的。FIRST 3 (s1, s2, s3, s4)中，优先级由大到小为s1>s2>s3>s4 举例， ANY 3 (s1, s2, s3, s4) 的含义：s1, s2, s3, s4这四个standby 中的任意3个standby server应用wal变化之后，primary端的事务才能提交请务必注意：FIRST 和ANY关键字是大小写敏感的。如果这两个关键字被用于standby server的name，那么standby_name需要用双引号引起来。在PostgreSQL 9.6之前版本中使用的第三种语法已经受到支持。该语法与\"FIRST 1\"是相同作用。比如， FIRST 1 (s1, s2) 和 s1, s2 有相同的含义：要么s1要么s2被选为同步standby 表示匹配任何standby name 没有机制来确保standby name的唯一性。在重复发生时，匹配到的standby 会被视为更高的优先级。尽管确切地说哪个是不确定的注意：每个standby_name应该是有效地SQL 标识符，除非是。如果需要,你可以使用双引号。但是请注意：standby_names与standby application_name 比较时，是不区分大小写的，不论有无双引号。 如果synchronous_standby_names参数不指定值，同步复制不会启用。即使同步复制被启用，通过设置synchronous_commit配置参数为local或者off，单个事务可以被配置为不等待standby端的wal被应用。 本参数可以在postgresql.conf文件或者在the server command line设置 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 vacuum_defer_cleanup_age 参数值数据类型 integer 默认值 0 取值范围 0到1000000 参数单位 参数含义 该参数指定了在primary库上vacuum和HOT updates 会延迟清理dead row versions的事务的数量。默认值是零个事务，零个事务的含义是dead row versions会尽可能快的被removed掉。尽可能快是指dead row versions不会被任何open transaction可见。你可以在hot standby环境中的primary端为此参数设置为非零值，这允许standby端的query运行更长的时间以便完成，而不会遇到过早的dead row versions cleanup而导致冲突。由于本参数值是根据在primary上发生的写入事务的数量来衡量的，因此，很难预测会为standby query增加多长时间。 本参数可以在postgresql.conf文件或者在the server command line设置 你可以考虑在standby 端使用hot_standby_feedback配置参数作为vacuum_defer_cleanup_age的替代。本参数不会防止那些已经达到age（由old_snapshot_threshold配置参数指定）的dead rows的清理。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 max_logical_replication_workers 参数值数据类型 integer 默认值 4 取值范围 0到262143 参数单位 参数含义 本参数设置Logical replication workers进程的最大数量，此处的worker进程包括两种：apply workers 和table synchronization workers。Logical replication workers进程的数量从max_worker_processes配置参数定义的池中获取。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 max_sync_workers_per_subscription 参数值数据类型 integer 默认值 2 取值范围 0到262143 参数单位 参数含义 指定了每个subscription中synchronization workers的最大数量。本参数控制在subscription初始化 或者 新表加入时的initial data copy的并行度。目前，每个table只能指定一个synchronization workersynchronization workers从max_logical_replication_workers配置参数定义的池中取得。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 restore_command 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 恢复时指定archive wal的位置 restore_command = 'cp /mnt/server/archivedir/%f \"%p\"' restore_command = 'copy \"C:\\\\server\\\\archivedir\\\\%f\" \"%p\"' # Windows restore_command对应的命令执行成功之后，restore_command返回零， %f代表archived wal文件名 %p代表copy destination path name %r代表含有最后一个有效的resart point的file的文件名 %%代表一个%字符(当需要写入%字符的情况下使用) 本参数需要在pg instance启动之前进行设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 archive_cleanup_command 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数定义了在每个restart point时所执行的shell命令。 archive_cleanup_command参数的目的是提供一个清理不再被standby server所需要的老的archived wal file的机制。 %r代表最后一个有效的restart point的wal file。该wal file是最早一个必须保留的文件，以便允许restore操作可以被restart。注意： restart point 是一个 point ，该point用于standby server重启recovery操作。因此，所有早于%r的文件可以被安全的清理掉。本信息可以用来truncate掉archive wal file，以便满足从当前restore可以restart的最小需求。 pg_archivecleanup实用命令常被用在单个standby配置的archive_cleanup_command参数中 archive_cleanup_command = 'pg_archivecleanup /mnt/server/archivedir %r' %%代表一个%字符(当需要写入%字符的情况下使用)如果命令返回非零值，一个警告日志消息会被写入。一个例外是：当命令被一个signal终止或者shell中有错误时，一个fatal error会被抛出。 本参数可以在postgresql.conf设置，也可以在server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 recovery_end_command 参数值数据类型 String 默认值 空字符串 取值范围 参数单位 参数含义 本参数用于定义在recovery结束时候的需要执行的shell命令。如果命令返回非零值，一个警告日志消息会被写入，然后数据库会继续启动。一个例外是：当命令被一个signal终止或者shell中错误时，数据库不会继续启动。%r代表最后一个有效的restart point的wal file 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 exit_on_error 参数值数据类型 Bool 默认值 off 取值范围 on和off 参数单位 参数含义 当设置为on时，任何error均会导致session被终止。默认情况下，本参数值为off，只有FATAL error才会终止session。 postgres=# set exit_on_error=on; SET postgres=# show exit_on_error ; -[RECORD 1]-+--- exit_on_error | on postgres=# begin; BEGIN postgres=# select \\ form t1; FATAL: syntax error at or near \"form\" LINE 1: select \\* form t1; ^ server closed the connection unexpectedly This probably means the server terminated abnormally before or while processing the request. The connection to the server was lost. Attempting reset: Succeeded.postgres=# 是否可session级修改 是 修改后何时生效 Reload即可生效Session级别修改可以立即生效。 参数名称 restart_after_crash 参数值数据类型 bool 默认值 on 取值范围 参数单位 参数含义 当设置为on时，PostgreSQL会在backend crash之后重新初始化(即:restart)。在某些情况下，比如PostgreSQL被Clusterware调用时，需要禁用PostgreSQL的restart(将该参数值设置为off)以便clusterware获得PostreSQL的控制权并采取适当的操作。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 data_sync_retry 参数值数据类型 bool 默认值 off 取值范围 off 和on 参数单位 参数含义 如果设置为off(默认值),当PostgreSQL flush数据到文件系统上遇到failure时，PostgreSQL会抛出一个panic级别的error。这会导致数据库服务crash掉。本参数只能在数据库服务启动之前设置。在某些操作系统中，当回写(write-back)失败之后，kernel的page cache中的数据状态是未知的(unknown).在一些情况下，它可能会被完全忘记，设置为尝试是不安全的，在实际上数据已经丢失的情况下，第二次尝试可能会报告成功，在这些场景中，避免数据损失的唯一方法是解决掉硬件问题之后从wal中恢复。当设置为on时，PostgreSQL会报告一个error并会继续运行，data flushing操作会在稍后的checkpoint中尝试。注意：仅在调查操作系统对buffered data的处理以防回写失败的情况下，才将其设置为on。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 archive_mode 参数值数据类型 enum 默认值 off 取值范围 off,on,always 参数单位 参数含义 指定归档模式。on表示启用归档模式，当wal_level配置参数值为minimal时, archive_mode 不能设置为on。当本参数值设置为always时，standby 实例会为收到的每个wal segments调用归档命令,也就是standby实例上也会进行wal的归档操作。本参数仅仅能在pg启动时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 archive_command 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 定义对wal进行归档的命令。当archive_mode配置参数启用并且archive_command配置参数是空字符串时，wal archiving暂时被禁用，但是pg会继续积累wal segment文件。archive_command参数值设置为/bin/true会禁用归档，但这样会导致wal文件归档中断，归档中断是无法进行归档恢复的，请注意这一点。使用举例如下： archive_command = 'test ! -f /mnt/server/archivedir/%f && cp %p /mnt/server/archivedir/%f' # Unix archive_command = 'copy \"%p\" \"C:\\\\server\\\\archivedir\\\\%f\"' # Windows 退出码是零代表本命令正常运行。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 archive_timeout 参数值数据类型 integer 默认值 0 取值范围 0到1073741823 参数单位 若是不指定单位，那默认的单位是秒 参数含义 强制进行wal segments归档的时长。使用本参数归档出来的wal segment与正常的wal segment是相同的大小。因此，本参数值设置太小会对存储空间有浪费。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 bgwriter_delay 参数值数据类型 integer 默认值 200 取值范围 10到10000 参数单位 若是不指定单位，那默认的单位是毫秒(ms) 参数含义 在每一轮循环中，background writer都会为一定数量的脏缓冲区发出写操作，然后background writer进行睡眠，睡眠的时间为bgwriter_delay参数值，然后再唤醒，然后重复。设置bgwriter_delay为不是10的倍数的值可能与将其设置为10的下一个更高的倍数具有相同的结果。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 bgwriter_lru_maxpages 参数值数据类型 integer 默认值 100 取值范围 0到1073741823 参数单位 参数含义 每轮bgwriter进程写入LRU Page的最大数量。本参数设置为零会禁用background writing行为，但是background writer进程依然会存在。本参数可以在postgresql.conf或者server command line设置 是否可session级修改 否 修改后何时生效 Reload即可 参数名称 bgwriter_lru_multiplier 参数值数据类型 real 默认值 2.0 取值范围 0到10 参数单位 参数含义 每轮写dirty buffer的数量基于服务器进程(即:backend process)在最近几轮中所需要的新buffer数量。最近的平均需要量乘以本参数值就是下一轮需要的新buffers数量的估计值。Dirty buffers会一直写入，直到有那么多干净的、可重用的buffers为止。因此，本参数值为1.0表示准确预计了需要的buffers数量。较大的本参数值提供了一些缓冲，以防需求激增，而较小的本参数值会有意将写dirty buffers操作留给服务器进程(即:backend process)完成。本参数可以在postgresql.conf或者server command line设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 bgwriter_flush_after 参数值数据类型 integer 默认值 Linux平台下是64，也就是512KB；其他平台下是0 取值范围 源码中BLCKSZ 符号常量为8KB时，取值范围是0到256；源码中BLCKSZ 符号常量不为8KB时，等比例计算即可。 参数单位 8KB 参数含义 当background writer写入的数据量超过本参数值之后，尝试强制操作系统发送这些写入的数据到底层存储中。这么做会限制kernel中page cache的dirty data的数量，减少在checkpoint末尾发生fsync时发生卡顿的可能性。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_statement_stats 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入系统性能统计信息。本参数on时，其他参数（log_parser_stats 、log_planner_stats、log_executor_stats）必须设置为off在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 09:55:21.621 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,6,\"\",2021-03-31 09:51:32 CST,3/0,0,LOG,00000,\"QUERY STATISTICS\",\"! system usage stats: ! 0.000657 s user, 0.000000 s system, 0.000657 s elapsed ! [0.003464 s user, 0.001403 s system total] ! 6288 kB max resident size ! 0/8 [0/24] filesystem blocks in/out ! 0/128 [0/1295] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [11/2] voluntary/involuntary context switches\",,,,,\"select \\*from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是 修改后何时生效 Session级修改立即生效；非session级修改reload生效，仅仅superuser能修改本参数 参数名称 log_parser_stats 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入解析统计信息。在本参数设置为on的情况下，log_statement_stats配置参数无法设置为on，设置为on时会有如下的错误： postgres=# alter system set log_statement_stats='on'; ERROR: invalid value for parameter \"log_statement_stats\": 1 DETAIL: Cannot enable \"log_statement_stats\" when \"log_parser_stats\", \"log_planner_stats\", or \"log_executor_stats\" is true. postgres=#在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,41,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"PARSER STATISTICS\",\"! system usage stats: ! 0.000019 s user, 0.000000 s system, 0.000019 s elapsed ! [0.015481 s user, 0.001403 s system total] ! 7720 kB max resident size ! 0/0 [0/56] filesystem blocks in/out ! 0/0 [0/1673] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent ! 0/0 [39/6] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,42,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"statement: select \\* from pg_database;\",,,,,,,,,\"psql\",\"client backend\" 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,43,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"PARSE ANALYSIS STATISTICS\",\"! system usage stats: ! 0.000031 s user, 0.000000 s system, 0.000030 s elapsed ! [0.015565 s user, 0.001403 s system total] ! 7720 kB max resident size ! 0/0 [0/56] filesystem blocks in/out ! 0/0 [0/1673] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent ! 0/0 [39/6] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,44,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"REWRITER STATISTICS\",\"! system usage stats: ! 0.000003 s user, 0.000000 s system, 0.000003 s elapsed ! [0.015582 s user, 0.001403 s system total] ! 7720 kB max resident size ! 0/0 [0/56] filesystem blocks in/out ! 0/0 [0/1673] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [39/6] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是，但是不能在log_statement_stats=on时设置本参数为on 修改后何时生效 Session级修改立即生效非session级修改reload生效，仅仅superuser能修改本参数 参数名称 log_planner_stats 参数值数据类型 boolean 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入规划器统计信息。在本参数设置为on的情况下，log_statement_stats配置参数无法设置为on，设置为on时会有如下的错误： postgres=# alter system set log_statement_stats='on'; ERROR: invalid value for parameter \"log_statement_stats\": 1 DETAIL: Cannot enable \"log_statement_stats\" when \"log_parser_stats\", \"log_planner_stats\", or \"log_executor_stats\" is true. postgres=#在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 10:24:39.323 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,63,\"\",2021-03-31 09:51:32 CST,3/3491,0,LOG,00000,\"PLANNER STATISTICS\",\"! system usage stats: ! 0.000041 s user, 0.000006 s system, 0.000045 s elapsed ! [0.017384 s user, 0.002173 s system total] ! 7740 kB max resident size ! 0/0 [0/80] filesystem blocks in/out ! 0/0 [0/1674] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [55/10] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是，但是不能在log_statement_stats=on时设置本参数为on 修改后何时生效 Session级修改立即生效;非session级修改reload生效，仅仅superuser能修改本参数 参数名称 log_executor_stats 参数值数据类型 Bool 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入执行器统计信息。在本参数设置为on的情况下，log_statement_stats配置参数无法设置为on，设置为on时会有如下的错误： postgres=# alter system set log_statement_stats='on'; ERROR: invalid value for parameter \"log_statement_stats\": 1 DETAIL: Cannot enable \"log_statement_stats\" when \"log_parser_stats\", \"log_planner_stats\", or \"log_executor_stats\" is true. postgres=#在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 10:27:59.858 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,71,\"\",2021-03-31 09:51:32 CST,3/3496,0,LOG,00000,\"EXECUTOR STATISTICS\",\"! system usage stats: ! 0.000031 s user, 0.000000 s system, 0.000030 s elapsed ! [0.018581 s user, 0.003320 s system total] ! 7744 kB max resident size ! 0/0 [0/104] filesystem blocks in/out ! 0/0 [0/1676] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [71/14] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是，但是不能在log_statement_stats=on时设置本参数为on 修改后何时生效 Session级修改立即生效非session级修改reload生效，仅仅superuser能修改本参数 参数名称 jit_provider 数据类型 string 默认值 llvmjit 取值范围 参数单位 参数含义 本参数值指定了jit provider library的名称，本参数仅仅可以在pg启动时设置。若是指定了不存在的library，则jit不可用，但是不会有报错抛出。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 local_preload_libraries 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数指定在连接启动时被预加载的一个或多个共享库。多个库名称以逗号分隔，其中每个库名的解释与load命令相同。库名之间的空格会被忽略，若是需要在库名中包括空格或者逗号，请用双引号将库名引起来。参数值仅仅在连接开始时生效，后续更改无效。如果找不到指定的库，则连接会将失败(即:连接将无法登陆到数据库中)。本参数任何user都可以设置。因此，被加载的共享库仅仅限于在$libdir/plugins目录下。数据库管理员有责任确保该目录下的共享库的安全性。本参数的参数值可以明确指定目录，比如$libdir/plugins/mylib，或者仅仅指定library名称，比如mylib，mylib的作用等同于$libdir/plugins/mylib本参数的目的是允许非授权用户加载debugging或者performance-measurement 库到特定session中，而无需load命令。为此，可以在客户端上使用设置了本参数的PGOPTIONS环境变量或者使用alter role set 是否可session级修改 是 修改后何时生效 Session级修改立即生效非session级修改reload生效 参数名称 session_preload_libraries 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数值指定了在连接启动时被预加载的一个或者多个共享库。多个库名称以逗号分隔，其中每个库名的解释与load命令相同。库名之间的空格会被忽略，若是需要在库名中包括空格或者逗号，请用双引号将库名引起来。参数值仅仅在连接开始时生效，后续更改无效。如果找不到指定的库，则连接会将失败(即:连接将无法登陆到数据库中)。本参数的目的是允许加载debugging或者performance-measurement 库到特定session中，而无需load命令。比如，使用alter role set 语句设置本参数可以将auto_explain针对所有session生效。与shared_preload_libraries不同，在会话开始时（而不是在首次使用时）加载库没有太大的性能优势。但是，使用连接池时有一些优点。 是否可session级修改 可以，只有superuser可以改变本参数值 修改后何时生效 Session级修改立即生效非session级修改reload生效，reload之后，仅仅对新启动的session生效。 参数名称 shared_preload_libraries 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数值指定了在PostgreSQL数据库启动时被预加载的一个或者多个共享库。多个库名称以逗号分隔，其中每个库名的解释与load命令相同。库名之间的空格会被忽略，若是需要在库名中包括空格或者逗号，请用双引号将库名引起来。该参数只能在PostgreSQL数据库启动时设置，若是找不到指定的库，PostgreSQL数据库启动会失败。设置了本参数之后，新的backend process的启动时间会略有延长，即使这个backedn process不用这个共享库。因此，本参数推荐用于哪些在绝大多数session中使用的共享库。另外，由于本参数需要重启PostgreSQL数据库生效，因此，若是用于短期的debugging目的，请使用session_preload_libraries参数。请注意，在Windows主机上，在服务器启动时预加载库不会减少启动每个新服务器进程所需的时间。每个服务器进程将重新加载所有预加载库。但是，对于需要在postmaster启动时执行操作的库，本参数在windows主机上是很有用的。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 deadlock_timeout 数据类型 string 默认值 1000 取值范围 1到2147483647 参数单位 毫秒 参数含义 在检查是否存在deadlock条件之前，等待一个lock的时间长度。死锁检查是相对昂贵的，因此，pg不会在每次等待lock时都运行死锁检测。PostgreSQL会乐观的认为死锁在生产应用程序中并不常见，只需要等待一段时间后再检查死锁。这是您在实践中想要设置的最小值，在负载很重的pg中，可能需要提高本参数值。在理想情况下，本参数值应该超过典型的transaction 时间，以提高在PostgreSQL决定检查死锁之前将释放锁的几率。仅仅superuser可以修改本参数值。在log_lock_waits参数启用的情况下，deadlock_timeout参数值也决定了一个有关lock wait的 log message被写入运行日志之前的等待时间。如果你正在尝试调查锁定延迟(locking delays)，你可能希望设置比正常deadlock_timeout参数值更短的时间。 是否可session级修改 是(仅仅superuser可修改) 修改后何时生效 Session级修改立即生效(仅仅superuser)；非session级别修改Reload即可生效 参数名称 max_locks_per_transaction 数据类型 integer 默认值 64 取值范围 10到2147483647 参数单位 参数含义 共享锁表(shared lock table)追踪max_locks_per_transaction \\* (max_connections + max_prepared_transactions) 个lock objects，因此，一次只能锁定这么多不同的对象。本参数控制分配给每个事务的object lock的平均数量。单个事务可以锁定更多的对象，只要所有事务的锁都在锁表(lock table)中. 本参数值不是可以锁定的行数；可以锁定的行数是无限的。max_locks_per_transaction的默认值是64在历史上是被证明足够的，但是如果在多个事务中有涉及多个不同的表的SQL语句(比如一个有多个子表的父表的SQL语句),则可能提高该数值。本参数只能在PostgreSQL数据库启动时设置。当存在standby server时，standby server上的本参数值要大于或者等于master server上的本参数值，否则，查询语句在standby server上是不被允许的。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_pred_locks_per_transaction 数据类型 Integer 默认值 64 取值范围 10到2147483647 参数单位 参数含义 共享谓词锁表(shared predicate lock table)追踪max_pred_locks_per_transaction \\* (max_connections + max_prepared_transactions) 个lock objects，因此，一次只能锁定这么多不同的对象。本参数控制分配给每个事务的object lock的平均数量。单个事务可以锁定更多的对象，只要所有事务的锁都在锁表(lock table)中. 本参数值不是可以锁定的行数；可以锁定的行数是无限的。本参数默认值64在测试环境中是足够的，但是如果你的client在单个串行化事务中用到了多个不同的table，你可能需要加大本参数值。本参数只能在PostgreSQL数据库启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_pred_locks_per_relation 数据类型 Integer 默认值 -2 取值范围 -2147483648到2147483647 参数单位 参数含义 本参数控制在将锁提升为覆盖整个relation之前，单个relation中能有多少pages或者tuples能被predicate-locked 。本参数值大于等于零时表示绝对限制，本参数值为负值时表示max_pred_locks_per_transaction/本参数值的绝对值。本参数可以在postgresql.conf设置也可以在server command line设置。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 max_pred_locks_per_page 数据类型 integer 默认值 2 取值范围 0到2147483647 参数单位 参数含义 本参数控制在将锁提升为覆盖整个page之前，单个page中能有多少tuples能被predicate-locked。本参数可以在postgresql.conf设置也可以在server command line设置。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 vacuum_cost_delay 数据类型 Real 默认值 0，零代表禁用了基于成本的vacuum delay特性。 取值范围 0到100 参数单位 毫秒 参数含义 vacuum_cost_limit参数值是一个累积vacuum io操作的成本值，当vacuum操作达到vacuum_cost_limit参数值之后，vacuum操作会停下来休眠一段时间，该段时间是vacuum_cost_delay参数值指定的，然后，PostgreSQL会重置计数器并继续vacuum操作。本参数的参数值建议尽量小，大值是没有帮助的。 是否可session级修改 是 修改后何时生效 session修改会立即生效；非session级修改reload即可生效。 参数名称 vacuum_cost_page_hit 数据类型 Integer 默认值 1 取值范围 0到10000 参数单位 参数含义 对shared buffer中发现的一个buffer进行vacuum的估计成本，本成本代表：锁定buffer pool的成本+查找shared hash table的成本+扫描page中内容的成本。 是否可session级修改 是 修改后何时生效 session修改会立即生效;非session级修改reload即可生效。 参数名称 vacuum_cost_page_miss 数据类型 Integer 默认值 10 取值范围 0到10000 参数单位 参数含义 对不在shared buffer中的一个buffer(只能从disk读取)进行vacuum的估计成本,本成本代表：锁定buffer pool的成本+查找shared hash table的成本+从disk中读取该block到buffer的成本+扫描page中内容的成本。 是否可session级修改 是 修改后何时生效 session修改会立即生效;非session级修改reload即可生效。 参数名称 vacuum_cost_page_dirty 数据类型 Integer 默认值 20 取值范围 0到10000 参数单位 参数含义 Vacuum改变一个之前是干净的block的估计成本。本成本代表：额外的io操作以便将dirty block刷新到disk中。 是否可session级修改 是 修改后何时生效 session修改会立即生效；非session级修改reload即可生效。 参数名称 vacuum_cost_limit 数据类型 Integer 默认值 200 取值范围 1到10000 参数单位 参数含义 本参数值是一个累积vacuum io操作的成本值，当vacuum操作达到本参数值之后，vacuum操作会停下来休眠一段时间。 是否可session级修改 是 修改后何时生效 session修改会立即生效；非session级修改reload即可生效。 参数名称 max_wal_senders 数据类型 Integer 默认值 10 取值范围 0到262143，0表示复制被禁用 参数单位 参数含义 同时运行wal sender process的最大数量。运行wal sender process的场景包括流复制和以streaming模式进行base backup(译者注：其实还包括逻辑复制使用逻辑复制槽的场景，但是在PostgreSQL文档中并没有写明这一点)。本参数仅能在PostgreSQL启动时进行设置。配置参数waL_level必须设置为replica或者更高的值以便允许来自standby server的连接。在流复制环境中，在流复制备库上的该参数值至少要等于或者大于流复制主库上的该参数值，否则在备库上的query是不允许的。 是否可session级修改 否 修改后何时生效 重启PG instanc生效 参数名称 max_replication_slots 数据类型 Integer 默认值 10 取值范围 0到262143 参数单位 参数含义 设置PostgreSQL能支持的复制槽的最大数量，本参数仅能在PostgreSQL启动时进行设置。当本参数值小于当前已经存在的replicat slot时，会导致PG instance无法启动。配置参数waL_level必须设置为replica或者更高的值以便允许replication slot被用到。 是否可session级修改 否 修改后何时生效 重启PG instanc生效 参数名称 wal_keep_size 数据类型 Integer 默认值 0，0表示PostgreSQL不会为流复制环境保留额外的wal segments， 取值范围 0到2147483647 参数单位 MB，若是设置时不带单位，则默认为MB 参数含义 指定在pg_wal目录中保留的wal segment file的最小大小，以便流复制环境中的standby server 从主库中fetch wal file。如果连接到发送服务器的standby server超过本参数值的大小，发送服务器可能会删除掉standby server仍然需要的wal segments，此时，复制连接会终止，下游的连接最终也会因此失败，若是wal进行了归档，standby server可以从归档中进行fetch segments，以便恢复流复制。本参数值仅仅设置保留在pg_wal中的wal segments的最小大小。系统可能需要保留更多的wal segments以便进行wal 归档或者从检查点中恢复。本参数值仅仅在postgresql.conf中进行设置或者在server command line中进行设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 max_slot_wal_keep_size 数据类型 Integer 默认值 -1，-1表示复制槽保留不限数量的wal files。 取值范围 -1到2147483647 参数单位 MB 参数含义 用来指定复制槽保留pg_wal目录中wal file的最大大小。当一个复制槽的restart_lsn落后于current_lsn超过本参数值时，使用了复制槽的standby server不能再继续流复制，因为已经删除掉了需要的wal files 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_sender_timeout 数据类型 Integer 默认值 60000 取值范围 0到2147483647，0代表禁用timeout机制。 参数单位 毫秒，若是指定时不带单位，默认为为毫秒数 参数含义 当处于inactive状态多长时间(多长时间由本参数指定)之后，终止掉复制连接。这对于发送服务器检测standby crash或者网络中断很有用。当主备节点分布于不同的地理位置时，不同地理位置的节点使用不同的本参数值会带来管理上的灵活性。对于低延迟网络连接，较小的本参数值有助于快速的连接故障检测；对于地理位置较远的高延迟网络连接，较高的本参数值有助于判断standby server的健康状况。 是否可session级修改 是 修改后何时生效 Session修改立即生效，允许非superuser修改本参数值非session级修改reload生效。 参数名称 authentication_timeout 数据类型 Integer 默认值 60 取值范围 1到600 参数单位 秒，如果指定本参数值时不带单位，默认为秒 参数含义 允许完成连接认证的最长时间。如果客户端没有在本参数值指定的时间内完成连接认证，PostgreSQL数据库会关闭此次连接。本参数用于防止大量的客户端无限制的占用连接。本参数仅仅可以在postgresql.conf设置或者在server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 password_encryption 数据类型 enum 默认值 md5 取值范围 md5和scram-sha-256，写on也可以，on与md5有相同的作用。 参数单位 参数含义 本参数决定了create role或者alter role语句中指定的密码的加密算法。请注意，老的客户端版本不支持SCRAM-SHA-256密码加密算法，具体请见下面的描述。有两个关键的标准去确定是否支持SCRAM密码加密算法。 正在运行PostgreSQL 10及其更高版本 你用来连接到PostgreSQL数据库的驱动有SCRAM兼容性。PostgreSQL社区已经提供了一个驱动清单(https://wiki.postgresql.org/wiki/List_of_drivers#Drivers)如果您的系统满足上述两个标准，您可以使用SCRAM密码加密算法。请注意：要把现有的环境从md5升级到scram-sha-256，可以在确保所有在用的客户端已经足以支持SCRAM之后，在postgresql.conf中设置password_encryption = 'scram-sha-256'，然后让所有用户设置新口令并且在pg_hba.conf中将认证方法说明改为scram-sha-256。 是否可session级修改 是 修改后何时生效 Session级修改立即生效；非session级修改reload生效 参数名称 krb_server_keyfile 数据类型 string 默认值 FILE:<sysconfdir>/krb5.keytab，注意,sysconfdir代表sysconfdir目录，该目录可以用pg_config --sysconfdir操作系统命令获得。 取值范围 参数单位 参数含义 设置服务器Kerberos key file的位置。如果本参数设置为空字符串(empty string),PostgreSQL会忽略这个空字符串，此时系统默认值会被使用。本参数值在postgresql.conf或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 krb_caseins_users 数据类型 bool 默认值 off，off代表大小写敏感 取值范围 参数单位 参数含义 本参数用于控制GSSAPI user names是否被视为大小写不敏感，本参数值仅能在postgresql.conf或者server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 db_user_namespace 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 若本参数值设置为on，那么创建的用户名的格式是username@dbname，当client传递username时，@符以及database name会被附加到username的末尾，并且PostgreSQL数据库查询用户是查找username@dbname这个格式的用户。当你在SQL环境中使用username@dbname这个格式的用户名时，请使用引号引起来username@dbname。当本参数值设置为on时，你依然可以建立普通的全局users，在客户端指定username时加上@即可，当PostgreSQL查找username之前，@符号会被去掉。 本参数会导致客户端和服务器端的用户名称表示方法不同。认证始终检查服务器端的username。因此，认证方法必须被配置为服务器端的username。因为md5密码加密算法在客户端和服务器端均使用username作为salt，因此，md5密码加密算法不能与本参数值一起使用。 本参数值仅能在postgresql.conf或者server command line中设置。请注意，本特性的目的是在一个完整的解决方案找到之前作为临时措施，到那时，本参数会被remove掉。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 track_activities 数据类型 boolean 默认值 on 取值范围 on 和off 参数单位 参数含义 当设置为on时，当每个session中执行SQL语句或者命令时，PostgreSQL会收集信息（包括命令执行的时间信息）。收集的信息仅仅对superuser和执行命令所在session的owner可见。 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效;非session级修改reload生效 参数名称 track_activity_query_size 数据类型 Integer 默认值 1024 取值范围 100到1048576 参数单位 Byte，当指定本参数不带单位时，默认是Bytes 参数含义 为每个active的session保留当前执行命令的文本的大小，该文本对应pg_stat_activity.query列。本参数仅仅能在PostgreSQL start时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 track_counts 数据类型 Bool 默认值 on 取值范围 on和off 参数单位 参数含义 当设置为on时，针对database activity收集统计信息。Autovacuum进程需要这些收集的信息。 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效；非session级修改reload生效 参数名称 track_io_timing 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 是否为database中的 io call/activity启用计时（即:io操作消耗了多少时间）。io计时信息体现在如下方面： pg_stat_database中 explain 带buffer时的输出中 pg_stat_statements中当设置为on时,PostgreSQL会重复查询操作系统的当前时间，这个可能会导致在某些平台下有大量开销。您可以使用pg_test_timing工具(该工具在bin目录下)来衡量系统的计时开销。仅superuser可以更改本参数值 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效非session级修改reload生效 参数名称 track_functions 数据类型 enum 默认值 none，none的含义是禁用本功能 取值范围 none,pl,all 参数单位 参数含义 启用追踪函数调用计数和消耗的时间。参数值pl的含义是仅仅对procedural-language函数进行追踪，参数值all的含义是追踪SQL和C语言函数。仅superuser可以更改本参数值 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效；非session级修改reload生效 参数名称 stats_temp_directory 数据类型 string 默认值 pg_stat_tmp 取值范围 参数单位 参数含义 本参数值用于指定存储临时统计信息的目录。本参数值可以是绝对路径，也可以是相对于data directory的相对路径本参数值指定为基于RAM的文件系统会减少物理IO并提升性能。本参数仅能在postgresql.conf中设置或者在server command line设置。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 data_directory 数据类型 string 默认值 取值范围 参数单位 参数含义 本参数值表示PostgreSQL的数据目录的位置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 config_file 数据类型 string 默认值 在数据目录中 取值范围 参数单位 参数含义 本参数值指定了postgresql.conf的位置本参数值仅仅可以在postgres command line设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 hba_file 数据类型 string 默认值 在数据目录中 取值范围 参数单位 参数含义 本参数值指定了pg_hba.conf的位置；本参数值仅仅可以在PostgreSQL启动时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 ident_file 数据类型 string 默认值 在数据目录中 取值范围 参数单位 参数含义 本参数值指定了pg_ident.conf的位置本参数值仅仅可以在PostgreSQL启动时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 external_pid_file 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 指定了将postmaster的pid写入哪个文件本参数值仅仅可以在PostgreSQL启动时设置。 postgres=# alter system set external_pid_file='/home/pg131/postmasterpid.txt'; ALTER SYSTEM postgres=# exit [pg131@VM-0-8-centos ~]$ pg_ctl restart waiting for server to shut down…​. done server stopped waiting for server to start…​.2021-04-09 10:09:21.776 CST [5977] LOG: redirecting log output to logging collector process 2021-04-09 10:09:21.776 CST [5977] HINT: Future log output will appear in directory \"log\". done server started [pg131@VM-0-8-centos ~]$ cd [pg131@VM-0-8-centos ~]$ ll total 136260 -rw-rw-r-- 1 pg131 pg131 0 Mar 10 20:47 12345aa drwxrwxr-x 2 pg131 pg131 4096 Apr 9 10:09 archive drwx------ 20 pg131 pg131 4096 Apr 9 10:09 data -rw-rw-r-- 1 pg131 pg131 72 Mar 23 10:34 para.sql drwxrwxr-x 6 pg131 pg131 4096 Dec 20 14:32 postgresql-13.1 -rw-r—​r-- 1 pg131 pg131 139499520 Dec 20 14:07 postgresql-13.1.tar -rw-r—​r-- 1 pg131 pg131 5 Apr 9 10:09 postmasterpid.txt drwxrwxr-x 7 pg131 pg131 4096 Dec 20 14:45 soft drwx------ 3 pg131 pg131 4096 Dec 20 16:04 ts1 [pg131@VM-0-8-centos ~]$ cat postmasterpid.txt 5977 [pg131@VM-0-8-centos ~]$ pg_ctl status pg_ctl: server is running (PID: 5977) /home/pg131/soft/bin/postgres[pg131@VM-0-8-centos ~]$ 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 log_min_messages 数据类型 enum 默认值 warning 取值范围 debug5,debug4,debug3,debug2,debug1,info,notice,warning,error,log,fatal,panic 参数单位 参数含义 控制哪种message level写入系统日志。取值范围见上。在上面的取值范围中，前一个取值包括其后的取值。在上面的取值范围中，级别越在后边，写入系统日志中的messages就越少。注意: 本参数的参数值log的级别与client_min_messages配置参数的参数值log的级别不同。仅允许superuser可以修改本参数值。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效；其他级别修改reload生效 参数名称 log_min_error_statement 数据类型 enum 默认值 error，表示那些导致了errors、log messages、fatal errors、panics的SQL语句会被记录到日志中。 取值范围 debug5,debug4,debug3,debug2,debug1,info,notice,warning,error,log,fatal,panic 参数单位 参数含义 控制导致何种级别错误的SQL语句内容被记录到日志中。为了关闭对错误SQL语句的记录，请设置被参数值为panic仅允许superuser可以修改本参数值。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_min_duration_statement 数据类型 integer 默认值 -1，-1表示禁用本功能。 取值范围 -1到2147483647 参数单位 毫秒，若是不指定单位的话。 参数含义 所有运行超过本参数值的SQL语句会打印到系统日志中。本参数对追踪未优化的SQL语句有很大帮助。本参数值为零表示打印所有的SQL语句。仅允许superuser可以修改本参数值。本参数会覆盖log_min_duration_sample参数，这意味持续时间超过本参数值的SQL语句将不进行采样，而是始终记录下来。对于使用扩展查询协议的客户端，Parse、Bind、Execute阶段会被各自独立记录下来。本参数与log_statement参数合用时，由于log_statement参数起作用而记录到log中的SQL文本不会被重复记录。在不使用syslog时，推荐使用log_line_prefix参数，以便记录PID或者Session ID。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_min_duration_sample 数据类型 Integer 默认值 -1，-1表示禁用本功能。 取值范围 -1到2147483647，参数值为零时，记录所有语句的sample 参数单位 毫秒 参数含义 举例说明，如果本参数值设置为100ms，那么运行时间超过100ms的SQL语句会被考虑进行采样。仅允许superuser可以修改本参数值。当流量太大而无法记录所有查询时，启用此参数会很有帮助。本参数的优先级比log_min_duration_statement参数优先级要低。这意味着持续时间超过log_min_duration_statement参数值的SQL语句不受采样限制，并且总是被记录下来。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_statement_sample_rate 数据类型 Real 默认值 1 取值范围 0到1 参数单位 参数含义 确定将记录的持续时间超过log_min_duration_sample参数值的的语句的比率。例如0.5意味着统计上每两个给定语句中就有一个被记录的机会。默认值为1.0，意味着记录所有采样语句。本参数值设置为0表示禁用sampled statement-duration logging。仅允许superuser可以修改本参数值。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_transaction_sample_rate 数据类型 Real 默认值 0，参数值为0表示不记录任何其他事务的语句 取值范围 0到1，参数值为1表示记录所有事物的所有语句 参数单位 参数含义 设置除其他原因记录的语句之外，所有记录语句的事务的百分比，本参数适用于每个新事务，而不考虑其语句的持续时间。采样是随机的，例如0.1意味着在统计上，每十个交易中有一个机会记录任何给定的事务。本参数值有助于构建一个事务的采样。仅允许superuser可以修改本参数值。另外请注意：与所有statement-logging选项一样，本参数会显著增加系统负载。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 checkpoint_timeout 数据类型 Integer 默认值 300 取值范围 30到86400 参数单位 秒 参数含义 设置两次automatic WAL checkpoints的时间间隔。增加本参数值会导致进行crash recovery的时间长度增大。本参数值仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 checkpoint_completion_target 数据类型 Real 默认值 0.5 取值范围 0到1 参数单位 参数含义 指定了checkpoint 完成的目标，即:checkpoint需要在checkpoint_timeout\\* checkpoint_completion_target的时间长度内完成。本参数值仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 checkpoint_flush_after 数据类型 Integer 默认值 32，注意：linux平台下本参数默认值为32，Windows平台下本参数默认值为0 取值范围 0到256，如果BLCKSZ符号常量值不是8KB，请根据比例进行修改。 参数单位 8KB，若是指定参数值时不带单位，默认是BLCKSZ bytes，在不修改BLCKSZ符号常量值的情况下，该符号常量值为8KB。 参数含义 每当在执行检查点时写入的数据量超过本参数值时，会尝试强制操作系统向底层存储发出这些写入操作。这样会限制kernel page cache中dirty data的总量，减少了在checkpoint的末尾发出fsync命令时，或者当操作系统在后台以更大的批写入数据时，PostgreSQL暂停的可能性。这通常会大大减少事务延迟，但也有一些情况，特别是在工作负载大于shared_buffers，但小于操作系统page cache的情况下，性能可能会降低。本参数值仅可以在postgresql.conf中设置或者在server command line进行设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 checkpoint_warning 数据类型 integer 默认值 30 取值范围 0到2147483647，0会禁用warning。 参数单位 秒。若是指定本参数值不带单位，默认是秒 参数含义 如果由于填充WAL段文件而导致的检查点发生的时间间隔比本参数值更小（这表明应该加大max_wal_size配置参数值），则向服务器日志写入一条消息。当checkpoint_timeout参数值小于checkpoint_warning参数值时，warning不会产生。本参数值仅可以在postgresql.conf中设置或者在server command line进行设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 max_wal_size 数据类型 Integer 默认值 1024 取值范围 2到2147483647 参数单位 MB 参数含义 触发automatic checkpoints的最大wal 大小。这是软限制，wal size可以在如下情况下超过本参数值的限制：重负载，失败的archive_command命令，以及高的wal_keep_size参数值设置。增加本参数值会导致进行crash recovery的时间长度增大。本参数值仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 min_wal_size 数据类型 Integer 默认值 80 取值范围 2到2147483647 参数单位 MB 参数含义 只要WAL Disk的使用低于本参数值，旧的wal文件总是在checkpoint发生时被循环使用，而不是删除wal文件。这可以用来确保保留足够的WAL空间来处理WAL使用中的峰值，例如在运行大型批处理作业时。此参数只能在postgresql.conf文件文件或在server command line上。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 dynamic_library_path 数据类型 string 默认值 $libdir, $libdir代表的绝对路径请见pg_config --pkglibdir命令的输出 取值范围 参数单位 参数含义 如果需要打开可动态加载的模块，并且create function 或LOAD命令中指定的文件名没有目录组件（即名称不包含斜杠），系统将在该路径中搜索所需的文件。本参数值必须是由冒号分隔(针对Linux平台)或者分号分隔(针对Windows平台)的绝对路径列表。如果本参数值以特殊字符串$libdir开头，那么编译后的PostgreSQL package library directory会取代$libdir, PostgreSQL package library directory请见pg_config --pkglibdir命令的结果。若是本参数值被设置为空字符串，表示自动path search功能会被关闭。superuser可以在运行时更改此参数，但这样做的设置只会持续到客户端连接结束，因此应将此方法保留用于开发目的。 是否可session级修改 是，此时仅限于superuser在session级别修改。 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 gin_fuzzy_search_limit 数据类型 integer 默认值 0，0表示无限制 取值范围 0到2147483647 参数单位 参数含义 GIN索引扫描返回的结果集大小的软上限。开发GIN索引的主要目的是在PostgreSQL中创建对高度可伸缩的全文搜索的支持，而且通常情况下，全文搜索会返回非常大的结果集。此外，当查询包含非常频繁的单词时，这种情况经常发生，因此大的结果集甚至没有用处。因为从磁盘读取许多元组并对它们进行排序可能需要花费大量时间，这对于生产来说是不可接受的。（请注意，索引搜索本身非常快。）为了便于此类查询的受控执行，GIN对返回的行数有一个可配置的软上限，这就是gin_fuzzy_search_limit配置参数。默认设置为0（表示无限制）。如果设置了非零限制，则返回的集是随机选择的整个结果集的子集。\"软\"意味着返回结果的实际数量可能与指定的限制有所不同，这取决于查询和系统随机数生成器的质量。从经验来看，以千为单位的本参数值（例如，5000-20000）工作正常。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 tcp_keepalives_count 数据类型 默认值 0，0表示取操作系统的默认值， 取值范围 0到2147483647 参数单位 参数含义 指定在服务器与客户端的连接被视为已断开之前可以被丢弃的TCP keepalive messages.在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为零。本参数在Windows操作系统上不被支持，因此，在Windows操作系统上，本参数值必须为0。本参数仅仅支持符合如下条件的操作系统：那些支持TCP_KEEPCNT或者等价socket option的操作系统。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 tcp_keepalives_idle 数据类型 integer 默认值 0，0表示取操作系统的默认值 取值范围 0到2147483647 参数单位 秒 参数含义 指定在没有网络活动的情况下，经过多长时间之后，操作系统应该向Client端发送TCP keepalive message.本参数仅仅支持符合如下条件的操作系统：那些支持TCP_KEEPIDLE或者等价socket option的操作系统。因此，在Windows以及其他操作系统中，本参数值必须为0，在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为0。在Windows操作系统上，本参数值为0时，本参数值将会被视为2小时，因为，Windows不提供读取系统默认值的方法。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 tcp_keepalives_interval 数据类型 Integer 默认值 0，0表示取操作系统的默认值 取值范围 0到2147483647 参数单位 秒 参数含义 指定TCP keepalive message重传的时间间隔。本参数仅仅支持符合如下条件的操作系统：那些支持TCP_KEEPINTVL或者等价socket option的操作系统。因此，在Windows以及其他操作系统中，本参数值必须为零，在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为零。在Windows操作系统上，本参数值为零时，本参数值将会被视为1秒，因为，Windows不提供读取系统默认值的方法。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 tcp_user_timeout 数据类型 Integer 默认值 0，0表示取操作系统的默认值 取值范围 0到2147483647 参数单位 毫秒 参数含义 指定在强制关闭TCP连接之前，传输的数据可能保持未确认状态的时间长度本参数仅仅支持符合如下条件的操作系统：那些支持TCP_USER_TIMEOUT或者等价socket option的操作系统。因此，在其他操作系统中，本参数值必须为零，在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为零。在Windows操作系统上，本参数不受支持，此时本参数值必须设置为零。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或者禁用genetic query optimization，生产环境中不应该将本参数设置为off。配置参数geqo_threshold提供了对GEQO更细粒度的控制。遗传查询优化器（GEQO）是一种使用启发式搜索进行查询计划的算法。这减少了用于复杂查询（那些包含许多关系的查询）的计划时间，但所产生的计划的成本有时不如普通穷举搜索算法所找到的计划。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_threshold 数据类型 integer 默认值 12 取值范围 2到2147483647 参数单位 参数含义 本参数是启用geqo的阈值，即:当from之后的关系数量至少有本参数值指定的个数时，才启用geqo。注意一个FULL OUTER JOIN只被计为一个FROM项。对于简单的SQL语句，通常最好使用常规的穷举搜索planner，但是对于具有许多表的查询，穷举搜索花费的时间太长，通常比执行次优计划的代价要长，因此，本阈值是管理geqo的一种方法。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_effort 数据类型 Integer 默认值 5 取值范围 1到10 参数单位 参数含义 控制GEQO中计划时间和查询计划质量之间的权衡。此变量必须是1到10之间的整数。默认值为5。较大的值会增加生成查询计划所花费的时间，但也会增加选择高效查询计划的可能性。本参数实际上并不直接做任何事情；它只用于计算影响geqo行为的其他变量的默认值。如果愿意，可以手动设置其他参数。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_pool_size 数据类型 Integer 默认值 0，0表示PostgreSQL会根据SQL语句中表的数量以及geqo_effort参数值选择一个合适的值。 取值范围 0到2147483647 参数单位 参数含义 控制GEQO使用的pool size，即:遗传群体中的个体数，至少是2，有用的值通常是100到1000. 参数值为0表示PostgreSQL会根据SQL语句中表的数量以及geqo_effort参数值选择一个合适的值。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_generations 数据类型 Integer 默认值 0 取值范围 0到2147483647 参数单位 参数含义 控制GEQO使用的generations，即算法的迭代次数。它必须至少是一个，并且有用的值通常是100到1000。如果设置为零（默认设置），则PostgreSQL会根据geqo_pool_size选择合适的值。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_selection_bias 数据类型 real 默认值 2 取值范围 1.5到2 参数单位 参数含义 控制GEQO使用的选择偏差。选择偏差是population内部的选择压力 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_seed 数据类型 real 默认值 0 取值范围 0到1 参数单位 参数含义 控制GEQO用于通过连接顺序搜索空间选择随机路径的随机数生成器的初始值，更改该值将更改所探索的连接路径集。并可能导致找到更好或更差的最佳路径 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 recovery_target 数据类型 string 默认值 空串 取值范围 immediate 参数单位 参数含义 此参数指定恢复应在达到一致状态后尽快结束，即尽早结束。从联机备份还原时，这意味着在备份结束的时间点结束恢复。 是否可session级修改 否 修改后何时生效 本参数不支持修改。 参数名称 recovery_target_name 数据类型 string 默认值 空串 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的命名还原点,该还原点由函数pg_create_restore_point()创建。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_time 数据类型 string 默认值 空串 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的time stamp，精确的停止点受到recovery_target_inclusive配置参数影响。此参数的值是一个时间戳，其格式与timestamp with time zone数据类型所接受的格式相同，只是您不能使用时区缩写（除非在配置文件的前面设置了timezone_abbreviations变量）。首选样式是使用UTC的数字偏移量，或者您可以编写完整的时区名称，例如，Europe/Helsinki，而不是EEST 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_xid 数据类型 string 默认值 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的trasaction ID。请记住，虽然事务ID在事务开始时按顺序分配，但事务可以按不同的数字顺序完成。要恢复的事务是在指定事务之前提交的事务（也可以选择包括指定事务），精确的停止点受到recovery_target_inclusive配置参数影响。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_lsn 数据类型 string 默认值 空串 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的LSN，精确的停止点受到recovery_target_inclusive配置参数影响。本参数使用系统数据类型pg_lsn解析。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_inclusive 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当本参数设置为on时指定到达指定的recovery target之后停止当本参数设置为off时指定到达指定的recovery target之前停止本参数与如下参数配合使用：recovery_target_lsn、recovery_target_time、recovery_target_xid。本参数控制含有精确recovery target(恢复目标)的LSN、commit time、或者trasaction id是否被包括恢复(recovery)之内。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_timeline 数据类型 string 默认值 latest 取值范围 参数单位 参数含义 指定recovery进入一个特定的timeline，本参数值可以是一个数字的timeline id或者一个特定的值，值current的含义：recover到做base backup时的那个timeline；值latest表示recover到在archive 中找到的最新的timeline，值latest适用于standby server。您通常只需要在复杂的重新恢复情况下设置本参数，在这种情况下，您需要返回到在时间点恢复之后达到的状态。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_action 数据类型 enum 默认值 pause 取值范围 pause,promote,shutdown 参数单位 参数含义 指定了一旦达到recovery target，PostgreSQL该采取的操作。默认值pause表示恢复暂停(pause)，值promote表示恢复过程完成并且PostgreSQL将会启动以接受外部连接，值shutdown表示在达到recovery target之后会stop PostgreSQL。pause值的预期用途是允许对数据库执行查询，以检查此恢复目标是否是最理想的恢复点,pg_wal_replay_resume()函数表示paused state resumed，这意味着恢复走到了终点。若是recovery target不是期望的那个stopping point，那么请关闭PostgreSQL，改变recovery target到更为之后的一个target，然后重启recovery即可。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 default_statistics_target 数据类型 integer 默认值 100 取值范围 1到10000 参数单位 参数含义 为没有通过ALTER TABLE SET STATISTICS设置column-specific target的表列设置默认统计目标。较大的本参数值会增加analyze的时间，但也会提升planner估计的质量。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 constraint_exclusion 数据类型 enum 默认值 partition 取值范围 partition,on,off on表示检查所有表的约束 off表示不检查约束partition表示仅仅对继承的子表以及union all子查询检查约束。 参数单位 参数含义 控制planner是否使用constraints优化查询,如果表的约束保证没有与查询匹配的行，table scan会被跳过。本参数经常与传统的继承树一起使用以提高性能，比如如下的例子： CREATE TABLE parent(key integer, …​); CREATE TABLE child1000(check (key between 1000 and 1999)) INHERITS(parent); CREATE TABLE child2000(check (key between 2000 and 2999)) INHERITS(parent); …​ SELECT \\* FROM parent WHERE key = 2400;当本参数启用时，select语句就不会扫描child1000这个表。目前，默认情况下仅对经常用于通过继承树实现表分区的情况启用约束排除。为所有表启用它会带来额外的计划开销，这在简单查询中非常明显，而且对于简单查询通常不会产生任何好处。如果没有使用传统继承进行分区的表，则可能希望完全关闭它。（请注意，分区表的等效功能由单独的配置参数enable_partition_pruning控制。） 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 cursor_tuple_fraction 数据类型 real 默认值 0.1 取值范围 0到1 参数单位 参数含义 设置planner’s估计，本参数设置较少的值时，会使planner倾向于对cursors使用\"fast start\" plan，这会快速的检索前几行，而获取所有行可能需要很长时间。较大的值更强调总的估计时间。在最大设置为1.0时，游标的规划与常规查询完全相同，只考虑总的估计时间，而不考虑第一行的交付时间。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 from_collapse_limit 数据类型 integer 默认值 8 取值范围 1到2147483647 参数单位 参数含义 如果生成的FROM列表包含的项目不超过本参数值，那么planner将会把子查询合并到上面的查询中。较小的值会减少计划时间，但可能会产生较差的查询计划。设置本参数值为geqo_threshold参数值或者更大可能会触发GEQO Planner的使用，进而导致非优化的执行计划。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 jit 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 确定jit编译是否被PostgreSQL使用。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 join_collapse_limit 数据类型 integer 默认值 8 取值范围 1到2147483647 参数单位 参数含义 每当生成的items列表不超过本参数值时,planner就会将explicit join结构（FULL JOINs除外）重写为FROM items列表。较小的值会减少计划时间，但可能会产生较差的查询计划.默认情况下，此参数值设置为与from_collapse_limit参数值相同，这适用于大多数用途。将其设置为1可防止explicit join的任何重新排序。因此，查询中指定的显式连接顺序将是连接关系的实际顺序。因为查询规划器并不总是选择最佳的连接顺序，所以高级用户可以选择将此参数值临时设置为1，然后显式指定所需的连接顺序。设置本参数值为geqo_threshold参数值或者更大可能会触发GEQO Planner的使用，进而导致非优化的执行计划。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 parallel_leader_participation 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 允许leader进程在Gather和Gather Merge node下执行查询计划，而不是等待worker进程。将此值设置为off可降低worker进程被阻塞的可能性，因为leader进程读取元组的速度不够快，但需要leader进程等待工作进程启动，然后才能生成第一个元组。Leader进程能够帮助或阻碍性能的程度取决于计划类型、worker数量和查询持续时间。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 force_parallel_mode 数据类型 enum 默认值 off 取值范围 off,on,regress off表示仅仅当预期能提升性能时，才使用parallel mode on表示对被视为safe的查询语句强制使用并行查询。regress表示除了有本参数值设置为\"on\"的相同的所有效果之外，还有一些旨在促进自动回归测试的附加效果 参数单位 参数含义 是否使用parallel query功能。更具体地说，将此值设置为on将在任何查询计划的顶部添加一个Gather节点，这看起来是安全的，因此查询在并行worker进程中运行，即使并行worker进程不可用或无法使用，也将禁止在并行查询上下文中禁止的操作（如启动子事务），除非planner认为这将导致查询失败，如果设置此选项时出现故障或意外结果，则查询使用的某些函数可能需要标记为PARALLEL UNSAFE（或者，可能是PARALLEL RESTRICTED）通常，来自并行worker进程的消息包含一个context line，但是设置为regress会抑制该context line，以便输出与非并行执行中的输出相同。此外，通过此设置，添加到计划中的Gather nodes将隐藏在EXPLAIN的输出中，以便当本参数值设置为off时，输出与获得的结果相匹配。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 plan_cache_mode 数据类型 enum 默认值 auto 取值范围 auto,force_generic_plan,force_custom_plan 参数单位 参数含义 可以使用自定义或generic的plan执行prepared statements（explicitly prepared或implicitly generated，例如通过PL/pgSQL），针对每一次的执行，自定义plan使用特定的一组参数值被重新生成，generic plans不依赖本plan_cache_mode参数值，可以被多次的执行重复使用。此外，使用generic plans会节省 planning time，但是，如果理想的plan强烈依靠本参数，那么，generic plan可能效率低下。这些选项之间的选择通常是自动进行的，但它可以被plan_cache_mode配置参数覆盖。在执行缓存的计划时，而不是在准备计划时，会考虑本参数值 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 array_nulls 数据类型 bool 默认值 on，本参数值为on时表示允许输入包括NULL的数组值 取值范围 on和off 参数单位 参数含义 本参数控制数组输入解析器是否将未加引号的NULL识别为指定NULL数组元素。8.2版本之前的PostgreSQL不支持数组中的null值，因此将null视为使用字符串值\"null\"指定普通数组元素。为了向后兼容需要旧行为的应用程序，可以关闭此参数。请注意，即使本参数值是off，也可以创建包含NULL的数组值 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 backslash_quote 数据类型 enum 默认值 safe_encoding 取值范围 safe_encoding,on,off on表示始终允许\\' off表示始终拒绝'safe_encoding表示仅当客户端编码不允许在多字节字符中使用ASCII\\时才允许. 参数单位 参数含义 本参数用于设置字符串的引用符是否是'符号。SQL标准中是'',而PostgreSQL历史上也接受了'符号，但是，使用'会带来安全风险，这是因为，在一些客户端字符集编码中，存在多字节字符，其中最后一个字节在数字上等同于ASCII \\.如果客户端没有正确的转义，那么SQL注入攻击是可能的。这种风险可以通过使PostgreSQL数据库拒绝引用符被backslash反斜杠转义的查询来防止。请注意，在标准一致性字符串文本中，无论如何，\\仅仅表示\\。此参数仅影响对非标准一致性文本的处理，包括转义字符串语法（E'…'） 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 escape_string_warning 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当设置为on时，表示当\\出现在一个普通的字符串('…'这样的)中并且standard_conforming_strings参数值为off时，一个warning会发出。希望使用反斜杠作为转义的应用程序应该修改为使用转义字符串语法（E'…'）. 因为根据SQL标准，普通字符串的默认行为现在是将反斜杠视为普通字符. 可以启用本参数来帮助定位需要更改的代码 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 lo_compat_privileges 数据类型 bool 默认值 off 取值范围 参数单位 参数含义 对large object，为权限检查启用面向backward的兼容性模式。在PostgreSQL9.0之前，large object并不具有访问权限，因此，large object总是能被所有的user进行读和写。设置本参数值为on将会禁用新权限的检查，这会与PostgreSQL9.0之前的版本保持兼容。 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 operator_precedence_warning 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 启用此选项后，解析器将对自PostgreSQL 9.4以来由于运算符优先级的更改而可能已更改含义的任何构造发出警告。对于审核应用程序以查看优先级更改是否破坏了任何内容非常有用；但这并不意味着在生产环境中保持打开状态，因为它将警告某些完全有效、符合标准的SQL代码 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 quote_all_identifiers 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当数据库生成SQL语句时，即使标识符不是关键字，也会将标识符引用起来。这会影响explain的输出以及诸如pg_get_viewdef函数的结果，比如下面的例子： postgres=# select pg_get_viewdef('company_view'); -[RECORD 1]--+-------------------- pg_get_viewdef SELECT company.id,+ company.name, company.age FROM company; postgres=# set quote_all_identifiers=on; SET postgres=# select pg_get_viewdef('company_view'); -[RECORD 1]--+------------------------ pg_get_viewdef SELECT \"company\".\"id\",+ \"company\".\"name\", \"company\".\"age\" FROM \"company\"; postgres=# 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 standard_conforming_strings 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数控制普通字符串文本（'…'）是否按SQL标准中的指定按字面含义处理反斜杠.从PostgreSQL9.1开始，默认值为on。此参数的存在也可以被视为表示支持转义字符串语法（E'…'）如果应用程序希望反斜杠被视为转义字符，则应使用转义字符串语法 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 synchronize_seqscans 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 这允许对大表的顺序扫描彼此同步，以便并发扫描在大约相同的时间读取相同的块，从而共享I/O工作负载。启用本参数后，扫描可能从表的中间开始，然后\"环绕\"结尾以覆盖所有行，以便与正在进行的扫描活动同步。这可能导致没有ORDERBY子句的查询返回的行顺序发生不可预知的更改。将此参数设置为off可确保与PostgreSQL 8.3之前的行为，即顺序扫描始终从表的开头开始 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 listen_addresses 数据类型 string 默认值 localhost 取值范围 参数单位 参数含义 设置数据库服务器监听的hostname或者IP地址，监听多个hostname或者IP地址时，请用逗号隔开。 \\*号表示监听所有可用的IP地址， 0:0:0:0监听代表所有IPV4的地址， ::代表监听所有IPV6的地址。当本参数值为空时，PostgreSQL不会监听任何的IP，此时，只有Unix-domain sockets才可以用于连接到数据库中。本参数控制哪些IP地址上接受连接尝试，这可以帮助防止在不安全的IP地址上重复出现恶意连接请求。该参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 port 数据类型 integer 默认值 5432 取值范围 1到65535 参数单位 参数含义 PostgreSQL监听的TCP端口。请注意，PostgreSQL监听的所有IP地址都使用相同的端口号，本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_connections 数据类型 integer 默认值 100 取值范围 1到262143 参数单位 参数含义 本参数值指定了PostgreSQL数据库的最大连接数。本参数只能在PostgreSQL启动时设置。在流复制备库上，必须将本参数值设置为与主库相同或者比主库参数值大,否则，后备服务器将不允许查询操作。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 superuser_reserved_connections 数据类型 integer 默认值 3 取值范围 0到262143 参数单位 参数含义 为superusers保留的connection \"slots\"的数量，当PostgreSQL的活跃的并发连接的数量等于max_connections参数值减去superuser_reserved_connections参数值之后，仅仅可以面向superuser建立新的连接，并且，replication connection也是建立不了的。本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 unix_socket_directories 数据类型 string 默认值 在Linux中默认值为/tmp；在Windows中默认值为空 取值范围 参数单位 参数含义 本参数指定了PostgreSQL监听客户端连接的Unix-domain socket(s)目录，多个目录可以用逗号隔开，两个目录之间的空格会被忽略，如果你想在目录名中包括逗号或者空格，请使用双引号引起来。本参数值为空时表示不监听任何的Unix-domain socket(s)目录，这意味着只有TCP/IP sockets可以被用来连接到PostgreSQL中。本参数只能在PostgreSQL启动时设置。在本参数值指定的目录下，会有名为s.PGSQL.nnnn的socket file，nnnn是PostgreSQL使用的端口号，在该目录下，还有一个名为.s.PGSQL.nnnn.lock的文件。这两个文件请不要手工删除。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 unix_socket_group 数据类型 string 默认值 空字符串，空字符串表示使用启动PostgreSQL 的user的默认组。 取值范围 参数单位 参数含义 设置Unix-domain socket(s).的owning group(请注意：socket的owning user总是启动PostgreSQL的user)。本参数与unix_socket_permissions结合使用可以将其作为Unix-domain connections的额外访问控制机制。本参数只能在PostgreSQL启动时设置。本参数不支持在Windows上使用，在Windows上，本参数取任何值都会被忽略。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 unix_socket_permissions 数据类型 integer 默认值 0777，0777表示任何人都可以连接 取值范围 0到511 参数单位 参数含义 本参数用于设置Unix-domain socket(s)的访问权限。Unix-domain socket使用通常的Unix文件系统权限集。参数值预计是被chmod和umask系统调用接受的数字模式。（要使用惯用的八进制格式，数字必须以0（零）开头。）合理的参数值是0770（仅用户和组，另请参见unix_socket_group）和0700（仅用户）。请注意，对于Unix-domain socket，仅写许可权是重要的，因此设置或撤消读或执行许可权没有意义。本参数只能在PostgreSQL启动时设置。在完全忽略socket permissions的操作系统上（特别是自solaris10起的Solaris），此参数是不相关的。在此类操作系统上，可以通过将unix目录指向一个搜索权限仅限于所需访问用户的目录来实现类似的效果。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 bonjour 数据类型 boolean 默认值 off 取值范围 off和on 参数单位 参数含义 本参数控制是否通过Bonjour机制通告PostgreSQL的存在。本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 bonjour_name 数据类型 string 默认值 空字符串。空字符串表示使用计算机名 取值范围 参数单位 参数含义 本参数用于设置Bonjour service name。当PostgreSQL软件没有编译Bonjour support时，本参数会被忽略。本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 effective_io_concurrency 数据类型 integer 默认值 1 取值范围 0到1000，0表示禁止发布异步I/O请求 参数单位 参数含义 有效利用disk子系统的并发disk io的数量。目前，本参数仅仅影响bitmap heap scans。对于HDD(机械硬盘)，本参数的起始值应该是PostgreSQL database用到的、组成raid 0 条带或者raid 1镜像的单独 disk drive的个数。但是，如果PostgreSQL数据库经常忙于并发会话中发出的多个查询，则较低的值可能足以使磁盘阵列保持繁忙。高于使磁盘保持忙碌的本参数值只会导致额外的CPU开销，固态盘(SSD)以及其他基于内存的存储可以经常处理很多并发请求，因此，此时最佳的值可能是几百。异步IO请求取决于一个有效的posix_fadvise函数，该函数在有些操作系统上是缺少的。在该函数缺少时，设置本参数为非零值会导致错误发生。在有些操作系统上(比如Solaris),本函数存在但是并没有实际的执行任何操作。在受到支持的操作系统上，本参数值的默认值为1，否则为零。本参数值可以被tablespace级别的同名参数值所覆盖。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 maintenance_io_concurrency 数据类型 integer 默认值 10 取值范围 0到1000 参数单位 参数含义 本参数类似于effective_io_concurrency参数，但本参数用于代表许多客户端会话执行的维护工作。在受到支持的操作系统上，本参数值的默认值为10，否则为零。本参数值可以被tablespace级别的同名参数值所覆盖。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 max_worker_processes 数据类型 integer 默认值 8 取值范围 0到262143 参数单位 参数含义 本参数值指定了PostgreSQL所能支持的并发worker进程的最大数量。本参数值仅能在PostgreSQL启动时设置。当运行在standby server环境中时，在standby server上的本参数值必须大于或者等于在master server上的本参数值。否则，query是不能运行在standby server上。当改变本参数值时，请考虑调整max_parallel_workers,参数，max_parallel_maintenance_workers参数，max_parallel_workers_per_gather参数 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_parallel_workers_per_gather 数据类型 integer 默认值 2 取值范围 0到1024，0表示禁用parallel query execution 参数单位 参数含义 本参数指定了可以被单个Gather或者Gather Merge Node启动的worker进程的最大数量。Parallel Workers进程从由max_worker_processes配置参数建立的进程池中获取，受限于max_parallel_workers配置参数。请注意，请求的workers的数量在运行时并不一定实际可用，如果这个情况发生，执行计划会以少于期望个数的workers去运行，这可能是低效的。请注意，parallel queries可能比非parallel queries消耗更多的资源，因为每个工作进程是一个完全独立的进程，其对系统的影响与附加用户会话大致相同。在为此设置选择值时，以及在配置控制资源利用率的其他设置（如work_mem）时，都应考虑到这一点。诸如work_mem之类的资源限制单独应用于每个worker，这意味着所有进程的总利用率可能比任何单个进程的正常利用率高得多。例如，一个使用4个worker的并行查询可能会使用多达5倍的CPU时间、内存、I/O带宽。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 max_parallel_maintenance_workers 数据类型 integer 默认值 2 取值范围 0到1024，0表示禁用由实用命令使用的parallel workers 参数单位 参数含义 可以被单个实用命令启动的parallel worker的最大数量。当前，支持parallel workers的并行实用命令有如下几种： create index 建立B-Tree时； vacuum 不带full选项时； Parallel Workers进程从由max_worker_processes配置参数建立的进程池中获取，受限于max_parallel_workers配置参数。请注意，请求的workers的数量在运行时并不一定实际可用，如果这个情况发生，执行计划会以少于期望个数的workers去运行，这可能是低效的。注意，并行实用程序命令不应该比等效的非并行操作消耗更多的内存，这种策略不同于并行查询，在并行查询中资源限制通常适用于每个worker进程。并行实用命令将资源限制maintenance_work_mem视为应用于整个实用命令的限制，而不考虑并行工作进程的数量。然而，并行实用命令仍然可能消耗更多的CPU资源和I/O带宽。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 max_parallel_workers 数据类型 integer 默认值 8 取值范围 0到1024 参数单位 参数含义 本参数值指定了PostgreSQL支持的parallel操作的worker的最大数量。当增加或者减少本参数值时，请考虑同时调整max_parallel_maintenance_workers 配置参数和 max_parallel_workers_per_gather配置参数。请注意：当本参数值高于max_worker_processes参数值时不会有任何作用。因为，因为parallel workers是从max_worker_processes配置参数所建立的工作进程池中获取的。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 backend_flush_after 数据类型 integer 默认值 0，0表示禁用强制writeback 取值范围 0到256 参数单位 8KB 参数含义 每当单个backend进程写入的数据超过本参数值时，尝试强制操作系统向底层存储发出这些写入操作。这样做将限制内核page cache中脏数据的数量，减少在检查点结束时发出fsync时，或者当操作系统在后台以更大的批写入数据时暂停的可能性。这通常会大大减少事务延迟，但也有一些情况，特别是在工作负载大于共享缓冲区，但小于操作系统page cache的情况下，性能可能会降低。此参数可能在某些平台下无效。如果指定该值时没有单位，则将其作为块，即源码中的符号常量BLCKSZ个字节，通常为8KB。如果BLCKSZ不是8KB，则最大值与之成比例缩放。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 old_snapshot_threshold 数据类型 intger 默认值 -1，-1代表禁用本特性，表示snapshot age 是无限制的。 取值范围 -1到86400 参数单位 分钟 参数含义 设置在使用snapshot时可以使用查询快照而不会出现\"snapshot too old\"错误的最短时间。处于dead状态的且超过本参数值的数据可以被vacuum掉。这有助于防止snapshot长期使用时出现表膨胀。为了防止由于清除快照可见的数据而产生不正确的结果，当快照早于本参数值并且快照用于读取自创建快照以来已修改的页面时，将生成一个错误。对生产环境有用的值可能从几个小时到几天不等。只允许使用较小的值（如0或1min），因为它们有时可能对测试有用。虽然允许高达60d的设置，但请注意，在许多工作负载中，在更短的时间内可能会出现极端膨胀或事务ID环绕(transaction ID wraparound)。启用本特性后，relation末尾释放的空间将无法释放给操作系统，因为这可能会删除检测\"snapshot too old\"条件所需的信息。分配给relation的所有空间都与该relation保持关联，以便仅在该relation中重用，除非显式释放（例如，使用VACUUM FULL）。此参数并不试图保证在任何特定情况下都会生成错误。事实上，如果正确的结果可以从一个已经物化的结果集中生成，则即使引用表中的底层行已被vacuum，也不会生成错误。某些表不能安全地提前vacuum，因此不受此设置的影响，例如系统目录(system catalogs)。对于这样的表，此设置既不会减少膨胀，也不会在扫描时产生\"snapshot too old\"错误的可能性。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 primary_conninfo 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数值指定了连接到sending server的连接字符串(连接信息）.在本连接字符串中缺少的option，PostgreSQL会到环境变量中取寻找。若是环境变量中也没有，那么就用默认值。连接字符串应该指定如下信息： Sending Server的主机名或者IP地址、 Sending Server的端口号（若是跟standby server端口号不同） Username(该用户在sending-server端有合适的权限) Password：请注意，密码可以在primary_conninfo中提供，也可以在standby server上的~/.pgpass文件中提供（此时，是用replication作为database name）。请不要在primary_conninfo中指定database的名字本参数仅能在postgresql.conf文件或者server command line中设置。当在wal receiver process正在运行的情况下修改本参数值，wal receiver process会被发信号通知关闭然后再以新参数值去启动，除非primary_conninfo配置参数为空字符串。当PostgreSQL没有处于standby mode的情况下，本参数无效。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 primary_slot_name 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数指定了当要连接到sending-server端时，要使用的sending-server端的已经存在的replication slot，用来控制sending-server端的wal removal。本参数仅能在postgresql.conf文件或者server command line中设置。当在wal receiver process正在运行的情况下修改本参数值，wal receiver process会被发信号通知关闭然后再以新参数值去启动，本参数在如下情况下没有作用：PostgreSQL没有处于standby mode或者primary_conninfo参数值没有设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 promote_trigger_file 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数值指定了一个trigger file，该文件用于standby server结束recovery状态进行角色提升。本参数值没有设置时，你可以使用pg_ctl promote或者使用函数pg_promote()进行角色提升。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 hot_standby 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数控制standby server是否允许运行只读查询语句。本参数仅能在server start时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_standby_archive_delay 数据类型 integer 默认值 30000 取值范围 -1到2147483647，-1表示允许standby server一直在等待直到冲突的query执行完毕。 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。当hot standby在被启用的状态下，本参数决定了standby server在取消掉standby中运行的且与WAL日志应用有冲突的查询语句之前的等待时间。本参数适用于从wal 归档读取wal data的情况。当不指定单位时，本参数的单位是毫秒。本参数仅能在postgresql.conf文件或者server command line中设置。 本参数值与查询语句在取消之前可以运行的最长时间不同。相反，本参数值是：一旦standby server从sending-server收到wal data，所允许的应用wal data的最长时间。因此，如果一个查询在一个wal segment之前导致了显著的延迟，那么在standby server再次赶上之前，后续冲突查询的容忍时间(宽限时间)将少得多。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 max_standby_streaming_delay 数据类型 integer 默认值 30000 取值范围 -1到2147483647，-1表示允许standby server一直在等待直到冲突的query执行完毕。 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。当hot standby在被启用的状态下，本参数决定了standby server在取消掉standby中运行的且与WAL日志应用有冲突的查询语句之前的等待时间。本参数适用于wal data通过流复制被接收的情况。当不指定单位时，本参数的单位是毫秒。本参数仅能在postgresql.conf文件或者server command line中设置。本参数值与查询语句在取消之前可以运行的最长时间不同。相反，本参数值是从主服务器接收到WAL数据后允许应用该数据的最长总时间，因此，如果一个查询导致了显著的延迟，那么在后备服务器再次赶上之前，后续冲突查询的容忍时间(宽限时间)将少得多 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_receiver_create_temp_slot 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数用于指定当永久的复制槽没有配置(复制槽配置请见配置参数primary_slot_name)的情况下，wal receiver是否在远程PG Cluster上创建一个临时的复制槽。本参数仅能在postgresql.conf文件或者server command line中设置。当wal receiver进程正在运行的同时本参数被改变时，wal receiver进程会关闭并重启以读取到新值。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_receiver_status_interval 数据类型 integer 默认值 10 取值范围 0到2147483，0表示完全禁止状态更新。 参数单位 秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数指定了备库上的wal receiver 进程向 primary库或者sending-server库发送复制进度(replication progress)的最短时间频率。备库将报告它写到的wal的位置，它flush 到disk的位置以及它应用wal的位置。本参数值是两次报告之间的最大时间间隔。每次写入(write)或刷新(flush)位置更改时都会发送更新，或者至少与本参数指定的频率相同。因此，应用的位置可能稍微落后于真实位置。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 hot_standby_feedback 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。是否允许一个hot standby的备库发送关于正在备库执行的查询的feedback给primary库或者upstream库。本参数值可以消除由于cleanup records导致的query cancels，但是会导致primary库上发生膨胀。feedback信息的发送频率不会超过配置参数wal_receiver_status_interval。当是级联复制环境时，feedback会通过upstream(即:中间节点)会最终到达primary库。Standby节点除了向upstream反馈信息外，没有其他用途。本参数不会覆盖primary端的配置参数old_snapshot_threshold的行为。Standby端上超过primary端age阈值的snapshot会变为invalid，这导致standby端上的查询被取消。这是因为配置参数old_snapshot_threshold的目的是在时间上对dead row导致膨胀提供绝对的限制，否则会因为standby的配置违反该限制。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_receiver_timeout 数据类型 integer 默认值 60000 取值范围 0到2147483647，0代表禁用本timeout检测机制。 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。当本备端与sending-server端或者primary端的inactive状态超过本参数值时，终止掉复制连接。本参数对receiving standby server检测primary node crash或者网络故障很有用。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_retrieve_retry_interval 数据类型 integer 默认值 5000 取值范围 1到2147483647 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数控制standby server遇到从所有源头(streaming replication、本地pg_wal目录、WAL Archive位置)获取wal而获取不到时的等待时间。本参数对处于in recovery的节点需要控制等待新的wal data可用的时间很有用。比如，在archive recovery中，通过降低本参数值，在检测一个新wal log file时，可以使recovery更快响应。在一个wal file产生很少的PostgreSQL中，增加本参数值会减少访问wal archive的请求次数。这在那些基础设施的访问次数会被考虑在内的云环境中非常有用。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 recovery_min_apply_delay 数据类型 integer 默认值 0，0代表无延迟。 取值范围 0到2147483647 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。默认情况下，standby server会及时的restore 来自于sending server端的WAL 记录。数据的延迟copy可能很有用，它提供了纠正数据丢失错误的机会。本参数值指定了允许你指定延迟recovery的时间长度。比如，你设置本参数值为5分钟，那么只有当standby端的系统时间比master端报告的提交时间至少晚5分钟时，standby端才会重放每个事物的提交。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 seq_page_cost 数据类型 real 默认值 1 取值范围 0到1.79769e+308 参数单位 参数含义 本参数设置了顺序读取磁盘上的page的成本。在table以及index上的表空间级别的同名本参数可以覆盖配置参数中的该参数值。增大本参数值会使索引扫描看起来相对更昂贵 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 random_page_cost 数据类型 real 默认值 4 取值范围 0到1.79769e+308 参数单位 参数含义 本参数设置了非顺序读取磁盘上的page的成本。在table以及index上的表空间级别的同名本参数可以覆盖配置参数中的该参数值。相对于seq_page_cost减少本参数值会使PostgreSQL更倾向于使用索引扫描。随机访问机械硬盘要比四次顺序访问还要昂贵。但是，使用较低的默认值(4.0)，因为大多数对磁盘的随机访问(如索引读取)都假定在cache中。默认值可以认为是将随机访问模型比顺序访问慢40倍，同时，期望90%的随机读取都是在cache中。如果您认为90%的缓存比率对于您的工作负载来说是一个不正确的假设，那么你可以增加本参数值以反映随机存储读取的真实成本。相应的，如果你的数据可能完全在cache中，比如在database大小小于服务器内存，减少本参数值是合适的。那些相对于顺序读具有低随机读成本的存储（比如固态驱动器），也可以使用较低本参数值（比如1.1） 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 cpu_tuple_cost 数据类型 real 默认值 0.01 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置在执行一个SQL语句时planner预计的处理每行数据的成本 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 cpu_index_tuple_cost 数据类型 real 默认值 0.005 取值范围 0到1.79769e+308 参数单位 参数含义 本参数用于设置在索引扫描期间planner预计的处理每个index entry的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 cpu_operator_cost 数据类型 real 默认值 0.0025 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置在执行一个SQL语句时planner预计的处理执行的每个operator或者function的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 parallel_setup_cost 数据类型 real 默认值 1000 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置planner预估的启动parallel worker processes的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 parallel_tuple_cost 数据类型 real 默认值 0.1 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置了从一个parallel worker进程将一个tuple传输到另外一个parallel worker进程的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 min_parallel_table_scan_size 数据类型 integer 默认值 1024，默认情况下是8MB 取值范围 0到715827882 参数单位 8KB 参数含义 本参数设置了考虑使用parallel scan的table的数据(data)的最小大小，对于并行顺序扫描，table的数据(data)的最小大小总是等于table的大小。但是当index被用到时，table的数据(data)的最小大小通常会笑。如果本参数不带单位，取的是源码中符号常量BLCKSZ的值，通常为8KB。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 min_parallel_index_scan_size 数据类型 integer 默认值 64，默认情况下是512KB 取值范围 0到715827882 参数单位 8KB 参数含义 本参数设置了考虑使用parallel scan的index的数据(data)的最小大小，请注意：一个并行索引扫通常不会扫过整个index。本参数是planner认为扫描实际会涉及的页数，本参数也用来决定一个特定的索引是否可以参与并行vacuum。如果本参数不带单位，取的是源码中符号常量BLCKSZ的值。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 effective_cache_size 数据类型 integer 默认值 524288 取值范围 1到2147483647 参数单位 8KB 参数含义 本参数设置了planner对单个SQL语句可用的disk cache的有效大小。这被纳入使用索引的成本估算中。值越大，使用索引扫描的可能性越大；值越小，使用顺序扫描的可能性越大。设置本参数时，应该同时考虑PostgreSQL的shared buffers和kernel的page cache用于PostgreSQL数据文件的部分，虽然这两个位置都可能存在一些数据。另外，还要考虑在不同表上预计的并发SQL语句数量，因为他们必须共享可用空间。本参数不影响PostgreSQL分配的shared buffers大小，也不会保留kernel的page cache。它仅用于估算目的。系统也不坚定在两次查询之间数据扔保留在磁盘cache中。如果本参数不带单位，取的是源码中符号常量BLCKSZ的值。通常为8KB。如果BLCKSZ不是8KB，则默认值按照定比例缩放。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 jit_above_cost 数据类型 real 默认值 100000 取值范围 -1到1.79769e+308。-1表示禁用JIT编译 参数单位 参数含义 当SQL语句的查询成本高于本参数值时，JIT编译会被激活，若是JIT编译被enable的情况下。执行JIT会消耗planning time但是可以加速查询执行。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 jit_inline_above_cost 数据类型 real 默认值 500000 取值范围 -1到1.79769e+308，-1表示禁用inline 参数单位 参数含义 当SQL语句的查询成本高于本参数值时，JIT编译会尝试inline functions and operators。Inlining会增加planning time，但是会提升执行速度。将本参数值设置为低于jit_above_cost是没有意义的。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 jit_optimize_above_cost 数据类型 real 默认值 500000 取值范围 -1到1.79769e+308，-1表示禁用expensive optimizations 参数单位 参数含义 当SQL语句的查询成本高于本参数值时，JIT编译会尝试进行expensive optimizations。这种优化会增加planning time，但是会提升执行速度。将本参数值设置为低于jit_above_cost是没有意义的，将本参数值设置为高于jit_inline_above_cost也是没有益处的。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 autovacuum 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 控制PostgreSQL是否允许autovacuum launcher daemon.autovacuum进程能正常工作的前提条件是配置参数track_counts设置为启用。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。即使本参数被禁用，若是需要防止事务id wraparound，PostgreSQL会启动autovacuum进程。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_autovacuum_min_duration 数据类型 integer 默认值 0，0表示记录所有autovacuum操作到PostgreSQL运行日志中 取值范围 -1到2147483647，-1表示禁止autovacuum进程执行信息打印到PostgreSQL运行日志中。 参数单位 毫秒 参数含义 当autovacuum进程运行的时间长度大于等于本参数值时，autovacuum进程执行时的每个动作会被记录到PostgreSQL运行日志中。举例，若是本参数值设置为250ms，那么所有运行时间长度大于等于250ms的automatic vacuum 和analyze操作会记录到PostgreSQL运行日志中。当本参数值被设置为-1之外的值时，若是autovacumm由于冲突所或者并发dropped relation的原因被skipped掉，一条信息会打印到PostgreSQL运行日志中。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_max_workers 数据类型 integer 默认值 3 取值范围 1到262143 参数单位 参数含义 本参数设置了同时可以运行的autovacuum worker进程的最大个数。本参数仅仅可以在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 autovacuum_naptime 数据类型 integer 默认值 60 取值范围 1·到2147483 参数单位 秒 参数含义 本参数指定了两次autovacuum进程运行的最小时间间隔。在每次autovacuum进程运行时，autovacuum守护进程检查数据库并针对需要的表发出vacuum和analyze命令。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_threshold 数据类型 integer 默认值 50 取值范围 0到2147483647 参数单位 参数含义 本参数用于设置作为vacuum操作候选表的条件，即:更新或者删除掉多少条记录可以被作为vacuum操作的候选表。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_insert_threshold 数据类型 integer 默认值 1000 取值范围 -1到2147483647，-1表示即使有本参数值数量的insert记录，autovacuum守护进程也不会针对该表触发vacuum操作。 参数单位 参数含义 本参数用于设置作为vacuum操作候选表的条件，即:插入多少条记录可以触发针对该表的vacuum。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_analyze_threshold 数据类型 integer 默认值 50 取值范围 0到2147483647 参数单位 参数含义 本参数用于设置作为analyze操作候选表的条件，即:插入、更新、删除多少条记录可以触发针对该表的analyze。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_scale_factor 数据类型 real 默认值 0.2，0.2表示表大小的20% 取值范围 0到100 参数单位 参数含义 本参数设置了表大小的一个比率，当决定一个表是否触发vacuum操作时，该比率会被配置参数autovacuum_vacuum_threshold考虑在内。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_insert_scale_factor 数据类型 real 默认值 0.2，0.2表示表大小的20% 取值范围 0到100 参数单位 参数含义 本参数设置了表大小的一个比率，当决定一个表是否触发vacuum操作时，该比率会被配置参数autovacuum_vacuum_insert_threshold考虑在内。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_analyze_scale_factor 数据类型 real 默认值 0.1 取值范围 0到100 参数单位 参数含义 本参数设置了表大小的一个比率，当决定一个表是否触发vacuum操作时，该比率会被配置参数autovacuum_analyze_threshold考虑在内。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_freeze_max_age 数据类型 integer 默认值 200000000 取值范围 100000到2000000000 参数单位 参数含义 指定表的pg_class.relfrozenxid字段在强制执行vacuum操作以防止事务ID wraparound之前可以达到的最大年龄，请注意，即使autovacuum进程被禁用，PostgreSQL也会为了防止wraparound而启动autovacuum进程。Vacuum还允许从pg_xact子目录中删除旧文件，这就是为什么默认值是相对较低的2亿个事务的原因。此参数只能在服务器启动时设置，但是可以通过更改表存储参数来减少单个表的设置值。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 autovacuum_multixact_freeze_max_age 数据类型 integer 默认值 400000000 取值范围 10000到2000000000 参数单位 参数含义 指定表的pg_class. relminmxid字段在强制执行vacuum操作以防止multixact ID wraparound之前可以达到的最大年龄，请注意，即使autovacuum进程被禁用，PostgreSQL也会为了防止wraparound而启动autovacuum进程。Vacuuming multixacts还允许从pg_multixact/members 和pg_multixact/offsets子目录中删除旧文件，这就是为什么默认值是相对较低的4亿个事务的原因。此参数只能在服务器启动时设置，但是可以通过更改表存储参数来减少单个表的设置值。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 autovacuum_vacuum_cost_delay 数据类型 real 默认值 2 取值范围 -1到100，-1表示使用配置参数vacuum_cost_delay的参数值。 参数单位 毫秒 参数含义 本参数是在autovacuum中vacuum操作用到的cost delay值。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_cost_limit 数据类型 integer 默认值 -1，-1表示使用配置参数vacuum_cost_limit的参数值。 取值范围 -1到10000 参数单位 参数含义 本参数是在autovacuum中vacuum操作用到的cost limit值。请注意，如果有多个autovacuum workers，则本参数值按比例分布在运行中的autovacuum workers之间，以便每个autovacuum worker该值的总和不超过本参数值。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 是否启用ssl连接 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_ca_file 数据类型 string 默认值 空字符串，空字符串表示没有CA File被加载，并且Client身份验证不会被执行。 取值范围 参数单位 参数含义 本参数指定了包括SSL server certificate authority (CA)的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_cert_file 数据类型 string 默认值 server.crt 取值范围 参数单位 参数含义 本参数指定了包括SSL server certificate的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_crl_file 数据类型 string 默认值 空字符串，空字符串表示没有CRL文件被加载。 取值范围 参数单位 参数含义 本参数指定了包括SSL server certificate revocation list (CRL)的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_key_file 数据类型 string 默认值 server.key 取值范围 参数单位 参数含义 本参数指定了包括SSL server private key的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_ciphers 数据类型 string 默认值 HIGH:MEDIUM:+3DES:!aNULL 取值范围 参数单位 参数含义 设置允许被SSL连接使用的SSL ciphers suites的清单。有关此设置的语法和支持值的列表，请参阅OpenSSL package中的ciphers manual page。有使用TLS版本1.2及更低版本的连接才会受到影响。当前没有控制TLS版本1.3连接使用的密码选择的设置。默认值通常是一个合理的选择，除非您有特定的安全要求。本参数仅能在postgresql.conf文件或者server command line中设置。下面解释一下默认值的含义： HIGH：使用HIGH Group的Cipher suites（例如，AES、Camellia、3DES）MEDIUM：使用MEDIUM Group的Cipher suites（例如，RC4, SEED） +3DES：针对HIGH的OpenSSL默认排序是存在问题的，因为3DES排序高于AES128.这是错误的，因为3DES比ASE128提供了少的安全性，并且3DES更慢。在所有除了HIGH和MEDIUM的ciphers外，+3DES进行了重新排序 !aNULL:禁用掉没有经过身份验证的匿名cipher suites。此类cipher suites易于遭受中间人攻击，因此不应该使用。可用的cipher suites在不同OpenSSL版本中的细节差异很大。使用操作系统命令openssl ciphers -v 'HIGH:MEDIUM:+3DES:!aNULL’可以看到当前安装的OpenSSL库的实际细节。请注意：这个list会在运行时基于server key type被过滤 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_prefer_server_ciphers 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 指定是否优先使用server端的SSL cipher，还是使用client端的。本参数仅能在postgresql.conf文件或者server command line中设置。更老的PostgreSQL版本没有本参数，更老的PostgreSQL版本总是优先使用client端的SSL cipher。本参数仅能在postgresql.conf文件或者server command line中设置。本参数的目的是用于向后兼容性。优先使用server端通常更好，因为更可能的是server端的配置是适当的 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_ecdh_curve 数据类型 string 默认值 prime256v1 取值范围 参数单位 参数含义 指定要在ECDH密钥交换中使用的曲线的名称。它需要所有连接的客户端支持。它不需要与服务器的椭圆曲线密钥使用的曲线相同。本参数仅能在postgresql.conf文件或者server command line中设置。最常见曲线的OpenSSL名称是：prime256v1（NIST P-256）、secp384r1（NIST P-384）、secp521r1（NIST P-521）。可用曲线的完整列表可以通过操作系统命令openssl ecparam -list_curves显示。但并不是所有这些都可以在TLS中使用。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_min_protocol_version 数据类型 enum 默认值 TLSv1.2 取值范围 {TLSv1,TLSv1.1,TLSv1.2,TLSv1.3} 参数单位 参数含义 设置使用的SSL/TLS协议的最小版本。OpenSSLLibrary的更老的版本不支持所有的值。如果选择了不支持的设置，将引发错误。TLS 1.0之前的协议版本（即SSL version 2 和3）始终处于禁用状态。默认值是TLSv1.2，该默认值是满足本文档编写时的工业最佳实践。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_max_protocol_version 数据类型 enum 默认值 空字符串，空字符串意味着运行任何的协议版本。 取值范围 {\"\",TLSv1,TLSv1.1,TLSv1.2,TLSv1.3} 参数单位 参数含义 设置使用的SSL/TLS协议的最大版本，这对测试或者有些组件使用新版本协议出现问题是有用的。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_dh_params_file 数据类型 string 默认值 空字符串，空字符串意味着使用默认DH参数编译。 取值范围 参数单位 参数含义 指定包含Diffie-Hellman参数的文件名，该参数用于所谓的临时DH SSL密码族。如果攻击者设法破解众所周知的编译在DH中的参数，那么使用自定义DH参数可以减少暴露。您可以使用操作系统命令openssl dhparam-out dhparams.pem 2048创建自己的DH参数文件本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_passphrase_command 数据类型 string 默认值 空字符串，空字符串表示使用内置的提示机制。 取值范围 参数单位 参数含义 设置需要获取用于解密SSL文件（如私钥）的密码短语时要调用的外部命令。命令必须将密码短语打印到标准输出，并以代码0退出。在参数值中，%p替换为提示字符串(写%%表示文本值%)。请注意提示字符串可能包含空格，因此请确保引用足够的引号。命令实际上不必提示用户输入密码短语，它可以从一个文件中读取它，从一个keychain工具中获取它，或者类似的。由用户来确定所选机制是否足够安全本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_passphrase_command_supports_reload 数据类型 bool 默认值 off，off表示在reload时，ssl_passphrase_command配置参数会被忽略，并且在passphrase被需要时，SSL configuration不会被reload 取值范围 on和off 参数单位 参数含义 当一个key file需要passphrase时，本参数决定由ssl_passphrase_command配置参数设置的passphrase command在configuration reload期间是否被调用本参数仅能在postgresql.conf文件或者server command line中设置。该设置适用于需要TTY进行提示的命令，该命令在PostgreSQL运行时可能不可用。例如，如果密码短语是从文件获取的，则将本参数设置为on可能是合适的 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 shared_buffers 数据类型 integer 默认值 16384，即：128MB 取值范围 16到1073741823 参数单位 8KB，若是指定本参数值时不带单位，则取自源码中的符号常量BLCKSZ大小，默认是8KB。非默认的BLCKSZ大小会改变本参数的最小值。本参数值仅仅能在PostgreSQL启动时设置。建议将OS物理内存的25%分配给本参数值。因为PostgreSQL依赖于操作系统的cache（也就是文件系统的page cache），因此将超过40%的RAM分配给本参数值的情况不太可能比分配较小的缓冲区效果更好。较大的本参数值通常需要相应的增加max_wal_size, 以便在较长时间内分散写入大量新数据或更改数据的过程。 参数含义 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 huge_pages 数据类型 enum 默认值 try，try的含义是PostgreSQL会尝试使用huge pages，如果尝试失败将返回默认值。on的含义是请求huge pages失败将阻止PostgreSQL启动，off的含义是不会启用huge pages。目前，本参数值仅支持Linux和Windows平台。当在其他平台下本参数值设置为try时，本参数值会被忽略。 huge pages的使用导致了更小的page tables以及耗费在内存管理方面的更小的CPU时间，这提高了性能。在Windows平台上，huge pages被称之为large pages。开启large pages的步骤： 0.安装PostgreSQL 数据库 1.关闭windows uac控制面板—​系统和安全—​更改用户账户控制设置—​改为’从不通知'，点击确定。 2.在windows组策略编辑器，赋予运行PostgreSQL的操作系统用户’锁定内存页’的权力（Lock Pages in Memory）计算机配置\\Windows 设置\\安全设置\\本地策略\\用户权利分配\\下的\"锁定内存页\"这个策略。 3.将windows服务中的postgresql-x64-11这个服务改为手动启动。 4.重新启动OS 5.设置postgresql.conf的配置参数huge_pages=on 6.设置postgresql.conf的配置参数shared_buffers=2048MB 7.以管理员身份打开cmd窗口，执行pg_ctl start -D e:\\postgresql\\11\\data启动PostgreSQL 8.验证是否启用请注意：当启用large pages时，不能用Windows服务中的PostgreSQL服务启动postgresql。请注意，本参数设置仅影响main shared memory area. Linux、FreeBSD和Illumos等操作系统也可以自动使用huge pages（也称为\"super\" pages 或者 \"large\" pages）进行正常内存分配，而无需PostgreSQL的显式请求。在Linux上，这被称为\"transparent huge pages\"（THP）。众所周知，对于某些Linux版本的某些用户来说，该特性会导致PostgreSQL的性能下降，因此目前不鼓励使用它（与显式使用huge_pages）。 取值范围 {off,on,try} 参数单位 参数含义 控制shared_buffers是否使用huge pages。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 temp_buffers 数据类型 integer 默认值 1024 取值范围 100到1073741823 参数单位 8KB 参数含义 设置每个session使用的temporary buffers的最大值，供临时表使用。指定本参数值时若是不带单位，以blocks为单位，取自源码中符号常量BLCKSZ,通常为8KB。若是BLCKSZ不为8KB，则本参数按照等比例变化。本参数值可以在单个session中更改，但只能在session中首次使用临时表之前更改；随后更改该值的尝试不会对该session产生任何影响。session将根据需要分配临时缓冲区，直到到达本参数值指定的限制。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 max_prepared_transactions 数据类型 integer 默认值 100 取值范围 0到262143，0代表禁用prepared-transaction特性。 参数单位 设置同时可以处于prepared状态的最大的事务个数。若是你不打算使用prepared transaction，请将本参数值设置为零以防止偶然建立prepared transaction。如果你使用prepared transaction，你可能希望本参数值与max_connections一样大。这样每个session都可以有一个prepared transaction pending。当有流复制环境是，你必须确保在备库上该参数值要大于等于在主库上的该参数值，否则，不能在备库上运行查询语句。 参数含义 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 work_mem 数据类型 integer 默认值 4096 取值范围 64到2147483647 参数单位 KB 参数含义 在写入磁盘临时文件之前，被查询操作（比如sort或者hash table）使用的内存最大值。若是指定本参数值时不带单位，那么是以KB为单位。请注意，针对复杂的查询，sort或者hash操作可能以parallel去运行，在开始向磁盘的临时文件写入数据之前，每个操作被允许最多使用本参数值对应的内存大小。同事，正在运行的多个session会并发执行这些操作。因此，使用的所有内存可以是本参数值的若干倍，当设置本参数值时，需要明确记住这个事实。Sort操作被如下使用：order by、distinct、merge joins；hash table被如下使用：hash joins、hash-base aggregation、在in子句中的基于hash的处理。 基于哈希的操作通常比基于排序的等效操作对内存可用性更加敏感。哈希表可用的内存是通过work_mem*hash_mem_multiplier来计算的。这使得基于哈希的操作使用的内存量可能超过通常的work_mem数量。 是否可session级修改 是 修改后何时生效 Session级修改立即生效其他修改Reload即可生效 参数名称 hash_mem_multiplier 数据类型 real 默认值 1 取值范围 1到1000 参数单位 参数含义 本参数值用于计算hash-base操作可以使用的最大内存。该最大内存是由work_mem*hash_mem_multiplier决定的。在经常有大量查询操作的环境中，考虑增加本参数值，特别是当简单的增加work_mem会导致内存压力时（内存压力通常以间歇性内存不足的形式出现）。设置我1.5或者2.0可能对混合负载有效。在work_mem参数值已经增加到40MB或者更多时，2.0-8.0或者更大范文内的更高设置可能会有效。 是否可session级修改 是 修改后何时生效 Session级修改立即生效其他修改Reload即可生效 参数名称 maintenance_work_mem 数据类型 integer 默认值 65536 取值范围 1024到2147483647 参数单位 KB 参数含义 指定用于维护操作(maintenance operations)的最大可用的内存。维护操作包括：vacuum,create index, ALTER TABLE ADD FOREIGN KEY.若指定本参数值时不带单位，那默认的单位是KB。较大的本参数值有助于提升vacuuming以及restore database时的性能。请注意：当autovacuum运行时，最多有本参数值\\* autovacuum_max_workers的内存被使用，因此，请小心设置本参数值，不要设置太高。可以使用配置参数autovacuum_work_mem来单独控制autovacuum时的内存使用。 是否可session级修改 是 修改后何时生效 Session级修改立即生效其他修改Reload即可生效 参数名称 autovacuum_work_mem 数据类型 integer 默认值 -1，-1表示使用maintenance_work_mem配置参数值 取值范围 -1到2147483647 参数单位 KB 参数含义 本参数用于设置每个autovacuum worker process使用的最大内存大小。若指定本参数值时不带单位，那默认的单位是KB。当vacuum的行为运行在其他上下文环境中时，本参数不生效。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 logical_decoding_work_mem 数据类型 integer 默认值 65536，即64MB 取值范围 64到2147483647 参数单位 KB 参数含义 本参数用于设置在解码后的变化写入到本地disk之前，逻辑解码使用的最大内存大小。本参数限制了逻辑流复制连接使用的内存大小。由于每个复制连接只使用此大小的单个buffer，并且通常不会有许多这样的连接(受到max_wal_senders配置参数限制)。因此可以将本参数值设置为比work_mem高，从而减少写入磁盘的解码变化量。 是否可session级修改 是 修改后何时生效 session级修改立即生效其他修改Reload即可生效 参数名称 max_stack_depth 数据类型 integer 默认值 2048 取值范围 100到2147483647 参数单位 KB 参数含义 本参数值指定PostgreSQL execution stack的最大安全深度。本参数值的理想设置是kernel执行的实际stack size limit（可以用操作系统命令ulimit -s或者ulimit -a进行查看）减去大约1MB的安全裕度。所以需要安全裕度，是因为并不是在服务器中的每个例程中都检查stack depth，而是只在可能递归的关键例程中检查stack depth。若指定本参数值时不带单位，那默认的单位是KB。本参数默认值为2MB，该值相对保守，不太可能有crash的风险。但是，它可能太小，无法执行复杂的函数。只有superuser才能更改此设置。本参数值设置为高于kernel的限制将意味着一个失控的递归函数可以crash掉一个单独的backend process。在PostgreSQL可以确定内核限制的平台上，服务器将不允许把本参数值设置为不安全的值。但是并非所有平台都提供该信息。因此建议谨慎选择本参数值。 是否可session级修改 是，仅限于superuser可进行session级修改。 修改后何时生效 session级修改立即生效其他修改Reload即可生效 参数名称 shared_memory_type 数据类型 enum 默认值 mmap 取值范围 {sysv,mmap,windows}, sysv,mmap适用于Linux平台；windows适用于windows平台。 参数单位 参数含义 指定shared memory的实现方式。 mmap是指使用mmap分配的anonymous shared memory； sysv是指通过shmget分配的 System V shared memory ；windows是指Windows shared memory。通常不鼓励使用sysv值，因为它通常需要非默认的内核设置来允许较大的分配，而sysv值在任何平台上都不是默认的。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 dynamic_shared_memory_type 数据类型 enum 默认值 posix 取值范围 {posix,sysv,mmap,windows} 其中posix,sysv,mmap适用于Linux平台；windows适用于Windows平台 参数单位 参数含义 指定PostgreSQL使用的dynamic shared memory的实现方式。 posix是指使用shm_open分配的POSIX shared memory; sysv是指使用shmget分配的System V shared memory； windows是指Windows shared memory； mmap是指使用存在的data directory中的memory-mapped files模拟的shared memory。通常不鼓励使用mmap值，这在任何平台上都不是默认值，因为操作系统可能会反复将修改过的页面写回磁盘，从而增加系统I/O负载；但是，当pg_dynshmem子目录存储在RAM磁盘上，或者其他共享内存工具不可用时，它可能对调试有用。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 log_destination 数据类型 string 默认值 stderr 取值范围 Linux平台下的取值有：stderr、csvlog、syslogWindows平台下的取值有：stderr、csvlog、eventlog 参数单位 参数含义 PostgreSQL支持多种记录PostgreSQL消息的方法。包括stderr、csvlog、syslog，在windows平台下还有eventlog。当本参数值是一个列表时，请使用逗号分隔每个值。本参数仅能在postgresql.conf中设置或者在server command line中设置。当本参数值中含有csvlog时，日志条目被输出为csv格式（comma separated value），这便于程序加载log内容。需要将配置参数logging_collector设置为on才可以生成csv格式的log。当本参数值含有stderr或者csvlog时，current_logfiles文件被建立用来记录当前正在被logging collector进程使用的logfile的位置。这提供了一个查找当前正在使用的log的简便方法。 current_logfiles文件的内容如下（举例）: stderr log/postgresql.log csvlog log/postgresql.csv当新的logfile被建立或者log_destination被加载时，current_logfiles文件会被重建。当本参数值不含有stderr或者csvlog，以及logging collector被禁用时，current_logfiles文件会被删除掉。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 logging_collector 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 本参数会启用logging collector进程，该进程是一个background process，用来捕获发送到stderr的log messages并重定向这些信息到log file中。这个方法比记录到syslog中要有用。因为有些类型的messages不会显示在syslog的输出中（一个例子是dynamic-linker failure messages，另外一个是脚本产生的error messages，比如archive_command）.本参数仅仅可以在PostgreSQL启动时设置。logging collector设计为永不丢失消息。这意味着在负载极高的情况下，当logging collector落后时，服务器进程可能会在尝试发送额外日志消息时被阻止。相反，syslog更喜欢在无法写入消息时丢弃消息，这意味着在这种情况下它可能无法记录某些消息，但不会阻塞系统的其余部分。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 log_directory 数据类型 string 默认值 log 取值范围 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数用于确定log file被建立在哪个目录下。本参数值可以是绝对路径，也可以是相对于data directory的相对路径。本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_filename 数据类型 string 默认值 postgresql-%Y-%m-%d_%H%M%S.log 取值范围 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数用于确定所创建的log file的文件名。本参数值被视为strftime pattern，因此可以使用%-escapes指定随时间变化的文件名(请注意，如果存在任何依赖于时区的%-转义，则计算将在配置参数log_timezone指定的时区中完成。）支持的%-转义与开放组的strftime规范中列出的那些类似。请注意，系统的strftime不是直接使用的，因此特定于平台（非标准）的扩展不起作用。如果本参数值中不带转义符，你应使用log rotation 工具以避免日志填充满整个磁盘。若是在log_destination配置参数中启用了csv格式的输出，那么会将.CSV附加到带时间戳的日志文件名. 以创建CSV格式输出的文件名(如果日志文件名以.log结尾，则替换后缀。）本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_file_mode 数据类型 string 默认值 0600， 0600表示只有PostgreSQL的owner可以读取或者写入log files 取值范围 0到511 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，在Unix/Linux系统中本参数用于设置logfile的权限（在Microsoft Windows平台上，本参数被忽略）。 参数值应该是以chmod和umask系统调用接受的格式指定的数字模式。 0600表示只有PostgreSQL的owner可以读取或者写入log files.其他通常有用的设置是0640，0640允许owner的group的成员去读取log files。请注意，要使用这样的设置，您需要更改log_directory目录以将文件存储在data directory目录之外的某个位置。无论如何，让日志文件具有广泛的可读性是不明智的，因为它们可能包含敏感数据本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_rotation_age 数据类型 integer 默认值 1440 取值范围 0到35791394，0表示禁用掉基于time的new log file创建。 参数单位 分钟 参数含义 在logging_collector配置参数设置为on的情况下，本参数确定了一个单独的log file能使用的最长时间。在将此数量的数据发送到日志文件后，将创建一个新的日志文件。若指定本参数值时不带单位，那默认的单位是分钟。默认值是24小时。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_rotation_size 数据类型 integer 默认值 10240 取值范围 0到2097151，0表示禁用掉基于size的new log file创建 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数确定了一个单独的log file能使用的最大大小。将此数量的数据发送到日志文件后，将创建一个新的日志文件。若指定本参数值时不带单位，那默认的单位是KB。默认值是10MB。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_truncate_on_rotation 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数将导致PostgreSQL截断（覆盖）而不是附加到任何同名的现有日志文件。请注意，只有在由于基于time(时间)的rotate而打开新文件时才会发生截断，而不是在服务器启动或基于size的rotate期间。当本参数值设置off时，在所有情况下都会附加到已有文件中。将本参数值与日志文件名（如postgresql-%H.log）结合使用会生成24个每小时一次的日志文件，然后周期性地覆盖它们。本参数仅能在postgresql.conf中设置或者在server command line中设置。举例：为了达到如下目的：保留7天的log，每天的log文件的文件名是server_log.Mon,server_log.Tue等等，然后用本周的log自动覆盖上周的log。该需求需要设置log_filename为server_log.%a, log_truncate_on_rotation为on，log_rotation_age为1440举例：为了达到如下目的：保留24小时的log，每小时一个log file，如果log file大小超过1GB时也需要很快进行rotate。该需求需要设置log_filename为server_log.%H%M, log_truncate_on_rotation为on，log_rotation_age为60，log_rotation_size为1000000.在log_filename中包括%M允许任何基于size的且可能在同一个小时内有多个log file的rotate 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_facility 数据类型 enum 默认值 local0 取值范围 {local0,local1,local2,local3,local4,local5,local6,local7} 参数单位 参数含义 当配置参数log_destination被设置为syslog时，本参数用于确定使用的syslog facility。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_ident 数据类型 string 默认值 postgres 取值范围 参数单位 参数含义 本参数设置了在操作系统syslog日志中用于标识PostgreSQL消息的关键字。本参数仅仅适用于Linux平台，因为只有Linux平台才有syslog.虽然在Windows平台下也能修改本参数值，但是该修改没有任何意义。本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_sequence_numbers 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当配置参数log_destination被设置为syslog并且本参数值设置为on时，PostgreSQL会在每条消息的前面加一个依次增大的顺序号，同一个命令的多条输出会以[2-1],[2-2]之类的次序标出：（如下截取自linux的/var/log/messages）image::PostgreSQL13%E6%96%87%E6%A1%A3%E4%B9%8BPG%E5%8F%82%E6%95%B0%E5%8F%82%E8%80%83%E6%89%8B%E5%86%8C_html_b376f05a85e6462b.png[\"\",357,140] 本参数设置为off的效果如下：（如下截取自linux的/var/log/messages）image::PostgreSQL13%E6%96%87%E6%A1%A3%E4%B9%8BPG%E5%8F%82%E6%95%B0%E5%8F%82%E8%80%83%E6%89%8B%E5%86%8C_html_340e68e94edcda77.png[\"\",361,131]本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_split_messages 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当配置参数log_destination被设置为syslog并且本参数值设置为on时，消息被按行分割，长的行会被分割，以便填满1024Bytes，这是传统syslog实现上的典型大小限制。当设置为off时，PostgreSQL日志消息将按照原样传递给syslog服务，并由syslog服务来处理可能非常庞大的消息。如果syslog最终记录到一个文本文件中，那么无论哪种方式，效果都是一样的，最好保持该设置处于on状态，因为大多数syslog实现要么不能处理大型消息，要么需要专门配置来处理它们。但是，如果syslog最终要写入其他介质，那么将消息逻辑地保持在一起可能是必要的或更有用的。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 event_source 数据类型 bool 默认值 PostgreSQL 取值范围 参数单位 参数含义 本参数仅仅适用于Windows平台，因为只有Windows平台才有event log，本参数值会体现在Windows操作系统的事件查看器的\"Windows日志\"-\"应用程序\"-\"来源\"一列。虽然在Linux平台下也能修改本参数值，但是该修改没有任何意义。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 block_size 数据类型 Integer 默认值 8192 取值范围 8192 参数单位 参数含义 数据库使用的disk block的大小，该参数也影响其他的配置参数，比如shared_buffers，该参数通过编译时./configure命令的—​with-blocksize选项确定 是否可session级修改 否，不可修改。 修改后何时生效 不可修改。 参数名称 data_checksums 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 显示data checksums是否启用。本参数值通过initdb时的-k选项或者—​data-checksums选项确定。本参数值可以使用pg_checksums进行修改，pg_checksums命令具体用法请见pg_checksums --help 是否可session级修改 否 修改后何时生效 立即生效 参数名称 data_directory_mode 数据类型 integer 默认值 0700 取值范围 0到511 参数单位 参数含义 在linux/unix中显示data_directory参数值对应的目录的权限；在windows系统中，本参数值总是显示为0700 是否可session级修改 否 修改后何时生效 不可修改 参数名称 debug_assertions 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 显示PG在编译时是否启用assertion。本参数值通过编译时./configure命令的—​enable-cassert选项确定。 是否可session级修改 否 修改后何时生效 不可修改 参数名称 integer_datetimes 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 支持64bit整型的date和time。本参数通过编译时./configure命令确定。注意：configure的—​disable-integer-datetimes选项，是\"obsolete option, no longer supported\" 是否可session级修改 否 修改后何时生效 不可修改 参数名称 lc_collate 数据类型 string 默认值 en_US.UTF-8(根据操作系统环境变量不同而不同，见os命令locale的返回结果) 取值范围 参数单位 参数含义 字符串的排序规则(String sort order)。本参数值通过initdb时的—​lc-collate确定，若是不指定该选项，initdb会取环境变量 是否可session级修改 否 修改后何时生效 在PG Database级别确定，确定后不可修改，只读参数 参数名称 lc_ctype 数据类型 string 默认值 en_US.UTF-8(根据操作系统环境变量不同而不同，见os命令locale的返回结果) 取值范围 参数单位 参数含义 字符分类(是否区分大小写，什么是字符等)。本参数通过initdb时的—​lc-ctype确定，若是不指定该选项，initdb会取环境变量 是否可session级修改 否 修改后何时生效 在PG Database级别确定，确定后不可修改，只读参数。 参数名称 max_function_args 数据类型 integer 默认值 100 取值范围 参数单位 参数含义 返回函数参数的最大个数， 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 max_identifier_length 数据类型 integer 默认值 63 取值范围 参数单位 参数含义 返回标识符的最大长度 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 max_index_keys 数据类型 integer 默认值 32 取值范围 参数单位 参数含义 一个index最多能含有多少列 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 segment_size 数据类型 integer 默认值 131072 取值范围 参数单位 8KB 参数含义 本参数值确定了每个disk file中的page的数量。本参数值通过编译时./configure命令执行的—​with-segsize=SEGSIZE选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 server_encoding 数据类型 string 默认值 UTF8 取值范围 参数单位 参数含义 数据库的编码 (即:字符集),本参数通过initdb时的-E或者—​encoding参数确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 server_version 数据类型 string 默认值 默认值是当前PostgreSQL版本，格式为主版本号.次版本号 取值范围 参数单位 参数含义 返回PostgreSQL的版本号 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 server_version_num 数据类型 integer 默认值 130000，返回主版本号 取值范围 参数单位 参数含义 返回PostgreSQL的版本号 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 ssl_library 数据类型 string 默认值 OpenSSL 取值范围 参数单位 参数含义 返回PostgreSQL软件编译时的SSL library。本参数值通过./configure的—​with-openssl选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 wal_block_size 数据类型 integer 默认值 8192 取值范围 参数单位 参数含义 返回WAL中disk block的大小。本参数值通过./configure的—​with-wal-blocksize选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 wal_segment_size 数据类型 string 默认值 16777216 取值范围 参数单位 Byte 参数含义 返回WAL Segments的大小。本参数值通过initdb的—​wal-segsize选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 DateStyle 数据类型 string 默认值 ISO, MDY 取值范围 参数单位 参数含义 本参数用于设置date和time的显示格式，以及解释不明确日期输入值的规则。由于历史原因，本参数值包括两个部分：第一部分：output format specification (ISO, Postgres, SQL, or German)；第二部分：the input/output specification for year/month/day ordering (DMY, MDY, or YMD)。上述两者可以单独设置，也可以一起设置。请注意，关键词Euro和European是DMY的同义词；关键词US和NonEnro是MDY的同义词；Initdb生成配置参数文件时，会根据os的locale命令的lc_time结果来设置本参数值。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 IntervalStyle 数据类型 enum 默认值 postgres 取值范围 postgres,postgres_verbose,sql_standard,iso_8601 参数单位 参数含义 本参数用于设置interval values的显示格式。参数值sql_standard表示产生匹配SQL标准interval常量的输出；参数值postgres表示当DateStyle参数值设置为ISO时，产生匹配PostgreSQL8.4版本之前的输出；参数值postgres_verbose表示当DateStyle参数值设置为非ISO时，表示当DateStyle参数值设置为ISO时，参数值iso_8601表示产生匹配在ISO 8601标准的4.4.3.2章节中定义的时间间隔带\"format with designators\"匹配的输出。本参数值也会影响模糊的interval输入的解释。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 TimeZone 数据类型 String 默认值 内置的默认值是GMT，一般情况下，本参数值依据initdb时的操作系统时区而定，即：initdb时的操作系统时区是什么，initdb后postgresql.conf中的本参数值就是什么。注意：CentoOS7/8的操作系统当前时区请使用操作系统命令timedatectl status查询。 取值范围 参数单位 参数含义 为显示以及翻译timestamp设置时区。可用的时区名称可以通过pg_timezone_names视图进行查询。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 timezone_abbreviations 数据类型 string 默认值 Default 取值范围 参数单位 参数含义 被PostgreSQL接受的datetime输入的时区简写。默认值Default在世界上大部分地区都有效。另外的有效值是Australia和India。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 extra_float_digits 数据类型 string 默认值 1 取值范围 -15到3 参数单位 参数含义 设置浮点值(float4,float8以及地理数据类型)的显示的位数。extra_float_digits大于０时，原有精度最多增加3位， 小于0时， 精度最多减掉相应数值， 等于０时， float4精度为6位， float8精度为15 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 client_encoding 数据类型 string 默认值 取自Database encoding 取值范围 参数单位 参数含义 本参数用于设置客户端字符集。 是否可session级修改 是 修改后何时生效 Reload即可生效。 参数名称 lc_messages 数据类型 string 默认值 与系统locale设置有关 取值范围 参数单位 参数含义 本参数用于设置消息显示的语言。可接受的参数值是跟系统相关的。本参数值会影响发送到服务器运行日志以及客户端的消息，不正确的本参数值会影响服务器日志的可读性。 是否可session级修改 是 修改后何时生效 Reload即可生效，且只有超级用户才能更改此设置。 参数名称 lc_monetary 数据类型 string 默认值 与系统locale设置有关 取值范围 参数单位 参数含义 设置用于格式化货币金额的语言环境，可接受的参数值是跟系统相关的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 lc_numeric 数据类型 string 默认值 与系统locale设置有关 取值范围 参数单位 参数含义 本参数值用于设置格式化数字。可接受的参数值是跟系统相关的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 lc_time 数据类型 string 默认值 取值范围 参数单位 参数含义 本参数值用于设置格式化的日期和时间。可接受的参数值是跟系统相关的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_text_search_config 数据类型 string 默认值 依据initdb时的lc_ctype对应的设置而定 取值范围 参数单位 参数含义 本参数值用于设置当全文检索函数中不明确指定配置项时所用的全文检索配置项。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 wal_level 数据类型 enum 默认值 replica 取值范围 {minimal,replica,logical} 参数单位 参数含义 本参数定义了有多少信息被写入到WAL日志中。参数值replica表示WAL日志中写入了足够的信息以支持WAL Archiving和replicatin（包括在standby server上的read-only查询），参数值minimal表示WAL日志中移除了除了需要从crash或者immeidate shutdown进行recovery之外的信息。参数值logical添加了必要信息以支持逻辑解码(logical decoding)，在本参数使用logical参数值的情况下，会使得WAL日志的数量变大，特别是很多表被配置为replica identity full并且很多delete和update被执行的情况下。本参数仅仅可以在PostgreSQL Start时修改。每个更高级别的参数值会包括更低级别参数值中记录的所有内容。但是最少的WAL不能包含足够的信息来从基本备份和WAL日志中重建数据，因此必须使用replica参数值来提供更多的信息以启用WAL归档（archive_mode）和流复制.在9.6之前的版本中，此参数值还可以是archive和hot_standby。在PG13版本中，这些仍被接受，但已映射到replica参数值上。当本参数设置为minimal时，如下操作会更快，因为如下操作在创建或重写它们的事务的其余部分中，没有记录永久关系的信息： ALTER …​ SET TABLESPACE CLUSTER CREATE TABLE REFRESH MATERIALIZED VIEW（无CONCURRENTLY） REINDEX TRUNCATE 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 fsync 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当本参数值为on时，通过发出fsync系统调用或者各种等价的方法(参见wal_sync_method配置参数)，PostgreSQL会尝试确保更新被物理的写入disk。这就保证了PostgreSQL Cluster可以在操作系统或者硬件crash之后能recover(恢复)到一个一致的状态。当本参数值为off时，会带来性能优势，在电源故障或者系统crash时，这会导致不可恢复的数据损坏。仅仅当数据库的全部数据来源自外部数据的情况下，才建议将本参数值设置为off关闭fsync的场景包括：从备份文件中加载数据到PG中；使用PG进行数据的批处理，然后数据库会被废弃并重建；一个经常被重建的只读数据库克隆，并且不会用于failover.高质量的硬件不足以让本参数值设置为off基于可靠恢复的原因将本参数值从off改为on时，需要强制将kernel中修改过的buffer刷新到持久存储中，可以通过如下方法达到本目的： PostgreSQL Cluster关闭；当fsync参数值为on时运行initdb –sync-only,运行sync操作系统命令，unmount掉文件系统或者重启服务器操作系统。在很多场景中，针对非关键事务将synchronous_commit参数值设置为off可以提供比将fsync参数值设置为off更大的潜在优势，不会造成数据损坏等风险。本参数值只能在postgresql.conf文件或server command line中设置。如果关闭此参数，请考虑关闭full_page_writes。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 synchronous_commit 数据类型 enum 默认值 on 取值范围 {local,remote_write,remote_apply,on,off} 参数单位 参数含义 本参数值指定了在PostgreSQL数据库返回一个成功的提示给客户端之前，必须处理多少WAL数据。如果synchronous_standby_names是空，那么本参数有意义的参数值为on和off；remote_apply、remote_write、和local提供了与on参数值一样的本地同步级别。所有非off模式的本地行为是等待本地WAL刷新到Disk中。在off模式下，没有等待，因此存在一个延迟，这个延迟是给client报告成功与以后保证事务安全以防server crash之间的延迟（最大的延迟是三倍的wal_writer_delay参数值）。与fsync参数值不同，将此参数设置为off不会造成数据库不一致的任何风险。操作系统或者数据库的crash可能会导致一些最近据称已提交的事务丢失，但数据库状态将与这些事务完全中止时的状态相同，因此，当性能比事务持久性的精确确定性更重要时，将本参数设置为off是一个有用的选择。如果synchronous_standby_names是非空，本参数值用来控制事务提交是否等待在standby server上的WAL Record被处理。 当本参数值为remote_apply时,commit会等待直到从当前的同步standby指示它们已经收到事务的commit record并应用( applied),因此，它可以在standby 端被query可见，并且在standby端被写入持久性的存储中。这会导致比之前设置的更大的提交延迟，因为primary端会等standby端的WAL replay。 当本参数值为on时，commit会等待直到当前同步standby指示已经收到事务的commit record并且已经flush到持久性存储中。这确保了事务不会丢失,除非primary端和所有同步standby端的存储都发生损坏。 当本参数值为remote_write时,commit会等待直到当前同步standby指示已经收到事务的commit record并且已经写入到它们的文件系统中。本参数确保了在一个PostgreSQL 实例crash之后的数据持久，但是不针对standby端的操作系统级别的crash，因为，数据在standby端可能没有flush到持久性存储中。当参数值为local时，会导致commits一直在等待直到local flush到disk，而不是复制。当同步复制使用时，通常不需要这么做，但是为了完整而提供了这种方法。 此参数可随时更改；任何一个事务的行为都由提交时生效的设置决定。因此，让一些事务同步提交，而其他事务异步提交是可能的，也是有用的。例如，要在默认值相反时使单个多语句事务异步提交，请在事务中发出SET LOCAL synchronous_commit TO OFF 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 wal_sync_method 数据类型 enum 默认值 Linux和FreeBSD下默认值是fdatasyncWindows下默认值是open_datasync 取值范围 {fsync,fdatasync,open_sync,open_datasync} 参数单位 参数含义 本参数值指定了强制将WAL写入Disk时使用的方法。当fsync参数值为off时，本参数值无关紧要。 open_datasync：使用open()的O_DSYNC选项写WAL Files fdatasync:每次commit时调用fdatasync() fsync:每次commit时调用fsync() fsync_writethrough:每次commit时调用fsync(),对disk write cache强制write-through open_sync：使用open()的O_SYNC选项写WAL Files open_*选项使用O_DIRECT(若是可用)，不是上述所有选项都在所有平台下可用。默认值是在该平台下支持的上述列表中的第一个方法，除了在Linux平台和FreeBSD平台下fdatasync是默认值。默认值不一定理想，可能有必要更次此设置或系统配置的其他方面，以创建crash的安全配置或实现最佳性能。本参数值只能在postgresql.conf文件或server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 full_page_writes 数据类型 bool 默认值 on 取值范围 参数单位 参数含义 当本参数值为on时，在一个checkpoint发生之后，当第一个修改发生时，将full page（即：该修改所在的disk page中的全部内容）写入WAL日志中。这是需要的，因为在操作系统crash期间正在进行的page write可能仅部分完成，从而导致磁盘上的页包含新旧数据的混合。通常存储在WAL中的行级更改数据不足以在crash之后的恢复中完全还原一个page。存储full page image可以保证页面可以正确还原，但代价是增加必须写入WAL的数据量(因为WAL replay总是从检查点开始，所以在检查点之后的每个页面的第一次更改期间这样做就足够了。因此，降低整页写入成本的一种方法是增加checkpoint interval参数。）关闭此参数可加快正常操作，但在系统发生故障后，可能会导致不可恢复的数据损坏或静默数据损坏。风险类似于关闭fsync，虽然较小，但应该仅基于为fsync参数建议的相同情况关闭本参数。关闭此参数不会影响将WAL存档用于时间点恢复（PITR）。此参数只能在postgresql.conf文件或server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_log_hints 数据类型 bool 默认值 off 取值范围 参数单位 参数含义 当本参数值设置为on时，在一个checkpoint发生之后，当第一个修改发生时，将full page（即：该修改所在的disk page中的全部内容）写入WAL日志中。即使对于所谓的提示位的非关键修改也是如此。如果data ckecksums被启用，hint bit updates总是被记录在wal中，此时本参数值被忽略。你可以使用本参数值来测试当数据库cluster启用checksum之后的会生成多少额外的wal-logging.本参数仅能在server start时设置。 是否可session级修改 否 修改后何时生效 PG instance重启生效 参数名称 wal_compression 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当本参数值为on时，在如下情况下PostgreSQL压缩一个全部的page image并写入到WAL中：full_page_writes参数值为on或者执行一次base backup。压缩的page image会在wal replay期间进行解压。启用本参数可以降低WAL日志的产生量而不会增加不可恢复的数据损坏的风险，但是在wal压缩以及解压过程中会有额外的cpu消耗。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，非session修改relaod生效 参数名称 wal_init_zero 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当本参数值为on时，会导致新的wal file以零填充，在某些文件系统中，这确保了在需要写入wal records之前的空间已被分配。但是，Copy-On-Write (COW)文件系统不会从本参数中获益，因此可以选择跳过不必要的工作。如果设置为off，则在创建文件时仅写入最后一个字节，以使其具有预期的大小。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，非session修改relaod生效 参数名称 wal_recycle 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数值为on时，会导致wal file被循环使用(通过renaming wal file的方式)，避免了创建新wal file的需要。在COW文件系统中，本参数可能让create new wal file更快。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，非session修改relaod生效 参数名称 wal_buffers 数据类型 integer 默认值 -1，即:shared_buffers配置参数值的1/32大小，不小于64KB也不大于一个wal segment大小。请注意这是默认值的取值范围，不是手工设置本参数值的取值范围。 取值范围 -1到262143 参数单位 8KB 参数含义 分配给尚未写入到disk中的wal data的共享内存大小；如果自动选择太大或太小，则可以手动设置本参数值，但是任何小于32 KB的正数参数值.将被视为32 KB.如果指定的该值不带单位，则单位是WAL块的个数，即源码中符号常量XLOG_BLCKSZ，通常为8KB. 本参数值只能在服务器启动时设置。当每次事务提交时，wal buffer中的内容都会被写入disk中，因此，极大的本参数值不可能带来显著的益处。 是否可session级修改 否 修改后何时生效 PG instance重启生效 参数名称 wal_writer_delay 数据类型 integer 默认值 200 取值范围 1到10000 参数单位 毫秒 参数含义 指定了wal writer进程写wal buffer到disk中的频率，以时间去衡量，当wal writer进程flush wal之后，wal writer进程会休眠本参数值一段时间，除非被一个异步提交事务很快唤醒。如果最后一次flush在小于wal_writer_delay参数值之前发生 以及 小于wal_writer_flush_after参数值对应的WAL生成量，那么WAL被写入操作系统而不是flush到disk中。请注意，在很多操作系统中，sleep delays的有效精度是10毫秒， 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_writer_flush_after 数据类型 Integer 默认值 128，即128*8KB=1MB 取值范围 0到2147483647 参数单位 8KB 参数含义 本参数值指定了wal writer进程flush wal的频率，以wal的大小来衡量。如果最后一次flush在小于wal_writer_delay参数值之前发生 以及 小于wal_writer_flush_after参数值对应的WAL生成量，那么WAL被写入操作系统而不是flush到disk中。本参数值为0代表wal data总是被立即被flush到disk中。如果指定的该值不带单位，则单位是WAL块的个数，即源码中符号常量XLOG_BLCKSZ，通常为8KB. 本参数值只能在postgresql.conf或者server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_skip_threshold 数据类型 Integer 默认值 2048 取值范围 0到2147483647 参数单位 KB 参数含义 当wal_level参数值为minimal并且在create或者rewriting一个永久关系之后进行事务提交，本参数控制怎么持久化新的数据。如果数据小于本参数值，PostgreSQL会将这些数据写入wal log，否则，PostgreSQL会针对受影响的文件使用一个fsync。取决于你的存储属性，如果这些commit正在拖慢并发事务，增大或者降低本参数值可能会有帮助. 本参数值不带单位时，默认以KB为单位。 是否可session级修改 是 修改后何时生效 Reload即可生效。 参数名称 commit_delay 数据类型 Integer 默认值 0，零表示没有延迟。 取值范围 0到100000 参数单位 毫秒 参数含义 设置本参数值会增加在wal flush启动之前的延迟。如果系统负载足够高以至于在给定的时间间隔内准备好提交其他事务，则这可以通过允许经由单个 WAL flush以提交大量事务来提高组提交吞吐量。但是，每次wal flush会增加延迟，最大延迟是本参数指指定的时间。如果没有其他事务会变成准备提交状态，延迟的时间是被浪费的，因此，如果当wal flush被初始化时，至少有commit_siblings个其他事务是active的情况下，延迟才被执行。同样，如果fsync被关闭，delay是不会被执行的。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，其他修改reload生效 参数名称 commit_siblings 数据类型 Integer 默认值 5 取值范围 0到1000 参数单位 参数含义 在执行配置参数commit_delay指定的延迟之前，需要的最小的并发打开的事务数。值越大，在延迟间隔期间至少有一个其他事务准备好提交的可能性就越大 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 allow_system_table_mods 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 允许修改系统表的结构以及对系统表进行某些其他有风险的操作。否则即使对于超级用户也是不允许的。不明智地使用此设置可能会导致无法挽回的数据丢失或严重损坏数据库系统。只有超级用户才能更改此设置。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，其他修改reload生效 参数名称 backtrace_functions 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数值是以逗号分隔的C函数名称。如果一个错误被抛出并且错误发生地方的内部C函数名称与本参数值list中的值相匹配，然后backtrace与error message会被写入server log。不是所有的平台都支持backtrace功能，backtrace的质量取决于编译选项。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，其他修改reload生效 参数名称 ignore_system_indexes 数据类型 boolean 默认值 off 取值范围 off和on 参数单位 参数含义 当读取system table时忽略system index（但是当修改表时，依然会更新索引）。本参数用于从损坏的索引中恢复的场景中。本参数当session启动后无法改变 是否可session级修改 否 修改后何时生效 Reload即可生效，但是仅仅针对reload之后的新session生效。 参数名称 post_auth_delay 数据类型 Integer 默认值 0，零表示禁用延迟。 取值范围 0到2147 参数单位 秒 参数含义 在PostgreSQL构建认证阶段之后，当一个新server process被fork出来时的延迟的时间。本参数值用来给开发人员一个用debugger attach到server process的机会。 是否可session级修改 否 修改后何时生效 Reload即可生效，但是仅仅针对reload之后的新session生效。 参数名称 pre_auth_delay 数据类型 Integer 默认值 0，零表示禁用延迟。 取值范围 0到60 参数单位 秒 参数含义 在PostgreSQL构建认证阶段之前，在一个新server process被fork出来之后的延迟的时间。本参数值用来给开发人员一个使用debugger attach到server process以跟踪在认证阶段异常行为的机会。本参数只能在postgresql.conf中或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 trace_notify 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 为LISTEN和NOTIFY命令生成大量的debugging输出。配置参数client_min_messages或者log_min_messages必须设置为DEBUG1或者更低才能发送该输出到client或者server log 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 trace_recovery_messages 数据类型 enum 默认值 log，log不会影响log决策 取值范围 {debug5,debug4,debug3,debug2,debug1,log,notice,warning,error} 参数单位 参数含义 启用记录恢复相关的调试输出，否则将不会被记录。此参数允许用户覆盖log_min_messages的正常设置，但仅限于特定消息。本参数的目的是调试hot standby。其他值导致该优先级或更高优先级的与恢复相关的调试消息被记录，就好像它们具有LOG优先级一样。针对配置参数log_min_messages的通用设置会导致无条件发送messages到server log中。本参数只能在postgresql.conf中或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 trace_sort 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当本参数值为on时，在sort操作期间，会发出有关资源使用的信息。本参数仅仅针对PostgreSQL编译时TRACE_SORT macro被定义时才有效（默认情况下，TRACE_SORT是被定义的） 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 trace_locks 数据类型 bool 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_lwlocks 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_userlocks 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_lock_oidmin 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_lock_table 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 debug_deadlocks 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 log_btree_build_stats 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 wal_consistency_checking 数据类型 String 默认值 空串，空串意味着禁用本特性。 取值范围 参数单位 参数含义 本参数的目的是检查WAL redo routine中的错误。当启用本参数时，与WAL记录一起修改的任何buffer的full page image都会添加到记录中。如果该记录随后被重放，系统将首先应用每个记录，然后测试由该记录修改的buffer是否与存储的image匹配。在某些情况下（如提示位），可以接受较小的变化，并将被忽略。任何意外的差异都将导致致命错误，从而终止恢复。本参数值可以设置为all，all表示检查所有记录，本参数值也可以设置为逗号分隔的resource manager列表，受到支持的resource manager 是heap、heap2、btree、hash、gin、gist、sequence、spgist、brin、generic 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效 参数名称 wal_debug 数据类型 bool 默认值 取值范围 参数单位 参数含义 当设置为on时，发出与wal相关的debugging out。本参数仅仅当PostgreSQL编译时定义了WAL_DEBUG macro的情况下才适用。 是否可session级修改 修改后何时生效 参数名称 ignore_checksum_failure 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 本参数仅仅在data checksums启用的情况下才生效。在读取过程中检查到checksum失败通常会导致PostgreSQL报告一个error，从而中止当前事务。本参数值设置为on会导致PostgreSQL忽略故障（但是依然会报告警告）并继续处理。此行为可能导致crash、传播或隐藏corruption或其他严重问题。但是，如果block header是正常的，可能允许您越过错误并检索可能仍然存在于表中的未损坏的tuple。如果block header已损坏，那么即使启用本选项，也会报告错误。 是否可session级修改 是，仅限于superuser在session级修改 修改后何时生效 Reload即可生效 参数名称 zero_damaged_pages 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 检测到一个损坏的page header通常会导致PostgreSQL报错一个error，并终止当前事务。设置本参数值为on会导致系统报告warning，将内存中损坏的页面清零，然后继续处理。这种行为会破坏数据，即：损坏页面上的所有行。但是，本参数确实允许您克服错误并从表中可能存在的任何未损坏的页面中检索行。如果由于硬件或软件错误而发生损坏，它对于恢复数据很有用。在您放弃从表的损坏页恢复数据的希望之前，您通常不应设置本参数。。清零页不会被强制写入磁盘，因此建议在再次关闭此参数之前重新创建表或索引。本参数值只能由超级用户更改。 是否可session级修改 是，仅限于superuser 修改后何时生效 Reload即可生效 参数名称 ignore_invalid_pages 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当本参数值是off时，在recovery期间检测wal record中存在invalid pages之后，PostgreSQL会发出一个PANIC-level的error，进而会终止recovery。当本参数值设置为on时会导致PostgreSQL忽略wal record中的invalid pages（但是依然会报warning）继续进行recovery。本行为可能会导致crash，data loss，传播或者隐藏corruption，或者其他严重问题。但是，本参数会允许你绕过的PANIC-level的error，进而完成recovery，进而启动PostgreSQL。本参数仅能在PostgreSQL启动时设置。本参数仅仅在recovery或者standby mode下生效。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 jit_debugging_support 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 注册JIT编译函数给debugger使用，本参数仅能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 jit_dump_bitcode 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 写出LLVM的bitcode以便利于JIT debugging， 是否可session级修改 是，仅限于superuser 修改后何时生效 Reload即可生效 参数名称 jit_expressions 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当 JIT 编译被启用时，本参数用于确定表达式是否被 JIT 编译 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 jit_profiling_support 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 如果 LLVM 具有所需的功能，则发出允许perf分析 JIT 生成的函数所需的数据。这会将文件写入$HOME/.debug/jit/下; 用户负责在需要时执行清理。默认设置是off。该参数只能在服务器启动时设置。本参数仅能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 jit_tuple_deforming 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当JIT编译被启用的状态下，本参数用于确定允许不允许tuple deforming进行jit 编译 是否可session级修改 是 修改后何时生效 Reload接口生效 参数名称 enable_bitmapscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对位图扫描计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_gathermerge 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对gather merge计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_hashagg 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对hashed aggregation计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_hashjoin 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对hash-join计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_incremental_sort 数据类型 bool 默认值 on 取值范围 on和off 数单位 参数含义 启用或禁用query planner对incremental sort steps的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_indexscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对index-scan的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_indexonlyscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对index-only-scan的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_material 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对materialization的使用，完全抑制物化是不可能的，但是关闭本参数可以防止query planner插入物化节点，除非在需要正确性的情况下。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_mergejoin 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对merge-join的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_nestloop 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对nested-loop的使用。完全抑制nested-loop连接是不可能的，但是如果有其他方法可用，关闭本参数会阻止query planner使用nested-loop。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_parallel_append 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对parallel-aware append的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_parallel_hash 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对parall hash join的使用，在hash-join被禁用的情况下，本参数值无效。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_partition_pruning 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner的partitioned table中消除partition。这也控制计划器生成查询计划的能力。允许查询执行器在查询执行期间删除（忽略）分区 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_partitionwise_join 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对partitionwise join的使用，这允许在partitioned table上的join在匹配的partition上执行join。Partitionwise 目前仅适用于连接条件包括所有分区键的情况，这些分区键必须是相同的数据类型并且具有一对一匹配的子分区集。由于分区连接规划在规划期间可以使用明显更多的 CPU 时间和内存，本参数默认值为off 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_partitionwise_aggregate 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对partitionwise grouping or aggregation的使用，这允许在partitioned table上的partitionwise grouping or aggregation会在每个partition上单独执行。如果GROUP BY子句不包含分区键，则只能在每个分区的基础上执行部分聚合，并且必须稍后执行最终确定。由于partitionwise grouping or aggregation在规划期间可能会使用更多的 CPU 时间和内存，本参数值为off 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_seqscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对sequential scan的使用。完全抑制顺序扫描是不可能的，但是如果有其他方法可用，关闭本参数会阻止query planner使用sequential scan。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_sort 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对explicit sort steps的使用。完全抑制显式排序是不可能的，但是如果有其他方法可用，关闭本参数会阻止query planner使用显式排序。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_tidscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对TID scan的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 application_name 数据类型 string 默认值 psql 取值范围 参数单位 参数含义 本参数值的长度小于NAMEDATALEN值（NAMEDATALEN是源码中的符号常量，默认是64个字符）。本参数被连接到服务器上的应用程序设置，本参数值会显示在pg_stat_activity视图中，也会显示在csv log中。本参数值也可以被包括在配置参数log_line_prefix的参数值中。只有可打印的ASCII字符才可以用于本参数值。其他字符会被问号(?)取代。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_print_parse 数据类型 bool 默认值 取值范围 参数单位 参数含义 在运行日志中记录query的parse tree。本消息在LOG消息级别的情况下被发出，因此，默认情况下，本消息会出现在运行日志中，但不会发给client。您可以通过调整client_min_messages配置参数、log_min_messages配置参数让消息发送给client。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_print_rewritten 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在运行日志中记录query的query rewriter output,。本消息在LOG消息级别的情况下被发出，因此，默认情况下，本消息会出现在运行日志中，但不会发给client。您可以通过调整client_min_messages配置参数、log_min_messages配置参数让消息发送给client。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_print_plan 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在运行日志中记录query的execution plan。本消息在LOG消息级别的情况下被发出，因此，默认情况下，本消息会出现在运行日志中，但不会发给client。您可以通过调整client_min_messages配置参数、log_min_messages配置参数让消息发送给client。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_pretty_print 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数设置为on时，会将debug_print_parse, debug_print_rewritten, or debug_print_plan配置参数产生的消息进行缩进，这会导致比设置为off时的compact格式更具有可读性但输出会更长。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 log_checkpoints 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 将checkpoints 和restartpoints记录到运行日志中，一些统计信息被包括到消息中，如写入buffer的数量以及写buffer的耗时等。本参数仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_connections 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当本参数值为on时，每一次尝试连接到PostgreSQL数据库以及客户端成功完成身份认证都会被记录到运行日志中。请注意：有些客户端程序(如psql)会连接两次以确定password是否需要，在该情况下，会有重复的\"connection received\"消息出现，该消息不一定表示有问题。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 log_disconnections 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当本参数值为on时， session终止的信息会被记录到运行日志中。日志输出类似log_connections配置参数的输出信息，以及session的持续时间。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 log_error_verbosity 数据类型 enum 默认值 default 取值范围 {terse,default,verbose} 参数单位 参数含义 设置日志消息的详细程度。控制在PG运行日志中为记录的每条消息写入的详细程度。Terse排除了DETAIL、HINT、QUERY、和CONTEXT 级别的信息。Verbose输出包括SQLSTATE错误代码以及源码文件名称、函数名称、生成错误的行号。仅有superuser能修改本参数值 是否可session级修改 是，仅限于superuser 修改后何时生效 Reload即可生效 参数名称 log_hostname 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 默认情况下，在运行日志中显示连接主机的 IP 地址。打开此参数也会导致记录主机名。请注意，根据您的主机名解析设置，这可能会造成不可忽视的性能损失。此参数只能在postgresql.conf文件中或在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_line_prefix 数据类型 string 默认值 %m [%p] 取值范围 参数单位 参数含义 控制运行日志中每行开头的输出内容。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 log_lock_waits 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当一个session的等待时间长于配置参数deadlock_timeout参数值以获得锁时是否生成日志消息。这在锁定等待是否导致性能不佳时非常有用。只有superuser才能更改本设置。 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效。 参数名称 log_parameter_max_length 数据类型 integer 默认值 -1 取值范围 -1到1073741823 参数单位 Byte 参数含义 如果本参数值大于零，则使用非错误语句日志消息记录的每个绑定参数值将被修剪为本参数值个字节。零禁用非错误语句日志的绑定参数的日志记录。-1（默认）允许完整记录绑定参数。如果此值未指定单位，则将其视为字节。只有超级用户才能更改此设置。 此设置仅影响由于log_statement、log_duration和相关设置而打印的日志消息。此设置的非零值会增加一些开销，特别是如果参数以二进制形式发送，则需要转换为文本。 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效 参数名称 log_parameter_max_length_on_error 数据类型 integer 默认值 0 取值范围 -1到1073741823 参数单位 Byte 参数含义 如果大于零，错误消息中报告的每个绑定参数值都会被修剪到本参数值个字节。零（默认值）禁用在错误消息中包含绑定参数。-1允许完整打印绑定参数。如果此值未指定单位，则将其视为字节此设置的非零值会增加开销，因为PostgreSQL需要在每个语句开始时将参数值的文本表示存储在内存中，无论最终是否发生错误。以二进制形式发送绑定参数时的开销比以文本形式发送时要大，因为前者需要数据转换，而后者只需要复制字符串 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 log_statement 数据类型 enum 默认值 all 取值范围 {none,ddl,mod,all} none是off的意思， ddl的含义是记录所有数据定义语句，如create、alter、drop; mod的含义是记录DDL语句之外，还会记录数据修改语句，如insert、update、delete、truncate以及copy from语句。PREPARE、EXECUTE和EXPLAIN ANALYZE语句所包含的命令的类型适当时，也会记录这些语句。all的含义是记录所有语句。 参数单位 参数含义 控制在PG的运行日志中写入哪些类型的SQL语句。对于使用扩展查询协议的客户端来说，记录到运行日志是从一个执行消息被收到开始的。绑定参数值也会被记录。请注意，即使是本参数值设置为all，运行日志中也不会显示那些包含简单语法错误的SQL语句。因为仅仅在基本的解析完成并确定SQL语句的type之后，log message才会被发出。在扩展查询协议的情况下，本参数值同样不会记录在执行阶段之前(即:在parser分析或planner期间)失败的语句。设置log_min_error_statement为error(或者更低)以记录此类语句 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效 参数名称 log_replication_commands 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 是否将每个复制命令记录到PG运行日志中 是否可session级修改 是，仅限于superuser修改 修改后何时生效 Reload即可生效 参数名称 log_temp_files 数据类型 integer 默认值 -1，代表禁用本log功能 取值范围 -1到2147483647 参数单位 KB 参数含义 控制是否记录本临时文件的文件名和大小。临时文件被创建用以排序、hash以及临时查询结果。当本参数值被启用时，当临时文件被删除时，一个日志条目会被发出。本参数值为0时会记录所有的临时文件信息。本参数值为正数时表示仅仅会记录那些临时文件大小大于等于本参数值的临时文件。本参数值不指定单位时，默认值以KB为单位。本参数仅仅superuser可以修改。 是否可session级修改 是，仅限于superuser进行修改。 修改后何时生效 Reload即可生效 参数名称 log_timezone 数据类型 String 默认值 内置默认值是GMT，但该内置默认值通常在postgresql.conf中被覆盖; initdb将在会设置本参数值以便与系统环境相对应。 取值范围 参数单位 参数含义 设置在log message中使用的time zone。与TimeZone配置参数不同，此值是PostgreSQL Cluster范围的，因此所有会话将一致地报告时间戳。此参数只能在postgresql.conf文件中或在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 client_min_messages 数据类型 enum 默认值 notice 取值范围 {debug5,debug4,debug3,debug2,debug1,log,notice,warning,error} 参数单位 参数含义 控制将哪些消息级别发送到客户端。有效值为DEBUG5，DEBUG4，DEBUG3，DEBUG2，DEBUG1，LOG，NOTICE，WARNING和ERROR。每个级别包括其后的所有级别。级别越靠后，发送的消息就越少。请注意，本参数值log的级别与log_min_messages配置参数值log的级别是不同的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 search_path 数据类型 string 默认值 \"$user\", public 取值范围 参数单位 参数含义 当一个object(table, data type, function等等)不带前面的schema name时，本参数用来指定哪个schema name会被搜索到。当在不同的schema name中有相同的object name时，在search_path中第一个匹配的object的那个schema会被使用。若是一个object没有被search_path中的任何schema匹配到，那么，这个object使用时需要带schema name(即:前面带上 \"schema.\").本参数值需要以逗号分隔，当本参数值包括不存在schema name 或者user没有对该schema name的usage权限时，此类参数值会被忽略。本参数值中出现的$user，其含义是current_user，若是存在与该user同名的schema name并且该user有对该schema name的usage权限（若不是这样，$user会被忽略）不论在本参数值中包括不包括pg_catalog，pg_catalog总会被搜索到，若是在本参数值中包括pg_catalog，那么会按照在本参数值中出现的顺序去搜，若是在本参数值中不包括pg_catalog,那么，PostgreSQL会在本参数值之前去搜索pg_catalog.类似的，当前的session临时表schema pg_temp_nnn 若是存在的话，会总被搜索到。可以在本参数值中使用pg_temp来明确指定要对pg_temp_nnn进行搜索。若是在本参数值中不明确列出，那么，PostgreSQL会首先搜索pg_temp_nnn（在pg_catalog之前）。临时schema name仅仅被用来搜索relation(表、视图、序列等等)和data type name，临时schema name不能用来搜索function name或者operator name当一个object被建立时其前面不指定任何schema name时，该object的schema name是在本参数值中搜索到的第一个有效的schema。当本参数值为空时，一个错误会被抛出。 本参数的默认值是\"$user\", public，该默认值支持对一个数据库的共享使用（即:没有user有私有的schemas，并且都对public共享使用）、每个user schema的私有使用以及混合使用。其他的影响可以通过alter语句修改本参数值来获得，这个修改可以是全局的也可以每个user的。可以通过函数current_schemas来检查当前使用的有效的schema name，这与search_path的参数值并不完全一致，因为函数current_schemas显示了在本参数值中出现的item是如何解析的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 row_security 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数控制是否抛出错误以代替应用行安全策略。on代表正常应用行安全策略，off代表查询失败除非应用了至少行安全策略。在限制了行可见性导致错误的结果的情况下，可以将本参数值改为off。在默认情况下，pg_dump会进行该修改(即:修改本参数值为off)。本参数对可以绕过行安全策略的roles是无效的，即:superuser和那些带有bypassrls属性的role。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_table_access_method 数据类型 string 默认值 heap 取值范围 参数单位 参数含义 本参数指定了create table或者create materialized views不带访问方法时table或者materialized views的访问方法，以及select …into语句使用时用到的访问方法。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_tablespace 数据类型 string 默认值 空字符串，空字符串代表当前database的默认tablespace 取值范围 参数单位 参数含义 在create object(table和index)语句不明确指定tablespace情况下，本参数值指定了该object对应的tablespace。如果指定的本参数值与现有的任何tablespace不匹配，那么PostgreSQL会自动使用当前database的默认tablespace。如果一个非默认表空间被指定，那么user必须带有对该非默认表空间的create的权限，否则创建会失败本参数不适用于临时表。临时表对应的配置参数是temp_tablespaces。本参数也不适用于create database语句。默认情况下，一个新的database从template数据库中继承了tablespace。若是本参数值是非空字符串，然后partitioned table被创建，那么，该partitioned table的表空间被设置为该非空字符串，该非空字符串也是将来partition被建立的默认表空间，即使本配置参数值之后被更改。 是否可session级修改 是 修改后何时生效 Reload即可生效。 参数名称 temp_tablespaces 数据类型 string 默认值 空字符串，空字符串代表所有的temporary objects都会被创建在当前数据库的默认表空间中。 取值范围 参数单位 参数含义 在create temporary object(temp tables 和temp tables 上的indexes)语句不明确指定tablespace情况下，本参数值指定了该object对应的temporary tablespace。诸如排序生成的临时文件也被建立到这个temp_tablespaces指定的临时表空间中。本参数值是一系列的tablespace name的清单，当有多个tablespace name的情况下，每次创建临时对象时，PostgreSQL 都会从列表中随机选择一个tablespace name。除了在事务中，连续创建的临时对象被放置在列表中的连续表空间中。如果列表中选定的是空字符串，PostgreSQL会自动使用当前数据库的默认表空间。当temp_tablespaces以交互方式设置时，指定不存在的表空间会报错，指定用户没有CREATE权限的表空间也会报错。但是，当使用先前设置的值时，将忽略不存在的表空间，以及用户缺乏CREATE权限的表空间。此规则适用于在postgresql.conf设置本参数值的情况下 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 check_function_bodies 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当设置为off时，它会在CREATE FUNCTION期间禁用函数体字符串的验证。禁用验证可避免验证过程的副作用，并避免由于前向引用等问题导致的误报。在代表其他用户加载函数之前，将此参数设置为off；pg_dump会自动执行此操作 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_transaction_isolation 数据类型 enum 默认值 read committed 取值范围 {serializable,\"repeatable read\",\"read committed\",\"read uncommitted\"} 参数单位 参数含义 本参数用来控制新事务的隔离级别 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_transaction_read_only 数据类型 bool 默认值 off，off的意思是read/write 取值范围 off和on 参数单位 参数含义 本参数用来控制每个新事务的默认只读状态。一个只读事务不能改变(alter)一个非临时表. 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_transaction_deferrable 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当运行在serializable隔离级别中时，一个deferrable的只读事务在它被允许继续处理之前可能会被延迟。然而，一旦它开始执行，它就不会产生任何确保可串行化所需的开销；所以序列化代码没有理由因为并发更新而强制中止该只读事务，这确保了对长时间运行的只读事务是很合适的。本参数控制每个新事务的默认的deferrable状态。当前，本参数对read-write事务是无效的，对隔离级别低于serializable的事务也是无效的 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 session_replication_role 数据类型 enum 默认值 origin 取值范围 {origin,replica,local} 参数单位 参数含义 控制针对当前session的与复制相关的triggers和rules的触发(firing),设置本参数需要superuser权限，并且会导致之前cache起来的查询计划被丢弃掉。本参数的用意是逻辑复制系统在应用复制的更改时将本参数值设置为replica，这样做的结果是，触发器和规则（没有从默认配置更改）不会在复制副本上触发。详细信息，请参阅ALTER TABLE子句ENABLE TRIGGER和ENABLE RULE PostgreSQL在内部将参数值origin和local视为相同的值，第三方复制软件可能把这两个参数用于他们的目的，比如，使用local来指示一个session的变化不会被复制的session。因为外键是通过trigger实现的，因此，设置本参数为replica也会disable掉所有的外键约束检查。 是否可session级修改 是，仅限于superuser可进行修改。 修改后何时生效 Reload即可生效 参数名称 statement_timeout 数据类型 integer 默认值 0，零代表禁用超时机制 取值范围 0到2147483647 参数单位 毫秒 参数含义 终止掉运行时间超过本参数值时间长度的SQL语句。如果log_min_error_statement配置参数被设置为error或者更低级别，超时的SQL语句也会被记录下来。超时时间是如下计算的：从SQL语句到PostgreSQL数据库服务器端开始算，直到该语句被服务器处理完成。如果单个简单查询消息(single simple-Query message)中出现多个SQL语句，则将分别对每条语句应用超时.( PostgreSQL13之前的版本通常将超时处理为应用于整个查询字符串)，在扩展查询协议中，当任何与查询相关的消息（Parse、Bind、Execute、descripe）到达PostgreSQL数据库时，超时就开始计时，并通过完成Execute或Sync消息而取消。在postgresql.conf中设置本参数值是不推荐的，因为这会影响所有session 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 lock_timeout 数据类型 integer 默认值 0，零代表禁用超时机制 取值范围 0到2147483647 参数单位 毫秒 参数含义 终止掉在尝试获取表、索引、行或其他数据库对象上的锁的等待时间超过本参数值的任何语句。时间限制分别适用于每次锁获取尝试，该限制既适用于显式锁定请求（如LOCK TABLE，或SELECT FOR UPDATE without NOWAIT），也适用于隐式获取的锁。如果指定的值没有单位，则以毫秒为单位。值为零（默认值）将禁用超时。与statement_timeout配置参数不同，本超时只能在锁等待时发生。请注意，如果statement_timeout非零，设置lock_timeout为相同或更大的值是毫无意义的，因为语句超时总是首先触发。如果log_min_error_statement设置为ERROR或更低，则将记录超时的语句。在postgresql.conf中设置本参数值是不推荐的，因为这会影响所有session 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 idle_in_transaction_session_timeout 数据类型 integer 默认值 0，零代表禁用超时机制 取值范围 0到2147483647 参数单位 毫秒 参数含义 终止掉那些处于open transaction状态的并且idle时间长度超过本参数值的任何会话。这允许释放该会话持有的任何锁，并重用连接槽；它还允许对仅对此事务可见的元组进行vacuum。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 vacuum_freeze_table_age 数据类型 integer 默认值 150000000 取值范围 0到2000000000 参数单位 参数含义 如果表的pg_class.relfrozenxid字段值已经达到本参数值，那么vacuum会执行一次侵略性的扫描（aggressive scan）。侵略性的扫描（aggressive scan）不同于常规vacuum(regular VACUUM), 侵略性的扫描（aggressive scan）会查看每个page以确定page中是否包括未冻结的xids或者mxids，而不是仅仅查看page中是否包括dead tuples。尽管用户可以将此值设置为 0 到 20 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_freeze_max_age 的95% ，以便有机会定期在为表启动反环绕自动清理(anti-wraparound autovacuum)之前手工运行VACUUM。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 vacuum_freeze_min_age 数据类型 integer 默认值 50000000 取值范围 0到1000000000 参数单位 参数含义 本参数用于指定VACUUM在扫描表时用于决定是否冻结行版本的截止年龄(cutoff age，以事物计算), 尽管用户可以将此值设置为 0 到 10 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_freeze_max_age参数值的一半，这样强制 autovacuum之间的时间不会过短。 是否可session级修改 修改后何时生效 参数名称 vacuum_multixact_freeze_table_age 数据类型 integer 默认值 150000000 取值范围 0到2000000000 参数单位 参数含义 如果表的pg_class. relminmxid字段值已经达到本参数值，那么vacuum会执行一次侵略性的扫描（aggressive scan）。侵略性的扫描（aggressive scan）不同于常规vacuum(regular VACUUM), 侵略性的扫描（aggressive scan）会查看每个page以确定page中是否包括未冻结的xids或者mxids，而不是仅仅查看page中是否包括dead tuples。尽管用户可以将此值设置为 0 到 20 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_multixact_freeze_max_age的95% ，以便有机会定期在为表启动反环绕自动清理(anti-wraparound autovacuum)之前手工运行VACUUM。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 vacuum_multixact_freeze_min_age 数据类型 integer 默认值 5000000 取值范围 0到1000000000 参数单位 参数含义 本参数用于指定VACUUM在扫描表时用于决定是否冻结行版本的multixacts截止年龄(cutoff age), 尽管用户可以将此值设置为 0 到 10 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_multixact_freeze_max_age参数值的一半，这样强制 autovacuum之间的时间不会过短。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 bytea_output 数据类型 enum 默认值 hex 取值范围 {escape,hex} 参数单位 参数含义 本配置参数定义了对数据类型bytea的输出格式。 bytea数据类型在输入时对两种格式(escape和hex)均接受。escape代表传统的PostgreSQL格式。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 xmlbinary 数据类型 enum 默认值 base64 取值范围 {base64,hex} 参数单位 参数含义 设置二进制值在 XML 中的编码方式。例如，这适用于通过函数xmlelement或xmlforest将bytea值转换为XML的情况。可能的值为base64和hex，它们都在 XML 模式标准中定义。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 xmloption 数据类型 enum 默认值 content 取值范围 {content,document} 参数单位 参数含义 本参数用于设置在XML和字符串值之间转换时DOCUMENT或CONTENT是隐式的。在PostgreSQL中，也可以使用下面的SQL标准来设置:SET XML OPTION { DOCUMENT | CONTENT }; 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 gin_pending_list_limit 数据类型 integer 默认值 4096 取值范围 64到2147483647 参数单位 KB 参数含义 设置GIN索引的挂起列表(pending list)的最大大小，该列表在启用fastupdate时使用。如果列表的大小超过此最大值，则通过将列表中的条目批量移动到索引的主数据结构来清除列表。如果指定此值时没有单位，则将其作为千字节。默认值为4MB。通过更改索引存储参数，可以为单个GIN索引重写此设置. 是否可session级修改 是 修改后何时生效 Reload即可生效","title":"","component":"ivorysql-doc","version":"master","name":"pg_parameters_reference","url":"/ivorysql-doc/master/pg_reference/pg_parameters_reference.html","titles":[{"text":"1. 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Replication / Master Server","hash":"replication-master-server","id":13},{"text":"1.6.1. synchronous_standby_names","hash":"synchronous_standby_names","id":14},{"text":"1.6.2. vacuum_defer_cleanup_age","hash":"vacuum_defer_cleanup_age","id":15},{"text":"1.7. Replication / Subscribers","hash":"replication-subscribers","id":16},{"text":"1.7.1. max_logical_replication_workers","hash":"max_logical_replication_workers","id":17},{"text":"1.7.2. max_sync_workers_per_subscription","hash":"max_sync_workers_per_subscription","id":18},{"text":"1.8. Write-Ahead Log / Archive Recovery","hash":"write-ahead-log-archive-recovery","id":19},{"text":"1.8.1. restore_command","hash":"restore_command","id":20},{"text":"1.8.2. archive_cleanup_command","hash":"archive_cleanup_command","id":21},{"text":"1.8.3. recovery_end_command","hash":"recovery_end_command","id":22},{"text":"1.9. Error Handling","hash":"error-handling","id":23},{"text":"1.9.1. exit_on_error","hash":"exit_on_error","id":24},{"text":"1.9.2. restart_after_crash","hash":"restart_after_crash","id":25},{"text":"1.9.3. data_sync_retry","hash":"data_sync_retry","id":26},{"text":"1.10. Write-Ahead Log / Archiving","hash":"write-ahead-log-archiving","id":27},{"text":"1.10.1. archive_mode","hash":"archive_mode","id":28},{"text":"1.10.2. archive_command","hash":"archive_command","id":29},{"text":"1.10.3. archive_timeout","hash":"archive_timeout","id":30},{"text":"1.11. Resource Usage / Background Writer","hash":"resource-usage-background-writer","id":31},{"text":"1.11.1. bgwriter_delay","hash":"bgwriter_delay","id":32},{"text":"1.11.2. bgwriter_lru_maxpages","hash":"bgwriter_lru_maxpages","id":33},{"text":"1.11.3. bgwriter_lru_multiplier","hash":"bgwriter_lru_multiplier","id":34},{"text":"1.11.4. bgwriter_flush_after","hash":"bgwriter_flush_after","id":35},{"text":"1.12. Statistics / Monitoring","hash":"statistics-monitoring","id":36},{"text":"1.12.1. log_statement_stats","hash":"log_statement_stats","id":37},{"text":"1.12.2. log_parser_stats","hash":"log_parser_stats","id":38},{"text":"1.12.3. log_planner_stats","hash":"log_planner_stats","id":39},{"text":"1.12.4. log_executor_stats","hash":"log_executor_stats","id":40},{"text":"1.13. Client Connection Defaults / Shared Library Preloading","hash":"client-connection-defaults-shared-library-preloading","id":41},{"text":"1.13.1. jit_provider","hash":"jit_provider","id":42},{"text":"1.13.2. local_preload_libraries","hash":"local_preload_libraries","id":43},{"text":"1.13.3. session_preload_libraries","hash":"session_preload_libraries","id":44},{"text":"1.13.4. shared_preload_libraries","hash":"shared_preload_libraries","id":45},{"text":"1.14. Lock Management","hash":"lock-management","id":46},{"text":"1.14.1. deadlock_timeout","hash":"deadlock_timeout","id":47},{"text":"1.14.2. max_locks_per_transaction","hash":"max_locks_per_transaction","id":48},{"text":"1.14.3. max_pred_locks_per_transaction","hash":"max_pred_locks_per_transaction","id":49},{"text":"1.14.4. max_pred_locks_per_relation","hash":"max_pred_locks_per_relation","id":50},{"text":"1.14.5. max_pred_locks_per_page","hash":"max_pred_locks_per_page","id":51},{"text":"1.15. Resource Usage / Cost-Based Vacuum Delay","hash":"resource-usage-cost-based-vacuum-delay","id":52},{"text":"1.15.1. vacuum_cost_delay","hash":"vacuum_cost_delay","id":53},{"text":"1.15.2. vacuum_cost_page_hit","hash":"vacuum_cost_page_hit","id":54},{"text":"1.15.3. vacuum_cost_page_miss","hash":"vacuum_cost_page_miss","id":55},{"text":"1.15.4. vacuum_cost_page_dirty","hash":"vacuum_cost_page_dirty","id":56},{"text":"1.15.5. vacuum_cost_limit","hash":"vacuum_cost_limit","id":57},{"text":"1.16. Replication / Sending Servers","hash":"replication-sending-servers","id":58},{"text":"1.16.1. max_wal_senders","hash":"max_wal_senders","id":59},{"text":"1.16.2. max_replication_slots","hash":"max_replication_slots","id":60},{"text":"1.16.3. wal_keep_size","hash":"wal_keep_size","id":61},{"text":"1.16.4. max_slot_wal_keep_size","hash":"max_slot_wal_keep_size","id":62},{"text":"1.16.5. wal_sender_timeout","hash":"wal_sender_timeout","id":63},{"text":"1.17. Connections and Authentication / Authentication","hash":"connections-and-authentication-authentication","id":64},{"text":"1.17.1. authentication_timeout","hash":"authentication_timeout","id":65},{"text":"1.17.2. password_encryption","hash":"password_encryption","id":66},{"text":"1.17.3. krb_server_keyfile","hash":"krb_server_keyfile","id":67},{"text":"1.17.4. krb_caseins_users","hash":"krb_caseins_users","id":68},{"text":"1.17.5. db_user_namespace","hash":"db_user_namespace","id":69},{"text":"1.18. Statistics / Query and Index Statistics Collector","hash":"statistics-query-and-index-statistics-collector","id":70},{"text":"1.18.1. track_activities","hash":"track_activities","id":71},{"text":"1.18.2. track_activity_query_size","hash":"track_activity_query_size","id":72},{"text":"1.18.3. track_counts","hash":"track_counts","id":73},{"text":"1.18.4. track_io_timing","hash":"track_io_timing","id":74},{"text":"1.18.5. track_functions","hash":"track_functions","id":75},{"text":"1.18.6. stats_temp_directory","hash":"stats_temp_directory","id":76},{"text":"1.19. File Locations","hash":"file-locations","id":77},{"text":"1.19.1. data_directory","hash":"data_directory","id":78},{"text":"1.19.2. config_file","hash":"config_file","id":79},{"text":"1.19.3. hba_file","hash":"hba_file","id":80},{"text":"1.19.4. ident_file","hash":"ident_file","id":81},{"text":"1.19.5. external_pid_file","hash":"external_pid_file","id":82},{"text":"1.20. Reporting and Logging / When to Log","hash":"reporting-and-logging-when-to-log","id":83},{"text":"1.20.1. log_min_messages","hash":"log_min_messages","id":84},{"text":"1.20.2. log_min_error_statement","hash":"log_min_error_statement","id":85},{"text":"1.20.3. log_min_duration_statement","hash":"log_min_duration_statement","id":86},{"text":"1.20.4. log_min_duration_sample","hash":"log_min_duration_sample","id":87},{"text":"1.20.5. log_statement_sample_rate","hash":"log_statement_sample_rate","id":88},{"text":"1.20.6. log_transaction_sample_rate","hash":"log_transaction_sample_rate","id":89},{"text":"1.21. Checkpoints","hash":"checkpoints","id":90},{"text":"1.21.1. checkpoint_timeout","hash":"checkpoint_timeout","id":91},{"text":"1.21.2. checkpoint_completion_target","hash":"checkpoint_completion_target","id":92},{"text":"1.21.3. checkpoint_flush_after","hash":"checkpoint_flush_after","id":93},{"text":"1.21.4. checkpoint_warning","hash":"checkpoint_warning","id":94},{"text":"1.21.5. max_wal_size","hash":"max_wal_size","id":95},{"text":"1.21.6. min_wal_size","hash":"min_wal_size","id":96},{"text":"1.22. dynamic_library_path","hash":"dynamic_library_path","id":97},{"text":"1.23. gin_fuzzy_search_limit","hash":"gin_fuzzy_search_limit","id":98},{"text":"1.24. tcp_keepalives_count","hash":"tcp_keepalives_count","id":99},{"text":"1.25. tcp_keepalives_idle","hash":"tcp_keepalives_idle","id":100},{"text":"1.26. tcp_keepalives_interval","hash":"tcp_keepalives_interval","id":101},{"text":"1.27. tcp_user_timeout","hash":"tcp_user_timeout","id":102},{"text":"1.28. Query Tuning / Genetic Query Optimizer","hash":"query-tuning-genetic-query-optimizer","id":103},{"text":"1.28.1. geqo","hash":"geqo","id":104},{"text":"1.28.2. geqo_threshold","hash":"geqo_threshold","id":105},{"text":"1.28.3. geqo_effort","hash":"geqo_effort","id":106},{"text":"1.28.4. geqo_pool_size","hash":"geqo_pool_size","id":107},{"text":"1.28.5. geqo_generations","hash":"geqo_generations","id":108},{"text":"1.28.6. geqo_selection_bias","hash":"geqo_selection_bias","id":109},{"text":"1.28.7. geqo_seed","hash":"geqo_seed","id":110},{"text":"1.29. Write-Ahead Log / Recovery Target","hash":"write-ahead-log-recovery-target","id":111},{"text":"1.29.1. recovery_target","hash":"recovery_target","id":112},{"text":"1.29.2. recovery_target_name","hash":"recovery_target_name","id":113},{"text":"1.29.3. recovery_target_time","hash":"recovery_target_time","id":114},{"text":"1.29.4. recovery_target_xid","hash":"recovery_target_xid","id":115},{"text":"1.29.5. recovery_target_lsn","hash":"recovery_target_lsn","id":116},{"text":"1.29.6. recovery_target_inclusive","hash":"recovery_target_inclusive","id":117},{"text":"1.29.7. recovery_target_timeline","hash":"recovery_target_timeline","id":118},{"text":"1.29.8. recovery_target_action","hash":"recovery_target_action","id":119},{"text":"1.30. Query Tuning / Other Planner Options","hash":"query-tuning-other-planner-options","id":120},{"text":"1.30.1. default_statistics_target","hash":"default_statistics_target","id":121},{"text":"1.30.2. constraint_exclusion","hash":"constraint_exclusion","id":122},{"text":"1.30.3. cursor_tuple_fraction","hash":"cursor_tuple_fraction","id":123},{"text":"1.30.4. from_collapse_limit","hash":"from_collapse_limit","id":124},{"text":"1.30.5. jit","hash":"jit","id":125},{"text":"1.30.6. join_collapse_limit","hash":"join_collapse_limit","id":126},{"text":"1.30.7. parallel_leader_participation","hash":"parallel_leader_participation","id":127},{"text":"1.30.8. force_parallel_mode","hash":"force_parallel_mode","id":128},{"text":"1.30.9. plan_cache_mode","hash":"plan_cache_mode","id":129},{"text":"1.31. 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Connections and Authentication / Connection Settings","hash":"connections-and-authentication-connection-settings","id":139},{"text":"1.32.1. listen_addresses","hash":"listen_addresses","id":140},{"text":"1.32.2. port","hash":"port","id":141},{"text":"1.32.3. max_connections","hash":"max_connections","id":142},{"text":"1.32.4. superuser_reserved_connections","hash":"superuser_reserved_connections","id":143},{"text":"1.32.5. unix_socket_directories","hash":"unix_socket_directories","id":144},{"text":"1.32.6. unix_socket_group","hash":"unix_socket_group","id":145},{"text":"1.32.7. unix_socket_permissions","hash":"unix_socket_permissions","id":146},{"text":"1.32.8. bonjour","hash":"bonjour","id":147},{"text":"1.32.9. bonjour_name","hash":"bonjour_name","id":148},{"text":"1.33. Resource Usage / Asynchronous Behavior","hash":"resource-usage-asynchronous-behavior","id":149},{"text":"1.33.1. effective_io_concurrency","hash":"effective_io_concurrency","id":150},{"text":"1.33.2. maintenance_io_concurrency","hash":"maintenance_io_concurrency","id":151},{"text":"1.33.3. max_worker_processes","hash":"max_worker_processes","id":152},{"text":"1.33.4. max_parallel_workers_per_gather","hash":"max_parallel_workers_per_gather","id":153},{"text":"1.33.5. max_parallel_maintenance_workers","hash":"max_parallel_maintenance_workers","id":154},{"text":"1.33.6. max_parallel_workers","hash":"max_parallel_workers","id":155},{"text":"1.33.7. backend_flush_after","hash":"backend_flush_after","id":156},{"text":"1.33.8. old_snapshot_threshold","hash":"old_snapshot_threshold","id":157},{"text":"1.34. Replication / Standby Servers","hash":"replication-standby-servers","id":158},{"text":"1.34.1. primary_conninfo","hash":"primary_conninfo","id":159},{"text":"1.34.2. primary_slot_name","hash":"primary_slot_name","id":160},{"text":"1.34.3. promote_trigger_file","hash":"promote_trigger_file","id":161},{"text":"1.34.4. hot_standby","hash":"hot_standby","id":162},{"text":"1.34.5. max_standby_archive_delay","hash":"max_standby_archive_delay","id":163},{"text":"1.34.6. max_standby_streaming_delay","hash":"max_standby_streaming_delay","id":164},{"text":"1.34.7. wal_receiver_create_temp_slot","hash":"wal_receiver_create_temp_slot","id":165},{"text":"1.34.8. wal_receiver_status_interval","hash":"wal_receiver_status_interval","id":166},{"text":"1.34.9. hot_standby_feedback","hash":"hot_standby_feedback","id":167},{"text":"1.34.10. wal_receiver_timeout","hash":"wal_receiver_timeout","id":168},{"text":"1.34.11. wal_retrieve_retry_interval","hash":"wal_retrieve_retry_interval","id":169},{"text":"1.34.12. recovery_min_apply_delay","hash":"recovery_min_apply_delay","id":170},{"text":"1.35. Query Tuning / Planner Cost Constants","hash":"query-tuning-planner-cost-constants","id":171},{"text":"1.35.1. seq_page_cost","hash":"seq_page_cost","id":172},{"text":"1.35.2. random_page_cost","hash":"random_page_cost","id":173},{"text":"1.35.3. cpu_tuple_cost","hash":"cpu_tuple_cost","id":174},{"text":"1.35.4. cpu_index_tuple_cost","hash":"cpu_index_tuple_cost","id":175},{"text":"1.35.5. cpu_operator_cost","hash":"cpu_operator_cost","id":176},{"text":"1.35.6. parallel_setup_cost","hash":"parallel_setup_cost","id":177},{"text":"1.35.7. parallel_tuple_cost","hash":"parallel_tuple_cost","id":178},{"text":"1.35.8. min_parallel_table_scan_size","hash":"min_parallel_table_scan_size","id":179},{"text":"1.35.9. min_parallel_index_scan_size","hash":"min_parallel_index_scan_size","id":180},{"text":"1.35.10. effective_cache_size","hash":"effective_cache_size","id":181},{"text":"1.35.11. jit_above_cost","hash":"jit_above_cost","id":182},{"text":"1.35.12. jit_inline_above_cost","hash":"jit_inline_above_cost","id":183},{"text":"1.35.13. jit_optimize_above_cost","hash":"jit_optimize_above_cost","id":184},{"text":"1.36. autovacuum","hash":"autovacuum","id":185},{"text":"1.36.1. autovacuum","hash":"autovacuum-2","id":186},{"text":"1.36.2. log_autovacuum_min_duration","hash":"log_autovacuum_min_duration","id":187},{"text":"1.36.3. autovacuum_max_workers","hash":"autovacuum_max_workers","id":188},{"text":"1.36.4. autovacuum_naptime","hash":"autovacuum_naptime","id":189},{"text":"1.36.5. autovacuum_vacuum_threshold","hash":"autovacuum_vacuum_threshold","id":190},{"text":"1.36.6. autovacuum_vacuum_insert_threshold","hash":"autovacuum_vacuum_insert_threshold","id":191},{"text":"1.36.7. autovacuum_analyze_threshold","hash":"autovacuum_analyze_threshold","id":192},{"text":"1.36.8. autovacuum_vacuum_scale_factor","hash":"autovacuum_vacuum_scale_factor","id":193},{"text":"1.36.9. autovacuum_vacuum_insert_scale_factor","hash":"autovacuum_vacuum_insert_scale_factor","id":194},{"text":"1.36.10. autovacuum_analyze_scale_factor","hash":"autovacuum_analyze_scale_factor","id":195},{"text":"1.36.11. autovacuum_freeze_max_age","hash":"autovacuum_freeze_max_age","id":196},{"text":"1.36.12. autovacuum_multixact_freeze_max_age","hash":"autovacuum_multixact_freeze_max_age","id":197},{"text":"1.36.13. autovacuum_vacuum_cost_delay","hash":"autovacuum_vacuum_cost_delay","id":198},{"text":"1.36.14. autovacuum_vacuum_cost_limit","hash":"autovacuum_vacuum_cost_limit","id":199},{"text":"1.37. Connections and Authentication / SSL","hash":"connections-and-authentication-ssl","id":200},{"text":"1.37.1. ssl","hash":"ssl","id":201},{"text":"1.37.2. ssl_ca_file","hash":"ssl_ca_file","id":202},{"text":"1.37.3. ssl_cert_file","hash":"ssl_cert_file","id":203},{"text":"1.37.4. ssl_crl_file","hash":"ssl_crl_file","id":204},{"text":"1.37.5. ssl_key_file","hash":"ssl_key_file","id":205},{"text":"1.37.6. ssl_ciphers","hash":"ssl_ciphers","id":206},{"text":"1.37.7. ssl_prefer_server_ciphers","hash":"ssl_prefer_server_ciphers","id":207},{"text":"1.37.8. ssl_ecdh_curve","hash":"ssl_ecdh_curve","id":208},{"text":"1.37.9. ssl_min_protocol_version","hash":"ssl_min_protocol_version","id":209},{"text":"1.37.10. ssl_max_protocol_version","hash":"ssl_max_protocol_version","id":210},{"text":"1.37.11. ssl_dh_params_file","hash":"ssl_dh_params_file","id":211},{"text":"1.37.12. ssl_passphrase_command","hash":"ssl_passphrase_command","id":212},{"text":"1.37.13. ssl_passphrase_command_supports_reload","hash":"ssl_passphrase_command_supports_reload","id":213},{"text":"1.38. Resource Usage / Memory","hash":"resource-usage-memory","id":214},{"text":"1.38.1. shared_buffers","hash":"shared_buffers","id":215},{"text":"1.38.2. huge_pages","hash":"huge_pages","id":216},{"text":"1.38.3. temp_buffers","hash":"temp_buffers","id":217},{"text":"1.38.4. max_prepared_transactions","hash":"max_prepared_transactions","id":218},{"text":"1.38.5. work_mem","hash":"work_mem","id":219},{"text":"1.38.6. hash_mem_multiplier","hash":"hash_mem_multiplier","id":220},{"text":"1.38.7. maintenance_work_mem","hash":"maintenance_work_mem","id":221},{"text":"1.38.8. autovacuum_work_mem","hash":"autovacuum_work_mem","id":222},{"text":"1.38.9. logical_decoding_work_mem","hash":"logical_decoding_work_mem","id":223},{"text":"1.38.10. max_stack_depth","hash":"max_stack_depth","id":224},{"text":"1.38.11. shared_memory_type","hash":"shared_memory_type","id":225},{"text":"1.38.12. dynamic_shared_memory_type","hash":"dynamic_shared_memory_type","id":226},{"text":"1.39. Reporting and Logging / Where to Log","hash":"reporting-and-logging-where-to-log","id":227},{"text":"1.39.1. log_destination","hash":"log_destination","id":228},{"text":"1.39.2. logging_collector","hash":"logging_collector","id":229},{"text":"1.39.3. log_directory","hash":"log_directory","id":230},{"text":"1.39.4. log_filename","hash":"log_filename","id":231},{"text":"1.39.5. log_file_mode","hash":"log_file_mode","id":232},{"text":"1.39.6. log_rotation_age","hash":"log_rotation_age","id":233},{"text":"1.39.7. log_rotation_size","hash":"log_rotation_size","id":234},{"text":"1.39.8. log_truncate_on_rotation","hash":"log_truncate_on_rotation","id":235},{"text":"1.39.9. syslog_facility","hash":"syslog_facility","id":236},{"text":"1.39.10. syslog_ident","hash":"syslog_ident","id":237},{"text":"1.39.11. syslog_sequence_numbers","hash":"syslog_sequence_numbers","id":238},{"text":"1.39.12. syslog_split_messages","hash":"syslog_split_messages","id":239},{"text":"1.39.13. event_source","hash":"event_source","id":240},{"text":"1.40. 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大幅降低了管理和维护的代价。对开发者来说，IvorySQL提供了简单的接口、极简的解决方案和与第三方工具的无缝集成。对数据分析专家来说，IvorySQL 提供了便捷的数据访问能力。 瀚高支持：由领先的PostgreSQL数据库提供商瀚高股份提供支持。 IvorySQL基于PostgreSQL，具有完整的SQL、坚如磐石的可靠性和庞大的生态系统。 IvorySQL数据库的主要应用场景： 企业数据库 如 ERP、交易系统、财务系统涉及资金、客户等信息，数据不能丢失且业务逻辑复杂，选择 IvorySQL 作为数据底层存储，一是可以帮助您在数据一致性前提下提供高可用性，二是可以用简单的编程实现复杂的业务逻辑。 含 LBS 的应用 大型游戏、O2O 等应用需要支持世界地图、附近的商家，两个点的距离等能力，PostGIS 增加了对地理对象的支持，允许您以 SQL 运行位置查询，而不需要复杂的编码，帮助您更轻松理顺逻辑，更便捷的实现 LBS，提高用户粘性。 数据仓库和大数据 IvorySQL 更多数据类型和强大的计算能力，能够帮助您更简单搭建数据库仓库或大数据分析平台，为企业运营加分。 建站或 App IvorySQL 良好的性能和强大的功能，可以有效的提高网站性能，降低开发难度。 数据库迁移 如果需要将Oracle数据库迁移到PostgreSQL数据库，可以直接使用IvorySQL数据库进行迁移。 IvorySQL是一个功能强大的开源对象关系数据库管理系统(ORDBMS)。 用于安全地存储数据，支持最佳做法，并允许在处理请求时检索它们。除此之外，还兼容了Oracle的语法，适用于使用Oracle的场景。 大小写转换 LIKE操作符 匿名块 函数与存储过程 内置数据类型与内置函数 端口与IP XML函数 sequence 包 不可见列 RowID OUT 参数 %TYPE、%ROWTYPE NLS 参数 Force View 嵌套子函数 sys_guid 函数 空字符串转null CALL 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IvorySQL简介","hash":"ivorysql简介","id":1},{"text":"1.1. 概述","hash":"概述","id":2},{"text":"1.2. 产品目标和范围","hash":"产品目标和范围","id":3},{"text":"1.3. 核心特性","hash":"核心特性","id":4},{"text":"1.4. 竞争优势","hash":"竞争优势","id":5},{"text":"1.5. 技术生态","hash":"技术生态","id":6},{"text":"1.6. 核心应用场景","hash":"核心应用场景","id":7},{"text":"2. 主要、基本功能","hash":"主要基本功能","id":8},{"text":"3. 与Oracle的兼容性","hash":"与oracle的兼容性","id":9}]},"2":{"id":2,"text":"yum源快速安装数据库，请参考从yum源安装ivorysql数据库。 想要获取更多安装方式，请参考安装指南。 主节点数据库需要安装并启动 集群中各节点均需关闭防火墙才能正常通信。 $ sudo systemctl stop firewalld 为了从主节点向备节点搭建流复制，主节点需要对data目录下的postgresql.conf和pg_hba.conf两个文件进行配置。 postgresql.conf 将以下配置追加到postgresql.conf文件末尾： listen_addresses = '*' max_connections = 100 wal_level = replica max_wal_senders = 5 hot_standby = on pg_hba.conf 将以下配置追加到pg_hba.conf文件末尾： host all all 0.0.0.0/0 trust host replication all 0.0.0.0/0 trust 示例中的pg_hba的配置，仅作为demo用来测试，这种配置会导致数据库密码失效，请根据环境实际情况进行配置 $ pg_ctl restart Yum源快速安装数据库，请参考从yum源安装ivorysql数据库。 想要获取更多安装方式，请参考安装指南。 备节点数据库只需要安装，不需要启动 集群中各节点均需关闭防火墙才能正常通信。 $ sudo systemctl stop firewalld 在备节点上执行以下命令，创建一个主节点的基础备份，即搭建流复制： $ sudo pg_basebackup -F p -P -X fetch -R -h <primary_ip> -p <primary_port> -U ivorysql -D /usr/local/ivorysql/ivorysql-5/data -h为主节点ip； -p为主节点数据库端口号，默认为5432； -U为数据库用户； -D为要创建的备节点数据库数据目录。 其他参数使用方法，请使用pg_basebackup --help命令获取。 将以下配置写入~/.bash_profile文件： PATH=/usr/local/ivorysql/ivorysql-5/bin:$PATH export PATH PGDATA=/usr/local/ivorysql/ivorysql-5/data export PGDATA source该文件使环境变量生效： $ source ~/.bash_profile $ pg_ctl -D /usr/local/ivorysql/ivorysql-5/data start 在主节点上执行以下命令可以看到walsender： $ ps -ef |grep postgres ... ivorysql 11176 8067 0 21:54 ? 00:00:00 postgres: walsender ivorysql 192.168.31.102(53416) streaming 0/7000060... 而备节点则可看到walreceiver： $ ps -ef | grep postgres ... ivorysql 6567 6139 0 21:54 ? 00:00:00 postgres: walreceiver streaming 0/7000060 ... 在主节点上psql连接数据库，并查看集群状态： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# select * from pg_stat_replication; pid | usesysid | usename | application_name | client_addr | client_hostname | client_port | backend_start | backend_ xmin | state | sent_lsn | write_lsn | flush_lsn | replay_lsn | write_lag | flush_lag | replay_lag | sync_priority | sync_state | reply_time -------+----------+----------+------------------+----------------+-----------------+-------------+-------------------------------+--------- -----+-----------+-----------+-----------+-----------+------------+-----------+-----------+------------+---------------+------------+------ ------------------------- 11176 | 10 | ivorysql | walreceiver | 192.168.31.102 | | 53416 | 2024-12-18 21:54:52.041847-05 | | streaming | 0/7000148 | 0/7000148 | 0/7000148 | 0/7000148 | | | | 0 | async | 2024- 12-18 22:52:07.325111-05 (1 row) 这里192.168.31.102为备节点的ip，async表示数据同步方式为异步流复制。 === 使用集群 集群中所有的写操作均在主节点执行，读操作则主备节点都可以执行。主节点的数据通过流复制同步到备节点。主节点写操作的结果在任何一个备节点都能够查询到。 例如，在主节点创建一个新的数据库test，并在主节点进行查询： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# create database test; CREATE DATABASE ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows) 在备节点查询： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows)","title":"集群搭建","component":"ivorysql-doc","version":"master","name":"cluster_setup","url":"/ivorysql-doc/master/cluster_setup.html","titles":[{"text":"1. 主节点","hash":"主节点","id":1},{"text":"1.1. 安装并启动数据库","hash":"安装并启动数据库","id":2},{"text":"1.2. 关闭防火墙","hash":"关闭防火墙","id":3},{"text":"1.3. 配置文件","hash":"配置文件","id":4},{"text":"1.4. 重启主节点数据库服务","hash":"重启主节点数据库服务","id":5},{"text":"2. 备节点","hash":"备节点","id":6},{"text":"2.1. 安装数据库","hash":"安装数据库","id":7},{"text":"2.2. 关闭防火墙","hash":"关闭防火墙-2","id":8},{"text":"2.3. 搭建流复制","hash":"搭建流复制","id":9},{"text":"2.4. 配置环境变量","hash":"配置环境变量","id":10},{"text":"2.5. 启动备节点数据库服务","hash":"启动备节点数据库服务","id":11},{"text":"3. 集群的使用","hash":"集群的使用","id":12},{"text":"3.1. 查看集群状态","hash":"查看集群状态","id":13}]},"3":{"id":3,"text":"IvorySQL在开源PostgreSQL数据库的基础上提供独特的附加功能。 IvorySQL致力于通过创新和建立在开源数据库解决方案之上为其终端用户提供价值。我们的目标是为中小型企业提供一个具有高性能、可扩展性、可靠性和易于使用的解决方案。 IvorySQL提供的扩展功能将使用户能够建立高性能和可扩展的PostgreSQL数据库集群，具有更好的数据库兼容性和管理。这简化了从其他DBMS迁移到PostgreSQL的过程，增强了数据库管理经验。 在对原有的PostgreSQL改动最小的前提下，实现对Oracle的兼容。我们需要实现双parser、双端口、模式PL/pgSQL实现PL/iSQL的框架。实现流程图如下： 下图描述了对PostgreSQL现有系统表的变化以及所做的补充。 看看你能否访问数据库服务器的第一个例子就是试着创建一个数据库。一台运行着的IvorySQL服务器可以管理许多数据库。通常我们会为每个项目和每个用户单独使用一个数据库。 你的站点管理员可能已经为你创建了可以使用的数据库。如果这样你就可以省略这一步，并且跳到下一节。 要创建一个新的数据库，在我们这个例子里叫`mydb`，你可以使用下面的命令： $ createdb mydb 如果不产生任何响应则表示该步骤成功，你可以跳过本节的剩余部分。 如果你看到类似下面这样的信息： createdb: command not found 那么就是IvorySQL没有安装好。或者是根本没安装，或者是你的shell搜索路径没有设置正确。尝试用绝对路径调用该命令试试： $ /usr/local/pgsql/bin/createdb mydb 在你的站点上这个路径可能不一样。和你的站点管理员联系或者看看安装指导获取正确的位置。 另外一种响应可能是这样： createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory Is the server running locally and accepting connections on that socket? 这意味着该服务器没有启动，或者在`createdb`期望去连接它的时候没有在监听。同样，你也要查看安装指导或者咨询管理员。 另外一个响应可能是这样： createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: FATAL: role \"joe\" does not exist 在这里提到了你自己的登录名。如果管理员没有为你创建IvorySQL用户帐号，就会发生这些现象。（IvorySQL用户帐号和操作系统用户帐号是不同的。） 如果你是管理员，参阅 第 22 章 获取创建用户帐号的帮助。你需要变成安装IvorySQL的操作系统用户的身份（通常是 postgres）才能创建第一个用户帐号。也有可能是赋予你的IvorySQL用户名和你的操作系统用户名不同；这种情况下，你需要使用`-U`选项或者使用`PGUSER`环境变量指定你的IvorySQL用户名。 如果你有个数据库用户帐号，但是没有创建数据库所需要的权限，那么你会看到下面的信息： createdb: error: database creation failed: ERROR: permission denied to create database 并非所有用户都被许可创建新数据库。如果IvorySQL拒绝为你创建数据库，那么你需要让站点管理员赋予你创建数据库的权限。出现这种情况时请咨询你的站点管理员。如果你自己安装了IvorySQL，那么你应该以你启动数据库服务器的用户身份登录然后参考手册完成权限的赋予工作。http://www.postgresql.org/docs/17/tutorial-createdb.html#ftn.id-1.4.3.4.10.4[[1\\]] 你还可以用其它名字创建数据库。IvorySQL允许你在一个站点上创建任意数量的数据库。数据库名必须是以字母开头并且小于63个字符长。一个方便的做法是创建和你当前用户名同名的数据库。许多工具假设该数据库名为缺省数据库名，所以这样可以节省你的敲键。要创建这样的数据库，只需要键入： $ createdb 如果你再也不想使用你的数据库了，那么你可以删除它。比如，如果你是数据库`mydb`的所有人（创建人），那么你就可以用下面的命令删除它： $ dropdb mydb （对于这条命令而言，数据库名不是缺省的用户名，因此你就必须声明它）。这个动作将在物理上把所有与该数据库相关的文件都删除并且不可取消，因此做这中操作之前一定要考虑清楚。 更多关于 createdb 和 dropdb 的信息可以分别在 createdb和 dropdb中找到。 你可以通过指定表的名字和所有列的名字及其类型来创建表∶ CREATE TABLE weather ( city varchar(80), temp_lo int, -- 最低温度 temp_hi int, -- 最高温度 prcp real, -- 湿度 date date ); 你可以在 psql 输入这些命令以及换行符。psql 可以识别该命令直到分号才结束。 你可以在SQL命令中自由使用空白（即空格、制表符和换行符）。这就意味着你可以用和上面不同的对齐方式键入命令，或者将命令全部放在一行中。两个划线（“--”）引入注释。任何跟在它后面直到行尾的东西都会被忽略。SQL是对关键字和标识符大小写不敏感的语言，只有在标识符用双引号包围时才能保留它们的大小写（上例没有这么做）。 varchar(80) 指定了一个可以存储最长80个字符的任意字符串的数据类型。int 是普通的整数类型。real 是一种用于存储单精度浮点数的类型。date 类型应该可以自解释（没错，类型为`date`的列名字也是`date`。这么做可能比较方便或者容易让人混淆 — 你自己选择）。 IvorySQL支持标准的SQL类型`int`、smallint、real、double precision、char(N)、varchar(N)、date、time、timestamp`和`interval，还支持其他的通用功能的类型和丰富的几何类型。IvorySQL中可以定制任意数量的用户定义数据类型。因而类型名并不是语法关键字，除了SQL标准要求支持的特例外。 第二个例子将保存城市和它们相关的地理位置： CREATE TABLE cities ( name varchar(80), location point ); 类型`point`就是一种IvorySQL特有数据类型的例子。 最后，我们还要提到如果你不再需要某个表，或者你想以不同的形式重建它，那么你可以用下面的命令删除它： DROP TABLE tablename; 当一个表被创建后，它不包含任何数据。在数据库发挥作用之前，首先要做的是插入数据。一次插入一行数据。你也可以在一个命令中插入多行，但不能插入不完整的行。即使只知道其中一些列的值，也必须创建完整的行。 要创建一个新行，使用 INSERT命令。这条命令要求提供表的名字和其中列的值。例如，考虑 第 5 章中的产品表： CREATE TABLE products ( product_no integer, name text, price numeric ); CREATE TABLE new_products ( product_no int , name varchar(255), price DECIMAL(10, 2), release_date DATE ); 一个插入一行的命令将是： INSERT INTO products VALUES (1, 'Cheese', 9.99); 数据的值是按照这些列在表中出现的顺序列出的，并且用逗号分隔。通常，数据的值是文字（常量），但也允许使用标量表达式。 上面的语法的缺点是你必须知道表中列的顺序。要避免这个问题，你也可以显式地列出列。例如，下面的两条命令都有和上文那条 命令一样的效果： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99); INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1); 许多用户认为明确列出列的名字是个好习惯。 如果你没有获得所有列的值，那么你可以省略其中的一些。在这种情况下，这些列将被填充为它们的缺省值。例如： INSERT INTO products (product_no, name) VALUES (1, 'Cheese'); INSERT INTO products VALUES (1, 'Cheese'); 第二种形式是IvorySQL的一个扩展。它从使用给出的值从左开始填充列，有多少个给出的列值就填充多少个列，其他列的将使用缺省值。 为了保持清晰，你也可以显式地要求缺省值，用于单个的列或者用于整个行： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', DEFAULT); INSERT INTO products DEFAULT VALUES; 你可以在一个命令中插入多行： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99), (2, 'Bread', 1.99), (3, 'Milk', 2.99); INSERT INTO new_products (product_no, name, price, release_date) VALUES (1, 'A', 100.00, '2025-05-29'), (2, 'B', 150.50, '2024-11-20'), (3, 'C', 200.75, '2025-05-29'); 也可以插入查询的结果（可能没有行、一行或多行）： INSERT INTO products (product_no, name, price) SELECT product_no, name, price FROM new_products WHERE release_date = '2025-05-29'; 这提供了用于计算要插入的行的SQL查询机制（ 第 7 章）的全部功能。 提示 在一次性插入大量数据时，考虑使用 COPY命令。它不如 INSERT命令那么灵活，但是更高效。 两个查询的结果可以用集合操作并、交、差进行组合。语法是: query1 UNION [ALL] query2 query1 INTERSECT [ALL] query2 query1 EXCEPT [ALL] query2 query1*和query2*都是可以使用以上所有特性的查询。集合操作也可以嵌套和级连，例如: query1 UNION query2 UNION query3 实际执行的是： (query1 UNION query2) UNION query3 UNION`有效地把*`query2的结果附加到query1*的结果上（不过我们不能保证这就是这些行实际被返回的顺序）。此外，它将删除结果中所有重复的行，就像`DISTINCT`做的那样，除非你使用了`UNION ALL`。 INTERSECT`返回那些同时存在于*`query1和query2*的结果中的行，除非声明了`INTERSECT ALL`，否则所有重复行都被消除。 EXCEPT`返回所有在*`query1的结果中但是不在query2的结果中的行（有时侯这叫做两个查询的*差）。同样的，除非声明了`EXCEPT ALL`，否则所有重复行都被消除。 为了计算两个查询的并、交、差，这两个查询必须是“并操作兼容的”，也就意味着它们都返回同样数量的列，并且对应的列有兼容的数据类型，如 第 10.5 节中描述的那样。 当优化器判断对于某一个特定的查询，并行查询是最快的执行策略时，优化器将创建一个查询计划。该计划包括一个 *Gather*或者*Gather Merge*节点。下面是一个简单的例子： EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%'; QUERY PLAN ------------------------------------------------------------------------------------- Gather (cost=1000.00..217018.43 rows=1 width=97) Workers Planned: 2 -> Parallel Seq Scan on pgbench_accounts (cost=0.00..216018.33 rows=1 width=97) Filter: (filler ~~ '%x%'::text) (4 rows) 在所有的情形下，`Gather`或*Gather Merge*节点都只有一个子计划，它是将被并行执行的计划的一部分。如果`Gather`或*Gather Merge*节点位于计划树的最顶层，那么整个查询将并行执行。如果它位于计划树的其他位置，那么只有查询中在它之下的那一部分会并行执行。在上面的例子中，查询只访问了一个表，因此除`Gather`节点本身之外只有一个计划节点。因为该计划节点是`Gather`节点的孩子节点，所以它会并行执行。 使用 EXPLAIN命令, 你能看到规划器选择的工作者数量。当查询执行期间到达`Gather`节点时，实现用户会话的进程将会请求和规划器选中的工作者数量一样多的 后台工作者进程。规划器将考虑使用的后台工作者的数量被限制为最多 max_parallel_workers_per_gather个。任何时候能够存在的后台工作者进程的总数由 max_worker_processes和 max_parallel_workers限制。因此，一个并行查询可能会使用比规划中少的工作者来运行，甚至有可能根本不使用工作者。最优的计划可能取决于可用的工作者的数量，因此这可能会导致不好的查询性能。如果这种情况经常发生，那么就应当考虑一下提高`max_worker_processes`和`max_parallel_workers`的值，这样更多的工作者可以同时运行；或者降低`max_parallel_workers_per_gather`，这样规划器会要求少一些的工作者。 为一个给定并行查询成功启动的后台工作者进程都将会执行计划的并行部分。这些工作者的领导者也将执行该计划，不过它还有一个额外的任务：它还必须读取所有由工作者产生的元组。当整个计划的并行部分只产生了少量元组时，领导者通常将表现为一个额外的加速查询执行的工作者。反过来，当计划的并行部分产生大量的元组时，领导者将几乎全用来读取由工作者产生的元组并且执行`Gather`或`Gather Merge`节点上层计划节点所要求的任何进一步处理。在这些情况下，领导者所作的执行并行部分的工作将会很少。 当计划的并行部分的顶层节点是`Gather Merge`而不是`Gather`时，它表示每个执行计划并行部分的进程会产生有序的元组，并且领导者执行一种保持顺序的合并。相反，`Gather`会以任何方便的顺序从工作者读取元组，这会破坏可能已经存在的排序顺序。 有几种设置会导致查询规划器在任何情况下都不生成并行查询计划。为了让并行查询计划能够被生成，必须配置好下列设置。 max_parallel_workers_per_gather必须被设置为大于零的值。这是一种特殊情况，更加普遍的原则是所用的工作者数量不能超过`max_parallel_workers_per_gather`所配置的数量。 此外，系统一定不能运行在单用户模式下。因为在单用户模式下，整个数据库系统运行在单个进程中，没有后台工作者进程可用。 如果下面的任一条件为真，即便对一个给定查询通常可以产生并行查询计划，规划器都不会为它产生并行查询计划： 查询要写任何数据或者锁定任何数据库行。如果一个查询在顶层或者CTE中包含了数据修改操作，那么不会为该查询产生并行计划。一种例外是，CREATE TABLE …​ AS、`SELECT INTO`以及`CREATE MATERIALIZED VIEW`这些创建新表并填充它的命令可以使用并行计划。 查询可能在执行过程中被暂停。只要在系统认为可能发生部分或者增量式执行，就不会产生并行计划。例如：用 DECLARE CURSOR创建的游标将永远不会使用并行计划。类似地，一个`FOR x IN query LOOP .. END LOOP`形式的 PL/pgSQL 循环也永远不会使用并行计划，因为当并行查询进行时，并行查询系统无法验证循环中的代码执行起来是安全的。 使用了任何被标记为`PARALLEL UNSAFE`的函数的查询。大多数系统定义的函数都被标记为`PARALLEL SAFE`，但是用户定义的函数默认被标记为`PARALLEL UNSAFE`。参见 第 15.4 节中的讨论。 该查询运行在另一个已经存在的并行查询内部。例如，如果一个被并行查询调用的函数自己发出一个 SQL 查询，那么该查询将不会使用并行计划。这是当前实现的一个限制，但是或许不值得移除这个限制，因为它会导致单个查询使用大量的进程。 即使对于一个特定的查询已经产生了并行查询计划，在一些情况下执行时也不会并行执行该计划。如果发生这种情况，那么领导者将会自己执行该计划在`Gather`节点之下的部分，就好像`Gather`节点不存在一样。上述情况将在满足下面的任一条件时发生： 因为后台工作者进程的总数不能超过 max_worker_processes，导致不能得到后台工作者进程。 由于为并行查询目的启动的后台工作者数量不能超过 max_parallel_workers这一限制而不能得到后台工作者。 客户端发送了一个执行消息，并且消息中要求取元组的数量不为零。执行消息可见 扩展查询协议中的讨论。因为 libpq当前没有提供方法来发送这种消息，所以这种情况只可能发生在不依赖 libpq 的客户端中。如果这种情况经常发生，那在它可能发生的会话中设置 max_parallel_workers_per_gather为零是一个很好的主意，这样可以避免产生连续运行时次优的查询计划。 因为每个工作者只执行完成计划的并行部分，所以不可能简单地产生一个普通查询计划并使用多个工作者运行它。每个工作者都会产生输出结果集的一个完全，因而查询并不会比普通查询运行得更快甚至还会产生不正确的结果。相反，计划的并行部分一定被查询优化器在内部当作一个*部分计划*，即它必须被构建出来，这样每一个执行该计划的进程将以无重复地方式产生输出行的一个子集，即保证每一个所需要的输出行正好只被一个合作进程生成。通常，这意味着该查询的驱动表上的扫描必须是一种可并行的扫描。 当前支持下列可并行的表扫描。 在一个*并行顺序扫描\\*中，表块将在合作进程之间被划分。一次会分发一个块，这样对表的访问还是保持顺序方式。 在一个*并行位图堆扫描\\*中，一个进程被选为领导者。这个进程执行对一个或者多个索引的扫描并且构建出一个位图指示需要访问哪些表块。这些表块接着会在合作进程之间划分（和并行顺序扫描中一样）。换句话说，堆扫描以并行方式进行但底层的索引扫描不是并行。 在一个*并行索引扫描\\*或者*并行只用索引的扫描\\*中，合作进程轮流从索引读取数据。当前，并行索引扫描仅有B-树索引支持。每一个进程将认领一个索引块并且扫描和返回该索引块引用的所有元组，其他进程可以同时地从一个不同的索引块返回元组。并行B-树扫描的结果会以每个工作者进程内的顺序返回。 其他扫描类型（例如非B-树索引的扫描）可能会在未来支持并行扫描。 正如在非并行计划中那样，驱动表可能被使用嵌套循环、哈希连接或者归并连接连接到一个或者多个其他表。连接的内侧可以是任何类型的被规划器支持的非并行计划，假设它能够安全地在并行工作者中运行。根据连接类型，内侧还可以是一种并行计划。 在一个*嵌套循环连接\\*中，内侧总是非并行的。尽管它会被完全执行，如果内侧是一个索引扫描也会很高效，因为外侧元组以及在索引中查找值的循环会被划分到多个合作进程。 在一个*归并连接\\*中，内侧总是一个非并行计划并且因此会被完全执行。这可能是不太高效的，特别是在排序必须被执行时，因为在每一个合作进程中工作数据和结果数据是重复的。 在一个哈希连接\\（没有“并行”前缀）中，每个合作进程都会完全执行内侧以构建哈希表的相同。如果哈希表很大或者该计划开销很大，这种方式就很低效。在一个并行哈希连接\\*中，内侧是一个*并行哈希\\，它把构建共享哈希表的工作划分到多个合作进程。 IvorySQL通过按两个阶段进行聚集来支持并行聚集。首先，每个参与到查询并行部分的进程执行一个聚集步骤，为该进程注意到的每个分组产生一个部分结果。这在计划中反映为一个`Partial Aggregate`节点。然后，部分结果通过`Gather`或者`Gather Merge`被传输到领导者。最后，领导者对来自所有工作者的结果进行重新聚集得到最终的结果。这在计划中反映为一个`Finalize Aggregate`节点。 因为`Finalize Aggregate`节点运行在领导者进程上，如果查询产生的分组数相对于其输入行数来说比较大，则查询规划器不会喜欢它。例如，在最坏的情况下，`Finalize Aggregate`节点看到的分组数可能与所有工作者进程在`Partial Aggregate`阶段看到的输入行数一样多。对于这类情况，使用并行聚集显然得不到性能收益。查询规划器会在规划过程中考虑这一点并且不太会在这种情况下选择并行聚集。 并行聚集并非在所有情况下都被支持。每一个聚集都必须是对并行 安全的并且必须有一个组合函数。如果该聚集有一个类型为`internal`的转移状态，它必须有序列化和反序列化函数。更多细节请参考 CREATE AGGREGATE。如果任何聚集函数调用包含`DISTINCT`或`ORDER BY`子句，则不支持并行聚集。对于有序集聚集或者当查询涉及`GROUPING SETS`时，也不支持并行聚集。只有在查询中涉及的所有连接也是该计划并行部分的组成部分时，才能使用并行聚集。 只要当IvorySQL需要从多个源中整合行到一个单一结果集时，它会使用`Append`或`MergeAppend`计划节点。在实现`UNION ALL`或扫描分区表时常常会发生这种情况。就像这些节点可以被用在任何其他计划中一样，它们可以被用在并行计划中。不过，在并行计划中，规划器使用的是`Parallel Append`节点。 当一个`Append`节点被用在并行计划中时，每个进程将按照子计划出现的顺序执行子计划，这样所有的参与进程会合作执行第一个子计划直到它被完成，然后同时移动到第二个计划。而在使用`Parallel Append`时，执行器将把它的子计划尽可能均匀地散布在参与进程中，这样多个子计划会被同时执行。这避免了竞争，也避免了子计划在那些不执行它的进程中产生启动代价。 此外，和常规的`Append`节点不同（在并行计划中使用时仅有部分子计划），Parallel Append`节点既可以有部分子计划也可以有非部分子计划。非部分子计划将仅被单个进程扫描，因为扫描它们不止一次会产生重复的结果。因此涉及到追加多个结果集的计划即使在没有有效的部分计划可用时，也能实现粗粒度的并行。例如，考虑一个针对分区表的查询，它只能通过使用一个不支持并行扫描的索引来实现。规划器可能会选择常规`Index Scan`计划的`Parallel Append。每个索引扫描必须被单一的进程执行完，但不同的扫描可以由不同的进程同时执行。 enable_parallel_append可以被用来禁用这种特性。 如果我们想要一个查询能产生并行计划但事实上又没有产生，可以尝试减小 parallel_setup_cost或者 parallel_tuple_cost。当然，这个计划可能比规划器优先产生的顺序计划还要慢，但也不总是如此。如果将这些设置为很小的值（例如把它们设置为零）也不能得到并行计划，那就可能是有某种原因导致查询规划器无法为你的查询产生并行计划。可能的原因可见 第 15.2 节和 第 15.4 节。 在执行一个并行计划时，可以用`EXPLAIN (ANALYZE,VERBOSE)`来显示每个计划节点在每个工作者上的统计信息。这些信息有助于确定是否所有的工作被均匀地分发到所有计划节点以及从总体上理解计划的性能特点。 ABORT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ABORT`回滚当前事务并且导致由该事务所作的所有更新被丢弃。这个命令的行为与标准SQL命令 `ROLLBACK的行为一样，并且只是为了历史原因存在。 WORK TRANSACTION 可选关键词。它们没有效果。 AND CHAIN 如果规定了`AND CHAIN` ，新事务立即启动，具有与刚刚完成的事务相同的事务特征(参见 http://www.postgresql.org/docs/17/sql-set-transaction.html[`SET TRANSACTION`])。否则，不会启动新事务。 使用 COMMIT成功地终止一个事务。 在一个事务块之外发出`ABORT`会发出一个警告消息并且不会产生效果。 中止所有更改： ABORT; 这个命令是一个因为历史原因而存在的IvorySQL扩展。`ROLLBACK`是等效的标准SQL命令。 BEGIN [ WORK | TRANSACTION ] [ transaction_mode [, ...] ] 其中 transaction_mode 是以下之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE BEGIN 开始一个事务块，也就是说所有 BEGIN 命令之后的所有语句将被在一个事务中执行，直到给出一个显式的 COMMIT或者 ROLLBACK。默认情况下（没有 BEGIN ），IvorySQL在“自动提交”模式中执行事务，也就是说每个语句都在自己的事务中执行并且在语句结束时隐式地执行一次提交（如果执行成功，否则会完成一次回滚）。 在一个事务块内的语句会执行得更快，因为事务的开始/提交也要求可观的CPU和磁盘活动。在进行多个相关更改时，在一个事务内执行多个语句也有助于保证一致性：在所有相关更新还没有完成之前，其他会话将不能看到中间状态。 如果指定了隔离级别、读/写模式或者延迟模式，新事务也会有那些特性，就像执行了 SET TRANSACTION一样。 WORK``TRANSACTION 可选的关键词。它们没有效果。 这个语句其他参数的含义请参考 SET TRANSACTION。 START TRANSACTION具有和`BEGIN` 相同的功能。 使用 COMMIT或者 ROLLBACK来终止一个事务块。 在已经在一个事务块中时发出`BEGIN`将惹出一个警告消息。事务状态不会被影响。要在一个事务块中嵌套事务，可以使用保存点（见 SAVEPOINT）。 由于向后兼容的原因，连续的 transaction_modes 之间的逗号可以被省略。 开始一个事务块： BEGIN; BEGIN 是一种IvorySQL语言扩展。它等效于SQL标准的命令 START TRANSACTION，它的参考页包含额外的兼容性信息。 DEFERRABLE transaction_mode 是一种IvorySQL语言扩展。 附带地，`BEGIN`关键词被用于嵌入式SQL中的一种不同目的。在移植数据库应用时，我们建议小心对待事务语义。 `COMMIT`提交当前事务。所有由该事务所作的更改会变得对他人可见并且被保证在崩溃发生时仍能持久。 WORK TRANSACTION 可选的关键词。它们没有效果。 AND CHAIN 如果指定了 AND CHAIN，则立即启动与刚刚完成的事务具有相同事务特征（参见 SET TRANSACTION）的新事务。否则，没有新事务被启动。 使用 ROLLBACK中止一个事务。 当不在一个事务内时发出 COMMIT 不会产生危害，但是它会产生一个警告消息。当`COMMIT AND CHAIN`不在事务内时是一个错误。 要提交当前事务并且让所有更改持久化： COMMIT; 命令 `COMMIT`符合SQL标准。表单`COMMIT TRANSACTION`为IvorySQL扩展。 COMMIT PREPARED transaction_id `COMMIT PREPARED`提交一个处于预备状态的事务。 transaction_id 要被提交的事务的事务标识符。 要提交一个预备的事务，你必须是原先执行该事务的同一用户或者超级用户。但是不需要处于执行该事务的同一会话中。 这个命令不能在一个事务块中执行。该预备事务将被立刻提交。 pg_prepared_xacts系统视图中列出了所有当前可用的预备事务。 提交由事务标识符`foobar`标识的事务： COMMIT PREPARED 'foobar'; COMMIT PREPARED 是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 END [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] END 提交当前事务。所有该事务做的更改便得对他人可见并且被保证发生崩溃时仍然是持久的。这个命令是一种IvorySQL扩展，它等效于 COMMIT。 WORK TRANSACTION 可选关键词，它们没有效果。 AND CHAIN 如果规定了`AND CHAIN`，则立即启动与刚完成事务具有相同事务特征(参见 SET TRANSACTION)的新事务。否则，没有新事务被启动。 使用 ROLLBACK可以中止一个事务。 当不在一个事务中时发出`END`没有危害，但是会产生一个警告消息。 要提交当前事务并且让所有更改持久化： END; END 是一种IvorySQL扩展，它提供和 COMMIT等效的功能，后者在SQL标准中指定。 PREPARE TRANSACTION transaction_id `PREPARE TRANSACTION`为两阶段提交准备当前事务。在这个命令之后，该事务不再与当前会话关联。相反，它的状态被完全存储在磁盘上，并且有很高的可能性它会被提交成功（即便在请求提交前发生数据库崩溃）。 一旦被准备好，事务稍后就可以分别用 COMMIT PREPARED或者 ROLLBACK PREPARED提交或者回滚。可以从任何会话而不仅仅是执行原始事务的会话中发出这些命令。 从发出命令的会话的角度来看，`PREPARE TRANSACTION`不像`ROLLBACK`命令：在执行它之后，就没有活跃的当前事务，并且该预备事务的效果也不再可见（如果该事务被提交，效果将重新变得可见）。 如果由于任何原因`PREPARE TRANSACTION`命令失败，它会变成一个`ROLLBACK`：当前事务会被取消。 transaction_id 一个任意的事务标识符，`COMMIT PREPARED`或者`ROLLBACK PREPARED`以后将用这个标识符来标识这个事务。该标识符必须写成一个字符串,并且长度必须小于200字节。它也不能与任何当前已经准备好的事务的标识符相同。 PREPARE TRANSACTION`并不是设计为在应用或者交互式会话中使用。它的目的是允许一个外部事务管理器在多个数据库或者其他事务性来源之间执行原子的全局事务。除非你在编写一个事务管理器，否则你可能不会用到`PREPARE TRANSACTION。 这个命令必须在一个事务块中使用。事务块用 BEGIN开始。 当前在已经执行过任何涉及到临时表或者会话的临时命名空间、创建带`WITH HOLD`的游标或者执行`LISTEN`、`UNLISTEN`或 `NOTIFY`的事务中，不允许`PREPARE`该事务。这些特性与当前会话绑定得太过紧密，所以对一个要被准备的事务来说没有什么用处。 如果用`SET`（不带`LOCAL`选项）修改过事务的任何运行时参数，这些效果会持续到`PREPARE TRANSACTION`之后，并且将不会被后续的任何`COMMIT PREPARED`或`ROLLBACK PREPARED`所影响。因此，在这一 方面`PREPARE TRANSACTION`的行为更像`COMMIT`而不是`ROLLBACK`。 所有当前可用的准备好事务被列在 pg_prepared_xacts系统视图中。 让一个事务处于准备好状态太久是不明智的。这将会干扰`VACUUM`回收存储的能力，并且在极限情况下可能导致数据库关闭以阻止事务ID回卷（见 第 25.1.5 节）。还要记住，该事务会继续持有它已经持有的锁。该特性的设计用法是，只要一个外部事务管理器已经验证其他数据库也准备好了要提交，一个准备好的事务将被正常地提交或者回滚。 如果没有建立一个外部事务管理器来跟踪准备好的事务并且确保它们被迅速地结束，最好禁用准备好事务特性（设置 max_prepared_transactions为零）。这将防止意外地创建准备好事务，不然该事务有可能被忘记并且最终导致问题。 为两阶段提交准备当前事务，使用`foobar`作为事务标识符： PREPARE TRANSACTION 'foobar'; `PREPARE TRANSACTION`是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 ROLLBACK [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] `ROLLBACK`回滚当前事务并且导致该事务所作的所有更新都被抛弃。 WORK TRANSACTION 可选关键词，没有效果。 AND CHAIN 如果指定了`AND CHAIN`，则立即启动与刚刚完成事务具有相同事务特征（参见 SET TRANSACTION）的新事务。否则，不会启动任何新事务。 使用 COMMIT可成功地终止一个事务。 在一个事务块之外发出`ROLLBACK`会发出一个警告并且不会有效果。事务块之外的`ROLLBACK AND CHAIN`是一个错误。 要中止所有更改： ROLLBACK; 命令`ROLLBACK`符合SQL标准。窗体`ROLLBACK TRANSACTION`是一个IvorySQL扩展。 ROLLBACK PREPARED transaction_id `ROLLBACK PREPARED`回滚一个处于准备好状态的事务。 transaction_id 要被回滚的事务的事务标识符。 要回滚一个准备好的事务，你必须是原先执行该事务的同一个用户或者是一个超级用户。但是你必须处在执行该事务的同一个会话中。 这个命令不能在一个事务块内被执行。准备好的事务会被立刻回滚。 pg_prepared_xacts系统视图中列出了当前可用的所有准备好的事务。 用事务标识符`foobar`回滚对应的事务： ROLLBACK PREPARED 'foobar'; `ROLLBACK PREPARED`是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 SAVEPOINT savepoint_name `SAVEPOINT`在当前事务中建立一个新保存点。 保存点是事务内的一种特殊标记，它允许所有在它被建立之后执行的命令被回滚，把该事务的状态恢复到它处于保存点时的样子。 savepoint_name 给新保存点的名字。 使用 ROLLBACK TO回滚到一个保存点。使用 RELEASE SAVEPOINT销毁一个保存点，但保持在它被建立之后执行的命令的效果。 保存点只能在一个事务块内建立。可以在一个事务内定义多个保存点。 要建立一个保存点并且后来撤销在它建立之后执行的所有命令的效果： BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); ROLLBACK TO SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); COMMIT; 上面的事务将插入值 1 和 3，但不会插入 2。 要建立并且稍后销毁一个保存点： BEGIN; INSERT INTO table1 VALUES (3); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (4); RELEASE SAVEPOINT my_savepoint; COMMIT; 上面的事务将插入 3 和 4。 当建立另一个同名保存点时，SQL要求之前的那个保存点自动被销毁。在IvorySQL中，旧的保存点会被保留，不过在进行回滚或释放时只能使用最近的那一个（用 `RELEASE SAVEPOINT`释放较新的保存点将会导致较旧的保存点再次变得可以被`ROLLBACK TO SAVEPOINT`和`RELEASE SAVEPOINT`访问）。在其他方面，`SAVEPOINT`完全符合SQL。 SET CONSTRAINTS { ALL | name [, ...] } { DEFERRED | IMMEDIATE } `SET CONSTRAINTS`设置当前事务内约束检查的行为。`IMMEDIATE`约束在每个语句结束时被检查。`DEFERRED`约束直到事务提交时才被检查。每个约束都有自己的`IMMEDIATE`或`DEFERRED`模式。 在创建时，一个约束会被给定三种特性之一：DEFERRABLE INITIALLY DEFERRED、DEFERRABLE INITIALLY IMMEDIATE`或者`NOT DEFERRABLE。第三类总是`IMMEDIATE`并且不会受到`SET CONSTRAINTS`命令的影响。前两类在每个事务开始时都处于指定的模式，但是它们的行为可以在一个事务内用`SET CONSTRAINTS`更改。 带有一个约束名称列表的`SET CONSTRAINTS`只更改那些约束（都必须是可延迟的）的模式。每一个约束名称都可以是模式限定的。如果没有指定模式名称，则当前的模式搜索路径将被用来寻找第一个匹配的名称。`SET CONSTRAINTS ALL`更改所有可延迟约束的模式。 当`SET CONSTRAINTS`把一个约束的模式从`DEFERRED`改成`IMMEDIATE`时，新模式会有追溯效果：任何还没有解决的数据修改（本来会在事务结束时被检查）会转而在`SET CONSTRAINTS`命令的执行期间被检查。如果任何这种约束被违背，SET CONSTRAINTS`将会失败（并且不会改变该约束模式）。这样，`SET CONSTRAINTS 可以被用来在一个事务中的特定点强制进行约束检查。 当前，只有`UNIQUE`、PRIMARY KEY、REFERENCES（外键）以及`EXCLUDE`约束受到这个设置的影响。`NOT NULL`以及`CHECK`约束总是在一行被插入或修改时立即检查（不是在语句结束时）。没有被声明为`DEFERRABLE`的唯一和排除约束也会被立刻检查。 被声明为“约束触发器”的触发器的引发也受到这个设置的控制 — 它们会在相关约束被检查的同时被引发。 因为IvorySQL并不要求约束名称在模式内唯一（但是在表内要求唯一），可能有多于一个约束匹配指定的约束名称。在这种情况下`SET CONSTRAINTS`将会在所有的匹配上操作。对于一个非模式限定的名称，一旦在搜索路径中的某个模式中发现一个或者多个匹配，路径中后面的模式将不会被搜索。 这个命令只修改当前事务内约束的行为。在事务块外部发出这个命令会产生一个警告并且也不会有任何效果。 这个命令符合SQL标准中定义的行为，但有一点限制：在 IvorySQL中，它不会应用在`NOT NULL`和`CHECK`约束上。还有，IvorySQL会立刻检查非可延迟的唯一约束，而不是按照标准建议的在语句结束时检查。 SET TRANSACTION transaction_mode [, ...] SET TRANSACTION SNAPSHOT snapshot_id SET SESSION CHARACTERISTICS AS TRANSACTION transaction_mode [, ...] 其中transaction_mode是下列之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE `SET TRANSACTION`命令设置当前会话的特性。`SET SESSION CHARACTERISTICS`设置一个会话后续事务的默认事务特性。在个体事务中可以用`SET TRANSACTION`覆盖这些默认值。 可用的事务特性是事务隔离级别、事务访问模式（读/写或只读）以及可延迟模式。此外，可以选择一个快照，不过只能用于当前事务而不能作为会话默认值。 一个事务的隔离级别决定当其他事务并行运行时该事务能看见什么数据： READ COMMITTED 一个语句只能看到在它开始前提交的行。这是默认值。 REPEATABLE READ 当前事务的所有语句只能看到这个事务中执行的第一个查询或者数据修改语句之前提交的行。 SERIALIZABLE 当前事务的所有语句只能看到这个事务中执行的第一个查询或者数据修改语句之前提交的行。如果并发的可序列化事务间的读写模式可能导致一种那些事务串行（一次一个）执行时不可能出现的情况，其中之一将会被回滚并且得到一个 `serialization_failure`错误。 SQL标准定义了一种额外的级别：READ UNCOMMITTED。在IvorySQL中`READ UNCOMMITTED`被视作`READ COMMITTED`。 一个事务执行了第一个查询或者数据修改语句（SELECT、INSERT、DELETE、UPDATE、FETCH`或`COPY）之后就无法更改事务隔离级别。更多有关事务隔离级别和并发控制的信息可见 第 13 章。 事务的访问模式决定该事务是否为读/写或者只读。读/写是默认值。当一个事务为只读时，如果SQL命令`INSERT`、UPDATE、DELETE`和`COPY FROM`要写的表不是一个临时表，则它们不被允许。不允许`CREATE、ALTER`以及`DROP`命令。不允许`COMMENT、GRANT、REVOKE、TRUNCATE。如果`EXPLAIN ANALYZE`和`EXECUTE`要执行的命令是上述命令之一，则它们也不被允许。这是一种高层的只读概念，它不能阻止所有对磁盘的写入。 只有事务也是`SERIALIZABLE`以及`READ ONLY`时，`DEFERRABLE`事务属性才会有效。当一个事务的所有这三个属性都被选择时，该事务在第一次获取其快照时可能会阻塞，在那之后它运行时就不会有`SERIALIZABLE`事务的开销并且不会有任何牺牲或者被一次序列化失败取消的风险。这种模式很适合于长时间运行的报表或者备份。 SET TRANSACTION SNAPSHOT`命令允许新的事务使用与一个现有事务相同的*快照*运行。已经存在的事务必须已经把它的快照用`pg_export_snapshot 函数（见 第 9.27.5 节）导出。该函数会返回一个快照标识符，SET TRANSACTION SNAPSHOT`需要被给定一个快照标识符来指定要导入的快照。在这个命令中该标识符必须被写成一个字符串，例如’000003A1-1'。SET TRANSACTION SNAPSHOT`只能在一个事务的开始执行，并且要在该事务的第一个查询或者数据修改语句（`SELECT、INSERT、DELETE、UPDATE、FETCH`或 `COPY）之前执行。此外，该事务必须已经被设置为`SERIALIZABLE`或者`REPEATABLE READ`隔离级别（否则，该快照将被立刻抛弃，因为`READ COMMITTED`模式会为每一个命令取一个新快照）。如果导入事务使用了`SERIALIZABLE`隔离级别，那么导入快照的事务必须也使用该隔离级别。还有，一个非只读可序列化事务不能导入来自只读事务的快照。 如果执行`SET TRANSACTION`之前没有`START TRANSACTION`或者`BEGIN`，它会发出一个警告并且不会有任何效果。 可以通过在`BEGIN`或者`START TRANSACTION`中指定想要的`transaction_modes来省掉`SET TRANSACTION。但是在`SET TRANSACTION SNAPSHOT`中该选项不可用。 会话默认的事务模式也可以通过配置参数 default_transaction_isolation、http://www.postgresql.org/docs/17/runtime-config-client.html#GUC-DEFAULT-TRANSACTION-READ-ONLY[default_transaction_read_only]和 default_transaction_deferrable来设置或检查（实际上`SET SESSION CHARACTERISTICS`只是用`SET`设置这些变量的等效体）。这意味着可以通过配置文件、ALTER DATABASE 等方式设置默认值。详见 第 20 章。 当前事务的模式可以类似的通过配置参数 transaction_isolation、 transaction_read_only、和 transaction_deferrable来设置或检查。设置这其中一个参数的作用与相应的`SET TRANSACTION`选项相同，在它何时可以完成方面，也有相同的限制。但是，这些参数不能在配置文件中设置，或者从活动SQL以外的任何来源来设置。 要用一个已经存在的事务的同一快照开始一个新事务，首先要从该现有事务导出快照。这将会返回快照标识符，例如： BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SELECT pg_export_snapshot(); pg_export_snapshot --------------------- 00000003-0000001B-1 (1 row) 然后在一个新开始的事务的开头把该快照标识符用在一个`SET TRANSACTION SNAPSHOT`命令中： BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SET TRANSACTION SNAPSHOT '00000003-0000001B-1'; SQL标准中定义了这些命令，不过`DEFERRABLE`事务模式和`SET TRANSACTION SNAPSHOT`形式除外，这两者是IvorySQL扩展。 SERIALIZABLE`是标准中默认的事务隔离级别。在IvorySQL中默认值是普通的`READ COMMITTED，但是你可以按上述的方式更改。 在SQL标准中，可以用这些命令设置一个其他的事务特性：诊断区域的尺寸。这个概念与嵌入式SQL有关，并且因此没有在IvorySQL服务器中实现。 SQL标准要求连续的 transaction_modes 之间有逗号，但是出于历史原因IvorySQL允许省略逗号。 START TRANSACTION [ transaction_mode [, ...] ] 其中 transaction_mode 是下列之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE 这个命令开始一个新的事务块。如果指定了隔离级别、读写模式或者可延迟模式，新的事务将会具有这些特性，就像执行了 SET TRANSACTION一样。这和 BEGIN命令一样。 这些参数对于这个语句的含义可参考 SET TRANSACTION。 在标准中，没有必要发出`START TRANSACTION`来开始一个事务块：任何SQL命令会隐式地开始一个块。IvorySQL的行为可以被视作在每个命令之后隐式地发出一个没有跟随在`START TRANSACTION`（或者`BEGIN`）之后的`COMMIT`并且因此通常被称作 “自动提交”。为了方便，其他关系型数据库系统也可能会提供自动提交特性。 DEFERRABLE transaction_mode 是一种IvorySQL语言扩展。 SQL标准要求在连续的 transaction_modes 之间有逗号，但是由于历史原因IvorySQL允许省略逗号。 SQL输入由一个 命令 序列组成。一个命令由一个 记号 的序列构成，并由一个分号（“;”）终结。输入流的末端也会标志一个命令的结束。具体哪些记号是合法的与具体命令的语法有关。 一个记号可以是一个 关键词、一个 标识符、一个 带引号的标识符、一个 literal（或常量）或者一个特殊字符符号。记号通常以空白（空格、制表符、新行）来分隔，但在无歧义时并不强制要求如此（唯一的例子是一个特殊字符紧挨着其他记号）。 例如，下面是一个（语法上）合法的SQL输入： SELECT * FROM MY_TABLE; UPDATE MY_TABLE SET A = 5; INSERT INTO MY_TABLE VALUES (3, 'hi there'); 这是一个由三个命令组成的序列，每一行一个命令（尽管这不是必须地，在同一行中可以有超过一个命令，而且命令还可以被跨行分割）。 另外，注释 也可以出现在SQL输入中。它们不是记号，它们和空白完全一样。 根据标识命令、操作符、参数的记号不同，SQL的语法不很一致。最前面的一些记号通常是命令名，因此在上面的例子中我们通常会说一个“SELECT”、一个“UPDATE”和一个“INSERT”命令。但是例如`UPDATE`命令总是要求一个`SET`记号出现在一个特定位置，而`INSERT`则要求一个`VALUES`来完成命令。每个命令的精确语法规则在 第 VI 部分中介绍。 上例中的`SELECT`、`UPDATE`或`VALUES`记号是 关键词 的例子，即SQL语言中具有特定意义的词。记号`MY_TABLE`和`A`则是 标识符 的例子。它们标识表、列或者其他数据库对象的名字，取决于使用它们的命令。因此它们有时也被简称为“名字”。关键词和标识符具有相同的词法结构，这意味着我们无法在没有语言知识的前提下区分一个标识符和关键词。一个关键词的完整列表可以在 附录 C中找到。 SQL标识符和关键词必须以一个字母（a-z，也可以是带变音符的字母和非拉丁字母）或一个下划线（ _ ）开始。后续字符可以是字母、下划线（_）、数字（0-9）或美元符号（$）。注意根据SQL标准的字母规定，美元符号是不允许出现在标识符中的，因此它们的使用可能会降低应用的可移植性。SQL标准不会定义包含数字或者以下划线开头或结尾的关键词，因此这种形式的标识符不会与未来可能的标准扩展冲突。 系统中一个标识符的长度不能超过`NAMEDATALEN`-1字节，在命令中可以写超过此长度的标识符，但是它们会被截断。默认情况下，`NAMEDATALEN`的值为64，因此标识符的长度上限为63字节。如果这个限制有问题，可以在`src/include/pg_config_manual.h`中修改`NAMEDATALEN`常量。 关键词和不被引号修饰的标识符是大小写不敏感的。因此： UPDATE MY_TABLE SET A = 5; 可以等价地写成： uPDaTE my_TabLE SeT a = 5; 一个常见的习惯是将关键词写成大写，而名称写成小写，例如： UPDATE my_table SET a = 5; 这里还有第二种形式的标识符：受限标识符*或*被引号修饰的标识符。它是由双引号（\"）包围的一个任意字符序列。一个受限标识符总是一个标识符而不会是一个关键字。因此`\"select\"`可以用于引用一个名为“select”的列或者表，而一个没有引号修饰的`select`则会被当作一个关键词，从而在本应使用表或列名的地方引起解析错误。在上例中使用受限标识符的例子如下： UPDATE \"my_table\" SET \"a\" = 5; 受限标识符可以包含任何字符，除了代码为0的字符（如果要包含一个双引号，则写两个双引号）。这使得可以构建原本不被允许的表或列的名称，例如包含空格或花号的名字。但是长度限制依然有效。 引用标识符也使其区分大小写，而未引用的名称总是折叠成小写。例如，标识符`FOO`、foo`和`\"foo\"`在IvorySQL中被认为是相同的，但是`\"Foo\"`和`\"FOO\"`与这三个不同，并且彼此不同。（在IvorySQL中，将不带引号的名称折叠为小写与SQL标准不兼容，SQL标准规定不带引号的名称应折叠为大写。因此，根据标准，`foo`应等同于`\"FOO\"`而不是`\"foo\"。如果您想编写可移植应用程序，建议您始终引用某个特定的名称，或者永远不要引用它。） 一种受限标识符的变体允许包括转义的用代码点标识的Unicode字符。这种变体以`U&`（大写或小写U跟上一个花号）开始，后面紧跟双引号修饰的名称，两者之间没有任何空白，如`U&\"foo\"`（注意这里与操作符`&`似乎有一些混淆，但是在`&`操作符周围使用空白避免了这个问题）。在引号内，Unicode字符可以以转义的形式指定：反斜线接上4位16进制代码点号码或者反斜线和加号接上6位16进制代码点号码。例如，标识符`\"data\"`可以写成： U&\"d\\0061t\\+000061\" 下面的例子用斯拉夫语字母写出了俄语单词“slon”（大象）： U&\"\\0441\\043B\\043E\\043D\" 如果希望使用其他转义字符来代替反斜线，可以在字符串后使用`UESCAPE`子句，例如： U&\"d!0061t!+000061\" UESCAPE '!' 转义字符可以是除了16进制位、加号、单引号、双引号、空白字符之外的任意单个字符。请注意，转义字符在 UESCAPE 之后用单引号而不是双引号书写。 为了在标识符中包括转义字符本身，将其写两次即可。 4位或6位转义形式都可以被用来定义UTF-16代理对来组成代码点大于U+FFFF的字符，尽管6位形式的存在使得这种做法变得不必要（代理对并不被直接存储，而是绑定成一个单独的代码点）。 如果服务器编码不是UTF-8，则由其中一个转义序列标识的Unicode代码点转换为实际的服务器编码；如果不可能，则报告错误。 在IvorySQL中有三种 隐式类型常量：字符串、位串和数字。常量也可以被指定显式类型，这可以使得它被更精确地展示以及更有效地处理。这些选择将会在后续小节中讨论。 在SQL中，一个字符串常量是一个由单引号（ ' ）包围的任意字符序列，例如’This is a string'`。为了在一个字符串中包括一个单引号，可以写两个相连的单引号，例如’Dianne''s horse'`。注意这和一个双引号（\"）不同。 两个只由空白及至少一个新行分隔的字符串常量会被连接在一起，并且将作为一个写在一起的字符串常量来对待。例如： SELECT 'foo' 'bar'; 等同于： SELECT 'foobar'; 但是： SELECT 'foo' 'bar'; 则不是合法的语法（这种有些奇怪的行为是SQL指定的，IvorySQL遵循了该标准）。 IvorySQL也接受“转义”字符串常量，这也是SQL标准的一个扩展。一个转义字符串常量可以通过在开单引号前面写一个字母`E`（大写或小写形式）来指定，例如`E’foo'`（当一个转义字符串常量跨行时，只在第一个开引号之前写`E`）。在一个转义字符串内部，一个反斜线字符（\\）会开始一个C风格的 反斜线转义 序列，在其中反斜线和后续字符的组合表示一个特殊的字节值（如 表 4.1中所示）。 表 4.1. 反斜线转义序列 反斜线转义序列 解释 \\b 退格 \\f 换页 \\n 换行 \\r 回车 \\t 制表符 *`o*, `*`oo*, `*`ooo*` (o = 0–7) 八进制字节值 \\x*`h, `\\x`hh` (h* = 0–9, A–F) 十六进制字节值 \\u*`xxxx, `\\U`xxxxxxxx` (x* = 0–9, A–F) 16 或 32-位十六进制 Unicode 字符值 跟随在一个反斜线后面的任何其他字符被当做其字面意思。因此，要包括一个反斜线字符，请写两个反斜线（\\\\）。在一个转义字符串中包括一个单引号除了普通方法’'`之外，还可以写成`\\'`。 你要负责保证你创建的字节序列由服务器字符集编码中合法的字符组成，特别是在使用八进制或十六进制转义时。一个有用的替代方法是使用Unicode转义或替代的Unicode转义语法，如 第 4.1.2.3 节中所述；然后服务器将检查字符转换是否可行。 小心 如果配置参数 standard_conforming_strings为`off`，那么IvorySQL对常规字符串常量和转义字符串常量中的反斜线转义都识别。不过，在IvorySQL中该参数的默认值为`on`，意味着只在转义字符串常量中识别反斜线转义。这种行为更兼容标准，但是可能打断依赖于历史行为（反斜线转义总是会被识别）的应用。作为一种变通，你可以设置该参数为`off`，但是最好迁移到符合新的行为。如果你需要使用一个反斜线转义来表示一个特殊字符，为该字符串常量写上一个`E`。在`standard_conforming_strings`之外，配置参数 escape_string_warning和 backslash_quote也决定了如何对待字符串常量中的反斜线。代码零的字符不能出现在一个字符串常量中。 IvorySQL也支持另一种类型的字符串转义语法，它允许用代码点指定任意Unicode字符。一个Unicode转义字符串常量开始于`U&`（大写或小写形式的字母U，后跟花号），后面紧跟着开引号，之间没有任何空白，例如`U&'foo'`（注意这产生了与操作符`&`的混淆。在操作符周围使用空白来避免这个问题）。在引号内，Unicode字符可以通过写一个后跟4位十六进制代码点编号或者一个前面有加号的6位十六进制代码点编号的反斜线来指定。例如，字符串’data'`可以被写为 U&'d\\0061t\\+000061' 下面的例子用斯拉夫字母写出了俄语的单词“slon”（大象）： U&'\\0441\\043B\\043E\\043D' 如果想要一个不是反斜线的转义字符，可以在字符串之后使用`UESCAPE`子句来指定，例如： U&'d!0061t!+000061' UESCAPE '!' 转义字符可以是出一个十六进制位、加号、单引号、双引号或空白字符之外的任何单一字符。 要在一个字符串中包括一个表示其字面意思的转义字符，把它写两次。 4位或6位转义形式可用于指定UTF-16代理项对，以组成代码点大于U+FFFF的字符，尽管从技术上讲，6位形式的可用性使得这是不必要的（代理项对不是直接存储的，而是合并到单个代码点中。） 如果服务器编码不是UTF-8，则由这些转义序列之一标识的Unicode代码点将转换为实际的服务器编码；如果不可能，则会报告错误。 此外，字符串常量的Unicode转义语法仅在配置参数 standard_conforming_strings开启时才有效。这是因为否则这种语法可能会混淆解析SQL语句的客户端，可能导致SQL注入和类似的安全问题。如果该参数设置为off，则此语法将被拒绝并显示错误消息。 虽然用于指定字符串常量的标准语法通常都很方便，但是当字符串中包含了很多单引号或反斜线时很难理解它，因为每一个都需要被双写。要在这种情形下允许可读性更好的查询，IvorySQL提供了另一种被称为“美元引用”的方式来书写字符串常量。一个美元引用的字符串常量由一个美元符号（$）、一个可选的另个或更多字符的“标签”、另一个美元符号、一个构成字符串内容的任意字符序列、一个美元符号、开始这个美元引用的相同标签和一个美元符号组成。例如，这里有两种不同的方法使用美元引用指定字符串“Dianne’s horse”： $$Dianne's horse$$ $SomeTag$Dianne's horse$SomeTag$ 注意在美元引用字符串中，单引号可以在不被转义的情况下使用。事实上，在一个美元引用字符串中不需要对字符进行转义：字符串内容总是按其字面意思写出。反斜线不是特殊的，并且美元符号也不是特殊的，除非它们是匹配开标签的一个序列的一部分。 可以通过在每一个嵌套级别上选择不同的标签来嵌套美元引用字符串常量。这最常被用在编写函数定义上。例如： $function$ BEGIN RETURN ($1 ~ $q$[\\t\\r\\n\\v\\\\]$q$); END; $function$ 这里，序列`$q$[\\t\\r\\n\\v\\\\]$q$表示一个美元引用的文字串[\\t\\r\\n\\v\\\\]，当该函数体被IvorySQL执行时它将被识别。但是因为该序列不匹配外层的美元引用的定界符$function$`，它只是一些在外层字符串所关注的常量中的字符而已。 一个美元引用字符串的标签（如果有）遵循一个未被引用标识符的相同规则，除了它不能包含一个美元符号之外。标签是大小写敏感的，因此`$tag$String content$tag$是正确的，但是$TAG$String content$tag$`不正确。 一个跟着一个关键词或标识符的美元引用字符串必须用空白与之分隔开，否则美元引用定界符可能会被作为前面标识符的一部分。 美元引用不是SQL标准的一部分，但是在书写复杂字符串文字方面，它常常是一种比兼容标准的单引号语法更方便的方法。当要表示的字符串常量位于其他常量中时它特别有用，这种情况常常在过程函数定义中出现。如果用单引号语法，上一个例子中的每个反斜线将必须被写成四个反斜线，这在解析原始字符串常量时会被缩减到两个反斜线，并且接着在函数执行期间重新解析内层字符串常量时变成一个。 位串常量看起来像常规字符串常量在开引号之前（中间无空白）加了一个`B`（大写或小写形式），例如`B'1001'`。位串常量中允许的字符只有`0`和`1`。 作为一种选择，位串常量可以用十六进制记号法指定，使用一个前导`X`（大写或小写形式）,例如`X'1FF'`。这种记号法等价于一个用四个二进制位取代每个十六进制位的位串常量。 两种形式的位串常量可以以常规字符串常量相同的方式跨行继续。美元引用不能被用在位串常量中。 在这些一般形式中可以接受数字常量： digits digits.[digits][e[+-]digits] [digits].digits[e[+-]digits] digitse[+-]digits 其中 digits 是一个或多个十进制数字（0到9）。如果使用了小数点，在小数点前面或后面必须至少有一个数字。如果存在一个指数标记（e），在其后必须跟着至少一个数字。在该常量中不能嵌入任何空白或其他字符。注意任何前导的加号或减号并不实际被考虑为常量的一部分，它是一个应用到该常量的操作符。 这些是合法数字常量的例子： 42 3.5 4. .001 5e2 1.925e-3 如果一个不包含小数点和指数的数字常量的值适合类型`integer`（32 位），它首先被假定为类型`integer`。否则如果它的值适合类型`bigint`（64 位），它被假定为类型`bigint`。再否则它会被取做类型`numeric`。包含小数点和/或指数的常量总是首先被假定为类型`numeric`。 一个数字常量初始指派的数据类型只是类型转换算法的一个开始点。在大部分情况中，常量将被根据上下文自动被强制到最合适的类型。必要时，你可以通过造型它来强制一个数字值被解释为一种指定数据类型。例如，你可以这样强制一个数字值被当做类型`real`（float4）： REAL '1.23' -- string style 1.23::REAL -- IvorySQL (historical) style 这些实际上只是接下来要讨论的一般造型记号的特例。 一种任意类型的一个常量可以使用下列记号中的任意一种输入： type 'string' 'string'::type CAST ( 'string' AS type ) 字符串常量的文本被传递到名为 type 的类型的输入转换例程中。其结果是指定类型的一个常量。如果对该常量的类型没有歧义（例如，当它被直接指派给一个表列时），显式类型造型可以被忽略，在那种情况下它会被自动强制。 字符串常量可以使用常规SQL记号或美元引用书写。 也可以使用一个类似函数的语法来指定一个类型强制： typename ( 'string' ) 但是并非所有类型名都可以用在这种方法中，详见 第 4.2.9 节。 如 第 4.2.9 节中讨论的，::、CAST()`以及函数调用语法也可以被用来指定任意表达式的运行时类型转换。要避免语法歧义，type 'string'语法只能被用来指定简单文字常量的类型。type 'string'` 语法上的另一个限制是它无法对数组类型工作，指定一个数组常量的类型可使用`::`或`CAST()`。 CAST()`语法符合SQL。`type 'string'`语法是该标准的一般化：SQL指定这种语法只用于一些数据类型，但是IvorySQL允许它用于所有类型。带有::`的语法是IvorySQL的历史用法，就像函数调用语法一样。 一个操作符名是最多`NAMEDATALEN`-1（默认为63）的一个字符序列，其中的字符来自下面的列表： \\+ - * / < > = ~ ! @ # % ^ & | ` ? 不过，在操作符名上有一些限制： --`和/*`不能在一个操作符名的任何地方出现，因为它们将被作为一段注释的开始。 一个多字符操作符名不能以`+或-`结尾，除非该名称也至少包含这些字符中的一个： ~ ! @ # % ^ & | ` ? 例如，@-`是一个被允许的操作符名，但*-`不是。这些限制允许IvorySQL解析SQL兼容的查询而不需要在记号之间有空格。 当使用非SQL标准的操作符名时，你通常需要用空格分隔相邻的操作符来避免歧义。例如，如果你定义了一个名为`@的前缀操作符，你不能写`X*@Y，你必须写`X* @Y`来确保IvorySQL把它读作两个操作符名而不是一个。 一些不是数字字母的字符有一种不同于作为操作符的特殊含义。这些字符的详细用法可以在描述相应语法元素的地方找到。这一节只是为了告知它们的存在以及总结这些字符的目的。 跟随在一个美元符号（ $ ）后面的数字被用来表示在一个函数定义或一个预备语句中的位置参数。在其他上下文中该美元符号可以作为一个标识符或者一个美元引用字符串常量的一部分。 圆括号（ () ）具有它们通常的含义，用来分组表达式并且强制优先。在某些情况中，圆括号被要求作为一个特定 SQL 命令的固定语法的一部分。 方括号（ [] ）被用来选择一个数组中的元素。更多关于数组的信息见 第 8.15 节。 逗号（ , ）被用在某些语法结构中来分割一个列表的元素。 分号（ ; ）结束一个 SQL 命令。它不能出现在一个命令中间的任何位置，除了在一个字符串常量中或者一个被引用的标识符中。 冒号（ : ）被用来从数组中选择“切片”（见 第 8.15 节）。在某些 SQL 的“方言”（例如嵌入式 SQL）中，冒号被用来作为变量名的前缀。 星号（ * ）被用在某些上下文中标记一个表的所有域或者组合值。当它被用作一个聚集函数的参数时，它还有一种特殊的含义，即该聚集不要求任何显式参数。 句点（ . ）被用在数字常量中，并且被用来分割模式、表和列名。 一段注释是以双横杠开始并且延伸到行结尾的一个字符序列，例如： -- This is a standard SQL comment 另外，也可以使用 C 风格注释块： /* multiline comment * with nesting: /* nested block comment */ */ 这里该注释开始于 / 并且延伸到匹配出现的 /。这些注释块可按照SQL标准中指定的方式嵌套，但和C中不同。这样我们可以注释掉一大段可能包含注释块的代码。 在进一步的语法分析前，注释会被从输入流中被移除并且实际被替换为空白。 表 4.2显示了IvorySQL中操作符的优先级和结合性。大部分操作符具有相同的优先并且是左结合的。操作符的优先级和结合性被硬写在解析器中。如果您希望以不同于优先级规则所暗示的方式解析具有多个运算符的表达式，请添加括号。 表 4.2. 操作符优先级（从高到低） 操作符/元素 结合性 描述 . 左 表/列名分隔符 :: 左 IvorySQL-风格的类型转换 [ ] 左 数组元素选择 + - 右 一元加、一元减 ^ 左 指数 * / % 左 乘、除、模 + - 左 加、减 （任意其他操作符） 左 所有其他本地以及用户定义的操作符 BETWEEN IN LIKE ILIKE SIMILAR 范围包含、集合成员关系、字符串匹配 < > = ⇐ >= <> 比较操作符 IS ISNULL NOTNULL IS TRUE、IS FALSE、IS NULL、`IS DISTINCT FROM`等 NOT 右 逻辑否定 AND 左 逻辑合取 OR 左 逻辑析取 注意该操作符有限规则也适用于与上述内建操作符具有相同名称的用户定义的操作符。例如，如果你为某种自定义数据类型定义了一个“”操作符，它将具有和内建的“”操作符相同的优先级，不管你的操作符要做什么。 当一个模式限定的操作符名被用在`OPERATOR`语法中时，如下面的例子： SELECT 3 OPERATOR(pg_catalog.+) 4; `OPERATOR`结构被用来为“任意其他操作符”获得 表 4.2中默认的优先级。不管出现在`OPERATOR()`中的是哪个指定操作符，这都是真的。 注意 版本9.5之前的PostgreSQL使用的操作符优先级规则略有不同。特别是，⇐、>= 和 `<>`习惯于被当作普通操作符，`IS`测试习惯于具有较高的优先级。并且在一些认为`NOT`比`BETWEEN`优先级高的情况下，`NOT BETWEEN`和相关的结构的行为不一致。为了更好地兼容SQL标准并且减少对 逻辑上等价的结构不一致的处理，这些规则也得到了修改。在大部分情况下，这些变化不会导致行为上的变化，或者可能会产生“no such operator”错误，但可以通过增加圆括号解决。不过在一些极端情况中，查询可能在没有被报告解析错误的情况下发生行为的改变。 值表达式被用于各种各样的环境中，例如在`SELECT`命令的目标列表中、作为`INSERT`或`UPDATE`中的新列值或者若干命令中的搜索条件。为了区别于一个表表达式（是一个表）的结果，一个值表达式的结果有时候被称为一个 标量。值表达式因此也被称为 标量表达式（或者甚至简称为 表达式）。表达式语法允许使用算数、逻辑、集合和其他操作从原始部分计算值。 一个值表达式是下列之一： 一个常量或文字值 一个列引用 在一个函数定义体或预备语句中的一个位置参数引用 一个下标表达式 一个域选择表达式 一个操作符调用 一个函数调用 一个聚集表达式 一个窗口函数调用 一个类型转换 一个排序规则表达式 一个标量子查询 一个数组构造器 一个行构造器 另一个在圆括号（用来分组子表达式以及重载优先级）中的值表达式 在这个列表之外，还有一些结构可以被分类为一个表达式，但是它们不遵循任何一般语法规则。这些通常具有一个函数或操作符的语义并且在 第 9 章中的合适位置解释。一个例子是`IS NULL`子句。 我们已经在 第 4.1.2 节中讨论过常量。下面的小节会讨论剩下的选项。 一个列可以以下面的形式被引用： correlation.columnname correlation 是一个表（有可能以一个模式名限定）的名字，或者是在`FROM`子句中为一个表定义的别名。如果列名在当前索引所使用的表中都是唯一的，关联名称和分隔用的句点可以被忽略（另见 第 7 章）。 一个位置参数引用被用来指示一个由SQL语句外部提供的值。参数被用于SQL函数定义和预备查询中。某些客户端库还支持独立于SQL命令字符串来指定数据值，在这种情况中参数被用来引用那些线外数据值。一个参数引用的形式是： $number 例如，考虑一个函数`dept`的定义： CREATE FUNCTION dept(text) RETURNS dept AS $$ SELECT * FROM dept WHERE name = $1 $$ LANGUAGE SQL; 这里 $1 引用函数被调用时第一个函数参数的值。 如果一个表达式得到了一个数组类型的值，那么可以抽取出该数组值的一个特定元素： expression[subscript] 或者抽取出多个相邻元素（一个“数组切片”）： expression[lower_subscript:upper_subscript] （这里，方括号 [ ] 表示其字面意思）。每一个 下标 自身是一个表达式，它将四舍五入到最接近的整数值。 通常，数组 表达式 必须被加上括号，但是当要被加下标的表达式只是一个列引用或位置参数时，括号可以被忽略。还有，当原始数组是多维时，多个下标可以被连接起来。例如： mytable.arraycolumn[4] mytable.two_d_column[17][34] $1[10:42] (arrayfunction(a,b))[42] 最后一个例子中的圆括号是必需的。详见 第 8.15 节。 如果一个表达式得到一个组合类型（行类型）的值，那么可以抽取该行的指定域: expression.fieldname 通常行 表达式 必须被加上括号，但是当该表达式是仅从一个表引用或位置参数选择时，圆括号可以被忽略。例如： mytable.mycolumn $1.somecolumn (rowfunction(a,b)).col3 （因此，一个被限定的列引用实际上只是域选择语法的一种特例）。一种重要的特例是从一个组合类型的表列中抽取一个域： (compositecol).somefield (mytable.compositecol).somefield 这里需要圆括号来显示 compositecol 是一个列名而不是一个表名，在第二种情况中则是显示 mytable 是一个表名而不是一个模式名。 你可以通过书写 .* 来请求一个组合值的所有域： (compositecol).* 这种记法的行为根据上下文会有不同，详见 第 8.16.5 节。 对于一次操作符调用，有两种可能的语法： expression operator expression（二元中缀操作符） operator expression（一元前缀操作符） 其中 operator 记号遵循 第 4.1.3 节 的语法规则，或者是关键词`AND`、`OR`和`NOT`之一，或者是一个如下形式的受限定操作符名： OPERATOR(schema.operatorname) 哪个特定操作符存在以及它们是一元的还是二元的取决于由系统或用户定义的那些操作符。 第 9 章 描述了内建操作符。 一个函数调用的语法是一个函数的名称（可能受限于一个模式名）后面跟上封闭于圆括号中的参数列表： function_name ([expression [, expression ... ]] ) 例如，下面会计算 2 的平方根： sqrt(2) 当在一个某些用户不信任其他用户的数据库中发出查询时，在编写函数调用时应遵守 第 10.3 节 中的安全防范措施。 内建函数的列表在 第 9 章 中。其他函数可以由用户增加。 参数可以有选择地被附加名称。详见 第 4.3 节。 注意 一个采用单一组合类型参数的函数可以被有选择地称为域选择语法，并且反过来域选择可以被写成函数的风格。也就是说，记号`col(table)`和`table.col`是可以互换的。这种行为是非 SQL 标准的但是在IvorySQL中被提供，因为它允许函数的使用来模拟“计算域”。 一个 聚集表达式 表示在由一个查询选择的行上应用一个聚集函数。一个聚集函数将多个输入减少到一个单一输出值，例如对输入的求和或平均。一个聚集表达式的语法是下列之一： aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ FILTER ( WHERE filter_clause ) ] 这里 aggregate_name 是一个之前定义的聚集（可能带有一个模式名限定），并且 expression 是任意自身不包含聚集表达式的值表达式或一个窗口函数调用。可选的 order_by_clause 和 filter_clause 描述如下。 第一种形式的聚集表达式为每一个输入行调用一次聚集。第二种形式和第一种相同，因为 ALL 是默认选项。第三种形式为输入行中表达式的每一个可区分值（或者对于多个表达式是值的可区分集合）调用一次聚集。第四种形式为每一个输入行调用一次聚集，因为没有特定的输入值被指定，它通常只对于 count() 聚集函数有用。最后一种形式被用于 *有序集 聚集函数，其描述如下。 大部分聚集函数忽略空输入，这样其中一个或多个表达式得到空值的行将被丢弃。除非另有说明，对于所有内建聚集都是这样。 例如，count(*) 得到输入行的总数。 count(f1) 得到输入行中 f1 为非空的数量，因为 count 忽略空值。而 count(distinct f1) 得到 f1 的非空可区分值的数量。 一般地，交给聚集函数的输入行是未排序的。在很多情况中这没有关系，例如不管接收到什么样的输入， min 总是产生相同的结果。但是，某些聚集函数（例如 array_agg 和 string_agg ）依据输入行的排序产生结果。当使用这类聚集时，可选的 order_by_clause 可以被用来指定想要的顺序。order_by_clause 与查询级别的 ORDER BY 子句（如 第 7.5 节 所述）具有相同的语法，除非它的表达式总是仅有表达式并且不能是输出列名称或编号。例如： SELECT array_agg(a ORDER BY b DESC) FROM table; 在处理多参数聚集函数时，注意 ORDER BY 出现在所有聚集参数之后。例如，要这样写： SELECT string_agg(a, ',' ORDER BY a) FROM table; 而不能这样写： SELECT string_agg(a ORDER BY a, ',') FROM table; -- 不正确 后者在语法上是合法的，但是它表示用两个`ORDER BY`键来调用一个单一参数聚集函数（第二个是无用的，因为它是一个常量）。 如果在 order_by_clause 之外指定了 DISTINCT ，那么所有的 ORDER BY 表达式必须匹配聚集的常规参数。也就是说，你不能在 DISTINCT 列表没有包括的表达式上排序。 注意 在一个聚集函数中指定 DISTINCT 以及 ORDER BY 的能力是一种IvorySQL扩展。按照到目前为止的描述，如果一般目的和统计性聚集中 排序是可选的，在要为它排序输入行时可以在该聚集的常规参数 列表中放置 ORDER BY 。有一个聚集函数的子集叫做 有序集聚集 ，它要求一个 order_by_clause，通常是因为该聚集的计算只对其输入行的特定顺序有意义。有序集聚集的典 型例子包括排名和百分位计算。按照上文的最后一种语法，对于 一个有序集聚集， order_by_clause 被写在 WITHIN GROUP (…​) 中。 order_by_clause 中的表达式 会像普通聚集参数一样对每一个输入行计算一次，按照每个 order_by_clause 的要求排序并 且交给该聚集函数作为输入参数（这和非 WITHIN GROUP order_by_clause 的情况不同，在其中表达 式的结果不会被作为聚集函数的参数）。如果有在 WITHIN GROUP 之前的参数表达式，会把它们称 为 直接参数 以便与列在 order_by_clause 中的 聚集参数 相区分。与普通聚集参数不同，针对 每次聚集调用只会计算一次直接参数，而不是为每一个输入行 计算一次。这意味着只有那些变量被 GROUP BY 分组时，它们才能包含这些变量。这个限制同样适用于根本不在 一个聚集表达式内部的直接参数。直接参数通常被用于百分数 之类的东西，它们只有作为每次聚集计算用一次的单一值才有意 义。直接参数列表可以为空，在这种情况下，写成 () 而不是 (*)（实际上 IvorySQL接受两种拼写，但是只有第一种符合 SQL 标准）。 有序集聚集的调用例子： SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_cont ----------------- 50489 这会从表 households 的 income 列得到第 50 个百分位或者中位的值。 这里`0.5`是一个直接参数，对于百分位部分是一个 在不同行之间变化的值的情况它没有意义。 如果指定了 FILTER ，那么只有对 filter_clause 计算为真的输入行会被交给该聚集函数，其他行会被丢弃。例如： SELECT count(*) AS unfiltered, count(*) FILTER (WHERE i < 5) AS filtered FROM generate_series(1,10) AS s(i); unfiltered | filtered ------------+---------- 10 | 4 (1 row) 预定义的聚集函数在 第 9.21 节 中描述。其他聚集函数可以由用户增加。 一个聚集表达式只能出现在 SELECT 命令的结果列表或是 HAVING 子句中。在其他子句（如 WHERE ）中禁止使用它，因为那些子句的计算在逻辑上是在聚集的结果被形成之前。 当一个聚集表达式出现在一个子查询中（见 第 4.2.11 节 和 第 9.23 节），聚集通常在该子查询的行上被计算。但是如果该聚集的参数（以及 filter_clause，如果有）只包含外层变量则会产生一个异常：该聚集则属于最近的那个外层，并且会在那个查询的行上被计算。该聚集表达式从整体上则是对其所出现于的子查询的一种外层引用，并且在那个子查询的任意一次计算中都作为一个常量。只出现在结果列表或 HAVING 子句的限制适用于该聚集所属的查询层次。 一个*窗口函数调用*表示在一个查询选择的行的某个部分上应用一个聚集类的函数。和非窗口聚集函数调用不同，这不会被约束为将被选择的行分组为一个单一的输出行 — 在查询输出中每一个行仍保持独立。不过，窗口函数能够根据窗口函数调用的分组声明（ PARTITION BY 列表）访问属于当前行所在分组中的所有行。一个窗口函数调用的语法是下列之一： function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) 其中 window_definition 的语法是 [ existing_window_name ] [ PARTITION BY expression [, ...] ] [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ] [ frame_clause ] 可选的 frame_clause 是下列之一 { RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ] { RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end [ frame_exclusion ] 其中 frame_start 和 frame_end 可以是下面形式中的一种 UNBOUNDED PRECEDING offset PRECEDING CURRENT ROW offset FOLLOWING UNBOUNDED FOLLOWING 而 frame_exclusion 可以是下列之一 EXCLUDE CURRENT ROW EXCLUDE GROUP EXCLUDE TIES EXCLUDE NO OTHERS 这里，expression 表示任何自身不含有窗口函数调用的值表达式。 window_name 是对定义在查询的 WINDOW 子句中的一个命名窗口声明的引用。还可以使用在 WINDOW 子句中定义命名窗口的相同语法在圆括号内给定一个完整的 window_definition，详见 SELECT 参考页。值得指出的是，OVER wname 并不严格地等价于 OVER (wname …​)，后者表示复制并修改窗口定义，并且在被引用窗口声明包括一个帧子句时会被拒绝。 PARTITION BY 选项将查询的行分组成为 分区，窗口函数会独立地处理它们。PARTITION BY 工作起来类似于一个查询级别的 GROUP BY 子句，不过它的表达式总是只是表达式并且不能是输出列的名称或编号。如果没有 PARTITION BY，该查询产生的所有行被当作一个单一分区来处理。ORDER BY 选项决定被窗口函数处理的一个分区中的行的顺序。它工作起来类似于一个查询级别的 ORDER BY 子句，但是同样不能使用输出列的名称或编号。如果没有 ORDER BY，行将被以未指定的顺序被处理。 frame_clause 指定构成 窗口帧 的行集合，它是当前分区的一个子集，窗口函数将作用在该帧而不是整个分区。帧中的行集合会随着哪一行是当前行而变化。在 RANGE、ROWS 或者 GROUPS 模式中可以指定帧，在每一种情况下，帧的范围都是从 frame_start 到 frame_end。如果 frame_end 被省略，则末尾默认为 CURRENT ROW。 UNBOUNDED PRECEDING 的一个 frame_start 表示该帧开始于分区的第一行，类似地 UNBOUNDED FOLLOWING 的一个 frame_end 表示该帧结束于分区的最后一行。 在 RANGE 或 GROUPS 模式中，CURRENT ROW 的一个 frame_start 表示帧开始于当前行的第一个 平级 行（被窗口的 ORDER BY 子句排序为与当前行等效的行），而 CURRENT ROW 的一个 frame_end 表示帧结束于当前行的最后一个平级行。在 ROWS 模式中，CURRENT ROW 就表示当前行。 在 offset PRECEDING 以及 offset FOLLOWING 帧选项中，offset 必须是一个不包含任何变量、聚集函数或者窗口函数的表达式。offset 的含义取决于帧模式： 在 ROWS 模式中， offset 必须得到一个非空、非负的整数，并且该选项表示帧开始于当前行之前或者之后指定数量的行。 在 GROUPS 模式中，offset 也必须得到一个非空、非负的整数，并且该选项表示帧开始于当前行的平级组之前或者之后指定数量的*平级组*，这里平级组是在 ORDER BY 顺序中等效的行集合（要使用 GROUPS 模式，在窗口定义中就必须有一个 ORDER BY 子句）。 在 RANGE 模式中，这些选项要求 ORDER BY 子句正好指定一列。offset 指定当前行中那一列的值与它在该帧中前面或后面的行中的列值的最大差值。offset 表达式的数据类型会随着排序列的数据类型而变化。对于数字的排序列，它通常是与排序列相同的类型，但对于日期时间排序列它是一个 interval。例如，如果排序列是类型 date 或者 timestamp，我们可以写 RANGE BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING。offset 仍然要求是非空且非负，不过“非负”的含义取决于它的数据类型。 在任何一种情况下，到帧末尾的距离都受限于到分区末尾的距离，因此对于离分区末尾比较近的行来说，帧可能会包含比较少的行。 注意在 ROWS 以及 GROUPS 模式中， 0 PRECEDING 和 0 FOLLOWING 与 CURRENT ROW 等效。通常在 RANGE 模式中，这个结论也成立（只要有一种合适的、与数据类型相关的“零”的含义）。 frame_exclusion 选项允许当前行周围的行被排除在帧之外，即便根据帧的开始和结束选项应该把它们包括在帧中。EXCLUDE CURRENT ROW 会把当前行排除在帧之外。EXCLUDE GROUP 会把当前行以及它在顺序上的平级行都排除在帧之外。EXCLUDE TIES 把当前行的任何平级行都从帧中排除，但不排除当前行本身。EXCLUDE NO OTHERS 只是明确地指定不排除当前行或其平级行的这种默认行为。 默认的帧选项是 RANGE UNBOUNDED PRECEDING，它和 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW 相同。如果使用 ORDER BY，这会把该帧设置为从分区开始一直到当前行的最后一个 ORDER BY 平级行的所有行。如果不使用 ORDER BY，就意味着分区中所有的行都被包括在窗口帧中，因为所有行都成为了当前行的平级行。 限制是 frame_start 不能是 UNBOUNDED FOLLOWING 、frame_end 不能是 UNBOUNDED PRECEDING，并且在上述 frame_start 和 frame_end 选项的列表中 frame_end 选择不能早于 frame_start 选择出现 — 例如不允许 RANGE BETWEEN CURRENT ROW AND offset PRECEDING，但允许 ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING ，虽然它不会选择任何行。 如果指定了 FILTER ，那么只有对 filter_clause 计算为真的输入行会被交给该窗口函数，其他行会被丢弃。只有是聚集的窗口函数才接受 FILTER 。 内建的窗口函数在 表 9.60 中介绍。用户可以加入其他窗口函数。此外，任何内建的或者用户定义的通用聚集或者统计性聚集都可以被用作窗口函数（有序集和假想集聚集当前不能被用作窗口函数）。 使用 * 的语法被用来把参数较少的聚集函数当作窗口函数调用，例如 count(*) OVER (PARTITION BY x ORDER BY y)。星号（*）通常不被用于窗口相关的函数。窗口相关的函数不允许在函数参数列表中用 DISTINCT 或 ORDER BY。 只有在 SELECT 列表和查询的 ORDER BY 子句中才允许窗口函数调用。 更多关于窗口函数的信息可以在 第 3.5 节、 第 9.22 节 以及 第 7.2.5 节 中找到。 一个类型造型指定从一种数据类型到另一种数据类型的转换。IvorySQL接受两种等价的类型造型语法： CAST ( expression AS type ) expression::type CAST 语法遵从 SQL，而用 :: 的语法是IvorySQL的历史用法。 当一个造型被应用到一种未知类型的值表达式上时，它表示一种运行时类型转换。只有已经定义了一种合适的类型转换操作时，该造型才会成功。注意这和常量的造型（如 第 4.1.2.7 节 中所示）使用不同。应用于一个未修饰串文字的造型表示一种类型到一个文字常量值的初始赋值，并且因此它将对任意类型都成功（如果该串文字的内容对于该数据类型的输入语法是可接受的）。 如果一个值表达式必须产生的类型没有歧义（例如当它被指派给一个表列），通常可以省略显式类型造型，在这种情况下系统会自动应用一个类型造型。但是，只有对在系统目录中被标记为“OK to apply implicitly”的造型才会执行自动造型。其他造型必须使用显式造型语法调用。这种限制是为了防止出人意料的转换被无声无息地应用。 还可以用像函数的语法来指定一次类型造型： typename ( expression ) 不过，这只对那些名字也作为函数名可用的类型有效。例如，double precision 不能以这种方式使用，但是等效的 float8 可以。还有，如果名称 interval、time 和 timestamp 被用双引号引用，那么由于语法冲突的原因，它们只能以这种风格使用。因此，函数风格的造型语法的使用会导致不一致性并且应该尽可能被避免。 注意 函数风格的语法事实上只是一次函数调用。当两种标准造型语法之一被用来做一次运行时转换时，它将在内部调用一个已注册的函数来执行该转换。简而言之，这些转换函数具有和它们的输出类型相同的名字，并且因此“函数风格的语法”无非是对底层转换函数的一次直接调用。显然，一个可移植的应用不应当依赖于它。详见 CREATE CAST。 COLLATE 子句会重载一个表达式的排序规则。它被追加到它适用的表达式： expr COLLATE collation 这里 collation 可能是一个受模式限定的标识符。COLLATE 子句比操作符绑得更紧，需要时可以使用圆括号。 如果没有显式指定排序规则，数据库系统会从表达式所涉及的列中得到一个排序规则，如果该表达式没有涉及列，则会默认采用数据库的默认排序规则。 COLLATE 子句的两种常见使用是重载 ORDER BY 子句中的排序顺序，例如： SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE \"C\"; 以及重载具有区域敏感结果的函数或操作符调用的排序规则，例如： SELECT * FROM tbl WHERE a > 'foo' COLLATE \"C\"; 注意在后一种情况中，COLLATE 子句被附加到我们希望影响的操作符的一个输入参数上。COLLATE 子句被附加到该操作符或函数调用的哪个参数上无关紧要，因为被操作符或函数应用的排序规则是考虑所有参数得来的，并且一个显式的 COLLATE 子句将重载所有其他参数的排序规则（不过，附加非匹配 COLLATE 子句到多于一个参数是一种错误。详见 第 24.2 节）。因此，这会给出和前一个例子相同的结果： SELECT * FROM tbl WHERE a COLLATE \"C\" > 'foo'; 但是这是一个错误： SELECT * FROM tbl WHERE (a > 'foo') COLLATE \"C\"; 因为它尝试把一个排序规则应用到 > 操作符的结果，而它的数据类型是非可排序数据类型 boolean。 一个标量子查询是一种圆括号内的普通 SELECT 查询，它刚好返回一行一列（关于书写查询可见 第 7 章）。`SELECT`查询被执行并且该单一返回值被使用在周围的值表达式中。将一个返回超过一行或一列的查询作为一个标量子查询使用是一种错误（但是如果在一次特定执行期间该子查询没有返回行则不是错误，该标量结果被当作为空）。该子查询可以从周围的查询中引用变量，这些变量在该子查询的任何一次计算中都将作为常量。对于其他涉及子查询的表达式还可见 第 9.23 节。 例如，下列语句会寻找每个州中最大的城市人口： SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name) FROM states; 一个数组构造器是一个能构建一个数组值并且将值用于它的成员元素的表达式。一个简单的数组构造器由关键词 ARRAY、一个左方括号 [ 、一个用于数组元素值的表达式列表（用逗号分隔）以及最后的一个右方括号 ] 组成。例如： SELECT ARRAY[1,2,3+4]; array --------- {1,2,7} (1 row) 默认情况下，数组元素类型是成员表达式的公共类型，使用和 UNION 或 CASE 结构（见 第 10.5 节）相同的规则决定。你可以通过显式将数组构造器造型为想要的类型来重载，例如： SELECT ARRAY[1,2,22.7]::integer[]; array ---------- {1,2,23} (1 row) 这和把每一个表达式单独地造型为数组元素类型的效果相同。关于造型的更多信息请见 第 4.2.9 节。 多维数组值可以通过嵌套数组构造器来构建。在内层的构造器中，关键词 ARRAY 可以被忽略。例如，这些语句产生相同的结果： SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]]; array --------------- {{1,2},{3,4}} (1 row) SELECT ARRAY[[1,2],[3,4]]; array --------------- {{1,2},{3,4}} (1 row) 因为多维数组必须是矩形的，处于同一层次的内层构造器必须产生相同维度的子数组。任何被应用于外层 ARRAY 构造器的造型会自动传播到所有的内层构造器。 多维数组构造器元素可以是任何得到一个正确种类数组的任何东西，而不仅仅是一个子- ARRAY 结构。例如： CREATE TABLE arr(f1 int[], f2 int[]); INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]); SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr; array ------------------------------------------------ {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}} (1 row) 你可以构造一个空数组，但是因为无法得到一个无类型的数组，你必须显式地把你的空数组造型成想要的类型。例如： SELECT ARRAY[]::integer[]; array ------- {} (1 row) 也可以从一个子查询的结果构建一个数组。在这种形式中，数组构造器被写为关键词 ARRAY 后跟着一个加了圆括号（不是方括号）的子查询。例如： SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%'); array ------------------------------------------------------------------ {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412} (1 row) SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i)); array ---------------------------------- {{1,2},{2,4},{3,6},{4,8},{5,10}} (1 row) 子查询必须返回一个单一列。如果子查询的输出列是非数组类型， 结果的一维数组将为该子查询结果中的每一行有一个元素， 并且有一个与子查询的输出列匹配的元素类型。如果子查询的输出列 是一种数组类型，结果将是同类型的一个数组，但是要高一个维度。 在这种情况下，该子查询的所有行必须产生同样维度的数组，否则结果 就不会是矩形形式。 用 ARRAY 构建的一个数组值的下标总是从一开始。更多关于数组的信息，请见 第 8.15 节。 一个行构造器是能够构建一个行值（也称作一个组合类型）并用值作为其成员域的表达式。一个行构造器由关键词 ROW、一个左圆括号、用于行的域值的零个或多个表达式（用逗号分隔）以及最后的一个右圆括号组成。例如： SELECT ROW(1,2.5,'this is a test'); 当在列表中有超过一个表达式时，关键词 ROW 是可选的。 一个行构造器可以包括语法 rowvalue .*，它将被扩展为该行值的元素的一个列表，就像在一个顶层 SELECT 列表（见 第 8.16.5 节）中使用 .* 时发生的事情一样。例如，如果表 t 有列 f1 和 f2，那么这些是相同的： SELECT ROW(t.*, 42) FROM t; SELECT ROW(t.f1, t.f2, 42) FROM t; 注意 在PostgreSQL 8.2 以前，.* 语法不会在行构造器中被扩展，这样写 ROW(t.*, 42) 会创建一个有两个域的行，其第一个域是另一个行值。新的行为通常更有用。如果你需要嵌套行值的旧行为，写内层行值时不要用 .*，例如 ROW(t, 42)。 默认情况下，由一个 ROW 表达式创建的值是一种匿名记录类型。如果必要，它可以被造型为一种命名的组合类型 — 或者是一个表的行类型，或者是一种用 CREATE TYPE AS 创建的组合类型。为了避免歧义，可能需要一个显式造型。例如： CREATE TABLE mytable(f1 int, f2 float, f3 text); CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- 不需要造型因为只有一个 getf1() 存在 SELECT getf1(ROW(1,2.5,'this is a test')); getf1 ------- 1 (1 row) CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric); CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- 现在我们需要一个造型来指示要调用哪个函数： SELECT getf1(ROW(1,2.5,'this is a test')); ERROR: function getf1(record) is not unique SELECT getf1(ROW(1,2.5,'this is a test')::mytable); getf1 ------- 1 (1 row) SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype)); getf1 ------- 11 (1 row) 行构造器可以被用来构建存储在一个组合类型表列中的组合值，或者被传递给一个接受组合参数的函数。还有，可以比较两个行值，或者用 IS NULL 或 IS NOT NULL 测试一个行，例如： SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows 详见 第 9.24 节。如 第 9.23 节 中所讨论的，行构造器也可以被用来与子查询相连接。 子表达式的计算顺序没有被定义。特别地，一个操作符或函数的输入不必按照从左至右或其他任何固定顺序进行计算。 此外，如果一个表达式的结果可以通过只计算其一部分来决定，那么其他子表达式可能完全不需要被计算。例如，如果我们写： SELECT true OR somefunc(); 那么 somefunc() 将（可能）完全不被调用。如果我们写成下面这样也是一样： SELECT somefunc() OR true; 注意这和一些编程语言中布尔操作符从左至右的“短路”不同。 因此，在复杂表达式中使用带有副作用的函数是不明智的。在 WHERE 和 HAVING 子句中依赖副作用或计算顺序尤其危险，因为在建立一个执行计划时这些子句会被广泛地重新处理。这些子句中布尔表达式（ AND / OR / NOT 的组合）可能会以布尔代数定律所允许的任何方式被重组。 当有必要强制计算顺序时，可以使用一个 CASE 结构（见 第 9.18 节）。例如，在一个 WHERE 子句中使用下面的方法尝试避免除零是不可靠的： SELECT ... WHERE x > 0 AND y/x > 1.5; 但是这是安全的： SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END; 一个以这种风格使用的 CASE 结构将使得优化尝试失败，因此只有必要时才这样做（在这个特别的例子中，最好通过写 y > 1.5*x 来回避这个问题）。 不过，CASE 不是这类问题的万灵药。上述技术的一个限制是， 它无法阻止常量子表达式的提早计算。如 第 38.7 节 中所述，当查询被规划而不是被执行时，被标记成 IMMUTABLE 的函数和操作符可以被计算。因此 SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab; 很可能会导致一次除零失败，因为规划器尝试简化常量子表达式。即便是 表中的每一行都有 x > 0（这样运行时永远不会进入到 ELSE 分支）也是这样。 虽然这个特别的例子可能看起来愚蠢，没有明显涉及常量的情况可能会发生 在函数内执行的查询中，因为因为函数参数的值和本地变量可以作为常量 被插入到查询中用于规划目的。例如，在PL/pgSQL函数 中，使用一个 IF -THEN -ELSE 语句来 保护一种有风险的计算比把它嵌在一个 CASE 表达式中要安全得多。 另一个同类型的限制是，一个 CASE 无法阻止其所包含的聚集表达式 的计算，因为在考虑 SELECT 列表或 HAVING 子句中的 其他表达式之前，会先计算聚集表达式。例如，下面的查询会导致一个除零错误， 虽然看起来好像已经这种情况加以了保护： SELECT CASE WHEN min(employees) > 0 THEN avg(expenses / employees) END FROM departments; min() 和 avg() 聚集会在所有输入行上并行地计算， 因此如果任何行有 employees 等于零，在有机会测试 min() 的结果之前，就会发生除零错误。取而代之的是，可以使用 一个 WHERE 或 FILTER 子句来首先阻止有问题的输入行到达一个聚集函数。 IvorySQL允许带有命名参数的函数被使用 位置 或 命名 记号法调用。命名记号法对于有大量参数的函数特别有用，因为它让参数和实际参数之间的关联更明显和可靠。在位置记号法中，书写一个函数调用时，其参数值要按照它们在函数声明中被定义的顺序书写。在命名记号法中，参数根据名称匹配函数参数，并且可以以任何顺序书写。对于每一种记法，还要考虑函数参数类型的效果，这些在 第 10.3 节 有介绍。 在任意一种记号法中，在函数声明中给出了默认值的参数根本不需要在调用中写出。但是这在命名记号法中特别有用，因为任何参数的组合都可以被忽略。而在位置记号法中参数只能从右往左忽略。 IvorySQL也支持 混合 记号法，它组合了位置和命名记号法。在这种情况中，位置参数被首先写出并且命名参数出现在其后。 下列例子将展示所有三种记号法的用法： CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false) RETURNS text AS $$ SELECT CASE WHEN $3 THEN UPPER($1 || ' ' || $2) ELSE LOWER($1 || ' ' || $2) END; $$ LANGUAGE SQL IMMUTABLE STRICT; 函数 concat_lower_or_upper 有两个强制参数，a 和 b。此外，有一个可选的参数 uppercase，其默认值为 false。a 和 b 输入将被串接，并且根据 uppercase 参数被强制为大写或小写形式。这个函数的剩余细节对这里并不重要（详见 第 38 章）。 在IvorySQL中，位置记号法是给函数传递参数的传统机制。一个例子： SELECT concat_lower_or_upper('Hello', 'World', true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 所有参数被按照顺序指定。结果是大写形式，因为 uppercase 被指定为 true。另一个例子： SELECT concat_lower_or_upper('Hello', 'World'); concat_lower_or_upper ----------------------- hello world (1 row) 这里，uppercase 参数被忽略，因此它接收它的默认值 false，并导致小写形式的输出。在位置记号法中，参数可以按照从右往左被忽略并且因此而得到默认值。 在命名记号法中，每一个参数名都用 ⇒ 指定来把它与参数表达式分隔开。例如： SELECT concat_lower_or_upper(a => 'Hello', b => 'World'); concat_lower_or_upper ----------------------- hello world (1 row) 再次，参数 uppercase 被忽略，因此它被隐式地设置为 false。使用命名记号法的一个优点是参数可以用任何顺序指定，例如： SELECT concat_lower_or_upper(a => 'Hello', b => 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) SELECT concat_lower_or_upper(a => 'Hello', uppercase => true, b => 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 为了向后兼容性，基于 \":=\" 的旧语法仍被支持： SELECT concat_lower_or_upper(a := 'Hello', uppercase := true, b := 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 混合记号法组合了位置和命名记号法。不过，正如已经提到过的，命名参数不能超越位置参数。例如： SELECT concat_lower_or_upper('Hello', 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 在上述查询中，参数 a 和 b 被以位置指定，而 uppercase 通过名字指定。在这个例子中，这只增加了一点文档。在一个具有大量带默认值参数的复杂函数中，命名的或混合的记号法可以节省大量的书写并且减少出错的机会。 注意 命名的和混合的调用记号法当前不能在调用聚集函数时使用（但是当聚集函数被用作窗口函数时它们可以被使用）。 ALTER TABLE [ IF EXISTS ] [ ONLY ] name [ * ] action; action: ADD ( add_coldef [, ... ] ) | MODIFY ( modify_coldef [, ... ] ) | DROP [ COLUMN ] ( column_name [, ... ] ) add_coldef: column_name data_type modify_coldef: column_name data_type alter_using alter_using: USING expression name 表名. column_name 列名. data_type 列类型. expression 值表达式. ADD keyword 增加表的列, 可以增加一个列或多个列. MODIFY keyword 修改表的列, 可以修改一个列或多个列. DROP keyword 删除表的列, 可以删除一个列或多个列. USING keyword 修改列的值. ADD: create table tb_test1(id int, flg char(10)); alter table tb_test1 add (name varchar); ALTER TABLE tb_test1 ADD adress varchar, ADD num int, ADD flg1 char; \\d tb_test1 Table \"public.tb_test1\" Column | Type | Collation | Nullable | Default --------+-----------------+-----------+----------+--------- id | pg_catalog.int4 | | | flg | char(10) | | | name | varchar2(4000) | | | adress | varchar2(4000) | | | num | pg_catalog.int4 | | | flg1 | char(1) | | | MODIFY: create table tb_test2(id int, flg char(10), num varchar); insert into tb_test2 values('1', 2, '3'); ALTER TABLE tb_test2 ALTER COLUMN id TYPE char; \\d tb_test2 Table \"public.tb_test2\" Column | Type | Collation | Nullable | Default --------+----------------+-----------+----------+--------- id | char(1) | | | flg | char(10) | | | num | varchar2(4000) | | | DROP: create table tb_test3(id int, flg1 char(10), flg2 char(11), flg3 char(12), flg4 char(13), flg5 char(14), flg6 char(15)); ALTER TABLE tb_test3 DROP id; \\d tb_test3 Table \"public.tb_test3\" Column | Type | Collation | Nullable | Default --------+----------+-----------+----------+--------- flg1 | char(10) | | | flg2 | char(11) | | | flg3 | char(12) | | | flg4 | char(13) | | | flg5 | char(14) | | | flg6 | char(15) | | | [ WITH [ RECURSIVE ] with_query [, ...] ] DELETE [ FROM ] [ ONLY ] table_name [ * ] [ [ AS ] alias ] [ USING using_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name 表名. alias 表别名. using_list 一个表表达式的列表，它允许在WHERE条件中出现 来自其他表的列. condition 一个返回boolean类型值的表达式. cursor_name 要在WHERE CURRENT OF情况中使用的游标的名称. output_expression 在每一行被删除后，会被DELETE计算并且返回的表达式. output_name 被返回列的名称. create table tb_test4(id int, flg char(10)); insert into tb_test4 values(1, '2'), (5, '6'); delete from tb_test4 where id = 1; table tb_test4; id | flg ----+------------ 5 | 6 (1 row) [ WITH [ RECURSIVE ] with_query [, ...] ] UPDATE [ ONLY ] table_name [ * ] [ [ AS ] alias ] SET { [ table_name | alias ] column_name = { expression | DEFAULT } | ( [ table_name | alias ] column_name [, ...] ) = [ ROW ] ( { expression | DEFAULT } [, ...] ) | ( [ table_name | alias ] column_name [, ...] ) = ( sub-SELECT ) } [, ...] [ FROM from_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name 表名. alias 表别名. column_name 列名. expression 值表达式. sub-SELECT select子句. from_list 表表达式. condition 一个返回boolean类型值的表达式. cursor_name 要在WHERE CURRENT OF情况中使用的游标 的名称. output_expression 在每一行被删除后，会被DELETE计算并且返回的表达式. output_name 被返回列的名称. create table tb_test5(id int, flg char(10)); insert into tb_test5 values(1, '2'), (3, '4'), (5, '6'); update tb_test5 a set a.id = 33 where a.id = 3; table tb_test5; Id | flg ----+------------ 1 | 2 5 | 6 33 | 4 (3 rows) set compatible_mode to oracle; create table students(student_id varchar(20) primary key , student_name varchar(40), student_pid int); select student_id,student_name from students group by student_id; ERROR: column \"students.student_name\" must appear in the GROUP BY clause or be used in an aggregate function SELECT 100 AS value FROM DUAL UNION SELECT 200 AS value FROM DUAL UNION SELECT 100 AS value FROM DUAL; value ------- 100 200 (2 rows) select_statement MINUS [ ALL | DISTINCT ] select_statement; select_statement 任何没有ORDER BY、LIMIT、 FOR NO KEY UPDATE、FOR UPDATE、 FOR SHARE和FOR KEY SHARE子句的 SELECT语句. ALL keyword 包含重复行结果. DISTINCT keyword 显示的消除重复行. select * from generate_series(1, 3) g(i) MINUS select * from generate_series(1, 3) g(i) where i = 1; i --- 2 3 (2 rows) 使用 q\\' 转义特殊字符。q\\' 转义字符通常在! [] {} () \\<> 和其他转义字符之后使用, 您也可以使用 \\, 也可以使用字母, 数字, \\=, +, -, *, \\&, \\$, \\%, #, 等, 不允许使用空格。 select q''' is goog ''; ?column? ------------ ' is goog (1 row) SELECT [ database {schema} | schema ] sequence {nextval | currval}; sequence 序列名. create sequence sq; select sq.nextval; nextval ----------- 1 (1 row) select sq.currval; nextval ----------- 1 (1 row) 将给定的不带时区的时间戳转换为指定的带时区的时间戳，如果指定时区或者时间戳为NULL，则返回NULL。 参数 描述 day 不带时区的时间戳 tz 指定的时区 select from_tz('2021-11-08 09:12:39','Asia/Shanghai') from dual; from_tz ----------------------------------- 2021-11-08 09:12:39 Asia/Shanghai (1 row) select from_tz('2021-11-08 09:12:39','SAST') from dual; from_tz -------------------------- 2021-11-08 09:12:39 SAST select from_tz(NULL,'SAST') from dual; from_tz --------- (1 row) select from_tz('2021-11-08 09:12:31',NULL) from dual; from_tz --------- (1 row) 获取当前数据库系统的时间戳。 select systimestamp(); systimestamp ------------------------------- 2021-12-02 14:38:59.879642+08 (1 row) select systimestamp; statement_timestamp ------------------------------- 2021-12-02 14:39:33.262828+08 将给定的带时区的时间戳转换为不带时区的UTC时间。 参数 描述 day 需要转化带时区的时间戳 select sys_extract_utc('2018-03-28 11:30:00.00 +09:00'::timestamptz) from dual; sys_extract_utc --------------------- 2018-03-28 02:30:00 (1 row) select sys_extract_utc(NULL) from dual; sys_extract_utc ----------------- (1 row) 获取当前会话的时区。 select sessiontimezone() from dual; sessiontimezone ----------------- PRC (1 row) set timezone to UTC; select sessiontimezone(); sessiontimezone ----------------- UTC (1 row) next_day 返回由格式名相同的第一个工作日的日期，该日期晚于当前日期。 无论日期的数据类型如何，返回类型始终为 DATE。 返回值具有与参数日期相同的小时、分钟和秒部分。 参数 描述 value 开始时间戳 weekday 星期几，可以是 \"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" 或者 0,1,2,3,4,5,6,0代表星期天 select next_day(to_timestamp('2020-02-29 14:40:50', 'YYYY-MM-DD HH24:MI:SS'), 'Tuesday') from dual; next_day --------------------- 2020-03-03 14:40:50 (1 row) select next_day('2020-07-01 19:43:51 +8'::timestamptz, 1) from dual; next_day --------------------- 2020-07-05 19:43:51 (1 row) last_day返回档期日期所在月份的最后一天。 参数 描述 value 指定的时间戳 select last_day(timestamp '2020-05-17 13:27:19') from dual; last_day --------------------- 2020-05-31 13:27:19 (1 row) select last_day('2020-11-29 19:20:40 +08'::timestamptz) from dual; last_day --------------------- 2020-11-30 19:20:40 (1 row) add_months 返回日期加上整数月份。 date 参数可以是日期时间值或任何可以隐式转换为 DATE 的值。 整数参数可以是整数或任何可以隐式转换为整数的值。 参数 描述 day oracle.date类型，需要被改变的时间戳 value 一个整形数据，需要增加的月数 select add_months(date '2020-02-15',7) from dual; add_months --------------------- 2020-09-15 00:00:00 (1 row) select add_months(timestamp '2018-12-15 19:12:09',12) from dual; add_months --------------------- 2019-12-15 19:12:09 (1 row) sysdate 返回数据库服务器的操作系统时间。 select sysdate; statement_sysdate --------------------- 2021-12-09 16:20:34 (1 row) select sysdate(); sysdate --------------------- 2021-12-09 16:21:39 (1 row) 转换第一个时区的时间到第二个时区的时间. 时区包括了 \"ast\", \"adt\", \"bst\", \"bdt\", \"cst\", \"cdt\", \"est\", \"edt\", \"gmt\", \"hst\", \"hdt\", \"mst\", \"mdt\", \"nst\", \"pst\", \"pdt\", \"yst\", \"ydt\". 参数 描述 day 需要被转换的时间戳 tz1 时间戳的时区 tz2 目标时区 select new_time(timestamp '2020-12-12 17:45:18', 'AST', 'ADT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'BST', 'BDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'CST', 'CDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) trunc函数返回一个日期, 按照指定格式被截断, fmt包括 \"Y\", \"YY\", \"YYY\", \"YYYY\",\"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". 参数 描述 value 指定的日期（oracle.date, timestamp, timestamptz） fmt 指定的格式, 如果被省略, 默认为 \"DDD\" select trunc(timestamp '2020-07-28 19:16:12', 'Q'); trunc --------------------- 2020-07-01 00:00:00 (1 row) select trunc(timestamptz '2020-09-27 18:30:21 + 08', 'MONTH'); trunc ------------------------ 2020-09-01 00:00:00+08 (1 row) round函数返回一个日期,按照指定的格式四舍五入, fmt 包括了 \"Y\", \"YY\", \"YYY\", \"YYYY\",\"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". 参数 描述 value 被转换的日期（oracle.date, timestamp, timestamptz） fmt 指定的格式, 如果被省略, 默认为 \"DDD\" select round(timestamp '2050-06-12 16:40:55', 'IYYY'); round --------------------- 2050-01-03 00:00:00 (1 row) TO_CHAR（str,[fmt]） 根据给定的格式将输入参数转换为 TEXT 数据类型的值。 如果省略 fmt，则数据将转换为系统默认格式的 TEXT 值。 如果 str 为 null，则该函数返回 null。 str 输入参数 （任意类型）。 fmt 输入格式参数,详见格式fmt。 select to_char('3 2:20:05'); to_char ----------------- 3 days 02:20:05 (1 row) select to_char('4.00'::numeric); to_char --------- 4 (1 row) select to_char(NULL); to_char --------- (1 row) select to_char(123,'xx'); to_char --------- 7b (1 row) TO_NUMBER(str,[fmt1]) 根据给定的格式将输入参数 str 转换为 NUMREIC 数据类型的值。 如果省略 fmt1，则数据将转换为系统默认格式的 NUMERIC 值。 如果 str 是 NUMERIC，则该函数返回 str。如果 str 计算结果为 null，则该函数返回 null。 如果它不能转换为 NUMERIC 数据类型，则该函数返回错误。 str 输入参数包括以下数据类型（double precision，numeric，text，integer等，但必须隐式转换为numeric）。 fmt1 输入格式参数，详见格式fmt1。 select to_number(1210.73::numeric, 9999.99::numeric); to_number ----------- 1210.73 (1 row) select to_number(NULL); to_number ----------- (1 row) select to_number('123'::text); to_number ----------- 123 (1 row) TO_DATE(str,[fmt]) 根据给定的格式将输入参数 str 转换为日期数据类型的值。 如果省略 fmt，则数据将转换为系统默认格式的日期值。 如果 str 为 null，则该函数返回 null。 如果 fmt 是 J，对于 Julian，则 char 必须是整数。 如果无法转换为 DATE，则该函数返回错误。 str 输入参数（integer，text，可以隐式转换为上述类型，符合日期格式的字符串）。 fmt 输入格式参数，详见格式fmt。 select to_date('50-11-28 ','RR-MM-dd '); to_date --------------------- 1950-11-28 00:00:00 (1 row) select to_date(2454336, 'J'); to_date --------------------- 2007-08-23 00:00:00 (1 row) select to_date('2019/11/22', 'yyyy-mm-dd'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('20-11-28 10:14:22','YY-MM-dd hh24:mi:ss'); to_date --------------------- 2020-11-28 10:14:22 (1 row) select to_date('2019/11/22'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('2019/11/27 10:14:22'); to_date --------------------- 2019-11-27 10:14:22 (1 row) select to_date('2020','RR'); to_date --------------------- 2020-01-01 00:00:00 (1 row) select to_date(NULL); to_date --------- (1 row) select to_date('-4712-07-23 14:31:23', 'syyyy-mm-dd hh24:mi:ss'); to_date ---------------------- -4712-07-23 14:31:23 (1 row) TO_TIMESTAMP(str,[fmt]) 根据给定的格式将输入参数 str 转换为不带时区的时间戳。 如果省略 fmt，则数据将转换为系统默认格式中不带时区值的时间戳。 如果 str 为 null，则该函数返回 null。 如果无法转换为不带时区的时间戳，则该函数返回错误。 str 输入参数（double precision,text，可以隐式转换为上述类型）。 fmt 输入格式参数，详见格式fmt。 select to_timestamp(1212121212.55::numeric); to_timestamp --------------------------- 2008-05-30 12:20:12.55 (1 row) select to_timestamp('2020/03/03 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp ------------------------ 2020-03-03 13:13:18 (1 row) select to_timestamp(NULL,NULL); to_timestamp -------------- (1 row) TO_YMINTERVAL(str) 将输入参数 str 时间间隔转换为年到月范围内的时间间隔。 只处理年月，其他部分省略。 如果输入参数为NULL，函数返回NULL，如果输入参数格式错误，函数返回错误。 str 输入参数（text，可以隐式转换为文本类型，必须是时间间隔格式。 兼容 SQL 标准的 SQL 间隔格式， ISO 持续时间格式与 ISO 8601:2004 标准兼容）。 select to_yminterval('P1Y-2M2D'); to_yminterval --------------- +000000000-10 (1 row) select to_yminterval('P1Y2M2D'); to_yminterval --------------- +000000001-02 (1 row) select to_yminterval('-01-02'); to_yminterval --------------- -000000001-02 (1 row) TO_DSINTERVAL(str) 将输入参数 str 的时间间隔转换为天到秒范围内的时间间隔。 输入参数包括：日、时、分、秒和微秒。 如果输入参数为NULL，函数返回NULL，如果输入参数包含年月或格式错误，函数返回错误。 str 输入参数（text，可以隐式转换为文本类型，必须是时间间隔格式。 兼容 SQL 标准的 SQL 间隔格式， ISO 持续时间格式与 ISO 8601:2004 标准兼容）。 select to_dsinterval('100 00 :02 :00'); to_dsinterval ------------------------------- +000000100 00:02:00.000000000 (1 row) select to_dsinterval('-100 00:02:00'); to_dsinterval ------------------------------- -000000100 00:02:00.000000000 (1 row) select to_dsinterval(NULL); to_dsinterval --------------- (1 row) TO_TIMESTAMP_TZ(str,[fmt]) 根据给定的格式将输入参数 str 转换为带时区的时间戳。 如果省略 fmt，则数据将转换为具有系统默认格式带时区值的时间戳。 如果 str 为 null，则该函数返回 null。 如果无法转换为带时区的时间戳，则该函数返回错误。 str 输入参数（text，可以隐式转换为文本类型）。 fmt 输入格式参数，详见格式fmt。 select to_timestamp_tz('2019','yyyy'); to_timestamp_tz ------------------------ 2019-01-01 00:00:00+08 (1 row) select to_timestamp_tz('2019-11','yyyy-mm'); to_timestamp_tz ------------------------ 2019-11-01 00:00:00+08 (1 row) select to_timestamp_tz('2003/12/13 10:13:18 +7:00'); to_timestamp_tz ------------------------ 2003-12-13 11:13:18+08 (1 row) select to_timestamp_tz('2019/12/13 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp_tz ------------------------ 2019-12-13 13:13:18+08 (1 row) select to_timestamp_tz(NULL); to_timestamp_tz ----------------- (1 row) GREATEST(expr1,expr2,…​) 获取一个或多个表达式的输入列表中的最大值。 如果任何 expr 的计算结果为 NULL，则该函数返回 NULL。 expr1` 输入参数（任意类型）。 `expr2` 输入参数（任意类型）。 `... select greatest('a','b','A','B'); greatest ---------- b (1 row) select greatest(',','.','/',';','!','@','?'); greatest ---------- @ (1 row) select greatest('瀚','高','数','据','库'); greatest ---------- 高 (1 row) SELECT greatest('HARRY', 'HARRIOT', 'HARRA'); greatest ---------- HARRY (1 row) SELECT greatest('HARRY', 'HARRIOT', NULL); greatest ---------- (1 row) SELECT greatest(1.1, 2.22, 3.33); greatest ---------- 3.33 (1 row) SELECT greatest('A', 6, 7, 5000, 'E', 'F','G') A; a --- G (1 row) LEAST(expr1,expr2,…​) 获取一个或多个表达式的输入列表中的最小值。 如果任何 expr 的计算结果为 NULL，则该函数返回 NULL。 expr1` 输入参数（任意类型）。 `expr2` 输入参数（任意类型）。 `... SELECT least(1,' 2', '3' ); least ------- 1 (1 row) SELECT least(NULL, NULL, NULL); least ------- (1 row) SELECT least('A', 6, 7, 5000, 'E', 'F','G') A; a ------ 5000 (1 row) select least(1,3,5,10); least ------- 1 (1 row) select least('a','A','b','B'); least ------- A (1 row) select least(',','.','/',';','!','@'); least ------- ! (1 row) select least('瀚','高','据','库'); least ------- 库 (1 row) SELECT least('HARRY', 'HARRIOT', NULL); least ------- (1 row) 模式 描述 HH 一天中的小时 （01-12） HH12 一天中的小时 （01-12） HH24 一天中的小时 （00-23） MI 分钟 （00-59）minute (00-59) SS 秒（00-59） MS 毫秒（000-999） US 微秒（000000-999999） SSSS 午夜后的秒（0-86399） AM, am, PM or pm 正午指示器（不带句号） A.M., a.m., P.M. or p.m. 正午指示器（带句号） Y,YYY 带逗号的年（4 位或者更多位） YYYY 年（4 位或者更多位） YYY 年的后三位 YY 年的后两位 Y 年的最后一位 IYYY ISO 8601 周编号方式的年（4 位或更多位） IYY ISO 8601 周编号方式的年的最后 3 位 IY ISO 8601 周编号方式的年的最后 2 位 I ISO 8601 周编号方式的年的最后一位 BC, bc, AD或者ad 纪元指示器（不带句号） B.C., b.c., A.D.或者a.d. 纪元指示器（带句号） MONTH 全大写形式的月名（空格补齐到 9 字符） Month 全首字母大写形式的月名（空格补齐到 9 字符） month 全小写形式的月名（空格补齐到 9 字符） MON 简写的大写形式的月名（英文 3 字符，本地化长度可变） Mon 简写的首字母大写形式的月名（英文 3 字符，本地化长度可变） mon 简写的小写形式的月名（英文 3 字符，本地化长度可变） MM 月编号（01-12） DAY 全大写形式的日名（空格补齐到 9 字符） Day 全首字母大写形式的日名（空格补齐到 9 字符） day 全小写形式的日名（空格补齐到 9 字符） DY 简写的大写形式的日名（英语 3 字符，本地化长度可变） Dy 简写的首字母大写形式的日名（英语 3 字符，本地化长度可变） dy 简写的小写形式的日名（英语 3 字符，本地化长度可变） DDD 一年中的日（001-366） IDDD ISO 8601 周编号方式的年中的日（001-371，年的第 1 日时第一个 ISO 周的周一） DD 月中的日（01-31） D 周中的日，周日（1）到周六（7） ID 周中的 ISO 8601 日，周一（1）到周日（7） W 月中的周（1-5）（第一周从该月的第一天开始） WW 年中的周数（1-53）（第一周从该年的第一天开始） IW ISO 8601 周编号方式的年中的周数（01 - 53；新的一年的第一个周四在第一周） CC 世纪（2 位数）（21 世纪开始于 2001-01-01） J 儒略日（从午夜 UTC 的公元前 4714 年 11 月 24 日开始的整数日数） Q 季度（to_date和to_timestamp会忽略） RM 大写形式的罗马计数法的月（I-XII；I 是 一月） rm 小写形式的罗马计数法的月（i-xii；i 是 一月） TZ 大写形式的时区名称 tz 小写形式的时区名称 OF 时区偏移量 模式 描述 9 带有指定位数的值 0 带前导零的值 . (period) 小数点 , (comma) 分组（千）分隔符 PR 尖括号内的负值 S 带符号的数字（使用区域） L 货币符号（使用区域） D 小数点（使用区域） G 分组分隔符（使用区域） MI 在指定位置的负号（如果数字 < 0） PL 在指定位置的正号（如果数字 > 0） SG 在指定位置的正/负号 RN 罗马数字（输入在 1 和 3999 之间） TH or th 序数后缀 V 移动指定位数（参阅注解） EEEE 科学记数的指数 NLS_LENGTH_SEMANTICS 使您能够使用字节或字符长度语义创建 CHAR 和 VARCHAR2 列。 现有列不受影响。 在这种情况下，默认语义是 BYTE。 SET NLS_LENGTH_SEMANTICS TO [NONE | BYTE | CHAR]; BYTE:数据以字节长度来存储。 CHAR:数据以字符长度来存储。 NONE:数据使用原生PostgreSQL存储方式。 create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 SET NLS_LENGTH_SEMANTICS TO BYTE; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- byte (1 row) insert into test values ('李老师您好'); 2021-12-14 15:28:11.906 HKT [6774] ERROR: value too long for type varchar2(5 byte) 2021-12-14 15:28:11.906 HKT [6774] STATEMENT: insert into test values ('李老师您好'); ERROR: value too long for type varchar2(5 byte) 具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。 VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 PL/iSQL 是 IvorySQL 的过程语言，用于为 IvorySQL 编写自定义函数、过程和包。 PL/iSQL 派生自 PostgreSQL 的 PL/pgsql，并增加了一些功能，但在语法上 PL/iSQL 更接近 Oracle 的 PL/SQL。 本文档描述了 PL/iSQL 程序的基本结构和构造。 iSQL 是一种程序化的块结构语言，支持四种不同的 程序类型，即 PACKAGES、PROCEDURES、FUNCTIONS 和 TRIGGERS。 iSQL 对每种类型的受支持程序使用相同的块结构。 一个块最多由三个部分组成：声明部分，可执行文件，和异常部分。 而声明和异常部分是可选的。 [DECLARE declarations] BEGIN statements [ EXCEPTION WHEN <exception_condition> THEN statements] END; 一个块至少可以由一个可执行部分组成 在 BEGIN 和 END 关键字中包含一个或多个 iSQL 语句。 CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS BEGIN NULL; END; / 所有关键字都不区分大小写。 标识符被隐式转换为小写，除非双引号， 就像它们在普通 SQL 命令中一样。 声明部分可用于声明变量和游标，并取决于使用块的上下文， 声明部分可以以关键字 DECLARE 开头。 CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS DECLARE quantity integer := 30; c_row pg_class%ROWTYPE; r_cursor refcursor; CURSOR c1 RETURN pg_proc%ROWTYPE; BEGIN NULL; end; / 可选的异常部分也可以包含在 BEGIN - END 块中。 异常部分以关键字 EXCEPTION 开始，一直持续到它出现的块的末尾。 如果块内的语句抛出异常，程序控制转到异常部分，根据异常和异常部分的内容，可能会或不会处理抛出的异常。 CREATE OR REPLACE FUNCTION reraise_test() RETURN void AS BEGIN BEGIN RAISE syntax_error; EXCEPTION WHEN syntax_error THEN BEGIN raise notice 'exception % thrown in inner block, reraising', sqlerrm; RAISE; EXCEPTION WHEN OTHERS THEN raise notice 'RIGHT - exception % caught in inner block', sqlerrm; END; END; EXCEPTION WHEN OTHERS THEN raise notice 'WRONG - exception % caught in outer block', sqlerrm; END; / 注意 与 PL/pgSQL 类似，PL/iSQL 使用 BEGIN/END 对语句进行分组， 并且不要将它们与用于事务控制的同名 SQL 命令混淆。 PL/iSQL 的 BEGIN/END 仅用于分组； 他们不开始或结束事务 要从 psql 客户端创建 PL/iSQL 程序，您可以使用类似于 PL/pgSQL 的$$语法 CREATE FUNCTION func() RETURNS void as $$ .. end$$ language plisql; 或者，您可以使用不带 $$ 的 Oracle 兼容语法的引用和语言规范， 并使用 /（正斜杠） 结束程序定义。 /（正斜杠）必须在换行符上 CREATE FUNCTION func() RETURN void AS … END; / CREATE [OR REPLACE] PROCEDURE procedure_name [(parameter_list)] is [DECLARE] -- variable declaration BEGIN -- stored procedure body END; / CREATE [OR REPLACE] FUNCTION function_name ([parameter_list]) RETURN return_type AS [DECLARE] -- variable declaration BEGIN -- function body return statement END; / CREATE [ OR REPLACE ] PACKAGE [schema.] *package_name* [invoker_rights_clause] [IS | AS] item_list[, item_list ...] END [*package_name*]; invoker_rights_clause: AUTHID [CURRENT_USER | DEFINER] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] function_declaration: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype; procedure_declaration: PROCEDURE procedure_name [(parameter_declaration[, ...])] type_definition: record_type_definition | ref_cursor_type_definition cursor_declaration: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype; item_declaration: cursor_declaration | cursor_variable_declaration | record_variable_declaration | variable_declaration | record_type_definition: TYPE record_type IS RECORD ( variable_declaration [, variable_declaration]... ) ; ref_cursor_type_definition: TYPE type IS REF CURSOR [ RETURN type%ROWTYPE ]; cursor_variable_declaration: curvar curtype; record_variable_declaration: recvar { record_type | rowtype_attribute | record_type%TYPE }; variable_declaration: varname datatype [ [ NOT NULL ] := expr ] parameter_declaration: parameter_name [IN] datatype [[:= | DEFAULT] expr] CREATE [ OR REPLACE ] PACKAGE BODY [schema.] package_name [IS | AS] [item_list[, item_list ...]] | item_list_2 [, item_list_2 ...] [initialize_section] END [package_name]; initialize_section: BEGIN statement[, ...] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] item_list_2: [ function_declaration function_definition procedure_declaration procedure_definition cursor_definition ] function_definition: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype [IS | AS] [declare_section] body; procedure_definition: PROCEDURE procedure_name [(parameter_declaration[, ...])] [IS | AS] [declare_section] body; cursor_definition: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype IS select_statement; body: BEGIN statement[, ...] END [name]; statement: [<<LABEL>>] pl_statments[, ...]; { CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... [ START WITH condition ] | START WITH condition CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... } CONNECT BY 查询语法以 CONNECT BY 关键字开头，这些关键字定义了父行和子行之间的分层相互依赖关系。 必须通过在 CONNECT BY 子句的条件部分指定 PRIOR 关键字来进一步限定结果. PRIOR PRIOR 关键字是一元运算符，它将前一行与当前行联系起来。 此关键字可以用在相等条件的左边或右边。 START WITH 该子句指定从哪一行开始层次结构。 NOCYCLE 无操作语句。 目前只有语法支持。 该子句表示即使存在循环也返回数据。 LEVEL 返回层次结构中当前行的级别，从根节点的 1 开始，之后每级别递增 1。 CONNECT_BY_ROOT expr 返回层次结构中当前行的父列。 SYS_CONNECT_BY_PATH(col, chr) 它是一个返回从根到当前节点的列值的函数，由字符“chr”分隔。 目前此功能有以下限制： 附加列可用于大多数表达式，如函数调用、CASE 语句和通用表达式，但有一些不受支持的列，如 ROW、TYPECAST、COLLATE、GROUPING 子句等 两个或多个列相同的情况下，可能需要输出列名，例如 > SELECT CONNECT_BY_ROOT col AS \"col1\", CONNECT_BY_ROOT col AS \"col2\" .... 不支持间接运算符或“*” 不支持循环检测（Loop detection） CREATE UNIQUE INDEX [IF NOT EXISTS] name ON table_name [USING method] (columns) GLOBAL CREATE UNIQUE INDEX myglobalindex on mytable(bid) GLOBAL; 在创建全局唯一索引期间，系统会对所有现有分区执行索引扫描，如果发现来自其他分区的重复项而不是当前分区，则会引发错误。例如： 命令 create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); create table gidxpart2 partition of gidxpart for values from (100000) to (199999); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); insert into gidxpart (a, b, c) values (150000, 572814, 'inserted second on gidxpart2'); create unique index on gidxpart (b) global; 输出 ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated. 在全局唯一索引创建过程中，系统会对所有现有分区执行索引扫描，如果在其他分区而不是当前分区中发现重复项，则会引发错误。 命令 create table gidx_part (a int, b int, c text) partition by range (a); create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (10); create table gidxpart2 partition of gidxpart for values from (10) to (100); create unique index gidx_u on gidxpart using btree(b) global; insert into gidxpart values (1, 1, 'first'); insert into gidxpart values (11, 11, 'eleventh'); insert into gidxpart values (2, 11, 'duplicated (b)=(11) on other partition'); 输出 ERROR: duplicate key value violates unique constraint \"gidxpart2_b_idx\" DETAIL: Key (b)=(11) already exists. 将新表附加到具有全局唯一索引的分区表时，系统将对所有现有分区进行重复检查。 如果在现有分区中发现与附加表中的元组匹配的重复项，则会引发错误并且附加失败。 附加需要所有现有分区上的共享锁（SHARE LOCK）。 如果其中一个分区正在进行并发 INSERT，则附加将等待它先完成。 这可以在未来的版本中改进 运行命令 create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); create unique index on gidxpart (b) global; create table gidxpart2 (a int, b int, c text); insert into gidxpart2 (a, b, c) values (150000, 572814, 'dup inserted on gidxpart2'); alter table gidxpart attach partition gidxpart2 for values from (100000) to (199999); 输出 ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated.","title":"开发者指南","component":"ivorysql-doc","version":"master","name":"developer_guide","url":"/ivorysql-doc/master/developer_guide.html","titles":[{"text":"1. 概览","hash":"概览","id":1},{"text":"1.1. 架构概述","hash":"架构概述","id":2},{"text":"1.2. 系统表的变化","hash":"系统表的变化","id":3},{"text":"2. 数据库建模（第一章创建库+第二章创建表）","hash":"数据库建模第一章创建库第二章创建表","id":4},{"text":"2.1. 创建一个数据库","hash":"创建一个数据库","id":5},{"text":"2.2. 创建一个新表","hash":"创建一个新表","id":6},{"text":"3. 写入数据（SQL写入）参考第 6 章 数据操纵","hash":"写入数据sql写入参考第-6-章-数据操纵","id":7},{"text":"4. 查询数据 参考 第七章查询的组合查询  第十五章 并行查询","hash":"查询数据-参考-第七章查询的组合查询-第十五章-并行查询","id":8},{"text":"4.1. 组合查询","hash":"组合查询","id":9},{"text":"4.2. 并行查询","hash":"并行查询","id":10},{"text":"4.2.1. 并行查询如何工作","hash":"并行查询如何工作","id":11},{"text":"4.2.2. 何时会用到并行查询？","hash":"何时会用到并行查询","id":12},{"text":"4.2.3. 并行计划","hash":"并行计划","id":13},{"text":"4.2.3.1. 并行扫描","hash":"并行扫描","id":14},{"text":"4.2.3.2. 并行连接","hash":"并行连接","id":15},{"text":"4.2.3.3. 并行聚集","hash":"并行聚集","id":16},{"text":"4.2.3.4. 并行Append","hash":"并行append","id":17},{"text":"4.2.3.5. 并行计划小贴士","hash":"并行计划小贴士","id":18},{"text":"5. 事务（参考Sql命令）","hash":"事务参考sql命令","id":19},{"text":"5.1. 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COMMIT PREPARED — 提交一个早前为两阶段提交预备的事务","hash":"commit-prepared-提交一个早前为两阶段提交预备的事务","id":40},{"text":"5.4.1. 大纲","hash":"大纲-4","id":41},{"text":"5.4.2. 描述","hash":"描述-3","id":42},{"text":"5.4.3. 参数","hash":"参数-4","id":43},{"text":"5.4.4. 注解","hash":"注解-4","id":44},{"text":"5.4.5. 例子","hash":"例子-2","id":45},{"text":"5.4.6. 兼容性","hash":"兼容性-4","id":46},{"text":"5.5. END - 提交当前事务","hash":"end-提交当前事务","id":47},{"text":"5.5.1. 大纲","hash":"大纲-5","id":48},{"text":"5.5.2. 描述","hash":"描述-4","id":49},{"text":"5.5.3. 参数","hash":"参数-5","id":50},{"text":"5.5.4. 注解","hash":"注解-5","id":51},{"text":"5.5.5. 示例","hash":"示例-3","id":52},{"text":"5.5.6. 兼容性","hash":"兼容性-5","id":53},{"text":"5.6. PREPARE TRANSACTION — 为两阶段提交准备当前事务","hash":"prepare-transaction-为两阶段提交准备当前事务","id":54},{"text":"5.6.1. 大纲","hash":"大纲-6","id":55},{"text":"5.6.2. 描述","hash":"描述-5","id":56},{"text":"5.6.3. 参数","hash":"参数-6","id":57},{"text":"5.6.4. 注解","hash":"注解-6","id":58},{"text":"5.6.5. 小心","hash":"小心","id":59},{"text":"5.6.6. 例子","hash":"例子-3","id":60},{"text":"5.6.7. 兼容性","hash":"兼容性-6","id":61},{"text":"5.7. ROLLBACK — 中止当前事务","hash":"rollback-中止当前事务","id":62},{"text":"5.7.1. 大纲","hash":"大纲-7","id":63},{"text":"5.7.2. 描述","hash":"描述-6","id":64},{"text":"5.7.3. 参数","hash":"参数-7","id":65},{"text":"5.7.4. 注解","hash":"注解-7","id":66},{"text":"5.7.5. 示例","hash":"示例-4","id":67},{"text":"5.7.6. 兼容性","hash":"兼容性-7","id":68},{"text":"5.8. ROLLBACK PREPARED — 取消一个之前为两阶段提交准备好的事务","hash":"rollback-prepared-取消一个之前为两阶段提交准备好的事务","id":69},{"text":"5.8.1. 大纲","hash":"大纲-8","id":70},{"text":"5.8.2. 描述","hash":"描述-7","id":71},{"text":"5.8.3. 参数","hash":"参数-8","id":72},{"text":"5.8.4. 注解","hash":"注解-8","id":73},{"text":"5.8.5. 例子","hash":"例子-4","id":74},{"text":"5.8.6. 兼容性","hash":"兼容性-8","id":75},{"text":"5.9. SAVEPOINT — 在当前事务中定义一个新的保存点","hash":"savepoint-在当前事务中定义一个新的保存点","id":76},{"text":"5.9.1. 大纲","hash":"大纲-9","id":77},{"text":"5.9.2. 描述","hash":"描述-8","id":78},{"text":"5.9.3. 参数","hash":"参数-9","id":79},{"text":"5.9.4. 注解","hash":"注解-9","id":80},{"text":"5.9.5. 示例","hash":"示例-5","id":81},{"text":"5.9.6. 兼容性","hash":"兼容性-9","id":82},{"text":"5.10. SET CONSTRAINTS — 为当前事务设置约束检查时机","hash":"set-constraints-为当前事务设置约束检查时机","id":83},{"text":"5.10.1. 大纲","hash":"大纲-10","id":84},{"text":"5.10.2. 描述","hash":"描述-9","id":85},{"text":"5.10.3. 注解","hash":"注解-10","id":86},{"text":"5.10.4. 兼容性","hash":"兼容性-10","id":87},{"text":"5.11. SET TRANSACTION — 设置当前事务的特性","hash":"set-transaction-设置当前事务的特性","id":88},{"text":"5.11.1. 大纲","hash":"大纲-11","id":89},{"text":"5.11.2. 描述","hash":"描述-10","id":90},{"text":"5.11.3. 注解","hash":"注解-11","id":91},{"text":"5.11.4. 示例","hash":"示例-6","id":92},{"text":"5.11.5. 兼容性","hash":"兼容性-11","id":93},{"text":"5.12. START TRANSACTION — 开始一个事务块","hash":"start-transaction-开始一个事务块","id":94},{"text":"5.12.1. 大纲","hash":"大纲-12","id":95},{"text":"5.12.2. 描述","hash":"描述-11","id":96},{"text":"5.12.3. 参数","hash":"参数-10","id":97},{"text":"5.12.4. 兼容性","hash":"兼容性-12","id":98},{"text":"6. Sql参考（第 4 章 SQL语法）","hash":"sql参考第-4-章-sql语法","id":99},{"text":"6.1. 词法结构","hash":"词法结构","id":100},{"text":"6.1.1. 标识符和关键词","hash":"标识符和关键词","id":101},{"text":"6.1.2. 常量","hash":"常量","id":102},{"text":"6.1.2.1. 字符串常量","hash":"字符串常量","id":103},{"text":"6.1.2.2. C风格转义的字符串常量","hash":"c风格转义的字符串常量","id":104},{"text":"6.1.2.3. 带有 Unicode 转义的字符串常量","hash":"带有-unicode-转义的字符串常量","id":105},{"text":"6.1.2.4. 美元引用的字符串常量","hash":"美元引用的字符串常量","id":106},{"text":"6.1.2.5. 位串常量","hash":"位串常量","id":107},{"text":"6.1.2.6. 数字常量","hash":"数字常量","id":108},{"text":"6.1.2.7. 其他类型的常量","hash":"其他类型的常量","id":109},{"text":"6.1.3. 操作符","hash":"操作符","id":110},{"text":"6.1.4. 特殊字符","hash":"特殊字符","id":111},{"text":"6.1.5. 注释","hash":"注释","id":112},{"text":"6.1.5.1. 操作符优先级","hash":"操作符优先级","id":113},{"text":"6.2. 值表达式","hash":"值表达式","id":114},{"text":"6.2.1. 列引用","hash":"列引用","id":115},{"text":"6.2.2. 位置参数","hash":"位置参数","id":116},{"text":"6.2.3. 下标","hash":"下标","id":117},{"text":"6.2.4. 域选择","hash":"域选择","id":118},{"text":"6.2.5. 操作符调用","hash":"操作符调用","id":119},{"text":"6.2.6. 函数调用","hash":"函数调用","id":120},{"text":"6.2.7. 聚集表达式","hash":"聚集表达式","id":121},{"text":"6.2.8. 窗口函数调用","hash":"窗口函数调用","id":122},{"text":"6.2.9. 类型转换","hash":"类型转换","id":123},{"text":"6.2.10. 排序规则表达式","hash":"排序规则表达式","id":124},{"text":"6.2.11. 标量子查询","hash":"标量子查询","id":125},{"text":"6.2.12. 数组构造器","hash":"数组构造器","id":126},{"text":"6.2.13. 行构造器","hash":"行构造器","id":127},{"text":"6.2.14. 表达式计算规则","hash":"表达式计算规则","id":128},{"text":"6.3. 调用函数","hash":"调用函数","id":129},{"text":"6.3.1. 使用位置记号","hash":"使用位置记号","id":130},{"text":"6.3.2. 使用命名记号","hash":"使用命名记号","id":131},{"text":"6.3.3. 使用混合记号","hash":"使用混合记号","id":132},{"text":"7. 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PL/iSQL 程序的结构","hash":"plisql-程序的结构","id":254},{"text":"7.13.2. psql 对 PL/iSQL 程序的支持","hash":"psql-对-plisql-程序的支持","id":255},{"text":"7.13.3. PL/iSQL 程序语法","hash":"plisql-程序语法","id":256},{"text":"7.13.3.1. PROCEDURES","hash":"procedures","id":257},{"text":"7.13.3.2. FUNCTIONS","hash":"functions","id":258},{"text":"7.13.3.3. PACKAGES","hash":"packages","id":259},{"text":"7.13.3.3.1. PACKAGE HEADER","hash":"package-header","id":260},{"text":"7.13.3.3.2. PACKAGE BODY","hash":"package-body","id":261},{"text":"7.14. 层级查询","hash":"层级查询","id":262},{"text":"7.14.1. 语法","hash":"语法-8","id":263},{"text":"7.14.2. 附加列","hash":"附加列","id":264},{"text":"7.14.3. 限制","hash":"限制","id":265},{"text":"8. 全局唯一索引","hash":"全局唯一索引","id":266},{"text":"8.1. 创建全局唯一索引","hash":"创建全局唯一索引","id":267},{"text":"8.1.1. 语法","hash":"语法-9","id":268},{"text":"8.1.2. 示例","hash":"示例-23","id":269},{"text":"8.1.3. 全局唯一性保证","hash":"全局唯一性保证","id":270},{"text":"8.2. 插入和更新","hash":"插入和更新","id":271},{"text":"8.2.1. 插入和更新的全局唯一性保证","hash":"插入和更新的全局唯一性保证","id":272},{"text":"8.2.2. 示例","hash":"示例-24","id":273},{"text":"8.3. 附加和分离","hash":"附加和分离","id":274},{"text":"8.3.1. 附加语句的全球唯一性保证","hash":"附加语句的全球唯一性保证","id":275},{"text":"8.3.2. 示例","hash":"示例-25","id":276}]},"4":{"id":4,"text":"PostgreSQL 服务端提供了对 GB18030 字符集的全面支持。GB18030是中国国家标准，旨在包含所有汉字和多种少数民族文字，实现与Unicode的统一。在PostgreSQL中正确配置和使用GB18030字符集，对于处理和存储需要符合此标准的中文数据至关重要。 服务端 GB18030 支持应当具备以下特性： 1. 支持 GB18030 作为服务端编码：initdb -E GB18030 可用，SHOW server_encoding 显示为 GB18030。 2. 提供 GB18030 <→ UTF8 的双向转换。 3. 支持多字节边界判定。 PostgreSQL已支持GB18030-2000版本作为客户端编码，通过扩展的方式支持GB18030_2022字符集与UTF的转换。 修改pg_enc来实现可指定GB18030作为服务端编码，PostgreSQL编码框架中增加底层函数以供pg内核调用。 设置一个全局变量is_load_gb18030_2022,默认为true,当用户指定-E选项时,在get_encoding_id中判断其设置的是否为gb18030_2022,如果是,将其字符串转为gb18030,然后将is_load_gb18030_2022 设为true,如果-E 选项为Gb18030,将其设为false。 在适当位置判断是否要加载插件,如果是,执行load_gb18030_2022,并将ivorysql.conf中的shared_preload_library添加gb18030_2022。 if (encoding_name && *encoding_name) { encoding_name_modify = pg_strdup(encoding_name); if(pg_strcasecmp(encoding_name,\"gb18030_2022\") == 0) { encoding_name_modify = pg_strdup(\"gb18030\"); is_load_gb18030_2022 = true; } else if(pg_strcasecmp(encoding_name,\"gb18030\") == 0) is_load_gb18030_2022 = false; if ((enc = pg_valid_server_encoding((const char *)encoding_name_modify)) >= 0) return enc; } wchar.c，增加 GB18030 的函数指针： pg_gb180302wchar_with_len(const unsigned char *from, pg_wchar *to, int len) gb18030 → wchar pg_wchar2gb18030_with_len(const pg_wchar *from, unsigned char *to, int len) wchar → gb18030 pg_gb18030_mblen(const unsigned char *s)：返回 1/2/4。 pg_gb18030_dsplen(const unsigned char *s)：ASCII 显示宽度 1；其它按 1 处理（与UTF8一致）。 pg_gb18030_verifier(const unsigned char *s, int len)：校验字节范围，拒绝非法序列。 接收数据: 如果一个使用 UTF-8 编码的客户端连接上来，服务端在接收到数据后，会调用其内部的 utf8_to_gb18030 函数，将数据转换为 GB18030 格式，然后才进行验证和存储。 发送数据: 当该客户端执行 SELECT 查询时，服务端会从磁盘/内存中读取原生的 GB18030 数据，然后调用 gb18030_to_utf8 函数将其转换为 UTF-8 格式，最后再通过网络协议发送给客户端。 新增GB18030-2022.xml数据文件，通过perl脚本解析为map文件，提供 gb18030_to_utf8() 与 utf8_to_gb18030()，优先表驱动，覆盖不到的区间通过算法映射。 static inline uint32 unicode_to_utf8word(uint32 c) { uint32 word; if (c <= 0x7F) { word = c; } else if (c <= 0x7FF) { word = (0xC0 | ((c >> 6) & 0x1F)) << 8; word |= 0x80 | (c & 0x3F); } else if (c <= 0xFFFF) { word = (0xE0 | ((c >> 12) & 0x0F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } else { word = (0xF0 | ((c >> 18) & 0x07)) << 24; word |= (0x80 | ((c >> 12) & 0x3F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } return word; } static uint32 conv_18030_2022_to_utf8(uint32 code) { #define conv18030(minunicode, mincode, maxcode) \\ if (code >= mincode && code <= maxcode) \\ return unicode_to_utf8word(gb_linear(code) - gb_linear(mincode) + minunicode) conv18030(0x0452, 0x8130D330, 0x8136A531); conv18030(0x2643, 0x8137A839, 0x8138FD38); conv18030(0x361B, 0x8230A633, 0x8230F237); conv18030(0x3CE1, 0x8231D438, 0x8232AF32); conv18030(0x4160, 0x8232C937, 0x8232F837); conv18030(0x44D7, 0x8233A339, 0x8233C931); conv18030(0x478E, 0x8233E838, 0x82349638); conv18030(0x49B8, 0x8234A131, 0x8234E733); conv18030(0x9FA6, 0x82358F33, 0x8336C738); conv18030(0xE865, 0x8336D030, 0x84308534); conv18030(0xFA2A, 0x84309C38, 0x84318537); conv18030(0xFFE6, 0x8431A234, 0x8431A439); conv18030(0x10000, 0x90308130, 0xE3329A35); /* No mapping exists */ return 0; }","title":"GB18030 字符集服务端支持","component":"ivorysql-doc","version":"master","name":"gb18030","url":"/ivorysql-doc/master/gb18030.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. initdb时通过-E指定GB18030或GB18030_2022","hash":"initdb时通过-e指定gb18030或gb18030_2022","id":3},{"text":"2.2. 多字节处理","hash":"多字节处理","id":4},{"text":"2.3. 与客户端的交互","hash":"与客户端的交互","id":5}]},"5":{"id":5,"text":"IvorySQL安装方式包括以下5种： yum源安装 docker安装 rpm安装 源码安装 deb安装 本章将详细介绍每种方式的安装、运行及卸载过程。想要更快获得IvorySQL，请参阅快速开始。 同样，安装前请先创建一个用户，并赋予其root权限，安装、使用和卸载均以该用户执行。这里以ivorysql用户为例。 创建或编辑IvorySQL yum源配置文件/etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 保存退出后，安装IvorySQL5 $ sudo dnf install -y ivorysql5-5.0 从Docker Hub上获取IvorySQL镜像 $ docker pull ivorysql/ivorysql:5.0-ubi8 运行IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 -e参数说明 参数名 是否必填 描述 IVORYSQL_USER 否 数据库用户，默认 ivorysql IVORYSQL_PASSWORD 是 数据库用户密码 IVORYSQL_DB 否 数据库名称，默认 ivorysql POSTGRES_HOST_AUTH_METHOD 否 修改主机身份验证方式，参考值：md5 POSTGRES_INITDB_ARGS 否 为initdb是添加额外参数，参考值：\"--data-checksums\" PGDATA 否 将数据目录定义到其他路径文件夹下（例如子目录），默认 /var/lib/ivorysql/data POSTGRES_INITDB_WALDIR 否 定义IvorySQL transaction文件夹路径，默认在数据目录（PGDATA）的子目录中。 不推荐将POSTGRES_HOST_AUTH_METHOD参数填写为trust，这样将会使IVORYSQL_PASSWORD设置失效。 如果POSTGRES_HOST_AUTH_METHOD参数设置为scram-sha-256，同时需将POSTGRES_INITDB_ARGS设置为—​auth-host=scram-sha-256，才会使数据库正确进行初始化。 安装依赖 $ sudo dnf install -y lz4 libicu libxslt python3 获取rpm包 $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-9d890e9-20251120.x86_64.rpm 安装rpm包 使用以下命令安装所有rpm包： $ sudo yum --disablerepo=* localinstall *.rpm 数据库将被安装在/usr/ivory-5/路径下。 安装依赖 $ sudo dnf install -y bison readline-devel zlib-devel openssl-devel uuid-devel $ sudo dnf groupinstall -y 'Development Tools' 获取IvorySQL源代码 $ git clone https://github.com/IvorySQL/IvorySQL.git $ cd IvorySQL $ git checkout -b IVORY_REL_5_STABLE origin/IVORY_REL_5_STABLE 配置 在IvorySQL目录下，执行以下命令进行配置，请使用—​prefix指定安装目录: $ ./configure --prefix=/usr/local/ivorysql/ivorysql-5 编译 执行以下命令进行编译： $ make 编译完毕，安装之前可先执行make check或make all-check-world测试刚刚编译的结果。 安装 执行以下命令安装，数据库将被安装在上述由—​prefix指定的路径下： $ sudo make install 安装依赖 $ sudo apt -y install pkg-config libreadline-dev libicu-dev libldap2-dev uuid-dev tcl-dev libperl-dev python3-dev bison flex openssl libssl-dev libpam-dev libxml2-dev libxslt-dev libossp-uuid-dev libselinux-dev gettext 获取deb包 $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-a50789d-20250304.amd64.deb 安装deb包 $ sudo dpkg -i IvorySQL-5.0-a50789d-20250304.amd64.deb 数据库将被安装在/usr/ivory-5/路径下。 参考yum源安装、rpm安装、源码安装、deb安装的用户，需要手动启动数据库。 赋权 执行以下命令为安装用户赋权，示例用户为ivorysql，安装目录为/usr/ivory-5/： $ sudo chown -R ivorysql:ivorysql /usr/ivory-5/ 配置环境变量 将以下配置写入用户的~/.bash_profile文件并使用source命令该文件使环境变量生效： PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile 数据库初始化 $ mkdir /usr/ivory-5/data $ initdb -D /usr/ivory-5/data 其中-D参数用来指定数据库的数据目录。更多参数使用方法，请使用initdb --help命令获取。 启动数据库服务 $ pg_ctl -D /usr/ivory-5/data -l ivory.log start 其中-D参数用来指定数据库的数据目录，如果[配置环境变量] 配置了PGDATA，则该参数可以省略。-l参数用来指定日志文件。更多参数使用方法，请使用pg_ctl --help命令获取。 查看确认数据库启动成功： $ ps -ef | grep postgres ivorysql 130427 1 0 02:45 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 130428 130427 0 02:45 ? 00:00:00 postgres: checkpointer ivorysql 130429 130427 0 02:45 ? 00:00:00 postgres: background writer ivorysql 130431 130427 0 02:45 ? 00:00:00 postgres: walwriter ivorysql 130432 130427 0 02:45 ? 00:00:00 postgres: autovacuum launcher ivorysql 130433 130427 0 02:45 ? 00:00:00 postgres: logical replication launcher ivorysql 130445 130274 0 02:45 pts/1 00:00:00 grep --color=auto postgres psql连接数据库： $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# 其中-d参数用来指定想要连接到的数据库名称。IvorySQL默认使用ivorysql数据库，但较低版本的IvorySQL首次使用时需用户先连接postgres数据库，然后自己创建ivorysql数据库。较高版本的IvorySQL则已为用户创建好ivorysql数据库，可以直接连接。 更多参数使用方法，请使用psql --help命令获取。 Docker运行IvorySQL时，需要添加额外参数，参考：psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 使用任何一种方法卸载前请先停止数据库服务并做好数据备份。 执行以下命令依次卸载： $ sudo dnf remove -y ivorysql5-5.0 执行以下命令，使IvorySQL容器停止运行，并删除IvorySQL容器和镜像： $ docker stop ivorysql $ docker rm ivorysql $ docker rmi ivorysql/ivorysql:5.0-ubi8 执行以下命令卸载并清理文件夹： $ sudo yum remove --disablerepo=* ivorysql5\\* $ sudo rm -rf /usr/ivory-5 执行以下命令卸载数据库并清理文件夹： $ sudo make uninstall $ make clean $ sudo rm -rf /usr/ivory-5 执行以下命令卸载数据库并清理文件夹： $ sudo dpkg -P IvorySQL-5.0 $ sudo rm -rf /usr/ivory-5","title":"安装指南","component":"ivorysql-doc","version":"master","name":"installation_guide","url":"/ivorysql-doc/master/installation_guide.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. yum源安装","hash":"yum源安装","id":2},{"text":"3. docker安装","hash":"docker安装","id":3},{"text":"4. rpm安装","hash":"rpm安装","id":4},{"text":"5. 源码安装","hash":"源码安装","id":5},{"text":"6. deb安装","hash":"deb安装","id":6},{"text":"7. 启动数据库","hash":"启动数据库","id":7},{"text":"8. 数据库连接","hash":"数据库连接","id":8},{"text":"9. 卸载IvorySQL","hash":"卸载ivorysql","id":9},{"text":"9.1. yum源安装的卸载","hash":"yum源安装的卸载","id":10},{"text":"9.2. docker安装的卸载","hash":"docker安装的卸载","id":11},{"text":"9.3. rpm安装的卸载","hash":"rpm安装的卸载","id":12},{"text":"9.4. 源码安装的卸载","hash":"源码安装的卸载","id":13},{"text":"9.5. deb安装的卸载","hash":"deb安装的卸载","id":14}]},"6":{"id":6,"text":"数据库迁移是指将数据从一个数据库转移到另一个数据库的过程，两端的数据库可能是PostgreSQL, IvorySQL, MySQL, Oracle, SQL Server等。迁移过程是一个具有挑战性的复杂过程，需要对数据库的原理以及各自的特性了如指掌。如果应用已经部署到生产环境并处于正常运行状态，为了保持业务不中断运行，并且不发生数据丢失，数据库迁移后需进行平滑的应用迁移。 迁移后数据库和系统应符合以下要求： 迁移后的数据库系统应完全承载原数据库系统的数据。避免迁移过程中数据丢失导致新的数据库系统数据不完整。 迁移后的数据库系统应完全适配原有数据库的功能。避免迁移后因数据类型或语法、函数的不支持，且无替代方案，导致整个业务系统的无法运行或抛错。 迁移后的数据库应适配整个业务系统的上下游，稳定可靠的保障整个业务系统的运行。 迁移后的数据库各方面综合性能不能弱于原数据库，为整个业务系统提供性能保证。 Ora2Pg 是一个免费的工具，用于将 Oracle 数据库迁移到 IvorySQL 兼容的模式。它连接您的 Oracle 数据库，自动扫描并提取它的结构或数据，然后生成可以装载到 IvorySQL 数据库的 SQL 脚本。Ora2Pg 可以从逆向工程 Oracle 数据库到大型企业数据库迁移，或者简单地将一些 Oracle 数据复制到 IvorySQL 数据库中。它非常容易使用，并且不需要任何 Oracle 数据库知识，而不需要提供连接到Oracle数据库所需的参数。 Ora2Pg 由一个 Perl 脚本（ora2pg）以及一个 Perl 模块（ Ora2Pg.pm）组成，唯一需要做的事情就是修改它的配置文件 ora2pg.conf，设置连接 Oracle 数据库的 DSN 和一个可选的 SCHEMA 名称。完成之后，只需要设置导出的类型：TABLE（包括约束和索引）、VIEW、MVIEW、TABLESPACE、SEQUENCE、INDEXES、TRIGGER、GRANT、FUNCTION、PROCEDURE、PACKAGE、PARTITION、TYPE、INSERT 或 COPY、FDW、QUERY、KETTLE 以及 SYNONYM。 默认情况下，Ora2Pg 导出一个 SQL 文件，可以通过 IvorySQL 客户端工具 psql 执行导出的SQL文件。当进行数据迁移时，可以在配置文件中设置一个目标数据库的 DSN，直接将数据从Oracle导入到 IvorySQL 数据库中。 对象 ora2pg是否支持 view 是 trigger 是，某些情况下需要手工修改脚本 sequence 是 function 是 procedure 是，某些情况下需要手工修改脚本 type 是，某些情况下需要手工修改脚本 materialized view 是，某些情况下需要手工修改脚本 linux环境 Oracle版本 IvorySQL版本 Centos Stream 9 19.0.0.0 4.2 [root@localhost /]# dnf install -y perl perl-ExtUtils-CBuilder perl-ExtUtils-MakeMaker [root@localhost /]# perl -v This is perl 5, version 16, subversion 3 (v5.16.3) built for x86_64-linux-thread-multi (with 44 registered patches, see perl -V for more detail) Copyright 1987-2012, Larry Wall Perl may be copied only under the terms of either the Artistic License or the GNU General Public License, which may be found in the Perl 5 source kit. Complete documentation for Perl, including FAQ lists, should be found on this system using \"man perl\" or \"perldoc perl\". If you have access to the Internet, point your browser at http://www.perl.org/, the Perl Home Page. DBI，Database Independent Interface，是 Perl 语言连接数据库的接口 下载地址 https://mirrors.sjtug.sjtu.edu.cn/cpan/modules/by-module/Plack/TIMB/DBI-1.643.tar.gz # tar zxvf DBI-1.643.tar.gz # cd DBI-1.643/ # perl Makefile.PL # make && make install 下载地址：https://sourceforge.net/projects/ora2pg/ 设置环境变量; 加载环境变量;因为必须定义ORACLE_HOME环境变量;本例在ivorysql用户下配置环境变量 export ORACLE_HOME=/opt/oracle/product/19c/dbhome_1 # tar -zxvf DBD-Oracle-1.76.tar.gz # source /home/postgres/.bashrc # cd DBD-Oracle-1.76 # perl Makefile.PL # make && make install 下载地址：https://sourceforge.net/projects/ora2pg/ # tar -xjf ora2pg-24.0.tar.bz2 # cd ora2pg-24.0 # perl Makefile.PL # make && make install 默认安装在/usr/local/bin/目录下 检查软件环境： # vi check.pl #!/usr/bin/perl use strict; use ExtUtils::Installed; my $inst= ExtUtils::Installed->new(); my @modules = $inst->modules(); foreach(@modules) { my $ver = $inst->version($_) || \"???\"; printf(\"%-12s -- %s\\n\", $_, $ver); } exit; # perl check.pl DBD::Oracle -- 1.76 DBD::Pg -- 3.8.0 DBI -- 1.643 Ora2Pg -- 24.0 Perl -- 5.16.3 设置环境变量 export PERL5LIB=<your_install_dir> #export PERL5LIB=/usr/local/bin/ === 源端准备工作 更新oracle统计信息 提高性能 BEGIN DBMS_STATS.GATHER_SCHEMA_STATS('SH'); DBMS_STATS.GATHER_SCHEMA_STATS('SCOTT'); DBMS_STATS.GATHER_SCHEMA_STATS('HR'); DBMS_STATS.GATHER_DATABASE_STATS; DBMS_STATS.GATHER_DICTIONARY_STATS; END;/ 查询源端对象的类型 ```bash SYS@PROD1>set pagesize 200 SYS@PROD1>select distinct OBJECT_TYPE from dba_objects where OWNER in ('SH','SCOTT','HR') ; OBJECT_TYPE ------------------- INDEX PARTITION TABLE PARTITION SEQUENCE PROCEDURE LOB X TRIGGER DIMENSION X MATERIALIZED VIEW TABLE INDEX VIEW 11 rows selected. 配置ora2pg.conf: 默认情况下，Ora2Pg会查找/etc/ora2pg/ora2pg.conf配置文件，如果文件存在，您只需执行：/usr/local/bin/ora2pg cat /etc/ora2pg/ora2pg.conf.dist | grep -v ^# |grep -v ^$ >ora2pg.conf vi ora2pg.conf # cat ora2pg.conf ORACLE_HOME /opt/oracle/product/19c/dbhome_1 ORACLE_DSN dbi:Oracle:host=localhost;sid=ORCLCDB;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 # 将用户导入到PostgreSQL数据库中 DISABLE_UNLOGGED 1 #避免将NOLOGGING属性设为UNLOGGED SKIP fkeys ukeys checks #跳过外键 唯一 和检查约束 TYPE TABLE,VIEW,GRANT,SEQUENCE,TABLESPACE,PROCEDURE,TRIGGER,FUNCTION,PACKAGE,PARTITION,TYPE,MVIEW,QUERY,DBLINK,SYNONYM,DIRECTORY,TEST,TEST_VIEW NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh.sql 只能同时执行一种类型的导出，因此TYPE指令必须是唯一的。如果您有多个，则只会在文件中找到最后一个。但我测试就可以同时导出多个类型的。 请注意，您可以通过向TYPE指令提供以逗号分隔的导出类型列表来链接多个导出，但在这种情况下，您不能将COPY或INSERT与其他导出类型一起使用。 某些导出类型不能或不应该直接加载到 IvorySQL 数据库中，仍然需要很少的手动编辑。GRANT，TABLESPACE，TRIGGER，FUNCTION，PROCEDURE，TYPE，QUERY和PACKAGE导出类型就是这种情况，特别是如果您有PLSQL代码或Oracle特定SQL。 对于TABLESPACE，您必须确保系统上存在文件路径，对于SYNONYM，您可以确保对象的所有者和模式对应于新的PostgreSQL数据库设计。 建议导出表结构时，一个类型一个类型的操作，避免其它错误相互影响。 设置Oracle数据库DSN后，您可以执行ora2pg以查看它是否有效： # ora2pg -t SHOW_VERSION -c ora2pg.conf Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 估算从Oracle到PostgreSQL的迁移过程的成本并不容易。为了获得对此迁移成本的良好评估，Ora2Pg将检查所有数据库对象，所有函数和存储过程，以检测是否仍有一些对象和PL / SQL代码无法由Ora2Pg自动转换。 Ora2Pg具有内容分析模式，该模式检查Oracle数据库以生成有关Oracle数据库包含的内容和无法导出的内容的文本报告。 # ora2pg -t SHOW_REPORT --estimate_cost -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 11/11 objects types (100.0%) end of objects auditing. ------------------------------------------------------------------------------- Ora2Pg v24.0 - Database Migration Report ------------------------------------------------------------------------------- Version Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 Schema SH Size 287.25 MB ------------------------------------------------------------------------------- Object Number Invalid Estimated cost Comments Details ------------------------------------------------------------------------------- DATABASE LINK 0 0 0 Database links will be exported as SQL/MED IvorySQL's Foreign Data Wrapper (FDW) extensions using oracle_fdw. DIMENSION 5 0 0 GLOBAL TEMPORARY TABLE 0 0 0 Global temporary table are not supported by PostgreSQL and will not be exported. You will have to rewrite some application code to match the PostgreSQL temporary table behavior. INDEX 20 0 3.4 14 index(es) are concerned by the export, others are automatically generated and will do so on PostgreSQL. Bitmap will be exported as btree_gin index(es) and hash index(es) will be exported as b-tree index(es) if any. Domain index are exported as b-tree but commented to be edited to mainly use FTS. Cluster, bitmap join and IOT indexes will not be exported at all. Reverse indexes are not exported too, you may use a trigram-based index (see pg_trgm) or a reverse() function based index and search. Use 'varchar_pattern_ops', 'text_pattern_ops' or 'bpchar_pattern_ops' operators in your indexes to improve search with the LIKE operator respectively into varchar, text or char columns. 11 bitmap index(es). 1 domain index(es). 2 b-tree index(es). INDEX PARTITION 196 0 0 Only local indexes partition are exported, they are build on the column used for the partitioning. JOB 0 0 0 Job are not exported. You may set external cron job with them. MATERIALIZED VIEW 2 0 6 All materialized view will be exported as snapshot materialized views, they are only updated when fully refreshed. SYNONYM 0 0 0 SYNONYMs will be exported as views. SYNONYMs do not exists with PostgreSQL but a common workaround is to use views or set the PostgreSQL search_path in your session to access object outside the current schema. TABLE 11 0 1.1 1 external table(s) will be exported as standard table. See EXTERNAL_TO_FDW configuration directive to export as file_fdw foreign tables or use COPY in your code if you just want to load data from external files. Total number of rows: 1063384. Top 10 of tables sorted by number of rows:. sales has 918843 rows. costs has 82112 rows. customers has 55500 rows. supplementary_demographics has 4500 rows. times has 1826 rows. promotions has 503 rows. products has 72 rows. countries has 23 rows. channels has 5 rows. sales_transactions_ext has 0 rows. Top 10 of largest tables:. TABLE PARTITION 56 0 5.6 Partitions are exported using table inheritance and check constraint. Hash and Key partitions are not supported by PostgreSQL and will not be exported. 56 RANGE partitions.. VIEW 1 0 1 Views are fully supported but can use specific functions. ------------------------------------------------------------------------------- Total 291 0 17.10 17.10 cost migration units means approximatively 1 man-day(s). The migration unit was set to 5 minute(s) ------------------------------------------------------------------------------ Migration level : A-1 ------------------------------------------------------------------------------- Migration levels: A - Migration that might be run automatically B - Migration with code rewrite and a human-days cost up to 5 days C - Migration with code rewrite and a human-days cost above 5 days Technical levels: 1 = trivial: no stored functions and no triggers 2 = easy: no stored functions but with triggers, no manual rewriting 3 = simple: stored functions and/or triggers, no manual rewriting 4 = manual: no stored functions but with triggers or views with code rewriting 5 = difficult: stored functions and/or triggers with code rewriting ------------------------------------------------------------------------------- # ora2pg -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 12/12 tables (100.0%) end of table export. [========================>] 1/1 views (100.0%) end of output. [========================>] 0/0 sequences (100.0%) end of output. [========================>] 0/0 procedures (100.0%) end of procedures export. [========================>] 0/0 triggers (100.0%) end of output. [========================>] 0/0 functions (100.0%) end of functions export. [========================>] 0/0 packages (100.0%) end of output. [========================>] 56/56 partitions (100.0%) end of output. [========================>] 0/0 types (100.0%) end of output. [========================>] 2/2 materialized views (100.0%) end of output. [========================>] 0/0 dblink (100.0%) end of output. [========================>] 0/0 synonyms (100.0%) end of output. [========================>] 2/2 directory (100.0%) end of output. Fixing function calls in output files.... 配置ora2pg.conf 的TYPE 为COPY 或 INSERT # cp ora2pg.conf sh_data.conf # vi sh_data.conf ORACLE_HOME /opt/oracle/product/19c/dbhome_1 ORACLE_DSN dbi:Oracle:host=localhost;sid=ORCLCDB;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 DISABLE_UNLOGGED 1 SKIP fkeys ukeys checks TYPE COPY NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh_data.sql 导出数据 # ora2pg -c sh_data.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 5/5 rows (100.0%) Table CHANNELS (5 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1995 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_H1_1997 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1996 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). …………………………………………………………… [========================>] 4500/4500 rows (100.0%) Table SUPPLEMENTARY_DEMOGRAPHICS (4500 recs/sec) [=======================> ] 1061558/1063384 total rows (99.8%) - (45 sec., avg: 23590 recs/sec). [========================>] 1826/1826 rows (100.0%) Table TIMES (1826 recs/sec) [========================>] 1063384/1063384 total rows (100.0%) - (45 sec., avg: 23630 recs/sec). [========================>] 1063384/1063384 rows (100.0%) on total estimated data (45 sec., avg: 23630 recs/sec) Fixing function calls in output files... 查看导出的文件： [root@test01 ora2pg]# ls -lrt *.sql -rw-r--r-- 1 root root 15716 Jul 2 21:21 TABLE_sh.sql -rw-r--r-- 1 root root 858 Jul 2 21:21 VIEW_sh.sql -rw-r--r-- 1 root root 2026 Jul 2 21:21 TABLESPACE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 SEQUENCE_sh.sql -rw-r--r-- 1 root root 2382 Jul 2 21:21 GRANT_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 TRIGGER_sh.sql -rw-r--r-- 1 root root 346 Jul 2 21:21 PROCEDURE_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 PACKAGE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 FUNCTION_sh.sql -rw-r--r-- 1 root root 6771 Jul 2 21:21 PARTITION_sh.sql -rw-r--r-- 1 root root 341 Jul 2 21:21 TYPE_sh.sql -rw-r--r-- 1 root root 342 Jul 2 21:21 QUERY_sh.sql -rw-r--r-- 1 root root 950 Jul 2 21:21 MVIEW_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 SYNONYM_sh.sql -rw-r--r-- 1 root root 926 Jul 2 21:21 DIRECTORY_sh.sql -rw-r--r-- 1 root root 343 Jul 2 21:21 DBLINK_sh.sql -rw-r--r-- 1 root root 55281235 Jul 2 17:11 sh_data.sql 以同样的方法分别导出HR，SCOTT用户数据。 创建ORCL 数据库 # su - ivorysql Last login: Tue Jul 2 20:04:30 CST 2019 on pts/3 $ createdb orcl $ psql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Collate | Ctype | ICU Locale | Locale Provider | Access privileges -----------+----------+-----------+---------+-------+------------+-----------------+----------------------- ivorysql | ivorysql | SQL_ASCII | C | C | | libc | orcl | ivorysql | SQL_ASCII | C | C | | libc | postgres | ivorysql | SQL_ASCII | C | C | | libc | template0 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql (5 rows) ivorysql=# 创建SH,HR,SCOTT 用户： $ psql orcl psql (18.0) Type \"help\" for help. orcl=# orcl=# create user sh with password 'sh'; CREATE ROLE 由于有物化视图，在TABLE_sh.sql 里包含了物化视图的索引，会创建失败。需先创建表，在创建物化视图，最后创建索引。 取消物化视图索引，后面单独创建： CREATE INDEX fw_psc_s_mv_chan_bix ON fweek_pscat_sales_mv (channel_id); CREATE INDEX fw_psc_s_mv_promo_bix ON fweek_pscat_sales_mv (promo_id); CREATE INDEX fw_psc_s_mv_subcat_bix ON fweek_pscat_sales_mv (prod_subcategory); CREATE INDEX fw_psc_s_mv_wd_bix ON fweek_pscat_sales_mv (week_ending_day); CREATE TEXT SEARCH CONFIGURATION en (COPY = pg_catalog.english); ALTER TEXT SEARCH CONFIGURATION en ALTER MAPPING FOR hword, hword_part, word WITH unaccent, english_stem; psql orcl -f tab.sql ALTER TABLE PARTITION sh.sales OWNER TO sh; COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT ALTER TABLE ALTER TABLE ALTER TABLE ……………………………… cat psql orcl -f GRANT_sh.sql CREATE USER SH WITH PASSWORD 'change_my_secret' LOGIN; ALTER TABLE sh.fweek_pscat_sales_mv OWNER TO sh; GRANT ALL ON sh.fweek_pscat_sales_mv TO sh; 物化视图需要相关查询权限,所以导入权限,注意这里要跟上用户 $ psql orcl sh -f MVIEW_sh.sql SELECT 0 SELECT 0 CREATE INDEX CREATE INDEX CREATE INDEX CREATE INDEX $ psql orcl -f VIEW_sh.sql SET SET SET CREATE VIEW $ psql orcl -f PARTITION_sh.sql SET SET SET CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE ………………………… $ psql orcl -f sh_data.sql SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 4500 SET COPY 1826 COMMIT 源库,目标端抽取部份对象对比： SYS@PROD1>select count(*) from sh.products; COUNT(*) ---------- 72 orcl=# select count(*) from sh.products; count ------- 72 (1 row) --------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.channels; COUNT(*) ---------- 5 orcl=# select count(*) from sh.channels; count ------- 5 (1 row) -------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.customers ; COUNT(*) ---------- 55500 orcl=# select count(*) from sh.customers ; count ------- 55500 (1 row) 使用时，两个选项—​project_base和—​init_project向Ora2Pg表明他必须创建一个项目模板，其中包含工作树，配置文件和从Oracle数据库导出所有对象的脚本。 生成通用配置文件。 1.创建脚本export_schema.sh以自动执行所有导出。2.创建脚本import_all.sh以自动执行所有导入。例： mkdir -p /ora2pg/migration # ora2pg --project_base /ora2pg/migration/ --init_project test_project Creating project test_project. /ora2pg/migration//test_project/ schema/ dblinks/ directories/ functions/ grants/ mviews/ packages/ partitions/ procedures/ sequences/ synonyms/ tables/ tablespaces/ triggers/ types/ views/ sources/ functions/ mviews/ packages/ partitions/ procedures/ triggers/ types/ views/ data/ config/ reports/ Generating generic configuration file Creating script export_schema.sh to automate all exports. Creating script import_all.sh to automate all imports.","title":"迁移指南","component":"ivorysql-doc","version":"master","name":"migration_guide","url":"/ivorysql-doc/master/migration_guide.html","titles":[{"text":"1. 迁移概述","hash":"迁移概述","id":1},{"text":"2. 迁移工具Ora2Pg","hash":"迁移工具ora2pg","id":2},{"text":"3. 迁移Oracle数据库至IvorySQL","hash":"迁移oracle数据库至ivorysql","id":3},{"text":"3.1. 环境准备","hash":"环境准备","id":4},{"text":"3.2. 依赖环境安装","hash":"依赖环境安装","id":5},{"text":"3.2.1. 安装Perl","hash":"安装perl","id":6},{"text":"3.2.2. 安装DBI模块","hash":"安装dbi模块","id":7},{"text":"3.2.3. 安装DBD-Oracle","hash":"安装dbd-oracle","id":8},{"text":"3.3. 安装Ora2pg","hash":"安装ora2pg","id":9},{"text":"3.4. ora2pg导出表结构","hash":"ora2pg导出表结构","id":10},{"text":"3.4.1. 测试连接","hash":"测试连接","id":11},{"text":"3.4.2. 迁移成本评估","hash":"迁移成本评估","id":12},{"text":"3.4.3. 导出SH表构","hash":"导出sh表构","id":13},{"text":"3.4.4. 导出SH用户数据","hash":"导出sh用户数据","id":14},{"text":"3.5. 在IvorySQL环境中创建orcl库","hash":"在ivorysql环境中创建orcl库","id":15},{"text":"4. 迁移门户","hash":"迁移门户","id":16},{"text":"4.1. 导入表结构","hash":"导入表结构","id":17},{"text":"4.2. 给对象授权","hash":"给对象授权","id":18},{"text":"4.3. 导入物化视图结构","hash":"导入物化视图结构","id":19},{"text":"4.4. 导入视图","hash":"导入视图","id":20},{"text":"4.5. 导入分区表","hash":"导入分区表","id":21},{"text":"4.6. 导入数据","hash":"导入数据","id":22},{"text":"5. 数据验证","hash":"数据验证","id":23},{"text":"6. 生成迁移模板","hash":"生成迁移模板","id":24}]},"7":{"id":7,"text":"由于IvorySQL基于PostgreSQL开发而成，运维人员在阅读理解本节内容时，建议同时参阅 手册。 IvorySQL版本号由主要版本和次要版本组成。例如，IvorySQL 3.2 中的3是主要版本，2是次要版本。 ​发布次要版本是不会改变内存的存储格式，因此总是和相同的主要版本兼容。例如，IvorySQL 3.2 和Ivory SQL 3.0 以及后续的 Ivory SQL 3.x 兼容。对于这些兼容版本的升级非常简单，只要关闭数据库服务，安装替换二进制的可执行文件，重新启动服务即可。 ​接下来，我们主要讨论IvorySQL的跨版本升级问题，例如，从 IvorySQL 2.3 升级到 IvorySQL 3.4 。主要版本的升级可能会修改内部数据的存储格式，因此需要执行额外的操作。常用的跨版本升级方法和适用场景如下： 升级方法 适用场景 停机时间 通过pg_dumpall升级数据 中小型数据库，例如小于100GB支持跨平台数据迁移 取决于数据库的大小 pg_upgrade工具 大中型数据库，例如大于100GB本机就地升级 几分钟 逻辑复制 大中型数据库，例如大于100GB跨平台支持 几秒钟 注意事项： 新的主版本通常会引入一些用户可见的不兼容性，因此可能需要应用程序编程上的改变。所有用户可见的更改都被列在 说明中，请特别注意标有“Migration”的小节。尽管你可能从一个主版本升级到另一个，而不用升级中间版本，你应该阅读全部中间版本的主要发行说明。 传统的跨版本升级方法利用pg_dump/pg_dumpall逻辑备份导出数据库，然后在新版本中通过pg_restore进行还原。导出旧版本数据库时推荐使用新版本的pg_dump/pg_dumpall工具，可以利用最新的并行导出和还原功能，同时可以减少数据库膨胀问题。 ​逻辑备份与还原非常简单但速度比较慢，停机时间取决于数据库的大小，因此适合中小型数据库的升级。 ​下面介绍这种升级方法的具体操作，假如当前IvorySQL软件的安装目录位于/usr/local/pgsql，同时数据目录位于/usr/local/pgsql/data，我们在同一台服务器上进行升级。 执行逻辑备份之前停止应用程序，确保没有数据更新，因为备份开始后的更新不会被导出。如有必要，可以修改/usr/local/pgsql/data/pg_hba.conf文件禁止其他人访问数据库。然后备份数据库： pg_dumpall > outputfile 如果已经安装了新版本的IvorySQL，可以使用新版本的pg_dumpall命令备份旧版本数据库。 2.停止旧版本的后台服务： pg_ctl stop 或者通过其他方式停止后台服务。 3.如果安装目录没有包含特定版本标识，可以将目录改名，必要时可以再修改回来。可以使用类似以下的命令重命名目录： mv /usr/local/pgsql /usr/local/pgsql.old 4.安装新版本IvorySQL软件，假如安装目录仍然是/usr/local/pgsql。 5.初始化一个新的数据库集群，需要使用数据库专用用户（通常是postgres；如果升级版本，应该已经存在该用户）执行操作： /usr/local/pgsql/bin/initdb -D /usr/local/pgsql/data 6.将旧版本配置文件pg_hba.conf 和 postgresql.conf 等中的改动在对应的新配置文件中再次进行修改。 7.使用数据库专用用户启动新版本的后台服务： /usr/local/pgsql/bin/postgres -D /usr/local/pgsql/data 8.最后利用新版本的psql命令还原数据： /usr/local/pgsql/bin/psql -d postgres -f outputfile 为了减少停机时间，可以将新版本的IvorySQL安装到另一个目录，同时使用不同的端口启动服务。然后同时执行数据库的导出和导入： pg_dumpall -p 5432 | psql -d postgres -p 5433 ​执行以上操作时，新旧版本的后台服务同时运行，新版本使用5433端口，旧版本使用5432端口。 pg_upgrade 工具是PostgreSQL 内置的跨版本升级工具，能够对数据库就地升级，不需要执行导出和导入操作。IvorySQL源自于PG，因此也能够使用pg_upgrade 工具进行大版本升级。 下面简要介绍一下CentOS8平台上如何使用 pg_upgrade 将IvorySQL升级到最新的5.0版本。 pg_upgrade 提供了升级前的兼容性检查（-c 或者 --check 选项）功能，可以发现插件、数据类型不兼容等问题。如果指定了—​link 选项，新版本服务可以直接使用原有的数据库文件而不需要执行复制，通常可以在几分钟内完成升级操作。 常用的参数包括： -b bindir，--old-bindir=bindir：旧的 IvorySQL 可执行文件目录； -B bindir，--new-bindir=bindir：新的 IvorySQL 可执行文件目录； -d configdir，--old-datadir=configdir：旧版本的数据目录； -D configdir，--new-datadir=configdir：新版本的数据目录； -c，--check：只检查升级兼容性，不更改任何数据； -k，--link：硬链接方式升级； 升级准备： 首先停止旧版本的IvorySQL4.6数据库： /usr/ivory-4/bin/pg_ctl -D ./data stop 然后安装新版本的IvorySQL5.0数据库： dnf install -y ivorysql5-5.0 初始化新版IvorySQL5.0数据目录： /usr/ivory-5/bin/initdb -D ./data 检查版本兼容性： /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ --check 最后出现 “Clusters are compatible” 表明两个版本之间的数据不存在兼容性问题，可以进行升级。 正式升级： /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ 看到 Upgrade Complete 说明升级已经顺利完成。 更新统计信息： pg_upgrade 会创建新的系统表，并重用旧的数据进行升级，统计信息并不会随升级过程迁移，所以在启用新版本之前，应该首先重新收集统计信息，避免没有统计信息导致错误的查询计划。 启动新版本数据库 /usr/ivory-5/bin/pg_ctl -D ./data -l logfile start 手动运行vacuum命令 vacuum --all --analyze-in-stage -h 127.0.0.1 -p 1521 升级后清理 rm -rf /home/ivorysql/test/4.6/data pg_upgrade 文档概述了上述所需的步骤。 我们也可以使用IvorySQL的已更新版本逻辑复制来创建一个后备服务器，逻辑复制支持在不同主版本的IvorySQL之间的复制。后备服务器可以在同一台计算机或者不同的计算机上。一旦它和主服务器（运行旧版本的IvorySQL）同步好，你可以切换主机并且将后备服务器作为主机，然后关闭旧的数据库实例。这样一种切换使得一次升级的停机时间只有数秒。 这种升级方法可以用内置的逻辑复制工具和外部的逻辑复制系统如pglogical，Slony，Londiste，和Bucardo。 pg_upgrade 支持将原生 PostgreSQL 集群升级到 IvorySQL。当源集群为原生 PostgreSQL ，而目标为 IvorySQL 集群时，必须使用 -g（--using-ora-pg）参数。 从 PostgreSQL 升级到 IvorySQL 属于“跨产品”迁移，建议在生产环境操作前完成完整的测试验证，并制定详细的回滚方案。 参数 说明 -g / --using-ora-pg 表示源集群为 PostgreSQL ，而目标为 IvorySQL 集群。启用此选项后，pg_upgrade 在连接两端集群时统一使用 PostgreSQL 标准端口，从而正确处理升级。 # 使用 IvorySQL 的 initdb 初始化新集群 /opt/ivorysql/bin/initdb -D /data/ivorysql/data /usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop 使用 -c 仅做兼容性检查，不修改任何数据： /opt/ivorysql/bin/pg_upgrade \\ -b /usr/lib/postgresql/16/bin \\ # 源 PG 可执行文件目录 -B /opt/ivorysql/bin \\ # 目标 IvorySQL 可执行文件目录 -d /data/pg/data \\ # 源 PG 数据目录 -D /data/ivorysql/data \\ # 目标 IvorySQL 数据目录 -g \\ # 源为 PostgreSQL，目标为 IvorySQL -c # 仅检查，不升级 /opt/ivorysql/bin/pg_upgrade \\ -b /usr/lib/postgresql/16/bin \\ -B /opt/ivorysql/bin \\ -d /data/pg/data \\ -D /data/ivorysql/data \\ -g /opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start # 验证数据库是否正常 /opt/ivorysql/bin/psql -p 5432 -c \"SELECT version();\" 升级完成后，pg_upgrade 会生成一个清理脚本： ./delete_old_cluster.sh 参数 长选项 说明 -b --old-bindir 源集群可执行文件目录 -B --new-bindir 目标集群可执行文件目录 -d --old-datadir 源集群数据目录 -D --new-datadir 目标集群数据目录 -g --using-ora-pg 源为 PostgreSQL 升级到 IvorySQL -p --old-port 源集群端口 -P --new-port 目标集群 PG 端口 -q --old-oraport 源集群 Oracle 端口 -Q --new-oraport 目标集群 Oracle 端口 -j --jobs 并行进程数 -k --link 使用硬链接替代文件复制 -c --check 仅检查兼容性，不执行升级 -v --verbose 输出详细日志 -g 参数仅在 源为纯 PostgreSQL 集群、目标为 IvorySQL 时使用。若源集群已是 IvorySQL，则无需此参数。 升级期间源集群和目标集群均须处于停止状态。 升级前务必对源集群做完整备份。 IvorySQL基于PostgreSQL开发，版本更新频率与PostgreSQL版本更新频率保持一致，每年更新一个大版本，每季度更新一个小版本。IvorySQL目前发布的版本有1.0到5.0，分别基于PostgreSQL 14.0到18.0进行开发，最新版本为IvorySQL 5.0，基于PostgreSQL 18.0进行开发。IvorySQL 的所有版本全部都做到了向下兼容。相关版本特性可以查看 官网。 IvorySQL使用 角色 的概念管理数据库访问权限。一个角色可以看成是一个数据库用户或者是数据库用户组，这取决于角色被怎样设置。角色可以拥有数据库对象（例如，表和函数）并且能够把那些对象上的权限赋予给其他角色来控制谁能访问哪些对象。此外，还可以把一个角色中的 成员资格 授予给另一个角色，这样允许成员角色使用被赋予给另一个角色的权限。 角色的概念把“用户”和“组”的概念都包括在内。 数据库角色在概念上已经完全与操作系统用户独立开来。 事实上可能维护一个对应关系会比较方便，但是这并非必需。 数据库角色在一个数据库集簇安装范围内是全局的（而不是独立数据库内）。 要创建一个角色，可使用CREATE ROLE SQL 命令： CREATE ROLE name; name遵循 SQL 标识符的规则：或是未经装饰没有特殊字符，或是用双引号包围（实际上，你将总是给该命令要加上额外选项，例如LOGIN。更多细节可见下文）。 要移除一个已有的角色，使用相似的DROP ROLE命令： DROP ROLE name; 为了方便，createuser和dropuser程序被提供作为这些 SQL 命令的包装器，它们可以从 shell 命令行调用： createuser name dropuser name 要决定现有角色的集合，检查pg_roles系统目录，例如： SELECT rolname FROM pg_roles; psql程序的\\du元命令也可以用来列出现有角色。 为了引导数据库系统，一个刚刚被初始化好的系统总是包含一个预定义角色。这个角色总是一个“superuser”，并且默认情况下（除非在运行initdb时修改）它的名字和初始化数据库集簇的操作系统用户相同。习惯上，这个角色将被命名为postgres。为了创建更多角色，你首先必须以初始角色的身份连接。 每一个到数据库服务器的连接都是使用某个特定角色名建立的，并且这个角色决定发起连接的命令的初始访问权限。要使用一个特定数据库连接的角色名由客户端指示，该客户端以一种应用相关的风格发起连接请求。例如，psql程序使用-U命令行选项来指定要以哪个角色连接。很多应用假定该名字默认是当前操作系统用户（包括createuser和psql）。因此在角色和操作系统用户之间维护一个名字对应关系通常是很方便的。 一个给定客户端连接能够用来连接的数据库角色的集合由该客户端的认证设置决定，因此，一个客户端不止限于以匹配其操作系统用户的角色连接，就像一个人的登录名不需要匹配她的真实名字一样。因为角色身份决定一个已连接客户端可用的权限集合，在设置一个多用户环境时要小心地配置权限。 一个数据库角色可以有一些属性，它们定义角色的权限并且与客户端认证系统交互。 把用户分组在一起来便于管理权限常常很方便：那样，权限可以被授予一整个组或从一整个组回收。在IvorySQL中通过创建一个表示组的角色来实现，并且然后将在该组角色中的 成员关系 授予给单独的用户角色。 由于角色可以拥有数据库对象并且能持有访问其他对象的特权，删除一个角色常常并非一次DROP ROLE就能解决。 任何被该用户所拥有的对象必须首先被删除或者转移给其他拥有者，并且任何已被授予给该角色的权限必须被收回。 更多有关数据库访问管理的细节，可以参阅 手册。 IvorySQL基于PostgreSQL，具有完整的SQL，其定义数据对象可以参考 手册。在此基础之上，IvorySQL为兼容Oracle，还做了一些Oracle专有数据对象的兼容。 具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。 VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 IvorySQL基于PostgreSQL开发，具有完全的SQL，查询数据具体的操作可以参考 手册。 IvorySQL实现了部分的SQL/MED规定，允许我们使用普通SQL查询来访问位于IvorySQL之外的数据。这种数据被称为外部数据（注意这种用法不要和外键混淆，后者是数据库中的一种约束）。 外部数据可以在一个外部数据包装器的帮助下被访问。一个外部数据包装器是一个库，它可以与一个外部数据源通讯，并隐藏连接到数据源和从它获取数据的细节。在contrib模块中有一些外部数据包装器，参见 文档。其他类型的外部数据包装器可以在第三方产品中找到。如果这些现有的外部数据包装器都不能满足你的需要，可以自己编写一个，参见 手册。 要访问外部数据，我们需要建立一个外部服务器对象，它根据它所支持的外部数据包装器所使用的一组选项定义了如何连接到一个特定的外部数据源。接着我们需要创建一个或多个外部表，它们定义了外部数据的结构。一个外部表可以在查询中像一个普通表一样地使用，但是在IvorySQL服务器中外部表没有存储数据。不管使用什么外部数据包装器，IvorySQL会要求外部数据包装器从外部数据源获取数据，或者在更新命令的情况下传送数据到外部数据源。 访问远程数据可能需要在外部数据源的授权。这些信息通过一个用户映射提供，它基于当前的IvorySQL角色提供了附加的数据例如用户名和密码。 由于包含着有价值的数据，IvorySQL数据库应当被定期地备份。虽然过程相当简单，但清晰地理解其底层技术和假设是非常重要的。 有三种不同的基本方法来备份 IvorySQL 数据： SQL转储 文件系统级备份 连续归档 SQL 转储方法的思想是创建一个由SQL命令组成的文件，当把这个文件回馈给服务器时，服务器将利用其中的SQL命令重建与转储时状态一样的数据库。 IvorySQL为此提供了工具pg_dump。这个工具的基本用法是： pg_dump dbname > dumpfile 正如你所见，pg_dump把结果输出到标准输出。我们后面将看到这样做有什么用处。 尽管上述命令会创建一个文本文件，pg_dump可以用其他格式创建文件以支持并行 和细粒度的对象恢复控制。 ​ pg_dump是一个普通的IvorySQL客户端应用（尽管是个 相当聪明的东西）。这就意味着你可以在任何可以访问该数据库的远端主机上进行备份工作。但是请记住 pg_dump不会以任何特殊权限运行。具体说来，就是它必须要有你想备份的表的读权限，因此为了备份整个数据库你几乎总是必须以一个数据库超级用户来运行它（如果你没有足够的特权来备份整个数据库，你仍然可以使用诸如-n schema 或-t table选项来备份该数据库中你能够访问的部分）。 ​ 要声明pg_dump连接哪个数据库服务器，使用命令行选项-h host和 -p port。 默认主机是本地主机或你的HOST环境变量指定的主机。 类似地，默认端口是环境变量PORT或（如果PORT不存在）内建的默认值。 （服务器通常有相同的默认值，所以还算方便。） ​ pg_dump默认使用与当前操作系统用户名同名的数据库用户名进行连接。 要使用其他名字，要么声明-U选项，要么设置环境变量PGUSER。请注意pg_dump的连接也要通过客户认证机制。 ​ pg_dump对于其他备份方法的一个重要优势是，pg_dump的输出可以很容易地在新版本的IvorySQL中载入，而文件级备份和连续归档都是极度的服务器版本限定的。pg_dump也是唯一可以将一个数据库传送到一个不同机器架构上的方法，例如从一个32位服务器到一个64位服务器。 ​ 由pg_dump创建的备份在内部是一致的， 也就是说，转储表现了pg_dump开始运行时刻的数据库快照，且在pg_dump运行过程中发生的更新将不会被转储。pg_dump工作的时候并不阻塞其他的对数据库的操作。（但是会阻塞那些需要排它锁的操作，比如大部分形式的ALTER TABLE） pg_dump生成的文本文件可以由psql程序读取。 从转储中恢复的常用命令是： psql dbname < dumpfile 其中dumpfile就是pg_dump命令的输出文件。这条命令不会创建数据库dbname，你必须在执行psql前自己从template0创建（例如，用命令createdb -T template0 dbname）。psql支持类似pg_dump的选项用以指定要连接的数据库服务器和要使用的用户名。参阅psql的手册获取更多信息。 非文本文件转储可以使用pg_restore工具来恢复。 ​ 在开始恢复之前，转储库中对象的拥有者以及在其上被授予了权限的用户必须已经存在。如果它们不存在，那么恢复过程将无法将对象创建成具有原来的所属关系以及权限（有时候这就是你所需要的，但通常不是）。 ​ 默认情况下，psql脚本在遇到一个SQL错误后会继续执行。你也许希望在遇到一个SQL错误后让psql退出，那么可以设置ON_ERROR_STOP变量来运行psql，这将使psql在遇到SQL错误后退出并返回状态3： psql --set ON_ERROR_STOP=on dbname < infile 不管怎样，你将只能得到一个部分恢复的数据库。作为另一种选择，你可以指定让整个恢复作为一个单独的事务运行，这样恢复要么完全完成要么完全回滚。这种模式可以通过向psql传递-1或—​single-transaction命令行选项来指定。在使用这种模式时，注意即使是很小的一个错误也会导致运行了数小时的恢复被回滚。但是，这仍然比在一个部分恢复后手工清理复杂的数据库要更好。 ​ pg_dump和psql读写管道的能力使得直接从一个服务器转储一个数据库到另一个服务器成为可能，例如： pg_dump -h host1 dbname | psql -h host2 dbname 重要：pg_dump产生的转储是相对于template0。这意味着在template1中加入的任何语言、过程等都会被pg_dump转储。结果是，如果在恢复时使用的是一个自定义的template1，你必须从template0创建一个空的数据库，正如上面的例子所示。 ​ 一旦完成恢复，在每个数据库上运行ANALYZE是明智的举动，这样优化器就有有用的统计数据了。 pg_dump每次只转储一个数据库，而且它不会转储关于角色或表空间（因为它们是集簇范围的）的信息。为了支持方便地转储一个数据库集簇的全部内容，提供了pg_dumpall程序。pg_dumpall备份一个给定集簇中的每一个数据库，并且也保留了集簇范围的数据，如角色和表空间定义。该命令的基本用法是： pg_dumpall > dumpfile 转储的结果可以使用psql恢复： psql -f dumpfile postgres （实际上，你可以指定恢复到任何已有数据库名，但是如果你正在将转储载入到一个空集簇中则通常要用（postgres）。在恢复一个pg_dumpall转储时常常需要具有数据库超级用户访问权限，因为它需要恢复角色和表空间信息。如果你在使用表空间，请确保转储中的表空间路径适合于新的安装。 pg_dumpall工作时会发出命令重新创建角色、表空间和空数据库，接着为每一个数据库pg_dump。这意味着每个数据库自身是一致的，但是不同数据库的快照并不同步。 集簇范围的数据可以使用pg_dumpall的—​globals-only选项来单独转储。如果在单个数据库上运行pg_dump命令，上述做法对于完全备份整个集簇是必需的。 在一些具有最大文件尺寸限制的操作系统上创建大型的pg_dump输出文件可能会出现问题。幸运地是，pg_dump可以写出到标准输出，因此你可以使用标准Unix工具来处理这种潜在的问题。有几种可能的方法： 使用压缩转储。你可以使用你喜欢的压缩程序，例如gzip： pg_dump dbname | gzip > filename.gz 恢复： gunzip -c filename.gz | psql dbname 或者： cat filename.gz | gunzip | psql dbname 使用split。split命令允许你将输出分割成较小的文件以便能够适应底层文件系统的尺寸要求。例如，让每一块的大小为2G字节： pg_dump dbname | split -b 2G - filename 恢复： cat filename* | psql dbname 如果使用GNU split，可能会把它和gzip一起使用： pg_dump dbname | split -b 2G -−filter='gzip > $FILE.gz' 它可以使用zcat恢复。 使用pg_dump的自定义转储格式。. 如果 IvorySQL 所在的系统上安装了zlib压缩库，自定义转储格式将在写出数据到输出文件时对其压缩。这将产生和使用gzip时差不多大小的转储文件，但是这种方式的一个优势是其中的表可以被有选择地恢复。下面的命令使用自定义转储格式来转储一个数据库： pg_dump -Fc dbname > filename ​自定义格式的转储不是psql的脚本，只能通过pg_restore恢复，例如： pg_restore -d dbname filename ​更多细节可以参阅 手册。 ​对于非常大型的数据库，你可能需要将split配合其他两种方法之一进行使用。 ​使用pg_dump的并行转储特性。 为了加快转储一个大型数据库的速度，你可以使用pg_dump的并行模式。它将同时转储多个表。你可以使用-j参数控制并行度。并行转储只支持“目录”归档格式。 pg_dump -j num -F d -f out.dir dbname ​你可以使用pg_restore -j来以并行方式恢复一个转储。它只能适合于“自定义”归档或者“目录”归档，但不管归档是否由pg_dump -j创建。 另外一种备份策略是直接复制 IvorySQL用于存储数据库中数据的文件，你可以采用任何你喜欢的方式进行文件系统备份，例如： tar -cf backup.tar /usr/local/pgsql/data 但是这种方法有两个限制，使得这种方法不实用，或者说至少比pg_dump方法差： 为了得到一个可用的备份，数据库服务器必须被关闭。例如阻止所有连接的半路措施是不起作用的（部分原因是tar和类似工具无法得到文件系统状态的一个原子的快照，还有服务器内部缓冲的原因）。不用说，在恢复数据之前你也需要关闭服务器。 如果你已经深入地了解了数据库的文件系统布局的细节，你可能会有兴趣尝试通过相应的文件或目录来备份或恢复特定的表或数据库。这种方法也不会起作用，因为包含在这些文件中的信息只有配合提交日志文件（pg_xact/*）才有用，提交日志文件包含了所有事务的提交状态。一个表文件只有和这些信息一起才有用。当然也不可能只恢复一个表及相关的pg_xact数据，因为这会导致数据库集簇中所有其他表变得无用。因此文件系统备份值适合于完整地备份或恢复整个数据库集簇。 ​另一种文件系统备份方法是创建一个数据目录的“一致快照”，如果文件系统支持此功能（并且你相信它的实现正确）。典型的过程是创建一个包含数据库的卷的“冻结快照”，然后从该快照复制整个数据目录（如上，不能是部分复制）到备份设备，最后释放冻结快照。即使在数据库服务器运行时，这种方式也有效。但是，以这种方式创建的备份保存的文件看起来就像数据库没有被正确关闭时的状态。因此，当你从备份数据上启动数据库服务器时，它会认为上一次的服务器实例崩溃了并尝试重放WAL日志。这不是问题，只是需要注意（当然WAL文件必须要包括在备份中）。你可以在拍摄快照之前执行一次CHECKPOINT以便节省恢复时间。 ​如果你的数据库跨越多个文件系统，可能没有任何方式可以对所有卷获得完全同步的冻结快照。例如，如果你的数据文件和WAL日志放置在不同的磁盘上，或者表空间在不同的文件系统中，可能没有办法使用快照备份，因为快照必须是同步的。在这些情况下，一定要仔细阅读你的文件系统文档以了解其对一致快照技术的支持。 ​如果没有可能获得同步快照，一种选择是将数据库服务器关闭足够长的时间以建立所有的冻结快照。另一种选择是执行一次连续归档基础备份，因为这种备份对于备份期间发生的文件系统改变是免疫的。这要求在备份过程中允许连续归档，恢复时使用连续归档恢复。 ​还有一种选择是使用rsync来执行一次文件系统备份。其做法是先在数据库服务器运行时执行rsync，然后关闭数据库服务器足够长时间来做一次rsync --checksum （--checksum是必需的，因为rsync的文件修改 时间粒度只能精确到秒）。第二次rsync会比第一次快，因为它只需要传送相对很少的数据，由于服务器是停止的，所以最终结果将是一致的。这种方法允许在最小停机时间内执行一次文件系统备份。 注意一个文件系统备份通常会比一个SQL转储体积更大（例如pg_dump不需要转储索引的内容，而是转储用于重建索引的命令）。但是，做一次文件系统备份可能更快。 在任何时间，IvorySQL在数据集簇目录的pg_wal/子目录下都保持有一个预写式日志（WAL）。这个日志存在的目的是为了保证崩溃后的安全：如果系统崩溃，可以“重放”从最后一次检查点以来的日志项来恢复数据库的一致性。该日志的存在也使得第三种备份数据库的策略变得可能：我们可以把一个文件系统级别的备份和WAL文件的备份结合起来。当需要恢复时，我们先恢复文件系统备份，然后从备份的WAL文件中重放来把系统带到一个当前状态。这种方法比之前的方法管理起来要更复杂，但是有其显著的优点： 我们不需要一个完美的一致的文件系统备份作为开始点。备份中的任何内部不一致性将通过日志重放（这和崩溃恢复期间发生的并无显著不同）来修正。因此我们不需要文件系统快照功能，只需要tar或一个类似的归档工具。 由于我们可以结合一个无穷长的WAL文件序列用于重放，可以通过简单地归档WAL文件来达到连续备份。这对于大型数据库特别有用，因为在其中不方便频繁地进行完全备份。 并不需要一直重放WAL项一直到最后。我们可以在任何点停止重放，并得到一个数据库在当时的一致快照。这样，该技术支持时间点恢复： 在得到你的基础备份以后，可以将数据库恢复到它在其后任何时间的状态。 如果我们连续地将一系列WAL文件输送给另一台已经载入了相同基础备份文件的机器， 我们就得到了一个热后备系统： 在任何时间点我们都能提出第二台机器， 它差不多是数据库的当前副本。 注意: pg_dump 和 pg_dumpall不会产生文件系统级别的备份，并且不能用于连续归档方案。这类转换是逻辑的并且不包含足够的信息用于WAL重放。 ​ 就简单的文件系统备份技术来说，这种方法只能支持整个数据库集簇的恢复，却无法支持其中一个子集的恢复。另外，它需要大量的归档存储：一个基础备份的体积可能很庞大，并且一个繁忙的系统将会产生大量需要被归档的WAL流量。尽管如此，在很多需要高可靠性的情况下，它是首选的备份技术。 ​ 要使用连续归档（也被很多数据库厂商称为“在线备份”）成功地恢复，你需要一个从基础备份时间开始的连续的归档WAL文件序列。为了开始，在你建立第一个基础备份之前，你应该建立并测试用于归档WAL文件的过程。对应地，我们首先讨论归档WAL文件的机制。关于如何建立归档和备份的方式以及操作过程中的要点，请参阅 手册。 copy 在 IvorySQL表和标准文件之间移动数据。COPY TO 把一个表的内容复制到一个文件，而COPY FROM 则从一个文件复制数据到一个表（把数据追加到表中原有数据）。COPY TO 也能复制一个SELECT查询的结果。 ​ 如果指定了一个列列表，COPY TO将只把指定列的数据复制到文件。对于COPY FROM，文件中的每个字段将按顺序插入到指定列中。COPY FROM命令的列列表中没有指定的表列则会采纳其默认值。 ​ 带一个文件名的COPY指示IvorySQL服务器直接从一个文件读取或者写入到一个文件。该文件必须是IvorySQL用户（运行服务器的用户ID）可访问的并且应该以服务器的视角来指定其名称。当指定了PROGRAM时，服务器执行给定的命令并且从该程序的标准输出读取或者写入到该程序的标准输入。该程序必须以服务器的视角指定，并且必须是IvorySQL用户可执行的。在指定STDIN或者STDOUT时，数据会通过客户端和服务器之间的连接传输。 ​ 运行COPY的每个后端将在pg_stat_progress_copy视图中报告其进度。 COPY table_name [ ( column_name [, ...] ) ] FROM { 'filename' | PROGRAM 'command' | STDIN } [ [ WITH ] ( option [, ...] ) ] [ WHERE condition ] COPY { table_name [ ( column_name [, ...] ) ] | ( query ) } TO { 'filename' | PROGRAM 'command' | STDOUT } [ [ WITH ] ( option [, ...] ) ] 其中 option 可以是下列之一： FORMAT format_name FREEZE [ boolean ] DELIMITER 'delimiter_character' NULL 'null_string' HEADER [ boolean ] QUOTE 'quote_character' ESCAPE 'escape_character' FORCE_QUOTE { ( column_name [, ...] ) | * } FORCE_NOT_NULL ( column_name [, ...] ) FORCE_NULL ( column_name [, ...] ) ENCODING 'encoding_name' 详细参数设置，请参阅 手册。 在成功完成时，一个COPY命令会返回一个形为 COPY count count 是被复制的行数。 注意： 如果命令不是COPY …​ TO STDOUT或者等效的psql命令\\copy …​ to stdout, psql将只打印这个命令标签。这是为了防止弄混命令标签和刚刚打印的数据。 COPY TO只能用于普通表，而不能用于视图，并且不能从子表或子分区复制行。 例如，COPY table TO 复制与SELECT * FROM ONLY table 相同的行。 语法COPY (SELECT * FROM table) TO …​ 可用于转储一个继承层次结构、分区表或视图中的所有行。 COPY FROM可以被用于普通表、外部表、分区表或者具有INSTEAD OF INSERT触发器的视图。 ​ 你必须拥有被COPY TO读取的表上的选择特权， 以及被COPY FROM插入的表上的插入特权。 拥有在命令中列出的列上的特权就足够了。 ​ 如果对表启用了行级安全性，相关的SELECT策略将应用于COPY table TO语句。当前，有行级安全性的表不支持COPY FROM。不过可以使用等效的INSERT语句。 ​ COPY命令中提到的文件会被服务器（而不是 客户端应用）直接读取或写入。因此它们必须位于数据库服务器（不是客户 端）的机器上或者是数据库服务器可以访问的。它们必须是 IvorySQL 用户（运行服务器的用户 ID）可访问的并且是可读或者可写的。类似地，用PROGRAM 指定的命令也会由服务器（不是客户端应用）直接执行，它也必须是 IvorySQL 用户可以执行的。 只允许数据库超级用户或者授予了角色pg_read_server_files、 pg_write_server_files及pg_execute_server_program 之一的用户COPY一个文件或者命令， 因为它允许读取或者写入服务器有特权访问的任何文件或者运行服务器有特权访问的程序。 ​ 不要把COPY和 psql指令 \\copy 弄混。\\copy会调用 COPY FROM STDIN或者COPY TO STDOUT，然后读取/存储一个 psql客户端可访问的文件中的数据。 因此，在使用\\copy时，文件的可访 问性和访问权利取决于客户端而不是服务器。 ​ 我们推荐在COPY中使用的文件名总是 指定为一个绝对路径。在COPY TO的 情况下服务器会强制这一点，但是对于 COPY FROM你可以选择从一个用相对 路径指定的文件中读取。该路径将根据服务器进程（而不是客户端） 的工作目录（通常是集簇的数据目录）解释。 ​ 用PROGRAM执行一个命令可能会受到操作系统 的访问控制机制（如 SELinux）的限制。 ​ COPY FROM将调用目标表上的任何触发器 和检查约束。但是它不会调用规则。 ​ 对于标识列，COPY FROM命令将总是写上输入数据中提供的列值，这和INSERT的选项OVERRIDING SYSTEM VALUE的行为一样。 ​ COPY输入和输出受到 DateStyle的影响。为了确保到其他 可能使用非默认DateStyle设置的 IvorySQL 安装的可移植性，在使用 COPY TO前应该把 DateStyle设置为ISO。避免转储把 IntervalStyle设置为 sql_standard的数据也是一个好主意，因为负的区间值可能会 被具有不同IntervalStyle设置的服务器解释错误。 ​ 即使数据会被服务器直接从一个文件读取或者写入一个文件而不通过 客户端，输入数据也会被根据ENCODING选项或者当前 客户端编码解释，并且输出数据会被根据ENCODING或 者当前客户端编码进行编码。 ​ COPY会在第一个错误处停止操作。这在 COPY TO的情况下不会导致问题，但是 在COPY FROM中目标表将已经收到了一 些行。这些行将不会变得可见或者可访问，但是它们仍然占据磁盘空间。 如果在一次大型的复制操作中出现错误，这可能浪费相当可观的磁盘空间。 你可能希望调用VACUUM来恢复被浪费的 空间。 ​ FORCE_NULL和FORCE_NOT_NULL可以被同时 用在同一列上。这会导致把已被引用的空值串转换为空值并且把未引用的空值 串转换为空串。 在使用text格式时，读取或写入的是一个文本文件，其中每一行就是表中的一行。一行中的列被定界字符分隔。列值本身是由输出函数产生的或者是可被输入函数接受的属于每个属性数据类型的字符串。在为空值的列的位置使用指定的空值串。如果输入文件的任何行包含比预期更多或者更少的列，COPY FROM将会抛出一个错误。 ​ 数据的结束可以表示为一个只包含反斜线-点号（\\.）的单一行。从一个文件读取时，数据结束标记并不是必要的，因为文件结束符就已经足够用了。只有使用3.0客户端协议之前的客户端应用复制数据时才需要它。 ​ 反斜线字符（\\）可以被用在COPY数据中来引用被用作行或者列界定符的字符。特别地，如果下列字符作为一个列值的一部分出现，它们 必须被前置一个反斜线：反斜线本身、新行、回车以及 当前的定界符字符。 ​ COPY TO会不加任何反斜线返回指定的空值串。 相反，COPY FROM会在移除反斜线之前把输入 与空值串相匹配。因此，一个空值串（例如\\N）不会与实 际的数据值\\N（它会被表示为\\\\N）搞混。 ​ COPY FROM识别下列特殊的反斜线序列： 序列 表示 \\b 退格（ASCII 8） \\f 换页（ASCII 12） \\n 新行（ASCII 10） \\r 回车（ASCII 13） \\t 制表（ASCII 9） \\v 纵向制表（ASCII 11） \\digits 反斜线后跟一到三个八进制数字表示该数字代码对应的字节 \\xdigits 反斜线加x后跟一到三个十六进制数字表示该数字代码对应的字节 当前，COPY TO不会发出一个八进制或十六进制位 反斜线序列，但是它确实把上面列出的其他序列用于那些控制字符。 ​ 任何上述表格中没有提到的其他反斜线字符将被当作表示其本身。不过，要注意 增加不必要的反斜线，因为那可能意外地产生一个匹配数据结束标记（ \\.）或者空值串（默认是\\N）的字符串。这些字符串 将在完成任何其他反斜线处理之前被识别。 ​ 强烈建议产生COPY数据的应用把数据新行和回车分别 转换为\\n和\\r序列。当前可以把一个数据回车表示为 一个反斜线和回车，把一个数据新行表示为一个反斜线和新行。不过，未来的发行 可能不会接受这些表示。如果在不同的机器之间（例如从 Unix 到 Windows） 传输COPY文件，它们也很容易受到破坏。 ​ 所有反斜杠序列都在编码转换后进行解释。 用八进制和十六进制数字反斜杠序列指定的字节必须在数据库编码中形成有效字符。 ​ COPY TO将用一个 Unix 风格的新行（ “\\n”）终止每一行。运行在 Microsoft Windows 上的服务器则会输出回车/新行（“\\r\\n”），不过只对 COPY到一个服务器文件这样做。为了做到跨平台一致， COPY TO STDOUT总是发送“\\n”而 不管服务器平台是什么。COPY FROM能够处理以 新行、回车或者回车/新行结尾的行。为了减少由作为数据的未加反斜线的新行 或者回车带来的风险，如果输出中的行结束并不完全相似， COPY FROM将会抱怨。 这种格式选项被用于导入和导出很多其他程序（例如电子表格）使用的逗号 分隔值（CSV）文件格式。不同于 IvorySQL标准文本格式使用的转义 规则，它产生并且识别一般的 CSV 转义机制。 ​ 每个记录中的值用DELIMITER字符分隔。如果值包含 定界符字符、QUOTE字符、NULL字符串、 一个回车或者换行字符，那么整个值会被加上QUOTE字符 作为前缀或者后缀，并且在该值内QUOTE字符或者 ESCAPE字符的任何一次出现之前放上转义字符。在输出 指定列中非NULL值时，还可以使用 FORCE_QUOTE来强制加上引用。 CSV格式没有标准方式来区分NULL值和空字符串。 IvorySQL的COPY用引用来处理 这种区分工作。NULL被按照NULL参数字符串输出 并且不会被引用，而匹配NULL参数字符串的非NULL 值会被加上引用。例如，使用默认设置时，NULL被写作一个未 被引用的空字符串，而一个空字符串数据值会被写成带双引号（\"\"）。 值的读取遵循类似的规则。你可以用FORCE_NOT_NULL来防止 对指定列的NULL输入比较。你还可以使用 FORCE_NULL把带引用的空值字符串数据值转换成NULL。 ​ 因为反斜线在CSV格式中不是一种特殊字符，数据结束标记 \\.也可以作为一个数据值出现。为了避免任何解释误会，在 一行上作为孤项出现的\\.数据值输出时会自动被引用，并且 输入时如果被引用，则不会被解释为数据结束标记。如果正在载入一个由 另一个应用创建的文件并且其中具有一个未被引用的列且可能具有 \\.值，你可能需要在输入文件中引用该值。 注意 CSV格式中，所有字符都是有意义的。一个被空白或者其他 非 DELIMITER字符围绕的引用值将包括那些字符。在导入 来自用空白填充CSV行到固定长度的系统的数据时，这可能 会导致错误。如果出现这种情况，在导入数据到 IvorySQL.之前，你可能需要预处理该 CSV文件以移除拖尾的空白。 注意 CSV 格式将识别并且产生带有包含嵌入的回车和换行的引用值的 CSV 文件。因此文件并不限于文本格式文件的每个表行一行的形式。 注意 很多程序会产生奇怪的甚至偶尔是不合常理的 CSV 文件，因此该文件 格式更像是一种习惯而不是标准。因此你可能会碰到一些无法使用这种 机制导入的文件，并且COPY也可能产生其他程序无 法处理的文件。 binary格式选项导致所有数据被以二进制格式 而不是文本格式存储/读取。它比文本和CSV格式要 快一些，但是二进制格式文件在不同的机器架构和 IvorySQL 版本之间的可移 植性要差些。还有，二进制格式与数据格式非常相关。例如不能从 一个smallint列中输出二进制数据并且把它读入到一个 integer列中，虽然这样做在文本格式中是可行的。 ​ binary文件格式由一个文件头、零个或者更多个包含 行数据的元组以及一个文件尾构成。头部和数据都以网络字节序表示。 文件头 文件头由 15 字节的固定域构成，后面跟着一个变长的头部扩展区。 固定域有： 签名 11-字节的序列PGCOPY\\n\\377\\r\\n\\0 — 注意 零字节是签名的一个必要的部分（该签名是为了能容易地发现文件被 无法正确处理 8 位字符编码的传输所破坏。这个签名将被行尾翻译过 滤器、删除零字节、删除高位或者奇偶修改等改变）。 标志域 32-位整数位掩码，用以表示该文件格式的重要方面。位被编号为 从 0 （LSB）到 31（MSB）。 注意这个域以网络字节序存放（最高有效位在前），所有该文件格式 中使用的整数域都是这样。16-31 位被保留用来表示严重的文件格式 问题， 读取者如果在这个范围内发现预期之外的被设置位，它应该 中止。0-15 位被保留用来表示向后兼容的格式问题，读取者应该简单 地略过这个范围内任何预期之外的被设置位。当前只定义了一个标志 位，其他位必须为零： 位 16 如果为 1，表示数据中包含 OID；如果为 0，则不包含。IvorySQL不再支持Oid系统列，但是格式仍然包含该指示符。 头部扩展区长度 32-为整数，表示头部剩余部分的以字节计的长度，不包括其本身。 当前，这个长度为零，并且其后就紧跟着第一个元组。未来对该 格式的更改可能会允许在头部中表示额外的数据。如果读取者不知 道要对头部扩展区数据做什么，可以安静地跳过它。 ​ 头部扩展区域被预期包含一个能自我解释的块的序列。 该标志域并不想告诉读取者扩展数据是什么。详细的 头部扩展内容的设计留给后来的发行去做。 这种设计允许向后兼容的头部增加（增加头部扩展块或者设置低位标志位）以及 非向后兼容的更改（设置高位标志位来表示这类更改并且在需要时向扩展区域 中增加支持数据）。 元组 每一个元组由一个表示元组中域数量的 16 位整数计数开始（当前，一个表中 的所有元组都应该具有相同的计数，但是这可能不会总是为真）。然后是元组 中的每一个域，它是一个 32 位的长度字，后面则跟随着这么多个字节的域数 据（长度字不包括其本身，并且可以是零）。作为一种特殊情况，-1 表示一个 NULL 域值。在 NULL 情况下，后面不会跟随值字节。 ​ 在域之间没有对齐填充或者任何其他额外的数据。 ​ 当前，一个二进制格式文件中的所有数据值都被假设为二进制格式（格式代码一）。 可以预见未来的扩展可能会增加一个允许独立指定各列的格式代码的头部域。 ​ 要为实际的元组数据决定合适的二进制格式，你应该参考 IvorySQL源码，特别是用于各列 数据类型的*send和*recv函数（通常可 以在源码的src/backend/utils/adt/目录中找到 这些函数）。 ​ 如果文件中包含 OID，OID 域会紧跟在域计数字之后。它是一个普通域， 不过它没有被包含在域计数中。注意IvorySQL当前版本不支持oid系统列。 文件尾 文件位由一个包含 -1 的 16 位整数字组成。这很容易与一个 元组的域计数字区分开。 ​ 如果一个域计数字不是 -1 也不是期望的列数，读取者应该报告错误。 这提供了一种针对某种数据不同步的额外检查。 下面的例子使用竖线（|）作为域定界符把一个表复制到客户端： COPY country TO STDOUT (DELIMITER '|'); ​ 从一个文件中复制数据到country表中： COPY country TO STDOUT (DELIMITER '|'); ​ 只把名称以 'A' 开头的国家复制到一个文件中： COPY (SELECT * FROM country WHERE country_name LIKE 'A%') TO '/usr1/proj/bray/sql/a_list_countries.copy'; ​ 要复制到一个压缩文件中，你可以用管道把输出导到一个外部压缩程序： COPY country TO PROGRAM 'gzip > /usr1/proj/bray/sql/country_data.gz'; ​ 这里是一个适合于从STDIN复制到表中的数据： AF AFGHANISTAN AL ALBANIA DZ ALGERIA ZM ZAMBIA ZW ZIMBABWE 注意每一行上的空白实际是一个制表符。 ​ 下面是用二进制格式输出的相同数据。该数据是用 Unix 工具 od -c过滤后显示的。该表具有三列， 第一列类型是char(2)，第二列类型是text， 第三列类型是integer。所有行在第三列都是空值。 0000000 P G C O P Y \\n 377 \\r \\n \\0 \\0 \\0 \\0 \\0 \\0 0000020 \\0 \\0 \\0 \\0 003 \\0 \\0 \\0 002 A F \\0 \\0 \\0 013 A 0000040 F G H A N I S T A N 377 377 377 377 \\0 003 0000060 \\0 \\0 \\0 002 A L \\0 \\0 \\0 007 A L B A N I 0000100 A 377 377 377 377 \\0 003 \\0 \\0 \\0 002 D Z \\0 \\0 \\0 0000120 007 A L G E R I A 377 377 377 377 \\0 003 \\0 \\0 0000140 \\0 002 Z M \\0 \\0 \\0 006 Z A M B I A 377 377 0000160 377 377 \\0 003 \\0 \\0 \\0 002 Z W \\0 \\0 \\0 \\b Z I 0000200 M B A B W E 377 377 377 377 377 377 剩余的详细信息可以参阅 手册。 查询性能可能受多种因素影响。其中一些因素可以由用户控制，而其他的则属于系统下层设计的基本原理。 IvorySQL为每个收到查询产生一个查询计划。 选择正确的计划来匹配查询结构和数据的属性对于好的性能来说绝对是最关键的，因此系统包含了一个复杂的规划器来尝试选择好的计划。 你可以使用EXPLAIN命令察看规划器为任何查询生成的查询计划。 阅读查询计划是一门艺术，它要求一些经验来掌握，但是本节只试图覆盖一些基础。 ​ 这些例子使用EXPLAIN的默认“text”输出格式，这种格式紧凑并且便于阅读。如果你想把EXPLAIN的输出交给一个程序做进一步分析，你应该使用它的某种机器可读的输出格式（XML、JSON 或 YAML）。 查询计划的结构是一个计划结点的树。最底层的结点是扫描结点：它们从表中返回未经处理的行。 不同的表访问模式有不同的扫描结点类型：顺序扫描、索引扫描、位图索引扫描。 也还有不是表的行来源，例如VALUES子句和FROM中返回集合的函数，它们有自己的结点类型。如果查询需要连接、聚集、排序、或者在未经处理的行上的其它操作，那么就会在扫描结点之上有其它额外的结点来执行这些操作。 并且，做这些操作通常都有多种方法，因此在这些位置也有可能出现不同的结点类型。 EXPLAIN给计划树中每个结点都输出一行，显示基本的结点类型和计划器为该计划结点的执行所做的开销估计。 第一行（最上层的结点）是对该计划的总执行开销的估计；计划器试图最小化的就是这个数字。 ​ 这里是一个简单的例子，只是用来显示输出看起来是什么样的： EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) 由于这个查询没有WHERE子句，它必须扫描表中的所有行，因此计划器只能选择使用一个简单的顺序扫描计划。被包含在圆括号中的数字是（从左至右）： 估计的启动开销。在输出阶段可以开始之前消耗的时间，例如在一个排序节点里执行排序的时间。 估计的总开销。这个估计值基于的假设是计划结点会被运行到完成， 即所有可用的行都被检索。不过实际上一个结点的父节点可能很快停止读取所有可用的行（见下面的LIMIT例子）。 这个计划结点输出行数的估计值。同样，也假定该结点能运行到完成。 预计这个计划结点输出的行平均宽度（以字节计算）。 ​ 开销是用规划器的开销参数所决定的捏造单位来衡量的。传统上以取磁盘页面为单位来度量开销； 也就是seq_page_cost将被按照习惯设为1.0，其它开销参数将相对于它来设置。 本节的例子都假定这些参数使用默认值。 ​ 有一点很重要：一个上层结点的开销包括它的所有子结点的开销。还有一点也很重要：这个开销只反映规划器关心的东西。特别是这个开销没有考虑结果行传递给客户端所花费的时间，这个时间可能是实际花费时间中的一个重要因素；但是它被规划器忽略了，因为它无法通过修改计划来改变（我们相信，每个正确的计划都将输出同样的行集）。 ​ 行数值有一些小技巧，因为它不是计划结点处理或扫描过的行数，而是该结点发出的行数。这通常比被扫描的行数少一些， 因为有些被扫描的行会被应用于此结点上的任意WHERE子句条件过滤掉。 理想中顶层的行估计会接近于查询实际返回、更新、删除的行数。 ​ 回到我们的例子： EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) 这些数字的产生非常直接。如果你执行： SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1'; 你会发现tenk1有358个磁盘页面和10000行。 开销被计算为 （页面读取数*seq_page_cost）+（扫描的行数*cpu_tuple_cost）。默认情况下，seq_page_cost是1.0，cpu_tuple_cost是0.01， 因此估计的开销是 (358 * 1.0) + (10000 * 0.01) = 458。 ​ 现在让我们修改查询并增加一个WHERE条件： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000; QUERY PLAN ------------------------------------------------------------ Seq Scan on tenk1 (cost=0.00..483.00 rows=7001 width=244) Filter: (unique1 < 7000) 请注意EXPLAIN输出显示WHERE子句被当做一个“过滤器”条件附加到顺序扫描计划结点。 这意味着该计划结点为它扫描的每一行检查该条件，并且只输出通过该条件的行。因为WHERE子句的存在，估计的输出行数降低了。不过，扫描仍将必须访问所有 10000 行，因此开销没有被降低；实际上开销还有所上升（准确来说，上升了 10000 * cpu_operator_cost）以反映检查WHERE条件所花费的额外 CPU 时间。 ​ 这条查询实际选择的行数是 7000，但是估计的rows只是个近似值。如果你尝试重复这个试验，那么你很可能得到略有不同的估计。 此外，这个估计会在每次ANALYZE命令之后改变， 因为ANALYZE生成的统计数据是从该表中随机采样计算的。 ​ 现在，让我们把条件变得更严格： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) 这里，规划器决定使用一个两步的计划：子计划结点访问一个索引来找出匹配索引条件的行的位置，然后上层计划结点实际地从表中取出那些行。独立地抓取行比顺序地读取它们的开销高很多，但是不是所有的表页面都被访问，这么做实际上仍然比一次顺序扫描开销要少（使用两层计划的原因是因为上层规划结点把索引标识出来的行位置在读取之前按照物理位置排序，这样可以最小化单独抓取的开销。结点名称里面提到的“位图”是执行该排序的机制）。 ​ 现在让我们给WHERE子句增加另一个条件： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND stringu1 = 'xxx'; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.04..229.43 rows=1 width=244) Recheck Cond: (unique1 < 100) Filter: (stringu1 = 'xxx'::name) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) 新增的条件stringu1 = 'xxx’减少了估计的输出行计数， 但是没有减少开销，因为我们仍然需要访问相同的行集合。 请注意，stringu1子句不能被应用为一个索引条件，因为这个索引只是在unique1列上。 它被用来过滤从索引中检索出的行。因此开销实际上略微增加了一些以反映这个额外的检查。 ​ 在某些情况下规划器将更倾向于一个“simple”索引扫描计划： EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42; QUERY PLAN -------------------------------------------------------------------​----------- Index Scan using tenk1_unique1 on tenk1 (cost=0.29..8.30 rows=1 width=244) Index Cond: (unique1 = 42) 在这类计划中，表行被按照索引顺序取得，这使得读取它们开销更高，但是其中有一些是对行位置排序的额外开销。 你很多时候将在只取得一个单一行的查询中看到这种计划类型。 它也经常被用于拥有匹配索引顺序的ORDER BY子句的查询中， 因为那样就不需要额外的排序步骤来满足ORDER BY。在此示例中，添加 ORDER BY unique1将使用相同的计划，因为索引已经隐式提供了请求的排序。 ​ 规划器可以通过多种方式实现ORDER BY子句。上面的例子表明，这样的排序子句可以隐式实现。 规划器还可以添加一个明确的sort步骤： EXPLAIN SELECT * FROM tenk1 ORDER BY unique1; QUERY PLAN ------------------------------------------------------------------- Sort (cost=1109.39..1134.39 rows=10000 width=244) Sort Key: unique1 -> Seq Scan on tenk1 (cost=0.00..445.00 rows=10000 width=244) 如果计划的一部分保证对所需排序键的前缀进行排序，那么计划器可能会决定使用incremental sort步骤： EXPLAIN SELECT * FROM tenk1 ORDER BY four, ten LIMIT 100; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Limit (cost=521.06..538.05 rows=100 width=244) -> Incremental Sort (cost=521.06..2220.95 rows=10000 width=244) Sort Key: four, ten Presorted Key: four -> Index Scan using index_tenk1_on_four on tenk1 (cost=0.29..1510.08 rows=10000 width=244) 与常规排序相比，增量排序允许在对整个结果集进行排序之前返回元组，这尤其可以使用LIMIT查询进行优化。 它还可以减少内存使用和将排序溢出到磁盘的可能性，但其代价是将结果集拆分为多个排序批次的开销增加。 ​ 如果在WHERE引用的多个行上有独立的索引，规划器可能会选择使用这些索引的一个 AND 或 OR 组合： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​------------------ Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) Index Cond: (unique2 > 9000) 但是这要求访问两个索引，所以与只使用一个索引并把其他条件作为过滤器相比，它不一定能胜出。如果你变动涉及到的范围，你将看到计划也会相应改变。 下面是一个例子，它展示了LIMIT的效果： EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------ Limit (cost=0.29..14.48 rows=2 width=244) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..71.27 rows=10 width=244) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) 这是和上面相同的查询，但是我们增加了一个LIMIT这样不是所有的行都需要被检索，并且规划器改变了它的决定。注意索引扫描结点的总开销和行计数显示出好像它会被运行到完成。但是，限制结点在检索到这些行的五分之一后就会停止，因此它的总开销只是索引扫描结点的五分之一，并且这是查询的实际估计开销。之所以用这个计划而不是在之前的计划上增加一个限制结点是因为限制无法避免在位图扫描上花费启动开销，因此总开销会是超过那种方法（25个单位）的某个值。 ​ 让我们尝试连接两个表，使用我们已经讨论过的列： EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..118.62 rows=10 width=488) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) Index Cond: (unique2 = t1.unique2) 在这个计划中，我们有一个嵌套循环连接结点，它有两个表扫描作为输入或子结点。该结点的摘要行的缩进反映了计划树的结构。连接的第一个（或“outer”）子结点是一个与前面见到的相似的位图扫描。它的开销和行计数与我们从SELECT …​ WHERE unique1 < 10得到的相同，因为我们将WHERE子句unique1 < 10用在了那个结点上。t1.unique2 = t2.unique2子句现在还不相关，因此它不影响 outer 扫描的行计数。嵌套循环连接结点将为从 outer 子结点得到的每一行运行它的第二个（或“inner”）子结点。当前 outer 行的列值可以被插入 inner 扫描。这里，来自 outer 行的t1.unique2值是可用的，所以我们得到的计划和开销与前面见到的简单SELECT …​ WHERE t2.unique2 = constant情况相似（估计的开销实际上比前面看到的略低，是因为在t2上的重复索引扫描会利用到高速缓存）。循环结点的开销则被以 outer 扫描的开销为基础设置，外加对每一个 outer 行都要进行一次 inner 扫描 （10 * 7.87），再加上用于连接处理一点 CPU 时间。 ​ 在这个例子里，连接的输出行计数等于两个扫描的行计数的乘积，但通常并不是所有的情况中都如此， 因为可能有同时提及两个表的 额外WHERE子句，并且因此它只能被应用于连接点，而不能影响任何一个输入扫描。这里是一个例子： EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t2.unique2 < 10 AND t1.hundred < t2.hundred; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..49.46 rows=33 width=488) Join Filter: (t1.hundred < t2.hundred) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Materialize (cost=0.29..8.51 rows=10 width=244) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..8.46 rows=10 width=244) Index Cond: (unique2 < 10) 条件t1.hundred < t2.hundred不能在tenk2_unique2索引中被测试，因此它被应用在连接结点。这缩减了连接结点的估计输出行计数，但是没有改变任何输入扫描。 ​ 注意这里规划器选择了“物化”连接的 inner 关系，方法是在它的上方放了一个物化计划结点。这意味着t2索引扫描将只被做一次，即使嵌套循环连接结点需要读取其数据十次（每个来自 outer 关系的行都要读一次）。物化结点在读取数据时将它保存在内存中，然后在每一次后续执行时从内存返回数据。 ​ 在处理外连接时，你可能会看到连接计划结点同时附加有“连接过滤器”和普通“过滤器”条件。连接过滤器条件来自于外连接的ON子句，因此一个无法通过连接过滤器条件的行也能够作为一个空值扩展的行被发出。但是一个普通过滤器条件被应用在外连接条件之后并且因此无条件移除行。在一个内连接中这两种过滤器类型没有语义区别。 ​ 如果我们把查询的选择度改变一点，我们可能得到一个非常不同的连接计划： EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Hash Join (cost=230.47..713.98 rows=101 width=488) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) -> Hash (cost=229.20..229.20 rows=101 width=244) -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) 这里规划器选择了使用一个哈希连接，在其中一个表的行被放入一个内存哈希表，在这之后其他表被扫描并且为每一行查找哈希表来寻找匹配。同样要注意缩进是如何反映计划结构的：tenk1上的位图扫描是哈希结点的输入，哈希结点会构造哈希表。然后哈希表会返回给哈希连接结点，哈希连接结点将从它的 outer 子计划读取行，并为每一个行搜索哈希表。 ​ 另一种可能的连接类型是一个归并连接，如下所示： EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Merge Join (cost=198.11..268.19 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Sort (cost=197.83..200.33 rows=1000 width=244) Sort Key: t2.unique2 -> Seq Scan on onek t2 (cost=0.00..148.00 rows=1000 width=244) 归并连接要求它的输入数据被按照连接键排序。在这个计划中，tenk1数据被使用一个索引扫描排序，以便能够按照正确的顺序来访问行。但是对于onek则更倾向于一个顺序扫描和排序，因为在那个表中有更多行需要被访问（对于很多行的排序，顺序扫描加排序常常比一个索引扫描好，因为索引扫描需要非顺序的磁盘访问）。 ​ 一种查看变体计划的方法是强制规划器丢弃它认为开销最低的任何策略，这可以使用启用/禁用标志实现例如，如果我们并不认同在前面的例子中顺序扫描加排序是处理表onek的最佳方法，我们可以尝试： SET enable_sort = off; EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​----------------------- Merge Join (cost=0.56..292.65 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Index Scan using onek_unique2 on onek t2 (cost=0.28..224.79 rows=1000 width=244) 这显示规划器认为用索引扫描来排序onek的开销要比用顺序扫描加排序的方式高大约12%。当然，下一个问题是是否真的是这样。我们可以通过使用EXPLAIN ANALYZE来仔细研究一下，如下文所述。 可以通过使用EXPLAIN的ANALYZE选项来检查规划器估计值的准确性。通过使用这个选项，EXPLAIN会实际执行该查询，然后显示真实的行计数和在每个计划结点中累计的真实运行时间，还会有一个普通EXPLAIN显示的估计值。例如，我们可能得到这样一个结果： EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Nested Loop (cost=4.65..118.62 rows=10 width=488) (actual time=0.128..0.377 rows=10 loops=1) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) (actual time=0.057..0.121 rows=10 loops=1) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) (actual time=0.024..0.024 rows=10 loops=1) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) (actual time=0.021..0.022 rows=1 loops=10) Index Cond: (unique2 = t1.unique2) Planning time: 0.181 ms Execution time: 0.501 ms 注意“actual time”值是以毫秒计的真实时间，而cost估计值被以捏造的单位表示，因此它们不大可能匹配上。在这里面要查看的最重要的一点是估计的行计数是否合理地接近实际值。在这个例子中，估计值都是完全正确的，但是在实际中非常少见。 在某些查询计划中，可以多次执行一个子计划结点。例如，inner 索引扫描可能会因为上层嵌套循环计划中的每一个 outer 行而被执行一次。在这种情况下，loops值报告了执行该结点的总次数，并且 actual time 和行数值是这些执行的平均值。这是为了让这些数字能够与开销估计被显示的方式有可比性。将这些值乘上loops值可以得到在该结点中实际消耗的总时间。在上面的例子中，我们在执行tenk2的索引扫描上花费了总共 0.220 毫秒。 ​ 在某些情况中，EXPLAIN ANALYZE会显示计划结点执行时间和行计数之外的额外执行统计信息。例如，排序和哈希结点提供额外的信息： EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2 ORDER BY t1.fivethous; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------------​------ Sort (cost=717.34..718.09 rows=101 width=488) (actual time=7.761..7.774 rows=100 loops=1) Sort Key: t1.fivethous Sort Method: quicksort Memory: 77kB -> Hash Join (cost=230.47..713.98 rows=101 width=488) (actual time=0.711..7.427 rows=100 loops=1) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) (actual time=0.007..2.583 rows=10000 loops=1) -> Hash (cost=229.20..229.20 rows=101 width=244) (actual time=0.659..0.659 rows=100 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 28kB -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) (actual time=0.080..0.526 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.049..0.049 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.194 ms Execution time: 8.008 ms 排序结点显示使用的排序方法（尤其是，排序是在内存中还是磁盘上进行）和需要的内存或磁盘空间量。哈希结点显示了哈希桶的数量和批数，以及被哈希表所使用的内存量的峰值（如果批数超过一，也将会涉及到磁盘空间使用，但是并没有被显示）。 ​ 另一种类型的额外信息是被一个过滤器条件移除的行数： EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE ten < 7; QUERY PLAN -------------------------------------------------------------------​-------------------------------------- Seq Scan on tenk1 (cost=0.00..483.00 rows=7000 width=244) (actual time=0.016..5.107 rows=7000 loops=1) Filter: (ten < 7) Rows Removed by Filter: 3000 Planning time: 0.083 ms Execution time: 5.905 ms 这些值对于被应用在连接结点上的过滤器条件特别有价值。只有在至少有一个被扫描行或者在连接结点中一个可能的连接对被过滤器条件拒绝时，“Rows Removed”行才会出现。 ​ 一个与过滤器条件相似的情况出现在“有损”索引扫描中。例如，考虑这个查询，它搜索包含一个指定点的多边形： EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Seq Scan on polygon_tbl (cost=0.00..1.05 rows=1 width=32) (actual time=0.044..0.044 rows=0 loops=1) Filter: (f1 @> '((0.5,2))'::polygon) Rows Removed by Filter: 4 Planning time: 0.040 ms Execution time: 0.083 ms 规划器认为（非常正确）这个采样表太小不值得劳烦一次索引扫描，因此我们得到了一个普通的顺序扫描，其中的所有行都被过滤器条件拒绝。但是如果我们强制使得一次索引扫描可以被使用，我们看到： SET enable_seqscan TO off; EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------- Index Scan using gpolygonind on polygon_tbl (cost=0.13..8.15 rows=1 width=32) (actual time=0.062..0.062 rows=0 loops=1) Index Cond: (f1 @> '((0.5,2))'::polygon) Rows Removed by Index Recheck: 1 Planning time: 0.034 ms Execution time: 0.144 ms 这里我们可以看到索引返回一个候选行，然后它会被索引条件的重新检查拒绝。这是因为一个 GiST 索引对于多边形包含测试是 “有损的”：它确实返回覆盖目标的多边形的行，然后我们必须在那些行上做精确的包含性测试。 EXPLAIN有一个BUFFERS选项可以和ANALYZE一起使用来得到更多运行时统计信息： EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) (actual time=0.323..0.342 rows=10 loops=1) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) Buffers: shared hit=15 -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) (actual time=0.309..0.309 rows=0 loops=1) Buffers: shared hit=7 -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Buffers: shared hit=2 -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) (actual time=0.227..0.227 rows=999 loops=1) Index Cond: (unique2 > 9000) Buffers: shared hit=5 Planning time: 0.088 ms Execution time: 0.423 ms BUFFERS提供的数字帮助我们标识查询的哪些部分是对 I/O 最敏感的。 ​记住因为EXPLAIN ANALYZE实际运行查询，任何副作用都将照常发生，即使查询可能输出的任何结果被丢弃来支持打印EXPLAIN数据。如果你想要分析一个数据修改查询而不想改变你的表，你可以在分析完后回滚命令，例如： BEGIN; EXPLAIN ANALYZE UPDATE tenk1 SET hundred = hundred + 1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------- Update on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=14.628..14.628 rows=0 loops=1) -> Bitmap Heap Scan on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=0.101..0.439 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.079 ms Execution time: 14.727 ms ROLLBACK; 正如在这个例子中所看到的，当查询是一个INSERT、UPDATE或DELETE命令时，应用表更改的实际工作由顶层插入、更新或删除计划结点完成。这个结点之下的计划结点执行定位旧行以及/或者计算新数据的工作。因此在上面，我们看到我们已经见过的位图表扫描，它的输出被交给一个更新结点，更新结点会存储被更新过的行。还有一点值得注意的是，尽管数据修改结点可能要可观的运行时间（这里，它消耗最大份额的时间），规划器当前并没有对开销估计增加任何东西来说明这些工作。这是因为这些工作对每一个正确的查询计划都得做，所以它不影响计划的选择。 ​ 当一个UPDATE或者DELETE命令影响继承层次时， 输出可能像这样： EXPLAIN UPDATE parent SET f2 = f2 + 1 WHERE f1 = 101; QUERY PLAN -------------------------------------------------------------------​---------------- Update on parent (cost=0.00..24.53 rows=4 width=14) Update on parent Update on child1 Update on child2 Update on child3 -> Seq Scan on parent (cost=0.00..0.00 rows=1 width=14) Filter: (f1 = 101) -> Index Scan using child1_f1_key on child1 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child2_f1_key on child2 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child3_f1_key on child3 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) 在这个例子中，更新节点需要考虑三个子表以及最初提到的父表。因此有四个输入 的扫描子计划，每一个对应于一个表。为清楚起见，在更新节点上标注了将被更新 的相关目标表，显示的顺序与相应的子计划相同（这些标注是从 PostgreSQL 9.5 开始新增的，在以前的版本中读者必须通过 观察子计划才能知道这些目标表）。 ​ EXPLAIN ANALYZE显示的 Planning time是从一个已解析的查询生成查询计划并进行优化 所花费的时间，其中不包括解析和重写。 ​ EXPLAIN ANALYZE显示的Execution time包括执行器的启动和关闭时间，以及运行被触发的任何触发器的时间，但是它不包括解析、重写或规划的时间。如果有花在执行BEFORE执行器的时间，它将被包括在相关的插入、更新或删除结点的时间内；但是用来执行AFTER 触发器的时间没有被计算，因为AFTER触发器是在整个计划完成后被触发的。在每个触发器（BEFORE或AFTER）也被独立地显示。注意延迟约束触发器将不会被执行，直到事务结束，并且因此根本不会被EXPLAIN ANALYZE考虑。 在两种有效的方法中EXPLAIN ANALYZE所度量的运行时间可能偏离同一个查询的正常执行。首先，由于不会有输出行被递交给客户端，网络传输开销和 I/O 转换开销没有被包括在内。其次，由EXPLAIN ANALYZE所增加的度量开销可能会很可观，特别是在操作系统调用gettimeofday()很慢的机器上。你可以使用pg_test_timing工具来度量在你的系统上的计时开销。 ​ EXPLAIN结果不应该被外推到与你实际测试的非常不同的情况。例如，一个很小的表上的结果不能被假定成适合大型表。规划器的开销估计不是线性的，并且因此它可能为一个更大或更小的表选择一个不同的计划。一个极端例子是，在一个只占据一个磁盘页面的表上，你将几乎总是得到一个顺序扫描计划，而不管索引是否可用。规划器认识到它在任何情况下都将采用一次磁盘页面读取来处理该表，因此用额外的页面读取去查看一个索引是没有价值的（我们已经在前面的polygon_tbl例子中见过）。 ​ 在一些情况中，实际的值和估计的值不会匹配得很好，但是这并非错误。一种这样的情况发生在计划结点的执行被LIMIT或类似的效果很快停止。例如，在我们之前用过的LIMIT查询中： EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------ Limit (cost=0.29..14.71 rows=2 width=244) (actual time=0.177..0.249 rows=2 loops=1) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..72.42 rows=10 width=244) (actual time=0.174..0.244 rows=2 loops=1) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) Rows Removed by Filter: 287 Planning time: 0.096 ms Execution time: 0.336 ms 索引扫描结点的估计开销和行计数被显示成好像它会运行到完成。但是实际上限制结点在得到两个行之后就停止请求行，因此实际的行计数只有 2 并且运行时间远低于开销估计所建议的时间。这并非估计错误，这仅仅一种估计值和实际值显示方式上的不同。 ​ 归并连接也有类似的现象。如果一个归并连接用尽了一个输入并且其中的最后一个键值小于另一个输入中的下一个键值，它将停止读取另一个输入。在这种情况下，不会有更多的匹配并且因此不需要扫描第二个输入的剩余部分。这会导致不读取一个子结点的所有内容，其结果就像在LIMIT中所提到的。另外，如果 outer （第一个）子结点包含带有重复键值的行，inner（第二个）子结点会被倒退并且被重新扫描来找能匹配那个键值的行。EXPLAIN ANALYZE会统计相同 inner 行的重复发出，就好像它们是真实的额外行。当有很多 outer 重复时，对 inner 子计划结点所报告的实际行计数会显著地大于实际在 inner 关系中的行数。 ​ 由于实现的限制，BitmapAnd 和 BitmapOr 结点总是报告它们的实际行计数为零。 ​ 通常，EXPLAIN将显示规划器生成的每个计划节点。 但是，在某些情况下，执行器可以不执行某些节点，因为根据规划时不可用的参数值能确定这些节点无法产生任何行。 （当前，这仅会在扫描分区表的Append或MergeAppend节点的子节点中发生。） 发生这种情况时，将从EXPLAIN输出中省略这些计划节点，并显示Subplans Removed：N的标识。 如我们在上一节所见，查询规划器需要估计一个查询要检索的行数，这样才能对查询计划做出好的选择。 本节对系统用于这些估计的统计信息进行一个快速的介绍。 ​ 统计信息的一个部分就是每个表和索引中的项的总数，以及每个表和索引占用的磁盘块数。这些信息保存在pg_class表的reltuples和relpages列中。 我们可以用类似下面的查询查看这些信息： SELECT relname, relkind, reltuples, relpages FROM pg_class WHERE relname LIKE 'tenk1%'; relname | relkind | reltuples | relpages ----------------------+---------+-----------+---------- tenk1 | r | 10000 | 358 tenk1_hundred | i | 10000 | 30 tenk1_thous_tenthous | i | 10000 | 30 tenk1_unique1 | i | 10000 | 30 tenk1_unique2 | i | 10000 | 30 (5 rows) 这里我们可以看到tenk1包含 10000 行， 它的索引也有这么多行，但是索引远比表小得多（不奇怪）。 ​ 出于效率考虑，reltuples和relpages不是实时更新的 ，因此它们通常包含有些过时的值。它们被VACUUM、ANALYZE和几个 DDL 命令（例如CREATE INDEX）更新。一个不扫描全表的VACUUM或ANALYZE操作（常见情况）将以它扫描的部分为基础增量更新reltuples计数，这就导致了一个近似值。在任何情况中，规划器将缩放它在pg_class中找到的值来匹配当前的物理表尺寸，这样得到一个较紧的近似。 ​ 大多数查询只是检索表中行的一部分，因为它们有限制要被检查的行的WHERE子句。 因此规划器需要估算WHERE子句的选择度，即符合WHERE子句中每个条件的行的比例。 用于这个任务的信息存储在pg_statistic系统目录中。 在pg_statistic中的项由ANALYZE和VACUUM ANALYZE命令更新， 并且总是近似值（即使刚刚更新完）。 ​ 除了直接查看pg_statistic之外， 手工检查统计信息的时候最好查看它的视图pg_stats。pg_stats被设计为更容易阅读。 而且，pg_stats是所有人都可以读取的，而pg_statistic只能由超级用户读取（这样可以避免非授权用户从统计信息中获取一些其他人的表的内容的信息。pg_stats视图被限制为只显示当前用户可读的表）。例如，我们可以： SELECT attname, inherited, n_distinct, array_to_string(most_common_vals, E'\\n') as most_common_vals FROM pg_stats WHERE tablename = 'road'; attname | inherited | n_distinct | most_common_vals ---------+-----------+------------+------------------------------------ name | f | -0.363388 | I- 580 Ramp+ | | | I- 880 Ramp+ | | | Sp Railroad + | | | I- 580 + | | | I- 680 Ramp name | t | -0.284859 | I- 880 Ramp+ | | | I- 580 Ramp+ | | | I- 680 Ramp+ | | | I- 580 + | | | State Hwy 13 Ramp (2 rows) 注意，这两行显示的是相同的列，一个对应开始于road表（inherited=t）的完全继承层次， 另一个只包括road表本身（inherited=f）。 ​ ANALYZE在pg_statistic中存储的信息量（特别是每个列的most_common_vals中的最大项数和histogram_bounds数组）可以用ALTER TABLE SET STATISTICS命令为每一列设置， 或者通过设置配置变量default_statistics_target进行全局设置。 目前的默认限制是 100 个项。提升该限制可能会让规划器做出更准确的估计（特别是对那些有不规则数据分布的列）， 其代价是在pg_statistic中消耗了更多空间，并且需要略微多一些的时间来计算估计数值。 相比之下，比较低的限制可能更适合那些数据分布比较简单的列。 更多规划器对统计信息的使用可以参阅 手册。 常常可以看到由于查询子句中用到的多个列相互关联而运行着糟糕的执行计划的慢查询。规划器通常会假设多个条件是彼此独立的，这种假设在列值相互关联的情况下是不成立的。由于常规的统计信息天然的针对个体列的性质，它们无法捕捉到跨列关联的知识。不过，IvorySQL有能力计算多元统计信息，它能捕捉这类信息。 ​ 由于可能的列组合数非常巨大，所以不可能自动计算多元统计信息。可以创建扩展统计信息对象（更常被称为统计信息对象）来指示服务器获得跨感兴趣列集合的统计信息。 ​ 统计信息对象可以使用CREATE STATISTICS命令创建。这样一个对象的创建仅仅是创建了一个目录项来表示对统计信息有兴趣。实际的数据收集是由ANALYZE（或者是一个手工命令，或者是后台的自动分析）执行的。收集到的值可以在pg_statistic_ext_data目录中看到。 ​ ANALYZE基于它用来计算常规单列统计信息的表行样本来计算扩展统计信息。由于样本的尺寸会随着表或者表列的统计信息目标（如前一节所述）增大而增加，更大的统计信息目标通常将会导致更准确的扩展统计信息，同时也会导致更多花在计算扩展统计信息之上的时间。 ​ 下面的小节介绍当前支持的扩展统计信息类型。 最简单的一类扩展统计信息跟踪函数依赖，这是在数据库范式定义中使用的概念。如果列a的值的知识足以决定列b的值，即不会有两个行具有相同的a值但是有不同的b值，我们就说列b函数依赖于列a。在一个完全规范化的数据库中，函数依赖应该仅存在于主键和超键上。不过，实际上很多数据集合会由于各种原因无法被完全规范化，常见的例子是为了性能而有意地反规范化。即使在一个完全规范化的数据库中，也会有某些列之间的部分关联，这些可以表达成部分函数依赖。 ​ 函数依赖的存在直接影响了特定查询中估计的准确性。如果一个查询包含独立列和依赖列上的条件，依赖列上的条件不会进一步降低结果的尺寸。但是如果没有函数依赖的知识，查询规划器将假定条件是独立的，导致对结果尺寸的低估。 ​ 要告知规划器有关函数依赖的信息，ANALYZE可以收集跨列依赖的测度。评估所有列组之间的依赖程度可能会昂贵到不可实现，因此数据收集被限制为针对那些在一个统计信息对象中一起出现的列组（用dependencies选项定义）。建议只对强相关的列组创建dependencies统计信息，以避免ANALYZE以及后期查询规划中不必要的开销。 ​ 这里是一个收集函数依赖统计信息的例子： CREATE STATISTICS stts (dependencies) ON city, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxname, stxkeys, stxddependencies FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts'; stxname | stxkeys | stxddependencies ---------+---------+------------------------------------------ stts | 1 5 | {\"1 => 5\": 1.000000, \"5 => 1\": 0.423130} (1 row) 这里可以看到列1（邮编）完全决定列5（城市），因此系数为1.0，而城市仅决定42%的邮编，意味着有很多城市（58%）有多个邮编。 在为涉及函数依赖列的查询计算选择度时，规划器会使用依赖系数来调整针对条件的选择度估计，这样就不会产生低估。 当前只有在考虑简单等值条件（将列与常量值比较）和具有常量值的IN 子句时，函数依赖才适用。不会使用它们来改进比较两个列或者比较列和表达式的等值条件的估计， 也不会用它们来改进范围子句、LIKE或者任何其他类型的条件。 ​ 在用函数依赖估计时，规划器假定在涉及的列上的条件是兼容的并且因此是冗余的。如果它们是不兼容的，正确的估计将是零行，但是那种可能性不会被考虑。例如，给定一个这样的查询 SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '94105'; 规划器将会忽视city子句，因为它不改变选择度，这是正确的。不过，即便真地只有零行满足下面的查询，规划器也会做出同样的假设 SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '90210'; 不过，函数依赖统计信息无法提供足够的信息来排除这种情况。 ​ 在很多实际情况中，这种假设通常是能满足的。例如，在应用程序中可能有一个GUI仅允许选择兼容的城市和邮编值用在查询中。但是如果不是这样，函数依赖可能就不是一个可行的选项。 单列统计信息存储每一列中可区分值的数量。在组合多个列（例如GROUP BY a, b）时，如果规划器只有单列统计数据，则对可区分值数量的估计常常会错误，导致选择不好的计划。 ​ 为了改进这种估计，ANALYZE可以为列组收集可区分值统计信息。和以前一样，为每一种可能的列组合做这件事情是不切实际的，因此只会为一起出现在一个统计信息对象（用ndistinct选项定义）中的列组收集数据。将会为列组中列出的列的每一种可能的组合都收集数据。 ​ 继续之前的例子，ZIP代码表中的可区分值计数可能像这样： CREATE STATISTICS stts2 (ndistinct) ON city, state, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxkeys AS k, stxdndistinct AS nd FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts2'; -[ RECORD 1 ]------------------------------------------------------​-- k | 1 2 5 nd | {\"1, 2\": 33178, \"1, 5\": 33178, \"2, 5\": 27435, \"1, 2, 5\": 33178} (1 row) 这表示有三种列组合有33178个可区分值：ZIP代码和州、ZIP代码和城市、ZIP代码+城市+周（事实上对于表中给定的一个唯一的ZIP代码，它们本来就应该是相等的）。另一方面，城市和州的组合只有27435个可区分值。 ​ 建议只对实际用于分组的列组合以及分组数错误估计导致了糟糕计划的列组合创建ndistinct统计信息对象。否则，ANALYZE循环只会被浪费。 为每列存储的另一种统计信息是频繁值列表。 这样可以对单个列进行非常准确的估计，但是对于在多个列上具有条件的查询，可能会导致严重的错误估计。 ​ 为了改善这种估计，ANALYZE可以收集列组合上的MCV列表。 与功能依赖和n-distinct系数类似，对每种可能的列分组进行此操作都是不切实际的。 在这种情况下，甚至更是如此，因为MCV列表（与功能依赖性和n-distinct系数不同）存储了公共列值。 因此，仅收集在使用mcv选项定义的统计对象中同时出现的那些列组的数据。 ​ 继续前面的示例，邮政编码表的MCV列表可能类似于以下内容（与更简单的统计信息不同，它需要一个函数来检查MCV内容）: CREATE STATISTICS stts3 (mcv) ON city, state FROM zipcodes; ANALYZE zipcodes; SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid), pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts3'; index | values | nulls | frequency | base_frequency -------+------------------------+-------+-----------+---------------- 0 | {Washington, DC} | {f,f} | 0.003467 | 2.7e-05 1 | {Apo, AE} | {f,f} | 0.003067 | 1.9e-05 2 | {Houston, TX} | {f,f} | 0.002167 | 0.000133 3 | {El Paso, TX} | {f,f} | 0.002 | 0.000113 4 | {New York, NY} | {f,f} | 0.001967 | 0.000114 5 | {Atlanta, GA} | {f,f} | 0.001633 | 3.3e-05 6 | {Sacramento, CA} | {f,f} | 0.001433 | 7.8e-05 7 | {Miami, FL} | {f,f} | 0.0014 | 6e-05 8 | {Dallas, TX} | {f,f} | 0.001367 | 8.8e-05 9 | {Chicago, IL} | {f,f} | 0.001333 | 5.1e-05 ... (99 rows) 这表明城市和州的最常见组合是华盛顿特区，实际频率（在样本中）约为0.35%。 组合的基本频率（根据简单的每列频率计算）仅为0.0027％，导致两个数量级的低估。 ​ 建议仅在实际在条件中一起使用的列的组合上创建MCV统计对象，对于这些组合，错误估计组数会导致糟糕的执行计划。 否则，只会浪费ANALYZE和规划时间。 我们可以在一定程度上用显式JOIN语法控制查询规划器。要明白为什么需要它，我们首先需要一些背景知识。 在一个简单的连接查询中，例如： SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; 规划器可以自由地按照任何顺序连接给定的表。例如，它可以生成一个使用WHERE条件a.id = b.id连接 A 到 B 的查询计划，然后用另外一个WHERE条件把 C 连接到这个连接表。或者它可以先连接 B 和 C 然后再连接 A 得到同样的结果。 或者也可以连接 A 到 C 然后把结果与 B 连接 — 不过这么做效率不好，因为必须生成完整的 A 和 C 的迪卡尔积，而在WHERE子句中没有可用条件来优化该连接（IvorySQL执行器中的所有连接都发生在两个输入表之间， 所以它必须以这些形式之一建立结果）。 重要的一点是这些不同的连接可能性给出在语义等效的结果，但在执行开销上却可能有巨大的差别。 因此，规划器会对它们进行探索并尝试找出最高效的查询计划。 ​ 当一个查询只涉及两个或三个表时，那么不需要考虑很多连接顺序。但是可能的连接顺序数随着表数目的增加成指数增长。 当超过十个左右的表以后，实际上根本不可能对所有可能性做一次穷举搜索，甚至对六七个表都需要相当长的时间进行规划。 当有太多的输入表时，IvorySQL规划器将从穷举搜索切换为一种遗传概率搜索，它只需要考虑有限数量的可能性（切换的阈值用geqo_threshold运行时参数设置）。遗传搜索用时更少，但是并不一定会找到最好的计划。 ​ 当查询涉及外连接时，规划器比处理普通（内）连接时拥有更小的自由度。例如，考虑： SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); 尽管这个查询的约束表面上和前一个非常相似，但它们的语义却不同， 因为如果 A 里有任何一行不能匹配 B 和 C的连接表中的行，它也必须被输出。因此这里规划器对连接顺序没有什么选择：它必须先连接 B 到 C，然后把 A 连接到该结果上。 相应地，这个查询比前面一个花在规划上的时间更少。在其它情况下，规划器就有可能确定多种连接顺序都是安全的。例如，给定： SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id); 将 A 首先连接到 B 或 C 都是有效的。当前，只有FULL JOIN完全约束连接顺序。大多数涉及LEFT JOIN或RIGHT JOIN的实际情况都在某种程度上可以被重新排列。 显式连接语法（INNER JOIN、CROSS JOIN或无修饰的JOIN）在语义上和FROM中列出输入关系是一样的， 因此它不约束连接顺序。 即使大多数类型的JOIN并不完全约束连接顺序，但仍然可以指示IvorySQL查询规划器将所有JOIN子句当作有连接顺序约束来对待。例如，这里的三个查询在逻辑上是等效的： SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); 但如果我们告诉规划器遵循JOIN的顺序，那么第二个和第三个还是要比第一个花在规划上的时间少。 这个效果对于只有三个表的连接而言是微不足道的，但对于数目众多的表，可能就是救命稻草了。 ​ 要强制规划器遵循显式JOIN的连接顺序， 我们可以把运行时参数join_collapse_limit设置为 1(其它可能值在下文讨论)。 ​ 你不必为了缩短搜索时间来完全约束连接顺序，因为可以在一个普通FROM列表里使用JOIN操作符。例如，考虑： SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...; 如果设置join_collapse_limit = 1，那么这就强迫规划器先把 A 连接到 B， 然后再连接到其它的表上，但并不约束它的选择。在这个例子中，可能的连接顺序的数目减少了 5 倍。 ​ 按照这种方法约束规划器的搜索是一个有用的技巧，不管是对减少规划时间还是对引导规划器生成好的查询计划。 如果规划器按照默认选择了一个糟糕的连接顺序，你可以通过JOIN语法强迫它选择一个更好的顺序 — 假设你知道一个更好的顺序。我们推荐进行实验。 ​ 一个非常相近的影响规划时间的问题是把子查询压缩到它们的父查询中。例如，考虑： SELECT * FROM x, y, (SELECT * FROM a, b, c WHERE something) AS ss WHERE somethingelse; 这种情况可能在使用包含连接的视图时出现；该视图的SELECT规则将被插入到引用视图的地方，得到与上文非常相似的查询。 通常，规划器会尝试把子查询压缩到父查询里，得到： SELECT * FROM x, y, a, b, c WHERE something AND somethingelse; 这样通常会生成一个比独立的子查询更好些的计划（例如，outer 的WHERE条件可能先把 X 连接到 A 上，这样就消除了 A 中的许多行， 因此避免了形成子查询的全部逻辑输出）。但是同时，我们增加了规划的时间； 在这里，我们用五路连接问题替代了两个独立的三路连接问题。这样的差别是巨大的，因为可能的计划数的是按照指数增长的。 如果有超过from_collapse_limit个FROM项将会导致父查询，规划器将尝试通过停止提升子查询来避免卡在巨大的连接搜索问题中。你可以通过调高或调低这个运行时参数在规划时间和计划的质量之间取得平衡。 from_collapse_limit和join_collapse_limit的命名相似，因为它们做的几乎是同一件事：一个控制规划器何时将把子查询“平面化”，另外一个控制何时把显式连接平面化。通常，你要么把join_collapse_limit设置成和from_collapse_limit一样（这样显式连接和子查询的行为类似）， 要么把join_collapse_limit设置为 1（如果你想用显式连接控制连接顺序）。 但是你可以把它们设置成不同的值，这样你就可以细粒度地调节规划时间和运行时间之间的平衡。 第一次填充数据库时可能需要插入大量的数据。本节包含一些如何让这个处理尽可能高效的建议。 在使用多个INSERT时，关闭自动提交并且只在最后做一次提交（在普通 SQL 中，这意味着在开始发出BEGIN并且在结束时发出COMMIT。某些客户端库可能背着你就做了这些，在这种情况下你需要确定在你需要做这些时该库确实帮你做了）。如果你允许每一个插入都被独立地提交，IvorySQL要为每一个被增加的行做很多工作。在一个事务中做所有插入的一个额外好处是：如果一个行的插入失败则所有之前插入的行都会被回滚，这样你不会被卡在部分载入的数据中。 使用COPY在一条命令中装载所有记录，而不是一系列INSERT命令。 COPY命令是为装载大量行而优化过的； 它没INSERT那么灵活，但是在大量数据装载时导致的负荷也更少。 因为COPY是单条命令，因此使用这种方法填充表时无须关闭自动提交。 如果你不能使用COPY，那么使用PREPARE来创建一个预备INSERT语句也有所帮助，然后根据需要使用EXECUTE多次。这样就避免了重复分析和规划INSERT的负荷。不同接口以不同的方式提供该功能， 可参阅接口文档中的“预备语句”。 ​请注意，在载入大量行时，使用COPY几乎总是比使用INSERT快， 即使使用了PREPARE并且把多个插入被成批地放入一个单一事务。 ​同样的事务中，COPY比更早的CREATE TABLE或TRUNCATE命令更快。 在这种情况下，不需要写 WAL，因为在一个错误的情况下，包含新载入数据的文件不管怎样都将被移除。不过，只有当wal_level设置为minimal（此时所有的命令必须写 WAL）时才会应用这种考虑。 如果你正在载入一个新创建的表，最快的方法是创建该表，用COPY批量载入该表的数据，然后创建表需要的任何索引。在已存在数据的表上创建索引要比在每一行被载入时增量地更新它更快。 ​ 如果你正在对现有表增加大量的数据，删除索引、载入表然后重新创建索引可能是最好的方案。 当然，在缺少索引的期间，其它数据库用户的数据库性能将会下降。 我们在删除唯一索引之前还需要仔细考虑清楚，因为唯一约束提供的错误检查在缺少索引的时候会丢失。 和索引一样，“成批地”检查外键约束比一行行检查效率更高。 因此，先删除外键约束、载入数据然后重建约束会很有用。 同样，载入数据和约束缺失期间错误检查的丢失之间也存在平衡。 ​更重要的是，当你在已有外键约束的情况下向表中载入数据时， 每个新行需要一个在服务器的待处理触发器事件（因为是一个触发器的触发会检查行的外键约束）列表的条目。载入数百万行会导致触发器事件队列溢出可用内存， 造成不能接受的交换或者甚至是命令的彻底失败。因此在载入大量数据时，可能需要（而不仅仅是期望）删除并重新应用外键。如果临时移除约束不可接受，那唯一的其他办法可能是就是将载入操作分解成更小的事务。 在载入大量数据时，临时增大maintenance_work_mem配置变量可以改进性能。这个参数也可以帮助加速CREATE INDEX命令和ALTER TABLE ADD FOREIGN KEY命令。 它不会对COPY本身起很大作用，所以这个建议只有在你使用上面的一个或两个技巧时才有用。 临时增大max_wal_size配置变量也可以让大量数据载入更快。 这是因为向IvorySQL中载入大量的数据将导致检查点的发生比平常（由checkpoint_timeout配置变量指定）更频繁。无论何时发生一个检查点时，所有脏页都必须被刷写到磁盘上。 通过在批量数据载入时临时增加max_wal_size，所需的检查点数目可以被缩减。 当使用 WAL 归档或流复制向一个安装中载入大量数据时，在录入结束后执行一次新的基础备份比处理大量的增量 WAL 数据更快。为了防止载入时记录增量 WAL，通过将wal_level设置为minimal、将archive_mode设置为off以及将max_wal_senders设置为零来禁用归档和流复制。 但需要注意的是，修改这些设置需要重启服务。 除了避免归档器或 WAL 发送者处理 WAL 数据的时间之外，这样做将实际上使某些命令更快，因为如果wal_level是minimal并且当前子事务（或顶级事务）创建或截断了它们更改的表或索引，则它们根本不编写 WAL。（通过在最后执行一个fsync而不是写 WAL，它们能以更小地代价保证崩溃安全）。 不管什么时候你显著地改变了表中的数据分布后，我们都强烈推荐运行ANALYZE。这包括向表中批量载入大量数据。运行ANALYZE（或者VACUUM ANALYZE）保证规划器有表的最新统计信息。 如果没有统计数据或者统计数据过时，那么规划器在查询规划时可能做出很差劲决定，导致在任意表上的性能低下。需要注意的是，如果启用了 autovacuum 守护进程，它可能会自动运行ANALYZE。 pg_dump生成的转储脚本自动应用上面的若干个（但不是全部）技巧。 要尽可能快地载入pg_dump转储，你需要手工做一些额外的事情（请注意，这些要点适用于恢复一个转储，而不是创建它的时候。同样的要点也适用于使用psql载入一个文本转储或用pg_restore从一个pg_dump归档文件载入）。 默认情况下，pg_dump使用COPY，并且当它在生成一个完整的模式和数据转储时， 它会很小心地先装载数据，然后创建索引和外键。因此在这种情况下，一些指导方针是被自动处理的。你需要做的是： 为maintenance_work_mem和max_wal_size设置适当的（即比正常值大的）值。 如果使用 WAL 归档或流复制，在转储时考虑禁用它们。在载入转储之前，可通过将archive_mode设置为off、将wal_level设置为minimal以及将max_wal_senders设置为零（在录入dump前）来实现禁用。 之后，将它们设回正确的值并执行一次新的基础备份。 采用pg_dump和pg_restore的并行转储和恢复模式进行实验并且找出要使用的最佳并发任务数量。通过使用-j选项的并行转储和恢复应该能为你带来比串行模式高得多的性能。 考虑是否应该在一个单一事务中恢复整个转储。要这样做，将-1或—​single-transaction命令行选项传递给psql或pg_restore。 当使用这种模式时，即使是一个很小的错误也会回滚整个恢复，可能会丢弃已经处理了很多个小时的工作。根据数据间的相关性， 可能手动清理更好。如果你使用一个单一事务并且关闭了 WAL 归档，COPY命令将运行得最快。 如果在数据库服务器上有多个 CPU 可用，可以考虑使用pg_restore的—​jobs选项。这允许并行数据载入和索引创建。 之后运行ANALYZE。 一个只涉及数据的转储仍将使用COPY，但是它不会删除或重建索引，并且它通常不会触碰外键。 因此当载入一个只有数据的转储时，如果你希望使用那些技术，你需要负责删除并重建索引和外键。在载入数据时增加max_wal_size仍然有用，但是不要去增加maintenance_work_mem；不如说在以后手工重建索引和外键时你已经做了这些。并且不要忘记在完成后执行ANALYZE。 持久性是数据库的一个保证已提交事务的记录的特性（即使是发生服务器崩溃或断电）。 然而，持久性会明显增加数据库的负荷，因此如果你的站点不需要这个保证，IvorySQL可以被配置成运行更快。在这种情况下，你可以调整下列配置来提高性能。除了下面列出的，在数据库软件崩溃的情况下也能保证持久性。当这些设置被使用时，只有突然的操作系统停止会产生数据丢失或损坏的风险。 将数据库集簇的数据目录放在一个内存支持的文件系统上（即RAM磁盘）。这消除了所有的数据库磁盘I/O,但将数据存储限制到可用的内存量（可能有交换区）。 关闭fsync； 不需要将数据刷入磁盘。 关闭synchronous_commit；可能不需要在每次提交时强制把WAL写入磁盘。这种设置可能会在数据库崩溃时带来事务丢失的风险（但是没有数据破坏）。 关闭full_page_writes； 不需要警惕部分页面写入。 增加max_wal_size和checkpoint_timeout； 这会降低检查点的频率，但会增加/pg_wal的存储要求。 创建不做日志的表来避免WAL写入，不过这会让表在崩溃时不安全。","title":"运维管理指南","component":"ivorysql-doc","version":"master","name":"operation_guide","url":"/ivorysql-doc/master/operation_guide.html","titles":[{"text":"1. 升级IvorySQL版本","hash":"升级ivorysql版本","id":1},{"text":"1.1. 升级方案概述","hash":"升级方案概述","id":2},{"text":"1.2. 通过pg_dumpall升级数据","hash":"通过pg_dumpall升级数据","id":3},{"text":"1.3. 利用pg_upgrade 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VARCHAR2","hash":"varchar2","id":20},{"text":"5.1.1. 概述","hash":"概述","id":21},{"text":"5.1.2. 语法","hash":"语法","id":22},{"text":"5.1.3. 用例","hash":"用例","id":23},{"text":"6. 查询数据","hash":"查询数据","id":24},{"text":"7. 使用外部数据","hash":"使用外部数据","id":25},{"text":"8. 备份与恢复","hash":"备份与恢复","id":26},{"text":"8.1. SQL转储","hash":"sql转储","id":27},{"text":"8.1.1. 从转储中恢复","hash":"从转储中恢复","id":28},{"text":"8.1.2. 使用pg_dumpall","hash":"使用pg_dumpall","id":29},{"text":"8.1.3. 处理大型数据库","hash":"处理大型数据库","id":30},{"text":"8.2. 文件系统级别备份","hash":"文件系统级别备份","id":31},{"text":"8.3. 连续归档和时间点恢复（PITR）","hash":"连续归档和时间点恢复pitr","id":32},{"text":"9. 装卸数据","hash":"装卸数据","id":33},{"text":"9.1. 大纲","hash":"大纲","id":34},{"text":"9.2. 输出","hash":"输出","id":35},{"text":"9.3. 注解","hash":"注解","id":36},{"text":"9.4. 文件格式","hash":"文件格式","id":37},{"text":"9.4.1. 文本格式","hash":"文本格式","id":38},{"text":"9.4.2. CSV格式","hash":"csv格式","id":39},{"text":"9.4.3. 二进制格式","hash":"二进制格式","id":40},{"text":"9.5. 示例","hash":"示例","id":41},{"text":"10. 性能管理","hash":"性能管理","id":42},{"text":"10.1. 使用EXPLAIN","hash":"使用explain","id":43},{"text":"10.1.1. EXPLAIN基础","hash":"explain基础","id":44},{"text":"10.1.2. 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事后运行ANALYZE","hash":"事后运行analyze","id":63},{"text":"10.4.9. 关于pg_dump的一些注记","hash":"关于pg_dump的一些注记","id":64},{"text":"10.5. 非持久设置","hash":"非持久设置","id":65}]},"8":{"id":8,"text":"本文详细描述如何提交一个问题。 以IvorySQL为例，提交一个bug report IvorySQL仓库： https://github.com/IvorySQL/IvorySQL 点击New issue 1、bug report Title: 标题 ## Bug Report 对bug进行描述 ### IvorySQL Version 在IvorySQL哪个版本发现的问题 ### OS Version (uname -a) 系统版本 ### Configuration options ( config.status --config ) 配置参数 ### Current Behavior 当前的结果 ### Expected behavior/code 期望的结果 ### Step to reproduce 复现步骤 ### Additional context that can be helpful for identifying the problem 有助于识别问题的其它信息 2、Enhancement Title: 标题 ## Enhancement 对于期望强化的功能作一个描述 3、Feature Request Title: 标题 ## Feature Request 描述你期望实现的一个功能 点击 submit new issue 按钮, 提交即可。 提交问题以后，如果问题的描述和复现步骤清晰明确和可定位，会有人直接定位和解决该问题。但也有可能会出现，负责跟进该问题的开发者需要您提供更加详细的信息的情况，也感谢您的配合。","title":"问题提交指南","component":"ivorysql-doc","version":"master","name":"problem_report_guide","url":"/ivorysql-doc/master/problem_report_guide.html","titles":[{"text":"1. 确认问题所属仓库","hash":"确认问题所属仓库","id":1},{"text":"2. 创建新的Issue","hash":"创建新的issue","id":2},{"text":"2.1. 第1步 进入New issue 页面：","hash":"第1步-进入new-issue-页面","id":3},{"text":"2.2. 第2步：选择需要填写的issue类型","hash":"第2步选择需要填写的issue类型","id":4},{"text":"2.3. 第3步：提交","hash":"第3步提交","id":5}]},"9":{"id":9,"text":"[发布日期：2025 年 11 月 25 日] IvorySQL 5.0 基于 PostgreSQL 18.0，带来更强的 Oracle 兼容能力、PL/iSQL 增强以及全新的全球化特性，同时对打包、自动化和工具链进行全面更新。 有关完整更新列表，请访问我们的 文档站点。 PostgreSQL 18.0 新增异步 I/O（AIO）子系统，可提升顺序扫描、位图堆扫描、vacuum 等操作的性能。 pg_upgrade 现在会保留优化器统计信息。 支持 \"skip scan\" 查找，使多列 B-tree 索引能够在更多场景下使用。 提供用于生成按时间排序 UUID 的 uuidv7() 函数。 支持虚拟生成列（在读取时计算值），并将其设为生成列的默认模式。 增加 OAuth 认证能力。 在 INSERT、UPDATE、DELETE 和 MERGE 的 RETURNING 子句中支持 OLD 和 NEW。 对 PRIMARY KEY、UNIQUE 与 FOREIGN KEY 引入时间区间约束。 更多细节请参阅 PostgreSQL 18.0 发布说明。 Oracle 兼容 ROWID：Feature #126 让 IvorySQL 行标识符与 Oracle 语义保持一致，便于跨数据库工具协同。 PL/iSQL CALL 调用语法：Feature #764 新增 Oracle 风格的 CALL 入口，实现存储过程一致的调用体验。 PL/iSQL %ROWTYPE：Feature #765 允许变量复用整张表或游标行的结构，便于紧凑编写 PL/iSQL。 PL/iSQL %TYPE：Feature #766 支持变量继承既有列或变量的类型，降低类型漂移风险。 区分大小写兼容开关：Feature #767 在需要时可保留标识符大小写，以匹配 Oracle 行为。 NLS 参数兼容性：Feature #768 支持 NLS_DATE_FORMAT、NLS_TIMESTAMP_FORMAT 等 Oracle 风格 NLS 设置。 空字符串转 NULL：Feature #769 将长度为零的字符串转换为 NULL，以遵循 Oracle 的兼容规则。 解析器切换能力：Feature #770 可在 Oracle 与 PostgreSQL 解析器之间切换，实现会话级灵活性。 GB18030 数据库编码：Feature #771 为中国市场提供 GB18030 初始化和创建数据库选项。 Oracle 兼容 SYS_GUID：Feature #773 实现 Oracle SYS_GUID 函数，生成基于 RAW 的 GUID。 Oracle 兼容 SYS_CONTEXT：Feature #774 提供 Oracle SYS_CONTEXT API，用于查询会话与环境元数据。 Oracle 兼容 USERENV：Feature #775 引入 USERENV 函数，使会话可检查 Oracle 风格的上下文信息。 Oracle 兼容函数语法：Feature #776 支持 EDITIONABLE/NONEDITIONABLE、RETURN、IS、OUT …​ NOCOPY 等 Oracle 结构。 Oracle 兼容过程语法：Feature #777 支持包含 Oracle 选项的过程 DDL、EXEC 调用以及 ALTER PROCEDURE。 libpq OUT 参数传递：Feature #778 扩展客户端协议，使 OUT 参数可像 OCI 一样被消费。 过程 OUT 参数：Feature #779 存储过程现在可按 Oracle 约定声明 IN、OUT、IN OUT 模式。 函数 OUT 参数：Feature #780 函数支持 Oracle 风格 OUT（含 IN OUT）参数。 嵌套子程序：Feature #781 允许在子程序内部定义函数或过程，并支持重载。 Oracle 兼容 INSTR：Feature #782 与 Oracle INSTR 行为保持一致，覆盖子串搜索与位置判断。 Oracle 兼容 FORCE VIEW：Feature #783 允许在引用对象尚未存在时创建视图，重现 Oracle FORCE 选项。 Oracle 兼容 LIKE 运算符：Feature #784 对齐 Oracle 的通配符语义，确保匹配行为可预测。 在线体验：IvorySQL v5.0：Feature #887 上线交互式浏览器环境，用户可实时体验与评估 IvorySQL v5.0，无需安装。 全平台打包：Feature #949 为 X86、ARM、MIPS、龙芯架构等平台提供多架构安装介质。 容器化部署支持（Docker Compose & Docker Swarm）： 支持在 Docker Swarm 与 Docker Compose 中部署单实例数据库与高可用集群。 容器化部署支持（Kubernetes 基础版）： 使用 Helm 在 Kubernetes（K8S）中部署单实例数据库与高可用集群。 发布 IvorySQL Operator v5（Kubernetes 进阶版）： Operator v5 适配 IvorySQL v5.0，并同步升级系统组件版本与数据库扩展版本。 发布 IvorySQL Cloud v5（统一全生命周期与可视化控制平面）： 提供可视化托管控制平面，覆盖订阅、全生命周期编排以及生态集成。 pg_cron：Feature #882 通过 pg_cron 集成在数据库层内执行计划任务。 pgAudit：Feature #929 借助 PostgreSQL 标准日志能力输出详尽的会话 / 对象审计记录。 PostGIS：Feature #880 提供空间数据处理与地理分析能力。 pgRouting：Feature #881 引入网络与路径分析能力。 PGroonga：Feature #879 增强全文检索。 ddlx：Feature #877 支持 ddlx，便于高级模式洞察与自动化 DDL 生成。 pgsql-http：Feature #883 允许数据库内部发起 HTTP/HTTPS 请求，实现与外部 Web 服务的无缝通信。 system_stats：Feature #946 通过 system_stats 提供系统级统计信息。 plpgsql_check：Feature #915 在运行前对 PL/pgSQL 函数做静态分析，定位错误、警告与潜在问题。 pgvector：Feature #878 融合 pgvector，为 AI/ML 工作负载提供原生向量相似度检索。 修复 unused_oids 与 duplicate_oids 目录工具，使头文件扫描能准确检测冲突且无误报：Issue #841 为 libpq/ivytest 产物新增 .gitignore，避免生成的二进制与日志污染开发树：Issue #843 扩展 GitHub 工作流回归测试，覆盖 --with-libnuma 配置，防止未来在启用 NUMA 的主机上出问题：Issue #869 让 psql 用户可以通过 \\h create package 获取 CREATE PACKAGE 语法帮助，补齐 PL/iSQL 包的 CLI 文档：Issue #936 排除 MainLoop 悬空指针引发的并发压力下间歇性段错误：Issue #898 修复 oracle_test/modules/*/sql 的测试框架假设，让 Oracle 兼容测试套件再次端到端运行：Issue #897 更新 README.md 与 README_CN.md，同步 IvorySQL v5 特性、打包与快速上手信息：Issue #896 更正全局唯一索引的强制机制，使相关回归测试在所有支持平台上稳定通过：Issue #894 IvorySQL 的主要代码仓库： IvorySQL 数据库源码：https://github.com/IvorySQL/IvorySQL IvorySQL 官网：https://github.com/IvorySQL/Ivory-www IvorySQL 文档：https://github.com/IvorySQL/IvorySQL-docs IvorySQL Docker：https://github.com/IvorySQL/docker_library 以下人员（按字母顺序）作为补丁作者、提交者、审阅者、测试者或问题报告者，为本次发布做出了贡献。 ccwxl Cédric Villemain elodiefb Grant Zhou Imran Zaheer luss Martin Gerhardy msdnchina omstack otegami rophy shlei6067 sjw1933 Yasir Hussain Shah 初少林 崇鹏豪 高雪玉 矫顺田 类延良 李苑 梁翔宇 刘晓辉 吕新杰 牛世继 彭冲 潘振浩 石卓妍 隋戈 陶郑 童水森 王硕 薛晓刚 严少安 杨世华 赵法威","title":"","component":"ivorysql-doc","version":"master","name":"release_notes","url":"/ivorysql-doc/master/release_notes.html","titles":[{"text":"1. 版本概览","hash":"版本概览","id":1},{"text":"2. 增强内容","hash":"增强内容","id":2},{"text":"3. 新特性","hash":"新特性","id":3},{"text":"3.1. 新增21 项 Oracle 兼容能力","hash":"新增21-项-oracle-兼容能力","id":4},{"text":"3.2. 在线体验与多平台发行包","hash":"在线体验与多平台发行包","id":5},{"text":"3.3. 云原生与容器化","hash":"云原生与容器化","id":6},{"text":"3.4. 新增 10 个 PostgreSQL 扩展","hash":"新增-10-个-postgresql-扩展","id":7},{"text":"4. 缺陷修复","hash":"缺陷修复","id":8},{"text":"5. 源码仓库","hash":"源码仓库","id":9},{"text":"6. 贡献者名单","hash":"贡献者名单","id":10}]},"10":{"id":10,"text":"这部份包含IvorySQL客户端应用和工具的参考信息。不是所有这些命令都是通用工具，某些需要特殊权限。这些应用的共同特征是它们可以被运行在任何主机上，而不管数据库服务器在哪里。 当在命令行上指定用户和数据库名时，它们的大小写会被保留 — 空格或特殊字符的出现可能需要使用引号。表名和其他标识符的大小写不会被保留并且可能需要使用引号。 分类 工具名称 描述 客户端工具 clusterdb clusterdb是一个工具，它用来对一个IvorySQL数据库中的表进行重新聚簇。它会寻找之前已经被聚簇过的表，并且再次在最后使用过的同一个索引上对它们重新聚簇。没有被聚簇过的表将不会被影响。clusterdb是SQL命令 CLUSTER 的一个包装器。在通过这个工具和其他方法访问服务器来聚簇数据库之间没有实质性的区别。 createdb createdb创建一个新的IvorySQL数据库。通常，执行这个命令的数据库用户将成为新数据库的所有者。但是，如果执行用户具有合适的权限，可以通过 -O 选项指定一个不同的所有者。createdb是SQL命令 CREATE DATABASE的一个包装器。在通过这个工具和其他方法访问服务器来创建数据库之间没有实质性的区别。 createuser createuser创建一个新的IvorySQL用户（或者更准确些，是一个角色）。只有超级用户和具有 CREATEROLE 特权的用户才能创建新用户，因此createuser必须被以上两种用户调用。如果你希望创建一个新的超级用户，你必须作为一个超级用户连接，而不仅仅是具有 CREATEROLE 特权。作为一个超级用户意味着绕过数据库中所有访问权限检查的能力，因此超级用户访问权限不能轻易被授予。createuser是SQL命令 CREATE ROLE的一个包装器。在通过这个工具和其他方法访问服务器来创建用户之间没有实质性的区别。 dropdb ropdb毁掉一个现有的IvorySQL数据库。执行这个命令的用户必须是一个数据库超级用户或该数据库的拥有者。dropdb是SQL命令 DROP DATABASE 的一个包装器。在通过这个工具和其他方法访问服务器来删除数据库之间没有实质性的区别。 dropuser dropuser移除一个已有的IvorySQL用户。只有超级用户以及具有 CREATEROLE 特权的用户能够移除IvorySQL用户（要移除一个超级用户，你必须自己是一个超级用户）。dropuser是SQL命令 DROP ROLE 的一个包装器。在通过这个工具和其他方法访问服务器来删除用户之间没有实质性的区别。 ecpg ecpg 是用于 C 程序的嵌入式 SQL 预处理器。它通过将 SQL 调用替换为特殊函数调用把带有嵌入式 SQL 语句的 C 程序转换为普通 C 代码。输出文件可以被任何 C 编译器工具链处理。ecpg 将把命令行中给出的每一个输入文件转换为相应的 C 输出文件。 如果输入文件名没有任何扩展名，则假定为 .pgc。文件扩展名将由 .c 替换以构造输出文件名。 但是输出文件名可以使用 -o 选项覆盖。如果输入文件名只是 -，ecpg 从标准输入 读取程序（并写入标准输出，除非用 -o 重写）。 pg_amcheck pg_amcheck支持对一个或多个数据库运行 amcheck 的损坏检查函数，并提供选项来选择要检查的模式、表和索引、要执行的检查类型以及是否并行执行检查，如果是，按并行数建立连接并使用。当前仅支持表关系和btree索引。其他关系类型将自动跳过。如果指定了 dbname，则它应该是要检查的单个数据库的名称，并且不应该存在其他数据库选择选项。否则，如果存在任何数据库选择选项，将检查所有匹配的数据库。如果不存在此类选项，将选中默认数据库。数据库选择选项包括 --all，--database 和 --exclude-database。它们还包括 --relation，--exclude-relation， --table，--exclude-table，--index，和 --exclude-index，但仅当这些选项与三段式模式一起使用时（例如，mydb*.myschema*.myrel*）。最后，它们包括 --schema 和 --exclude-schema 当这些选项与两段式模式一起使用时（例如 mydb*.myschema* ）。 pg_basebackup pg_basebackup被用于获得一个正在运行的IvorySQL数据库集簇的基础备份。获得这些备份不会影响数据库的其他客户端，并且可以被用于时间点恢复，以及用作一个日志传送或流复制后备服务器的开始点。pg_basebackup对数据库群集的文件进行精确复制，同时确保服务器自动进入和退出备份模式。备份总是从整个数据库集簇获得，不可能备份单个数据库或数据库对象。关于选择性备份，必须使用一个像 pg_dump 的工具。备份通过一个使用复制协议常规IvorySQL连接制作。该连接必须由一个具有 REPLICATION 权限或者具有超级用户权限的用户ID建立，并且 pg_hba.conf必须允许该复制连接。该服务器还必须被配置，使 max_wal_senders 设置得足够高以提供至少一个walsender用于备份以及一个用于WAL流（如果使用流）。在同一时间可以有多个 pg_basebackup 运行，但是从性能的角度来说，只进行一次备份并且复制结果通常更好。pg_basebackup不仅能从主控机也能从后备机创建一个基础备份。要从后备机获得一个备份，设置后备机让它能接受复制连接（也就是，设置 max_wal_senders 和 hot_standby，并且适当配置其 pg_hba.conf ）。你将也需要在主控机上启用 full_page_writes。注意在来自后备机的备份中有一些限制：不会在被备份的数据库集簇中创建备份历史文件。 pg_basebackup不能强制后备服务器在备份结束时切换到新的WAL文件。 当正在使用 -X none 时，如果服务器上的写活动比较低，pg_basebackup可能需要等待很长时间，以便切换和归档备份所需要的最后的WAL文件。 在这种情况下，在主服务器上运行 pg_switch_wal 以触发立即的WAL文件切换可能是有用的。 如果在备份期间后备机被提升为主控机，备份会失败。 备份所需的所有 WAL 记录必须包含足够的全页写，这要求你在主控机上启用 full_page_writes 并且不使用一个类似pg_compresslog的工具以 archive_command 从 WAL 文件中移除全页写。每当pg_basebackup进行基本备份时，服务器的 pg_stat_progress_basebackup 视图将报告备份的进度。 pgbench pgbench是一种在IvorySQL上运行基准测试的简单程序。它可能在并发的数据库会话中一遍一遍地运行相同序列的 SQL 命令，并且计算平均事务率（每秒的事务数）。默认情况下，pgbench会测试一种基于 TPC-B 但是要更宽松的场景，其中在每个事务中涉及五个 SELECT、 UPDATE 以及 INSERT 命令。但是，通过编写自己的事务脚本文件很容易用来测试其他情况。 pg_config pg_config工具打印当前安装版本的IvorySQL的配置参数。它的设计目的之一是便于想与IvorySQL交互的软件包能够找到所需的头文件和库。 pg_dump pg_dumppg_dump是用于备份一种IvorySQL数据库的工具。即使数据库正在被并发使用，它也能创建一致的备份。pg_dump不阻塞其他用户访问数据库（读取或写入）。pg_dump只转储单个数据库。要备份一个集簇或者集簇中 对于所有数据库公共的全局对象（例如角色和表空间），应使用 pg_dumpall。转储可以被输出到脚本或归档文件格式。脚本转储是包含 SQL 命令的纯文本文件，它们可以用来重构数据库到它被转储时的状态。要从这样一个脚本恢复，将它输入到 psql。脚本文件甚至可以被用来在其他机器和其他架构上重构数据库。在经过一些修改后，甚至可以在其他 SQL 数据库产品上重构数据库。另一种可选的归档文件格式必须与 pg_restore 配合使用来重建数据库。它们允许pg_restore能选择恢复什么，或者甚至在恢复之前对条目重排序。归档文件格式被设计为在架构之间可移植。当使用归档文件格式之一并与pg_restore组合时，pg_dump提供了一种灵活的归档和传输机制。pg_dump可以被用来备份整个数据库，然后pg_restore可以被用来检查归档并/或选择数据库的哪些部分要被恢复。最灵活的输出文件格式是“自定义”格式（ -Fc ）和“目录”格式（ -Fd ）。它们允许选择和重排序所有已归档项、支持并行恢复并且默认是压缩的。“目录”格式是唯一一种支持并行转储的格式。当运行pg_dump时，我们应该检查输出中有没有任何警告（打印在标准错误上） pg_dumpall pg_dumpall工具可以一个集簇中所有的IvorySQL数据库写出到（“转储”）一个脚本文件。该脚本文件包含可以用作 psql 的输入SQL命令来恢复数据库。它会对集簇中的每个数据库调用 pg_dump 来完成该工作。pg_dumpall还转储对所有数据库公用的全局对象（ pg_dump 不保存这些对象），也就是说数据库角色和表空间都会被转储。 目前这包括适数据库用户和组、表空间以及适合所有数据库的访问权限等属性。因为pg_dumpall从所有数据库中读取表，所以你很可能需要以一个数据库超级用户的身份连接以便生成完整的转储。同样，你也需要超级用户特权执行保存下来的脚本，这样才能增加角色和组以及创建数据库。SQL 脚本将被写出到标准输出。使用 -f / --file 选项或者 shell 操作符可以把它重定向到一个文件。pg_dumpall需要多次连接到IvorySQL服务器（每个数据库一次）。如果你使用口令认证，可能每次都会要求口令。这种情况下使用一个 ~/.pgpass 会比较方便 pg_isready pg_isready是一个用来检查一个IvorySQL数据库服务器的连接状态的工具。其退出状态指定了连接检查的结果。 pg_receivewal pg_receivewal被用来从一个运行着的IvorySQL集簇以流的方式得到预写式日志。预写式日志会被使用流复制协议以流的方式传送，并且被写入到文件的一个本地目录。这个目录可以被用作归档位置来做一次使用时间点恢复的恢复。当预写式日志在服务器上被产生时，pg_receivewal实时以流的方式传输预写式日志，并且不像 archive_command 那样等待段完成。由于这个原因，在使用pg_receivewal时不必设置 archive_timeout。与 IvorySQL 后备服务器上的 WAL 接收进程不同，pg_receivewal默认只在一个 WAL 文件被关闭时才刷入 WAL 数据。要实时刷入 WAL 数据，必须指定选项 --synchronous。 由于pg_receivewal不应用于WAL，当 synchronous_commit 等于 remote_apply 时，你将不允许它成为同步备用。 如果发生这样的情况，它将成为一个永远不能拉起的备用数据库，并且会导致事务提交阻塞。 为了避免这种情况，你应该为 synchronous_standby_names 配置一个适当的值，或规定为pg_receivewal 的 application_name 与它不匹配，或将 synchronous_commit 的值更改为 remote_apply 以外的内容。预写式日志在一个常规IvorySQL连接上被以流式传送，并且使用复制协议。连接必须由一个具有 REPLICATION 权限的用户或者一个超级用户建立，并且 pg_hba.conf 必须允许复制连接。服务器也必须被配置一个足够高的 max_wal_senders 来至少留出一个可用会话给流。首先，扫描WAL段文件所写入的目录，并发现最新完成的段文件，作为下一个段文件的开始的起始点。 这是独立计算的，根据用于压缩每个段的压缩方法。 如果用前面的方法无法计算出起点，最新的WAL刷写位置用作由服务器通过 IDENTIFY_SYSTEM 命令的报告。 pg_recvlogical pg_recvlogical 控制逻辑解码复制槽以及来自这种复制槽的流数据。它会创建一个复制模式的连接，因此它受到和 pg_receivewal 相同的约束，还有逻辑复制的约束。pg_recvlogical 与逻辑解码SQL接口的peek和get模式没有等效性。它咋接收到数据以及干净地退出时，它会惰性地发送数据的确认。为了检查一个槽上还未消费的待处理数据，可以使用 pg_logical_slot_peek_changes。 pg_restore pg_restore是一个用来从 pg_dump 创建的非文本格式归档恢复IvorySQL数据库的工具。它将发出必要的命令把该数据库重建成它被保存时的状态。这些归档文件还允许pg_restore选择恢复哪些内容或者在恢复前对恢复项重排序。这些归档文件被设计为可以在不同的架构之间迁移。pg_restore可以在两种模式下操作。如果指定了一个数据库名称，pg_restore会连接那个数据库并且把归档内容直接恢复到该数据库中。否则，会创建一个脚本，其中包含着重建该数据库所必要的 SQL 命令，它会被写入到一个文件或者标准输出。这个脚本输出等效于pg_dump的纯文本输出格式。因此，一些控制输出的选项与pg_dump的选项类似。显然，pg_restore无法恢复不在归档文件中的信息。例如，如果归档使用“以 INSERT 命令转储数据”选项创建， pg_restore将无法使用 COPY 语句装载数据。 pg_verifybackup pg_verifybackup用于根据备份时服务器生成的 backup_manifest 检查使用 pg_basebackup 进行的数据库群集备份的完整性。备份必须以“普通”格式存储；“tar”格式的备份可以在解压缩后进行检查。需要注意的是，由pg_verifybackup执行的验证不包括也不可能包括运行中的服务器在尝试使用备份时执行的所有检查。 即使使用此工具，也应执行测试还原，并验证生成的数据库是否按预期工作，以及它们是否包含正确的数据。但是，pg_verifybackup可以检测到由于存储问题或用户错误而经常出现的许多问题。备份验证分四个阶段进行。首先，pg_verifybackup 读取 backup_manifest 文件。如果该文件不存在、无法读取、格式不正确或无法根据其内部校验和进行验证，pg_verifybackup 将以致命错误终止。其次，pg_verifybackup 将尝试验证当前存储在磁盘上的数据文件是否与服务器打算发送的数据文件完全相同，下面将介绍一些例外情况。 除了少数例外，额外和丢失的文件将被检测到。此步骤将忽略 postgresql.auto.conf、standby.signal 和 recovery.signal 的存在与否或对其的任何修改，因为预计这些文件可能是在备份过程中创建或修改的。它也不会抱怨目标目录中的 backup_manifest 文件或 pg_wal 中的任何内容，即使这些文件不会列在备份清单中。只检查文件；不验证目录的存在与否，除非间接验证：如果目录丢失，则它应该包含的任何文件也必然会丢失。 psql psql是一个IvorySQL的基于终端的前端。它让你能交互式地键入查询，把它们发送给IvorySQL，并且查看查询结果。或者，输入可以来自于一个文件或者命令行参数。此外，psql还提供一些元命令和多种类似 shell 的特性来为编写脚本和自动化多种任务提供便利。 reindexdb reindexdb是用于重建一个IvorySQL数据库中索引的工具。reindexdb是 SQL 命令 REINDEX的一个包装器。 在通过这个工具和其他方法访问服务器来重索引数据库之间没有实质性的区别。 vacuumdb vacuumdb是用于清理一个IvorySQL数据库的工具。vacuumdb也将产生由IvorySQL查询优化器所使用的内部统计信息。 服务器应用 initdb initdb 创建一个新的IvorySQL数据库集簇。一个数据库集簇是由一个单一服务器实例管理的数据库的集合。一个数据库集簇的创建包括创建存放数据库数据的目录、生成共享目录表（属于整个集簇而不是任何特定数据库的表）并且创建 template1 和 postgres 数据库。当你后来创建一个新的数据库时，任何在 template1 数据库中的东西都会被复制（因此，任何已安装在 template1 中的东西都会被自动地复制到后来创建的每一个数据库中）。 postgres 数据库是便于用户、工具和第三方应用使用的默认数据库。尽管 initdb 将尝试创建指定的数据目录，它可能没有权限（如果想要的数据目录的父目录被根用户拥有）。要在这样一种设置中初始化，作为 root 创建一个空数据目录，然后使用 chown 将该目录赋予给数据库用户账户，再然后 su 成为该数据库用户并运行 initdb。initdb 必须以将拥有该服务器进程的用户运行，因为该服务器需要访问 initdb 创建的文件和目录。因为该服务器不能作为 root 运行，你不能以 root 运行 initdb （事实上它会拒绝这样做）。由于安全原因，由 initdb 创建的新集簇默认将只能由集簇拥有者访问。--allow-group-access 选项允许与集簇拥有者同组的任何用户读取集簇中的文件。这对非特权用户执行备份很有用。initdb 初始化该数据库集簇的默认区域和字符集编码。当一个数据库被创建时，其字符集编码、排序顺序（ LC_COLLATE ）和字符集类（ LC_CTYPE ，例如大写、小写、数字）可以被独立设置。initdb 为 template1 数据库确定那些设置，它们将作为所有其他数据库的默认值。要修改默认排序顺序或字符集类，使用 --lc-collate 和 --lc-ctype 选项。除 C 或 POSIX 之外的排序顺序也有性能罚值。由于这些原因，在运行 initdb 时选择正确的区域很重要。余下的区域分类可以在服务器启动之后改变。你也可以使用 --locale 来为所有区域分类设置默认值，包括排序顺序和字符集类。所有服务器区域值（ lc_* ）可以通过 SHOW ALL 显示。要修改默认编码，使用 --encoding 。 pg_archivecleanup pg_archivecleanup被设计用作 archive_cleanup_command 在作为后备服务器运行时来清理 WAL 文件归档。 pg_archivecleanup也可以被用作一个单独的程序来清理 WAL 文件归档。要配置一个后备服务器以使用pg_archivecleanup，把下面 的内容放在 postgresql.conf 配置文件中：archive_cleanup_command = 'pg_archivecleanup archivelocation %r' 其中 archivelocation 是要从中移除 WAL 段文件的目录。当被用在 archive_cleanup_command中时，所有逻辑上在 %r 参数的值之前的 WAL 文件都将被从 archivelocation 移除。这能最小化需要被保留的文件数量， 同时能保留崩溃后重启的能力。如果对于这台特定的后备服务器， archivelocation 是一个短暂需要的区域，使用这个参数就是合适的，但是当 archivelocation 要用作一个长期的 WAL 归档 区域或者当多个后备服务器正在从这个归档位置恢复时，使用这个参数就 不合适。当被用作一个单独的程序时，所有逻辑上在 oldestkeptwalfile 之前的 WAL 文件将被从 archivelocation 中移除。在这种模式中，如果指定了 .partial 或者 .backup 文件名，则 只有该文件前缀将被用作 oldestkeptwalfile。这种对 .backup 文件名的处理允许你移除所有在一个特定基础备份之前归档的 WAL 文件而不出错。 pg_checksums pg_checksums在IvorySQL集簇中检查、启用或禁用数据校验和。运行pg_checksums之前，必须彻底关闭服务器。验证校验和时，如果没有校验和错误，则退出状态为零，如果检测到至少一个校验和失败，则退出状态为非零。启用或禁用校验和时，如果操作失败，则退出状态为非零。验证校验和时，集簇中的每个文件都要被扫描。启用校验和时，集簇中的每个文件都会被原地重写。禁用校验和时，仅更新 pg_control 文件。 pg_controldata pg_controldata 打印在 initdb 期间初始化的信息，例如目录版本。它也显示关于预写式日志和检查点处理的信息。这种信息是集簇范围的，并且不针对任何一个数据库。这个工具只能由初始化集簇的用户运行，因为它要求对数据目录的读访问。你可以在命令行中指定数据目录，或者使用环境变量 PGDATA。这个工具支持选项 -V 和 --version，它们打印pg_controldata版本并退出。它也支持选项 -? 和 --help，它们输出支持的参数。 pg_ctl pg_ctl是一个用于初始化IvorySQL数据库集簇，启动、停止或重启IvorySQL数据库服务器（ postgres），或者显示一个正在运行服务器的状态的工具。尽管服务器可以被手工启动，pg_ctl包装了重定向日志输出以及正确地从终端和进程组脱离等任务。它也提供了方便的选项用来控制关闭。 pg_resetwal pg_resetwal 会清除预写式日志（WAL）并且有选择地重置存储在 pg_control 文件中的一些其他控制信息。如果这些文件已经被损坏，某些时候就需要这个功能。当服务器由于这样的损坏而无法启动时，这只应该被用作最后的手段。在运行这个命令之后，就可能可以启动服务器，但是记住数据库可能包含由于部分提交事务产生的不一致数据。你应当立刻转储你的数据、运行 initdb 并且重新载入。重新载入后，检查不一致并且根据需要修复之。这个工具只能被安装服务器的用户运行，因为它要求对数据目录的读写访问。出于安全原因，你必须在命令行中指定数据目录。pg_resetwal 不使用环境变量 PGDATA。如果 pg_resetwal 抱怨它无法为 pg_control 决定合法数据，你可以通过指定 -f （强制）选项强制它继续。在这种情况下，丢失的数据将被替换为看似合理的值。可以期望大部分域是匹配的，但是下一个 OID、下一个事务 ID 和纪元、下一个多事务 ID 和偏移以及 WAL 开始位置域可能还是需要人工协助。这些域可以使用下面讨论的选项设置。如果你不能为所有这些域决定正确的值，-f 还是可以被使用，但是恢复的数据库还是值得怀疑：一次立即的转储和重新载入是势在必行的。在你转储之前不要在该数据库中执行任何数据修改操作，因为任何这样的动作都可能使破坏更严重。 pg_rewind pg_rewind是用于在集簇的时间线分叉以后，同步一个 IvorySQL 集簇和同一集簇的另一份拷贝的工具。一种典型的场景是在失效后让一个旧的主服务器重新上线，同时有一个后备机跟随着新的主机。成功回放后，目标数据目录的状态类似于源数据目录的基本备份。与进行新的基本备份或使用rsync等工具不同，pg_rewind不需要比较或复制集群中未更改的关系块。仅复制现有关系文件中更改的块；所有其他文件（包括新的关系文件、配置文件和WAL段）都将被完整复制。因此，当数据库很大并且集群之间只有一小部分块不同时，倒带操作比其他方法要快得多。pg_rewind检查源集簇和目标集簇的时间线历史来判断它们在哪一点分叉，并且期望在目标集簇的 pg_wal 目录中找到 WAL 来返回到分叉点。分叉点可能会在目标时间线、源时间线或者它们的共同祖先上找到。在典型的失效场景中，目标集簇在分叉后很快就被关闭，这不是问题，但是如果目标集簇在分叉后已经运行了很长时间，旧的 WAL 文件可能已经不存在了。在这样的情况下，您可以手动将它们从WAL存档复制到 pg_wal 目录，或使用 -c 选项运行pg_rewind以自动从WAL存档检索它们。pg_rewind的使用并不限于失效的场景，例如一个后备服务器可能被提升、运行一些写事务，然后被倒回再次成为一个后备。在运行pg_rewind之后，需要完成WAL重放以使数据目录处于一致状态。当目标服务器再次启动时，它将进入归档恢复，并从分歧点之前的最后一个检查点重放源服务器中生成的所有 WAL。当pg_rewind被运行时有某些 WAL 在源服务器上不可用，并且因此无法被pg_rewind会话所复制，则在目标服务器被启动时必须让这些 WAL 可用。 这可以通过在目标数据目录中创建一个 recovery.signal 文件并且在 postgresql.conf 中配置适合的 restore_command 来实现。pg_rewind要求目标服务器在 postgresql.conf 中启用了 wal_log_hints 选项，或者在用initdb初始化集簇时启用了数据校验。目前默认情况下这两者都没有被打开。http://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-FULL-PAGE-WRITES[full_page_writes] 也必须被设置为 on，这是默认的。 pg_test_fsync pg_test_fsync是想告诉你在特定的系统上，哪一种 wal_sync_method 最快，还可以在发生认定的 I/O 问题时提供诊断信息。不过，pg_test_fsync 显示的区别可能不会在真实的数据库吞吐量上产生显著的区别，特别是由于 很多数据库服务器被它们的预写日志限制了速度。 pg_test_fsync为 wal_sync_method 报告以微秒计的平均文件同步操作时间， 也能被用来提示用于优化 commit_delay 值的方法。 pg_test_timing pg_test_timing是一种度量在你的系统上计时开销以及确认系统时间绝不会回退的工具。收集计时数据很慢的系统会给出不太准确的 EXPLAIN ANALYZE 结果。 pg_upgrade pg_upgrade允许存储在IvorySQL数据文件中的数据被升级到一个较晚 的IvorySQL主版本而无需进行主版本升级通常所需的数据转储/重载。 对于次版本升级则不需要这个程序。IvorySQL主发行版本通常会加入新的特性，这些新特性常常会更改系统表的布局，但是内部数据存储格式很少会改变。pg_upgrade 使用这一事实来通过创建新系统表并且重用旧的用户数据文件来执行快速升级。 如果未来的主发行版本更改了数据存储格式，导致旧数据格式不可读，那么 pg_upgrade将无法用于此类升级。（社区将努力避免这种情况）。pg_upgrade会尽力（例如通过检查兼容的编译时设 置）确保新旧集簇在二进制上也是兼容的，包括 32/64 位二进制。保持外部模块也是二进制兼容的也很重要，不过pg_upgrade无法检查这一点。 pg_waldump pg_waldump 显示预写式日志（WAL），它主要用于调试或者教育目的。这个工具只能由安装该服务器的用户运行，因为它要求对数据目录的只读访问。 postgres postgres 是IvorySQL数据库服务器。一个客户端应用为了能访问一个数据库，它会（通过一个网络或者本地）连接到一个运行着的 postgres 实例。该 postgres 实例接着会开始一个独立的服务器进程来处理该连接。一个 postgres 实例总是管理正好一个数据库集簇的数据。一个数据库集簇是一个数据库的集合，它们被存储在一个共同的文件系统位置（“数据区”）上。 一个系统上可以同时运行多个 postgres 实例，只要它们使用不同的数据区和不同的通信端口（见下文）。postgres 启动时需要知道数据区的位置，该位置必须通过 -D 选项或 PGDATA 环境变量指定，对此是没有默认值的。通常，-D 或 PGDATA 会直接指向由 initdb 创建的数据区目录。默认情况下，postgres 会在前台启动并将日志消息打印到标准错误流。但在实际应用中，postgres 应当作为一个后台进程启动，而且多数是在系统启动时自动启动。postgres 还能在单用户模式中被调用。这种模式的主要用途是在启动过程中由 initdb 使用。有时候它也被用于调试或者灾难性恢复。注意，运行一个单用户模式服务器并不真地适合调试服务器，因为不会发生实际的进程间通信和锁定。当从 shell 中调用单用户模式时，用户可以输入查询并且结果会被以一种更适合开发者阅读（不适合普通用户）的形式打印在屏幕上。在单用户模式中，会话用户将被设置为 ID 为 1 的用户，并且这个用户会被隐式地赋予超级用户权限。该用户不必实际存在，因此单用户模式运行可以被用来对某些意外损坏的系统目录进行手工恢复。 clusterdb [connection-option…​] [ --verbose | -v ] [ --table | -t table ] …​ [dbname] clusterdb` [*`connection-option`*...] [ `--verbose` | `-v` ] `--all` | `-a clusterdb接受下列命令行参数： -a --all 聚簇所有数据库。 [-d] dbname [--dbname=] dbname 当不使用 -a/ --all 时，指定要被聚簇的数据库名称。 如果数据库名称未指定，则从环境变量 PGDATABASE 中读取数据库名称。 如果该环境变量也没有被设置，则使用为连接指定的用户名作数据库名。 dbname 可以是 connection string。 如果是这样，连接时的字符串参数将覆盖所有冲突的命令行选项。 -e --echo 回显clusterdb生成并发送给服务器的命令。 -q --quiet 不显示进度消息。 -t table --table=table 只聚簇 table。可以通过写多个 -t 开关来聚簇多个表。 -v --verbose 在处理期间打印详细信息。 -V --version 打印clusterdb版本并退出。 -? --help 显示关于clusterdb命令行参数的帮助并退出。clusterdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制clusterdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，clusterdb将自动提示要求一个口令。但是，clusterdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db= dbname 当使用 -a / --all 时，指定要连接到的数据库名称来发现哪些其他数据库应该被聚簇。 如果没有指定，将使用 postgres 数据库。而如果它也不存在，将使用 template1。 这可以是 connection string。如果是这样，连接时的字符串参数将覆盖所有冲突的命令行选项。 另外，连接到其他数据库时，除了数据库名字本身其他的连接时字符串参数将被重新使用。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可选的值为 `always` ， `auto` ， `never` 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 CLUSTER 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要聚簇数据库 test： $ clusterdb test 要聚簇在数据库 xyzzy 中的一个表 foo： $ clusterdb --table=foo xyzzy createdb [connection-option…​] [option…​] [dbname [description]] createdb接受下列命令行参数： dbname 指定要被创建的数据库名。该名称必须在这个集簇中所有IvorySQL数据库中唯一。默认是创建一个与当前系统用户同名的数据库。 description 指定与新创建的数据库相关联的一段注释。 -D tablespace --tablespace=tablespace 指定该数据库的默认表空间（这个名称被当做一个双引号引用的标识符处理）。 -e --echo 回显createdb生成并发送到服务器的命令。 -E encoding --encoding=encoding 指定要在这个数据库中使用的字符编码模式。 -l locale --locale=locale 指定要在这个数据库中使用的区域。这等效于同时指定`--lc-collate`和`--lc-ctype`。 --lc-collate=locale 指定要在这个数据库中使用的 LC_COLLATE 设置。 --lc-ctype=locale 指定要在这个数据库中使用的 LC_CTYPE 设置。 -O owner --owner=owner 指定拥有这个新数据库的数据库用户（这个名称被当做一个双引号引用的标识符处理）。 -T template --template=template 指定用于创建这个数据库的模板数据库（这个名称被当做一个双引号引用的标识符处理）。 -V --version 打印createdb版本并退出。 -? --help 显示关于createdb命令行参数的帮助并退出。选项 -D、-l 、 -E 、 -O`和 `-T 对应于底层 SQL 命令 CREATE DATABASE的选项，关于这些选项的信息可见该命令的内容。 createdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制createdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，createdb将自动提示要求一个口令。但是，createdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db=dbname 指定要连接到来发现哪些其他数据库应该被聚簇的数据库名。如果没有指定，将使用 postgres 数据库。而如果它也不存在（或者如果它就是要创建新数据库的名称），将使用 template1。这可以是 connection string。如果是这样，连接时字符串参数将覆盖所有冲突的命令行选项。 PGDATABASE 如果被设置，就是要创建的数据库名，除非在命令行中覆盖。 PGHOST PGPORT PGUSER 默认连接参数。如果没有在命令行或 PGDATABASE 指定要创建的数据库名， PGUSER 也决定要创建的数据库名。 PG_COLOR 规定在诊断消息中是否使用颜色。 可能的值为 always，auto 和 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 CREATE DATABASE和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要使用默认数据库服务器创建数据库 demo： $ createdb demo 要在主机 eden、端口 5000 上使用 template0 模板数据库创建数据库 demo，这里是命令行命令和底层SQL命令： $ createdb -p 5000 -h eden -T template0 -e demo CREATE DATABASE demo TEMPLATE template0; createuser — 定义一个新的IvorySQL用户账户 createuser [connection-option…​] [option…​] [username] createuser接受下列命令行参数： username 指定要被创建的IvorySQL用户的名称。这个名称必须与这个IvorySQL安装中所有现存角色不同。 -c number --connection-limit=number 为该新用户设置一个最大连接数。默认值为不设任何限制。 -d --createdb 新用户将被允许创建数据库。 -D --no-createdb 新用户将不被允许创建数据库。这是默认值。 -e --echo 回显createuser生成并发送给服务器的命令。 -E --encrypted 此选项已过时，但为了实现向后兼容仍然接受。 -g role --role=role 指定一个角色，这个角色将立即加入其中成为其成员。 如果要把这个角色加入到多个角色中作为成员， 可以写多个 -g 开关。 -i --inherit 新角色将自动继承把它作为成员的角色的特权。这是默认值。 -I --no-inherit 新角色将不会自动继承把它作为成员的角色的特权。 --interactive 如果在命令行没有指定用户名，提示要求用户名，并且在命令行没有指定选项 -d / -D、 -r / -R、 -s / `-S`时也提示。 -l --login 新用户将被允许登入（即，该用户名能被用作初始会话用户标识符）。这是默认值。 -L --no-login 新用户将不被允许登入（一个没有登录特权的角色仍然可以作为管理数据库权限的方式而存在）。 -P --pwprompt 如果给定，createuser将发出一个提示要求新用户的口令。如果你没有计划使用口令认证，这就不是必须的。 -r --createrole 新用户将被允许创建新的角色（即，这个用户将具有 CREATEROLE 特权）。 -R --no-createrole 新用户将不被允许创建新角色。这是默认值。 -s --superuser 新用户将成为一个超级用户。 -S --no-superuser 新用户将不会成为一个超级用户。这是默认值。 -V --version 打印createuser版本并退出。 --replication 新用户将具有 REPLICATION 特权，这在 CREATE ROLE 的文档中有更完整的描述。 --no-replication 新用户将不具有 REPLICATION 特权，这在 CREATE ROLE 的文档中有更完整的描述。 -? --help 显示有关createuser命令行参数的帮助并退出。 createuser也接受下列命令行参数作为连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接（不是要创建的用户名）。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制createuser在连接到一个数据库之前提示要求一个口令（用来连接到服务器，而不是新用户的口令）。这个选项不是必不可少的，因为如果服务器要求口令认证，createuser将自动提示要求一个口令。但是，createuser将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always， auto 和 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 CREATE ROLE 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要在默认数据库服务器上创建一个用户 joe： $ createuser joe 要在默认数据库服务器上创建一个用户 joe 并提示要求一些额外属性： $ createuser --interactive joe Shall the new role be a superuser? (y/n) n Shall the new role be allowed to create databases? (y/n) n Shall the new role be allowed to create more new roles? (y/n) n 要使用在主机 eden、端口 5000 上的服务器创建同一个用户 joe，并带有显式指定的属性，看看下面的命令： $ createuser -h eden -p 5000 -S -D -R -e joe CREATE ROLE joe NOSUPERUSER NOCREATEDB NOCREATEROLE INHERIT LOGIN; 要创建用户 joe 为一个超级用户并且立刻分配一个口令： $ createuser -P -s -e joe Enter password for new role: xyzzy Enter it again: xyzzy CREATE ROLE joe PASSWORD 'md5b5f5ba1a423792b526f799ae4eb3d59e' SUPERUSER CREATEDB CREATEROLE INHERIT LOGIN; 在上面的例子中，在录入新口令时新口令并没有真正地被回显，但是为了清晰，我们特意把它列了出来。如你所见，该口令在被发送给客户端之前会被加密。 dropdb — 移除一个IvorySQL数据库 dropdb [connection-option…​] [option…​] dbname dropdb接受下列命令行参数： dbname 指定要被移除的数据库的名字。 -e -−echo 回显dropdb生成并发送给服务器的命令。 -f -−force 在删除目标数据库之前，尝试终止与该数据库的所有现有连接。有关此选项的详细信息，请参见 DROP DATABASE。 -i -−interactive 在做任何破坏性的工作之前发出一个验证提示。 -V -−version 打印dropdb版本并退出。 -−if-exists 如果数据库不存在也不抛出一个错误。在这种情况下会发出一个提示。 -? -−help 显示有关dropdb命令行参数的帮助并退出。 dropdb也接受下列命令行参数作为连接参数： -h host -−host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作Unix域套接字的目录。 -p port -−port=port 指定服务器正在监听连接的TCP端口或本地Unix域套接字文件扩展。 -U username -−username=username 要作为哪个用户连接。 -w -−no-password 不发出口令提示。如果服务器要求口令认证并且没有可用的口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W -−password 强制dropdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，dropdb将自动提示要求一个口令。但是，dropdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 -−maintenance-db=dbname 指定一个数据库的名称，将连接到这个数据库以便删除目标数据库。如果没有指定，将使用 postgres 数据库。而如果它也不存在（或者它就是正在被删除的目标数据库），将使用 template1。这个值可以是一个 连接字符串。 如果是这样，连接字符串参数将覆盖任何有冲突的命令行选项。 PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto 以及 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 DROP DATABASE 和 psql 中找找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要在默认数据库服务器上毁掉数据库 demo： $ dropdb demo 要使用在主机 eden、端口5000上的服务器中毁掉数据库 demo，并带有验证和回显，看看下面的命令： $ dropdb -p 5000 -h eden -i -e demo Database \"demo\" will be permanently deleted. Are you sure? (y/n) y DROP DATABASE demo; dropuser — 移除一个IvorySQL用户账户 dropuser [connection-option…​] [option…​] [username] dropuser接受下列命令行参数： username 指定要移除的IvorySQL用户的名字。如果没有在命令行指定并且使用了 -i / -−interactive 选项，你将被提醒要求一个用户名。 -e -−echo 回显dropuser生成并发送给服务器的命令。 -i -−interactive 在实际移除该用户之前提示要求确认，并且在没有在命令行指定用户名提示要求一个用户名。 -V -−version 打印dropuser版本并退出。 -−if-exists 如果用户不存在时不要抛出一个错误。在这种情况下将发出一个提示。 -? -−help 显示有关dropuser命令行参数的帮助并退出。 dropuser也接受下列命令行参数作为连接参数： -h host -−host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作Unix域套接字的目录。 -p port -−port=port 指定服务器正在监听连接的TCP端口或本地Unix域套接字文件扩展。 -U username -−username=username 要作为哪个用户连接。 -w -−no-password 不发出口令提示。如果服务器要求口令认证并且没有可用的口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W -−password 强制dropuser在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，dropuser将自动提示要求一个口令。但是，dropuser将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、auto 以及 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 DROP ROLE 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 要从默认数据库服务器移除用户 joe： $ dropuser joe 要使用在主机 eden、端口5000上的服务器移除用户 joe，并带有验证和回显，可使用下面的命令： $ dropuser -p 5000 -h eden -i -e joe Role \"joe\" will be permanently removed. Are you sure? (y/n) y DROP ROLE joe; ecpg — 嵌入式 SQL C 预处理器 ecpg [option…​] file…​ -c 自动从 SQL 代码生成确定的 C 代码。当前，这对 EXEC SQL TYPE 起效。 -C mode 设置一个兼容性模式。mode 可以是 INFORMIX，INFORMIX_SE 或 ORACLE。 -D symbol 定义一个 C 预处理器符号。 -h 处理头文件。指定此选项后，输出文件扩展名变为 .h 而不是 .c，默认输入文件扩展名为 .pgh 而不是 .pgc。此外，将强制启用 -c 选项。 -i 分析系统也包括文件。 -I directory 指定一个额外的包括路径，用来寻找通过 EXEC SQL INCLUDE 包括的文件。默认值是 .（当前目录）、/usr/local/include 、在编译时定义的IvorySQL包括目录（默认：/usr/local/pgsql/include）以及 /usr/include。 -o `filename` 指定 ecpg 应该将它的所有输出写到给定的 filename。写 -o- 将所有输出发送到标准输出。 -r `option` 选择运行时行为。option 可以是下列之一：no_indicator 不使用指示器而使用特殊值来表示空值。历史上曾有数据库使用这种方法。prepare 在使用所有语句之前准备它们。libecpg 将保持一个预备语句的缓冲并当语句再被执行时重用该语句。如果缓冲满了，libecpg 将释放最少使用的语句。`questionmarks`为兼容性原因允许使用问号作为占位符。在很久以前这被用作默认值。 -t 打开事务的自动提交。在这种模式下，每一个 SQL 命令会被自动提交，除非它位于一个显式事务块中。在默认模式中，命令只有当 EXEC SQL COMMIT 被发出时才被提交。 -v 打印额外信息，包括版本和“包括”路径。 --version 打印ecpg版本并退出。 -? --help 显示关于ecpg命令行参数的帮助并退出。 在编译预处理好的 C 代码文件时，编译器需要能够找到IvorySQL包括目录中的ECPG头文件。因此，在调用编译器时，你可能必须使用 -I 选项（例如， -I/usr/local/pgsql/include ）。 使用带有嵌入式 SQL 的 C 代码的程序必须被链接到 libecpg 库，例如使用链接器选项 -L/usr/local/pgsql/lib -lecpg。 适合于安装的这些目录的值可以使用 pg_config 找到。 如果你有一个名为 prog1.pgc 的嵌入式 SQL C 源文件，你可以使用下列命令序列创建一个可执行程序： ecpg prog1.pgc cc -I/usr/local/pgsql/include -c prog1.c cc -o prog1 prog1.o -L/usr/local/pgsql/lib -lecpg pg_amcheck — 在一个或多个IvorySQL数据库中检查损坏 pg_amcheck [option…​] [dbname] 以下命令行选项控制要检查的内容： -a --all 检查所有数据库，但通过`--exclude-database`排除的数据库除外。 -d pattern --database=pattern 检查与指定的 pattern匹配的数据库，但`--exclude-database`排除的数据库除外。可以多次指定此选项。 -D pattern --exclude-database=pattern 排除与给定的 pattern匹配的数据库。可以多次指定此选项。 -i pattern --index=pattern 检查与指定的 pattern匹配的索引，除非它们被排除在外。可以多次指定此选项。这类似于 --relation 选项，只是它只适用于索引，而不适用于表。 -I pattern --exclude-index=pattern 排除与指定的 pattern匹配的索引。可以多次指定此选项。这类似于 --exclude-relation 选项，只是它只适用于索引，而不适用于表。 -r pattern --relation=pattern 检查与指定的 pattern匹配的关系，除非它们被排除在外。可以多次指定此选项。模式可能是非限定的，例如 myrel*，或者它们可能是模式限定的，如 myschema*.myrel* 或数据库限定和模式限定，例如 mydb*.myscheam*.myrel*。数据库限定模式将向要检查的数据库列表中添加匹配的数据库。 -R pattern --exclude-relation=pattern 排除与指定的 pattern匹配的关系。可以多次指定此选项。与`--relation`一样, pattern 可以是非限定的、模式限定的或数据库和模式限定的。 -s pattern --schema=pattern 检查模式中与指定的 pattern匹配的表和索引，除非另有排除。可以多次指定此选项。要仅选择与特定模式匹配的模式中的表，请考虑使用类似 --table=SCHEMAPAT.* --no-dependent-indexes。要仅选择索引，请考虑使用类似 --index=SCHEMAPAT. 的方法。模式模式可以是数据库限定的。例如，你可以编写 --schema=mydb.myschema* 在数据库中选择匹配 mydb* 的模式。 -S pattern --exclude-schema=pattern 排除模式中与指定的 pattern匹配的表和索引。可以多次指定此选项。与`--schema`一样，模式可以是数据库限定的。 -t pattern --table=pattern 检查与指定的 pattern匹配的表，除非它们被排除在外。可以多次指定此选项。这类似于`--relation`选项，只是它只适用于表，而不适用于索引。 -T pattern --exclude-table=pattern 排除与指定的 pattern 匹配的表。可以多次指定此选项。这类似于 --exclude-relation 选项，只是它只适用于表，而不适用于索引。 --no-dependent-indexes 默认情况下，如果选中了一个表，则该表的任何btree索引也将被选中，即使它们没有被诸如 --index 或 --relation 之类的选项显式选择。此选项将抑制该行为。 --no-dependent-toast 默认情况下，如果选中了一个表，则也将检查它的toast表（如果有），即使它没有通过诸如 --table 或 --relation 之类的选项显式选择。此选项将抑制该行为。 --no-strict-names 默认情况下，如果 --database， --table， --index，或 --relation 的参数不匹配任何对象，则这是一个致命错误。此选项将该错误降级为警告。 以下命令行选项用于控制表的检查： --exclude-toast-pointers 默认情况下，每当在表中遇到toast指针时，都会执行查找以确保它引用toast表中明显有效的条目。这些检查可能非常慢，可以使用此选项跳过这些检查。 --on-error-stop 在发现损坏的表的第一页上报告所有损坏后，停止处理该表关系，并转到下一个表或索引。请注意，索引检查总是在第一个损坏页之后停止。此选项仅与表关系相关。 --skip=option 如果给定 all-frozen，表损坏检查将跳过所有表中标记为全部冻结的页面。如果给定了 all-visible，则表损坏检查将跳过所有表中标记为所有可见的页面。默认情况下，不跳过任何页面。这可以指定为 none，但由于这是默认值，因此无需提及。 --startblock=block 从指定的块号开始检查。如果正在检查的表关系的块数少于此数，则会发生错误。此选项不适用于索引，可能仅在检查单个表关系时有用。请参阅 --endblock 了解更多注意事项。 --endblock=`block` 在指定的块号结束检查。如果正在检查的表关系的块数少于此数，则会发生错误。此选项不适用于索引，可能仅在检查单个表关系时有用。如果同时选中常规表和toast表，则此选项将同时适用于这两个表，但在验证toast指针时，仍然可以访问编号更高的toast块，除非使用 --exclude-toast-pointers 抑制。 以下命令行选项用来控制B树索引的检查： --heapallindexed 对于每个选中的索引，使用 amcheck's heapallindexed 选项验证索引中是否存在作为索引元组的所有堆元组。 --parent-check 对于检查的每个btree索引，使用 amcheck 的 bt_index_parent_check 函数，该函数在索引检查期间对父/子关系执行其他检查。默认情况是使用amcheck的 bt_index_check 函数，但请注意，使用 --rootdescend 选项会隐式选择 bt_index_parent_check。 --rootdescend 对于每个选中的索引，通过使用 amcheck's rootdescend 选项从根页面对每个元组执行新搜索，在叶级重新查找元组。使用此选项也会隐式选择 --parent-check 选项。这种形式的验证最初是为了帮助开发btree索引功能而编写的。它在帮助检测实践中发生的损坏类型方面可能用处有限，甚至毫无用处。它还可能导致损坏检查花费相当长的时间，并消耗服务器上相当多的资源。 当指定 --parent-check 选项或 --rootdescend 选项时，对B树索引执行的额外检查需要相对强的关系级锁。这些检查是唯一会阻止 INSERT， UPDATE，和 DELETE 命令并发数据修改的检查。 以下命令行选项用来控制与服务器的连接： -h hostname --host=hostname 指定运行服务器的计算机的主机名。如果该值以斜杠开头，它将用作Unix域套接字的目录。 -p port --port=port 指定服务器正在侦听连接的TCP端口或本地Unix域套接字文件扩展名。 -U --username=username 连接的用户名。 -w --no-password 永远不要发出密码提示。如果服务器需要密码身份验证，并且密码无法通过其他方式（如 .pgpass 文件，连接尝试将失败。在没有用户输入密码的批处理作业和脚本中，此选项非常有用。 -W --password 在连接到数据库之前，强制pg_amcheck提示输入密码。此选项从来都不是必需的，因为如果服务器要求密码身份验证，pg_amcheck将自动提示输入密码。但是，pg_amcheck将浪费一次连接尝试，以发现服务器需要密码。在某些情况下，键入 -W 以避免额外的连接尝试是值得的。 --maintenance-db=dbname 指定用于发现要检查的数据库列表的数据库或 连接字符串。如果既不使用 --all 也不使用任何包含数据库模式的选项，则不需要这种连接，并且该选项不起任何作用。否则，连接到正在检查的数据库时，也将使用此选项值中包含的数据库名称以外的任何连接字符串参数。如果省略此选项，则默认值为 postgres，或如果失败，则为 template1。 其它允许的选项： -e --echo 回显发送到服务器的所有SQL。 -j num --jobs=num 使用 num 个到服务器的并发连接，或每个要检查的对象使用一个连接，以较小者为准。默认情况是使用单个连接。 -P --progress 显示进度信息。进度信息包括已完成检查的关系数以及这些关系的总大小。它还包括最终要检查的关系的总数，以及这些关系的估计大小。 -v --verbose 打印更多消息。特别是，这将为每个正在检查的关系打印一条消息，并将增加服务器错误的详细程度。 -V --version 打印pg_amcheck版本并退出。 --install-missing --install-missing=`schema` 安装检查数据库所需的任何缺少的扩展。如果尚未安装，每个扩展的对象将安装到给定的 schema 中，或者如果未指定则安装到 pg_catalog 模式中。目前，唯一需要的扩展是 amcheck。 -? --help 显示有关pg_amcheck命令行参数的帮助，然后退出。 pg_basebackup — 获得一个IvorySQL集簇的一个基础备份 pg_basebackup [option…​] 下列命令行选项控制输出的位置和格式： -D directory --pgdata=directory 设置目标目录以将输出写入。如果该目录不存在，pg_basebackup将创建该目录（以及所有丢失的父目录）。如果已经存在，则必须为空。当备份处于 tar 模式中，目标目录可以被指定为 - （破折号），从而将 tar 文件写入 stdout。这个选项是必需的。 -F format --format=format 为输出选择格式。format 可以是下列之一： p plain 把输出写成平面文件，使用和源服务器数据目录和表空间相同的布局。 当集簇没有额外表空间时，整个数据库将被放在目标目录中。 如果集簇包含额外的表空间，主数据目录将被放置在目标目录中，但是所有其他表空间将被放在它们位于源服务器上的相同的绝对路径中。 (参见 --tablespace-mapping 来更改。)这是默认格式。t tar 将输出作为tar文件写入目标目录中。 主数据目录的内容将被写入名为 base.tar 的文件，而其他表空间将被写入以该表空间的OID命名的单独的tar文件。如果将目标目录指定为 -（破折号），则tar内容将被写入标准输出，该标准输出适用于管道传输至gzip（例如）。 只有当集簇没有额外表空间并且没有使用WAL流时才允许这样做。 -R --write-recovery-conf 创建一个 standby.signal 文件，并将连接设置附加到目标目录（或使用tar格式的基本存档文件中）的 postgresql.auto.conf 文件中。 这样可以简化使用备份结果设置后备服务器的过程。 postgresql.auto.conf 文件将记录连接设置（如果有）以及pg_basebackup所使用的复制槽，这样流复制后面就会使用相同的设置。 -T olddir=newdir --tablespace-mapping=olddir=newdir 在备份期间将目录 olddir 中的表空间重定位到 newdir 中。为使之有效，olddir 必须与源服务器上定义的表空间的路径规范完全匹配。（但如果备份中没有包含 olddir 中的表空间也不是错误）。同时，newdir 是接收主机文件系统中的目录。与主目标目录一样，newdir 不必已经存在，但是如果确实存在，则必须为空。olddir 和 newdir 必须是绝对路径。如果一个路径凑巧包含了一个 = 符号，可用反斜线对它转义。对于多个表空间可以多次使用这个选项。如果以这种方法重定位一个表空间，主数据目录中的符号链接会被更新成指向新位置。因此新数据目录已经可以被一个所有表空间位于更新后位置的新服务器实例使用。目前，这个选项仅以普通输出格式工作，如果选择了tar格式，请忽略它。 --waldir=waldir 指定用于预写式日志目录的位置。waldir 必须是绝对路径。只有当备份是平面文件模式时才能指定事务日志目录。 设置要写入WAL（预写日志）文件的目录。 默认情况下，WAL文件将放置在目标目录的 pg_wal 子目录中，但是此选项可用于将它们放置在其他位置。waldir 必须是绝对路径。与主目标目录一样，waldir 不必已经存在，但是如果确实存在，则必须为空。只有当备份是平面文件模式时才可以指定此选项。 -X method --wal-method=method 在备份中包括所需的WAL（预写式日志）文件。这包括所有在备份期间产生的预写式日志。除非指定了方法 none，可以在目标目录中启动postmaster而无需参考日志归档，从而使输出成为完全独立的备份。支持下列收集预写式日志的 方法：n none 不要在备份中包括预写式日志。f fetch 在备份末尾收集预写式日志文件。因此，有必要把源服务器的 wal_keep_size 参数设置得足够高，这样在备份末尾之前需要的日志数据不会被移除。如果所需的日志数据在传输之前已被回收，则备份将失败并且无法使用。如果使用tar格式，预写式日志文件将被包含在 base.tar 文件。s stream 在进行备份时，流式传输预写式日志数据。将开启一个到服务器的第二连接并且在运行备份时并行开始流传输预写式日志。因此，它将需要两个复制连接，而不仅仅是一个。 只要客户端可以跟上预写式日志数据，使用此方法就不需要在源服务器上保存额外的预写式日志。如果使用tar格式，预写式日志文件被写入到一个单独的名为 pg_wal.tar 的文件（如果服务器的版本超过10，该文件将被命名为 pg_xlog.tar ）。这个值是默认值。 -z --gzip 启用对 tar 文件输出的 gzip 压缩，使用默认的压缩级别。只有使用 tar 格式时压缩才可用，并且会在所有tar文件名后面自动加上后缀 .gz。 -Z level --compress=level 启用对 tar 文件输出的 gzip 压缩，并且制定压缩机别（0 到 9，0 是不压缩，9 是最佳压缩）。只有使用 tar 格式时压缩才可用，并且会在所有tar文件名后面自动加上后缀 .gz。 下列命令行选项控制备份的生成和程序的调用： -c fast|spread --checkpoint=fast|spread 将检查点模式设置为 fast（立刻）或 spread（默认）。 -C --create-slot 指定在启动备份之前应创建由 --slot 选项命名的复制插槽。如果插槽已存在，则会引发错误。 -l label --label=label 为备份设置标签。如果没有指定，将使用一个默认值“ pg_basebackup base backup ”。 -n --no-clean 默认情况下，当 pg_basebackup 因为一个错误而中止时，它会把它意识到无法完成该工作之前已经创建的目录（例如目标目录和预写式日志目录）都移除。这个选项可以禁止这种清洗，因此可以用于调试。注意不管哪一种方式都不会清除表空间目录。 -N --no-sync 默认情况下，pg_basebackup 将等待所有文件被安全地写到磁盘上。这个选项导致 pg_basebackup 不做这种等待就返回，这样会更快一些，但是也意味着后续发生的操作系统崩溃可能会使得这个基础备份损坏。通常这个选项对测试比较有用，在创建生产安装时不应该使用。 -P --progress 启用进度报告。启用这个选项将在备份期间发表一个大致的进度报告。由于数据库可能在备份期间改变，这仅仅是一种近似并且可能不会刚好在 100% 结束。特别地，当 WAL 日志被包括在备份中时，总数据量无法预先估计，并且在这种情况中估计的目标尺寸会在它经过不带 WAL 的总估计后增加。 -r rate --max-rate=rate 设置从源服务器收集数据的最大传输速率。这有助于限制pg_basebackup对服务器的影响。值以每秒千字节为单位。使用后缀 M 表示每秒兆字节数。后缀 k 也可以接受，没有任何影响。有效值介于每秒32千字节和每秒1024兆字节之间。此选项始终影响数据目录的传输。只有收集方法为 fetch 时，才会影响WAL文件的传输。 -S slotname --slot=slotname 这个选项仅能与 -X stream 一起使用。它导致WAL流使用指定的复制槽。如果该基础备份的目的是被用作一台使用复制槽的流复制后备，则它应该使用与 primary_slot_name 中相同的复制槽名称。这可以确保主服务器不会移除位于该基础备份结束与新备用数据库上流复制开始之间产生的任何所需的WAL数据。指定的复制槽必须已经存在，除非同时使用了选项 -C。如果这个选项没有被指定并且服务器支持临时复制槽（版本10以后），则会自动使用一个临时复制槽来进行WAL流。 -v --verbose 启用冗长模式。将在启动和关闭期间输出一些额外步骤，并且如果进度报告也被启用，还会显示当前正在被处理的确切文件名。 --manifest-checksums=algorithm 指定应应用于备份清单中包含的每个文件的校验和算法。目前，可用的算法有`NONE`、CRC32C、SHA224、SHA256、SHA384`和`SHA512。默认值为`CRC32C`。如果选择了`NONE`，备份清单将不包含任何校验和。否则，它将包含备份中使用指定算法的每个文件的校验和。此外，清单将始终包含自身内容的`SHA256`校验和。`SHA`算法比`CRC32C`算法占用大量CPU，因此选择其中一种算法可能会增加完成备份所需的时间。使用SHA散列函数可为希望验证备份是否遭到篡改的用户提供每个文件的加密安全摘要，而CRC32C算法提供的校验和计算速度更快。它擅长捕获由于意外更改引起的错误，但不能抵抗恶意修改。请注意，为了对有权访问备份的对手有用，备份清单必须安全地存储在其他位置，否则必须经过验证以确保自从进行备份以来未进行任何修改。 pg_verifybackup 可用于根据备份清单检查备份的完整性。 --manifest-force-encode 强制备份清单中的所有文件名采用十六进制编码。如果未指定此选项，则仅对非UTF8文件名进行十六进制编码。此选项主要用于测试读取备份清单文件的工具是否正确处理此情况。 --no-estimate-size 阻止服务器估计将要流式传输的备份数据总量，从而导致 pg_stat_progress_basebackup 视图中的 backup_total 列始终为 NULL 。如果没有此选项，则备份将从枚举整个数据库的大小开始，然后返回并发送实际内容。这可能会使备份花费的时间稍长一些，特别是在发送第一个数据之前，备份将花费更长的时间。 如果估计时间过长，此选项将有助于避免此类估计时间。使用 --progress 时不允许使用此选项。 --no-manifest 禁用备份清单的生成。如果未指定此选项，则服务器将生成并发送备份清单，可以使用 pg_verifybackup 进行验证。清单是备份中存在的每个文件的列表，可能包含的所有WAL文件除外。它还存储大小、上次修改时间和每个文件的可选校验和。 --no-slot 防止为备份创建临时复制插槽。默认情况下，如果选择了日志流，但没有用选项 -S 指定槽名称，则会创建一个临时复制插槽（如果源服务器支持）。这个选项的主要目的是允许在服务器没有空闲复制槽可用时制作基础备份。使用复制槽几乎总是最好的方式，因为它能防止备份期间所需的WAL被删除。 --no-verify-checksums 如果在取基础备份的服务器上启用了校验码验证，则禁用校验码验证。默认情况下，校验码会被验证并且校验码失败将会导致一种非零的退出状态。不过，基础备份在这种情况下将不会被移除，就好像使用了 --no-clean 选项一样。校验和验证失败也将报告在 pg_stat_database 视图中。 以下命令行选项控制到源服务器的连接： -d connstr --dbname=connstr 指定用于连接到服务器的参数，比如 连接字符串；这些将覆盖所有冲突的命令行选项。为了和其他客户端应用一致，该选项被称为 --dbname。但是因为pg_basebackup并不连接到集簇中的任何特定数据库，连接字符串中的任何数据库名将被忽略。 -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。默认值取自 PGHOST 环境变量（如果设置），否则会尝试一个 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。默认用 PGPORT 环境变量中的值（如果设置），或者一个编译在程序中的默认值。 -s interval --status-interval=interval 指定发送回源服务器的状态包之间的秒数。较小的值可以更准确地监视服务器的备份进度。一个零值完全禁用这种周期性的状态更新，不过当服务器需要时还是会有一个更新会被发送来避免超时导致的断开连接。默认值是 10 秒。 -U username --username=username 指定连接的用户名。 -w --no-password 防止发出口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_basebackup在连接到源服务器之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，pg_basebackup将自动提示要求一个口令。但是，pg_basebackup将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 其他选项也可用： -V --version 打印pg_basebackup版本并退出。 -? --help 显示有关pg_basebackup命令行参数的帮助并退出。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 环境变量 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 在备份的开始时，需要在源服务器上执行检查点。这可能需要一点时间（尤其在没有使用选项 --checkpoint=fast 时），在此期间pg_basebackup看起来处于闲置状态。 备份将包括数据目录和表空间中的所有文件，包括配置文件以及由第三方放在该目录中的任何额外文件，不过由IvorySQL管理的特定临时文件除外。但只有常规文件和目录会被拷贝，但用于表空间的符号链接会被保留。指向IvorySQL已知的特定目录的符号链接被拷贝为空目录。其他符号链接和特殊设备文件会被跳过。 在普通格式中，表空间将备份到源服务器上的相同路径，除非使用了 --tablespace-mapping 选项。如果没有这个选项并且表空间正在使用，在同一台服务器上进行普通格式的基础备份将无法工作，因为备份必须要写入到与原始表空间相同的目录位置。 在使用 tar 格式时，用户应负责在启动使用数据的 IvorySQL 服务器前解压每一个 tar 文件。如果有额外的表空间，用于它们的 tar 文件需要被解压到正确的位置。在这种情况下，服务器将根据包含在 base.tar 文件中的 tablespace_map 文件的内容为那些表空间创建符号链接。 pg_basebackup可以和具有相同或较低主版本的服务器一起工作. 如果在源集簇上启用了组权限，pg_basebackup将保留数据文件的组权限。 要创建服务器 mydbserver 的一个基础备份并将它存储在本地目录 /usr/local/pgsql/data 中： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data 要创建本地服务器的一个备份，为其中每一个表空间产生一个压缩过的 tar 文件，并且将它存储在目录 backup 中，在运行期间显示一个进度报告： $ pg_basebackup -D backup -Ft -z -P 要创建一个单一表空间本地数据库的备份并且使用bzip2压缩它： $ pg_basebackup -D - -Ft -X fetch | bzip2 > backup.tar.bz2 （如果在该数据库中有多个表空间，这个命令将失败）。 要创建一个本地数据库的备份，其中 /opt/ts 中的表空间被重定位到 ./backup/ts： $ pg_basebackup -D backup/data -T /opt/ts=$(pwd)/backup/ts pgbench — 在IvorySQL上运行一个基准测试 pgbench -i [option…​] [dbname] pgbench [option…​] [dbname] pgbench -i 会创建四个表 pgbench_accounts、 pgbench_branches、 pgbench_history 以及 pgbench_tellers，如果同名表已经存在会被先删除。如果你已经有同名表，一定注意要使用另一个数据库！ 在默认的情况下“比例因子”为 1，这些表初始包含的行数为： table # of rows --------------------------------- pgbench_branches 1 pgbench_tellers 10 pgbench_accounts 100000 pgbench_history 0 你可以使用 -s （比例因子）选项增加行的数量。-F （填充因子）选项也可以在这里使用。 一旦你完成了必要的设置，你就可以用不包括 -i 的命令运行基准，也就是： pgbench [ options ] dbname 在近乎所有的情况中，你将需要一些选项来做一次有用的测试。最重要的选项是 -c （客户端数量）、 -t （事务数量）、-T （时间限制）以及 -f （指定一个自定义脚本文件）。完整的列表见下文。 下面分成三个部分。数据库初始化期间使用的选项和运行基准时会使用不同的选项，但也有一些选项在两种情况下都使用。 pgbench接受下列命令行初始化参数： dbname 指定要测试的数据库的名称。 如果这个没有被指定，将使用环境变量 PGDATABASE 。 如果那个也没有设定，将使用指定要连接的用户名称。 -i --initialize 要求调用初始化模式。 -I init_steps --init-steps=init_steps 只执行选出的一组普通初始化步骤。init_steps 指定要被执行的初始化步骤，每一个步骤使用一个字符代表。每一个步骤都以指定的顺序被调用。默认是 dtgvp。可用的步骤是：d（删除）删除任何已有的pgbench表。t（创建表）创建标准pgbench场景使用的表，即 pgbench_accounts、pgbench_branches、pgbench_history`以及 `pgbench_tellers。g 或 G（生成数据、客户端或服务器端）生成数据并且装入到标准的表中，替换掉已经存在的任何数据。使用 g（客户端数据生成），数据在 pgbench 客户端生成，然后发送到服务器。这通过 COPY 广泛使用客户端/服务器带宽。使用 g 会导致日志记录每 100,000 行打印一条消息，同时为 pgbench_accounts 表生成数据。使用 G（服务器端数据生成），仅从 pgbench 客户端发送少量查询，然后在服务器中实际生成数据。 此变体不需要大量带宽，但服务器将完成更多工作。 使用 G`会导致日志记录在生成数据时不打印任何进度消息。默认的初始化行为使用客户端数据生成（相当于`g）。v（清理）在标准的表上调用 VACUUM。p（创建主键）在标准的表上创建主键索引。f（创建外键）在标准的表之间创建外键约束（注意这一步默认不会被执行）。 -F fillfactor --fillfactor= fillfactor 用给定的填充因子创建表 pgbench_accounts、pgbench_tellers 以及 pgbench_branches。默认是100。 -n --no-vacuum 在初始化期间不执行清理（这个选项会抑制 v 初始化步骤，即便在`-I` 中指定了该步骤）。 -q --quiet 把记录切换到安静模式，只是每 5 秒产生一个进度消息。默认的记录会每 100,000 行打印一个消息，这经常会在每秒钟输出很多行（特别是在好的硬件上）。如果在 -I 中指定了 G，则此设置无效。 -s scale_factor --scale= scale_factor 将生成的行数乘以比例因子。例如，-s 100 将在 pgbench_accounts 表中创建 10,000,000 行。默认为 1。当比例为 20,000 或更高时，用来保存账号标识符的列（ aid 列）将切换到使用更大的整数（ bigint ），这样才能足以保存账号标识符。 --foreign-keys 在标准的表之间创建外键约束（如果 f 在初始化步骤序列中不存在，这个选项会把它加入）。 --index-tablespace=index_tablespace 在指定的表空间而不是默认表空间中创建索引。 --partition-method=NAME 使用 NAME 方法创建一个分区的 pgbench_accounts 表。 预期值为 range 或 hash。 此选项要求 --partitions 设置为非零。 如果未指定，默认值为 range。 --partitions=NUM 创建一个分区 pgbench_accounts 表，其中 NUM 分区的大小与按比例缩放的帐户数几乎相等。 默认为 0，表示没有分区。 --tablespace=tablespace 在指定的表空间而不是默认表空间中创建表。 --unlogged-tables 把所有的表创建为非日志记录表而不是永久表。 pgbench接受下列命令行基准参数： -b scriptname[@weight] --builtin= scriptname[@weight] 把指定的内建脚本加入到要被执行的脚本列表中。 可用的内建脚本有： tpcb-like、 simple-update 和 select-only 。 这里也接受内建名称无歧义的前缀缩写。 如果用上特殊的名字 list，将会显示内建脚本的列表并且立刻退出。可选的，在 @ 后面写一个整数权重以调整选择这个脚本而不是其它的概率。 默认的权重为1。 详情如下。 -c clients --client= clients 模拟的客户端数量，也就是并发数据库会话数量。默认为 1。 -C --connect 为每一个事务建立一个新连接，而不是只为每个客户端会话建立一个连接。这对于度量连接开销有用。 -d --debug 打印调试输出。 -D varname = value --define= varname = value 定义一个由自定义脚本（见下文）使用的变量。允许多个 -D 选项。 -f filename[@weight] --file= filename[@weight] 把一个从 filename 读到的事务脚本加入到被执行的脚本列表中。可选的，在 @ 后写一个整数权重以调整选择这个脚本而非其他的概率。 默认权重为1. (要使用包含 @ 字符的脚本文件名，附加权重在后面以避免歧义，例如 filen@me@1 。) 详情如下。 -j threads --jobs= threads pgbench中的工作者线程数量。在多 CPU 机器上使用多于一个线程会有用。客户端会尽可能均匀地分布到可用的线程上。默认为 1。 -l --log 把与每一个事务相关的信息写到一个日志文件中。详见下文。 -L limit --latency-limit= limit 对持续超过 limit 毫秒的事务进行独立的计数和报告， 这些事务被认为是 迟到（late） 了的事务。在使用限流措施时（ --rate=…​ ），滞后于计划超过 limit 毫秒并且因此没有希望满足延迟限制的事务根本 不会被发送给服务器。这些事务被认为是 被跳过（skipped） 的事务，它们会被单独计数并且报告。 -M querymode --protocol=querymode 要用来提交查询到服务器的协议： simple ：使用简单查询协议。 extended 使用扩展查询协议。 prepared ：使用带预备语句的扩展查询语句。在 prepared 模式中，pgbench重用从第二次查询迭代开始的语法分析结果，因此pgbench运行速度比其他模式快。默认是简单查询协议。 -n --no-vacuum 在运行测试前不进行清理。如果你在运行一个不包括标准的表 pgbench_accounts 、 pgbench_branches 、 pgbench_history 和 pgbench_tellers 的自定义测试场景时，这个选项是必需的。 -N --skip-some-updates 运行内建的简单更新脚本。这是 -b simple-update 的简写。 -P sec --progress=sec 每 sec 秒显示进度报告。该报告包括运行了多长时间、从上次报告以来的 tps 以及从上次报告以来事务延迟的平均值和标准偏差。如果低于限流值（ -R ），延迟会相对于事务预定的开始时间（而不是实际的事务开始时间）计算，因此其中也包括了平均调度延迟时间。 -r --report-latencies 在基准结束后，报告平均的每个命令的每语句等待时间（从客户端的角度来说是执行时间）。详见下文。 -R rate --rate= rate 按照指定的速率执行事务而不是尽可能快地执行（默认行为）。该速率 以 tps（每秒事务数）形式给定。如果目标速率高于最大可能速率，则 该速率限制不会影响结果。该速率的目标是按照一条泊松分布的调度时间线开始事务。期望的开始 时间表会基于客户端第一次开始的时间（而不是上一个事务结束的时 间）前移。这种方法意味着当事务超过它们的原定结束时间时，更迟的 那些有机会再次追赶上来。当限流措施被激活时，运行结束时报告的事务延迟是从预订的开始时间计 算而来的，因此它包括每一个事务不得不等待前一个事务结束所花的时 间。该等待时间被称作调度延迟时间，并且它的平均值和最大值也会被 单独报告。关于实际事务开始时间的事务延迟（即在数据库中执行事务 所花的时间）可以用报告的延迟减去调度延迟时间计算得到。如果把 --latency-limit 和 --rate 一起使用， 当一个事务在前一个事务结束时已经超过了延迟限制时，它可能会滞后 非常多，因为延迟是从计划的开始时间计算得来。这类事务不会被发送 给服务器，而是一起被跳过并且被单独计数。一个高的调度延迟时间表示系统无法用选定的客户端和线程数按照指定 的速率处理事务。当平均的事务执行时间超过每个事务之间的调度间隔时，每一个后续事务将会落后更多，并且随着测试运行时间越长，调度延迟时间将持续增加。发生这种情况时，你将不得不降低指定的事务速率。 -s scale_factor --scale= scale_factor 在pgbench的输出中报告指定的比例因子。对于内建测试，这并非必需；正确的比例因子将通过对 pgbench_branches 表中的行计数来检测。不过，当只测试自定义基准（ -f 选项）时，比例因子将被报告为 1（除非使用了这个选项）。 -S --select-only 执行内建的只有选择的脚本。是 -b select-only 简写形式。 -t transactions --transactions= transactions 每个客户端运行的事务数量。默认为 10。 -T seconds --time= seconds 运行测试这么多秒，而不是为每个客户端运行固定数量的事务。-t 和 -T 是互斥的。 -v --vacuum-all 在运行测试前清理所有四个标准的表。在没有用 -n 以及 -v 时， pgbench将清理 pgbench_tellers 和 pgbench_branches 表，并且截断 pgbench_history。 --aggregate-interval=`seconds` 聚集区间的长度（单位是秒）。仅可以与 -l 选项一起使用。通过这个选项，日志会包含针对每个区间的概要数据，如下文所述。 --log-prefix=prefix 设置 --log 创建的日志文件的文件名前缀。默认是 pgbench_log。 --progress-timestamp 当显示进度（选项 -P ）时，使用一个时间戳（Unix 时间）取代从运行开始的秒数。单位是秒，在小数点后是毫秒精度。这可以有助于比较多种工具生成的日志。 --random-seed= seed 设置随机数生成器种子。为系统的随机数生成器提供种子，然后随机数生成器会产生一个初始生成器状态序列，每一个线程一个状态。seed 的值可以是： time （默认值，种子基于当前时间）、 rand （使用一种强随机源，如果没有可用的源则失败）或者一个无符号十进制整数值。一个pgbench脚本中会显式（ random…​ 函数）地或者隐式地（如 --rate 使用随机数生成器调度事务）调用随机数生成器。在被明确设置时，用作种子的值会显示在终端上。 还可以通过环境变量 PGBENCH_RANDOM_SEED 提供用于 seed 的值。为了确保所提供的种子影响所有可能的使用，把这个选项放在第一位或者使用环境变量。明确地设置种子允许准确地再生一个 pgbench 运行，对随机数而言。因为随机状态是针对每个线程管理，这意味着如果每一个线程有一个客户端并且没有外部或者数据依赖，则对于一个相同的调用就会有完全相同的 pgbench 运行。从一种统计的角度来看，再生运行不是什么好主意，因为它能隐藏性能可变性或者不正当地改进性能，即通过命中前一次运行的相同页面来改进性能。不过，它也可以对调试起到很大帮助作用，例如重新运行一种导致错误的棘手用例。请善用。 --sampling-rate=`rate` 采样率，在写入数据到日志时被用来减少日志产生的数量。如果给出这个选项，只有指定比例的事务被记录。1.0 表示所有事务都将被记录，0.05 表示只有 5% 的事务会被记录。在处理日志文件时，记得要考虑这个采样率。例如，当计算TPS值时，你需要相应地乘以这个数字（例如，采样率是 0.01，你将只能得到实际TPS的 1/100）。 --show-script= scriptname 在 stderr 上显示内置脚本 scriptname 的实际代码，并立即退出。 pgbench 还接受以下用于连接参数的常见命令行参数： -h hostname --host= hostname 数据库服务器的主机名 -p port --port=port 数据库服务器的端口号 -U login --username= login 要作为哪个用户连接 -V --version 打印pgbench版本并退出。 -? --help 显示有关pgbench命令行参数的信息，并且退出。 成功运行将以状态0退出。 退出状态为1表示静态问题，如无效的命令行选项。 运行过程中的错误，例如数据库错误或脚本中的问题将导致退出状态为2。 在后一种情况下，pgbench将打印部分结果。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数。 此实用程序与大多数其他 IvorySQL 实用程序一样，使用 libpq 支持的环境变量。 环境变量 PG_COLOR 指定是否在诊断消息中使用颜色。 可能的值是 always、 auto 和 never. pg_config — 获取已安装的IvorySQL的信息 pg_config [option…​] 要使用pg_config，提供一个或多个下列选项： --bindir 打印用户可执行文件的位置。例如使用这个选项来寻找 psql 程序。这通常也是 pg_config 程序所在的位置。 --docdir 打印文档文件的位置。 --htmldir 打印 HTML 文档文件的位置。 --includedir 打印客户端接口的 C 头文件的位置。 --pkgincludedir 打印其它 C 头文件的位置。 --includedir-server 打印用于服务器编程的 C 头文件的位置。 --libdir 打印对象代码库的位置。 --pkglibdir 打印动态可载入模块的位置，或者服务器可能搜索它们的位置（其它架构独立数据文件可能也被安装在这个目录）。 --localedir 打印区域支持文件的位置（如果在IvorySQL被编译时没有配置区域支持，这将是一个空字符串）。 --mandir 打印手册页的位置。 --sharedir 打印架构独立支持文件的位置。 --sysconfdir 打印系统范围配置文件的位置。 --pgxs 打印扩展 makefile 的位置。 --configure 打印当IvorySQL被配置编译时给予 configure 脚本的选项。这可以被用来重新得到相同的配置，或者找出是哪个选项编译了一个二进制包（不过注意二进制包通常包含厂商相关的自定补丁）。参见下面的例子。 --cc 打印用来编译IvorySQL的 CC 变量值。这显示被使用的 C 编译器。 --cppflags 打印用来编译IvorySQL的 CPPFLAGS 变量值。这显示在预处理时需要的 C 编译器开关（典型的是 -I 开关）。 --cflags 打印用来编译IvorySQL的 CFLAGS 变量值。这显示被使用的 C 编译器开关。 --cflags_sl 打印用来编译IvorySQL的 CFLAGS_SL 变量值。这显示被用来编译共享库的额外 C 编译器开关。 --ldflags 打印用来编译IvorySQL的 LDFLAGS 变量值。这显示链接器开关。 --ldflags_ex 打印用来编译IvorySQL的 LDFLAGS_EX 变量值。这只显示被用来编译可执行程序的链接器开关。 --ldflags_sl 打印用来编译IvorySQL的 LDFLAGS_SL 变量值。这只显示被用来编译共享库的链接器开关。 --libs 打印用来编译IvorySQL的 LIBS 变量值。这通常包含用于链接到IvorySQL中的外部库的 -l 开关。 --version 打印IvorySQL的版本。 -? --help 显示有关pg_config命令行参数的帮助并退出。 如果给定多于一个选项，将按照相同的顺序打印信息，每行一项。如果没有给定选项，将打印所有可用信息，并带有标签。 要重建当前 IvorySQL 安装的编译配置，可运行下列命令： eval ./configure `pg_config --configure` pg_config --configure 的输出包含 shell 引号，这样带空格的参数可以被正确地表示。因此，为了得到正确的结果需要使用 eval。 pg_dump — 把IvorySQL数据库抽取为一个脚本文件或其他归档文件 pg_dump [connection-option…​] [option…​] [dbname] 下列命令选项控制输出的内容和格式。 dbname 指定要被转储的数据库名。如果没有指定，将使用环境变量 PGDATABASE。如果环境变量也没有设置，则使用指定给该连接的用户名。 -a --data-only 只转储数据，而不转储模式（数据定义）。表数据、大对象和序列值都会被转储。这个选项类似于指定 --section=data，但是由于历史原因又不完全相同。 -b --blobs 在转储中包括大对象。这是当 --schema、 --table 或 --schema-only 被指定时的默认行为。因此，只有在请求转储一个特定方案或者表的情况中， -b 开关才对向转储中加入大对象有用。注意blobs是被考虑的数据，因此在使用 --data-only 时将被包括在内，但在使用 --schema-only 时则不会包括。 -B --no-blobs 在转储中排除大对象。当同时给定 -b 和 -B 时，行为是在数据被转储时输出大对象，请参考 -b 文档。 -c --clean 在输出创建数据库对象的命令之前输出清除（删除）它们的命令 （除非也指定了 --if-exists，如果任何对象不存在于 目的数据库中，恢复可能会产生一些伤害性的错误消息）。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 -C --create 使得在输出的开始是一个创建数据库本身并且重新连接到被创建的数据库的命令（通过这种形式的一个脚本，在运行脚本之前你连接的是目标安装中的哪个数据库都没有关系）。如果也指定了 --clean，脚本会在重新连接到目标数据库之前先删除它然后再重建。通过 --create，输出还会包括数据库的注释（如果有）以及与这个数据库相关的任何配置变量设置，也就是任何提到了这个数据库的 ALTER DATABASE …​ SET …​ 命令和 ALTER ROLE …​ IN DATABASE …​ SET …​ 命令。该数据库本身的访问特权也会被转储，除非指定有 --no-acl。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 -e pattern -−extension=pattern 只转储匹配 pattern 的扩展。 如果未指定此选项，目标数据库中的所有非系统扩展都将被转储。 通过写入多个 -e 开关，可以选择多个扩展。 pattern 参数可以理解为与psql’s \\d 命令使用的规则相同的模式， 所以还可以通过在模式中写入通配符来选择多个扩展。 在使用通配符时，如果需要防止来自扩展通配符的shell，请小心的引用模式。任何由 pg_extension_config_dump 注册的配置关系被包括在转储中，如果他的扩展已经被 -−extension 指定了。注意当指定了 -e ，pg_dump不尝试转储任何所选扩展可能依赖的其他数据库对象。 也就是说，这里不保证指定扩展转储的结果能够由他们自己成功恢复到一个干净的数据库中。 -E encoding --encoding=encoding 以指定的字符集编码创建转储。 在默认情况下，该转储会以该数据库的编码创建（另一种得到相同结果的方式是将 PGCLIENTENCODING 环境变量设置成想要的转储编码）。 -f file --file=file 将输出发送到指定文件。对于基于输出格式的文件这个参数可以被忽略，在那种情况下将使用标准输出。不过对于目录输出格式必须给定这个参数，在目录输出格式中指定的是一个目录而不是一个文件。在这种情况中，该目录会由 pg_dump 创建并且不需要以前就存在。 -F format --format=format 选择输出的格式。format 可以是下列之一： p plain 输出一个纯文本形式的SQL脚本文件（默认值）。c custom 输出一个适合于作为pg_restore输入的自定义格式归档。和目录输出格式一起，这是最灵活的输出格式，它允许在恢复时手动选择和排序已归档的项。这种格式在默认情况还会被压缩。 d directory 输出一个适合作为pg_restore输入的目录格式归档。这将创建一个目录，其中每个被转储的表和大对象都有一个文件，外加一个所谓的目录文件，该文件以一种pg_restore能读取的机器可读格式描述被转储的对象。一个目录格式归档能用标准 Unix 工具操纵，例如一个未压缩归档中的文件可以使用gzip工具压缩。这种格式默认情况下是被压缩的并且也支持并行转储。t tar 输出一个适合于输入到pg_restore中的 tar -格式归档。tar 格式可以兼容目录格式，抽取一个 tar 格式的归档会产生一个合法的目录格式归档。不过，tar 格式不支持压缩。还有，在使用 tar 格式时，表数据项的相对顺序不能在恢复过程中被更改。 -j njobs --jobs=njobs 通过同时归档 njobs 个表来运行并行转储。这个选项可能会减少执行转储所需的时间，但也会增加数据库服务器上的负载。你只能和目录输出格式一起使用这个选项，因为这是唯一一种让多个进程能在同一时间写其数据的输出格式。pg_dump将打开 njobs + 1 个到该数据库的连接，因此确保你的 max_connections 设置足够高以容纳所有的连接。在运行一次并行转储时请求数据库对象上的排他锁可能导致转储失败。 其原因是，pg_dump领导者进程会在工作者进程将要稍后转储的对象上请求共享锁，以便确保在转储运行时不会有人删除它们并让它们出错。 如果另一个客户端接着请求一个表上的排他锁，那个锁将不会被授予但是会被排入队列等待领导者进程的共享锁被释放。 因此，任何其他对该表的访问将不会被授予或者将排在排他锁请求之后。 这包括尝试转储该表的工作者进程。如果没有任何防范措施，这可能会是一种经典的死锁情况。 要检测这种冲突，pg_dump工作者进程使用 NOWAIT 选项请求另一个共享锁。 如果该工作者进程没有被授予这个共享锁，其他某人必定已经在同时请求了一个排他锁并且没有办法继续转储，因此pg_dump除了中止转储之外别无选择。对于一个一致的备份，数据库服务器需要支持同步的快照，在IvorySQL的主服务器和10的后备服务器中引入了一种特性。 有了这种特性，即便数据库客户端使用不同的连接，也可以保证他们看到相同的数据集。 pg_dump -j 使用多个数据库连接，它用领导者进程连接到数据一次，并且为每一个工作者任务再一次连接数据库。 如果没有同步快照特征，在每一个连接中不同的工作者任务将不能被保证看到相同的数据，这可能导致一个不一致的备份。 -n pattern --schema=pattern 只转储匹配 pattern 的模式，这会选择模式本身以及它所包含的所有对象。当没有指定这个选项时，目标数据库中所有非系统模式都将被转储。多个模式可以通过书写多个 -n 开关来选择。另外， pattern 参数可以被解释为一种根据psql的`\\d`命令所用的相同规则（请参见下面的 Patterns）编写的模式，这样多个模式也可以通过在该模式中书写通配字符来选择。在使用通配符时，如果需要阻止 shell 展开通配符需要小心引用该模式，注意当 -n 被指定时，pg_dump不会尝试转储所选模式可能依赖的任何其他数据库对象。因此，无法保证一次指定模式转储的结果能够仅凭其本身被成功地恢复到一个干净的数据库中。注意当 -n 被指定时，非模式对象（如二进制大对象）不会被转储。你可以使用 --blobs 开关将二进制大对象加回到该转储中。 -N pattern --exclude-schema=pattern 不转储匹配 pattern 模式的任何模式。该模式被根据 -n 所用的相同规则被解释。-N 可以被给定多次来排除匹配几个模式中任意一个的模式。当 -n 和 -N 都被给定时，该行为是只转储匹配至少一个 -n 开关但是不匹配 -N 开关的模式。如果只有 -N 而没有 -n，那么匹配 -N 的模式会被从一个正常转储中排除。 -O --no-owner 不输出设置对象拥有关系来匹配原始数据库的命令。默认情况下，pg_dump会发出`ALTER OWNER` 或 SET SESSION AUTHORIZATION 语句来设置被创建的数据库对象的拥有关系。除非该脚本被一个超级用户（或是拥有脚本中所有对象的同一个用户）启动，这些语句都将会失败。要使一个脚本能够被任意用户恢复，但把所有对象的拥有关系都给这个用户，可指定 -O。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用`pg_restore`时指定该选项。 -R --no-reconnect 这个选项已经废弃，但是为了向后兼容仍然能被接受。 -s --schema-only 只转储对象定义（模式），而非数据。这个选项是 --data-only 的逆选项。它和指定 --section=pre-data --section=post-data 相似，但是由于历史原因又不完全相同。（不要把这个选项和 --schema 选项混淆，后者在“schema”的使用上有不同的含义）。要为数据库中表的一个子集排除表数据，见 --exclude-table-data。 -S username --superuser=username 指定要在禁用触发器时使用的超级用户的用户名。只有使用 --disable-triggers 时，这个选项才相关（通常，最好省去这个选项，而作为超级用户来启动结果脚本来取而代之）。 -t pattern --table=pattern 只转储名字匹配 pattern 的表。通过写多个 -t 开关可以选择多个表。另外，pattern 参数可以被解释为一种根据psql’s \\d 命令所用的相同规则编写的模式，这样多个表也可以通过在该模式中书写通配字符来选择。在使用通配符时，如果需要阻止 shell 展开通配符需要小心引用该模式，当 -t 被使用时，-n 和 -N 开关不会有效果，因为被 -t 选择的表将被转储而无视那些开关，并且非表对象将不会被转储。注意当 -t 被指定时，pg_dump不会尝试转储所选表可能依赖的任何其他数据库对象。因此，无法保证一次指定表转储的结果能够仅凭其本身被成功地恢复到一个干净的数据库中。 -T pattern --exclude-table=pattern 不转储匹配 pattern 模式的任何表。该模式被根据 -t 所用的相同规则被解释。-T 可以被给定多次来排除匹配几个模式中任意一个的模式。当 -t 和 -T 都被给定时，该行为是只转储匹配至少一个 -t 开关但是不匹配 -T 开关的表。如果只有 -T 而没有 -t，那么匹配 -T 的表会被从一个正常转储中排除。 -v --verbose 指定冗长模式。这将导致pg_dump向标准错误输出详细的对象注释以及转储文件的开始/停止时间，还有进度消息。 重复该选项会导致在标准错误上出现附加的调试级消息。 -V --version pg_dump版本并退出。 -x --no-privileges --no-acl 防止转储访问特权（授予/收回命令）。 -Z 0..9 --compress=0..9 指定要使用的压缩级别。零意味着不压缩。对于自定义和目录归档格式，这会指定个体表数据段的压缩，并且默认是进行中等级别的压缩。对于纯文本输出，设置一个非零压缩级别会导致整个输出文件被压缩，就好像它被gzip处理过一样，但是默认是不压缩。tar 归档格式当前完全不支持压缩。 --binary-upgrade 这个选项用于就地升级功能。我们不推荐也不支持把它用于其他目的。这个选项在未来的发行中可能被改变而不做通知。 --column-inserts --attribute-inserts 将数据转储为带有显式列名的 INSERT 命令（ INSERT INTO table (column, …​) VALUES …​）。这将使得恢复过程非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。重新加载期间的任何错误都将导致有问题的 INSERT 相关的行将丢失，而不是整个表内容。 --disable-dollar-quoting 这个选项禁止在函数体中使用美元符号引用，并且强制它们使用 SQL 标准字符串语法被引用。 --disable-triggers 只有在创建一个只转储数据的转储时，这个选项才相关。它指示pg_dump包括在数据被重新载入时能够临时禁用目标表上的触发器的命令。如果你在表上有引用完整性检查或其他触发器，并且你在数据重新载入期间不想调用它们，请使用这个选项。当前，为 --disable-triggers 发出的命令必须作为超级用户来执行。因此，你还应当使用 -S 指定一个超级用户名，或者宁可作为一个超级用户启动结果脚本。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 --enable-row-security 只有在转储具有行安全性的表的内容时，这个选项才相关。默认情况下， pg_dump将把 row_security 设置为 off 来确保从该表中转储 出所有的数据。如果用户不具有足够能绕过行安全性的特权，那么会抛出 一个错误这个参数指示pg_dump将 row_security 设置为 on，允许用户只转储该表中 它们能够访问到的部分内容。注意如果当前你使用了这个选项，你可能还想得到 INSERT 格式的转储，因为恢复期间的 COPY FROM 不支持行安全性。 --exclude-table-data=pattern 不转储匹配 pattern 模式的任何表中的数据。该模式根据 -t 的相同规则被解释。--exclude-table-data 可以被给定多次来排除匹配多个模式的表。当你需要一个特定表的定义但不想要其中的数据时，这个选项就有用了。要排除数据库中所有表的数据，见 --schema-only。 --extra-float-digits=ndigits 在转储浮点数据时使用规定的 extra_float_digits 值，而不是最大可用精度。以备份目的生成的常规转储不使用此选项。 --if-exists 时间条件性命令（即增加一个 IF EXISTS 子句）来清除数据库和其他对象。 只有同时指定了 --clean 时，这个选项才可用。 --include-foreign-data=`foreignserver` 使用与 foreignserver 模式匹配的外部服务器转储任何外部表的数据。可以通过编写 多个 --include-foreign-data 开关来选择多个外部服务器。 同样，根据psql’s \\d 命令使用的相同规则， 将 foreignserver 参数解释为模式。 因此也可以通过在模式中写入通配符来选择多个外部服务器。使用通配符时， 如果需要，请小心引用该模式，以防止Shell扩展通配符。 唯一的例外是不允许使用空模式。注意指定 --include-foreign-data 时，pg_dump不会检查外部表是否可写。因此，不能保证可以成功还原外部表转储的结果。 --inserts 将数据转储为 INSERT 命令（而不是 COPY ）。这将使得恢复非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。重新加载期间的任何错误都将导致有问题的 INSERT 相关的行将丢失，而不是整个表内容。注意如果你已经重新安排了列序，该恢复可能会一起失败。--column-inserts 选项对于列序改变是安全的，但是会更慢。 --load-via-partition-root 在为一个分区表转储数据时，让 COPY 语句或者 INSERT 语句把包含它的分区层次的根而不是分区自身作为目标。这导致在数据被装载时，会为每一个行重新确定合适的分区。如果在一台服务器上重新装载数据时会出现行并不是总是落入到和原始服务器上相同的分区中的情况，这个选项就很有用。例如，如果分区列是文本类型并且两个系统中用于排序分区列的排序规则有着不同的定义，就会发生这种情况。在从用这个选项制作的归档恢复时，最好不要使用并行，因为pg_restore将不能准确地知道一个给定的归档数据项将把数据装载到哪个分区中。这会导致效率不高，因为在并行任务见会有锁冲突，或者甚至可能由于在所有的相关数据被装载前建立了外键约束而导致重新装载失败。 --lock-wait-timeout=`timeout` 在转储的开始从不等待共享表锁的获得。而是在指定的 timeout 内不能锁定一个表时失败。超时时长可以用 SET statement_timeout 接受的任何格式指定。 --no-comments 不转储注释。 --no-publications 不转储publication。 --no-security-labels 不转储安全标签。 --no-subscriptions 不转储订阅。 --no-sync 默认情况下，pg_dump 将等待所有文件被安全地写入磁盘。这个选项会让 pg_dump 不等待直接返回，这样会更快，但是也意味着后续的一次操作系统崩溃会让该转储损坏。通常这个选项对测试有用，但是不应该在从生产安装中转储数据时使用。 --no-tablespaces 不要输出选择表空间的命令。通过这个选项，在恢复期间所有的对象都会被创建在任何作为默认的表空间中。当发出归档(非文本)输出文件时，这个选项被忽略。对于归档格式，你可以在调用 pg_restore 时指定该选项。 --no-toast-compression 不要输出命令以设置 TOAST 压缩方法。 采用这个选项，所有列将按默认压缩设置进行恢复。 --no-unlogged-table-data 不转储非日志记录表的内容。这个选项对于表定义（模式）是否被转储没有影响，它只会限制转储表数据。当从一个后备服务器转储时，在非日志记录表中的数据总是会被排除。 --on-conflict-do-nothing 增加 ON CONFLICT DO NOTHING 到 INSERT commands。 除非规定了 --inserts, --column-inserts 或 --rows-per-insert ，否则此选项是无效的. --quote-all-identifiers 强制引用所有标识符。当从IvorySQL主版本与pg_dump不同的服务器上转储一个数据库时或者当输出准备载入到一个具有不同主版本的服务器时，推荐使用这个选项。默认情况下，pg_dump只对在其主版本中是被保留词的标识符加上引号。在转储其他版本服务器时，这种默认行为有时会导致兼容性问题，因为那些版本可能具有些许不同的被保留词集合。使用 --quote-all-identifiers 能阻止这种问题，但代价是转储脚本更难阅读。 --rows-per-insert=nrows 数据转储为 INSERT 命令（而不是 COPY ）。 控制每个 INSERT 命令的最大行数。 指定的值必须大于零。重新加载期间的任何错误都将导致有问题的`INSERT` 相关的行将丢失，而不是整个表内容。 --section=sectionname 只转储命名节。节的名称可以是 pre-data、 data 或 post-data。这个选项可以被指定多次来选择多个节。默认是转储所有节。数据节包含真正的表数据、大对象内容和序列值。数据后项包括索引、触发器、规则和除了已验证检查约束之外的约束的定义。数据前项包括所有其他数据定义项。 --serializable-deferrable 为转储使用一个 可序列化 事务，以保证所使用的快照与后来的数据库状态是一致的。但是这样做是在事务流中等待一个点，在该点上不能存在异常，这样就不会有转储失败或者导致其他事务带着 serialization_failure 回滚的风险。对于一个只为灾难恢复存在的转储，这个选项没什么益处。如果一个转储被用来在原始数据库持续被更新期间载入一份用于报表或其他只读负载的数据库拷贝时，这个选项就有所帮助。如果没有这个选项，转储可能会反映一个与最终提交事务的任何执行序列都不一致的状态。例如，如果使用了批处理技术，一个批处理在转储中可以显示为关闭，而其中的所有项都不出现。如果 pg_dump 被启动时没有读写事务在活动，则这个选项没有什么不同。如果有读写事务在活动，该转储的启动可能会被延迟一段不确定的时间。一旦开始运行，有没有这个开关的表现是相同的。 --snapshot=snapshotname 在做一个数据库的转储时指定一个同步的快照。在需要把转储和一个逻辑复制槽 或者一个并发会话同步时可以用上这个选项。在并行转储的情况下，将使用这个选项指定的快照名而不是取一个新快照。 --strict-names 要求每一个扩展( -e / --extension )，模式（ -n / --schema ）和表（ -t / --table ）限定符匹配要转储的数据库中至少一个扩展/模式/表。 注意，如果没有找到有这样的扩展/模式/表限定符匹配，即便没有 --strict-names ，pg_dump也将生成一个错误。这个选项对 -N / --exclude-schema、-T / --exclude-table 或者 --exclude-table-data 没有效果。无法匹配任何对象的排除模式不会被当作错误。 --use-set-session-authorization 输出 SQL-标准的 SET SESSION AUTHORIZATION 命令取代 ALTER OWNER 命令来确定对象的所有关系。这让该转储更加兼容标准，但是取决于该转储中对象的历史，该转储可能无法正常恢复。而且，一个使用 SET SESSION AUTHORIZATION 的转储将一定会要求超级用户特权来正确地恢复，而 ALTER OWNER 要求更少的特权。 -? --help 显示有关pg_dump命令行参数的帮助并退出。 下列命令行选项控制数据库连接参数。 -d dbname --dbname=dbname 指定要连接的数据库的名称。 这等效于在命令行上将 dbname 指定为第一个非选项参数。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖所有冲突的命令行选项。 -h host --host=host 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线，它被用作一个 Unix 域套接字的目录。默认是从 PGHOST 环境变量中取得（如果被设置），否则将尝试一次 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。默认是放在`PGPORT`环境变量中（如果被设置），否则使用编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_dump在连接到一个数据库之前提示要求一个口令。这个选项从来不是必须的，因为如果服务器要求口令认证，pg_dump将自动提示要求一个口令。但是，pg_dump将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下，值得键入`-W`来避免额外的连接尝试。 --role=rolename 指定一个用来创建该转储的角色名。这个选项导致pg_dump在连接到数据库后发出一个 SET ROLE rolename 命令。当已认证用户（由 -U 指定）缺少pg_dump所需的特权但是能够切换到一个具有所需权利的角色时，这个选项很有用。一些安装有针对直接作为超级用户登录的策略，使用这个选项可以让转储在不违反该策略的前提下完成。 PGDATABASE PGHOST PGOPTIONS PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。. 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 pg_dump在内部执行 SELECT 语句。如果你运行pg_dump时出现问题，确定你能够从正在使用的数据库中选择信息，例如 psql。此外，libpq前端-后端库所使用的任何默认连接设置和环境变量都将适用。 pg_dump的数据库活动会被统计收集器正常地收集。如果不想这样，你可以通过 PGOPTIONS 或 ALTER USER 命令设置参数 track_counts 为假。 如果你的数据库集簇对于 template1 数据库有任何本地添加，要注意将pg_dump的输出恢复到一个真正的空数据库。否则你很可能由于以增加对象的重复定义而得到错误。要创建一个不带任何本地添加的空数据库，从 template0 而不是 template1 复制它，例如： CREATE DATABASE foo WITH TEMPLATE template0; 当一个只含数据的转储被选中并且使用了选项 --disable-triggers 时，pg_dump在开始插入数据之前会发出命令禁用用户表上的触发器，并且接着在数据被插入之后发出命令重新启用它们。如果恢复中途被停止，系统目录可能会停留在一种错误状态。 pg_dump产生的转储文件不包含优化器用来做出查询计划决定的统计信息。因此，在从一个转储文件恢复后运行 ANALYZE 来确保最优性能是明智的。 在转储逻辑复制订阅时，pg_dump将生成使用 connect = false 选项的 CREATE SUBSCRIPTION 命令，这样恢复订阅时不会建立远程连接来创建复制槽或者进行初始的表拷贝。通过这种方式，可以无需到远程服务器的网络访问就能恢复该转储。然后就需要用户以一种合适的方式重新激活订阅。如果涉及到的主机已经改变，连接信息可能也必须被改变。在开启一次新的全表拷贝之前，截断目标表也可能是合适的。 要把一个数据库 mydb 转储到一个 SQL 脚本文件： $ pg_dump mydb > db.sql 要把这样一个脚本重新载入到一个（新创建的）名为 newdb 的数据库中： $ psql -d newdb -f db.sql 要转储一个数据库到一个自定义格式归档文件： $ pg_dump -Fc mydb > db.dump 要转储一个数据库到一个目录格式的归档： $ pg_dump -Fd mydb -f dumpdir 要用 5 个并行的工作者任务转储一个数据库到一个目录格式的归档： $ pg_dump -Fd mydb -j 5 -f dumpdir 要把一个归档文件重新载入到一个（新创建的）名为 newdb 的数据库： $ pg_restore -d newdb db.dump 把一个归档文件重新装载到同一个数据库（该归档正是从这个数据库中转储得来）中，丢掉那个数据库中的当前内容： $ pg_restore -d postgres --clean --create db.dump 要转储一个名为 mytab 的表： $ pg_dump -t mytab mydb > db.sql 要转储 detroit 模式中名称以 emp 开始的所有表，排除名为 employee_log 的表： $ pg_dump -t 'detroit.emp*' -T detroit.employee_log mydb > db.sql 要转储名称以 east 或者 west 开始并且以 gsm 结束的所有模式，排除名称包含词`test`的任何模式： $ pg_dump -n 'east*gsm' -n 'west*gsm' -N '*test*' mydb > db.sql 同样，用正则表达式记号法来合并开关： $ pg_dump -n '(east|west)*gsm' -N '*test*' mydb > db.sql 要转储除了名称以 ts_ 开头的表之外的所有数据库对象： $ pg_dump -T 'ts_*' mydb > db.sql 要在 -t 和相关开关中指定一个大写形式或混合大小写形式的名称，你需要双引用该名称，否则它会被折叠到小写形式 。但是双引号对于 shell 是特殊的，所以反过来它们必须被引用。因此， 要转储一个有混合大小写名称的表，你需要类似这样的东西： $ pg_dump -t \"\\\"MixedCaseName\\\"\" mydb > mytab.sql pg_dumpall — 将一个IvorySQL数据库集簇抽取到一个脚本文件中 pg_dumpall [connection-option…​] [option…​] pg_dumpall工具可以一个集簇中所有的IvorySQL数据库写出到（“转储”）一个脚本文件。该脚本文件包含可以用作 psql 的输入SQL命令来恢复数据库。它会对集簇中的每个数据库调用 pg_dump 来完成该工作。pg_dumpall还转储对所有数据库公用的全局对象（ pg_dump 不保存这些对象），也就是说数据库角色和表空间都会被转储。 目前这包括适数据库用户和组、表空间以及适合所有数据库的访问权限等属性。 因为pg_dumpall从所有数据库中读取表，所以你很可能需要以一个数据库超级用户的身份连接以便生成完整的转储。同样，你也需要超级用户特权执行保存下来的脚本，这样才能增加角色和组以及创建数据库。 SQL 脚本将被写出到标准输出。使用 -f / --file 选项或者 shell 操作符可以把它重定向到一个文件。 pg_dumpall需要多次连接到IvorySQL服务器（每个数据库一次）。如果你使用口令认证，可能每次都会要求口令。这种情况下使用一个 ~/.pgpass 会比较方便。 下列命令行选项用于控制输出的内容和格式。 -a --data-only 只转储数据，不转储模式（数据定义）。 -c --clean 包括在重建数据库之前清除（移除）它们的 SQL 命令。角色和表空间的 DROP 命令也会被加入进来。 -E encoding --encoding=encoding 用指定的字符集编码创建转储。默认情况下，转储使用数据库的编码创建（另一种得到相同结果的方法是设置 PGCLIENTENCODING 环境变量为想要的转储编码）。 -f filename --file=filename 将输出发送到指定的文件中。如果省略，将使用标准输出。 -g --globals-only 只转储全局对象（角色和表空间），而不转储数据库。 -O --no-owner 不输出用于设置对象所有权以符合原始数据库的命令。默认情况下，pg_dumpall发出 ALTER OWNER 或 SET SESSION AUTHORIZATION 语句来设置被创建的模式元素的所有权。除非脚本是由一个超级用户（或者是拥有脚本中所有对象的同一个用户）所运行，这些语句在脚本运行时会失败。要使得一个脚本能被任意用户恢复，但又不想给予该用户所有对象的所有权，可以指定 -O。 -r --roles-only 只转储角色，不转储数据库和表空间。 -s --schema-only 只转储对象定义（模式），不转储数据。 -S username --superuser=username 指定要在禁用触发器时使用的超级用户的用户名。只有使用 --disable-triggers 时，这个选项才相关（通常，最好省去这个选项，而作为超级用户来启动结果脚本来取而代之）。 -t --tablespaces-only 只转储表空间，不转储数据库和角色。 -v --verbose 指定细节模式。这将导致pg_dumpall向标准错误输出详细的对象注释以及转储文件的开始/停止时间，还有进度消息。 重复该选项会导致在标准错误上出现附加的调试级信息。 这个选项还会被传递到pg_dump。 -V --version 打印pg_dumpall版本并退出。 -x --no-privileges --no-acl 防止转储访问特权（授予/收回命令）。 --binary-upgrade 这个选项用于就地升级功能。我们不推荐也不支持把它用于其他目的。这个选项在未来的发行中可能被改变而不做通知。 --column-inserts --attribute-inserts 将数据转储为带有显式列名的 INSERT 命令（INSERT INTO table(column, …​) VALUES …​）。这将使得恢复过程非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。 --disable-dollar-quoting 这个选项禁止在函数体中使用美元符号引用，并且强制它们使用 SQL 标准字符串语法被引用。 --disable-triggers 只有在创建一个只转储数据的转储时，这个选项才相关。它指示pg_dumpall包括在数据被重新载入时能够临时禁用目标表上的触发器的命令。如果你在表上有引用完整性检查或其他触发器，并且你在数据重新载入期间不想调用它们，请使用这个选项。当前，为 --disable-triggers 发出的命令必须作为超级用户来执行。因此，你还应当使用 -S 指定一个超级用户名，或者宁可作为一个超级用户启动结果脚本。 --exclude-database=`pattern` 不要转储名字与 pattern 匹配的数据库。可以通过编写多个 --exclude-database 开关来排除多个模式。 pattern 参数被解释为模式，根据psql的 \\d 命令使用的相同规则 ，因此，通过在模式中编写通配符也可以排除多个数据库。 使用通配符时，请谨慎的引用模式，如果需要防止shell通配符扩展。 --extra-float-digits=ndigits 在转储浮点数据时使用extra_float_digits规定的值，而不是最大可用精度。备份目的进行的常规转储不使用此选项。 --if-exists 时间条件性命令（即增加一个 IF EXISTS 子句）来清除数据库和其他对象。 只有同时指定了 --clean 时，这个选项才可用。 --inserts 将数据转储为 INSERT 命令（而不是 COPY ）。这将使得恢复非常慢，这主要用于使转储能够被载入到非IvorySQL数据库中。注意如果你已经重新安排了列序，该恢复可能会一起失败。--column-inserts 选项对于列序改变是安全的，但是会更慢。 --load-via-partition-root 在为一个分区表转储数据时，让 COPY 语句或者 INSERT 语句把包含它的分区层次的根而不是分区自身作为目标。这导致在数据被装载时，会为每一个行重新确定合适的分区。如果在一台服务器上重新装载数据时会出现行并不是总是落入到和原始服务器上相同的分区中的情况，这个选项就很有用。例如，如果分区列是文本类型并且两个系统中用于排序分区列的排序规则有着不同的定义，就会发生这种情况。 --lock-wait-timeout=timeout 在转储的开始从不等待共享表锁的获得。 而是在指定的 timeout 内不能锁定一个表时失败。 超时时长可以用 SET statement_timeout 接受的任何格式指定。 --no-comments 不转储注释。 --no-publications 不转储publication。 --no-role-passwords 不为角色转储口令。在恢复完后，角色的口令将是空口令，并且在设置口令之前口令认证都不会成功。由于指定这个选项时并不需要口令值，角色信息将从目录视图 pg_roles 而不是 pg_authid 中读出。因此，如果对 pg_authid 的访问被某条安全性策略所限制，那么这个选项也会有所帮助。 --no-security-labels 不转储安全标签。 --no-subscriptions 不转储subscription。 --no-sync 默认情况下，pg_dumpall 将等待所有文件被安全地写入到磁盘。这个选项会让 pg_dumpall 不做这种等待而返回，这样会更快，但是意味着后续的操作系统崩溃可能留下被损坏的转储。通常来说，这个选项对测试有用，但不应该在从生产安装中转储数据时使用。 --no-tablespaces 不要输出选择表空间的命令。通过这个选项，在恢复期间所有的对象都会被创建在任何作为默认的表空间中。 -−no-toast-compression 不要输出命令以设置 TOAST 压缩方法。 采用这个选项，所有列将会以默认压缩设置来恢复。 --no-unlogged-table-data 不转储非日志记录表的内容。这个选项对于表定义（模式）是否被转储没有影响，它只会限制转储表数据。 --on-conflict-do-nothing 添加 ON CONFLICT DO NOTHING 到 INSERT 命令。 除非 --inserts 或 --column-inserts 也被规定，否则此选项不生效。 --quote-all-identifiers 强制引用所有标识符。在从一个与pg_dumpall主版本不同的IvorySQL服务器转储数据库时或者要将输出载入到一个不同主版本的服务器时，推荐使用这个选项。默认情况下，pg_dumpall只会对为其主版本中保留词的标识符加上引号。在与其他版本的具有不同保留词集合的服务器交互时，这有时会导致兼容性问题。使用 --quote-all-identifiers 可以阻止这类问题，但是代价是转储脚本会更加难读。 --rows-per-insert=nrows 将数据转储为 INSERT 命令（而不是 COPY ）。控制每个 INSERT 命令的最大行数。 指定的值必须是大于零的数。重新加载期间的任何错误都将导致仅丢失有问题的 INSERT 的行，而不是整个表内容。 --use-set-session-authorization 输出 SQL-标准的 SET SESSION AUTHORIZATION 命令取代 ALTER OWNER 命令来确定对象的所有关系。这让该转储更加兼容标准，但是取决于该转储中对象的历史，该转储可能无法正常恢复。 -? --help 显示有关pg_dumpall命令行参数的帮助并退出。 下列命令行选项控制数据库连接参数。 -d connstr --dbname=connstr 指定用于连接到服务器的参数，比如 连接字符串；这些将覆盖所有冲突的命令行选项。这个选项被称为 --dbname 是为了和其他客户端应用一致，但是因为pg_dumpall需要连接多个数据库，连接字符串中的数据库名将被忽略。使用 -l 选项指定一个数据库，该数据库被用于初始连接，这将转储全局对象并且发现需要转储哪些其他数据库。 -h host --host=host 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线，它被用作一个 Unix 域套接字的目录。默认是从 PGHOST 环境变量中取得（如果被设置），否则将尝试一次 Unix 域套接字连接。 -l dbname --database=dbname 指定要连接到哪个数据库转储全局对象以及发现要转储哪些其他数据库。如果没有指定，将会使用 postgres 数据库，如果 postgres 不存在，就使用 template1。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。默认是放在 PGPORT 环境变量中（如果被设置），否则使用编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_dumpall在连接到一个数据库之前提示要求一个口令。这个选项从来不是必须的，因为如果服务器要求口令认证，pg_dumpall将自动提示要求一个口令。但是，pg_dumpall将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下，值得键入 -W 来避免额外的连接尝试。注意对每个要被转储的数据库，口令提示都会再次出现。通常，最好设置一个 ~/.pgpass 文件来减少手工口令输入。 --role=`rolename` 指定一个用来创建该转储的角色名。这个选项导致pg_dump在连接到数据库后发出一个 SET ROLE rolename 命令。当已认证用户（由 -U 指定）缺少pg_dump所需的特权但是能够切换到一个具有所需权利的角色时，这个选项很有用。一些安装有针对直接作为超级用户登录的策略，使用这个选项可以让转储在不违反该策略的前提下完成。 PGHOST PGOPTIONS PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 因为pg_dumpall在内部调用pg_dump，所以， 一些诊断消息可以参考pg_dump。 即使当用户的目的是把转储脚本恢复到一个空的集簇中，--clean 选项也有用武之地。--clean 的使用让该脚本删除并且重建内建的 postgres 和 template1 数据库，确保这两个数据库保持与源集簇中相同的属性（例如locale和编码）。如果不用这个选项，这两个数据库将保持它们现有的数据库级属性以及任何已有的内容。 一旦恢复，建议在每个数据库上运行 ANALYZE，这样优化器就可以得到有用的统计信息。你也可以运行 vacuumdb -a -z 来分析所有数据库。 不应该预期转储脚本运行到结束都不出错。特别是由于脚本将为源集簇中已有的每一个角色发出 CREATE ROLE 语句，对于bootstrap超级用户当然会得到一个“role already exists”错误，除非目标集簇用一个不同的bootstrap超级用户名完成的初始化。这种错误是无害的并且应该被忽略。--clean 选项的使用很可能会产生额外的有关于不存在对象的无害错误消息，不过可以通过加上 --if-exists 减少这类错误消息。 pg_dumpall要求所有需要的表空间目录在进行恢复之前就必须存在；否则，数据库创建就会由于在非默认位置创建数据库而失败。 要转储所有数据库： $ pg_dumpall > db.out 要从这个文件重新载入数据库，你可以使用： $ psql -f db.out postgres 这里你连接哪一个数据库并不重要，因为由pg_dumpall创建的脚本将包含合适的命令来创建和连接到被保存的数据库。一个例外是，如果指定了 --clean，则开始时必须连接到 postgres 数据库，该脚本将立即尝试删除其他数据库，并且这种动作对于已连接上的这个数据库将会失败。 pg_isready — 检查一个IvorySQL服务器的连接状态 pg_isready [connection-option…​] [option…​] -d dbname --dbname=dbname 指定要连接的数据库名。dbname 可以是连接字符串。如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -h hostname --host=hostname 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。默认值取自 PGPORT 环境变量。如果环境变量没有设置，则默认值使用编译时指定的端口（通常是 5432）。 -q --quiet 不显示状态消息。当脚本编程时有用。 -t seconds --timeout=seconds 尝试连接时，在返回服务器不响应之前等待的最大秒数。设置为 0 则禁用。默认值是 3 秒。 -U username --username=username 作为用户 username 连接数据库，而不是用默认用户。 -V --version 打印pg_isready版本并退出。 -? --help 显示有关pg_isready命令行参数的帮助并退出。 如果服务器正常接受连接，pg_isready返回 0 给 shell；如果服务器拒绝连接（例如处于启动阶段）则返回 1；如果连接尝试没有被相应则返回 2；如果没有尝试（例如由于非法参数）则返回 3。 和大部分其他IvorySQL工具相似，pg_isready 也使用libpq支持的环境变量。 环境变量 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 要获得服务器状态，不一定需要提供正确的用户名、口令或数据库名。 不过，如果提供了不正确的值，服务器将会记录一次失败的连接尝试。 标准用法： $ pg_isready /tmp:5432 - accepting connections $ echo $? 0 使用连接参数运行连接到处于启动中的IvorySQL集簇： $ pg_isready -h localhost -p 5433 localhost:5433 - rejecting connections $ echo $? 1 使用连接参数运行连接到无响应的IvorySQL集簇： $ pg_isready -h someremotehost someremotehost:5432 - no response $ echo $? 2 pg_receivewal — 以流的方式从一个IvorySQL服务器得到预写式日志 pg_receivewal [option…​] -D directory --directory=directory 要把输出写到哪个目录。这个参数是必需的。 -E lsn --endpos=lsn 当接收到达指定的LSN时，自动停止复制并且以正常退出状态0退出。如果有一个记录的LSN正好等于 lsn，则该记录将会被处理。 --if-not-exists 当指定 --create-slot 并且具有指定名称的槽已经存在时不要抛出错误。 -n --no-loop 不要在连接错误上循环。相反，碰到一个错误时立刻退出。 --no-sync 这个选项导致 pg_receivewal 不强制WAL数据被刷回磁盘。这样会更快，但是也意味着接下来的操作系统崩溃会让WAL段损坏。通常，这个选项对于测试有用，但不应该在对生产部署进行WAL归档时使用。这个选项与 --synchronous 不兼容。 -s interval --status-interval=interval 指定发送回服务器的状态包之间的秒数。这允许我们更容易地监控服务器的进度。 一个零值完全禁用这种周期性的状态更新，不过当服务器需要时还是会有一个更新 会被发送来避免超时导致的断开连接。默认值是 10 秒。 -S slotname --slot=slotname 要求pg_receivewal使用一个已有的复制槽。在使用这个选项时， pg_receivewal将会报告给服务器一个刷写位置，指示每一个 段是何时被同步到磁盘的，这样服务器可以在不需要该段时移除它。当pg_receivewal的复制客户端在服务器 上被配置为一个同步后备时，那么使用一个复制槽将会向服务器报告刷写 位置，但只在一个 WAL 文件被关闭时报告。因此，该配置将导致主服务 器上的事务等待很长的时间并且无法令人满意地工作。要让这种配置工作 正确，还必须制定选项 --synchronous （见下文）。 --synchronous 在 WAL 数据被收到后立即刷入到磁盘。还要在刷写后立即向服务器回送 一个状态包（不考虑 --status-interval ）。如果pg_receivewal的复制客户端在服务器 上被配置为一个同步后备，应该指定这个选项来确保向服务器发送及时的反馈。 -v --verbose 启用冗长模式。 -Z level --compress=level 启用预写式日志上的gzip压缩，并且指定压缩级别（0到9，0是不压缩而9是最大压缩）。所有的文件名后都将被追加后缀 .gz。 下列命令行选项控制数据库连接参数。 -d connstr --dbname=connstr 指定用于连接到服务器的参数，作为 连接字符串；这些将覆盖所有冲突的命令行选项。为了和其他客户端应用一致，该选项被称为 --dbname。但是因为pg_receivewal并不连接到集簇中的任何特定数据库，连接字符串中的数据库名将被忽略。 -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。默认值取自 PGHOST 环境变量（如果设置），否则会尝试一个 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。默认用 PGPORT 环境变量中的值（如果设置），或者一个编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_receivewal在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，pg_receivewal将自动提示要求一个口令。但是，pg_receivewal将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 为了控制物理复制槽，pg_receivewal 可以执行下列两种动作之一： --create-slot 用 --slot 中指定的名称创建一个新的物理复制槽， 然后退出。 --drop-slot 删除 --slot 中指定的复制槽，然后退出。 其他选项也可用： -V --version 打印pg_receivewal版本并退出。 -? --help 显示有关pg_receivewal命令行参数的帮助并退出。 在被SIGINT信号终止（没有正常的方式结束它。因此这不是一种错误）时，pg_receivewal将以状态0退出。 对于致命错误或者其他信号，退出状态将不是零。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 环境变量 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 在使用pg_receivewal替代 archive_command 作为主要的 WAL 备份方法时， 强烈建议使用复制槽。否则，服务器可能会在预写式日志文件被备份好之前重用 或者移除它们，因为没有任何信息（不管是来自 archive_command 或是复制槽）能够指示 WAL 流已 经被归档到什么程度。不过要注意，如果接收者没有持续地取走 WAL 数据， 一个复制槽将会填满服务器的磁盘空间。 如果在源集簇上启用了组权限，pg_receivewal将保留接收到的WAL文件上的组权限。 要从位于 mydbserver 的服务器流式传送预写式日志并且将它存储在本地目录`/usr/local/pgsql/archive`： $ pg_receivewal -h mydbserver -D /usr/local/pgsql/archive pg_recvlogical — 控制 IvorySQL 逻辑解码流 pg_recvlogical [option…​] 必须至少要指定下列选项之一来选择一个动作： --create-slot 为 --dbname 指定的数据库用 --slot 指定的名称创建一个新的逻辑复制槽，使用 --plugin 指定的输出插件。 --drop-slot 删除名称由 --slot 指定的复制槽，然后退出。 --start 从 --slot 指定的逻辑复制槽开始进行流式传送更改，一直继续 到被一个信号终止。如果服务器端关机或者断开连接导致更改流结束，会进入一个 循环一直重试，通过指定 --no-loop 可以防止这种情况下进入循环重试。流格式由槽创建时指定的输出插件决定。连接必须是连接到用于创建该槽的同一个数据库上。 --create-slot 和 --start 可以被一起指定。 --drop-slot 不能和另一个动作组合在一起。 下面的命令行选项控制输出的位置和格式以及其他复制行为： -E `lsn` --endpos=`lsn` 在 --start 模式中，当接收过程到达指定的LSN时会自动地停止复制并且以正常的退出状态0退出。如果不处于 --start 模式时指定这个选项，则会发生错误。如果有一个记录的LSN正好等于 lsn，则该记录将被输出。--endpos 不会察觉到事务边界并且可能会在一个事务中间截断输出。任何部分输出的事务都将不会被消费，并且在下一次从该槽中读取时将会重放该事务。单个的消息不会被截断。 -f filename --file=filename 把接收到并且解码好的事务数据写入到一个文件。使用 - 可以写到stdout。 -F interval_seconds --fsync-interval=interval_seconds 指定pg_recvlogical发出 fsync() 调用确保输出文件被安全地刷到磁盘的频度。服务器将会偶尔要求客户端执行一次刷写并且把刷写位置报告给服务器。 这个设置可以在此之外更加频繁地执行刷写。指定间隔为 0 会完全禁止发出 fsync() 调用，但是仍会报告进度给服务器。在这种情况下，发生崩溃会导致数据丢失。 -I lsn --startpos=lsn 在 --start 模式中，从给定的 LSN 开始复制。 --if-not-exists 当指定 --create-slot 并且具有指定名称的槽已经存在时不要抛出错误。 -n --no-loop 当服务器连接丢失时，不要在循环中重试，直接退出。 -o name [=value] --option=name[=value] 如果指定了输出插件，把选项值 value 传递给选项 name。存在哪些选项以及它们的效果 取决于使用的输出插件。 -P plugin --plugin=plugi 在创建一个槽时使用指定的逻辑解码输出插件。如果该槽已经存在，这个选项没有效果。 -s interval_seconds --status-interval=interval_seconds 这个选项和 pg_receivewal 中的同名选项具有相同的效果。请参考那里的描述。 -S slot_name --slot=slot_name 在 --start 模式中，使用名为 slot_name 的已有逻辑复制槽。在 --create-slot 模式中，使用这个名称 创建该槽。在 --drop-slot 模式中，删除这个名称指定的槽。 -v --verbose 开启详细输出模式。 下列命令行选项控制数据库连接参数。 -d database --dbname=database 要连接的数据库。这个选项的详细含义请见动作的描述。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。默认为用户名。 -h hostname-or-ip --host=hostname-or-ip 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线， 它被用作一个 Unix 域套接字的目录。默认是从 `PGHOST`环境变量中取得（如果被设置）， 否则将尝试一次 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。 默认是放在 PGPORT 环境变量中（如果被设置）， 否则使用编译在程序中的默认值。 -U user --username=user 要作为哪个用户连接。默认是用当前操作系统用户名。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有 其他方式提供口令（例如一个 .pgpass 文件）， 那么连接尝试将失败。这个选项对于批处理任务和脚本有用， 因为在其中没有一个用户来输入口令。 -W --password 强制pg_dump在连接到一个数据库之前提示要求一个口令。这个选项不是必须的，因为如果服务器要求口令认证， pg_dump将自动提示要求一个口令。 但是，pg_dump将浪费一次连接尝试 来发现服务器想要一个口令。在某些情况下，值得键入 -W 来避免额外的连接尝试。 还有下列附加选项可用： -V --version 打印pg_recvlogical的版本并且退出。 -? --help 显示关于pg_recvlogical命令行参数的帮助，并且退出。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 环境变量`PG_COLOR`规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 如果在源服务器上启用了组权限，pg_recvlogical将会在接收到的WAL文件上保留组权限。 pg_restore — 从一个由pg_dump创建的归档文件恢复一个IvorySQL数据库 pg_restore [connection-option…​] [option…​] [filename] pg_restore接受下列命令行参数。 filename 指定要被恢复的归档文件（对于一个目录格式的归档则是目录）的位置。如果没有指定，则使用标准输入。 -a --data-only 只恢复数据，不恢复模式（数据定义）。如果在归档中存在，表数据、大对象和序列值会被恢复。这个选项类似于指定 --section=data，但是由于历史原因两者不完全相同。 -c --clean 在重新创建数据库对象之前清除（丢弃）它们（除非使用了 --if-exists，如果有对象在目标数据库中不存在，这可能会生成一些无害的错误消息）。 -C --create 在恢复一个数据库之前县创建它。如果还指定了 --clean，在连接到目标数据库之前丢弃并且重建它。如果使用 --create，pg_restore还会恢复数据库的注释（如果有）以及与其相关的配置变量设置，也就是任何提到过这个数据库的 ALTER DATABASE …​ SET …​ 和 ALTER ROLE …​ IN DATABASE …​ SET …​ 命令。不管是否指定 --no-acl，数据库本身的访问特权都会被恢复。在使用这个选项时，-d 提到的数据库只被用于发出初始的 DROP DATABASE 和 CREATE DATABASE 命令。所有要恢复到该数据库名中的数据都出现在归档中。 -d dbname --dbname=dbname 连接到数据库 dbname 并且直接恢复到该数据库中。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -e --exit-on-error 在发送 SQL 命令到该数据库期间如果碰到一个错误就退出。默认行为是继续并且在恢复结束时显示一个错误计数。 -f filename --file=filename 为生成的脚本指定输出文件，或在与 -l 选项一起使用时为列表指定输出文件。为 stdout用 -。 -F format --format=format 指定归档的格式。并不一定要指定该格式，因为pg_restore将会自动决定格式。如果指定，可以是下列之一：c custom 归档是pg_dump的自定义格式。d directory 归档是一个目录归档。t tar 归档是一个 tar 归档。 -I index --index=index 只恢复提及的索引的定义。可以通过写多个 -I 开关指定多个索引。 -j number-of-jobs --jobs=number-of-jobs 使用并发任务运行pg_restore中最耗时的步骤 — 载入数据、创建索引或者创建约束； 同时，最多使用 number-of-jobs 个并发会话。对于一个运行在多处理器机器上的服务器， 这个选项能够大幅度减少恢复一个大型数据库的时间。当发出脚本而不是直接连接到数据库服务器时，将忽略此选项。每一个任务是一个进程或者一个线程，这取决于操作系统，它们都使用一个独立的服务器连接。这个选项的最佳值取决于服务器、客户端以及网络的硬件设置。因素包括 CPU 的核心数和磁盘设置。一个好的建议是服务器上 CPU 的核心数，但是更大的值在很多情况下也能导致更快的恢复时间。当然，过高的值会由于超负荷反而导致性能降低。这个选项只支持自定义和目录归档格式。输入必须是一个常规文件或目录（例如，不能是一个管道或者标准输出）。还有，多任务不能和选项 --single-transaction 一起用。 -l --list 列出归档的内容的表格。这个操作的输出能被用作 -L 选项的输入。注意如果把 -n 或 -t 这样的过滤开关与 -l 一起使用，它们将会限制列出的项。 -L list-file --use-list=list-file 只恢复在 list-file 中列出的归档元素，并且按照它们出现在该文件中的顺序进行恢复。注意如果把 -n 或 -t 这样的过滤开关与 -L 一起使用，它们将会进一步限制要恢复的项。list-file 通常是编辑一个 -l 操作的输出来创建。行可以被移动或者移除，并且也可以通过在行首放一个（ ; ）将其注释掉。例子见下文。 -n schema --schema=schema 只恢复在被提及的模式中的对象。可以用多个 -n 开关来指定多个模式。这可以与`-t`选项组合在一起只恢复一个指定的表。 -N schema --exclude-schema=schema 不恢复所提及方案中的对象。可以用多个 -N 开关指定多个要被排除的方案。如果对同一个方案名称同时给出了 -n 和 -N，则 -N 会胜出并且该方案会被排除。 -O --no-owner 不要输出将对象的所有权设置为与原始数据库匹配的命令。默认情况下，pg_restore会发出 ALTER OWNER 或者 SET SESSION AUTHORIZATION 语句来设置已创建的模式对象的所有权。除非到该数据库的初始连接是一个超级用户（或者拥有脚本中所有对象的同一个用户）建立的，这些语句将会失败。通过 -O，任何用户名都可以被用于初始连接，并且这个用户将会拥有所有被创建的对象。 -P function-name(argtype [, …​]) --function=function-name(argtype [, …​]) 只恢复被提及的函数。要小心地拼写函数的名称和参数使它们正好就是出现在转储文件的内容表中的名称和参数。可以使用多个 -P 开关指定多个函数。 -R --no-reconnect 这个选项已被废弃，但是出于向后兼容性的目的，系统仍然还接受它。 -s --schema-only 只恢复归档中的模式（数据定义）不恢复数据。这个选项是 --data-only 的逆选项。它与指定 --section=pre-data --section=post-data 相似，但是由于历史原因并不完全相同。（不要把这个选项和 --schema 选项弄混，后者把词“schema”用于一种不同的含义）。 -S username --superuser=username 指定在禁用触发器时要用的超级用户名。只有使用 --disable-triggers 时这个选项才相关。 -t table --table=table 只恢复所提及的表的定义和数据。出于这个目的，“table”包括视图、物化视图、序列和外部表。可以写上多个 -t 开关可以选择多个表。这个选项可以和 -n 选项结合在一起指定一个特定模式中的表。注意在指定 -t 时，pg_restore不会尝试恢复所选表可能依赖的任何其他数据库对象。因此，无法确保能成功地把一个特定表恢复到一个干净的数据库中。注意这个标志的行为和pg_dump的 -t 标志不一样。在pg_restore中当前没有任何通配符匹配的规定，也不能在其 -t 选项中包括模式的名称。而且，虽然pg_dump的 -t 标志也会转储选中表的附属对象（例如索引），但是pg_restore的 -t 标志不包括这些附属对象。 -T trigger --trigger=trigger 只恢复所提及的触发器。可以用多个 -T 开关指定多个触发器。 -v --verbose 指定冗长模式。 这将导致pg_restore输出详细的对象注释和启动/停止时间到输出文件，并将推送消息输出到标准错误。 重复该选项会造成附加的调试级消息出现在标准报错上。 -V --version 打印该pg_restore的版本并退出。 -x --no-privileges --no-acl 阻止恢复访问特权（授予/收回命令）。 -1 --single-transaction 将恢复作为单一事务执行（即把发出的命令包裹在 BEGIN / COMMIT 中）。这可以确保要么所有命令完全成功，要么任何改变都不被应用。这个选项隐含了 --exit-on-error。 --disable-triggers 只有在执行一个只恢复数据的恢复时，这个选项才相关。它指示pg_restore在装载数据时执行命令临时禁用目标表上的触发器。如果你在表上有参照完整性检查或者其他触发器并且你不希望在数据载入期间调用它们时，请使用这个选项。目前，为 --disable-triggers 发出的命令必须以超级用户身份完成。因此你还应该用`-S`指定一个超级用户名，或者更好的方法是以一个IvorySQL超级用户运行pg_restore。 --enable-row-security 只有在恢复具有行安全性的表的内容时，这个选项才相关。默认情况下，pg_restore将把 row_security 设置为 off 来确保所有数据都被恢复到表中。如果用户不拥有足够绕过行安全性的特权，那么会抛出一个错误。这个参数指示pg_restore把 row_security 设置为on允许用户尝试恢复启用了行安全性的表的内容。如果用户没有从转储向表中插入行的权限，这仍将失败。注意当前这个选项还要求转储处于 INSERT 格式，因为 COPY FROM 不支持行安全性。 --if-exists 使用条件命令（即增加一个 IF EXISTS 子句）删除数据库对象。只有指定了 --clean 时，这个选项才有效。 --no-comments 即便归档中包含注释也不输出恢复注释的命令。 --no-data-for-failed-tables 默认情况下，即便表的创建命令失败（例如因为表已经存在），表数据也会被恢复。通过这个选项，对这类表的数据会被跳过。如果目标数据库已经包含了想要的表内容，这种行为又很有有用。例如，IvorySQL扩展（如PostGIS）的辅助表可能已经被载入到目标数据库中，指定这个选项就能阻止把重复的或者废弃的数据载入到这些表中。只有当直接恢复到一个数据库中时这个选项才有效，在产生 SQL脚本输出时这个选项不会产生效果。 --no-publications 即便归档中包含publication也不输出恢复publication的命令。 --no-security-labels 不要输出恢复安全标签的命令，即使归档中包含安全标签。 --no-subscriptions 即便归档中包含subscription也不输出恢复subscription的命令。 --no-tablespaces 不输出命令选择表空间。通过这个选项，所有的对象都会被创建在恢复时的默认表空间中。 --section=`sectionname` 只恢复提及的小节。小节的名称可以是 pre-data、data 或者 post-data。可以把这个选项指定多次来选择多个小节。默认值是恢复所有小节。数据小节包含实际的表数据以及大对象定义。Post-data 项由索引定义、触发器、规则和除已验证的检查约束之外的约束构成。Pre-data 项由所有其他数据定义项构成。 --strict-names 要求每一个模式（ -n / --schema）以及表（ -t / --table）限定词匹配备份文件中至少一个模式/表。 --use-set-session-authorization 输出 SQL 标准的 SET SESSION AUTHORIZATION 命令取代 ALTER OWNER 命令来决定对象拥有权。这会让转储更加兼容标准，但是依赖于转储中对象的历史，可能无法正确恢复。 -? --help 显示有关pg_restore命令行参数的帮助，并且退出。 pg_restore也接受下列用于连接参数的命令行参数： -h host --host=host 指定服务器正在运行的机器的主机名。如果该值开始于一个斜线，它被用作一个 Unix 域套接字的目录。默认是从 PGHOST 环境变量中取得（如果被设置），否则将尝试一次 Unix 域套接字连接。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展名。默认是放在 PGPORT 环境变量中（如果被设置），否则使用编译在程序中的默认值。 -U username --username=username 要作为哪个用户连接。 -w --no-password 不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那么连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制pg_restore在连接到一个数据库之前提示要求一个口令。这个选项不是必须的，因为如果服务器要求口令认证，pg_restore将自动提示要求一个口令。但是，pg_restore将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下，值得键入 -W 来避免额外的连接尝试。 --role=`rolename` 指定一个用来创建该转储的角色名。这个选项导致pg_restore在连接到数据库后发出一个 SET ROLE rolename 命令。当已认证用户（由 -U 指定）缺少pg_restore所需的特权但是能够切换到一个具有所需权利的角色时，这个选项很有用。一些安装有针对直接作为超级用户登录的策略，使用这个选项可以让转储在不违反该策略的前提下完成。 PGHOST PGOPTIONS PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 当使用 -d 选项指定一个直接数据库连接时，pg_restore在内部执行 SELECT 语句。如果你运行pg_restore时出现问题，确定你能够从正在使用的数据库中选择信息，例如 psql。此外，libpq前端-后端库所使用的任何默认连接设置和环境变量都将适用。 如果你的数据库集簇对于 template1 数据库有任何本地添加，要注意将pg_restore的输出载入到一个真正的空数据库。否则你很可能由于以增加对象的重复定义而得到错误。要创建一个不带任何本地添加的空数据库，从 template0 而不是 template1 复制它，例如： CREATE DATABASE foo WITH TEMPLATE template0; 下面将详细介绍pg_restore的局限性。 在恢复数据到一个已经存在的表中并且使用了选项 --disable-triggers 时，pg_restore会在插入数据之前发出命令禁用用户表上的触发器，然后在完成数据插入后重新启用它们。如果恢复在中途停止，可能会让系统目录处于错误的状态。 pg_restore不能有选择地恢复大对象，例如只恢复特定表的大对象。如果一个归档包含大对象，那么所有的大对象都会被恢复，如果通过 -L、 -t 或者其他选项进行了排除，它们一个也不会被恢复。 一旦完成恢复，应该在每一个被恢复的表上运行 ANALYZE，这样优化器能得到有用的统计信息。 假设我们已经以自定义格式转储了一个叫做 mydb 的数据库： $ pg_dump -Fc mydb > db.dump 要删除该数据库并且从转储中重新创建它： $ dropdb mydb $ pg_restore -C -d postgres db.dump -d 开关中提到的数据库可以是任何已经存在于集簇中的数据库，pg_restore只会用它来为 mydb 发出 CREATE DATABASE 命令。通过 -C，数据总是会被恢复到出现在归档文件的数据库名中。 要把转储重新载入到一个名为 newdb 的新数据库中： $ createdb -T template0 newdb $ pg_restore -d newdb db.dump 注意我们不使用 -C，而是直接连接到要恢复到其中的数据库。还要注意我们是从 template0 而不是 template1 创建了该数据库，以保证它最初是空的。 要对数据库项重排序，首先需要转储归档的表内容： $ pg_restore -l db.dump > db.list 列表文件由一个头部和一些行组成，这些行每一个都用于一个项，例如： ; ; Archive created at Mon Sep 14 13:55:39 2009 ; dbname: DBDEMOS ; TOC Entries: 81 ; Compression: 9 ; Dump Version: 1.10-0 ; Format: CUSTOM ; Integer: 4 bytes ; Offset: 8 bytes ; Dumped from database version: 8.3.5 ; Dumped by pg_dump version: 8.3.8 ; ; ; Selected TOC Entries: ; 3; 2615 2200 SCHEMA - public pasha 1861; 0 0 COMMENT - SCHEMA public pasha 1862; 0 0 ACL - public pasha 317; 1247 17715 TYPE public composite pasha 319; 1247 25899 DOMAIN public domain0 pasha 分号表示开始一段注释，行首的数字表明了分配给每个项的内部归档 ID。 文件中的行可以被注释掉、删除以及重排序。例如： 10; 145433 TABLE map_resolutions postgres ;2; 145344 TABLE species postgres ;4; 145359 TABLE nt_header postgres 6; 145402 TABLE species_records postgres ;8; 145416 TABLE ss_old postgres 把这样一个文件作为pg_restore的输入将会只恢复项 10 和 6，并且先恢复 10 再恢复 6。 $ pg_restore -L db.list db.dump pg_verifybackup — 验证IvorySQL集群的基础备份的完整性 pg_verifybackup [option…​] pg_verifybackup 接受以下命令行参数： -e --exit-on-error 检测到备份问题后立即退出。 如果没有指定这个选项，pg_verifybackup 将在检测到问题后继续检查备份，并将检测到的所有问题报告为错误。 -i path --ignore=path 在将备份中实际存在的数据文件列表与 backup_manifest 文件中列出的数据文件列表进行比较时，忽略指定的文件或目录，该文件或目录应表示为相对路径名。如果指定了目录，则此选项会影响以该位置为根的整个子树。 如果相对路径名与指定的路径名匹配，有关额外文件、丢失文件、文件大小差异或校验和不匹配的投诉将被抑制。 可以多次指定此选项。 -m path --manifest-path=path 使用指定路径的清单文件，而不是位于备份目录根目录中的清单文件。 -n --no-parse-wal 不要试图解析从该备份恢复所需的预写式日志数据。 -q --quiet 成功验证备份后不要打印任何内容。 -s --skip-checksums 不要验证数据文件校验和。但仍检查是否存在文件以及这些文件的大小。这样将会快得多，因为文件本身不需要读取。 -w path --wal-directory=path 尝试解析存储在指定目录中的 WAL 文件，而不是 pg_wal。 如果备份存储在与WAL存档不同的位置，则这可能很有用。 其他选项也可用： -V --version 打印 pg_verifybackup 版本并退出。 -? --help 显示有关pg_verifybackup命令行参数的帮助，然后退出。 要在 mydbserver 上创建服务器的基本备份并验证备份的完整性： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ pg_verifybackup /usr/local/pgsql/data 要在 mydbserver 上创建服务器的基本备份，请将清单移动到备份目录之外的某个位置，并验证备份： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/backup1234 $ mv /usr/local/pgsql/backup1234/backup_manifest /my/secure/location/backup_manifest.1234 $ pg_verifybackup -m /my/secure/location/backup_manifest.1234 /usr/local/pgsql/backup1234 要在忽略手动添加到备份目录的文件的同时验证备份，并跳过校验和验证： $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ edit /usr/local/pgsql/data/note.to.self $ pg_verifybackup --ignore=note.to.self --skip-checksums /usr/local/pgsql/data psql — IvorySQL的交互式终端 psql [option…​] [dbname [username]] -a --echo-all 把所有非空输入行按照它们被读入的形式打印到标准输出（不适用于交互式行读取）。这等效于把变量 ECHO 设置为 all。 -A --no-align 切换到非对齐输出模式（默认输出模式是 对齐 的）。这等效于 \\pset format unaligned。 -b --echo-errors 把失败的 SQL 命令打印到标准错误输出。这等效于把变量 ECHO 设置为 errors。 -c command --command=command 指定psql执行一个给定的命令字符串 command。这个选项可以重复多次并且以任何顺序与 -f 选项组合在一起。当 -c 或者 -f 被指定时，psql不会从标准输入读取命令，直到它处理完序列中所有的 -c 和 -f 选项之后终止。command 必须是一个服务器完全可解析的命令字符串（即不包含psql相关的特性）或者单个反斜线命令。因此不能在一个 -c 选项中混合SQL和psql元命令。要那样做，可以使用多个 -c 选项或者把字符串用管道输送到psql中，例如： psql -c '\\x' -c 'SELECT * FROM foo;' ` 或者 `echo '\\x \\\\ SELECT * FROM foo;' | psql ` （ `\\\\ 是分隔符元命令）。每一个被传递给 -c 的SQL命令字符串会被当做一个单独的请求发送给服务器。因此，即便该字符串包括多个SQL命令，服务器也会把它当做一个事务来执行，除非在该字符串中有显式的 BEGIN / COMMIT 命令把它划分成多个事务。此外，psql只会打印出该字符串中最后一个SQL命令的结果。这和从文件中读取同一字符串或者把同一字符串传给psql的标准输出时的行为不同，因为那两种情况下psql会独立地发送每一个SQL命令。由于这种行为，把多于一个SQL命令放在 -c 字符串中通常会得到意料之外的结果。最好使用多个 -c 命令或者把多个命令输送给psql的标准输入，按照上文所说的使用echo或者通过一个shell，例如： `psql <<EOF \\x SELECT * FROM foo; EOF ` --csv 切换到CSV（逗号分隔值）输出模式。 这相当于 \\pset format csv。 -d dbname --dbname=dbname 指定要连接的数据库的名称。这等效于指定 dbname 为命令行上的第一个非选项参数。dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -e --echo-queries 也把发送到服务器的所有 SQL 命令复制到标准输出。这等效于把变量 ECHO 设置为 queries。 -E --echo-hidden 回显 \\d 以及其他反斜线命令生成的实际查询。可以用它来学习psql的内部操作。这等效于把变量 ECHO_HIDDEN 设置为 on。 -f filename --file=filename 从文件 filename 而不是标准输入中读取命令。这个选项可以被重复多次，也可以以任意顺序与 -c 选项组合。当 -c 或者 -f 被指定时，psql不会从标准输入读取命令，直到它处理完序列中所有的 -c 和 -f 选项之后终止。除此以外，这个选项很大程度上等价于元命令 \\i。如果 filename 是 - （连字符），那么会读取标准输入直到遇见一个 EOF 指示或者 \\q 元命令。这种方式可以用把自多个文件的输入组合成一种交互式输入。不过注意在这种情况下不会使用 Readline（很像指定了 -n 的情况）。使用这个选项与 psql < filename 有细微的不同。通常，两种形式都可以做到我们所期望的，但是使用 -f 启用了一些好的特性，例如带有行号的错误消息。使用这个选项还有一丝机会可以降低启动开销。在另一方面，使用shell输入重定向的变体（理论上）保证会得到与手工输入时相同的输出。 -F separator --field-separator=separator 使用 separator 作为非对齐输出的域分隔符。这等效于 \\pset fieldsep 或者 \\f。 -h hostname --host=hostname 指定运行服务器的机器的主机名。如果这个值由一个斜线开始，它会被用作 Unix 域套接字的目录。 -H --html 切换到HTML输出模式。这等效于 \\pset format html 或者 \\H 命令。 -l --list 列出所有可用的数据库，然后退出。其他非连接选项会被忽略。这与元命令 \\list 类似。在使用这个选项时，psql将连接到数据库 postgres，除非在命令行上提及一个不同的数据（选项 -d 或非选项参数，可能是通过一个服务项，但不能通过一个环境变量）。 -L filename --log-file=filename 除了把所有查询输出写到普通输出目标之外，还写到文件 filename 中。 -n --no-readline 不使用Readline做行编辑并且不使用命令历史。在剪切和粘贴时，关掉 Tab 展开会有所帮助。 -o `filename` --output=`filename` 把所有查询输出放到文件 filename 中。这等效于命令 \\o。 -p port --port=port 指定服务器用于监听连接的 TCP 端口或者本地 Unix 域套接字文件扩展。默认是 PGPORT 环境变量的值，如果没有设置，则默认为编译时指定的端口号（通常是5432）。 -P assignment --pset=assignment 以 \\pset 的形式指定打印选项。注意，这里你必须用一个等号而不是空格来分隔名称和值。例如，要设置输出格式为LaTeX，应该写成 -P format=latex。 -q --quiet 指定psql应该安静地工作。默认情况下，它会打印出欢迎消息以及多种输出。如果使用了这个选项，以上那些就都不会输出。在使用 -c 选项时，配合这个选项很有用。这等效于设置变量 QUIET 为 on。 -R separator --record-separator=separator 把 separator 用作非对齐输出的记录分隔符。这等效于 \\pset recordsep 命令。 -s --single-step 运行在单步模式中。这意味着在每个命令被发送给服务器之前都会提示用户一个可以取消执行的选项。使用这个选项可以调试脚本。 -S --single-line 运行在单行模式中，其中新行会终止一个 SQL 命令，就像分号的作用一样。注意这种模式被提供给那些坚持使用它的用户，但是并不一定要使用它。特别地，如果在一行中混合了SQL和元命令，那对于没有经的用户来说，它们的执行顺序可能不总是那么清晰。 -t --tuples-only 关闭打印列名和结果行计数页脚等。这等效于 \\t 或者 \\pset tuples_only 命令。 -T table_options --table-attr=table_options 指定要替换HTML table 标签的选项。详见 \\pset tableattr。 -U username --username=username 作为用户 username 而不是默认用户连接到数据库（当然，你必须具有这样做的权限）。 -v assignment --set=assignment --variable=assignment 执行一次变量赋值，和 \\set 元命令相似。注意你必须在命令行上用等号分隔名字和值（如果有）。要重置一个变量，去掉等号就行。要把一个变量置为空值，使用等号但是去掉值。这些赋值在命令行处理期间被完成，因此反映连接状态的变量将在稍后被覆盖。 -V --version 打印psql版本并且退出。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且口令不能从其他来源（例如一个 .pgpass 文件）获得，那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。注意这个选项将对整个会话保持设置，并且因此它会影响元命令 \\connect 的使用，就像初始的连接尝试那样。 -W --password 强制psql在连接到一个数据库之前提示要求一个口令，即使口令不会被使用。如果服务器需要口令认证并且口令不能从其他来源获得，例如 .pgpass 文件，psql 在任何情况下都会提示输入口令。 然而，psql 将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。注意这个选项将对整个会话保持设置，并且因此它会影响元命令 \\connect 的使用，就像初始的连接尝试那样。 -x --expanded 打开扩展表格式模式。这等效于 \\x 或者 \\pset expanded 命令。 -X, --no-psqlrc 不读取启动文件（要么是系统范围的 psqlrc 文件，要么是用户的 ~/.psqlrc 文件）。 -z --field-separator-zero 设置非对齐输出的域分隔符为零字节。这等效于 \\pset fieldsep_zero。 -0 --record-separator-zero 设置非对齐输出的记录分隔符为零字节。例如，这对与 xargs -0 配合有关。这等效于 \\pset recordsep_zero。 -1 --single-transaction 这个选项只能被用于与一个或者多个 -c 以及/或者 -f 选项组合。它会让psql在第一个上述选项之前发出一条 BEGIN 命令并且在最后一个上述选项之后发出一条 COMMIT 命令，这样就把所有的命令都包裹在一个事务中。这个选项可以保证要么所有的命令都成功地完成，要么不应用任何更改。如果命令本身包含 BEGIN 、 COMMIT 或者 ROLLBACK，这个选项将不会得到想要的效果。还有，如果当个命令不能在一个事务块中执行，指定这个选项将导致整个事务失败。 -? --help[=`topic]` 显示有关psql的帮助并且退出。可选的 topic 参数（默认为 options ）选择要解释哪一部分的psql： commands 描述psql的反斜线命令； options 描述可以被传递给psql的命令行选项；而 variables 则显示有关psql配置变量的帮助。 如果psql正常完成，它会向 shell 返回 0。如果它自身发生一个致命错误（例如内存用完、找不到文件），它会返回 1。如果到服务器的连接出问题并且事务不是交互式的，它会返回 2。如果在脚本中发生错误，它会返回 3 并且变量 ON_ERROR_STOP 会被设置。 COLUMNS 如果 \\pset columns 为零，这个环境变量控制用于 wrapped 格式的宽度以及用来确定是否输出需要用到分页器或者切换到扩展自动模式中的垂直格式的宽度。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 PSQL_EDITOR EDITOR VISUAL \\e、 \\ef 以及 \\ev 命令所使用的编辑器。会按照列出的顺序检查这些变量，第一个被设置的将被使用。如果都没有被设置，默认是使用Unix系统上的 vi 或者Windows系统上的 notepad.exe。 PSQL_EDITOR_LINENUMBER_ARG 当 \\e、 \\ef 或者 \\ev 带有一个行号参数时，这个变量指定用于传递起始行号给用户编辑器的命令行参数。对于Emacs或者vi之类的编辑器，这个变量是一个加号。如果需要在选项名称和行号之间有空格，可以在该变量的值中包括一个结尾的空格。例如： PSQL_EDITOR_LINENUMBER_ARG='' PSQL_EDITOR_LINENUMBER_ARG='--line ' ` 在 Unix 系统上默认是 ` （对应于默认编辑器 vi ，且对很多其他常见编辑器可用）。在 Windows 系统上没有默认值。 PSQL_HISTORY 命令历史文件的替代位置。波浪线（ ~ ）扩展会被执行。 PSQL_PAGER PAGER 如果一个查询的结果在屏幕上放不下，它们会通过这个命令分页显示。典型的值是 more 或 less。通过把 PSQL_PAGER 或 PAGER 设置为空字符串可以禁用分页器的使用，调整 \\pset 命令与分页器相关的选项也能达到同样的效果。会按照列出的顺序检查这些变量，第一个被设置的将被使用。如果都没有被设置，则大部分平台上默认使用 more，但在Cygwin上使用 less。 PSQLRC 用户的 .psqlrc 文件的替代位置。波浪线（ ~ ）扩展会被执行。 SHELL 被 \\! 命令执行的命令。 TMPDIR 存储临时文件的目录。默认是 /tmp 。 和大部分其他IvorySQL工具一样，这个工具也使用libpq所支持的环境变量。 psqlrc and ~/.psqlrc 如果没有 -X 选项，在连接到数据库后但在接收正常的命令之前，psql会尝试依次从系统级的启动文件（ psqlrc ）和用户的个人启动文件（ ~/.psqlrc ）中读取并且执行命令。这些文件可以被用来设置客户端或者服务器，通常是一些 \\set 和 SET 命令。系统级的启动文件是 psqlrc，它应该在安装好的IvorySQL的“系统配置”目录中，最可靠的定位方法是运行 pg_config --sysconfdir 。默认情况下，这个目录将是 ../etc/ （相对于包含IvorySQL可执行文件的目录）。可以通过 PGSYSCONFDIR 环境变量显式地设置这个目录的名称。用户个人的启动文件是 .psqlrc，它应该在调用用户的主目录中。在 Windows 上，由于没有用户主目录的概念，个人的启动文件是 %APPDATA%\\postgresql\\psqlrc.conf 。用户启动文件的位置可以通过 PSQLRC 环境变量设置。系统级和用户个人的启动文件都可以弄成是针对特定psql版本的，方法是在文件名后面加上一个横线以及IvorySQL的主、次版本号，版本最为匹配的文件会优先于不那么匹配的文件读入。 .psql_history 命令行历史被存储在文件 ~/.psql_history 中，或者是 Windows 的文件 %APPDATA%\\postgresql\\psql_history 中。历史文件的位置可以通过 HISTFILE psql变量或者 PSQL_HISTORY 环境变量明确的设置。 psql和具有相同主版本或者更老的主版本服务器最为匹配。如果服务器的版本比psql本身要高，则反斜线命令尤其容易失败。运行 SQL 命令并且显示查询结果的一般功能应该也能和具有更新主版本的服务器一起使用，但是并非在所有的情况下都能保证如此。 如果你想用psql连接到多个具有不同主版本的服务器，推荐使用最新版本的psql。或者，你可以为每一个主版本保留一份psql拷贝，并且针对相应的服务器使用匹配的版本。但实际上，这种额外的麻烦是不必要的。 第一个例子展示了如何如何跨越多行输入一个命令。注意提示符的改变： testdb=> CREATE TABLE my_table ( testdb(> first integer not null default 0, testdb(> second text) testdb-> ; CREATE TABLE 现在再看看表定义： testdb=> \\d my_table Table \"public.my_table\" Column | Type | Collation | Nullable | Default --------+---------+-----------+----------+--------- first | integer | | not null | 0 second | text | | | 现在我们把提示符改一改： testdb=> \\set PROMPT1 '%n@%m %~%R%# ' peter@localhost testdb=> 假定已经用数据填充了这个表并且想看看其中的数据： peter@localhost testdb=> SELECT * FROM my_table; first | second -------+-------- 1 | one 2 | two 3 | three 4 | four (4 rows) 你可以用 \\pset 命令以不同的方式显示表： peter@localhost testdb=> \\pset border 2 Border style is 2. peter@localhost testdb=> SELECT * FROM my_table; +-------+--------+ | first | second | +-------+--------+ | 1 | one | | 2 | two | | 3 | three | | 4 | four | +-------+--------+ (4 rows) peter@localhost testdb=> \\pset border 0 Border style is 0. peter@localhost testdb=> SELECT * FROM my_table; first second ----- ------ 1 one 2 two 3 three 4 four (4 rows) peter@localhost testdb=> \\pset border 1 Border style is 1. peter@localhost testdb=> \\pset format csv Output format is csv. peter@localhost testdb=> \\pset tuples_only Tuples only is on. peter@localhost testdb=> SELECT second, first FROM my_table; one,1 two,2 three,3 four,4 peter@localhost testdb=> \\pset format unaligned Output format is unaligned. peter@localhost testdb=> \\pset fieldsep '\\t' Field separator is \" \". peter@localhost testdb=> SELECT second, first FROM my_table; one 1 two 2 three 3 four 4 或者使用短命令： peter@localhost testdb=> \\a \\t \\x Output format is aligned. Tuples only is off. Expanded display is on. peter@localhost testdb=> SELECT * FROM my_table; -[ RECORD 1 ]- first | 1 second | one -[ RECORD 2 ]- first | 2 second | two -[ RECORD 3 ]- first | 3 second | three -[ RECORD 4 ]- first | 4 second | four 此外，可以使用 \\g 为一个查询设置这些输出格式选项： peter@localhost testdb=> SELECT * FROM my_table peter@localhost testdb-> \\g (format=aligned tuples_only=off expanded=on) -[ RECORD 1 ]- first | 1 second | one -[ RECORD 2 ]- first | 2 second | two -[ RECORD 3 ]- first | 3 second | three -[ RECORD 4 ]- first | 4 second | four 这是一个示例，用 \\df 命令以发现名称匹配 int*pl 并且第二参数是 bigint 类型的函数: testdb=> \\df int*pl * bigint List of functions Schema | Name | Result data type | Argument data types | Type ------------+---------+------------------+---------------------+------ pg_catalog | int28pl | bigint | smallint, bigint | func pg_catalog | int48pl | bigint | integer, bigint | func pg_catalog | int8pl | bigint | bigint, bigint | func (3 rows) 如果需要，可以用 \\crosstabview 命令以交叉表的形式显示查询结果： testdb=> SELECT first, second, first > 2 AS gt2 FROM my_table; first | second | gt2 -------+--------+----- 1 | one | f 2 | two | f 3 | three | t 4 | four | t (4 rows) testdb=> \\crosstabview first second first | one | two | three | four -------+-----+-----+-------+------ 1 | f | | | 2 | | f | | 3 | | | t | 4 | | | | t (4 rows) 这第二个例子展示了表的“乘法”（连接），行按照序号降序排序且列按照独立的、升序的方式排序。 testdb=> SELECT t1.first as \"A\", t2.first+100 AS \"B\", t1.first*(t2.first+100) as \"AxB\", testdb(> row_number() over(order by t2.first) AS ord testdb(> FROM my_table t1 CROSS JOIN my_table t2 ORDER BY 1 DESC testdb(> \\crosstabview \"A\" \"B\" \"AxB\" ord A | 101 | 102 | 103 | 104 ---+-----+-----+-----+----- 4 | 404 | 408 | 412 | 416 3 | 303 | 306 | 309 | 312 2 | 202 | 204 | 206 | 208 1 | 101 | 102 | 103 | 104 (4 rows) reindexdb — 重索引一个IvorySQL数据库 reindexdb [connection-option…​] [option…​] [ -S | --schema schema ] …​ [ -t | --table table ] …​ [ -i | --index index ] …​ [dbname] reindexdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all reindexdb [connection-option…​] [option…​] -s | --system [dbname] reindexdb接受下列命令行参数： -a --all 重索引所有数据库。 --concurrently 使用 `CONCURRENTLY` 选项。 请参阅 http://www.postgresql.org/docs/17/sql-reindex.html[REINDEX]，其中详细解释了此选项的所有注意事项。 [-d] dbname dbname 当 -a / --all 未使用时，指定要重新索引的数据库的名称。 如果未指定，则从环境变量 PGDATABASE 中读取数据库名称。 如果未设置，则使用为连接指定的用户名。 dbname 可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 -e --echo 回显reindexdb生成并发送到服务器的命令。 -i index --index=index 只是重建 *`index`*。可以通过写多个 `-i` 开关来重建多个索引。 -j njobs --jobs=njobs 通过同时运行 njobs 命令并行执行 reindex 命令。 此选项可能会减少处理时间，但也会增加数据库服务器上的负载。reindexdb将打开到数据库的 njobs 连接，因此请确保 max_connections 设置足够高，可以容纳所有连接。请注意，此选项与 --index 和 --system 选项不兼容。 -q --quiet 不显示进度消息。 -s --system 仅索引数据库的系统目录。 -S schema --schema=schema 只对 schema 重建索引。 通过写多个 -S 开关可以指定多个要重建索引的模式。 -t table --table=table 只索引 table。可以通过写多个 -t 开关来重索引多个表。 --tablespace=tablespace 指定要重建索引的表空间。 （这个名字以双引号标识符来处理。） -v --verbose 在处理时打印详细信息。 -V --version 打印reindexdb版本并退出。 -? --help 显示有关reindexdb命令行参数的帮助并退出。 reindexdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制reindexdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，reindexdb将自动提示要求一个口令。但是，reindexdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db=dbname 当使用 -a / --all 时，指定要连接到的数据库名称以发现应重新索引哪些数据库。 如果未指定，将使用 postgres 数据库， 如果不存在，将使用 template1 。 这可以是 连接字符串。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 此外，在连接到其他数据库时，将重新使用除数据库名称本身之外的连接字符串参数。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 REINDEX和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 reindexdb可能需要多次连接到IvorySQL服务器，每一次都会询问一个口令。在这种情况下使用一个 ~/.pgpass 文件会更方便。 要重索引数据库 test： $ reindexdb test 要重索引名为 abcd 的数据库中的表 foo 和索引 bar： $ reindexdb --table foo --index bar abcd vacuumdb — 对一个IvorySQL数据库进行垃圾收集和分析 vacuumdb [connection-option…​] [option…​] [ -t | --table table [( column [,…​] )] ] …​ [dbname] vacuumdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all vacuumdb接受下列命令行参数： -a --all 清理所有数据库。 [-d] dbname dbname 指定要清理或分析的数据库的名称，当不使用 -a / --all`时。 如果未指定，则从环境变量 `PGDATABASE 中读取数据库名称。 如果环境变量也没有设置，指定给该连接的用户名将用作数据库名。 dbname 可以是 connection string。 如果是这样，连接时的字符串参数将覆盖任何冲突的命令行选项。 --disable-page-skipping 根据可见性地图的内容禁用跳过页面。 -e --echo 回显vacuumdb生成并发送给服务器的命令。 -f --full 执行“完全”清理。 -F --freeze 强有力地“冻结”元组。 --force-index-cleanup 总是移除指向死元组的索引条目。 -j njobs --jobs=njobs 通过同时运行 njobs 个命令来并行执行清理或者分析命令。这个选项可能会减少处理的时间， 但是它也会增加数据库服务器的负载。vacuumdb将开启 njobs 个到数据库的连接，因此请确认你的 max_connections 设置足够高以容纳所有的连接。注意如果某些系统目录被并行处理，使用这种模式加上 -f（ FULL ）选项可能会导致死锁失败。 --min-mxid-age mxid_age 仅在multixact ID 年龄至少为 mxid_age 的表上执行清空或分析命令。 此设置对于确定要处理的表的优先级比较有用，以防止multixact ID 回绕。对于此选项的用途，关系的multixact ID年龄是主关系及其关联的TOAST表的年龄中最大的，如果存在的话。由于vacuumdb发出的命令在需要时还将处理TOAST表的关系，它无需单独考虑。 --min-xid-age xid_age 仅在事务ID 年龄至少为 xid_age 的表上执行清空或分析命令。 此设置对于确定要处理的表的优先级比较有用，以防止事务ID 回绕。对于此选项的用途，关系的事务ID年龄是主关系及其关联的TOAST表的年龄中最大的，如果存在的话。由于vacuumdb发出的命令在需要时还将处理TOAST表的关系，它无需单独考虑。 --no-index-cleanup 不移除指向死元组的索引条目。 --no-process-toast 略过与要清理的表有关联的任何 TOAST 表。 --no-truncate 不要在表的结尾截断空页面。 -P parallel_workers --parallel=parallel_workers 指定 parallel vacuum 的并发数量。这允许清理利用多个 CPU 来处理索引。请参见 VACUUM。 -q --quiet 不显示进度消息。 --skip-locked 跳过无法立即锁定以进行处理的关系。 -t `table [ (column [,…​]) ]` --table=`table [ (column [,…​]) ]` 只清理或分析 table。列名只能和 --analyze 或 --analyze-only 选项一起被指定。通过写多个 -t 开关可以清理多个表。提示如果你指定列，你可能必须转义来自 shell 的括号（见下面的例子）。 -v --verbose 在处理期间打印详细信息。 -V --version 打印vacuumdb版本并退出。 -z --analyze 也计算优化器使用的统计信息。 -Z --analyze-only 只计算优化器使用的统计信息（不清理）。 --analyze-in-stages 与 --analyze-only 相似，只计算优化器使用的统计信息（不做清理）。 使用不同的配置设置运行分析的几个（目前是 3个）阶段以更快地产生可用的统计信息。这个选项对分析一个刚从转储恢复或者通过 pg_upgrade 得到的数据库有用。这个选项将尝试尽可能快地创建一些统计信息来让该数据库可用，然后在后续的阶段中产生完整的统计信息。 -? --help 显示有关vacuumdb命令行参数的帮助并退出。 vacuumdb也接受下列命令行参数用于连接参数： -h host --host=host 指定运行服务器的机器的主机名。如果该值以一个斜线开始，它被用作 Unix 域套接字的目录。 -p port --port=port 指定服务器正在监听连接的 TCP 端口或本地 Unix 域套接字文件扩展。 -U username --username=username 要作为哪个用户连接。 -w --no-password 从不发出一个口令提示。如果服务器要求口令认证并且没有其他方式提供口令（例如一个 .pgpass 文件），那儿连接尝试将失败。这个选项对于批处理任务和脚本有用，因为在其中没有一个用户来输入口令。 -W --password 强制vacuumdb在连接到一个数据库之前提示要求一个口令。这个选项不是必不可少的，因为如果服务器要求口令认证，vacuumdb将自动提示要求一个口令。但是，vacuumdb将浪费一次连接尝试来发现服务器想要一个口令。在某些情况下值得用 -W 来避免额外的连接尝试。 --maintenance-db=dbname 当使用 -a / --all 时，指定要连接到的数据库名称以发现应该清理的数据库。 如果未指定，将使用 postgres 数据库，如果不存在，将使用 template1。 这可以是 connection string。 如果是这样，连接字符串参数将覆盖任何冲突的命令行选项。 此外，在连接到其他数据库时，将重新使用除数据库名称本身之外的连接字符串参数。 PGDATABASE PGHOST PGPORT PGUSER 默认连接参数 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto 和 never。 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 在有困难时，可以在 VACUUM 和 psql 中找潜在问题和错误消息的讨论。数据库服务器必须运行在目标主机上。同样，任何libpq前端库使用的默认连接设置和环境变量都将适用于此。 vacuumdb可能需要多次连接到IvorySQL服务器，每次都询问一个口令。在这种情况下有一个 ~/.pgpass 文件会很方便。 要清理数据库 test： $ vacuumdb test 要清理和为优化器分析一个名为 bigdb 的数据库： $ vacuumdb --analyze bigdb 要清理在名为 xyzzy 的数据库中的一个表 foo，并且为优化器分析该表的 bar 列： $ vacuumdb --analyze --verbose --table='foo(bar)' xyzzy initdb — 创建一个新的IvorySQL数据库集簇 initdb [option…​] [ --pgdata | -D ] ` directory` -A authmethod --auth=authmethod 这个选项为本地用户指定在 pg_hba.conf 中使用的默认认证方法（ host 和`local` 行）。initdb 将使用指定的认证方法为非复制连接以及复制连接填充 pg_hba.conf 项。除非你信任你系统上的所有本地用户，不要使用 trust。为了安装方便，trust 是默认值。 --auth-host=authmethod 这个选项为通过 TCP/IP 连接的本地用户指定在 pg_hba.conf 中使用的认证方法（ host 行）。 --auth-local=authmethod 这个选项为通过 Unix 域套接字连接的本地用户指定在 pg_hba.conf 中使用的认证方法（ local 行）。 -D directory --pgdata=directory 这个选项指定数据库集簇应该存放的目录。这是 initdb 要求的唯一信息，但是你可以通过设定 PGDATA 环境变量来避免书写它，这很方便因为之后数据库服务器（ postgres ）可以使用同一个变量来找到数据库目录。 -E encoding --encoding=encoding 选择模板数据库的编码。这也将是后来创建的任何数据库的默认编码，除非你覆盖它。默认值来自于区域，或者如果该值不起作用则为 SQL_ASCII。 -g --allow-group-access 允许与集簇拥有者同组的用户读取 initdb 创建的所有集簇文件。 Windows会忽略此选项，因为它不支持POSIX样式的组权限。 -k --data-checksums 在数据页面上使用校验码来帮助检测 I/O 系统造成的损坏。 启用校验码将会引起显著的性能惩罚。 如果设置，则为所有对象计算校验和，在整个数据库中。 所有校验和失败都将报告在 pg_stat_database 视图。 --locale=locale 为数据库集簇设置默认区域。如果这个选项没有被指定，该区域将从 initdb 所运行的环境中继承。 --lc-collate=locale --lc-ctype=locale --lc-messages=locale --lc-monetary=locale --lc-numeric=locale --lc-time=locale 和 --locale 相似，但是只在指定的分类中设置区域。 --no-locale 等效于 --locale=C。 -N --no-sync 默认情况下，initdb 将等待所有文件被安全地写到磁盘。这个选项会导致 initdb 不等待就返回，这当然更快，但是也意味着一次后续的操作系统崩溃可能让数据目录损坏。通常，这个选项对测试有用，但是不应该在创建生产安装时使用。 -−no-instructions 默认情况下，initdb 将在输出的尾部写入如何启动集群的指令。 这个选项会导致那些指令被抛弃。 这主要由平台特定行为中封装 initdb 的工具来使用，那些指令有可能是错误的。 --pwfile=filename 让 initdb 从一个文件读取数据库超级用户的口令。该文件的第一行被当作口令。 -S --sync-only 安全地把所有数据库文件写入到磁盘并退出。这不会执行任何正常的initdb操作。 -T config --text-search-config=config 设置默认的文本搜索配置。详见 default_text_search_config。 -U username --username=username 选择数据库超级用户的用户名。这个的默认值是实际运行 initdb 的用户的名称。超级用户的名字是什么真的不重要，但是你可以选择保留常用的名字postgres，即使操作系统的用户名不同。 -W --pwprompt 让 initdb 提示要求为数据库超级用户给予一个口令。如果你没有计划使用口令认证，这就不重要。否则在你设置一个口令之前你就无法使用口令认证。 -X directory --waldir=directory 这个选项指定预写式日志会被存储在哪个目录中。 --wal-segsize=`size` 设置 WAL段尺寸，以兆字节为单位。这是WAL日志中每个文件的尺寸。默认的尺寸为16兆字节。该值必须位于2的1次幂和1024次幂（兆字节）之间。这个选项只能在初始化期间设置，并且之后不能更改。调整这个值来控制WAL日志传送或者归档可能会有用。此外，在有大量WAL的数据库中，每个目录中数量巨大的WAL文件可能会成为性能和管理问题。增加WAL文件尺寸将会降低WAL文件的数量。 其他较少使用的选项： -d --debug 打印来自引导后端的调试输出以及普通大众不那么感兴趣的一些消息。引导后端被程序 initdb 用来创建目录表。这个选项会生成大量极端无聊的输出。 -−discard-caches 使用 debug_discard_caches=1 选项运行引导程序后端。 这需要很长时间并且只适用于深度调试。 -L directory 指定 initdb 应从哪里寻找它的输入文件来初始化数据库集簇。这通常没有必要。如果你需要显式指定它们的位置，你应该被告知。 -n --no-clean 默认情况下，当 initdb 确定有一个错误阻止它完整地创建数据库集簇，它会移除在它发现无法完成任务之前创建的任何文件。这个选项会抑制这种整理并且对调试有用。 其他选项： -V --version 打印initdb版本并退出。 -? --help 显示有关initdb命令行参数的帮助并退出。 PGDATA 指定数据库集簇应该被存放的目录，可以使用 -D 选项覆盖。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 TZ 指定创建的数据集簇的默认时区。值应该是一个完整的时区名称 和大部分其他IvorySQL工具相似，这个工具也使用libpq支持的环境变量。 initdb 可以通过 pg_ctl initdb 被调用。 pg_archivecleanup — 清理IvorySQL WAL 归档文件 pg_archivecleanup [option…​] archivelocation oldestkeptwalfile pg_archivecleanup接受下列命令行参数： -d 在 stderr 上打印很多调试日志输出。 -n 在 stdout 上打印将被移除的文件的名字（执行一次演习）。 -V --version 打印pg_archivecleanup版本并退出。 -x extension 提供一个扩展名，在决定所有的文件 是否应该被删除之前，将从文件名中剥离这个扩展名。这通常有助于清理已经 存储期间被压缩过并且被压缩程序增加了一个扩展名的归档。例如： -x .gz。 -? --help 显示pg_archivecleanup命令行参数的帮助并退出。 环境变量 PG_COLOR 指定是否在诊断消息中使用颜色。 可能的值是 always, auto 和 never。 在 Linux 或者 Unix 系统上，你可能会用： archive_cleanup_command = 'pg_archivecleanup -d /mnt/standby/archive %r 2>>cleanup.log' 其中归档目录位于后备服务器上，这样 archive_command 通过 NFS 来访问它，但是文件对于后备服务器来说是本地的。这将会 在 cleanup.log 中产生调试输出 从归档目录中移除不再需要的文件 pg_checksums — 在IvorySQL数据库集簇中启用、禁用或检查数据校验和 pg_checksums [option…​] [[ -D | --pgdata ] datadir] 下列命令选项可用： -D directory --pgdata=directory 指定存储数据库集簇的目录。 -c --check 检查校验和。如果未指定其它任何内容，这是缺省模式。 -d --disable 禁用校验和。 -e --enable 启用校验和。 -f filenode --filenode=filenode 仅验证文件节点为 filenode 的关系中的校验和。 -N --no-sync 缺省地，pg_checksums 会等待所有文件安全地写到磁盘上。该选项使得 pg_checksums 不等待就返回，这样更快，但意味着后续如果操作系统崩溃会让更新的数据目录损坏。一般地，该选项对测试有用，但不应用在生产安装上。当使用 --check 时，该选项无效。 -P --progress 启用进度报告。在检查或启用校验和时，打开该选项，会提供进度报告。 -v --verbose 启用详细输出。列出所有检查的文件 。 -V --version 打印pg_checksums版本并退出。 -? --help 显示关于pg_checksums命令行参数的帮助并退出。 PGDATA 指定数据库集簇存储的目录；可以用 -D 选项覆盖。 PG_COLOR 指定是否在诊断消息中使用颜色。可能的值为 always, auto, never。 在大型集簇中启用校验和的时间可能很长。在此操作期间，写到数据目录的集簇或其它程序必须是未启动的，否则可能出现数据丢失。 当复制设置与执行关系文件块的直接拷贝的工具（例如 pg_rewind）一起使用时，启用和禁用校验和会导致以不正确校验和形式出现的页面损坏，如果未在所有节点上执行一致的操作的话。故在复制设置中启用或禁用校验和时，推荐一致地切换所有集簇之前停止所有集簇。此外销毁所有备用数据库，在主数据库上执行操作，最后从头开始重建后备服务器，也是安全的。 如果在启用或禁用校验和时异常终止或杀掉pg_checksums，那么集簇的数据校验和配置保持不变，pg_checksums可以重新运行以执行相同操作。 pg_controldata — 显示一个IvorySQL数据库集簇的控制信息 pg_controldata [option] [[ -D | --pgdata ] datadir] PGDATA 默认的数据目录位置。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 pg_ctl — 初始化、启动、停止或控制一个IvorySQL服务器 pg_ctl init[db] [-D datadir] [-s] [-o initdb-options] pg_ctl start [-D datadir] [-l filename] [-W] [-t seconds] [-s] [-o options] [-p path] [-c] pg_ctl stop [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] pg_ctl restart [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] [-o options] [-c] pg_ctl reload [-D datadir] [-s] pg_ctl status [-D datadir] pg_ctl promote [-D datadir] [-W] [-t seconds] [-s] pg_ctl logrotate [-D datadir] [-s] pg_ctl kill signal_name process_id 在Microsoft Windows上，还有： pg_ctl register [-D datadir] [-N servicename] [-U username] [-P password] [-S a[uto] | d[emand] ] [-e source] [-W] [-t seconds] [-s] [-o options] pg_ctl unregister [-N servicename] -c --core-files 在可行的平台上尝试允许服务器崩溃产生核心文件，方法是提升在核心文件上的任何软性资源限制。这通过允许从一个失败的服务器进程中获得一个栈跟踪而有助于调试或诊断问题。 -D `datadir` --pgdata=`datadir` 指定数据库配置文件的文件系统位置。如果这个选项被忽略，将使用环境变量`PGDATA`。 -l `filename` --log=`filename` 追加服务器日志输出到*`filename`*。如果该文件不存在，它会被创建。umask被设置成 077，这样默认情况下不允许其他用户访问该日志文件。 -m mode --mode=mode 指定关闭模式。mode 可以是 smart、fast 或 immediate，或者这三者之一的第一个字母。如果这个选项被忽略，则 fast 是默认值。 -o options --options=options 指定被直接传递给 postgres 命令的选项。-o 可以被指定多次，所有给定的选项都会被传过去。这些选项应该通常被单引号或双引号包围来确保它们被作为一个组传递。 -o initdb-options --options=initdb-options 指定要直接传递给 initdb 命令的选项。-o 可以被指定多次，所有给定的选项都会被传过去。这些选项应该通常被单引号或双引号包围来确保它们被作为一个组传递。 -p path 指定 postgres 可执行程序的位置。默认情况下，postgres 可执行程序可以从 pg_ctl 相同的目录得到，或者如果没有在那里找到，则在硬写的安装目录中获得。除非你正在做一些不同寻常的事并且得到错误说没有找到 postgres 可执行程序，这个选项不是必需的。在 init 模式中，这个选项类似于指定了 initdb 可执行程序的位置。 -s --silent 只打印错误，不打印信息性的消息。 -t seconds --timeout=seconds 指定等待一个操作完成时要等待的最大秒数（见选项 -w ）。默认为 PGCTLTIMEOUT 环境变量的值，如果该环境变量没有设置则默认为60秒。 -V --version 打印pg_ctl版本并退出。 -w --wait 等待操作完成。模式 start、 stop、 restart、 promote 以及 register 支持这个选项，并且对那些模式是默认的。在等待时，pg_ctl 会一遍又一遍地检查服务器的PID文件，在两次检查之间会休眠一小段时间。当PID文件指示该服务器已经做好准备接受连接时，启动操作被认为完成。当服务器移除PID文件时，关闭操作被认为完成。pg_ctl 会基于启动或关闭的成功与否返回一个退出代码。如果操作在超时时间（见选项 -t ）内未能完成，则 pg_ctl 会以一个非零退出状态退出。但是注意该操作可能会在后台继续进行并且最终取得成功。 -W --no-wait 不等待操作完成。这是选项 -w 的对立面。如果禁用等待，所请求的动作会被触发，但是不会有关于其成功与否的反馈。在这种情况下，可能必须用服务器日志文件或外部监控系统来检查该操作的进度以及成功与否。在以前版本的IvorySQL中，这是除 stop 模式之外的模式的默认选项。 -? --help 显示有关pg_ctl命令行参数的帮助并退出。 如果一个指定的选项有效，但与选中的操作模式无关，则pg_ctl会忽略它。 -e source 作为一个 Windows 服务运行时，pg_ctl用来 在事件日志中记录日志的事件源的名称。默认是 IvorySQL。 注意这只控制由pg_ctl本身发送的消息，一旦开始， 服务器将使用 event_source 参数中指定的事件源。如果服务器在启动时很早（在该参数被设置前）就失败，它可能也会使用默认的事件源名称 IvorySQL 来记录。 -N servicename 要注册的系统服务的名称。这个名称将被用于服务名和显示名。默认是 IvorySQL。 -P password 用于运行该服务的用户的口令。 -S start-type 要注册的系统服务的启动类型。启动类型可以是 auto、 demand 或者两者之一的第一个字母。如果这个选项被忽略，则 auto 是默认值。 -U username 用于运行该服务的用户的用户名。对于域用户，使用格式 DOMAIN\\username。 PGCTLTIMEOUT 等待启动或者关闭完成时要等待的默认秒数限制。如果没有设置， 默认值是 60 秒。 PGDATA 默认的数据目录位置。 大部分的 pg_ctl 模式都要求知道数据目录的位置，因此 -D 选项是必需的，除非 PGDATA 被设置。 和大部分其他IvorySQL工具相似，pg_ctl 也使用libpq支持的环境变量。 更多影响服务器的变量请见 postgres。 postmaster.pid pg_ctl在数据目录中检查这个文件来判断服务器当前是否正在运行。 postmaster.opts 如果这个文件存在于数据目录中，pg_ctl（处于 restart 模式中）将把该文件的内容作为选项传递给postgres，除非通过 -o 选项进行了覆盖。这个文件的内容也会被显示在 status 模式中。 要启动服务器并且等到服务器接受连接： $ pg_ctl start 要使用端口 5433 启动服务器并且运行时不使用 fsync： $ pg_ctl -o \"-F -p 5433\" start 要停止服务器，使用： $ pg_ctl stop -m 选项允许控制服务器如何关闭： $ pg_ctl stop -m smart 重启服务器几乎等价于停止服务器并且再次启动它，不过 pg_ctl 默认会保存并重用被传递给之前的运行实例的命令行选项。要以和之前相同的选项重启服务器，使用： $ pg_ctl restart 但是如果指定了 -o，则会替换任何之前的选项。要使用端口 5433 重启并在重启时禁用 fsync： $ pg_ctl -o \"-F -p 5433\" restart 这里是pg_ctl状态输出的例子： $ pg_ctl status pg_ctl: server is running (PID: 13718) /usr/local/pgsql/bin/postgres \"-D\" \"/usr/local/pgsql/data\" \"-p\" \"5433\" \"-B\" \"128\" 第二行是在重启模式可能被调用的命令行。 pg_resetwal — 重置一个IvorySQL数据库集簇的预写式日志以及其他控制信息 pg_resetwal [ -f | --force ] [ -n | --dry-run ] [option…​] [ -D | --pgdata ] datadir -f --force 即使 pg_resetwal 无法从 pg_control 中确定有效的数据（如前面所解释的），也强迫 pg_resetwal 继续运行。 -n --dry-run -n / --dry-run 选项指示 pg_resetwal 打印从 pg_control 重构出来的值以及要被改变的值，然后不修改任何东西退出。这主要是一个调试工具，但是可以用来在允许 pg_resetwal 真正执行下去之前进行完整性检查。 -V --version 显示版本信息然后退出。 -? --help 显示帮助然后退出。 只有当 pg_resetwal 无法通过读取 pg_control 确定合适的值时，才需要下列选项。安全值可以按下文所述来确定。对于接收数字参数的值，可以使用前缀 0x 指定 16 进制值。 -c xid, xid --commit-timestamp-ids=xid, xid 手工设置提交时间可以检索到的最老的和最新的事务 ID。能检索到提交时间的最老事务 ID 的安全值（第一部分）可以通过在数据目录下 pg_commit_ts 目录中数字上最小的文件名来决定。反过来，能检索到提交时间的最新事务 ID 的安全值（第二部分）可以通过同一个目录中数字上最大的文件名来决定。文件名都是十六进制的。 -e xid_epoch --epoch=xid_epoch 手工设置下一个事务 ID 的 epoch。事务 ID 的 epoch 实际上并没有存储在数据库中的任何地方，除了被 pg_resetwal 设置在这个域中，所以只要关心的是数据库本身，任何值都可以用。你可能需要调整这个值来确保诸如Slony-I和Skytools之类的复制系统正确地工作 — 如果确实需要调整，应该可以从下游的复制数据库的状态中获得一个合适的值。 -l `walfile` --next-wal-file=`walfile` 通过指定下一个WAL段文件名称来手工设置WAL开始位置。下一个WAL段文件的名称应该比当前存在于数据目录下 pg_wal 目录中的任意 WAL 段文件名更大。这些名称也是十六进制的并且有三个部分。第一部分是“时间线 ID”并且通常应该被保持相同。例如，如果 00000001000000320000004A 是 pg_wal 中最大的项，则使用 -l 00000001000000320000004B 或更高的值。注意在使用非默认WAL段尺寸时，WAL文件名中的数字与系统函数和系统视图报告的LSN不同。这个选项要的是WAL文件名而不是LSN。注意 pg_resetwal 本身查看 pg_wal 中的文件并选择一个超出最新现存文件名的默认 -l 设置。因此，只有当你知道 WAL 段文件当前不在 pg_wal 中时，或者当 pg_wal 的内容完全丢失时，才需要对 -l 的手工调整，例如一个离线归档中的项。 -m mxid,mxid --multixact-ids=mxid,mxid 手工设置下一个和最老的多事务 ID。确定下一个多事务 ID（第一部分）的安全值的方法：在数据目录下的 pg_multixact/offsets 目录中查找最大的数字文件名，然后在它的基础上加一并且乘以 65536 (0x10000)。反过来，确定最老的多事务 ID（ -m 的第二部分）的方法：在同一个目录中查找最小的数字文件名并且乘以 65536。文件名是十六进制的数字，因此实现上述方法最简单的方式是以十六进制指定选项值并且追加四个零。 -o oid --next-oid=oid 手工设置下一个 OID。没有相对容易的方法来决定超过数据库中最大 OID 的下一个 OID。但幸运的是正确地得到下一个 OID 设置并不是决定性的。 -O mxoff --multixact-offset=mxoff 手工设置下一个多事务偏移量。确定安全值的方法：查找数据目录下 pg_multixact/members 目录中最大的数字文件名，然后在它的基础上加一并且乘以 52352 (0xCC80)。文件名是十六进制数字。没有像其他选项那样追加零的简单方法。 --wal-segsize=wal_segment_size 设置新的WAL段尺寸，以兆字节为单位。这个值必须被设为2的1次幂和1024次幂（兆字节）之间。更多信息请参考 initdb 的相同选项。注意虽然 pg_resetwal 将把WAL起始地址设置成超过最新的现有WAL段文件，但一些段尺寸的改变可能导致之前的WAL文件名被重用。如果WAL文件名重叠会导致归档策略出现问题，推荐把 -l 和这个选项一起使用来手动设置WAL起始地址。 -u xid --oldest-transaction-id=xid 手工设置最老的未冻结事务ID。一个安全值，可以通过在数据目录下的 pg_xact 目录中查找数字最小的文件名然后乘以1048576 (0x100000)的方式来确定。 注意该文件名是十六进制的。 以十六进制来指定选项值通常也是最简单的。 例如,如果 0007 是 pg_xact 中最小的记录，-u 0x700000 将有效(五个后补零提供适当的乘数)。 -x xid --next-transaction-id=xid 手工设置下一个事务 ID。确定安全值的方法：在数据目录下的 pg_xact 目录中查找最大的数字文件名，然后在它的基础上加一并且乘以 1048576 (0x100000)。注意文件名是十六进制的数字。通常以十六进制的形式指定该选项值也是最容易的。例如，如果 0011 是 pg_xact 中的最大项，-x 0x1200000 就可以（五个尾部的零就表示了前面说的乘数）。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 这个命令不能在服务器正在运行时被使用。如果在数据目录中发现一个服务器锁文件，pg_resetwal 将拒绝启动。如果服务器崩溃那么一个锁文件可能会被留下，在那种情况下你能移除该锁文件来让 pg_resetwal 运行。但是在你那样做之前，再次确认没有服务器进程仍然存活。 pg_resetwal 仅能在具有相同主版本的服务器上使用。 pg_rewind — 把一个IvorySQL数据目录与另一个从该目录中复制出来的数据目录同步 pg_rewind [option…​] { -D | --target-pgdata }*` directory`* { --source-pgdata=`directory` | --source-server=`connstr` } 如果在处理时pg_rewind失败，则目标的数据目录很可能不在可恢复的状态。在这种情况下，推荐创建一个新的备份。 由于 pg_rewind 完全从源复制配置文件，因此可能需要在重新启动目标服务器之前更正用于恢复的配置，特别是当目标服务器作为源的后备服务器重新引入时。 如果在倒带操作完成后重新启动服务器但未配置恢复，则目标可能会再次与主服务器分离。 如果pg_rewind发现它无法直接写入的文件，它将立刻失败。例如当源服务器和目标服务器为只读的SSL密钥及证书使用相同的文件映射，就会发生这种情况。如果在目标服务器上存在这样的文件，推荐在运行pg_rewind之前移除它们。在做了rewind之后，一些那样的文件可能已经被从源服务器拷贝，这样就有必要移除已经拷贝的数据并且恢复到rewind之前使用的链接集合。 pg_rewind接受下列命令行参数： -D directory --target-pgdata=directory 这个选项指定要与源数据目录同步的目标数据目录。在运行pg_rewind之前目标服务器必须被干净地关闭。 --source-pgdata=directory 指定要和目标服务器同步的源服务器的数据目录的文件系统路径。这个选项要求源服务器必须被干净地关闭。 --source-server=`connstr` 指定一个 libpq 连接串用于连接要与目标服务器同步的源IvorySQL服务器。 连接必须是常规（非复制）连接，角色具有足够权限执行源服务器上pg_rewind使用的函数（详请参阅备注部分）或超级用户角色。 这个选项要求源服务器正在运行并且接受连接。 -R --write-recovery-conf 创建 standby.signal 并将连接设置附加到输出目录中的 postgresql.auto.conf 中。--source-server 对于此选项是必需的。 -n --dry-run 做除了实际修改目标目录之外的其他所有事情。 -N --no-sync 默认情况下，pg_rewind 将等待所有文件安全地写入磁盘。 此选项会导致 pg_rewind 不等待即可返回，这更快，但意味着后续操作系统崩溃会使同步数据目录损坏。通常情况，此选项可用于测试，但不应使用于生产安装。 -P --progress 启用进度报告。在从源集簇拷贝数据时，打开这个选项将会发送一个近似的进度报告。 -c --restore-target-wal 如果在 pg_wal 目录中不再可用这些文件，请使用在目标群集配置中定义的 restore_command 从WAL存档中检索WAL文件。 --debug 打印冗长的调试输出，这主要对于调试pg_rewind的开发者有用。 --no-ensure-shutdown pg_rewind 要求目标服务器在倒带之前彻底关闭。 默认情况下，如果目标服务器没有完全关闭，pg_rewind 会以单用户模式启动目标服务器，先完成崩溃恢复，然后将其停止。 通过传递这个选项，如果服务器没有完全关闭，pg_rewind 会跳过这个并立即出错。 在这种情况下，用户应该自己处理这种情况。 -V --version 显示版本信息然后退出。 -? --help 显示帮助然后退出。 在使用 --source-server 选项时，pg_rewind也使用libpq支持的环境变量。 环境变量`PG_COLOR`规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 当使用在线群集作为源执行pg_rewind时，具有充足权限来执行pg_rewind在源群集上使用的函数的角色可以用来代替超级用户。 这里介绍如何创建这样的角色，在这里命名 rewind_user： CREATE USER rewind_user LOGIN; GRANT EXECUTE ON function pg_catalog.pg_ls_dir(text, boolean, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_stat_file(text, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text, bigint, bigint, boolean) TO rewind_user; 当使用近期升级的在线群集作为源执行pg_rewind时，必须在升级后执行 CHECKPOINT 以便其控制文件反映最新的时间线信息， pg_rewind使用这些信息检查目标群集是否可以使用指定的源群集倒回。 其基本思想是从源集簇拷贝所有文件系统级别的改变到目标集簇： 1.以源集簇的时间线历史从目标集簇分叉出来的点之前的最后一个检查点为起点，扫描目标集簇的 WAL 日志。对于每一个 WAL 记录，读取每一个被动过的数据块。这会得到在目标集簇中从源集簇被分支出去以后所有被更改过的数据块列表。如果某些 WAL 文件 不再可用，请尝试使用 -c 选项重新运行 pg_rewind 以在 WAL 存档中搜索丢失的文件。 2.使用直接的文件系统访问（ --source-pgdata ）或者 SQL （ --source-server ），把所有那些更改过的块从源集簇拷贝到目标集簇。 关系文件现在的状态相当于源和目标的WAL时间线偏离点之前最后一个完成的检查点的时刻加上偏离点之后目标上更改的任何块的源上的当前状态。 3.将所有其他文件，包括新的关系文件、WAL段、pg_xact 和配置文件，从源集群复制到目标集群。与基本备份类似，从源集群复制的数据中省略了目录 pg_dynshmem/、 pg_notify/、 pg_replslot/、 pg_serial/、 pg_snapshots/、 pg_stat_tmp/ 以及 pg_subtrans/ 的内容。文件 backup_label、 tablespace_map、 pg_internal.init、 postmaster.opts 以及 postmaster.pid，以及任何以 pgsql_tmp 开始的文件或目录都会被忽略。 4.创建一个 backup_label 文件，在故障转移时创建的检查点处开始WAL重放，并将 pg_control 文件配置为最小一致性LSN，该LSN定义为从活动源回放时的 pg_current_wal_insert_lsn() 结果，或从停止的源回放时的最后一个检查点LSN。 5.启动目标时，IvorySQL将重放所有必需的WAL，从而使数据目录处于一致状态。 pg_test_fsync — 为IvorySQL判断最快的 wal_sync_method pg_test_fsync [option…​] pg_test_fsync接受下列命令行选项： -f --filename 指定要写入测试数据到其中的文件名。这个文件必须位于和 pg_wal 目录所在或者将被放置的同一个文件系统中（ pg_wal 包含WAL文件）。默认是当前 目录中的 pg_test_fsync.out。 -s --secs-per-test 指定每次测试的秒数。每个测试的时间越长，测试的精度就越高，但是 它需要更多时间来运行。默认是 5 秒，这允许程序在 2 分钟以内完成。 -V --version 打印pg_test_fsync版本并且退出。 -? --help 显示有关pg_test_fsync命令行参数的帮助并且退出。 环境变量 PG_COLOR 指定是否在诊断消息中使用颜色。可能的值为 always、 auto 和 never. pg_test_timing — 度量计时开销 pg_test_timing [option…​] pg_test_timing接受下列命令行选项： -d duration --duration=duration 指定测试的持续时间，以秒计。更长的持续时间会给出更好一些的精确度，并且更可能发现系统时钟回退的问题。默认的测试持续时间是 3 秒。 -V --version 打印pg_test_timing版本并退出。 -? --help 显示有关pg_test_timing的命令行参数，然后退出。 好的结果将显示大部分（>90%）的单个计时调用用时都小于 1 微秒。每次循环的平均开销将会更低，低于 100 纳秒。下面的例子来自于一台使用了一份 TSC 时钟源码的 Intel i7-860 系统，它展示了非常好的性能： Testing timing overhead for 3 seconds. Per loop time including overhead: 35.96 ns Histogram of timing durations: &lt; us % of total count 1 96.40465 80435604 2 3.59518 2999652 4 0.00015 126 8 0.00002 13 16 0.00000 2 注意每次循环时间和柱状图用的单位是不同的。循环的解析度可以在几个纳秒（ns），而单个计时调用只能解析到一个微秒（us）。 当查询执行器使用 EXPLAIN ANALYZE 运行一个语句时，单个操作会被计时，总结也会被显示。你的系统的负荷可以通过使用psql程序计数行来检查： CREATE TABLE t AS SELECT * FROM generate_series(1,100000); \\timing SELECT COUNT(*) FROM t; EXPLAIN ANALYZE SELECT COUNT(*) FROM t; i7-860 系统测到运行该计数查询用了 9.8 ms 而 EXPLAIN ANALYZE 版本则需要 16.6 ms，每次处理都在 100,000 行上进行。6.8 ms 的差别意味着在每行上的计时负荷是 68 ns，大概是 pg_test_timing 估计的两倍。即使这样相对少量的负荷也造成了带有计时的计数语句耗时多出了 70%。在更大量的查询上，计时开销带来的问题不会有这么明显。 在一些较新的 Linux 系统上，可以在任何时候更改用来收集计时数据的时钟来源。第二个例子显示了在上述快速结果的相同系统上切换到较慢的 acpi_pm 时间源可能带来的降速： # cat /sys/devices/system/clocksource/clocksource0/available_clocksource tsc hpet acpi_pm # echo acpi_pm > /sys/devices/system/clocksource/clocksource0/current_clocksource # pg_test_timing Per loop time including overhead: 722.92 ns Histogram of timing durations: &lt; us % of total count 1 27.84870 1155682 2 72.05956 2990371 4 0.07810 3241 8 0.01357 563 16 0.00007 3 在这种配置中，上面的例子 EXPLAIN ANALYZE 用了 115.9 ms。其中有 1061 ns 的计时开销，还是用这个工具直接度量结果的一个小倍数。这么多的计时开销意味着实际的查询本身只占了时间的一个很小的分数，大部分的时间都耗在了计时所需的管理开销上。在这种配置中，任何涉及到很多计时操作的`EXPLAIN ANALYZE`都会受到计时开销的显著影响。 FreeBSD 也允许即时更改时间源，并且它会记录在启动期间有关计时器选择的信息： # dmesg | grep \"Timecounter\" Timecounter \"ACPI-fast\" frequency 3579545 Hz quality 900 Timecounter \"i8254\" frequency 1193182 Hz quality 0 Timecounters tick every 10.000 msec Timecounter \"TSC\" frequency 2531787134 Hz quality 800 # sysctl kern.timecounter.hardware=TSC kern.timecounter.hardware: ACPI-fast -> TSC 其他系统可能只允许在启动时设定时间源。在旧的 Linux 系统上，“clock”内核设置是做这类更改的唯一方法。并且即使在一些更近的系统上，对于一个时钟源你将只能看到唯一的选项 \"jiffies\"。Jiffies 是老的 Linux 软件时钟实现，当有足够快的计时硬件支持时，它能够具有很好的解析度，就像在这个例子中： $ cat /sys/devices/system/clocksource/clocksource0/available_clocksource jiffies $ dmesg | grep time.c time.c: Using 3.579545 MHz WALL PM GTOD PIT/TSC timer. time.c: Detected 2400.153 MHz processor. $ pg_test_timing Testing timing overhead for 3 seconds. Per timing duration including loop overhead: 97.75 ns Histogram of timing durations: &lt; us % of total count 1 90.23734 27694571 2 9.75277 2993204 4 0.00981 3010 8 0.00007 22 16 0.00000 1 32 0.00000 1 收集准确的计时信息在计算机上通常是使用具有不同精度的时钟硬件完成的。使用一些硬件，操作系统能几乎直接把系统时钟时间传递给程序。一个系统时钟也可以得自于一块简单地提供计时中断、在某个已知时间区间内的周期性滴答的芯片。在两种情况中，操作系统内核提供一个隐藏这些细节的时钟源。但是时钟源的精确度以及能多快返回结果会根据底层硬件而变化。 不精确的计时能够导致系统不稳定性。对任何时钟源的更改都要仔细地测试。操作系统默认是有时会更倾向于可靠性而不是最好的精确性。并且如果你在使用一个虚拟机器，应查看与之兼容的推荐时间源。在模拟计时器时虚拟硬件面临着额外的困难，并且提供商常常会建议每个操作系统的设置。 时间戳计数器（TSC）时钟源是当前一代 CPU 上最精确的一种。当操作系统支持 TSC 并且 TSC 可靠时，它是跟踪系统时间更好的方式。有多种方式会使 TSC 无法提供准确的计时源，这会让它不可靠。旧的系统能有一种基于 CPU 温度变化的 TSC 时钟，这让它不能用于计时。尝试在一些就的多核 CPU 上使用 TSC 可能在多个核心之间给出不一致的时间报告。这可能导致时间倒退，这个程序会检查这种问题。并且即使最新的系统，在非常激进的节能配置下也可能无法提供准确的 TSC 计时。 更新的操作系统可能检查已知的 TSC 问题并且当它们被发现时切换到一种更慢、更稳定的时钟源。如果你的系统支持 TSC 时间但是并不默认使用它，很可能是由于某种充分的理由才禁用它。某些操作系统可能无法正确地检测所有可能的问题，或者即便在知道 TSC 不精确的情况下也允许使用 TSC。 如果系统上有高精度事件计时器（HPET）并且 TSC 不准确，该系统将会更喜欢 HPET 计时器。计时器芯片本身是可编程的，最高允许 100 纳米的解析度，但是在你的系统时钟中可能见不到那么高的准确度。 高级配置和电源接口（ACPI）提供了一种电源管理（PM）计时器，Linux 把它称之为 acpi_pm。得自于 acpi_pm 的时钟最好时将能提供 300 纳秒的解析度。 在旧的 PC 硬件上使用的计时器包括 8254 可编程区间计时器（PIT）、实时时钟（RTC）、高级可编程中断控制器（APIC）计时器以及 Cyclone 计时器。这些计时器是以毫秒解析度为目标的。 pg_upgrade — 升级IvorySQL服务器实例 pg_upgrade -b oldbindir -B newbindir -d oldconfigdir -D newconfigdir [option…​] pg_upgrade接受下列命令行参数： -b bindir --old-bindir=bindir 旧的 IvorySQL 可执行文件目录； 环境变量 PGBINOLD -B bindir --new-bindir=bindir 新的 IvorySQL 可执行文件目录； 默认为pg_upgrade所在的目录； 环境变量 PGBINNEW -c --check 只检查集簇，不更改任何数据 -d configdir --old-datadir=configdir 旧的集簇数据目录；环境变量 PGDATAOLD -D configdir --new-datadir=configdir 新的集簇数据目录；环境变量 PGDATANEW -j --jobs=`njobs` 要同时使用的进程或线程数 -k --link 使用硬链接来代替将文件拷贝到新集簇 -o options --old-options options 直接传送给旧 postgres 命令的选项，多个选项可以追加在后面 -O options --new-options options 直接传送给新 postgres 命令的选项，多个选项可以追加在后面 -p port --old-port=port 旧的集簇端口号；环境变量 PGPORTOLD -P port --new-port=port 新的集簇端口号；环境变量 PGPORTNEW -r --retain 即使在成功完成后也保留 SQL 和日志文件 -s dir --socketdir=dir 用于升级期间postmaster套接字的目录;默认是当前目录; 环境变量 PGSOCKETDIR -U username --username=username 集簇的安装用户名；环境变量 PGUSER -v --verbose 启用详细的内部日志 -V --version 显示版本信息，然后退出 --clone 使用有效的文件克隆（在一些系统上也被称为“reflinks”），而不是将文件拷贝到新群集。 这可以导致数据文件接近瞬时的复制，从而获得 -k / --link 的速度优势，同时保留旧群集不受影响。文件克隆仅在某些操作系统和文件系统上得到支持。如果选中但不被支持，则 pg_upgrade运行将会出错。 目前，它支持在Linux（内核4.5或更高版本）上的Btrfs和XFS（在文件系统创建reflink支持），以及macOS上的APFS。 -? --help 显示帮助，然后退出 下面是用pg_upgrade执行一次升级的步骤： 1.移动旧集簇（可选） 如果你的安装目录不是版本相关的（例如 /usr/local/pgsql ）， 就有必要移动当前的 IvorySQL 安装目录，以免它干扰新的 IvorySQL安装。一旦当前的 IvorySQL服务器被关闭，就可以安全地重命名 IvorySQL 安装目录。假设旧目录是 /usr/local/pgsql，你可以这样： mv /usr/local/pgsql /usr/local/pgsql.old 来重命名该目录。 2.对于源码安装，编译新版本 用兼容旧集簇的 configure 标记编译新的 IvorySQL 源码。在开始升级之前，pg_upgrade 将检查 pg_controldata 来确保所有设置都是兼容的。 3.安装新的 IvorySQL 二进制文件 安装新服务器的二进制文件和支持文件。pg_upgrade 会被包含在默认的安装中。 对于源码安装，如果你希望把新服务器安装在一个自定义的位置， 可以使用 prefix 变量： make prefix=/usr/local/pgsql.new install 4.初始化新的 IvorySQL 集簇 使用 initdb 初始化新集簇。这里也要使用与 旧集簇相兼容的 initdb 标志。许多预编译的 安装程序会自动做这个步骤。这里没有必要启动新集簇。 5.安装扩展共享对象文件 许多扩展和自定义模块，无论是来自 contrib 或其他源，使用共享对象文件(或DLLs)，例如， pgcrypto.so。 如果旧集群使用过这些，匹配新服务器二进制的共享对象文件，必须安装在新集群中。 通常是通过操作系统命令。 不要加载模式定义，例如 CREATE EXTENSION pgcrypto，因为这些将从旧集群复制。 如果扩展更新是可用的，pg_upgrade将报告这一点，并创建一个脚本，可以稍后运行来更新它们。 6.拷贝定制的全文本检索文件 从旧集群向新集群拷贝任何定制化全文本检索文件(词典、同义词、辞典、停用词) 7.调整认证 pg_upgrade 将会多次连接到旧服务器和新服务器，因此 你可能想要在 pg_hba.conf 中把认证设置成 peer 或者使用一个 ~/.pgpass 文件。 8.停止两个服务器 确认两个数据库服务器都被停止使用，例如在 Unix 上可以： pg_ctl -D /opt/IvorySQL/1.5 stop pg_ctl -D /opt/IvorySQL/2.1 stop 或者在 Windows 上使用正确的服务名： NET STOP postgresql-1.5 NET STOP postgresql-2.1 直到后面的步骤之前，流复制和日志传送后备服务器可以保持运行。 9.为后备服务器升级做准备 升级后备服务器时，请对旧的主集簇和后备集簇运行pg_controldata以验证旧的后备服务器已经完全追上。验证“Latest checkpoint location”值在所有集簇中都匹配（如果旧后备服务器在旧的主服务器之前被关闭或者如果旧的后备服务器仍在运行，则将会出现失配）。此外，请确保在新的主集簇上的 postgresql.conf 文件中，wal_level 未设置为 minimal。 10.运行 pg_upgrade 总是应该运行新服务器而不是旧服务器的pg_upgrade二进制文件。 pg_upgrade要求制定新旧集簇的数据和可执行文件（ bin ）目录。 你也可以指定用户和端口值，以及你是否想要用链接或克隆来取代默认的复制行为对数据文件进行处理。 如果你使用链接模式，升级将会快很多（不需要文件拷贝）并且将使用 更少的磁盘空间，但是在升级后一旦启动新集簇，旧集簇就无法被访问。 链接模式也要求新旧集簇数据目录位于同一个文件系统中（表空间和 pg_wal 可以在不同的文件系统中）。 克隆模式提供了相同的速度以及磁盘空间优势，但不会导致新群集启动后旧群集不可用。 克隆模式还需要新旧数据目录位于同一文件系统中。 此模式仅在某些操作系统和文件系统上可用。 --jobs 选项允许多个 CPU 核心被用来复制/链接文件以及并行地转储和重载数据库模式。这个选项一个比较好的值是 CPU 核心数 和表空间数的最大值。这个选项可以显著地减少升级运行在一台多处理 器机器上的多数据库服务器的时间。 对于 Windows 用户，你必须以一个超级账号登录，并且以 `postgres`用户启动一个 shell 并且设置正确的路径： RUNAS /USER:postgres \"CMD.EXE\" SET PATH=%PATH%;C:\\Program Files\\IvorySQL\\14\\bin; 并且用带引号的目录运行pg_upgrade，例如： pg_upgrade.exe --old-datadir \"C:/Program Files/IvorySQL/1.5/data\" --new-datadir \"C:/Program Files/IvorySQL/2.1/data\" --old-bindir \"C:/Program Files/IvorySQL/1.5/bin\" --new-bindir \"C:/Program Files/IvorySQL/2.1/bin\" 一旦启动，pg_upgrade 将验证两个集簇是否兼容并且 执行升级。你可以使用 pg_upgrade --check 来只执行检查， 这种模式即使在旧服务器还在运行时也能使用。 pg_upgrade --check 也将列出任何在更新后需要做的手工调整。 如果你将要使用链接或克隆模式，你应该使用 --link 或 --clone 选项和 --check 一起来启用链接模式相关的检查。 pg_upgrade 要求在当前目录中的写权限。 显然，没有人可以在升级期间访问这些集簇。pg_upgrade 默认会在端口 50432 上运行服务器来避免意外的客户端连接。在做升级时， 可以对两个集簇使用相同的端口号，因为新旧集簇不会在同时被运行。不过， 在检查一个旧的运行中服务器时，新旧端口号必须不同。 如果在恢复数据库模式时发生错误，pg_upgrade 将会退出 并且你必须按照恢复旧集簇。要再次尝试 pg_upgrade，你将需要修改旧集簇，这样 pg_upgrade 模式会成功恢复。如果问题是一个 contrib 模块， 你可能需要从旧集簇中卸载该模块并且在升级后重新把它安装在新集簇中，不过 这样做的前提是该模块没有被用来存储用户数据。 11.升级流复制和日志传送后备服务器 如果使用链接模式并且有流复制或者日志 传送后备服务器，你可以遵照下面的 步骤对它们进行快速的升级。你将不用在这些后备服务器上运行 pg_upgrade，而是在主服务器上运行rsync。 到这里还不要启动任何服务器。 如果你没有使用链接模式、没有或不想使用rsync或者想用一种更容易的解决方案，请跳过这一节中的过程并且在pg_upgrade完成并且新的主集簇开始运行后重建后备服务器。 在后备服务器上安装新的 IvorySQL 二进制文件 确保新的二进制和支持文件被安装在所有后备服务器上。 确保不存在新的后备机数据目录 确保新的后备机数据目录不存在或者为空。如果 运行过initdb，请删除后备服务器的新数据目录。 安装扩展共享对象文件 在新的后备机上安装和新的主集簇中相同的扩展共享对象文件。 停止后备服务器 如果后备服务器仍在运行，现在使用上述的指令停止它们。 保存配置文件 从旧后备机的配置目录保存任何需要保留的配置文件，例如 `postgresql.conf`（以及它包含的任何文件）、 `postgresql.auto.conf`、`pg_hba.conf`， 因为这些文件在下一步中会被重写或者移除。 运行rsync 在使用链接模式时，后备服务器可以使用rsync快速升级。为了实现这一点，在主服务器上一个高于新旧数据库集簇目录的目录中为每个后备服务器运行这个命令： ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive old_cluster new_cluster remote_dir ``` 其中`old_cluster`和`new_cluster`是相对于主服务器上的当前目录的，而`remote_dir`是后备服务器上高于新旧集簇目录的一个目录。在主服务器和后备服务器上指定目录之下的目录结构必须匹配。指定远程目录的详细情况请参考rsync的手册，例如： ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive /opt/IvorySQL/1.5 \\ /opt/IvorySQL/2.1 standby.example.com:/opt/IvorySQL ``` 可以使用rsync的`--dry-run`选项验证该命令将做的事情。虽然在主服务器上必须为至少一台后备运行rsync，可以在一台已经升级过的后备服务器上运行rsync来升级其他的后备服务器，只要已升级的后备服务器还没有被启动。 这个命令所做的事情是记录由pg_upgrade的链接模式创建的链接，它们连接主服务器上新旧集簇中的文件。该命令接下来在后备服务器的旧集簇中寻找匹配的文件并且为它们在该后备的新集簇中创建链接。主服务器上没有被链接的文件会被从主服务器拷贝到后备服务器（通常都很小）。这提供了快速的后备服务器升级。不幸地是，rsync会不必要地拷贝与临时表和不做日志表相关的文件，因为通常在后备服务器上不存在这些文件。 如果你已经把`pg_wal`放在数据目录外面，也必须在那些目录上运行rsync。 配置流复制和日志传送后备服务器 为日志传送配置服务器（不需要运行`pg_start_backup()` 以及`pg_stop_backup()`或者做文件系统备份，因为从属机 仍在与主机同步）。 12.恢复 pg_hba.conf 如果你修改了 pg_hba.conf，则要将其恢复到原始的设置。 也可能需要调整新集簇中的其他配置文件来匹配旧集簇，例如 postgresql.conf （以及它包含的任何文件）和 postgresql.auto.conf。 13.启动新服务器 现在可以安全地启动新的服务器，并且可以接着启动任何rsync过的后备服务器。 14.升级后处理 如果需要做任何升级后处理，pg_upgrade 将在完成后发出警告。它也将 生成必须由管理员运行的脚本文件。这些脚本文件将连接到每一个需要做 升级后处理的数据库。每一个脚本应该这样运行： ``` psql --username=postgres --file=script.sql postgres ``` 这些脚本可以以任何顺序运行并且在运行之后立即删除。 小心 通常在重建脚本运行完成之前访问重建脚本中引用的表是不安全的，这样做 可能会得到不正确的结果或者很差的性能。没有在重建脚本中引用的表可以 随时被访问。 15.统计信息 由于 pg_upgrade 并未传输优化器统计信息，在升级的尾声 你将被指示运行一个命令来生成这些信息。你可能需要设置连接参数来匹配你的新集簇。 16.删除旧集簇 一旦你对升级表示满意，你就可以通过运行 pg_upgrade 完成时提到的脚本来删除旧集簇的 数据目录（如果在旧数据目录中有用户定义的表空间就不可能实现自动删除）。 你也可以删除旧安装目录（例如 bin、 share ）。 17.恢复到旧集簇 在运行 pg_upgrade 之后，如果你希望恢复到 旧集簇，有几个选项： 如果使用了 --check 选项, 则旧集群没有被修改；它可以被重新启动。 如果 --link 选项没有被使用, 旧集群没有被修改；它可以被重新启动。 如果使用了 --link 选项, 数据文件可能在新旧群集之间共享: 如果 pg_upgrade 在链接启动之前中止，旧集群没有被修改，它可以重新启动。 如果你没有启动新集群，旧集群没有被修改，当链接启动时，一个 .old 后缀会附加到 $PGDATA/global/pg_control。 如果要重用旧集群，从 $PGDATA/global/pg_control 移除 .old 后缀；你就可以重启旧集群。 如果你已经启动新群集，它已经写入了共享文件，并且使用旧群集会不安全。这种情况下，需要从备份中还原旧群集。 pg_upgrade创建不同的工作文件，如模式转储，在当前工作目录中。为了安全，请确保该目录不可被任何其他用户读取或者写入。 pg_upgrade在新旧数据目录中启动短期的postmasters。临时 Unix 套接字文件用于与这些postmasters通信，默认情况下，在当前工作目录中进行。 在某些情况下，当前目录的路径名称可能太长，无法成为有效的套接字名称。这种情况下你可以使用 -s 选项将套接字文件放在某些具有较短路径名称的目录中。 为了安全原因，请确保该目录不可被任何其他用户读取或者写入。（这在 Windows 上不受支持。） 如果失败、重建和重索引会影响你的安装，pg_upgrade 将会报告这些情况。用来重建表和索引的升级后脚本将会自动被建立。 如果你正在尝试自动升级很多集簇，你应该发现具有相同数据库模式的集簇 对所有集簇升级都要求同样的升级后步骤，这是因为升级后步骤是基于数据 库模式而不是用户数据。 对于部署测试，创建一个只有模式的旧集簇副本，在其中插入假数据并且升级。 pg_upgrade不支持包含使用这些 reg* OID-引用 系统数据类型的表列的数据库的升级： regcollation regconfig regdictionary regnamespace regoper regoperator regproc regprocedure (regclass, regrole, and regtype can be upgraded.) 如果你想要使用链接模式并且你不想让你的旧集簇在新集簇启动时被修改，考虑使用克隆模式。 如果(克隆模式)不可用，可以复制一份旧集簇并且在副本上以链接模式进行升级。要创建旧集簇的一 份合法拷贝，可以在服务器运行时使用 rsync 创建旧集簇的 一份脏拷贝，然后关闭旧服务器并且再次运行 rsync --checksum 把更改更新到该拷贝以让其一致（ --checksum 是必要的，因为 rsync 在判断文件修改时间的更改时的精度只能到秒级）。你可能想要排除 一些文件，例如 postmaster.pid。如果你的文件系统支持文 件系统快照或者 copy-on-write 文件副本，你可以使用它们来创建旧集簇和 表空间的一个备份，不过快照和副本必须被同时创建或者在数据库服务器关闭 期间被创建。 pg_waldump — 以人类可读的形式显示一个IvorySQL 数据库集簇的预写式日志 pg_waldump [option…​] [startseg [endseg]] 下列命令行选项控制输出的位置和格式： startseg 从指定的日志段文件开始读取。这也隐含地决定了要搜索文件的路径以及 要使用的时间线。 endseg 在读取指定的日志段文件后停止。 -b --bkp-details 输出有关备份块的细节。 -e end --end=end 在指定的WAL位置停止读取，而不是一直读取到日志流的末尾。 -f --follow 在到达可用 WAL 的末尾之后，保持每秒轮询一次是否有新的 WAL 出现。 -n limit --limit=limit 显示指定数量的记录，然后停止。 -p path --path=path 指定搜索日志段文件的目录或包含这些文件的包含 pg_wal 子目录的目录。 缺省值是在当前目录中搜索，当前目录的 pg_wal 子目录和 PGDATA 的 pg_wal 子目录。 -q --quiet 除错误外，不要打印任何输出。当您想知道一系列WAL记录是否可以成功解析但不关心记录内容时，此选项非常有用。 -r rmgr --rmgr=rmgr 只显示由指定资源管理器生成的记录。如果把 list 作为资源管理器名称 传递给这个选项，则打印出可用资源管理器名称的列表然后退出。 -s start --start=start 要从哪个WAL位置开始读取。默认是从找到的最早的文件的第一个可用日志记录开始。 -t `timeline` --timeline=`timeline` 要从哪个时间线读取日志记录。默认是使用 startseg （如果指定） 中的值，否则默认为 1。 -V --version 打印pg_waldump版本并且退出。 -x xid --xid=xid 只显示用给定事务 ID 标记的记录。 -z --stats[=record] 显示概括统计信息（记录的数量和尺寸以及全页镜像）而不是显示 每个记录。可以选择针对每个记录生成统计信息，而不是针对每个资源管理器生成。 -? --help 显示有关pg_waldump命令行参数的帮助并且退出。 PGDATA 数据目录；另请参阅 -p 选项。 PG_COLOR 规定在诊断消息中是否使用颜色。可能的值为 always、 auto、 never。 当服务器正在运行时可能会给出错误的结果。 只有指定的时间线 会被显示（如果没有指定，则显示默认时间线）。 其他时间线上的记录会被忽略。 pg_waldump不能读取具有后缀 .partial 的 WAL 文件。如果需要读取那些文件，需要从文件名中移除 .partial 后缀。 postgres — IvorySQL数据库服务器 postgres [选项…​] postgres 接受下列命令行参数。你也可以通过设置一个配置文件来减少键入大部分这些选项。有些（安全）选项还可以从连接的客户端以一种与应用相关只应用于会话的方法设置。例如，如果设置了 PGOPTIONS 环境变量，那么基于libpq的客户端将都把那个字符串传递给服务器，它将被服务器解释成 postgres 命令行选项。 -B nbuffers 设置被服务器进程使用的共享内存缓冲区数量。这个参数的默认值是initdb自动选择的。指定这个选项等效于设置 shared_buffers 配置参数。 -c name=value 设置一个命名的运行时参数。大多数其它命令行选项实际上都是这种参数赋值的短形式。-c 可以出现多次用于设置多个参数。 -C name 打印命名运行时参数的值，并且退出（详见上面的 -c 选项）。这可以被用在一个运行服务器上，并且从 postgresql.conf 中返回值，这些值可能被在这次调用中的任何参数修改过。它并不反映集簇启动时提供的参数。这个选项用于与一个服务器实例交互的其他程序来查询配置参数值，例如 pg_ctl。面向用户的应用应该使用 SHOW or the pg_settings 视图。 -d debug-level 设置调试级别。数值设置得越高，写到服务器日志的调试输出就越多。取值范围是从 1 到 5。还可以针对某个特定会话使用 -d 0 来阻止父 postgres 进程的服务器日志级别被传播到这个会话。 -D datadir 指定数据库配置文件的文件系统位置。 -e 把默认日期风格设置为“European”，也就是输入日期域的顺序是 DMY。这也导致在一些日期输出格式中把日打印在月之前。 -F 禁用 fsync 调用以提高性能，但是要冒系统崩溃时数据损坏的风险。指定这个选项等效于禁用 fsync 配置参数。 -h hostname 指定 postgres 监听来自客户端应用 TCP/IP 连接的 IP 主机名或地址。该值也可以是一个用逗号分隔的地址列表，或者 * 表示监听所有可用的地址。一个空值表示不监听任何 IP 地址，在这种情况下可以使用 Unix 域套接字连接到服务器。缺省只监听localhost。声明这个选项等效于设置listen_addresses配置参数。默认只监听localhost。指定这个选项等效于设置 listen_addresses 配置参数。 -i 允许远程客户端使用 TCP/IP （互联网域）连接。没有这个选项，将只接受本地连接。这个选项等效于在 postgresql.conf 中或者通过 -h 选项将 listen_addresses 设为 *。这个选项已经被废弃，因为它不允许访问 listen_addresses 的完整功能。所以最好直接设置 listen_addresses。 -k `directory` 指定 postgres 用来监听来自客户端应用连接的 Unix 域套接字的目录。这个值也可以是一个逗号分隔的目录列表。一个空值指定不监听任何 Unix 域套接字，在这种情况下只能用 TCP/IP 套接字来连接到服务器。默认值通常是 /tmp，但是可以在编译的时候修改。指定这个选项等效于设置 unix_socket_directories 配置参数。 -l 启用使用SSL的安全连接。要使这个选项可用，编译IvorySQL时必须打开SSL支持。有关使用SSL的更多信息。 -N max-connections 设置该服务器将接受的最大客户端连接数。该参数的默认值由initdb自动选择。指定这个选项等效于设置 max_connections 配置参数。 -p `port` 指定 postgres 用于监听客户端应用连接的 TCP/IP 端口或本地 Unix 域套接字文件扩展。默认为 PGPORT 环境变量的值。如果 PGPORT 没有设置，那么默认值是编译期间设立的值（通常是 5432）。如果你指定了一个非默认端口，那么所有客户端应用都必须用命令行选项或者 PGPORT 指定同一个端口。 -s 在每条命令结束时打印时间信息和其它统计信息。这个选项对测试基准和调节缓冲区数量有用处。 -S work-mem 指定在使用临时磁盘文件之前排序和散列表使用的基本内存量。 -V --version 打印postgres版本并退出。 --name=value 设置一个命名的运行时参数；其缩写形式是 -c。 --describe-config 这个选项会用制表符分隔的 COPY 格式导出服务器的内部配置变量、描述以及默认值。设计它的目的是用于管理工具。 -? --help 显示有关postgres的命令行参数，并且退出。 这里描述的选项主要被用于调试目的，并且在某些情况下可以协助恢复严重受损的数据库。在生产数据库环境中应该不会去使用它们。在这里列举它们只是为了让IvorySQL系统开发者使用。此外，这些选项可能在将来的版本中更改或删除而不另行通知。 -f { s | i | o | b | t | n | m | h } 禁止某种扫描和连接方法的使用：s 和 i 分别禁用顺序和索引扫描， o、 b 和 t 分别禁用只用索引扫描、位图索引扫描以及 TID 扫描，而 n、 m 和 h 则分别禁用嵌套循环、归并和哈希连接。顺序扫描和嵌套循环连接都不可能完全被禁用。-fs 和 -fn 选项仅仅是在有其他选择时不鼓励优化器使用这些计划类型。 -n 该选项主要用于调试导致服务器进程异常崩溃的问题。对付这种情况的一般策略是通知所有其它服务器进程，让它们终止并且接着重新初始化共享内存和信号量。这是因为一个错误的服务器进程可能在终止之前就已经对共享状态造成了破坏。该选项指定 postgres 将不会重新初始化共享数据结构。一个有经验的系统程序员这时就可以使用调试器检查共享内存和信号量状态。 -O 允许修改系统表的结构。这个选项用于 initdb。 -P 读取系统表时忽略系统索引（但在更改系统表时仍然更新索引）。这在从损坏的系统索引中恢复时有用。 -t pa[rser] | pl[anner] | e[xecutor] 打印与每个主要系统模块相关的查询的时间统计。这个选项不能和 -s 选项一起使用。 -T 该选项主要用于调试导致服务器进程异常崩溃的问题。对付这种情况的一般策略是通知所有其它服务器进程，让它们终止并且接着重新初始化共享内存和信号量。这是因为一个错误的服务器进程可能在终止之前就已经对共享状态造成了破坏。该选项指定 postgres 将通过发送 SIGSTOP 信号停止其他所有服务器进程，但是并不让它们终止。这样就允许系统程序员手动从所有服务器进程收集内核转储。 -v protocol 声明这次会话使用的前/后服务器协议的版本数。该选项仅在内部使用。 -W seconds 在一个新服务器进程被启动时，它实施认证过程之后会延迟这个选项所设置的秒数。这就留出了机会来用一个调试器附着在服务器进程上。 下面的选项仅适用于单用户模式（见 Single-User Mode）。 --single 选择单用户模式。这必须是命令行中的第一个选项。 database 指定要访问的数据库的名称。这必须是命令行中的最后一个参数。如果省略它，则默认为用户名。 -E 在执行命令之前回显所有命令到标准输出。 -j 使用跟着两个新行的分号而不是仅用新行作为命令终止符。 -r filename 将所有服务器日志输出发送到 filename 中。 只有在作为一个命令行选项提供时，这个选项才会兑现。 PGCLIENTENCODING 客户端使用的默认字符编码（客户端可以独立地覆盖它）。这个值也可以在配置文件中设置。 PGDATA 默认的数据目录位置。 PGDATESTYLE DateStyle 运行时参数的默认值（这个环境变量的使用已被废弃）。 PGPORT 默认端口号（在配置文件中设置更好） 一个提到了 semget 或 shmget 的错误消息可能意味着你需要配置内核来提供足够的共享内存和信号量。你也可以通过降低 shared_buffers 值减少IvorySQL的共享内存消耗， 或者降低 max_connections 值减少信号量消耗，这样可以推迟对内核的重新配置。 如果一个消息说另外一个服务器已经在运行，应该仔细地检查，例如根据你的系统可以用命令 $ ps ax | grep postgres 或 $ ps -ef | grep postgres 如果你确信没有冲突的服务器正在运行，那么你可以删除消息中提到的锁文件然后再次尝试。 如果一个失败消息指示它无法绑定到一个端口，可能意味着该端口已经被某些非IvorySQL进程所使用。如果你终止 postgres 并且立即使用相同的端口重启它，你也可能会得到这种错误。在这种情况系，你必须等待几秒直到操作系统关闭该端口，然后再重试。最后，如果你指定了一个操作系统认为需要保留的端口号，你可能也会得到这个错误。例如，很多版本的 Unix 认为低于 1024 的端口号是“可信的”并且只允许 Unix 超级用户访问它们。 实用命令 pg_ctl 可以用来安全方便地启动和关闭 postgres 服务器。 只要有可能，就不要使用 SIGKILL 杀死主 postgres 服务器。这样会阻止 postgres 在终止前释放它持有的系统资源（例如共享内存和信号量）。这样可能会导致启动新的 postgres 进程时出现问题。 要正常地终止 postgres 服务器，可以使用 SIGTERM、 SIGINT 或者 SIGQUIT 信号。第一个在退出前将等待所有客户端终止，第二个将强行断开所有客户端的连接，第三个会不做正确的关闭立即退出并且会导致重启时的恢复。 SIGHUP 信号会重新加载服务器配置文件。也可以向一个单独的服务器进程发送 SIGHUP 信号，但是这样做通常没什么意义。 要取消一个正在运行的查询，可以向运行该查询的进程发送 SIGINT 信号。要干净地终止一个后端进程，可向它发送`SIGTERM`。在 SQL 中可调用的与这两种动作等效的命令可参考 pg_cancel_backend 和 pg_terminate_backend。 postgres 服务器使用 SIGQUIT 来告诉子服务器进程终止但不做正常的清理。该信号不应该被用户使用。向一个服务器进程发送 SIGKILL 也是不明智的 — 主 postgres 进程将把这解释为一次崩溃，并且作为其标准崩溃恢复过程的一部分，它将强制所有的后代进程退出。 -- 选项在FreeBSD或OpenBSD上无法运行，应该使用 -c。这在受影响的系统里是个缺陷； 如果这个缺陷没有被修复，将来的IvorySQL版本将提供一种解决方案。 要启动一个单用户模式的服务器，使用这样的命令 postgres --single -D /usr/local/pgsql/data other-options my_database 用 -D 给服务器提供正确的数据库目录的路径，或者确保环境变量 PGDATA 被设置。同时还要指定你想在其中工作的特定数据库的名字。 通常，单用户模式的服务器会把换行符当做命令输入的终止符。它不明白分号的作用，因为那属于psql。要想把一个命令分成多行，必须在最后一个换行符以外的每个换行符前面敲一个反斜线。这个反斜线和旁边的新行都会被从输入命令中去掉。注意即使在字符串或者注释中也会这样做。 但是如果使用了 -j 命令行选项，那么单个新行将不会终止命令输入。相反，分号-新行-新行的序列才会终止命令输入。也就是说，输入一个紧跟着空行的分号。在这种模式下，反斜线-新行不会被特殊对待。此外，在字符串或者注释内的这类序列也不会被特殊对待。 不管在哪一种输入模式中，如果输入的一个分号不是正好在命令终止符之前或者不是命令终止符的一部分，它会被认为是一个命令分隔符。当真正输入一个命令终止符时，已经输入的多个语句将被作为一个单个事务执行。 要退出会话，输入EOF（通常是 Control + D ）。如果从上一个命令终止符以来已经输入了任何文本，那么EOF将被当作命令终止符，并且如果要退出则需要另一个EOF。 请注意单用户模式的服务器不会提供复杂的行编辑特性（例如没有命令历史）。但用户模式也不会做任何后台处理，例如自动检查点或者复制。 要用默认值在后台启动 postgres： $ nohup postgres >logfile 2>&1 </dev/null & 要用指定端口启动 postgres，例如 1234： $ postgres -p 1234 要使用psql连接到这个服务器，用 -p 选项指定这个端口： $ psql -p 1234 或者设置环境变量 PGPORT： $ export PGPORT=1234 $ psql 命名运行时参数可以用这些形式之一设置： $ postgres -c work_mem=1234 $ postgres --work-mem=1234 两种形式都覆盖 postgresql.conf 中可能存在的 work_mem 设置。请注意在参数名中的下划线在命令行可以写成下划线或连字符。除了用于短期的实验外，更好的习惯是编辑 postgresql.conf 中的设置， 而不是倚赖命令行开关来设置参数。","title":"工具参考","component":"ivorysql-doc","version":"master","name":"tools_reference","url":"/ivorysql-doc/master/tools_reference.html","titles":[{"text":"1. 工具一览表","hash":"工具一览表","id":1},{"text":"2. 客户端工具","hash":"客户端工具","id":2},{"text":"2.1. clusterdb","hash":"clusterdb","id":3},{"text":"2.1.1. 大纲","hash":"大纲","id":4},{"text":"2.1.2. 选项","hash":"选项","id":5},{"text":"2.1.3. 环境","hash":"环境","id":6},{"text":"2.1.4. 诊断","hash":"诊断","id":7},{"text":"2.1.5. 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pg_resetwal","hash":"pg_resetwal","id":156},{"text":"3.6.1. 大纲","hash":"大纲-26","id":157},{"text":"3.6.2. 选项","hash":"选项-24","id":158},{"text":"3.6.3. 环境","hash":"环境-22","id":159},{"text":"3.6.4. 注解","hash":"注解-13","id":160},{"text":"3.7. pg_rewind","hash":"pg_rewind","id":161},{"text":"3.7.1. 大纲","hash":"大纲-27","id":162},{"text":"3.7.2. 警告","hash":"警告-2","id":163},{"text":"3.7.3. 选项","hash":"选项-25","id":164},{"text":"3.7.4. 环境","hash":"环境-23","id":165},{"text":"3.7.5. 注解","hash":"注解-14","id":166},{"text":"3.7.6. 如何工作","hash":"如何工作","id":167},{"text":"3.8. pg_test_fsync","hash":"pg_test_fsync","id":168},{"text":"3.8.1. 大纲","hash":"大纲-28","id":169},{"text":"3.8.2. 选项","hash":"选项-26","id":170},{"text":"3.8.3. 环境","hash":"环境-24","id":171},{"text":"3.9. pg_test_timing","hash":"pg_test_timing","id":172},{"text":"3.9.1. 大纲","hash":"大纲-29","id":173},{"text":"3.9.2. 选项","hash":"选项-27","id":174},{"text":"3.9.3. 用法","hash":"用法","id":175},{"text":"3.9.4. 结果解读","hash":"结果解读","id":176},{"text":"3.9.5. 度量执行器计时开销","hash":"度量执行器计时开销","id":177},{"text":"3.9.6. 改变时间来源Changing time sources","hash":"改变时间来源changing-time-sources","id":178},{"text":"3.9.7. 时钟硬件和计时准确性","hash":"时钟硬件和计时准确性","id":179},{"text":"3.10. pg_upgrade","hash":"pg_upgrade","id":180},{"text":"3.10.1. 大纲","hash":"大纲-30","id":181},{"text":"3.10.2. 选项","hash":"选项-28","id":182},{"text":"3.10.3. 使用","hash":"使用","id":183},{"text":"3.10.4. 注解","hash":"注解-15","id":184},{"text":"3.11. pg_waldump","hash":"pg_waldump","id":185},{"text":"3.11.1. 大纲","hash":"大纲-31","id":186},{"text":"3.11.2. 选项","hash":"选项-29","id":187},{"text":"3.11.3. 环境","hash":"环境-25","id":188},{"text":"3.11.4. 注解","hash":"注解-16","id":189},{"text":"3.12. postgres","hash":"postgres","id":190},{"text":"3.12.1. 大纲","hash":"大纲-32","id":191},{"text":"3.12.2. 选项","hash":"选项-30","id":192},{"text":"3.12.3. 通用选项","hash":"通用选项","id":193},{"text":"3.12.4. 半内部选项","hash":"半内部选项","id":194},{"text":"3.12.5. 用于单用户模式的选项","hash":"用于单用户模式的选项","id":195},{"text":"3.12.6. 环境","hash":"环境-26","id":196},{"text":"3.12.7. 诊断","hash":"诊断-10","id":197},{"text":"3.12.8. 注解","hash":"注解-17","id":198},{"text":"3.12.9. 缺陷","hash":"缺陷","id":199},{"text":"3.12.10. 单用户模式","hash":"单用户模式","id":200},{"text":"3.12.11. 例子","hash":"例子-13","id":201}]},"11":{"id":11,"text":"IvorySQL 是唯一一款基于 PostgreSQL 研发的兼容 Oracle 的数据库。 从 Github 下载源码开始。 请前往 IvorySQL 发布页面。 IvorySQL 项目是瀚高软件提出的一个开源项目，旨在将 Oracle 兼容性功能添加到流行的 PostgreSQL 数据库中。 IvorySQL 开源并且可以免费使用，如果您有任何建议请联系 support@ivorysql.org IvorySQL v5.0 pdf 文档","title":"欢迎来到 IvorySQL","component":"ivorysql-doc","version":"master","name":"welcome","url":"/ivorysql-doc/master/welcome.html","titles":[{"text":"开始","hash":"开始","id":1},{"text":"发布","hash":"发布","id":2},{"text":"关于 IvorySQL","hash":"关于-ivorysql","id":3},{"text":"文档下载","hash":"文档下载","id":4}]},"12":{"id":12,"text":"为了满足PG模式和兼容Oracle模式， IvorySQL在initdb时，可以指定-m参数，来获取PG模式数据库或Oracle兼容模式数据库。 不指定-m参数时，默认为Oracle兼容模式 指定-m参数为pg时，数据库将不再兼容Oracle语法 initdb -m 初始化，需要判断不同的模式，其中Oracle模式下，需要执行postgres_oracle.bki的SQL语句; 启动时会根据初始化模式，判断是否为oracle兼容模式。 database_mode：用于表示初始化模式； database_mode=DB_PG，PG模式，且不可切换； database_mode=DB_ORACLE，Oracle兼容模式； 初始化PG模式： ./initdb -D ../data -m pg 初始化oracle兼容模式： ./initdb -D ../data -m oracle 或 ./initdb -D ../data","title":"双模式设计","component":"ivorysql-doc","version":"master","name":"dual_mode_design","url":"/ivorysql-doc/master/architecture/dual_mode_design.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"13":{"id":13,"text":"双parser框架主要分为两部分，包括SQL端和服务器端编程语言。 基本做法是新增一套兼容Oracle风格的语法和词法，在开启Oracle兼容的情况下，走Oracle风格的语法分析，生成相应的语法树。 具体方法： 在src/backend/下面，新建一个oracle_parser目录，将src/backend/parser/下的scan.l和gram.y复制到该目录下，改名成ora_gram.y和ora_scan.l，添加 Oracle风格的语法和词法分析代码，同时复制keywords.c到该目录下，用来存放自己的关键字。该oracle_parser目录编译成一个动态库 libparser_oracle.so。当开启Oracle兼容的时候，配置文件ivorysql.conf被嵌入到postgresql.conf文件的末尾。配置文件ivorysql.conf中的shared_preload_libraries参数中添加“liboracle_parser”，这样当数据库启动时能够自动载入liboracle_parser动态库。 新增ora_raw_parser 函数指针，当libparser_oracle.so动态库被加载时，该动态库中的 _PG_init() 函数将 oracle_raw_parser() 函数的地址赋值给 ora_raw_parser，_PG_fini()则在兼容模式切换时负责重置 ora_raw_parser 为空。 每一个后端进程调用BackendInitialize()函数，通过后端进程所连接的端口号来设置 port→connmode。如果端口是Oracle兼容端口，则设置connmode为’o’，否则设置为’p’。 这之后PostgresMain()调用InitIvorysql()，如果port→connmode是’o’，则调用函数 SetConfigOption(\"ivorysql.compatible_mode\", \"oracle\", PGC_USERSET, PGC_S_OVERRIDE); 由于这个参数设置了 assign_hook，因此当 SetConfigOption() 函数里执行assign_hook()时，相当于调用 assign_compatible_mode()，从而设置 sql_raw_parser = ora_raw_parser; 在对SQL语句进行分析时，函数pg_parse_query()→raw_parser() 通过函数指针 sql_raw_parser 调用standard parser() 或者 ora_raw_parser()。 下面的图演示了SQL语句分析时发生的事情。 在系统表pg_language中新增pliSQL语言。 具体实现方法： 将PG源码中的plpgsql目录，复制一份，改名为plisql，里面的文件名称，改成plisql开头，因为plpgsql是一个language，改造的plisql也是一个language，因此，plpgsql language的注册函数如plpgsql_validator, plpgsql_call_handler, plpgsql_inline_handler都需要改成plisql开头，里面其他函数的改名都改成以plisql开头。 plisql目录构建为一个插件，initdb时如果数据库模式是Oracle，则创建这个插件。这个插件会将pliSQL语言注册到数据库的系统表中。 pliSQL端没有自己的词法规约，它依赖SQL端的词法规约，因此，针对pliSQL，强制其走兼容Oracle的词法规约，主要改造点是在 plisql_scanner_init 函数中，里面应该调用ora_scanner_init()函数，plisql目录中的 internal_yylex() 函数调用 ora_core_yylex。 pliSQL语法规则在plisql/src/pl_gram.y中，兼容Oracle plSQL块的语法规则都在这个模块中实现。 SQL端创建函数，没有指定language的时候，如果当前是兼容Oracle模式，则默认language是plisql，如果是兼容pg模式，则默认language是plpgsql。oracle_parser中的ora_gram.y默认是plisql，pg parser中的gram.y默认是plpgsql。 匿名块没有指定language的时候，如果是兼容Oracle模式，默认plisql，如果是兼容pg模式，则默认plpgsql。 处理过程的ExecuteDoStmt函数同样根据兼容模式来决定默认language。","title":"Oracle兼容框架之双parser","component":"ivorysql-doc","version":"master","name":"dual_parser","url":"/ivorysql-doc/master/architecture/dual_parser.html","titles":[{"text":"1. SQL端词法语法分离","hash":"sql端词法语法分离","id":1},{"text":"2. 服务器端编程语言词法语法分离","hash":"服务器端编程语言词法语法分离","id":2}]},"14":{"id":14,"text":"在对原有的PostgreSQL改动最小的前提下，实现对Oracle兼容。我们需要实现双parser、双端口、模式PL/PGSQL实现PL/iSQL的框架。实现流程图如下： 保持了Ivorysql 5432端口兼容原有postgres情况，因此 Ivorysql采用另一个独立的端口登录，默认为1521。从该端口登录，默认采用Oracle兼容模式。如果需要从5432端口登录且还要进入兼容模式，则需要通过SET ivorysql.compatible_mode=oracle进行设置。 为了将Oracle语法与PostgreSQL语法间的相互干扰降到最低，因此新增parser模块，用于处理Oracle相关的语法。 新增PL/iSQL过程语言兼容Oracle的PL/SQL过程语言。","title":"Ivorysql框架设计","component":"ivorysql-doc","version":"master","name":"framework_design","url":"/ivorysql-doc/master/architecture/framework_design.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"2.1. 双端口设计","hash":"双端口设计","id":3},{"text":"2.2. parser模块设计","hash":"parser模块设计","id":4},{"text":"2.3. 新增PL/iSQL过程语言","hash":"新增plisql过程语言","id":5}]},"15":{"id":15,"text":"为了兼容Oracle，需要在原有的GUC变量基础之上增加一些用于控制数据库执行结果的变量，以达到和Oracle行为一致的目的。 为了更好地添加兼容的guc参数，以及为了更少地改动pg内核源码，我们需要设计一个框架将guc添加到一个统一的地方。 添加兼容版本的guc参数时，我们需统一在 ivy_guc.c 文件中添加。其中 Ivy_ConfigureNamesBool、Ivy_ConfigureNamesInt、Ivy_ConfigureNamesString、Ivy_ConfigureNamesReal 和 Ivy_ConfigureNamesEnum 分别表示5种不同类型的guc参数，添加guc参数时，将guc的值添加到对应数组中即可。 变量名 描述 ivorysql.compatible_mode 表示当前兼容哪种数据库（pg/oracle），可以通过show命令查看，set命令更改该变量，reset命令重置为连接时的数据库模式，reset all会影响该变量 ivorysql.database_mode 表示当前数据库的模式（pg/oracle），可以通过show命令查看，set/reset/reset all命令不影响该变量 ivorysql.datetime_ignore_nls_mask 表示日期格式是否会受NLS参数影响，默认为0，可以通过set命令设置，reset 命令重置，reset all命令会重置该变量 ivorysql.enable_emptystring_to_NULL 取值为（on/off）,该变量为on时，会将插入的空字符串转成NULL值存储 ivorysql.identifier_case_switch 设置字符大小写转换模式 ivorysql.listen_address 表示兼容模式监听的地址，在初始化数据库时，从ivorysql.conf文件中读取该配置，在配置文件中修改该值，需要重启数据库生效，可以通过show命令查看 ivorysql.port 表示兼容模式下连接的端口号，在初始化数据库时，从ivorysql.conf文件中读取该配置，在配置文件中修改该值，需要重启数据库生效，可以通过show命令查看 nls_date_format 表示默认的日期格式，可以通过show命令查看，默认为‘YYYY-MM-DD’，可以通过set命令设置，可以通过reset命令重置回默认值，reset all 命令会重置该变量 nls_length_semantics 兼容Oracle的同名参数，控制一个字符所占内存的大小 nls_timestamp_format 兼容Oracle的同名参数，控制带时间的日期格式 nls_timestamp_tz_format 兼容Oracle的同名参数，控制带时区的日期格式 shared_preload_libraries 在初始化数据库时，从ivorysql.conf文件中读取，可以通过show命令查看，在配置文件中修改该值，重启数据库生效。 该GUC变量的可选值为0-15 可选值 不经过nls格式化的类型 0 不屏蔽任何类型，所有时间格式均由nls格式化 1 date 2 timestamp 3 date、timestamp 4 timestamptz 5 date、timestamptz 6 timestamp、timestamptz 7 date、timestamp、timestamptz 8 timestampltz 9 date、timestampltz 10 timestamp、timestampltz 11 date、timestamp、timestampltz 12 timestamptz、timestampltz 13 date、timestamptz、timestampltz 14 timestamp、timestamptz、timestampltz 15 date、timestamp、timestamptz、timestampltz 使用方法(以date为例) 查看nls date 的格式及datetime_ignore_nls_mask值 ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# show nls_date_format; nls_date_format ----------------- YYYY-MM-DD (1 row) ivorysql=# show ivorysql.datetime_ignore_nls_mask; ivorysql.datetime_ignore_nls_mask ----------------------------------- 0 (1 row) 创建测试表 ivorysql=# create table test_nls_date(a int, created_at date); CREATE TABLE 插入数据 ivorysql=# insert into test_nls_date values(1, '2024/04/05'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 2024-04-05 (1 row) 修改nls_date_format ivorysql=# set nls_date_format to 'yy-mm-dd'; SET 插入nls格式数据并查看，插入成功 ivorysql=# insert into test_nls_date values(2, '24/04/15'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows) 将date类型改为不经过nls处理，插入相同格式数据，如改成 1 (3,5,7等都可以)，插入数据将报错，nls格式化将不对date的查询结果起效。 ivorysql=# set ivorysql.datetime_ignore_nls_mask to 1; SET ivorysql=# insert into test_nls_date values(3, '24/05/15'); ERROR: date/time field value out of range: \"24/05/15\" LINE 1: insert into test_nls_date values(3, '24/05/15'); ^ HINT: Perhaps you need a different \"datestyle\" setting. ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows)","title":"GUC框架","component":"ivorysql-doc","version":"master","name":"guc_framework","url":"/ivorysql-doc/master/architecture/guc_framework.html","titles":[{"text":"1. 新增GUC变量","hash":"新增guc变量","id":1},{"text":"1.1. 实现","hash":"实现","id":2},{"text":"1.2. 新增变量（目前）","hash":"新增变量目前","id":3},{"text":"1.3. 部分变量使用示例","hash":"部分变量使用示例","id":4},{"text":"1.3.1. ivorysql.datetime_ignore_nls_mask","hash":"ivorysql-datetime_ignore_nls_mask","id":5}]},"16":{"id":16,"text":"IvorySQL 在初始化过程中支持两种数据库模式： PG 模式：保持与原生 PostgreSQL 的兼容性 Oracle 模式（默认）：提供 Oracle 语法兼容及增强功能 用户可通过 initdb 命令参数指定初始化模式，实现不同场景下的兼容性需求。 initdb在开始阶段会解析输入的命令行参数。 参数 功能描述 可选值 默认值 -m 指定数据库模式 oracle/pg oracle -C 设置 Oracle 兼容的大小写转换模式 interchange/normal/lowercase interchange 参数解析流程： 1.继承 PostgreSQL 原有参数处理机制 2.新增模式选择参数 -m 的解析逻辑 3.增加大小写转换参数 -C 的处理模块 执行 setup_data_file_paths() 函数完成关键文件路径配置： if (DB_PG == database_mode) set_input(&bki_file, \"postgres.bki\"); else set_input(&bki_file, \"postgres_oracle.bki\"); 路径检查机制： 1.验证 BKI 文件的存在性（postgres_oracle.bki / postgres.bki） 2.确认配置文件模板的可用性 3.建立与数据库模式对应的系统目录结构 通过 initialize_data_directory() 函数完成核心初始化操作： 调用 create_data_directory() 创建主数据目录（PGDATA）。 通过 create_xlog_or_symlink() 建立 WAL 日志目录。 循环创建 base, global 等标准子目录。 调用set_null_conf()创建空的postgresql.conf。 Oracle 模式下额外创建 ivorysql.conf 配置文件。 调用setup_config()将配置信息写入postgresql.conf。在写入配置时，如果是oracle模式，则会额外向ivorysql.conf中写入配置信息。 执行 bootstrap_template1() 加载对应模式的 BKI 文件初始化template1模板数据库， IvorySQL会额外设置template1模板数据库的数据库模式(oracle/pg)和大小写转换模式。 load_plisql()：安装兼容 Oracle PL/SQL 的 PL/iSQL 过程语言 load_ivorysql_ora()：加载核心Oracle兼容层扩展 make_ivorysql()：创建默认的 ivorysql 数据库 当数据库模式是oracle时，会对用户名进行强制小写转换 提示 在 PostgreSQL 中，\"bki\" 是 \"Bootstrap Kit\" 的简写。Bootstrap Kit 是 PostgreSQL 内部用于引导（bootstrap）数据库系统的一组工具和文件集合。 Bootstrap Kit 主要用于初始化 PostgreSQL 数据库系统的基础结构，包括系统目录（system catalog）、系统表（system table）以及其他必要的系统对象。这些系统目录和表存储了 PostgreSQL 数据库系统的元数据信息，如数据库对象、表结构、索引信息等。 genbki.pl 处理系统表文件如pg_class.h,pg_namespace.h,pg_proc.h等，并生成postgres.bki供initdb加载，主要是加载初始化数据，如创建系统表，系统函数和预定义类型等。 在initdb前的编译阶段，会通过genbki.pl生成postgres.bki 和 postgres_ora.bki，postgres.bki保持PG原样，postgres_ora.bki在pg的基础上增加oracle 兼容的系统对象。","title":"initdb过程","component":"ivorysql-doc","version":"master","name":"initdb_process","url":"/ivorysql-doc/master/architecture/initdb_process.html","titles":[{"text":"1. 参数解析处理","hash":"参数解析处理","id":1},{"text":"2. 文件路径初始化","hash":"文件路径初始化","id":2},{"text":"3. 数据目录初始化流程","hash":"数据目录初始化流程","id":3},{"text":"3.1. 目录结构创建","hash":"目录结构创建","id":4},{"text":"3.2. 配置文件初始化","hash":"配置文件初始化","id":5},{"text":"3.3. 模板数据库引导","hash":"模板数据库引导","id":6},{"text":"3.4. Oracle兼容用户名处理","hash":"oracle兼容用户名处理","id":7}]},"17":{"id":17,"text":"IvorySQL Cloud平台是一个综合性的解决方案，它集成了IvorySQL数据库以及周边生态，以提供全面的数据库和资源管理功能。安装前需要上github编译安装好 前端：https://github.com/IvorySQL/ivory-cloud-web 后端：https://github.com/IvorySQL/ivory-cloud 搭建好K8S集群，并在集群master节点上安装ivory-operator https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE IvorySQL Cloud平台，目前支持Linux系统下的安装，以下为各部分对应的安装包： 组件 安装包 前端 dist 后端 cloudnative-1.0-SNAPSHOT.jar K8S集群 docker.io/ivorysql/ivory-operator:v5.0 docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 另外，云服务平台还需要用户安装以下组件： 后端数据库：负责存储和管理所有与云资源、用户信息、权限控制、计费信息等相关的数据。需要使用PG系列数据库，如PostgreSQL、瀚高数据库、IvorySQL等。 NGINX：支持云服务平台的web服务。 安装前，所有服务器都必须完成以下准备工作。并且将IvorySQL Cloud平台部署在K8S（1.23）的服务器上，且该K8S集群需要有默认的storage class. 所有服务器关闭防火墙，以保证他们之间的网络互通。 systemctl stop firewalld.service systemctl disable firewalld.service IvorySQL Cloud平台的后端数据库需用户自行安装，请参考IvorySQL官网。 # 克隆代码 git clone https://github.com/IvorySQL/ivory-cloud.git # 进入到项目根目录 cd ivory-cloud 请确保ivory-cloud\\cloudnative\\src\\main\\resources\\monitor文件夹，及其所有的子文件夹下的以.sh结尾的文件是unix格式的，如果不是，请执行dos2unix命令转换成unix格式。 dos2unix cloudnative\\src\\main\\resources\\monitor\\* # 编译 mvn clean mvn package -D maven.test.skip=true 打包完成后，可以在 ivory-cloud/cloudnative/target下找到cloudnative-1.0-SNAPSHOT.jar文件 在K8S服务器上执行如下操作： # 创建目录 mkdir -p /home/ivory # 将上一步编译好的文件ivory-cloud/cloudnative/target/cloudnative-1.0-SNAPSHOT.jar上传至上述目录 # 配置文件 ## 创建配置目录 mkdir -p /home/ivory/config ## 上传配置文件 将源代码ivory-cloud/cloudnative/src/main/resources目录里的如下三个配置文件上传至 /home/ivory/config application.yaml application-native.yaml spring_pro_logback.xml ## 修改配置文件，请将url、username、password修改为<<backend-db,后端数据库章节>>安装的数据库的信息。 ## /home/ivory/config/application-native.yaml datasource: druid: db-type: com.alibaba.druid.pool.DruidDataSource driver-class-name: org.postgresql.Driver url: jdbc:postgresql://127.0.0.1:5432/ivorysql username: ivorysql password: \"ivory@123\" # 安装jdk1.8 yum install -y java-1.8.0-openjdk.x86_64 [root@cloud ivory]# pwd /home/ivory/ [root@cloud ivory]# nohup java -jar cloudnative-1.0-SNAPSHOT.jar > log_native 2>&1 & [root@cloud ivory]# ps -ef |grep java root 77494 1 0 10月09 ? 00:03:07 java -jar cloudnative-1.0-SNAPSHOT.jar ## 获取代码 git clone https://github.com/IvorySQL/ivory-cloud-web.git ## 进入项目根目录 cd ivorysql-cloud-web ## 安装依赖 npm install ## 编译打包 npm run build:prod # 创建目录 [root@cloud opt]# mkdir -p /opt/cloud/web # 将前端构建后的dist文件夹置于/opt/cloud/web # 授权 [root@cloud web]# chmod 755 /opt/cloud/web/dist [root@cloud web]# chmod -R 777 /opt/cloud/web/dist 修改文件 [root@cloud dist]# pwd /home/cloud/web/dist [root@cloud dist]# vi config.js var PLATFROM_CONFIG = {}; // ip请更换为当前服务器地址 PLATFROM_CONFIG.baseUrl = \"http://192.168.31.43:8081/cloudapi/api/v1\" //true: need to show \"注册\" on login page //false: don't show \"注册\" on login page globalShowRegister = true //是否隐藏云原生数据库， true: 隐藏云原生数据库；false:显示云原生数据库 disableNative = false // 数据库类型 dbtype = \"IvorySQL\" dbversion = \"5.0\" IvorySQL Cloud平台服务器需要安装nginx，以支持云服务平台的web服务。nginx需要用户自行安装，这里提供一种安装方法作为参考。 [root@cloud web]# wget https://nginx.org/download/nginx-1.20.1.tar.gz [root@cloud web]# ls -lrt 总用量 3924 -rwxrwxr-x. 1 root root 1061461 5月 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 10月 9 16:43 dist.tar.gz drwxrwxrwx. 4 root root 103 10月 21 13:20 dist [root@host30 cloud]# yum -y install pcre-devel [root@host30 cloud]# yum -y install openssl openssl-devel nginx会被安装在configure时由—​prefix指定的目录下，例如这里的/opt/cloud/nginx： ## 解压缩nginx-1.20.1.tar.gz安装包 [root@cloud web]# tar -zxvf nginx-1.20.1.tar.gz ## 解压后生成nginx-1.20.1文件夹 [root@cloud web]# ls -lrt 总用量 3924 -rwxrwxr-x. 1 root root 1061461 5月 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 10月 9 16:43 dist.tar.gz drwxrwxr-x. 9 1001 1001 186 10月 9 16:53 nginx-1.20.1 drwxrwxrwx. 4 root root 103 10月 21 13:20 dist ## 配置导向 [root@cloud web]# cd nginx-1.20.1 [root@cloud nginx-1.20.1]# ./configure --prefix=/opt/cloud/nginx --with-http_ssl_module ## 编译安装 [root@cloud nginx-1.20.1]# make [root@cloud nginx-1.20.1]# make install 配置文件在/opt/cloud/nginx路径下，可以按照github上readme对nginx.conf进行对比修改。ip请配置为当前服务器的ip。 server { listen 9104; server_name 192.168.31.43; location / { root /opt/cloud/web/dist; index index.html index.htm; } error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } } [root@cloud sbin]# pwd /opt/cloud/nginx/sbin [root@cloud sbin]# ./nginx -c /opt/cloud/nginx/conf/nginx.conf [root@cloud sbin]# ps -ef | grep nginx root 2179 131037 0 09:46 pts/1 00:00:00 grep --color=auto nginx root 55047 1 0 10月21 ? 00:00:00 nginx: master process ./nginx -c /opt/cloud/nginx/conf/nginx.conf nobody 55048 55047 0 10月21 ? 00:00:00 nginx: worker process 请自行搭建K8S，此处描述为在K8S集群上安装ivory-operator和load镜像。 参见 https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE 网站上的readme 如果服务器可以直接访问到docker hub,可以跳过该章节。否则需要在所有的K8S集群节点提前load 如下docker镜像 docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/pgbouncer:ubi8-1.23.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 docker.io/prom/prometheus:v2.33.5 docker.io/prom/alertmanager:v0.22.2 docker.io/grafana/grafana:8.5.10","title":"安装说明","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_installation","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html","titles":[{"text":"1. IvorySQL Cloud平台安装","hash":"ivorysql-cloud平台安装","id":1},{"text":"2. 安装前准备","hash":"安装前准备","id":2},{"text":"2.1. 关闭防火墙","hash":"关闭防火墙","id":3},{"text":"2.2. 后端部署","hash":"后端部署","id":4},{"text":"2.2.1. 后端数据库","hash":"后端数据库","id":5},{"text":"2.2.2. 后端服务程序","hash":"后端服务程序","id":6},{"text":"2.2.2.1. 编译后端服务程序","hash":"编译后端服务程序","id":7},{"text":"2.2.2.2. 部署程序","hash":"部署程序","id":8},{"text":"2.2.3. 启动后端服务程序","hash":"启动后端服务程序","id":9},{"text":"2.3. 前端部署","hash":"前端部署","id":10},{"text":"2.3.1. 编译前端服务程序","hash":"编译前端服务程序","id":11},{"text":"2.3.2. 修改目录和文件权限","hash":"修改目录和文件权限","id":12},{"text":"2.3.3. 修改config.js","hash":"修改config-js","id":13},{"text":"2.4. 安装部署nginx","hash":"安装部署nginx","id":14},{"text":"2.4.1. 下载nginx安装包","hash":"下载nginx安装包","id":15},{"text":"2.4.2. 安装相关依赖","hash":"安装相关依赖","id":16},{"text":"2.4.3. 编译安装nginx","hash":"编译安装nginx","id":17},{"text":"2.4.4. 修改配置文件nginx.conf","hash":"修改配置文件nginx-conf","id":18},{"text":"2.4.5. 启动nginx","hash":"启动nginx","id":19},{"text":"2.5. operator部署","hash":"operator部署","id":20},{"text":"2.5.1. 安装ivory-operator","hash":"安装ivory-operator","id":21},{"text":"2.5.2. load镜像","hash":"load镜像","id":22}]},"18":{"id":18,"text":"IvorySQL Cloud是一个基于Web的服务平台，用户可以在任意电脑上通过浏览器进行访问，安装云服务平台的服务器IP为192.168.31.43，然后在浏览器输入http://192.168.31.43:9104/（9104为前端nginx.conf.default中设置的端口）即可进入登录页面页面： 进入登录页面，根据提示输入信息即可访问IvorySQL云服务平台： 点击页面右上角的头像，会显示当前登录用户名和“Log Out”。点击“Log Out”退出当前登录页面。点击用户名则保留当前登录页面： 平台登录admin用户后，点击左侧菜单栏的“K8S集群管理”按钮，进入集群管理页。 点击页面左上角“增加Kubernetes集群”按钮，输入集群信息后提交。 在集群管理页面，可以查看已添加集群的详细信息，同时对集群信息进行编辑和删除操作。 使用demo用户登录平台。 点击左侧菜单栏\"数据库订阅\"按钮，填写需要申请的数据库的各个参数，然后点击\"下一步：确认信息\"。 检查信息后，点击\"确定\" \"确定\"后，会自动跳转到\"数据库管理\"页面，查看订阅任务 显示云服务平台管理的数据库信息。 demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"重启\"选项。 检查信息后，点\"确认\" demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"实例ID\"列。 进入数据库详情页面，点击\"修改密码\"列的图标。 输入新的数据库密码，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"删除实例\"选项。 检查信息后，点击\"确定\"按钮。 此功能需用户自行配置相关插件，如topolvm demo用户登录平台。 点击左侧导航栏\"磁盘扩容\"，选中数据库，点击\"操作\"列的\"修改\"按钮；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"磁盘扩容\"选项。 检查并输入磁盘扩容的大小后，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"规格变更\"，选中数据库，点击\"操作\"列的\"修改\"按钮；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"规格变更\"选项。 检查信息并输入规格变更的大小后，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"数据库备份\"，选中数据库，点击\"操作\"列的\"备份\"；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"备份\"选项。 检查信息并输入备份名称后，点击\"确定\"按钮。 demo用户登录平台。 点击左侧导航栏\"数据库恢复\"，选中数据库，点击\"操作\"列的\"查看\"按钮；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"恢复\"选项。 选择需要备份的文件后，点\"操作\"列的\"恢复\" 输入需要恢复的数据库信息，密码为备份前的数据库密码 后续参考\"4.1数据库订阅\"的步骤。 demo用户登录平台。 点击左侧导航栏\"监控工具\"，选择数据库所在的集群；或者点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，在下拉选项中选择\"监控\"选项。 等待监控创建完毕后，再次重复（2）中操作，跳转新页面，输入用户名：admin，密码admin，点击login登录 选择放大镜图标，点击查看监控指标。 demo用户登录平台。 点击左侧导航栏\"数据库管理\"，选中数据库，点击\"操作\"列的\"更多\"按钮，选择\"登录\"选项。 跳转新页面后，输入数据库用户名 sysdba@ivyo.com 及数据库密码，点击\"Login\"按钮。 连接数据库，即可进行操作。","title":"使用说明","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_usage","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html","titles":[{"text":"1. 登录和退出","hash":"登录和退出","id":1},{"text":"1.1. 用户登录","hash":"用户登录","id":2},{"text":"1.2. 退出登录","hash":"退出登录","id":3},{"text":"2. 管理员功能","hash":"管理员功能","id":4},{"text":"2.1. 添加集群","hash":"添加集群","id":5},{"text":"2.2. 管理集群","hash":"管理集群","id":6},{"text":"3. demo用户功能","hash":"demo用户功能","id":7},{"text":"3.1. 数据库订阅","hash":"数据库订阅","id":8},{"text":"3.2. 数据库管理","hash":"数据库管理","id":9},{"text":"3.3. 数据库重启","hash":"数据库重启","id":10},{"text":"3.4. 修改密码","hash":"修改密码","id":11},{"text":"3.5. 删除实例","hash":"删除实例","id":12},{"text":"3.6. 磁盘扩容","hash":"磁盘扩容","id":13},{"text":"3.7. 规格变更","hash":"规格变更","id":14},{"text":"3.8. 数据库备份","hash":"数据库备份","id":15},{"text":"3.9. 数据库恢复","hash":"数据库恢复","id":16},{"text":"3.10. 数据库监控","hash":"数据库监控","id":17},{"text":"3.11. 可视化登录工具","hash":"可视化登录工具","id":18}]},"19":{"id":19,"text":"当前，PostgreSQL数据库的CALL语句存在以下限制： 不支持INTO子句； 无法调用有返回值的函数； 无法将结果赋值给客户端变量（即 Oracle 中的绑定变量 / host variables）。 为提升对 Oracle 的兼容性，IvorySQL 实现了对 CALL func(…​) INTO :var; 语法的支持，允许用户通过绑定变量（如 :x）接收函数返回值，并在行为（如精度检查、错误处理）上与 Oracle 保持一致。 由于 PostgreSQL/IvorySQL 本身不支持 SQL 层直接向客户端变量赋值，因此本方案采用 “客户端重写 + 服务端协同” 的方式实现： 当 CALL 语句包含绑定变量（如 :x）时： 客户端将其重写为一个特殊的匿名 PL 块（DO $$ ... $$）； 使用扩展查询协议发送，以便传递参数类型和精度信息； 服务端执行该匿名块，并将结果返回给客户端； 客户端再将结果写入对应的绑定变量。 当 CALL 语句不含绑定变量时： 行为与原生 PostgreSQL 完全一致，使用简单查询协议，不做任何重写。 为了在接口中兼容CALL [INTO]语句，需将其转换为匿名PL/iSQL块，并借助匿名块对OUT参数的支持来实现功能等价。这就要求get_parameter_description函数能够正确识别CALL语句，并在遇到CALL INTO时，返回重写后的PL语句。 相应地，get_hostvariables例程需要将这些信息（如是否为 CALL 语句、是否包含INTO、重写后的语句等）保存到HostVariable结构中。HostVariable的定义如下： typedef struct HostVariable { HostVariableEntry *hostvars; int length; bool isdostmt; bool iscallstmt; // 是否来自 CALL 语句 char *convertcall; // 重写后的语句 } HostVariable; 在服务端需要修改语法解析器部分，在ora_gram.y中添加CALL INTO语法规则，并在action部分生成重写后的PL语句，如“x := add(1,2);” CallStmt: CALL func_application { CallStmt *n = makeNode(CallStmt); n->funccall = castNode(FuncCall, $2); $$ = (Node *)n; } | CALL func_application INTO ORAPARAM { CallStmt *n = makeNode(CallStmt); OraParamRef *hostvar = makeNode(OraParamRef); char *callstr = NULL; n->funccall = castNode(FuncCall, $2); hostvar->number = 0; hostvar->location = @4; hostvar->name = $4; n->hostvariable = hostvar; callstr = pnstrdup(pg_yyget_extra(yyscanner)->core_yy_extra.scanbuf + @2, @3 - @2); n->callinto = psprintf(\"%s := %s;\", $4, callstr); pfree(callstr); $$ = (Node *)n; } ; CallStmt结构需要保存INTO子句和转换后的PL语句 typedef struct CallStmt { NodeTag type; FuncCall *funccall; /* from the parser */ FuncExpr *funcexpr; /* transformed call, with only input args */ List *outargs; /* transformed output-argument expressions */ OraParamRef *hostvariable; /* only used for get_parameter_description() */ char *callinto; /* rewrite CALL INTO to a PL assign stmt */ } CallStmt; 为区分普通 DO 语句和由 CALL 转换而来的匿名块，语法中新增GENERATED FROM CALL关键字： opt_do_from_where: GENERATED FROM CALL { $$ = true; } | /*EMPTY*/ { $$ = false; } ; 生成的 DoStmt 节点将设置 do_from_call = true，供执行器识别。 typedef struct DoStmt { NodeTag type; List *args; /* List of DefElem nodes */ List *paramsmode; /* List of parameters mode */ List *paramslen; /* List of length for parameter datatypes */ bool do_from_call; /* True if DoStmt is come from CallStmt */ } DoStmt; 在IVY接口中，占位符信息是通过一个名为get_parameter_description的集合返回函数（SRF）获取的。该函数需要能够识别输入语句的类型，并在遇到CALL INTO语句时，返回重写后的PL/iSQL赋值语句。 为此，IvorySQL对该函数的返回结构（TupleDesc）进行了扩展：新增了一个hint 字段，专门用于返回CALL INTO语句重写后的PL代码；对于其他类型的语句，该字段保持为NULL。 此外，原函数结果集的第一条元组的第一个字段原本仅用true/false来区分语句是否为匿名块。为了更准确地识别语句类型（尤其是CALL语句），现已将其修改为返回对应解析树的 CommandTag。 所有这些元数据信息最终会被封装到一个用户上下文结构中，以便在 SRF 函数的多次调用之间高效传递和复用。 { OraParamExtralData *extral; const char *cmdtag; char *callintoexpr; } outparam_fctx; 接口层 CALL涉及的ivy前缀的接口包括： IvyStmtExecute IvyStmtExecute2 IvyexecPreparedStatement IvyexecPreparedStatement2 在上述接口中，用户传入的CALL [INTO]语句会被重写为一种“特殊”的匿名块语句。为了明确标识这类由CALL转换而来的匿名块，在接口的语句类型定义中新增了一种专用类型。该类型的作用是在IvyHandleDostmt中正确识别此类语句，并生成形如 DO ... USING … — GENERATED FROM CALL 的执行语句。 typedef enum IvyStmtType { IVY_STMT_UNKNOW, IVY_STMT_DO, IVY_STMT_DOFROMCALL, /* new statement type */ IVY_STMT_DOHANDLED, IVY_STMT_OTHERS } IvyStmtType; 在重写 CALL 语句时，如果遇到调用函数的 CALL INTO 语句，接口需要对绑定变量的顺序进行内部调整。这一调整对用户是完全透明的：用户在绑定参数时，只需按照 CALL 语句中出现的顺序操作即可——即 INTO 子句中的变量在原语句中位于最后。 然而，在重写生成的特殊匿名块中，该 INTO 变量会作为赋值表达式的左值（即第一个参数）出现。因此，接口必须在内部将绑定顺序正确调整，确保执行逻辑与用户预期一致。 所有涉及此逻辑的接口例程都需要实现这一处理，相关例程如下： Ivyreplacenamebindtoposition Ivyreplacenamebindtoposition2 Ivyreplacenamebindtoposition3 在IvyexecPreparedStatement 和 IvyexecPreparedStatement2 这类接口中，用户需要显式提供每个参数的 paramvalues、paramlengths、paramformats 和 parammode。对于 CALL 语句，这些参数数组中的元素顺序必须根据重写后的匿名块结构进行位置调整，以确保绑定与执行逻辑一致。 其中，IvyexecPreparedStatement2 更为特殊：它要求用户额外提供一个 IvyBindOutInfo* 类型的输出绑定列表。该列表不仅用于绑定 OUT 参数，还被 IvyAssignPLISQLOutParameter 在获取 PL/iSQL 过程返回结果时用来识别每个 OUT 参数的数据类型。因此，在处理 CALL语句时，接口会先对用户传入的 IvyBindOutInfo*列表进行位置重排（将INTO对应的输出变量移至首位），再将其写入IvyPreparedStatement语句句柄中，供后续赋值使用。 关于输出参数的精度处理：当CALL语句中的输出绑定变量与实际返回值的精度不匹配时，系统可能报错，也可能自动截断——具体行为取决于绑定变量的数据类型是否与过程/函数声明的参数类型完全一致。 在PL/iSQL inline handler中，每个OUT参数的精确数据类型均可通过ParamListInfo在绑定阶段获取。如果当前执行的匿名块是由CALL语句转换而来的特殊DoStmt，那么在执行赋值时，系统会进行如下判断： 若ParamListInfo中记录的类型与函数/存储过程形参的类型完全相同，则采用强制类型转换赋值； 否则，采用隐式类型转换赋值。 这一机制旨在兼容Oracle的行为，确保在类型不完全匹配时仍能安全、合理地完成赋值。 -- 原始 CALL语句： CALL my_func(:in1, :in2) INTO :out; -- 重写为： do $$BEGIN :out := my_func(:in1, :in2); END$$ using out INOUT, in1 INOUT, in2 INOUT paramslength -1,-1,-1 GENERATED FROM CALL;","title":"CALL INTO","component":"ivorysql-doc","version":"master","name":"call_into","url":"/ivorysql-doc/master/compatibility_features_design/call_into.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 整体设计思路","hash":"整体设计思路","id":2},{"text":"3. 实现原理","hash":"实现原理","id":3},{"text":"3.1. 交互式终端","hash":"交互式终端","id":4},{"text":"3.2. 服务端","hash":"服务端","id":5}]},"20":{"id":20,"text":"为了满足Oracle的引用标识符大小写兼容，ivorysql设计了三种引用标识符的大小写转换模式。 如果在数据库初始化时附加了参数 -C，值可以为 normal/interchange/lowercase，则代码中 Initdb.c→main() 函数会处理该参数，根据参数值设置全局变量 caseswitchmode。然后 initdb 命令会以 -boot 模式启动一个 postgres 进程用于设置 template1 模板数据库，同时赋予参数 -C ivorysql.identifier_case_switch=caseswitchmode 给新进程。 这个新启动的后端进程会通过下面的代码将 identifier_case_switch 信息写入 pg_control 文件： BootstrapModeMain() -> BootStrapXLOG(); /* save database compatible level value */ ControlFile->dbmode = bootstrap_database_mode; ControlFile->casemode = identifier_case_switch; /* some additional ControlFile fields are set in WriteControlFile() */ WriteControlFile(); 当用户使用 pg_ctl 命令启动数据库时，postmaster 进程会读取 pg_control 文件的内容，代码调用路径为： PostmasterMain()-->SetCaseGucOption()-->GetCaseSwitchModeFromControl() 读取到参数值后调用 SetConfigOption() 函数进行赋值。 在每个新的后端进程开始的时候，由于是从 postmaster 进程 fork 而来，会自动拥有相同的 ivorysql.identifier_case_switch 参数值。首先处理 startup 包，其中如果包含 database 或者 user 这两个参数，则根据 identifier_case_switch 对参数值做相应的处理。 源代码为： BackendMain()->BackendInitialize()-->ProcessStartupPacket() 另外，在处理用户的 SQL 语句时，如果包含标识符，也会进行同样的处理，代码分布在： SplitIdentifierString()，quoteOneName() 和 SplitGUCList() 这几个函数中。","title":"引用标识符大小写转换","component":"ivorysql-doc","version":"master","name":"case_conversion","url":"/ivorysql-doc/master/compatibility_features_design/case_conversion.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"21":{"id":21,"text":"在Oracle数据库中，空字符串('')被视为NULL值，这是Oracle的一个重要特性。为了兼容Oracle的这一行为，IvorySQL提供了空字符串转NULL功能。当启用该功能后，SQL语句中的空字符串将自动转换为NULL值，从而保证Oracle应用程序在IvorySQL上的行为一致性。 参数 ivorysql.enable_emptystring_to_NULL 对应的GUC变量为 enable_emptystring_to_NULL。 在文件 ora_scan.l 中可以看到对这个变量的使用： case xe: yylval->str = litbufdup(yyscanner); if (strcmp(yylval->str, \"\") == 0 && ORA_PARSER == compatible_db && enable_emptystring_to_NULL) { return NULL_P; } return SCONST; 其中 xe 代表被引号包围的字符串： <xe> extended quoted strings (support backslash escape sequences) 上面代码的逻辑是，在词法分析时如果遇到空字符串，在空转NULL功能启用的情况下，返回 NULL_P，否则返回 SCONST。 在语法分析文件 ora_gram.y 里，对语句 insert into abc values(''); 是这样解析的： values_clause: VALUES '(' expr_list ')' { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1($3); $$ = (Node *) n; } expr_list: a_expr { $$ = list_make1($1); } a_expr: c_expr { $$ = $1; } c_expr: AexprConst { $$ = $1; } AexprConst: Iconst | Sconst { $$ = makeStringConst($1, @1); } | NULL_P { $$ = makeNullAConst(@1); } 以上代码对词法分析时返回的 NULL_P 或者 SCONST 分别构建对应的节点进行处理。","title":"空字符串转NULL功能","component":"ivorysql-doc","version":"master","name":"empty_string_to_null","url":"/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"22":{"id":22,"text":"Force View 提供 Oracle 兼容的 CREATE [OR REPLACE] FORCE VIEW 与 ALTER VIEW …​ COMPILE 行为，允许开发者先落地视图占位，再在依赖补齐后恢复正常执行。 系统将原始 SQL 及标识符大小写模式写入专属目录项，依赖恢复时可自动或手动编译为普通视图，且保持 Oracle 风格的告警与错误提示。 PG在删除基表时，如果有视图依赖于此基表，则不允许删除，需要使用cascade 关键字全部删除；Oracle删除基表时，允许直接删除基表，而保留force view并将force view 转为无效状态，且创建普通视图后，也可以删除此视图的基表，但把视图转为无效状态。 为了支持新的语法，需要增加对应的语法规则。且FORCE VIEW的状态在多个session之间是共享的，新增一张系统表用来存储FORCE VIEW的相关信息。 src/backend/oracle_parser/ora_gram.y 注册 Force View 关键字。 语法阶段为 ViewStmt→force 赋值，并在 AlterTableStmt 阶段生成 AT_ForceViewCompile 选项。 解析阶段保留 ViewStmt→stmt_literal，后续占位时需要复用原始文本。 /* Insert or update the pg_force_view catalog as needed */ if (need_store) { ...... StoreForceViewQuery(address.objectId, stmt->replace, ident_case, stmt->stmt_literal ? stmt->stmt_literal : queryString); } src/include/nodes/parsenodes.h 为 ViewStmt 增加 bool force、char *stmt_literal 字段。 AT_ForceViewCompile 在 parsenodes.h 与 tablecmds.c 成对定义，确保 ALTER VIEW …​ COMPILE 进入 AlterTable 流程。 parse_analyze 仍按普通路径执行，Force 模式只在解析报错后介入。 DefineView()（src/backend/commands/view.c）统一处理普通与 Force 视图。 代码在 PG_TRY/PG_CATCH 包裹下先尝试普通解析，捕获语义错误后根据 stmt→force 决定是否进入Force View 流程。 成功解析继续调用 StoreViewQuery() 写入 _RETURN 规则，失败则进入 Force View 分支，保证视图对象仍然存在。 src/include/catalog/pg_force_view.h 定义目录表，字段包含视图 OID (fvoid)、标识符大小写模式 (ident_case)、原始 SQL 文本 (source)。 唯一索引 pg_force_view_fvoid_index 搭配 syscache FORCEVIEWOID（src/include/catalog/syscache_info.h）实现按视图 OID 查找。 目录表开启 TOAST，长 SQL 文本不会被截断，可覆盖复杂迁移脚本。 CATALOG(pg_force_view,9120,ForceViewRelationId) { /* oid of force view */ Oid fvoid; /* see IDENT_CASE__xxx constants below */ char ident_case; #ifdef CATALOG_VARLEN /* variable-length fields start here */ /* sql definition */ text source; #endif } FormData_pg_force_view; bool rel_is_force_view(Oid relid) 位于 src/backend/commands/view.c，通过判断 _RETURN 规则是否存在确认视图是否处于 Force 状态。 Force View 仍登记为 relkind = RELKIND_VIEW，避免额外兼容分支。 pg_class.relhasrules 在占位状态设置为 false，配合作为判断依据。 DefineView() 在解析成功后调用 DefineVirtualRelation() 写入 pg_class、pg_attribute 等结构。 StoreViewQuery() 生成 _RETURN 规则并维护依赖关系。 该路径不会触及 pg_force_view，视图完成后立即可用。 CreateForceVirtualPlaceholder()（src/backend/commands/view.c）负责生成或复用占位视图： 若视图不存在，调用 DefineVirtualRelation() 建立基础对象，但不写入 _RETURN 规则。 若已有 Force View，复用当前记录，更新列定义或清理旧元数据。 若已有普通视图且声明了 OR REPLACE，调用 make_view_invalid() 失效原视图，再写入新的占位定义。 StoreForceViewQuery() 将 stmt_literal 与当前 ivorysql.identifier_case_switch 持久化到 pg_force_view，以便后续恢复大小写语义。 视图占位完成后向客户端返回 WARNING: View created with compilation errors，提示仍处于不可用状态。 make_view_invalid() 在依赖对象被 DROP/ALTER 等操作影响时调用。 函数删除 _RETURN 规则、清理 pg_depend 依赖、重置 pg_class.relhasrules 并清空 pg_attribute 列信息。 同步生成 CREATE FORCE VIEW …​ AS <pg_get_viewdef> 文本写入 pg_force_view，ident_case 设置为 IDENT_CASE_UNDEFINE 表示该文本由系统构造。 视图仍然存在并可在元数据中枚举，但实际访问会触发 Force 判定。 _RETURN 缺失导致执行器在打开视图时调用 compile_force_view()，若编译仍失败则抛出 view \"<schema>.<name>\" has errors。 用户可通过 ALTER VIEW …​ COMPILE 或再次 CREATE OR REPLACE FORCE VIEW 来恢复。 parse_relation.c 的 addRangeTableEntry() 和 parse_clause.c 的目标关系打开逻辑在判定 Force View 后调用 compile_force_view()。 该函数重新执行 raw_parser 与 parse_analyze，成功则再安装 _RETURN 规则，失败直接终止当前语句。 AT_ForceViewCompile 在 tablecmds.c Phase 2 阶段执行，先申请 AccessExclusiveLock 再调用 compile_force_view()。 成功编译返回常规 ALTER VIEW 完成信息；失败打印 WARNING: View altered with compilation errors，视图仍保持占位。 compile_force_view() 读取 pg_force_view.source 并构造 ViewStmt，随后调用 compile_force_view_internal()。 函数通过 checkViewColumns() 比对旧列，允许新增列但禁止类型不兼容的替换；新增列由 AT_AddColumnToView() 完成。 _RETURN 规则由 StoreViewQuery() 重新生成，最后 DeleteForceView() 删除目录项，视图恢复为普通状态。","title":"Force View","component":"ivorysql-doc","version":"master","name":"force_view","url":"/ivorysql-doc/master/compatibility_features_design/force_view.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 语法与解析入口","hash":"语法与解析入口","id":3},{"text":"2.1.1. Force View 语法支持","hash":"force-view-语法支持","id":4},{"text":"2.1.2. AST 字段扩展","hash":"ast-字段扩展","id":5},{"text":"2.1.3. 解析入口","hash":"解析入口","id":6},{"text":"2.2. Force View 元数据","hash":"force-view-元数据","id":7},{"text":"2.2.1. pg_force_view 目录","hash":"pg_force_view-目录","id":8},{"text":"2.2.2. 视图状态","hash":"视图状态","id":9},{"text":"2.3. 创建与替换流程","hash":"创建与替换流程","id":10},{"text":"2.3.1. 普通 view","hash":"普通-view","id":11},{"text":"2.3.2. Force view","hash":"force-view","id":12},{"text":"2.4. 依赖失效与回退","hash":"依赖失效与回退","id":13},{"text":"2.4.1. 主动失效逻辑","hash":"主动失效逻辑","id":14},{"text":"2.4.2. 失效后的可见行为","hash":"失效后的可见行为","id":15},{"text":"2.5. 自动与主动编译","hash":"自动与主动编译","id":16},{"text":"2.5.1. 自动编译触发点","hash":"自动编译触发点","id":17},{"text":"2.5.2. 主动编译","hash":"主动编译","id":18},{"text":"2.5.3. 列校验与元数据更新","hash":"列校验与元数据更新","id":19}]},"23":{"id":23,"text":"PostgreSQL支持函数(FUNCTION)和存储过程(PROCEDURE)，但是在语法上和Oracle有差异，为了让Oracle的PLSQL语句可以在IvorySQL上执行，也就是“语法兼容”，IvorySQL采取这样的解决方案：如果Oracle的子句在IvorySQL中存在相同功能的子句，则直接映射为对应的IvorySQL子句，否则只实现其语法，不实现其功能。 PL/iSQL是IvorySQL中的过程语言名称，专门用来兼容Oracle的PLSQL语句。为了兼容Oracle风格的函数与存储过程语法，需要对psql客户端工具、SQL端以及PL/iSQL端做相应处理。 Oracle的sqlplus工具使用斜线(/)来结束函数和存储过程，IvorySQL的客户端工具psql需要兼容同样的语法，也就是说我们常规的遇到分号发送语句给服务端的机制，当遇到Oracle风格的函数和存储过程命令时失效，改为使用斜线(/)发送命令。 为此在 PsqlScanStateData 结构体中增加如下字段： bool cancel_semicolon_terminator; /* not send command when semicolon found */ /* * State to track boundaries of Oracle ANONYMOUS BLOCK. * Case 1: Statements starting with << ident >> is Oracle anonymous block. */ int token_count; /* # of tokens, not blank or newline since start of statement */ bool anonymous_label_start; /* T if the first token is \"<<\" */ bool anonymous_label_ident; /* T if the second token is an identifier */ bool anonymous_label_end; /* T if the third token is \">>\" */ /* * Case 2: DECLARE BEGIN ... END is Oracle anonymous block syntax. * DECLARE can also be a PostgreSQL cursor declaration statement, we need to tell this. */ bool maybe_anonymous_declare_start; /* T if the first token is DECLARE */ int token_cursor_idx; /* the position of keyword CURSOR in SQL statement */ /* * Case 3: DECLARE BEGIN ... END is Oracle anonymous block syntax. * BEGIN can also be a PostgreSQL transaction statement. */ bool maybe_anonymous_begin_start; /* T if the first token is BEGIN */ 同时修改 ora_psqlscan.l，添加和修改相应的词法规则, 以下是代码片段示例： { if (is_oracle_slash(cur_state, cur_state->scanline)) { /* Terminate lexing temporarily */ cur_state->cancel_semicolon_terminator = false; cur_state->maybe_anonymous_declare_start = false; cur_state->maybe_anonymous_begin_start = false; cur_state->anonymous_label_start = false; cur_state->anonymous_label_ident = false; cur_state->anonymous_label_end = false; cur_state->start_state = YY_START; cur_state->token_count = 0; cur_state->token_cursor_idx = 0; cur_state->identifier_count = 0; cur_state->begin_depth = 0; cur_state->ora_plsql_expect_end_symbol = END_SYMBOL_INVALID; return LEXRES_SEMI; } ECHO; 避免将注释等场景中的斜线误判为结束符，为此在 oracle_fe_utils/ora_psqlscan.l 文件中新增 is_oracle_slash 函数接口用于检测： bool is_oracle_slash(PsqlScanState state, const char *line) { bool result = false; switch (state->start_state) { case INITIAL: case xqs: /* treat these like INITIAL */ { int len, i; bool has_slash = false; len = strlen(line); for (i = 0; i < len; i++) { /* allow special char */ if (line[i] == '\\t' || line[i] == '\\n' || line[i] == '\\r' || line[i] == ' ') continue; if (line[i] == '/') { if (has_slash) break; has_slash = true; continue; } /* others */ break; } if (i == len && has_slash) result = true; } break; default: break; } return result; } SQL端要能够识别函数和存储过程的创建语法，这是通过修改ora_base_yylex来实现的。这个函数预取并缓存token，如果是Oracle语法格式则组织一个SCONST发送给PLSQL端，否则从堆栈中获取之前预读的token，按照原生PG的逻辑进行处理。 在ora_base_yy_extra_type数据结构中增加如下字段： /* * The native PG only cache one-token info include yylloc, yylval and token * number in yyextra, IvorySQL cache multiple tokens info using two arrays. */ int max_pushbacks; /* the max size of cache array */ int loc_pushback; /* # of used tokens */ int num_pushbacks; /* # of cached tokens */ int *pushback_token; /* token number array */ TokenAuxData *pushback_auxdata; /* auxdata array */ OraBodyStyle body_style; int body_start; int body_level; 增加token堆栈的操作接口： push_back_token forward_token ora_internal_yylex internal_yylex  ora_base_yylex函数中在创建函数、过程、匿名块时会预读部分token，使用上述结构缓存到堆栈中，是为了构造一个符合Oracle PL/SQL语法的SCONST发送给PL/iSQL端去处理。具体请参考源代码。 该部分主要修改了pl_gram.y文件，以兼容PLSQL的函数和存储过程语法，在不影响PG原生的PL/pgSQL的前提下去兼容Oracle PL/SQL语法形式，如下是DECLARE部分兼容的代码示例，更多请参考IvorySQL源代码。 /* * The declaration section of the outermost block in Oracle does not have the DECLARE keyword. */ ora_outermost_pl_block: ora_decl_sect K_BEGIN proc_sect exception_sect K_END opt_label { PLiSQL_stmt_block *new; new = palloc0(sizeof(PLiSQL_stmt_block)); new->cmd_type = PLISQL_STMT_BLOCK; new->lineno = plisql_location_to_lineno(@2); new->stmtid = ++plisql_curr_compile->nstatements; new->label = $1.label; new->n_initvars = $1.n_initvars; new->initvarnos = $1.initvarnos; new->body = $3; new->exceptions = $4; check_labels($1.label, $6, @6); plisql_ns_pop(); $$ = (PLiSQL_stmt *)new; } ; ora_decl_sect: opt_block_label opt_ora_decl_start opt_ora_decl_stmts { if ($2) { if ($1 == NULL) { plisql_ns_push(NULL, PLISQL_LABEL_BLOCK); } } } opt_ora_decl_stmts { if ($4) { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; /* Remember variables declared in decl_stmts */ $$.n_initvars = plisql_add_initdatums(&($$.initvarnos)); } else { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); $$.n_initvars = 0; if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; $$.initvarnos = NULL; } } ; opt_ora_decl_start: K_DECLARE { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = true; } | /*EMPTY*/ { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = false; } ; opt_ora_decl_stmts: ora_decl_stmts { $$ = true; } | /*EMPTY*/ { $$ = false; } ora_decl_stmts: ora_decl_stmts ora_decl_stmt | ora_decl_stmt ; ora_decl_stmt: decl_statement ;","title":"PL/iSQL函数与存储过程","component":"ivorysql-doc","version":"master","name":"function_procedure","url":"/ivorysql-doc/master/compatibility_features_design/function_procedure.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 客户端工具psql","hash":"客户端工具psql","id":3},{"text":"2.2. SQL端","hash":"sql端","id":4},{"text":"2.3. PL/iSQL端","hash":"plisql端","id":5}]},"24":{"id":24,"text":"Oracle和IvorySQL中的 LIKE 语法是相同的，他们的区别在于表达式的类型，Oracle支持对数字、日期、字符串类型的列用 LIKE 关键字配合通配符来实现模糊查询。原生PostgreSQL只支持字符串类型，不支持日期、数字类型，IvorySQL通过扩展数据类型支持和操作符重载，实现了与 Oracle 兼容的 LIKE 操作符功能。 PostgreSQL的字符串基本类型是text，所以 LIKE 是以text为基础，其他PostgreSQL类型隐式转换成text，不用创建operator就能自动转换；IvorySQL中oracle兼容的字符串类型是varchar2，因此创建一个varchar2的 LIKE 操作符，其他oracle的类型也通过隐式转换成varchar2实现不用创建操作符，也能使用 LIKE 操作符。 在之前实现oracle兼容数据类型时，IvorySQL做了integer，float8，float4 等一些数据类型到varchar2的隐式转换，没有直接到text的。因此实现这些兼容类型的 LIKE 操作符兼容，有两种方式。一种需要针对每个类型添加一个 LIKE 操作符，另一种是做一个基本的varchar2的 LIKE 操作符。在第二种实现方式中，IvorySQL针对float8，integer，number等已经做了向varchar2类型的隐式转换，这些数据类型可以和varchar2用同一个操作符，这样在创建操作符的时候只需要创建varchar2类型的 LIKE 操作符就可以。 可以通过以下SQL语句查看已经存在的类型隐式转换 -- 查询已存在的隐式转换路径(9503为varchar2的OID) SELECT t1.typname AS source_type, t2.typname AS target_type FROM pg_cast C JOIN pg_type t1 ON C.castsource = t1.OID JOIN pg_type t2 ON C.casttarget = t2.OID WHERE C.casttarget = 9503; 经过查看可得，需要进行兼容的类型全部都在该表中做了隐式转换，所以可以直接将这些类型转换为text类型然后进行模糊查询。 CREATE OR REPLACE FUNCTION sys.varchar2like(varchar2, varchar2) RETURNS bool AS $$ SELECT $1::text LIKE $2::text; $$ LANGUAGE SQL IMMUTABLE STRICT; CREATE OPERATOR ~~ ( PROCEDURE = sys.varchar2like, LEFTARG = varchar2, RIGHTARG = varchar2 ); 第一段代码定义了一个名为sys.varchar2like的函数，它接收两个varchar2类型的参数，通过将它们转换为PostgreSQL原生的text类型后执行标准的LIKE模式匹配，最终返回布尔值表示是否匹配。 第二段代码创建了一个名为~~的操作符，它将使用前面定义的varchar2like函数作为实现，并指定该操作符左右两边的参数都必须是varchar2类型。 这样就在IvorySQL中建立了一个与Oracle兼容的LIKE操作符，当用户使用~~操作符时，实际上是通过类型转换后调用PostgreSQL原生的LIKE功能来完成模式匹配，从而实现了Oracle的LIKE语义兼容。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"like_operator","url":"/ivorysql-doc/master/compatibility_features_design/like_operator.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"1.1. 类型转换","hash":"类型转换","id":2},{"text":"1.2. 核心函数实现","hash":"核心函数实现","id":3}]},"25":{"id":25,"text":"嵌套子函数：定义在函数、存储过程或匿名块内部的函数或存储过程，也称为 subproc 或 inner 函数。 父函数：承载嵌套函数的外层函数、存储过程或匿名块，执行过程中负责实际触发子函数调用。 当 DECLARE 块里出现 function …​ is/as begin …​ end 结构时，pl_gram.y 会调用 plisql_build_subproc_function()（对应创建普通函数，这一阶段相当于在 pg_proc 中更新 catalog 中的注册信息）： 在 PLiSQL_function 的 subprocfuncs[] 数组中创建 PLiSQL_subproc_function 结构，记录名称、参数、返回类型、属性等，获得一个下标 fno 作为该子函数的标识。 调用 plisql_check_subprocfunc_properties() 校验声明与定义的合法组合。 保存到父函数的 datum 表：编译期的 PLiSQL_function→datums 描述子函数里的变量、记录字段，PLiSQL_execstate→datums 保存着执行过程中的变量。 如果子函数里使用了多态参数，在语法阶段保存到 subprocfunc→src，同时将 has_poly_argument 设成 true，执行时按不同实参类型重新编译。 父函数的 PLiSQL_function 结构多了一个 subprocfuncs 数组，里面每个元素就是刚才创建的 PLiSQL_subproc_function。 子函数结构体 PLiSQL_subproc_function 有一个哈希表指针 HTAB *poly_tab，默认为空。当子函数里使用了多态函数时，has_poly_argument 为 true，则会在初次编译时初始化 poly_tab。poly_tab 的 key 是 PLiSQL_func_hashkey，记录着子函数的 fno、输入参数类型等； value 是编译好的 PLiSQL_function *（plisql 函数的执行上下文）。 编译过程中，pg 解析器会生成一个 ParseState 结构，plisql_subprocfunc_ref() 会通过 ParseState→p_subprocfunc_hook() 找到父函数的 PLiSQL_function，调用 plisql_ns_lookup() 找到所有同名子函数的 fno，根据参数个数、类型找到最合适的多态函数。 FuncExpr 结构构造时会对子函数进行标记，方便后期执行阶段识别：function_from = FUNC_FROM_SUBPROCFUNC，parent_func 指向父级 PLiSQL_function，funcid = fno。 plisql_call_handler() 当 function_from == FUNC_FROM_SUBPROCFUNC，会用 parent_func + fno 找到对应的 PLiSQL_subproc_function： 如果不是多态：直接复用 subprocfunc→function 里的动作树。 如果是多态：先在 poly_tab 查有没有编译结果；没有就调用 plisql_dynamic_compile_subproc() 编译，放进 poly_tab 缓存。 子函数开始执行之前，plisql_init_subprocfunc_globalvar() 会把父函数的 datum 表中有关的变量 fork 一份，这样子函数可以获取到父函数的变量值，也不会污染父函数的变量空间；执行后由 plisql_assign_out_subprocfunc_globalvar() 把需要回写的变量更新到父函数的 datum 表。 pl_gram.y 新增子过程声明、嵌套定义的产生式，并在创建过程中记录 lastoutvardno、子过程信息等元数据。 支持在子函数内引用父过程变量、子过程以及自定义类型。 当 DECLARE 块内出现 function …​ is/as begin …​ end 结构时，pl_gram.y 会调用 plisql_build_subproc_function() 进行编译： 在 PLiSQL_function 的 subprocfuncs[] 中创建 PLiSQL_subproc_function 结构，记录名称、参数、返回类型和属性，分配下标 fno 作为子函数标识。 调用 plisql_check_subprocfunc_properties() 校验声明与定义属性组合是否合法，防止重复或缺失声明造成的语义错误。 父函数的 Datum 表在编译期和执行期分别缓存子函数能访问的变量： PLiSQL_function→datums 保存子函数编译阶段可见的变量与记录字段信息。 PLiSQL_execstate→datums 在执行阶段持有实时的变量数值，实现运行期访问。 若子函数包含多态参数，语法阶段会： 将子函数源文本拷贝到 subprocfunc→src。 设置 has_poly_argument = true，为后续按实参类型动态编译做好准备。 父函数的 PLiSQL_function 结构新增 subprocfuncs 数组，每个元素对应一个 PLiSQL_subproc_function。 PLiSQL_subproc_function 持有 HTAB *poly_tab 指针；当 has_poly_argument 为 true 时，在首次编译时初始化该缓存，键为 PLiSQL_func_hashkey（子函数 fno + 实参类型），值为编译后的 PLiSQL_function。 编译期间 PostgreSQL 解析器会构造 ParseState，plisql_subprocfunc_ref() 通过 ParseState→p_subprocfunc_hook() 连接父函数，调用 plisql_ns_lookup() 找到同名子函数的全部 fno，并依据参数个数与类型挑选最佳候选，实现重载分发。 构造 FuncExpr 时会标记嵌套调用信息，便于执行阶段识别： function_from = FUNC_FROM_SUBPROCFUNC。 parent_func 指向父级 PLiSQL_function。 funcid = fno，用于快速定位子函数定义。 plisql_subprocfunc_ref() 作为 ParseState→p_subprocfunc_hook 实现入口，复用名称空间查询逻辑。 plisql_get_subprocfunc_detail() 依据参数数量、类型与命名匹配规则挑选最优候选，是嵌套函数重载的关键。 plisql_call_handler() 判断 function_from 后，通过 parent_func + fno 找到目标 PLiSQL_subproc_function。 对普通子函数，直接复用 subprocfunc→function 缓存； 对多态子函数，先查询 poly_tab，未命中时调用 plisql_dynamic_compile_subproc() 动态编译并写入缓存。 plisql_init_subprocfunc_globalvar() 在子函数执行前拷贝父函数 Datum 表中的相关变量，保证子函数读取到外层最新状态。 plisql_assign_out_subprocfunc_globalvar() 在返回前回写 OUT/INOUT 变量，确保父子函数数据一致性且互不污染。 psqlscan.l 调整 proc_func_define_level 和 begin_depth 的入栈/出栈逻辑，确保嵌套函数体整体发送至 SQL 端。 只有当嵌套层级回到 0 且遇到分号时，才触发发送，避免子函数块被拆分。 普通函数通过 funcid 访问 pg_proc；嵌套函数依赖 FuncExpr.parent_func 承载的 PLiSQL_function。 为此实现一组函数指针（plisql_register_internal_func() 注册）供 SQL 层回调，按需获取嵌套函数名称、返回类型与 OUT 参数信息。","title":"嵌套子函数","component":"ivorysql-doc","version":"master","name":"nested_function","url":"/ivorysql-doc/master/compatibility_features_design/nested_function.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 嵌套子函数语法识别","hash":"嵌套子函数语法识别","id":3},{"text":"2.1.1. 识别嵌套写法","hash":"识别嵌套写法","id":4},{"text":"2.1.2. 数据项保存","hash":"数据项保存","id":5},{"text":"2.1.3. 保存多态函数模板","hash":"保存多态函数模板","id":6},{"text":"2.2. 父函数重新编译","hash":"父函数重新编译","id":7},{"text":"2.3. 调用时解析","hash":"调用时解析","id":8},{"text":"3. 模块设计","hash":"模块设计","id":9},{"text":"3.1. PL/iSQL 语法扩展","hash":"plisql-语法扩展","id":10},{"text":"3.2. 数据项保存","hash":"数据项保存-2","id":11},{"text":"3.3. 多态函数模板","hash":"多态函数模板","id":12},{"text":"3.4. 父函数重新编译","hash":"父函数重新编译-2","id":13},{"text":"3.5. 解析器钩子","hash":"解析器钩子","id":14},{"text":"3.6. FuncExpr 标记","hash":"funcexpr-标记","id":15},{"text":"3.7. 嵌套函数查找机制","hash":"嵌套函数查找机制","id":16},{"text":"3.8. 执行路径","hash":"执行路径","id":17},{"text":"3.9. 变量同步","hash":"变量同步","id":18},{"text":"3.10. PSQL 端语句发送","hash":"psql-端语句发送","id":19},{"text":"3.11. SQL 层返回值获取","hash":"sql-层返回值获取","id":20}]},"26":{"id":26,"text":"IvorySQL提供了兼容Oracle的NLS参数功能，包含如下参数。 参数名称 功能描述 ivorysql.datetime_ignore_nls_mask 表示日期格式是否忽略NLS参数影响，默认为0。 nls_length_semantics 兼容Oracle的同名参数，表示char/varchar/varchar2的类型修饰符的大小单位是字节还是字符。 nls_date_format 表示默认的日期格式，可以通过show命令查看，默认为‘YYYY-MM-DD’。 nls_timestamp_format 兼容Oracle的同名参数，控制带时间的日期格式。 nls_timestamp_tz_format 兼容Oracle的同名参数，控制带时区的日期时间格式。 nls_territory 兼容Oracle的同名参数，指定数据库的默认区域。 nls_iso_currency 兼容Oracle的同名参数，指定国家和区域对应的唯一货币符。 nls_currency 兼容Oracle的同名参数，指定显示本地货币的符号，对应数字字符串格式中占位符L。 IvorySQL中的数据类型存在一个属性修饰符 typmod ，是对类型的补充说明，比如在 VARCHAR(n) 类型中，n 就是类型修饰符。 在创建或修改表的列时可以指定长度类型，例如: ivorysql=# create table t1(name varchar2(2 byte)); 对于列类型为 CHAR 、 VARCHAR 和 VARCHAR2 字符型的列，当没有显式指定列的长度类型时，IvorySQL使用 nls_length_semantics 参数的值来决定长度类型，有 byte 和 char 两种值，默认为 byte 。 需要特别注意的是， nls_length_semantics 参数的值仅影响新创建的列，对已经存在的列不会产生任何影响。 在语法解析文件 ora_gram.y中，存在如下代码来根据 nls_length_semantics 把原本的 char/varchar/varchar2 类型改成 oracharchar 或者 oracharbyte ： CharacterWithLength: character '(' Iconst ')' { if (ORA_PARSER == compatible_db) { if (strcmp($1, \"bpchar\")) { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oravarcharchar\"; else $1 = \"oravarcharbyte\"; } else { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oracharchar\"; else $1 = \"oracharbyte\"; } $$ = OracleSystemTypeName($1); $$->typmods = list_make1(makeIntConst($3, @3)); $$->location = @1; } else { ... } } ; IvorySQL 中数据类型 oracharchar 和 oracharbyte 的修饰符输入输出函数包括： oravarcharchartypmodout() oravarcharbytetypmodout() oracharbytetypmodout() oracharchartypmodout() 上面这些函数调用C语言实现的函数 anychar_typmodout() ，后者根据 nls_length_semantics 的值来调整输出的内容是否包含 byte/char 的说明。 nls_length_semantics 另一个作用是限制表中的列长度： 根据上述代码在语法解析文件 ora_gram.y中，如果原本的 varchar 类型被转换成了 oracharchar 类型，则函数 oravarcharchar() 会被调用，而 pg_mbcharcliplen() 函数计算字符长度，而不是字节长度。 Datum oravarcharchar(PG_FUNCTION_ARGS) { VarChar *source = PG_GETARG_VARCHAR_PP(0); int32 typmod = PG_GETARG_INT32(1); bool isExplicit = PG_GETARG_BOOL(2); int32 len, maxlen; size_t maxmblen; char *s_data; len = VARSIZE_ANY_EXHDR(source); s_data = VARDATA_ANY(source); maxlen = typmod - VARHDRSZ; /* No work if typmod is invalid or supplied data fits it already */ if (maxlen < 0 || len <= maxlen) PG_RETURN_VARCHAR_P(source); maxmblen = pg_mbcharcliplen(s_data, len, maxlen); ... } 这个参数被定义为一个int值，低四位分别表示是否在相应的日期时间格式上忽略NLS参数的影响，掩码定义如下： #define ORADATE_MASK 0x01 #define ORATIMESTAMP_MASK 0x02 #define ORATIMESTAMPTZ_MASK 0x04 #define ORATIMESTAMPLTZ_MASK 0x08 在源代码中，这个GUC参数被用于下面这些函数： oradate_in() oratimestamp_in() oratimestampltz_in() oratimestamptz_in() 如果相应的掩码被设置，则调用原生PG的处理函数，否则调用兼容代码并忽略NLS格式。 这三个GUC参数，在函数 ora_do_to_timestamp() 中作为格式字符串，对输入的字符串进行格式检查与模式识别。 下面是其默认值，可以通过设置其值为\"pg\"使其失效。\"pg\"表示禁用NLS特定行为，恢复为PostgreSQL的默认行为。 char *nls_date_format = \"YYYY-MM-DD\"; char *nls_timestamp_format = \"YYYY-MM-DD HH24:MI:SS.FF6\"; char *nls_timestamp_tz_format = \"YYYY-MM-DD HH24:MI:SS.FF6 TZH:TZM\"; 目前，nls_territory 和 nls_iso_currency 支持CHINA与AMERICA两个值。 默认值如下: char *nls_territory = \"AMERICA\"; char *nls_currency = \"$\"; char *nls_iso_currency = \"AMERICA\"; 这三个参数将在oracle兼容函数 to_number() 中被使用。 to_number() 函数尚未实现。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"nls_parameter","url":"/ivorysql-doc/master/compatibility_features_design/nls_parameter.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"1.1. nls_length_semantics 参数","hash":"nls_length_semantics-参数","id":2},{"text":"1.2. GUC参数 datetime_ignore_nls_mask","hash":"guc参数-datetime_ignore_nls_mask","id":3},{"text":"1.3. GUC参数 nls_date_format/nls_timestamp_format/nls_timestamp_tz_format","hash":"guc参数-nls_date_formatnls_timestamp_formatnls_timestamp_tz_format","id":4},{"text":"1.4. GUC参数 nls_currency/nls_iso_currency/nls_territory","hash":"guc参数-nls_currencynls_iso_currencynls_territory","id":5}]},"27":{"id":27,"text":"IvorySQL提供了兼容Oracle的out参数功能，包括含有out参数的函数与过程、匿名块支持out参数、libpq支持out参数。 对于pliSQL函数，在创建函数时，系统表pg_proc中存储函数参数为全部参数个数（包括OUT参数个数）及对应的参数数据类型。 在处理函数参数的interpret_function_parameter_list()函数中，根据参数模式，判断如果是IN OUT，则参数不能有默认值。 在make_return_stmt函数中，去掉如果发现有out参数报错的处理。 通过修改FuncnameGetCandidates函数，在函数查找时匹配包括out参数在内的所有参数。 函数编译时，构造一个row变量来容纳OUT参数变量和返回值变量。修改编译好的函数返回类型（function→fn_rettype）为RECORDOID。 函数执行时，在ExecInitFunc函数中调用新函数ExecInitFuncOutParams， 构造OUT参数计算节点，通过计算函数plisql_out_param将函数返回值和OUT参数值从tuple中分离，并且为OUT参数向外赋值。 为了支持冒号占位符形式的绑定变量，修改ora_scan.l文件，添加语法，遇到冒号占位符返回ORAPARAM。在ora_gram.y文件中，在c_expr和赋值plassign_target的语法中，添加对ORAPARAM添加处理，构造一个OraParamRef节点。 新增 DO + USING 语法： DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, ...] 修改ora_gram.y文件，增加DO+USING语法支持。DoStmt结构体中增加 paramsmode 用于存储匿名块中的绑定变量模式等信息的列表。 对PBE过程的修改包括：exec_parse_message函数中，根据应用接口传过来的参数类型oid，识别出实参的模式；在exec_bind_message函数中，对于匿名块DO+USING，识别出USING后面参数的模式，向执行器传递参数信息。 含OUT参数匿名块的执行 在PortalStart函数中对匿名块语句，调用CreateTupleDescFromParams函数，增加参数构造描述信息。 PLiSQL_function成员fn_prokind增加新值PROKIND_ANONYMOUS_BLOCK用来表示匿名块。 在plisql_exec_function函数中，对有out参数的匿名块做判断，调用plisql_anonymous_return_out_parameter函数完成对OUT参数构造PLiSQL_row类型的变量，最后将row类型的变量求值当作匿名块函数的返回值。 修改Libpq以支持按位置和按参数名字绑定，涉及SQL端，PLiSQL端，libpq接口端。 SQL端：在服务器端实现系统函数 get_parameter_description，该函数根据SQL语句，返回变量名字与位置的关系。这个函数被用在libpq接口函数中。 返回的第一行name显示SQL类型，后面行依次显示占位符名字、位置信息。 ``` ivorysql=# select * from get_parameter_description('insert into t values(:x, :y);'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows) ``` 对于匿名块语句，尚不支持。 PLiSQL端：主要是PL/iSQL块根据参数位置或参数名称调整参数内部标识。 执行函数需要从绑定句柄中获取参数的值与类型的信息； 对out参数的返回列名称做一个特殊处理。如果是out参数，那么其列名称为_column_xxx，其中xxx是out参数的位置，从而根据绑定位置与返回的位置从结果集中给out参数赋值； 在PLiSQL执行端，根据参数名字出现的位置，调整名字转换成内部标识的变量，如$number。在返回到客户端的时候，发送描述信息给libpq， 从而达到返回的列名是由参数名字构造，LIBPQ端根据列名来给out参数赋值。 libpq接口端：提供准备、绑定、执行函数，这些函数与OCI接口相应函数类似。 一般调用流程如下： 使用IvyHandleAlloc分配语句句柄和错误句柄。 调用IvyStmtPrepare准备语句。 调用IvyBindByPos或IvyBindByName 绑定参数。 调用IvyStmtExecute 执行，可重复执行。 调用IvyFreeHandle 释放语句句柄和错误句柄。 另外还实现了Ivyconnectdb，Ivystatus，Ivyexec，IvyresultStatus，IvyCreatePreparedStatement，IvybindOutParameterByPos，IvyexecPreparedStatement，IvyexecPreparedStatement2，Ivynfields，Ivyntuples，Ivyclear等一系列接口函数。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"out_parameter","url":"/ivorysql-doc/master/compatibility_features_design/out_parameter.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"1.1. 含有out参数的函数","hash":"含有out参数的函数","id":2},{"text":"1.2. 匿名块支持out参数","hash":"匿名块支持out参数","id":3},{"text":"1.3. libpq中调用含out参数的函数","hash":"libpq中调用含out参数的函数","id":4}]},"28":{"id":28,"text":"IvorySQL提供了兼容Oracle RowID的功能。RowID是一种伪列，在创建表时由数据库自动生成，对于数据库中的每一行，RowID 伪列返回该行的地址。 RowID 应当具备以下特性： 1. 逻辑地标识每一行，且值唯一 2. 可以通过ROWID快速查询和修改表的其他列，自身不能被插入和修改 3. 用户可以控制是否开启此功能 在IvorySQL中系统列 ctid 字段代表了数据行在表中的物理位置，也就是行标识（tuple identifier），由一对数值组成（块编号和行索引），可以通过ctid快速的查找表中的数据行，这样和Oracle的RowID行为很相似，但是ctid值有可能会改变（例如当update/ vacuum full时），因此ctid不适合作为一个长期的行标识。 我们选择了表的oid加一个序列值组成的复合类型来作为RowID值，其中的序列是系统列。如果RowID功能被开启，则在建表的同时创建一个名为table-id_rowid_seq 的序列。同时在heap_form_tuple构造函数中，为 HeapTupleHeaderData 的长度增加8个字节，并标识td→t_infomask = HEAP_HASROWID 位来表示rowid的存在。 在开启了ROWID的GUC参数或建表时带上 WITH ROWID 选项，或对普通表执行 ALTER TABLE … SET WITH ROWID 时会通过增加序列创建命令来创建一个序列。 /* * Build a CREATE SEQUENCE command to create the sequence object, * and add it to the list of things to be done before this CREATE/ALTER TABLE */ seqstmt = makeNode(CreateSeqStmt); seqstmt->with_rowid = true; seqstmt->sequence = makeRangeVar(snamespace, sname, -1); seqstmt->options = lcons(makeDefElem(\"as\", (Node *) makeTypeNameFromOid(INT8OID, -1), -1), seqstmt->options); seqstmt->options = lcons(makeDefElem(\"nocache\", NULL, -1), seqstmt->options); 同时为了快速通过RowID伪列查询到一行数据，默认会在表的RowID列上创建一个UNIQUE索引，以提供快速查询功能。 RowID列作为系统属性列其实现是通过在 heap.c 中新增一个系统列来实现的。 /* * Compatible Oracle ROWID pseudo column. */ static const FormData_pg_attribute a7 = { .attname = {\"rowid\"}, .atttypid = ROWIDOID, .attlen = -1, .attnum = RowIdAttributeNumber, .attcacheoff = -1, .atttypmod = -1, .attbyval = false, .attalign = TYPALIGN_SHORT, .attstorage = TYPSTORAGE_PLAIN, .attnotnull = true, .attislocal = true, }; 在pg_class系统表中增加一个 bool 类型的字段 relhasrowid，用于标识建表时的 WITH ROWID选项，如果建表时带了WITH ROWID选项，则 relhasrowid为 t，否则为f。 用户在执行 ALTER table … SET WITH ROWID/ WITHOUT ROWID 命令时，也会修改这个值。 /* T if we generate ROWIDs for rows of rel */ bool relhasrowid BKI_DEFAULT(f); 在RowID的存储方面，如果启用了RowID 伪列功能，则在插入表之前 heap_form_tuple函数会根据参数TupleDesc 中tdhasrowid 是否为true 在 HeapTupleHeaderData 中增加8个字节来存储序列值。 在heap_prepare_insert 函数中获取序列的nextval值，存在HeapTupleHeader 相应的位置。 if (relation->rd_rel->relhasrowid) { // Get the sequence next value seqnum = nextval_internal(relation->rd_rowdSeqid, true); // Set the HeapTupleHeader HeapTupleSetRowId(tup, seqnum); }","title":"RowID","component":"ivorysql-doc","version":"master","name":"rowid","url":"/ivorysql-doc/master/compatibility_features_design/rowid.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"29":{"id":29,"text":"IvorySQL的sys_guid() 是一个强大的随机数产生函数，它产生并返回一个由16个字节组成的数据库级别唯一的标识符(原始值)。 IvorySQL的sys_guid()函数通过修改插件uuid-ossp的代码实现。为了充分利用uuid的多种基础库，采用如下逻辑： 如果系统有uuid-ossp，就使用uuid_make(); 如果系统有uuid-e2fs，就使用 uuid_generate_random(); 否则就调用 arc4random(); 同时修改代码使得IvorySQL能够自动载入uuid-ossp插件。","title":"sys_guid() 函数","component":"ivorysql-doc","version":"master","name":"sys_guid_function","url":"/ivorysql-doc/master/compatibility_features_design/sys_guid_function.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2}]},"30":{"id":30,"text":"IvorySQL提供了兼容Oracle的plsql数据类型功能，包括%TYPE、%ROWTYPE。 这是一个被动的过程。 当前的实现方案是记录函数（存储过程）与 tablename.columname 的依赖关系。当引用类型发生变化时，根据依赖关系，让函数（存储过程）缓存失效。这样一来，函数被调用时就会执行强制编译。从而确保函数获取最新的变量类型。 在系统表 pg_proc 中添加一个字段 prostatus 用来表示函数（存储过程）状态，有3种状态: validate(v)，invalidate(i)，N/A(n)。函数编译成功后，这个状态会被设置为valid。 在解析函数内容时，plisql_parse_cwordtype，plisql_parse_wordrowtype，plisql_parse_cwordrowtype 等函数识别出%TYPE， %ROWTYPE引用的对象，并记录在 plisql_referenced_object 链表中，最后添加到 pg_depend 系统表中。 增加一种新的依赖类型 DEPENDENCY_TYPE = 't'，表示%TYPE或%ROWTYPE依赖。在添加对象引用关系到pg_depend系统表时，设置依赖类型为 't'。 对表进行操作时（修改表类型、删除表等），查看系统表pg_depend，如果存在依赖类型deptype=’t’，并且依赖对象是函数，则调用函数 plisql_free_function 删除函数缓存，并修改 pg_proc 系统表中的函数状态prostatus 为 N/A(n)。 在 PLiSQL_type 结构体中添加成员 bool notnull; /* * Postgres data type */ typedef struct PLiSQL_type { bool notnull; /* the type is built by variable%type, * isnull or notnull of the variable */ 在负责解析%TYPE类型函数的plisql_parse_wordtype或plisql_parse_cwordtype函数中，判断如果引用变量类型指定了NOT NULL约束，为返回的datatype的成员 bool notnull 属性赋值为true。 在pl_gram.y 文件的 decl_statement 语法中，根据PLiSQL_type 的成员 bool notnull 为变量PLiSQL_variable *var的notnull属性赋值。这样就继承了引用变量的约束。 在 ora_gram.y 中为 func_type 添加%ROWTYPE支持。 | type_function_name attrs '%' ROWTYPE { $$ = makeTypeNameFromNameList(lcons(makeString($1), $2)); $$->row_type = true; $$->location = @1; } 在 TypeName 结构体中添加成员 bool row_type 用来标记是否指定了%ROWTYPE。 typedef struct TypeName { bool pct_type; /* %TYPE specified? */ bool row_type; /* %ROWTYPE specified? */ 在 LookupTypeName 函数中，如果 TypeName 的成员 row_type 为 TRUE，则根据TypeName的成员names中获得schema名与表名，然后获取表的typeoid。 在 ora_gram.y 中添加新的语法，支持 VALUES 后面不带括号’(‘。 values_clause_no_parens: VALUES columnref { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1(list_make1($2)); n->valuesIsrow = true; $$ = (Node *) n; } 为结构体 SelectStmt 添加一个字段 bool valuesIsrow 表示 values 是一个 row。 在为insert语句做 transform 时，也就是函数 transformInsertStmt 中，在处理 INSERT …​ VALUES 语句时，如果 valuesIsrow 为true，调用新函数 transformRowExpression，将 row_variable 转换成等价的 row_variable.field1,…​,row_variable.fieldN。 在UPDATE语句做transform时候，也就是transformUpdateStmt的时候，如果是Oracle兼容模式，调用新添加的 transformIvyUpdateTargetList 函数。 在这个新函数中，对于参数origTlist(即targetList)中没有名字为row的情况，按原来UPDATE的transform流程执行 transformUpdateTargetList 函数。 对参数origTlist中有名字为row的情况：因为PostgreSQL中row可以作为列名，而Oracle 中row是保留关键字，不可以作为列名，所以需要区分row是否是表中的列，如果row不是要更新的表中的列，则调用新函数 transformUpdateRowTargetList 把语句 UPDATE table_name SET ROW = row_variable [WHERE …]; 转换成等价的 UPDATE table_name SET table_name.column1 = row_variable.column1, table_name.column2 = row_variable.filed2,… table_name.columnN = row_variable.columnN [WHERE …]; 如果语句“UPDATE table_name SET ROW = row_variable”中的变量 row_variable不是组合类型，就按原来UPDATE的transform流程执行 transformUpdateTargetList 函数； 如果语句中的变量 row_variable 是组合类型，表中的名为row的那一列也是组合类型，并且两者的类型Oid也一样，则按原来UPDATE的transform流程执行 transformUpdateTargetList 函数； 其他所有情况，调用新函数 transformUpdateRowTargetList 处理；","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"type_rowtype","url":"/ivorysql-doc/master/compatibility_features_design/type_rowtype.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现说明","hash":"实现说明","id":2},{"text":"2.1. 引用类型发生变化时，如何确保%TYPE声明的变量也随之相应改变","hash":"引用类型发生变化时如何确保type声明的变量也随之相应改变","id":3},{"text":"2.2. %TYPE声明的变量继承引用变量的约束","hash":"type声明的变量继承引用变量的约束","id":4},{"text":"2.3. 表名%ROWTYPE或视图名%ROWTYPE作为函数或存储过程的参数类型和函数返回值类型","hash":"表名rowtype或视图名rowtype作为函数或存储过程的参数类型和函数返回值类型","id":5},{"text":"2.4. INSERT语句增强","hash":"insert语句增强","id":6},{"text":"2.5. UPDATE语句增强","hash":"update语句增强","id":7}]},"31":{"id":31,"text":"从Docker Hub上获取IvorySQL镜像 $ docker pull ivorysql/ivorysql:5.0-ubi8 运行IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 查看IvorySQL容器运行是否成功 $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql 从Docker Hub上获取IvorySQL镜像 [highgo@manager-node1 ~]$ podman pull ivorysql/ivorysql:5.0-ubi8 ✔ docker.io/ivorysql/ivorysql:5.0-ubi8 Trying to pull docker.io/ivorysql/ivorysql:5.0-ubi8... Getting image source signatures Copying blob 5885448c5c88 done | Copying blob 6c502b378234 done | Copying blob 8b4f2b90d6b6 done | Copying blob 9b000f2935f6 done | Copying blob 806f782da874 done | Copying blob e4c51845a9eb done | Copying blob dcb1e9a04275 done | Copying blob 285a279173f8 done | Copying blob 1f6f247b9ae0 done | Copying blob 3cc81bed8614 done | Copying blob 863c87bf25eb done | Copying blob 4f4fb700ef54 done | Copying config 88e1bbeda8 done | Writing manifest to image destination 88e1bbeda81c51d88e12cbd2b19730498f1343d1c64bb3dddc8ffcb08a1f965f 运行IvorySQL $ podman run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=123456 -d ivorysql/ivorysql:5.0-ubi8 查看IvorySQL容器运行是否成功 [highgo@manager-node1 ~]$ podman ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 368dee58d5ef docker.io/ivorysql/ivorysql:5.0-ubi8 postgres 20 seconds ago Up 20 seconds 0.0.0.0:5434->5432/tcp, 1521/tcp, 5866/tcp ivorysql [highgo@manager-node1 ~]$ podman exec -it ivorysql /bin/bash [root@8cc631eb413d /]# ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=#","title":"Docker & Podman 部署IvorySQL","component":"ivorysql-doc","version":"master","name":"docker_podman_deployment","url":"/ivorysql-doc/master/containerization/docker_podman_deployment.html","titles":[{"text":"1. docker方式运行","hash":"docker方式运行","id":1},{"text":"2. podman方式运行","hash":"podman方式运行","id":2}]},"32":{"id":32,"text":"准备三个网络互通的服务器，并搭建swarm集群。 测试集群名称及对应ip地址如下： manager-node1: 192.168.21.205 manager-node2: 192.168.21.164 manager-node3: 192.168.21.51 [root@manager-node1 docker-swarm]# docker node ls ID HOSTNAME STATUS AVAILABILITY MANAGER STATUS ENGINE VERSION y9d9wd9t2ncy4t9bvw6bg9sjs * manager-node1 Ready Active Reachable 26.1.4 iv17o6m9t9e06vd9iu1o6damd manager-node2 Ready Active Leader 25.0.4 vjnax76qj812mlvut6cv4qotl manager-node3 Ready Active Reachable 24.0.6 下载源码 [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-swarm 部署一个三节点的etcd [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-etcd.yml ivoryhac-etcd Creating network ivoryhac-etcd_etcd-net Creating service ivoryhac-etcd_etcd3 Creating service ivoryhac-etcd_etcd1 Creating service ivoryhac-etcd_etcd2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 可自定义数据库外挂目录，修改docker-swarm-ivypatroni.yml中的volumes，修改完成后修改目录权限及属主；示例如下 mkdir -p /home/ivorysql/{data,patroni} chown -R 1000:1000 /home/ivorysql/{data,patroni} chmod 700 /home/ivorysql/{data,patroni} 部署IvorySQL高可用集群 [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-ivypatroni.yml ivoryhac-patroni Since --detach=false was not specified, tasks will be created in the background. In a future release, --detach=false will become the default. Creating service ivoryhac-patroni_ivypatroni1 Creating service ivoryhac-patroni_ivypatroni2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 uzdvjq5j2gwt ivorysql-hac_ivypatroni1 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1521->1521/tcp, *:5866->5866/tcp fr0m9chu3ce8 ivorysql-hac_ivypatroni2 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1522->1521/tcp, *:5867->5866/tcp psql连接数据库的Oracle端口及PG端口 [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) 卸载IvorySQL集群 [root@manager-node1 ~] docker stack rm ivoryhac-patroni [root@manager-node1 ~] docker stack rm ivoryhac-etcd 下载源码 [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-compose 拷贝文件至其他服务器 将etcd和ivypatroni的docker-compose文件，分别拷贝到其他服务器上。 如测试服务器: 192.168.21.205 存放etcd1+ivorypatroni1， 192.168.21.164 存放etcd2+ivorypatroni2， 192.168.21.51 存放etcd3+ivorypatroni3 部署一个三节点的etcd，以node1为例 [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-etcd1.yml up -d [+] Running 1/1 ✔ Container etcd Started 0.1s 部署IvorySQL高可用集群 在每个节点上，部署ivyhac服务 以node1为例 [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-ivypatroni_1.yml up -d [+] Running 1/1 ✔ Container ivyhac1 Started 0.1s [root@manager-node1 docker-compose]# docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 736c0d188bdd ivorysql/docker-compose-ha-cluster:5.0-4.0.6-ubi8 \"/bin/sh /docker-ent…\" 18 seconds ago Up 17 seconds ivyhac1 9d8e04e4f819 quay.io/coreos/etcd:v3.5.8 \"/usr/local/bin/etcd\" 24 minutes ago Up 24 minutes etcd 此时，一主两备集群搭建完成 psql连接数据库的Oracle端口及PG端口 [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) 卸载IvorySQL集群 以node1为例 [root@manager-node1 ~] docker-compose -f ./docker-compose-ivypatroni_1.yml down [root@manager-node1 ~] docker-compose -f ./docker-compose-etcd1.yml down","title":"Docker Swarm & Docker Compose 部署IvorySQL高可用集群","component":"ivorysql-doc","version":"master","name":"docker_swarm_compose_deployment","url":"/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html","titles":[{"text":"1. docker swarm搭建IvorySQL HA Cluster","hash":"docker-swarm搭建ivorysql-ha-cluster","id":1},{"text":"2. docker compose搭建IvorySQL HA Cluster","hash":"docker-compose搭建ivorysql-ha-cluster","id":2}]},"33":{"id":33,"text":"进入k8s集群的master节点上，创建名为ivorysql的namespace [root@k8s-master ~]# kubectl create ns ivorysql 下载最新docker_library代码 [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git 进入单机目录 [root@k8s-master ~]# cd docker_library/k8s-cluster/single [root@k8s-master single]# vim statefulset.yaml #根据个人环境自行修改statefulset中的pvc信息及数据库密码 使用statefulset.yaml创建一个单机pod [root@k8s-master single]# kubectl apply -f statefulset.yaml service/ivorysql-svc created statefulset.apps/ivorysql created 等待pod创建成功 [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 0/1 ContainerCreating 0 47s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 47s NAME READY AGE statefulset.apps/ivorysql 0/1 47s [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 1/1 Running 0 2m39s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 2m39s NAME READY AGE statefulset.apps/ivorysql 1/1 2m39s psql连接IvorySQL的PG端口 [root@k8s-master single]# psql -U ivorysql -p 32106 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# exit psql连接IvorySQL的Oracle端口 [root@k8s-master single]# psql -U ivorysql -p 31887 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) 卸载单机容器 [root@k8s-master single]# kubectl delete -f statefulset.yaml 进入k8s集群的master节点上，创建名为ivorysql的namespace [root@k8s-master ~]# kubectl create ns ivorysql 下载最新docker_library代码 [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git 进入高可用集群目录 [root@k8s-master ~]# cd docker_library/k8s-cluster/ha-cluster/helm_charts [root@k8s-master single]# vim values.yaml #根据个人环境自行修改values.yaml中的pvc信息及集群规模等信息，数据库密码查看templates/secret.yaml并自行修改。 使用 Helm 命令部署高可用集群 [root@k8s-master helm_charts]# helm install ivorysql-ha-cluster -n ivorysql . NAME: ivorysql-ha-cluster LAST DEPLOYED: Wed Sep 10 09:45:36 2025 NAMESPACE: ivorysql STATUS: deployed REVISION: 1 TEST SUITE: None [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 42s pod/ivorysql-patroni-hac-1 0/1 Running 0 18s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 42s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 42s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 1/3 42s 等待所有 Pod 进入“Running”（运行中）状态，即表示集群已部署成功。 [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 88s pod/ivorysql-patroni-hac-1 1/1 Running 0 64s pod/ivorysql-patroni-hac-2 1/1 Running 0 41s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 88s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 88s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 3/3 88s 使用psql连接集群主节点的PG、Oracle端口 [root@k8s-master helm_charts]# psql -U ivorysql -p 32391 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32477 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# 使用psql连接集群备节点的PG、Oracle端口 [root@k8s-master helm_charts]# psql -U ivorysql -p 30111 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32654 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# 卸载高可用集群 [root@k8s-master helm_charts]# helm uninstall ivorysql-ha-cluster -n ivorysql 删除PVC [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-2 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-2 -n ivorysql","title":"k8s部署单机容器和高可用集群","component":"ivorysql-doc","version":"master","name":"k8s_deployment","url":"/ivorysql-doc/master/containerization/k8s_deployment.html","titles":[{"text":"1. 单机容器","hash":"单机容器","id":1},{"text":"2. 高可用集群","hash":"高可用集群","id":2}]},"34":{"id":34,"text":"Fork ivory-operator 仓库 并克隆到本地： YOUR_GITHUB_UN=\"<your GitHub username>\" git clone --depth 1 \"git@github.com:${YOUR_GITHUB_UN}/ivory-operator.git\" cd ivory-operator 执行以下命令完成安装： kubectl apply -k examples/kustomize/install/namespace kubectl apply --server-side -k examples/kustomize/install/default 在本教程中，我们将基于 examples/kustomize/ivory 目录中的示例进行构建。 当引用 YAML 清单中的嵌套对象时，我们将使用类似 kubectl explain 的 . 格式。例如，对于以下 YAML 文件： spec: hippos: appetite: huge 我们会用 spec.hippos.appetite 来表示最深层的元素。 kubectl explain 是一个非常有用的命令。你可以使用它来查看 ivorycluster.ivory-operator.ivorysql.org 自定义资源定义（CRD）的结构： kubectl explain ivorycluster 创建一个 Ivory 集群非常简单。使用 examples/kustomize/ivory 目录中的示例，只需运行： kubectl apply -k examples/kustomize/ivory IVYO 将在 ivory-operator 命名空间中创建一个名为 hippo 的简单 Ivory 集群。你可以通过以下命令跟踪 Ivory 集群的状态： kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo 你也可以使用以下命令跟踪 Ivory Pod 的状态： kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance IVYO 根据 examples/kustomize/ivory 目录中的 Kustomize 清单信息创建了 Ivory 集群。让我们通过查看 examples/kustomize/ivory/ivory.yaml 文件来更好地理解发生了什么： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 当我们运行 kubectl apply 命令时，实际上是在 Kubernetes 中创建了一个 ivorycluster 自定义资源。IVYO 检测到新增的 ivorycluster 资源后，开始创建在 Kubernetes 中运行 Ivory 所需的所有对象！ 还发生了什么？IVYO 从 metadata.name 读取值，为 Ivory 集群命名为 hippo。此外，IVYO 通过查看 spec.image 和 spec.backups.pgbackrest.image 的值，分别确定了 Ivory 和 pgBackRest 使用的容器镜像。spec.postgresVersion 的值也很重要，它帮助 IVYO 跟踪你使用的 Ivory 主版本。 IVYO 通过清单中的 spec.instances 部分知道要创建多少个 Ivory 实例。虽然 name 是可选的，但我们选择将其命名为 instance1。我们也可以在集群初始化期间创建多个副本和实例，但稍后在我们讨论 如何扩展并创建高可用 Ivory 集群 时会详细介绍。 ivorycluster 自定义资源中非常重要的一部分是 dataVolumeClaimSpec 部分。它描述了 Ivory 实例将使用的存储，建模自 Persistent Volume Claim。如果你没有提供 spec.instances.dataVolumeClaimSpec.storageClassName，则将使用 Kubernetes 环境中的默认存储类。 作为创建 Ivory 集群的一部分，我们还指定了备份存档的信息。IVYO 使用 pgBackRest，这是一个开源的备份与恢复工具，专为处理 TB 级备份而设计。在集群初始化期间，我们可以指定备份和归档（https://www.postgresql.org/docs/current/wal-intro.html[预写日志或 WAL]）的存储位置。我们将在本教程的 灾难恢复 部分更深入地讨论 ivorycluster 规范的这一部分，并了解如何将备份存储在 Amazon S3、Google GCS 和 Azure Blob Storage 中。 最常见的原因是 PVC 未绑定。请确保你在任何 volumeClaimSpec 中正确设置了存储选项。你可以随时更新设置并使用 kubectl apply 重新应用更改。 还要确保你有足够的持久卷可用：你的 Kubernetes 管理员可能需要配置更多持久卷。 如果你使用的是 OpenShift，可能需要将 spec.openshift 设置为 true。 创建 Ivory 集群是一回事，连接到它又是另一回事。让我们看看 IVYO 如何让连接 Ivory 集群变得简单！ IVYO 会创建一系列 Kubernetes Service，为访问 Ivory 数据库提供稳定的端点。这些端点让应用程序能够始终如一地连接到数据。要查看可用的 Service，可执行： kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo 输出示例： NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 3h14m hippo-ha-config ClusterIP None <none> <none> 3h14m hippo-pods ClusterIP None <none> <none> 3h14m hippo-primary ClusterIP None <none> 5432/TCP 3h14m hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 3h14m 大多数 Service 用于集群内部管理，无需关注。连接数据库时，只需关注名为 hippo-primary 的 Service。得益于 IVYO，你甚至无需手动指定它——这些信息已被写入 Secret！ 集群初始化时，IVYO 会引导创建一个数据库和用户，供应用程序使用。相关信息保存在名为 <clusterName>-pguser-<userName> 的 Secret 中。对于 hippo 集群，该 Secret 名为 hippo-pguser-hippo，包含以下键值： user：用户账户名 password：用户密码 dbname：用户默认可访问的数据库名 host：数据库主机名（指向主实例的 Service） port：数据库监听端口 uri： PostgreSQL 连接 URI，含完整登录信息 jdbc-uri： PostgreSQL JDBC 连接 URI，供 JDBC 驱动使用 所有连接均通过 TLS 进行。IVYO 自带证书中心（CA），支持使用 Ivory 的 verify-full SSL 模式，防止窃听与中间人攻击。你也可以稍后使用自定义 CA，详见 自定义集群 章节。 默认情况下，IVYO 部署的 Service 类型为 ClusterIP。根据暴露数据库的方式，你可能需要更改为其他 Service 类型 或指定 NodePort 值。 可通过以下字段调整 IVYO 管理的 Service： spec.service：控制连接主库的 Service spec.userInterface.pgAdmin.service：控制 pgAdmin 管理工具的 Service 例如，将主库 Service 改为 NodePort 并指定端口、注解与标签： spec: service: metadata: annotations: my-annotation: value1 labels: my-label: value2 type: NodePort nodePort: 32000 重新应用后，再次查看 Service： kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo 输出示例： NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha NodePort 10.105.57.191 <none> 5432:32000/TCP 48s hippo-ha-config ClusterIP None <none> <none> 48s hippo-pods ClusterIP None <none> <none> 48s hippo-primary ClusterIP None <none> 5432/TCP 48s hippo-replicas ClusterIP 10.106.18.99 <none> 5432/TCP 48s 查看 hippo-ha 的详细信息，顶部将显示自定义注解与标签已生效： Name: hippo-ha Namespace: ivory-operator Labels: my-label=value2 ivory-operator.ivorysql.org/cluster=hippo ivory-operator.ivorysql.org/patroni=hippo-ha Annotations: my-annotation: value1 使用默认 ClusterIP 类型时禁止设置 nodePort；该值必须在合法范围内且未被占用。此处提供的注解与标签优先级最高。若通过外部暴露 Service 并依赖 TLS 验证，需使用 IVYO 的 自定义 TLS 功能。 本教程以 Keycloak（开源身份管理应用）为例。Keycloak 可部署在 Kubernetes 上，并使用 Ivory 作为数据库。以下示例清单将 Keycloak 连接到已运行的 hippo 集群： kubectl apply --filename=- <<EOF apiVersion: apps/v1 kind: Deployment metadata: name: keycloak namespace: ivory-operator labels: app.kubernetes.io/name: keycloak spec: selector: matchLabels: app.kubernetes.io/name: keycloak template: metadata: labels: app.kubernetes.io/name: keycloak spec: containers: - image: quay.io/keycloak/keycloak:latest args: [\"start-dev\"] name: keycloak env: - name: DB_VENDOR value: \"ivory\" - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } - name: KEYCLOAK_ADMIN value: \"admin\" - name: KEYCLOAK_ADMIN_PASSWORD value: \"admin\" - name: KC_PROXY value: \"edge\" ports: - name: http containerPort: 8080 - name: https containerPort: 8443 readinessProbe: httpGet: path: /realms/master port: 8080 restartPolicy: Always EOF 注意环境变量部分： - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } 所有连接信息均来自 hippo-pguser-hippo Secret，无需硬编码或明文传递凭据，即可直接在 GitOps 流水线中使用！ Ivory 以稳定著称，通常“开箱即用”。但在 Kubernetes 这样的分布式环境中，仍可能遇到多种影响可用的事件： 数据库磁盘或其余硬件故障 网络不可达 节点操作系统崩溃 数据文件损坏 整个数据中心失联 Kubernetes 自身组件（如 Service）被误删 此外，常规运维（小版本升级、安全补丁、硬件更换）也会带来计划内停机。 好消息：IVYO 已针对上述场景提供保护。要让集群真正高可用，首先把它扩容。 IVYO 支持两种快速获得 HA 集群的方式： 调大 spec.instances.replicas 在 spec.instances 新增条目 本文采用方法 1，将副本数设为 2。对应 YAML 片段： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: \"{{< param imageIvorySQL >}}\" postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 # <── 新增副本 dataVolumeClaimSpec: accessModes: [\"ReadWriteOnce\"] resources: requests: storage: 1Gi backups: pgbackrest: image: \"{{< param imagePGBackrest >}}\" repos: - name: repo1 volume: volumeClaimSpec: accessModes: [\"ReadWriteOnce\"] resources: requests: storage: 1Gi 应用后稍等片刻，新副本自动初始化。通过以下命令可实时查看实例 Pod： kubectl -n ivory-operator get pods \\ -l ivory-operator.ivorysql.org/cluster=hippo,\\ ivory-operator.ivorysql.org/instance-set 上一篇《连接集群》提到，应用默认通过 hippo-primary Service 读写。我们人为删除它： kubectl -n ivory-operator delete svc hippo-primary 立刻再查询 Service 列表： kubectl -n ivory-operator get svc \\ -l ivory-operator.ivorysql.org/cluster=hippo 可见 hippo-primary 已被 IVYO 秒级重建。多数应用凭借重连逻辑几乎无感知。 首先找到当前主库 Pod 对应的 StatefulSet 名字： PRIMARY_STS=$(kubectl -n ivory-operator get sts \\ -l ivory-operator.ivorysql.org/cluster=hippo,\\ ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[0].metadata.name}') echo $PRIMARY_STS 假设输出为 hippo-instance1-zj5s，直接删除： kubectl -n ivory-operator delete sts \"$PRIMARY_STS\" 再次查看 StatefulSet： kubectl -n ivory-operator get sts \\ -l ivory-operator.ivorysql.org/cluster=hippo IVYO 会立即重建被删对象，并自动将原副本重新加入集群。同时，另一实例已被提升为新主： kubectl -n ivory-operator get pods \\ -l ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[0].metadata.labels.ivory-operator\\.ivorysql\\.org/instance}' 即使 IVYO 进程短暂离线，Patroni 仍能独立完成故障切换，确保应用读写不中断。 IvorySQL 支持同步复制，可进一步降低事务丢失风险。只需在集群里增加： spec: patroni: dynamicConfiguration: synchronous_mode: true 如需强制所有提交都同步到至少一个副本，可再加： synchronous_mode_strict: true Patroni 默认“可用性优先”，当同步副本全部失效时会退化为异步；若业务要求绝对同步，请启用 synchronous_mode_strict，此时无可用同步副本将拒绝写入。 preferredDuringSchedulingIgnoredDuringExecution —— 尽力分散，资源不足时允许同节点 requiredDuringSchedulingIgnoredDuringExecution —— 强制分散，找不到空闲节点则 Pending 示例 —— 强制让同一 instance-set 的 Pod 落在不同节点： affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 将数据库实例固定在带 workload-role=db 标签的节点： affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: workload-role operator: In values: [\"db\"] 相比反亲和的“0 或 1”限制，拓扑分布约束可按比例打散，粒度更细。字段模板： topologySpreadConstraints: - maxSkew: <整数> topologyKey: <标签键> whenUnsatisfiable: <DoNotSchedule | ScheduleAnyway> labelSelector: <对象> 示例 —— 5 个实例 Pod 在 3 节点间尽量均衡： instances: - name: instance1 replicas: 5 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: ivory-operator.ivorysql.org/instance-set: instance1 备份仓库主机（repo-host）也可同理配置，确保多集群场景下仓库 Pod 分散。 业务蒸蒸日上，流量激增，需要给 Ivory 集群扩容，却又担心 resize 造成停机？ IVYO 提供滚动升级机制，能在零感知或毫秒级中断内完成 CPU、内存、磁盘等所有规格的在线调整。 继续阅读前，请确保已按上一章《高可用》部署了 HA 集群（至少 2 副本）。 IVYO 把资源声明分散在多处，保持统一语义（与 Kubernetes 原生 resources 字段一致），并支持 QoS 类别设置： spec.instances.resources └ Ivory 主容器、init 容器、数据迁移 Job 的 CPU / 内存 spec.instances.sidecars.replicaCertCopy.resources └ 副本证书复制 sidecar spec.backups.pgbackrest.repoHost.resources └ pgBackRest 仓库主机及对应 init / 迁移 Job spec.backups.pgbackrest.sidecars.*.resources spec.backups.pgbackrest.jobs.resources spec.backups.pgbackrest.restore.resources spec.dataSource.ivorycluster.resources 示例：把 hippo 每个实例上限调整为 2 CPU、4 GiB 内存 apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: instances: - name: instance1 replicas: 2 resources: # <── 新增或修改 limits: cpu: \"2\" memory: 4Gi dataVolumeClaimSpec: accessModes: [\"ReadWriteOnce\"] resources: requests: storage: 1Gi kubectl apply -k examples/kustomize/ivory ---- 实时观察滚动过程（逐 Pod 重建）： watch kubectl -n ivory-operator get pods \\ -l ivory-operator.ivorysql.org/cluster=hippo,\\ ivory-operator.ivorysql.org/instance \\ -o custom-columns=NAME:.metadata.name,ROLE:.metadata.labels.ivory-operator\\.ivorysql\\.org/role,PHASE:.status.phase ---- 流程解析： 1. 先升级所有 副本 实例 → 新 Pod 就绪后旧 Pod 才删除 2. 执行受控主从切换（switchover）→ 应用仅感受到毫秒级重连 3. 最后升级原主库 → 再次选主完成 要求： - 底层 StorageClass 的 allowVolumeExpansion=true - 只能增不能减 需要调大的字段： spec.instances.dataVolumeClaimSpec.resources.requests.storage （数据目录） spec.backups.pgbackrest.repos[*].volume.volumeClaimSpec…​ （备份仓库） 示例：数据盘 1 GiB → 10 GiB，备份盘 1 GiB → 20 GiB spec: instances: - name: instance1 dataVolumeClaimSpec: resources: requests: storage: 10Gi # 1→10 backups: pgbackrest: repos: - name: repo1 volume: volumeClaimSpec: resources: requests: storage: 20Gi # 1→20 kubectl apply -k examples/kustomize/ivory ---- IVYO 会按 副本优先、主库最后 的顺序触发底层 pvc.spec.resources.requests.storage 修改，Kubelet 与存储插件完成文件系统在线扩容，Pod 无需重建，业务无感知。 部分公有云早期 StorageClass 或本地盘 CSI 驱动未开启扩容，仍可通过 “新增大容量实例集 → 切换 → 删除老实例集” 完成“曲线”扩容。 步骤示例： 保留原 instance1（1 GiB），新增 instance2（10 GiB） spec: instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: resources: requests: storage: 1Gi - name: instance2 # 新实例集 replicas: 2 dataVolumeClaimSpec: resources: requests: storage: 10Gi 等待 instance2 副本同步追上主库 提交仅含 instance2 的清单，IVYO 将自动： 把 instance2 某一副本提升为新主 删除 instance1 所有 Pod & PVC 完成“数据迁移” 结果：业务未中断，磁盘已换成 10 GiB，老 PVC 被释放，费用停止计费。 反向操作即可“缩容”磁盘（先建小盘实例集 → 切换 → 删大盘）。 节点剩余资源不满足 requests → 扩容节点或降低 requests PVC 申请过大 / StorageClass 不存在 → 检查存储类及配额 确认 StorageClass： kubectl get sc -o custom-columns=NAME:.metadata.name,ALLOW Expansion:.allowVolumeExpansion ---- 若返回 false 或空值，请： 换用支持扩容的 StorageClass，或 使用上文“场景 B”实例集替换方案 管理 Ivory 集群中多个实例的诀窍之一是确保所有配置更改都能传播到每个实例。这正是 IVYO 的用武之地：当您为集群进行 Ivory 配置更改时，IVYO 会将其应用到所有 Ivory 实例。 例如，在上一步中，我们分别添加了 CPU 和内存限制为 2.0 和 4Gi。让我们调整一些 Ivory 设置以更好地利用我们的新资源。我们可以在 spec.patroni.dynamicConfiguration 部分中进行此操作。以下是一个更新后的示例清单，其中调整了几个设置： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB 特别是，我们在 spec 中添加了以下内容： patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB 使用以下命令将这些更新应用到您的 Ivory 集群： kubectl apply -k examples/kustomize/ivory IVYO 将应用这些设置，并在必要时重新启动每个 Ivory 实例。您可以使用 Ivory 的 SHOW 命令验证更改是否已生效，例如： SHOW work_mem; 应该会产生类似以下的结果： work_mem ---------- 2MB IVYO 中的所有连接都使用 TLS 加密组件之间的通信。IVYO 设置了一个 PKI 和证书颁发机构 (CA)，允许您创建可验证的端点。但是，您可能希望根据组织要求引入不同的 TLS 基础设施。好消息是：IVYO 允许您这样做！ IVYO 有几个不同的 TLS 端点可以自定义，包括 Ivory 集群的端点以及控制 Ivory 实例之间如何进行身份验证的端点。让我们看看如何通过定义以下内容来自定义 TLS： 一个 spec.customTLSSecret，用于标识集群并加密通信；以及 一个 spec.customReplicationTLSSecret，用于复制身份验证。 要自定义 Ivory 集群的 TLS，您需要在 Ivory 集群的命名空间中创建两个 Secret。其中一个 Secret 将是 customTLSSecret，另一个将是 customReplicationTLSSecret。这两个 Secret 都包含要使用的 TLS 密钥（tls.key）、TLS 证书（tls.crt）和 CA 证书（ca.crt）。 注意：如果提供了 spec.customTLSSecret，则必须也提供 spec.customReplicationTLSSecret，并且两者都必须包含相同的 ca.crt。 自定义 TLS 和自定义复制 TLS Secret 应包含以下字段（如果您无法控制 Secret 的 data 中的字段名称，请参见下面的解决方法）： data: ca.crt: <value> tls.crt: <value> tls.key: <value> 例如，如果您本地计算机上存储有名为 ca.crt、hippo.key 和 hippo.crt 的文件，您可以运行以下命令从这些文件创建 Secret： kubectl create secret generic -n ivory-operator hippo-cluster.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=hippo.key \\ --from-file=tls.crt=hippo.crt 创建 Secret 后，您可以在 ivorycluster.ivory-operator.ivorysql.org 自定义资源中指定自定义 TLS Secret。例如，如果您创建了 hippo-cluster.tls Secret 和 hippo-replication.tls Secret，您可以将它们添加到您的 Ivory 集群中： spec: customTLSSecret: name: hippo-cluster.tls customReplicationTLSSecret: name: hippo-replication.tls 如果您无法控制 Secret 中的键值对，您可以创建一个映射来告诉 Ivory Operator 哪个键保存了预期的值。它看起来类似于以下内容： spec: customTLSSecret: name: hippo.tls items: - key: <tls.crt key in the referenced hippo.tls Secret> path: tls.crt - key: <tls.key key in the referenced hippo.tls Secret> path: tls.key - key: <ca.crt key in the referenced hippo.tls Secret> path: ca.crt 例如，如果 hippo.tls Secret 中的 tls.crt 位于名为 hippo-tls.crt 的键中，tls.key 位于名为 hippo-tls.key 的键中，ca.crt 位于名为 hippo-ca.crt 的键中，那么您的映射将如下所示： spec: customTLSSecret: name: hippo.tls items: - key: hippo-tls.crt path: tls.crt - key: hippo-tls.key path: tls.key - key: hippo-ca.crt path: ca.crt 注意：尽管自定义 TLS 和自定义复制 TLS Secret 共享相同的 ca.crt，但它们不共享相同的 tls.crt： 您的 spec.customTLSSecret TLS 证书应具有与主服务名称匹配的通用名称 (CN) 设置。这是集群名称后缀为 -primary 的名称。例如，对于我们的 hippo 集群，这将是 hippo-primary。 您的 spec.customReplicationTLSSecret TLS 证书应具有与预设复制用户 _ivoryrepl 匹配的通用名称 (CN) 设置。 与其他更改一样，您可以使用 kubectl apply 推出 TLS 自定义设置。 有几种方法可以将您自己的自定义 Kubernetes 标签 添加到您的 Ivory 集群。 集群：您可以通过编辑自定义资源的 spec.metadata.labels 部分将标签应用于集群中的任何 IVYO 托管对象。 Ivory：您可以通过编辑 spec.instances.metadata.labels 将标签应用于 Ivory 实例集及其对象。 pgBackRest：您可以通过编辑 ivoryclusters.spec.backups.pgbackrest.metadata.labels 将标签应用于 pgBackRest 及其对象。 有几种方法可以将您自己的自定义 Kubernetes 注解 添加到您的 Ivory 集群。 集群：您可以通过编辑自定义资源的 spec.metadata.annotations 部分将注解应用于集群中的任何 IVYO 托管对象。 Ivory：您可以通过编辑 spec.instances.metadata.annotations 将注解应用于 Ivory 实例集及其对象。 pgBackRest：您可以通过编辑 spec.backups.pgbackrest.metadata.annotations 将注解应用于 pgBackRest 及其对象。 IVYO 允许您使用 pod 优先级类 通过设置 Ivory 集群上的 priorityClassName 字段来指示 pod 的相对重要性。这可以通过以下方式完成： 实例：优先级是按实例集定义的，并应用于该实例集中的所有 Pod，方法是编辑自定义资源的 spec.instances.priorityClassName 部分。 专用仓库主机：在规范的 repoHost 部分下定义的优先级通过编辑自定义资源的 spec.backups.pgbackrest.repoHost.priorityClassName 部分应用于专用仓库主机。 备份（手动和计划）：优先级在 spec.backups.pgbackrest.jobs.priorityClassName 部分下定义，并将该优先级应用于所有 pgBackRest 备份作业（手动和计划）。 还原（数据源或就地）：通过编辑自定义资源的 spec.dataSource.ivorycluster.priorityClassName 部分为“数据源”还原或就地还原定义优先级。 数据迁移：规范中第一个实例集（数组位置 0）定义的优先级用于 PGDATA 和 WAL 迁移作业。pgBackRest 仓库迁移作业将使用应用于 repoHost 的优先级类。 IvorySQL 通过将更改存储在其https://www.postgresql.org/docs/current/wal-intro.html[预写日志 (WAL)] 中来提交事务。由于访问和使用 WAL 文件的方式通常与数据文件不同，并且在高性能情况下，可能需要将 WAL 文件放在单独的存储卷上。使用 IVYO，可以通过在您的 ivorycluster 规范中为您所需的实例添加 walVolumeClaimSpec 块来实现，无论是在创建集群时还是之后的任何时间： spec: instances: - name: instance walVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 稍后可以通过从实例中移除 walVolumeClaimSpec 部分来移除此卷。请注意，在更改 WAL 目录时，会小心避免丢失任何 WAL 文件。只有在先前配置的卷上不再有任何 WAL 文件时，IVYO 才会删除 PVC。 IVYO 可以在集群创建和初始化过程中为您运行 SQL。IVYO 使用 psql 客户端运行 SQL，因此您可以使用元命令连接到不同的数据库、更改错误处理或设置和使用变量。其功能在 psql 文档 中有所描述。 Ivory 集群规范接受对包含您的 init SQL 文件的 ConfigMap 的引用。更新您的集群规范以包括 ConfigMap 名称 spec.databaseInitSQL.name 和您的 SQL 文件的数据键 spec.databaseInitSQL.key。例如，如果您使用以下命令创建 ConfigMap： kubectl -n ivory-operator create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql 您可以将以下部分添加到您的 ivorycluster 规范中： spec: databaseInitSQL: key: init.sql name: hippo-init-sql ConfigMap 必须与您的 Ivory 集群位于同一命名空间中。 在您将 ConfigMap 引用添加到您的规范后，使用 kubectl apply -k examples/kustomize/ivory 应用更改。IVYO 将创建您的 hippo 集群，并在集群启动后运行您的初始化 SQL。您可以通过检查 Ivory 集群上的 databaseInitSQL 状态来验证您的 SQL 是否已运行。在状态设置期间，您的 init SQL 不会再次运行。您可以使用 kubectl describe 命令检查集群状态： kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo 在某些情况下，由于 Kubernetes 处理 ivorycluster 状态的方式，IVYO 可能会多次运行您的 SQL 命令。请确保您在 init SQL 中定义的命令是幂等的。 现在 databaseInitSQL 已在您的集群状态中定义，请验证数据库对象是否已按预期创建。验证后，我们建议从您的规范中移除 spec.databaseInitSQL 字段。从规范中移除该字段也将从集群状态中移除 databaseInitSQL。 IVYO 使用 psql 交互式终端在您的数据库中执行 SQL 语句。语句使用标准输入和文件名标志传递（例如 psql -f -）。 SQL 语句以超级用户身份在默认维护数据库中执行。这意味着您可以完全控制创建数据库对象、扩展或运行您可能需要的任何 SQL 语句。 如果您正在创建用户或数据库，请参阅 用户/数据库管理 文档。通过规范的用户管理部分创建的数据库可以在您的初始化 sql 中引用。例如，如果定义了数据库 zoo： spec: users: - name: hippo databases: - \"zoo\" 您可以通过将以下 psql 元命令添加到您的 SQL 来连接到 zoo： \\c zoo create table t_zoo as select s, md5(random()::text) from generate_Series(1,5) s; 默认情况下，psql 会在每个 SQL 命令完成时提交它。要将多个命令组合成一个单独的 事务，请使用 BEGIN 和 COMMIT 命令。 BEGIN; create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; COMMIT; psql 的退出代码将决定何时设置 databaseInitSQL 状态。当 psql 返回 0 时，状态将被设置，并且不会再次运行 SQL。当 psql 返回错误退出代码时，状态将不会被设置。IVYO 将继续尝试执行 SQL，作为其协调循环的一部分，直到 psql 正常返回。如果 psql 以失败退出，您将需要编辑 ConfigMap 中的文件，以确保您的 SQL 语句将导致成功的 psql 返回。对 ConfigMap 进行实时更改的最简单方法是使用以下 kubectl edit 命令： kubectl -n <cluster-namespace> edit configmap hippo-init-sql 请务必将所有更改传回您的本地文件。另一个选项是在本地文件中进行更改，并使用 kubectl --dry-run 创建模板，并将输出通过管道传输到 kubectl apply： kubectl create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql --dry-run=client -o yaml | kubectl apply -f - 如果您编辑了 ConfigMap 但更改没有显示出来，您可能正在等待 IVYO 协调您的集群。一段时间后，IVYO 将自动协调集群，或者您可以通过对集群应用任何更改来触发协调（例如，使用 kubectl apply -k examples/kustomize/ivory）。 为了确保 psql 在您的 SQL 命令失败时返回失败退出代码，请在您的 SQL 文件中设置 ON_ERROR_STOP 变量： \\set ON_ERROR_STOP \\echo Any error will lead to exit code 3 create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; IVYO 内置了一些即用型便利功能，用于管理 Ivory 集群中的用户和数据库。然而，您可能有需要创建额外用户、调整用户权限或向集群添加额外数据库的需求。 有关 IVYO 中用户和数据库管理工作原理的详细信息，请参阅架构指南中的 用户管理 部分。 您可以通过在 ivorycluster 自定义资源中添加以下片段来创建新用户。让我们将其添加到我们的 hippo 数据库中： spec: users: - name: rhino 现在您可以应用更改，并看到新用户已创建。请注意以下事项： 该用户只能连接到默认的 ivory 数据库。 用户不会将任何连接凭据填充到 hippo-pguser-rhino Secret 中。 该用户是未特权的。 让我们创建一个名为 zoo 的新数据库，我们将允许 rhino 用户访问该数据库： spec: users: - name: rhino databases: - zoo 检查 hippo-pguser-rhino Secret。您现在应该看到 dbname 和 uri 字段已填充！ 我们可以通过使用 Ivory 提供的标准 角色属性 并将它们添加到 spec.users.options 来设置角色权限。假设我们希望 rhino 成为超级用户（在授予 Ivory 超级用户权限时要小心！）。您可以将以下内容添加到规范中： spec: users: - name: rhino databases: - zoo options: \"SUPERUSER\" 就这样：我们创建了一个名为 rhino 的 Ivory 用户，该用户具有超级用户权限，并且可以访问 rhino 数据库（尽管超级用户可以访问所有数据库！）。 假设您想从 rhino 中撤销超级用户权限。您可以通过以下方式执行此操作： spec: users: - name: rhino databases: - zoo options: \"NOSUPERUSER\" 如果您想添加多个权限，您可以在 options 中用空格分隔每个权限，例如： spec: users: - name: rhino databases: - zoo options: \"CREATEDB CREATEROLE\" 默认情况下，IVYO 不允许您访问 ivory 用户。但是，您可以通过执行以下操作来访问此帐户： spec: users: - name: ivory 这将创建一个模式为 <clusterName>-pguser-ivory 的 Secret，其中包含 ivory 帐户的凭据。对于我们的 hippo 集群，这将是 hippo-pguser-ivory。 IVYO 不会自动删除用户：将用户从规范中移除后，它将仍然存在于您的集群中。要删除用户及其所有对象，作为超级用户，您需要在用户拥有对象的每个数据库中运行 DROP OWNED，并在您的 Ivory 集群中运行 DROP ROLE。 例如，对于上面的 rhino 用户，您将运行以下命令： DROP OWNED BY rhino; DROP ROLE rhino; 请注意，您可能需要根据对象所有权结构运行 DROP OWNED BY rhino CASCADE; —— 请非常小心此命令！ IVYO 不会自动删除数据库：从规范中移除数据库的所有实例后，它将仍然存在于您的集群中。要完全删除数据库，您必须以 Ivory 超级用户身份运行 DROP DATABASE 命令。 例如，要删除 zoo 数据库，您将执行以下命令： DROP DATABASE zoo; 也许有人不小心删除了 users 表。也许你想把生产数据库克隆到降级环境。也许你想演练灾难恢复系统（这很重要！）。 无论哪种情况，了解如何使用 IVYO 执行“恢复”操作以便从特定时间点恢复数据，或出于其他目的克隆数据库都很重要。 我们来看看如何执行不同类型的恢复操作。首先，让我们了解自定义资源上的核心恢复属性。 IVYO 提供了从现有 ivorycluster 或远程云数据源（如 S3、GCS 等）恢复的能力。有关更多信息，请参阅 从 S3 / GCS / Azure Blob 存储中存储的备份克隆 部分。 请注意，您不能同时使用本地 ivorycluster 数据源和远程云数据源；如果同时填写了 dataSource.ivorycluster 和 dataSource.pgbackrest 字段，本地 ivorycluster 数据源将优先。 自定义资源上有几个重要属性需要了解，这些都是恢复过程中的关键。所有这些属性都分组在自定义资源的 spec.dataSource.ivorycluster 部分中。 请查看下表，了解每个属性在设置恢复操作时的工作原理。 spec.dataSource.ivorycluster.clusterName：您要从中恢复的集群的名称。这对应于另一个 ivorycluster 自定义资源的 metadata.name 属性。 spec.dataSource.ivorycluster.clusterNamespace：您要从中恢复的集群的命名空间。当集群存在于不同的命名空间时使用。 spec.dataSource.ivorycluster.repoName：用于恢复的 spec.dataSource.ivorycluster.clusterName 中的 pgBackRest 仓库的名称。可以是 repo1、repo2、repo3 或 repo4 之一。仓库必须存在于另一个集群中。 spec.dataSource.ivorycluster.options：IVYO 允许的任何额外 pgBackRest 恢复选项 或常规选项。例如，您可能希望设置 --process-max 以帮助提高大型数据库的性能；但您将无法设置 --target-action，因为该选项目前被禁止。（如果存在 --target，IVYO 总是将其设置为 promote，否则将其留空。） spec.dataSource.ivorycluster.resources：设置恢复作业的 资源限制和请求 可以确保其高效运行。 spec.dataSource.ivorycluster.affinity：自定义 Kubernetes 亲和性 规则约束恢复作业，使其仅在某些节点上运行。 spec.dataSource.ivorycluster.tolerations：自定义 Kubernetes 容忍度 允许恢复作业在 污点 节点上运行。 让我们通过一些示例来了解如何克隆和恢复我们的数据库。 让我们创建一个我们之前创建的 hippo 集群的克隆。我们知道我们的集群名为 hippo（基于其 metadata.name），并且我们只有一个名为 repo1 的备份仓库。 让我们称我们的新集群为 elephant。我们可以使用如下所示的清单创建 hippo 集群的克隆： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 请注意规范中的这一部分： spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 这部分告诉 IVYO 将 elephant 集群创建为 hippo 集群的独立副本。 以上就是克隆 Ivory 集群所需的全部操作！IVYO 将在新的持久卷声明 (PVC) 上创建数据副本，并致力于将集群初始化到规范。很简单！ 有人删除了用户表吗？您可能希望执行时间点恢复 (PITR) 以将数据库恢复到更改发生之前的状态。幸运的是，IVYO 可以帮助您做到这一点。 您可以使用为 IVYO 的灾难恢复功能提供支持的备份管理工具 pgBackRest 的 restore 命令来设置 PITR。您需要在 spec.dataSource.ivorycluster.options 上设置一些选项来执行 PITR。这些选项包括： --type=time：这告诉 pgBackRest 执行 PITR。 --target：执行 PITR 的目标位置。恢复目标的一个示例是 2021-06-09 14:15:11-04。此处指定的时区为 -04，即东部夏令时。有关其他时区选项，请参阅 pgBackRest 文档。 --set（可选）：选择从哪个备份开始 PITR。 开始前的一些快速说明： 要执行 PITR，您必须有一个在 PITR 时间之前完成的备份。换句话说，您不能对没有备份的时间执行 PITR！ 所有相关的 WAL 文件必须成功推送，以便恢复正确完成。 确保选择包含所需备份的正确仓库名称！ 考虑到这一点，让我们使用上面的 elephant 示例。假设我们要执行到 2021-06-09 14:15:11-04 的时间点恢复 (PITR)，我们可以使用以下清单： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 需要注意的部分是： spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" 请注意我们如何放入选项以指定进行 PITR 的位置。 使用上述清单，IVYO 将继续创建一个恢复其数据直到 2021-06-09 14:15:11-04 的新 Ivory 集群。此时，集群被提升，您可以从该特定时间点开始访问您的数据库！ 与上面描述的 PITR 恢复类似，您可能希望执行类似的回退到更改发生之前的状态，但不创建另一个 IvorySQL 集群。幸运的是，IVYO 也可以帮助您做到这一点。 您可以使用为 IVYO 的灾难恢复功能提供支持的备份管理工具 pgBackRest 的 restore 命令来设置 PITR。您需要在 spec.backups.pgbackrest.restore.options 上设置一些选项来执行 PITR。这些选项包括： --type=time：这告诉 pgBackRest 执行 PITR。 --target：执行 PITR 的目标位置。恢复目标的一个示例是 2021-06-09 14:15:11-04。 --set（可选）：选择从哪个备份开始 PITR。 开始前的一些快速说明： 要执行 PITR，您必须有一个在 PITR 时间之前完成的备份。换句话说，您不能对没有备份的时间执行 PITR！ 所有相关的 WAL 文件必须成功推送，以便恢复正确完成。 确保选择包含所需备份的正确仓库名称！ 要执行就地恢复，用户首先需要填写规范的恢复部分，如下所示： spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" 然后，要触发恢复，您需要使用以下命令注释 ivorycluster： kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-restore=id1 恢复完成后，可以禁用就地恢复： spec: backups: pgbackrest: restore: enabled: false 请注意我们如何放入选项以指定进行 PITR 的位置。 使用上述清单，IVYO 将继续重新创建您的 Ivory 集群，以恢复其数据直到 2021-06-09 14:15:11-04。此时，集群被提升，您可以从该特定时间点开始访问您的数据库！ 出于性能原因或为了将选定数据库移动到没有足够空间来恢复整个集群备份的计算机上，您可能需要从备份中恢复特定数据库。 pgBackRest 支持这种情况，但请务必确保这是您想要的。以这种方式恢复将从备份中恢复请求的数据库并使其可访问，但备份中的所有其他数据库在恢复后将无法访问。 例如，如果您的备份包含数据库 test1、test2 和 test3，并且您请求恢复 test2，则恢复完成后，test1 和 test3 数据库将无法访问。请查看 pgBackRest 文档中关于 恢复单个数据库的限制。 您可以使用类似于以下规范的规范从备份中恢复单个数据库： spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --db-include=hippo 其中 --db-include=hippo 将仅恢复 hippo 数据库的内容。 高级高可用性和灾难恢复策略涉及将您的数据库集群分布在数据中心之间，以帮助最大化正常运行时间。IVYO 提供了使用外部存储系统或 IvorySQL 流复制部署可以跨越多个 Kubernetes 集群的 ivorycluster 的方法。https://github.com/IvorySQL/ivory-operator/blob/master/docs/content/architecture/disaster-recovery.md[灾难恢复架构] 文档中提供了 IVYO 备用集群的高级概述。 本教程部分将描述如何创建三种不同类型的备用集群，一种使用外部存储系统，一种直接从主集群流式传输数据，一种利用外部存储和流式传输。这些示例集群可以在同一个 Kubernetes 集群中使用单个 IVYO 实例创建，也可以通过正确的存储和网络配置分布在不同的 Kubernetes 集群和 IVYO 实例中。 基于仓库的备用集群将从存储在外部存储中的 pgBackRest 仓库中恢复。主集群应使用基于云的 备份配置 创建。以下清单定义了一个 ivorycluster，其中 standby.enabled 设置为 true，并且 repoName 配置为指向主集群中配置的 s3 仓库： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" standby: enabled: true repoName: repo1 流式备用集群依赖于通过网络到主集群的经过身份验证的连接。主集群应可通过网络访问并允许 TLS 身份验证（默认启用 TLS）。在以下清单中，我们将 standby.enabled 设置为 true，并提供了指向主集群的 host 和 port。我们还定义了 customTLSSecret 和 customReplicationTLSSecret 以提供允许备用集群向主集群进行身份验证的证书。对于这种类型的备用集群，您必须使用 自定义 TLS： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: repos: - name: repo1 volume: volumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true host: \"192.0.2.2\" port: 5432 另一个选项是使用从主集群流式传输的外部 pgBackRest 仓库创建备用集群。通过此设置，如果流式复制落后，备用集群将继续从 pgBackRest 仓库恢复。在此清单中，我们启用了前两个示例中的设置： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true repoName: repo1 host: \"192.0.2.2\" port: 5432 在某些时候，您会希望提升备用集群以开始接受读取和写入。这具有将 WAL（事务归档）推送到 pgBackRest 仓库的净效应，因此我们需要确保我们不会意外创建脑裂场景。如果两个主实例尝试写入同一个仓库，则可能会发生脑裂。如果主集群仍处于活动状态，请确保在尝试提升备用集群之前 关闭 主集群。 一旦主集群处于非活动状态，我们可以通过移除或禁用其 spec.standby 部分来提升备用集群： spec: standby: enabled: false 此更改触发将备用领导者提升为 IvorySQL 主实例，并且集群开始接受写入。 您可以从存储在 AWS S3（或使用 S3 协议的存储系统）、GCS 或 Azure Blob 存储中的备份克隆 Ivory 集群，而无需活动的 Ivory 集群！方法与从现有 ivorycluster 克隆类似。如果您希望为人们提供数据集但将其压缩在更便宜的存储上，这很有用。 出于本示例的目的，假设您创建了一个名为 hippo 的 Ivory 集群，其备份存储在 S3 中，看起来类似于以下内容： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 manual: repoName: repo1 options: - --type=full repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" 确保 ivyo-s3-creds 中的凭据与您的 S3 凭据匹配。有关 使用 S3 部署 Ivory 集群进行备份 的更多详细信息，请参阅教程的 备份 部分。 为了从活跃集群创建新集群时获得最佳性能，请确保对前一个集群进行了最近的完整备份。上面的清单设置为进行完整备份。假设 hippo 是在 ivory-operator 命名空间中创建的，您可以使用以下命令触发完整备份： kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-backup=\"$( date '+%F_%H:%M:%S' )\" 等待备份完成。完成后，您可以删除 Ivory 集群。 现在，让我们将 hippo 备份中的数据克隆到一个名为 elephant 的新集群中。您可以使用类似于以下的清单： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/elephant/repo1 repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" 在此清单中需要注意以下几点。首先，请注意我们新的 ivorycluster 中的 spec.dataSource.pgbackrest 对象与旧的 ivorycluster 中的 spec.backups.pgbackrest 对象非常相似，但略有不同。主要区别是： 从基于云的数据源恢复时不需要镜像 从基于云的数据源恢复时，stanza 是必填字段 backups.pgbackrest 有一个 repos 字段，这是一个数组 dataSource.pgbackrest 有一个 repo 字段，这是一个单一对象 还要注意相似之处： 我们正在为两者重用密钥（因为新的恢复 pod 需要具有与原始备份 pod 相同的凭据） repo 对象是相同的 global 对象是相同的 这是因为 elephant ivorycluster 的新恢复 pod 将需要重用最初设置 hippo ivorycluster 时使用的配置和凭据。 在此示例中，我们正在创建一个新的集群，该集群也备份到同一个 S3 存储桶；只有 spec.backups.pgbackrest.global 字段已更改为指向不同的路径。这将确保新的 elephant 集群将预填充来自 hippo 备份的数据，但将备份到自己的文件夹，确保原始备份仓库得到适当保留。 部署此清单以创建 elephant Ivory 集群。观察它启动并运行： kubectl -n ivory-operator describe ivorycluster elephant 当它准备就绪时，您将看到预期实例的数量与就绪实例的数量相匹配，例如： Instances: Name: 00 Ready Replicas: 1 Replicas: 1 Updated Replicas: 1 前面的示例展示了如何使用现有的 S3 仓库预填充 ivorycluster，同时使用新的 S3 仓库进行备份。但是使用基于云的数据源的 ivorycluster 也可以使用本地仓库。 例如，假设一个名为 rhino 的 ivorycluster 旨在从原始的 hippo ivorycluster 预填充，清单将如下所示： apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: rhino spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi 虽然拥有 高可用性 和 灾难恢复 系统可以在您的 IvorySQL 集群出现问题时提供帮助，但监控可以帮助您预防问题的发生。此外，监控可以帮助您诊断和解决可能导致性能下降的问题，而不是停机。 让我们看看 IVYO 如何允许您在集群中启用监控。 让我们看看如何使用 Postgres Operator 示例 仓库中的 kustomize/ivory 示例将 IvorySQL Exporter sidecar 添加到您的集群中。 监控工具是使用自定义资源的 spec.monitoring 部分添加的。目前，唯一支持的监控工具是使用 pgMonitor 配置的 IvorySQL Exporter。 在 kustomize/ivory/ivory.yaml 文件中，将以下 YAML 添加到规范中： monitoring: pgmonitor: exporter: image: {{< param imagePostgresExporter >}} 保存您的更改并运行： kubectl apply -k kustomize/ivory IVYO 将检测到更改并将 Exporter sidecar 添加到集群中存在的所有 Ivory Pod 中。IVYO 还将完成允许 Exporter 连接到数据库并使用 IVYO 监控 堆栈收集指标的工作。 IVYO 允许您配置 exporter sidecar 以使用 TLS 加密。如果您通过 exporter 规范提供自定义 TLS Secret： monitoring: pgmonitor: exporter: customTLSSecret: name: hippo.tls 与 IVYO 可以配置的其他自定义 TLS Secret 一样，Secret 需要在与您的 PostgresCluster 相同的命名空间中创建。它还应该包含启用加密所需的 TLS 密钥 (tls.key) 和 TLS 证书 (tls.crt)。 data: tls.crt: <value> tls.key: <value> 为 exporter 配置 TLS 后，您将需要更新您的 Prometheus 部署以使用 TLS，并且与 exporter 的连接将被加密。查看 Prometheus 文档，了解有关为 Prometheus 配置 TLS 的更多信息。 在您的集群中启用 IvorySQL Exporter 后，请按照 IVYO 监控 中概述的步骤安装监控堆栈。这将允许您在 Kubernetes 中部署 pgMonitor 配置的 Prometheus、https://grafana.com/[Grafana] 和 Alertmanager 监控工具。这些工具将默认设置为连接到您的 Ivory Pod 上的 Exporter 容器。 虽然默认的 Kustomize 安装应该在大多数 Kubernetes 环境中工作，但可能需要根据您的特定需求进一步自定义项目。 例如，默认情况下，fsGroup 设置为 26，用于为组成 IVYO 监控堆栈的各种部署定义的 securityContext： securityContext: fsGroup: 26 在大多数 Kubernetes 环境中，此设置是必需的，以确保容器内的进程具有写入组成 IVYO 监控堆栈的每个 Pod 挂载的任何卷所需的权限。但是，在 OpenShift 环境中安装时（更具体地说，当使用 restricted 安全上下文约束时），应删除 fsGroup 设置，因为 OpenShift 将自动处理在 Pod 的 securityContext 中设置适当的 fsGroup。 此外，在同一部分中，可能还需要根据您的特定存储配置修改 supplmentalGroups 设置： securityContext: supplementalGroups : 65534 因此，应修改和/或修补（例如，使用额外的覆盖）kustomize/monitoring 下的以下文件，以确保 securityContext 为您的 Kubernetes 环境正确定义： deploy-alertmanager.yaml deploy-grafana.yaml deploy-prometheus.yaml 为了修改 IVYO 监控安装程序创建的各种存储资源（即 PersistentVolumeClaims）的配置，还可以修改 kustomize/monitoring/pvcs.yaml 文件。 此外，还可以通过修改以下配置资源来进一步自定义组成 IVYO 监控堆栈的各种组件（Grafana、Prometheus 和/或 AlertManager）的配置： alertmanager-config.yaml alertmanager-rules-config.yaml grafana-datasources.yaml prometheus-config.yaml 最后，请注意，可以通过修改 kustomize/monitoring/grafana-secret.yaml 文件中的 Grafana Secret 来更新 Grafana 的默认用户名和密码。 一旦 Kustomize 项目根据您的特定需求进行了修改，就可以使用 kubectl 和 Kustomize 安装 IVYO 监控： kubectl apply -k kustomize/monitoring 同样，一旦安装了 IVYO 监控，就可以使用 kubectl 和 Kustomize 卸载它： kubectl delete -k kustomize/monitoring 连接池有助于扩展和维护应用程序与数据库之间的整体可用性。IVYO 通过支持 PgBouncer 连接池和状态管理器来促进这一点。 让我们看看我们如何添加连接池并将其连接到我们的应用程序！ 让我们看看如何使用 Ivory Operator 仓库示例文件夹中的 kustomize/keycloak 示例添加连接池。 连接池是使用自定义资源的 spec.proxy 部分添加的。目前，唯一支持的连接池是 PgBouncer。 添加 PgBouncer 连接池的唯一必需属性是设置 spec.proxy.pgBouncer.image 属性。在 kustomize/keycloak/ivory.yaml 文件中，将以下 YAML 添加到规范中： proxy: pgBouncer: image: {{< param imageIvoryPGBouncer >}} （您也可以在 kustomize/examples/high-availability 示例中找到此示例）。 保存您的更改并运行： kubectl apply -k kustomize/keycloak IVYO 将检测到更改并创建一个新的 PgBouncer Deployment！ 设置起来相当容易，所以现在让我们看看如何将我们的应用程序连接到连接池。 当连接池部署到集群时，IVYO 会将附加信息添加到用户 Secret 中，以允许应用程序直接连接到连接池。回想一下，在此示例中，我们的用户 Secret 称为 keycloakdb-pguser-keycloakdb。描述用户 Secret： kubectl -n ivory-operator describe secrets keycloakdb-pguser-keycloakdb 您应该看到此 Secret 中包含几个新属性，允许您通过连接池连接到您的 Ivory 实例： pgbouncer-host：PgBouncer 连接池的主机名。这引用了 PgBouncer 连接池的 Service。 pgbouncer-port：PgBouncer 连接池正在侦听的端口。 pgbouncer-uri：一个 PostgreSQL 连接 URI，提供通过 PgBouncer 连接池登录到 Ivory 数据库的所有信息。 pgbouncer-jdbc-uri：一个 PostgreSQL JDBC 连接 URI，提供通过使用 JDBC 驱动程序的 PgBouncer 连接池登录到 Ivory 数据库的所有信息。请注意，默认情况下，连接字符串禁用 JDBC 管理预处理事务以实现 与 PgBouncer 一起使用的最佳方式。 打开 kustomize/keycloak/keycloak.yaml 中的文件。更新 DB_ADDR 和 DB_PORT 值如下： - name: DB_ADDR valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-port } } 这会更改 Keycloak 的配置，使其现在通过连接池连接。 应用更改： kubectl apply -k kustomize/keycloak Kubernetes 将检测到更改并开始部署新的 Keycloak Pod。完成后，Keycloak 现在将通过 PgBouncer 连接池连接到 Ivory！ IVYO 通过 TLS 部署每个集群和组件。这包括 PgBouncer 连接池。如果您使用自己的 自定义 TLS 设置，则需要在 spec.proxy.pgBouncer.customTLSSecret 中为 PgBouncer 提供 Secret 引用。 PgBouncer 的 TLS 证书应具有与 PgBouncer Service 名称匹配的通用名称 (CN)。这是集群的名称，后缀为 -pgbouncer。例如，对于我们的 hippo 集群，这将是 hippo-pgbouncer。对于 keycloakdb 示例，它将是 keycloakdb-pgBouncer。 要自定义 PgBouncer 的 TLS，您需要在您的 Ivory 集群的命名空间中创建一个 Secret，其中包含要使用的 TLS 密钥 (tls.key)、TLS 证书 (tls.crt) 和 CA 证书 (ca.crt)。Secret 应包含以下值： data: ca.crt: <value> tls.crt: <value> tls.key: <value> 例如，如果您本地计算机上存储有名为 ca.crt、keycloakdb-pgBouncer.key 和 keycloakdb-pgBouncer.crt 的文件，则可以运行以下命令： kubectl create secret generic -n ivory-operator keycloakdb-pgBouncer.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=keycloakdb-pgBouncer.key \\ --from-file=tls.crt=keycloakdb-pgBouncer.crt 您可以在您的 ivorycluster.ivory-operator.ivorysql.org 自定义资源中的 spec.proxy.pgBouncer.customTLSSecret.name 字段中指定自定义 TLS Secret，例如： spec: proxy: pgBouncer: customTLSSecret: name: keycloakdb-pgBouncer.tls PgBouncer 连接池是高度可定制的，从配置和 Kubernetes 部署角度来看都是如此。让我们探索一些您可以进行的自定义！ 可以通过 spec.proxy.pgBouncer.config 自定义 PgBouncer 配置。进行配置更改后，IVYO 会将它们推出到任何 PgBouncer 实例，并自动发出“重新加载”。 您可以通过以下几种方式自定义配置： spec.proxy.pgBouncer.config.global：接受键值对，这些更改全局应用于 PgBouncer。 spec.proxy.pgBouncer.config.databases：接受键值对，这些键值对代表 PgBouncer 数据库定义。 spec.proxy.pgBouncer.config.users：接受键值对，这些键值对代表 应用于特定用户的连接设置。 spec.proxy.pgBouncer.config.files：接受文件列表，这些文件挂载在 /etc/pgbouncer 目录中，并在使用 PgBouncer 的 include 指令 考虑任何其他选项之前加载。 例如，要将连接池模式设置为 transaction，您需要设置以下配置： spec: proxy: pgBouncer: config: global: pool_mode: transaction 有关 PgBouncer 配置 的参考，请参阅： https://www.pgbouncer.org/config.html 默认情况下，IVYO 部署一个 PgBouncer 实例。您可能希望运行多个 PgBouncer 实例以具有一定的冗余级别，尽管您仍然希望注意有多少连接将连接到您的 Ivory 数据库！ 您可以通过 spec.proxy.pgBouncer.replicas 属性管理部署的 PgBouncer 实例的数量。 您可以通过 spec.proxy.pgBouncer.resources 属性管理分配给 PgBouncer 实例的 CPU 和内存资源。spec.proxy.pgBouncer.resources 的布局应该很熟悉：它遵循设置 容器资源 的标准 Kubernetes 结构。 例如，假设我们想为 PgBouncer 实例设置一些 CPU 和内存限制。我们可以添加以下配置： spec: proxy: pgBouncer: resources: limits: cpu: 200m memory: 128Mi 由于 IVYO 使用 Deployment 部署 PgBouncer 实例，因此这些更改会使用滚动更新推出，以最大程度地减少应用程序与 Ivory 实例之间的中断！ 您可以通过 spec.proxy.pgBouncer.metadata.annotations 和 spec.proxy.pgBouncer.metadata.labels 属性分别为您的 PgBouncer 实例应用自定义注释和标签。请注意，对这两个属性中的任何一个的任何更改都将优先于您添加的任何其他自定义标签。 您可以通过 spec.proxy.pgBouncer.affinity 属性控制 pod 反亲和性、pod 亲和性和节点亲和性，特别是： spec.proxy.pgBouncer.affinity.nodeAffinity：控制 PgBouncer 实例的节点亲和性。 spec.proxy.pgBouncer.affinity.podAffinity：控制 PgBouncer 实例的 Pod 亲和性。 spec.proxy.pgBouncer.affinity.podAntiAffinity：控制 PgBouncer 实例的 Pod 反亲和性。 以上每个都遵循 设置亲和性的标准 Kubernetes 规范。 例如，要为 kustomize/keycloak 示例设置首选 Pod 反亲和性规则，您需要向配置中添加以下内容： spec: proxy: pgBouncer: affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: keycloakdb ivory-operator.ivorysql.org/role: pgbouncer topologyKey: kubernetes.io/hostname 您可以通过设置 容忍度 通过 spec.proxy.pgBouncer.tolerations 将 PgBouncer 实例部署到 具有污点的节点。此属性遵循 Kubernetes 标准容忍度布局。 例如，如果有一组具有 role=connection-poolers:NoSchedule 污点的节点，您希望将 PgBouncer 实例调度到这些节点，您可以应用以下配置： spec: proxy: pgBouncer: tolerations: - effect: NoSchedule key: role operator: Equal value: connection-poolers 请注意，设置容忍度并不一定意味着 PgBouncer 实例将被分配给具有这些污点的节点。容忍度充当密钥：它们允许您访问节点。如果您希望确保您的 PgBouncer 实例部署到特定节点，您需要将设置容忍度与节点亲和性相结合。 除了使用亲和性、反亲和性和容忍度之外，您还可以通过 spec.proxy.pgBouncer.topologySpreadConstraints 设置 拓扑分布约束。此属性遵循 Kubernetes 标准拓扑分布约束布局。 例如，由于我们的每个 pgBouncer Pod 都将设置标准的 ivory-operator.ivorysql.org/role: pgbouncer 标签，我们可以在确定 maxSkew 时使用此标签。在下面的示例中，由于我们有 3 个节点，maxSkew 为 1，并且我们将 whenUnsatisfiable 设置为 ScheduleAnyway，我们理想情况下应该在每个节点上看到 1 个 Pod，但如果其他约束阻止这种情况发生，我们的 Pod 可以分布得不那么均匀。 proxy: pgBouncer: replicas: 3 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/role: pgbouncer 如果您希望确保您的 PgBouncer 实例分布得更均匀（或根本不部署），您需要将 whenUnsatisfiable 更新为 DoNotSchedule。 有时您可能需要手动重启 IvorySQL。这可以通过向集群的 spec.metadata.annotations 部分添加或更新自定义注释来完成。IVYO 将检测到更改并执行 滚动重启。 例如，如果您在命名空间 ivory-operator 中有一个名为 hippo 的集群，您只需要使用以下命令修补 hippo ivorycluster： kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"metadata\":{\"annotations\":{\"restarted\":\"'\"$(date)\"'\"}}}}' 观察您的 hippo 集群：您将看到滚动更新已触发，重启已开始。 您可以通过将 spec.shutdown 属性设置为 true 来关闭 Ivory 集群。您可以通过编辑清单来执行此操作，或者在 hippo 集群的情况下，执行如下命令： kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"shutdown\": true}}' 这样做的结果是，此集群的所有 Kubernetes 工作负载都缩放为 0。您可以使用以下命令验证这一点： kubectl get deploy,sts,cronjob --selector=ivory-operator.ivorysql.org/cluster=hippo -n ivory-operator NAME READY AGE statefulset.apps/hippo-00-lwgx 0/0 1h NAME SCHEDULE SUSPEND ACTIVE cronjob.batch/hippo-repo1-full @daily True 0 要将已关闭的 Ivory 集群重新打开，您可以将 spec.shutdown 设置为 false。 您可以通过将 spec.paused 属性设置为 true 来暂停 Ivory 集群协调过程。您可以通过编辑清单来执行此操作，或者在 hippo 集群的情况下，执行如下命令： kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"paused\": true}}' 暂停集群将暂停对集群当前状态的任何更改，直到协调恢复。这允许您完全控制何时将 ivorycluster spec 的更改部署到 Ivory 集群。在暂停期间，除了“Progressing”条件外，不会更新任何状态。 要恢复 Ivory 集群的协调，您可以将 spec.paused 设置为 false 或从清单中删除该设置。 应尽可能频繁地使凭据失效并替换（轮换）它们，以最大限度地降低其被滥用的风险。与密码不同，每个 TLS 证书都有一个过期日期，因此替换它们是不可避免的。 实际上，IVYO 会在证书过期日期 之前 自动轮换其管理的客户端证书。将在其工作持续时间的 2/3 之后生成新的客户端证书；因此，例如，IVYO 创建的证书在 12 个月后过期，将在大约第 8 个月时被 IVYO 替换。这样做是为了让您不必担心遇到过期证书的问题或服务中断。 如果您想轮换单个客户端证书，您可以通过从其证书 Secret 中删除 tls.key 字段来重新生成现有集群的证书。 是时候轮换您的 IVYO 根证书了吗？您只需要删除 ivyo-root-cacert secret。IVYO 将无缝地重新生成并推出它，确保您的应用程序继续与 Ivory 集群通信，而无需更新任何配置或处理任何停机时间。 kubectl delete secret ivyo-root-cacert IVYO 仅更新包含生成的根证书的 secret。它不会触及自定义证书。 当您使用自己的 TLS 证书与 IVYO 时，您有责任适当地替换它们。方法如下。 IVYO 会自动检测并加载对 IvorySQL 服务器和复制 Secret 内容的更改，而不会停机。您或您的证书管理器只需要替换 spec.customTLSSecret 引用的 Secret 中的值。 如果您将 spec.customTLSSecret 更改为引用新的 Secret 或新的字段，IVYO 将执行 滚动重启。 更改 IvorySQL 证书颁发机构时，请确保同时更新 customReplicationTLSSecret。 有时您可能希望更改 HA 集群中的主节点。这可以通过使用 ivorycluster spec 的 patroni.switchover 部分来完成。它允许您在 ivoryclusters 中启用切换，将特定实例作为新的主节点，并在您的 ivorycluster 进入不良状态时运行故障转移。 让我们完成执行切换的过程！ 首先，您需要更新您的 spec 以准备您的集群以更改主节点。编辑您的 spec 以具有以下字段： spec: patroni: switchover: enabled: true 应用此更改后，IVYO 将寻找触发器以在您的集群中执行切换。您将通过将 ivory-operator.ivorysql.org/trigger-switchover 注释添加到您的自定义资源来触发切换。设置此注释的最佳方法是使用时间戳，这样您就知道何时启动了更改。 例如，对于我们的 hippo 集群，我们可以运行以下命令来触发切换： kubectl annotate -n ivory-operator ivorycluster hippo \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" 如果您想执行另一次切换，您可以重新运行注释命令并添加 --overwrite 标志： kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" IVYO 将检测到此注释并使用 Patroni API 请求更改当前主节点！ 随着 Patroni 的工作，您的数据库实例 Pod 上的角色将开始更改。新的主节点将具有 master 角色标签，旧的主节点将更新为 replica。 切换的状态将使用 status.patroni.switchover 字段进行跟踪。这将设置为您在触发器注释中定义的值。如果您使用时间戳作为注释，这是确定何时请求切换的另一种方法。 在实例 Pod 标签已更新且 status.patroni.switchover 已设置后，主节点已在您的集群上更改！ 更改主节点后，我们建议您通过将 spec.patroni.switchover.enabled 设置为 false 或完全从您的 spec 中删除该字段来禁用切换。如果该字段被删除，相应的状态也将从 ivorycluster 中删除。 切换主节点时，您可以选择的另一个选项是提供目标实例作为新的主节点。在执行切换时，此目标实例将用作候选节点。spec.patroni.switchover.targetInstance 字段接受您要切换到的实例的名称。 此名称可以在几个不同的地方找到；一个是 StatefulSet 的名称，另一个是数据库 Pod 上的 ivory-operator.ivorysql.org/instance 标签。以下命令可以帮助您确定谁是当前主节点以及用作 targetInstance 的名称： $ kubectl get pods -l ivory-operator.ivorysql.org/cluster=hippo \\ -L ivory-operator.ivorysql.org/instance \\ -L ivory-operator.ivorysql.org/role -n ivory-operator NAME READY STATUS RESTARTS AGE INSTANCE ROLE hippo-instance1-jdb5-0 3/3 Running 0 2m47s hippo-instance1-jdb5 master hippo-instance1-wm5p-0 3/3 Running 0 2m47s hippo-instance1-wm5p replica 在我们的示例集群中，hippo-instance1-jdb5 当前是主节点，这意味着我们希望在切换中定位 hippo-instance1-wm5p。现在您知道哪个实例当前是主节点以及如何找到您的 targetInstance，让我们更新您的集群 spec： spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p 应用此更改后，您将再次需要通过注释 ivorycluster 来触发切换（请参见上面的命令）。您可以通过检查 Pod 角色标签和 status.patroni.switchover 来验证切换是否已完成。 最后，当您的集群进入不健康状态时，我们可以选择进行故障转移。完成此操作所需的唯一 spec 更改是将 spec.patroni.switchover.type 字段更新为 Failover 类型。需要注意的是，执行故障转移时需要 targetInstance。基于上面的示例集群，假设 hippo-instance1-wm5p 仍然是副本，我们可以更新 spec： spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p type: Failover 应用此 spec 更改后，您的 ivorycluster 将准备好执行故障转移。同样，您需要通过注释 ivorycluster 来触发切换（请参见上面的命令），并验证 Pod 角色标签和 status.patroni.switchover 是否已相应更新。 切换过程中遇到的错误可能会使您的集群处于不良状态。如果您遇到问题，请在 operator 日志中找到问题，您可以更新 spec 以修复问题并应用更改。应用更改后，IVYO 将尝试再次执行切换。 总有一个时刻，您需要删除自己的集群。如果您一直在跟着示例操作，只需运行下面一条命令即可删除 Ivory 集群： kubectl delete -k examples/kustomize/ivory IVYO 会清理与该集群相关的所有对象。 关于数据保留：这取决于您 PVC 的 回收策略。如需了解 Kubernetes 如何管理数据保留，请参阅 Kubernetes 官方文档关于卷回收的说明。","title":"IvorySQL Operator部署IvorySQL","component":"ivorysql-doc","version":"master","name":"operator_deployment","url":"/ivorysql-doc/master/containerization/operator_deployment.html","titles":[{"text":"1. 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Do this. 1. Do that. 1. Done. 0. Do this. 1. Do that. 2. Done. 正确示范: 1. Do this. 2. Do that. 3. Done. 13、使用列表时，每一列表项的标识符（+、-、* 或数字）后只能空一格，后接列表内容。 正确示范 * Foo * Bar * Baz 1. Foo * Bar 1. Baz 14、列表（包括有序和无序列表）前后必须各空一行。 错误示范 Some text* Some* List 1. Some2. List Some text 正确示范 Some text * Some * List 1. Some 2. List Some text 15、代码块前后必须各空一行。 错误示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 正确示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 16、文档中禁止出现裸露的 URL。如果希望用户能直接点击并打开该 URL，则使用一对尖括号 (<URL>) 包裹该 URL。如果由于特殊情况必须使用裸露的 URL，不需要用户通过点击打开，则使用一对反引号 `URL` 包裹该 URL。 错误示范 For more information, see https://www.example.com/. 正确示范 For more information, see <https://www.example.com/>. 17、使用加粗、斜体等强调效果时，在强调标识符内禁止出现多余的空格。如不能出现 ** 加粗文本 **。 错误示范 Here is some ** bold ** text. Here is some * italic * text. Here is some more __ bold __ text. Here is some more _ italic _ text. 正确示范: Here is some **bold** text. Here is some *italic* text. Here is some more __bold__ text. Here is some more _italic_ text. 18、单个反引号包裹的代码块内禁止出现多余的空格。如不能出现 ` 示例文本`。 错误示范： some text some text 正确示范: some text 19、链接文本两边禁止出现多余的空格。如不能出现 某链接。 错误示范 [ a link ](https://www.example.com/) 正确示范: [a link](https://www.example.com/) 20、链接必须有链接路径。如不能出现空链接等情况。 错误示范 [an empty link]() [an empty fragment](#) 正确示范: [a valid link](https://example.com/) [a valid fragment](#fragment) 21、文档中的代码块统一使用三个反引号进行包裹，禁止使用缩进四格风格的代码块。 错误示范： Some text. # Indented code More text. 正确示范 ```ruby # Fenced code ​``` More text.","title":"asciidoc语法快速参考","component":"ivorysql-doc","version":"master","name":"asciidoc_syntax_reference","url":"/ivorysql-doc/master/contribution/asciidoc_syntax_reference.html","titles":[{"text":"1. 规范说明","hash":"规范说明","id":1},{"text":"2. 示例","hash":"示例","id":2}]},"36":{"id":36,"text":"IvorySQL 社区采用一套 闭环式开源协作流程，确保从问题提出到版本发布，每一个环节都能闭合反馈、持续改进。这一流程鼓励用户与开发者形成良性互动，让社区开发始终围绕实际需求持续演进。 整个协作闭环流程如下： 🐛 提问题（Issue） 用户或开发者在 GitHub 的 Issues 页面提交 Bug、功能建议或使用反馈。 💬 问题讨论（Discussion） 维护者与社区成员就问题展开讨论，确认问题性质与优先级，加入 ToDo List。 🛠️ 开发分支（Fork & Dev） 开发者认领 Issue，Fork 仓库并在本地开发测试，准备提交代码。 🚀 提交 Pull Request（PR） 将开发分支 Push 到 Fork 仓库后，向上游仓库发起合并请求。 🧐 代码评审（Review） 维护者或核心开发者对 PR 进行评审，提出修改建议并确保质量。 🔀 合并主分支（Merge） 审核通过后，PR 被合并至主分支，对应的 Issue 被关闭。 📦 版本发布（Release） 项目定期发布新版本（每季度小版本，每年大版本），包含最新的修复与功能。 🧪 用户测试（Test） 用户升级使用新版，反馈新问题，新的 Issue 随之产生，形成完整的反馈循环。 通过这套完整的闭环协作机制，IvorySQL 实现了问题响应 → 开发贡献 → 质量保障 → 发布反馈的全流程闭合，推动项目持续健康演进。 IvorySQL是一个由核心开发团队主导、社区共同维护的开源项目。我们欢迎用户、贡献者和维护者的加入，共同推动IvorySQL的发展。如果您希望看到您的代码或文档更改被添加到IvorySQL并出现在将来的版本中，本节的内容介绍是您需要知道的。 在参与贡献前，请确认您当前的参与身份，以便更高效地了解适合您的贡献方式： 用户 👉 欢迎提交问题反馈、功能建议，并参与社区讨论。 贡献者 👉 请先签署 CLA，然后选择您感兴趣的项目模块进行 Issue 认领和代码提交。 维护者 👉 请参考维护职责，包括社区规划、代码评审和协作机制建设。 无论您以何种身份加入，IvorySQL 社区都非常欢迎您的参与和支持！​IvorySQL社区欢迎并赞赏所有类型的贡献，期待您的加入！ 📢 请务必阅读并遵守我们的 IvorySQL社区行为准则。 无论您是要提交 Issue、参与讨论，还是贡献代码与文档，您都需要使用 GitHub 账号登录并与 IvorySQL 项目进行交互。 请参考https://docs.github.com/en/get-started/start-your-journey注册您的github账号，并熟悉Git工具和工作流。 IvorySQL源码托管在github: https://github.com/IvorySQL。 作为用户，您在使用 IvorySQL 过程中扮演着重要角色。我们鼓励您： 发现 Bug、性能缺陷或文档不准确？ 有新的功能建议或使用体验改进？ 如果您准备向社区上报 Bug 或者提交需求，请在 IvorySQL 社区对应的仓库上提交 Issue，并参考Issue 提交指南。 通过 邮件列表进行讨论 加入 GitHub讨论，补充信息或验证问题 在 微信、Discord等聊天群参与技术交流 我们欢迎代码、文档、测试等各类贡献。 在提交代码或文档贡献之前，为了确保代码合法合规，个人或企业贡献者需要签署贡献者许可协议(CLA)。签署CLA是IvorySQL社区接受贡献的必要条件，以确保您的贡献被合法分发。请根据下列链接下载CLA进行签署并将签署后的CLA发送至 cla@ivorysql.org。 个人贡献者 企业贡献者 未签署CLA的Pull Request将无法进入评审阶段。 我们将仓库划分为多个子项目，您可以从如下列表中找到感兴趣的项目及其代码仓库 代码仓库 描述 IvorySQL 负责社区IvorySQL数据库的开发和维护 Ivory-www 负责社区的官网开发和维护 ivory-operator 负责IvorySQL云原生数据库及周边工具开发和维护 docker_library 负责IvorySQL多架构的镜像构建开发和维护 ivory-cloud 负责IvorySQL云服务平台及周边生态开发和维护 Ivorysql_docs 负责社区的文档中心开发和维护 ivory-doc-builder 负责Ivorysql_docs的编译 Ivorysql_web 负责社区的文档中心网站维护 Ivorysql_wasm 负责IvorySQL在线易用体验网站的开发和维护 您可以将自己创建的Issue或者愿意处理的Issue分配给自己。 只需要在评论框内输入/assign，机器人就会将问题分配给您。 每个 Issue 下面可能已经有参与者的交流和讨论，如果您感兴趣，也可以在评论框中发表自己的意见参与 Issue 讨论。 对于提交一个PR应该保持一个功能，或者一个bug提交一次。禁止多个功能一次提交。 前往项目主页，点击Fork按钮，将IvorySQL项目Fork到您自己的GitHub账户中。 使用如下命令将项目克隆到本地进行开发： git clone https://github.com/$user/IvorySQL.git （将 $user 替换为你的 GitHub ID）。 git checkout -b feature/your-feature-name 在提交代码前，请确保通过回归测试 打开你 Fork 的仓库： https://github.com/$user/IvorySQL.git 点击 Compare & pull request 按钮填写PR信息 Fix test 功能描述 leave a comment 对该提交功能进行比较详细的描述 点击Create pull request 按钮即可提交。 维护者负责进行IvorySQL代码的管理，PR审查，主导版本发布与IvorySQL发展方向。 制定版本规划和 Roadmap 跟踪与评估社区需求 维护公开的 TODO 列表 参与 Pull Request 评审 审查安全问题，保障项目健康 优化协作机制（代码贡献指南、PR 模板等） 建立漏洞响应机制和行为守则 感谢每一位参与 IvorySQL 的开发者、文档编辑者、测试人员和使用者。正是有了你们的付出，IvorySQL 才能不断成长！我们欢迎所有人参与 IvorySQL 社区贡献，我们的目标是发展一个由贡献者组成的活跃、健康的社区。","title":"","component":"ivorysql-doc","version":"master","name":"community_contribution_guide","url":"/ivorysql-doc/master/contribution/community_contribution_guide.html","titles":[{"text":"1. 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Fork仓库","hash":"fork仓库","id":12},{"text":"2.3.4.2. 编码","hash":"编码","id":13},{"text":"2.3.4.3. 创建一个Pull Request并提交","hash":"创建一个pull-request并提交","id":14},{"text":"2.4. 维护者","hash":"维护者","id":15},{"text":"2.4.1. 社区规划","hash":"社区规划","id":16},{"text":"2.4.2. 代码管理","hash":"代码管理","id":17},{"text":"2.4.3. 流程与治理机制","hash":"流程与治理机制","id":18},{"text":"2.5. 致谢","hash":"致谢","id":19}]},"37":{"id":37,"text":"如果您提交的贡献是原创作品，那么您可以假设IvorySQL将作为整个IvorySQL版本的一部分发布给下游用户，该版本将遵循Apache许可证2.0版本。 如果您提交的内容不是原创作品，同样鼓励代码共享和尊重原作者的著作权，同样允许代码修改，再发布。请注意需要满足如下条件： 1、需要给代码的用户一份Apache许可证。 2、如果您修改了代码，需要在被修改的文件中说明。 3、在延伸的代码中（修改和有源代码衍生的代码中）需要带有原来代码中的协议、商标、专利声明和其他原来作者规定需要包含的说明。 4、如果再发布的产品中包含一个Notice文件，则在Notice文件中需要带有Apache许可证。您可以在Notice中增加自己的许可，但不可以表现为对Apache许可证构成更改。 最后，请记住，从非原始的工作中删除许可标头从来都不是一个好主意。即使您使用的文件部分最初在顶部有许可标头，您也应该保留它。与往常一样，如果您不太确定您的贡献所涉及的许可问题，请随时在开发人员邮件列表中联系我们。 您获得反馈和看到代码合并到项目中的机会在很大程度上取决于更改的粒度。如果您的想法发生了更大的变化，我们强烈建议您在花大量时间编写代码之前，先加入开发人员的邮件列表，并与我们分享您的建议。即使您的建议得到社区的验证，我们仍然建议您将实际工作作为一系列小型的、独立的提交来完成。这使得评审员的工作更加容易，并提高了反馈的及时性。 当谈到IvorySQL的C和C++部分时，我们尝试遵循PostgreSQL编码约定。除此之外： 对于C和Perl代码，如果需要，请运行pgindent。我们建议在查看更改时使用git diff—​color，这样您提交的代码中就不会出现任何虚假的空白问题。 所有贡献给IvorySQL的新功能都应该由与其一起贡献的回归测试覆盖。如果您不确定如何测试或记录您的工作，请在ivorysql-hackers邮件列表中提出问题，社区的开发人员将尽力帮助您。 至少，您应该始终运行make installcheck world，以确保您没有破坏任何东西。 如果您正在进行的更改涉及PostgreSQL和IvorySQL之间的通用功能，则可能会要求您将其转发到PostgreSQL。这不仅是为了我们不断减少两个项目之间的差异，而且是为了让与PostgreSQL相关的任何变化都能从对上游PostgreSQL社区更广泛的审查中受益。一般来说，将这两个代码库都放在手边是个好主意，这样您就可以确定您的更改是否需要前移。 一旦您准备好与IvorySQL核心团队和IvorySQL社区的其他成员共享您的工作，您应该将所有提交推送到从官方IvorySQL派生的分支的您自己的存储库中，并向我们发送请求。 假定提交的拉取请求通过验证检查，可供同行审查。同行审查是确保对IvorySQL的贡献具有高质量并与路线图和社区期望保持一致的过程。我们鼓励IvorySQL社区的每个成员审查请求并提供反馈。由于您不必成为核心团队成员就可以做到这一点，因此我们建议您向有兴趣成为IvorySQL长期贡献者的任何人提供一系列拉动式评论。 同行评审的一个结果可能是达成共识，即您需要以某些方式修改pull请求。GitHub允许您将其他提交推送到从中发送请求的分支中。这些额外的提交将对所有审阅者可见。 当同行评议收到参与者至少+1张+1和no-1张的选票时，同行评议会趋于一致。在这一点上，您应该期望核心团队成员之一将您的更改引入到项目中。 在补丁审查期间的任何时候，您都可能会因审查人员和核心团队成员的工作效率而遇到延迟。请耐心点，也不要气馁。如果您在几天内没有收到预期的反馈，请添加一条评论，要求更新pull请求本身，或者向邮件列表发送一封电子邮件。 有时，您会看到核心团队成员直接提交到存储库，而无需执行pull请求工作流。这仅适用于小的更改，我们使用的经验法则是：如果更改涉及任何可能导致测试失败的功能，那么它必须通过pull请求工作流。另一方面，如果更改发生在代码库的非功能部分（例如在注释块中修复打字错误），则核心团队成员可以决定直接提交到存储库。","title":"FAQ","component":"ivorysql-doc","version":"master","name":"faq","url":"/ivorysql-doc/master/contribution/faq.html","titles":[{"text":"1. IvorySQL贡献的许可","hash":"ivorysql贡献的许可","id":1},{"text":"2. 编码指南","hash":"编码指南","id":2},{"text":"3. 适用于上游PostgreSQL的更改","hash":"适用于上游postgresql的更改","id":3},{"text":"4. 补丁提交","hash":"补丁提交","id":4},{"text":"5. 补丁审查","hash":"补丁审查","id":5},{"text":"6. 直接提交到存储库","hash":"直接提交到存储库","id":6}]},"38":{"id":38,"text":"IvorySQL由一个核心开发团队维护，该团队拥有对GitHub上的IvorySQL主存储库的提交权限。同时，我们非常渴望从更广泛的IvorySQL社区中的成员那里获得贡献。如果您希望看到您的代码或文档更改被添加到IvorySQL并出现在将来的版本中，本节的内容介绍是您需要知道的。 ​IvorySQL社区欢迎并赞赏所有类型的贡献，期待您的加入！ 作为成员，贡献者和领导者，我们每个人都应阅读我们的 行为准则。我们承诺让每个人都能参与社区，成为一种无骚扰的体验，无论年龄、体型、有形或无形的残疾、种族、性特征、性别认同和表达、经验水平、教育、社会经济地位、国籍、个人外表、种族、宗教或性身份和取向如何。 ​ 我们承诺以有助于建立一个开放，热情，多样化，包容和健康社区的方式采取行动和互动。 团队是持续开放的团队，专注于IvorySQL项目的一部分。一个团队有其审查者、提交者和维护者，并拥有一个或多个存储库。团队级别的决策来自其维护者。IvorySQL开发人员的典型提升路径是从用户到审查者，然后是提交者和维护者。但获得更多的角色并不意味着你对其他社区成员拥有任何特权。IvorySQL社区中的每个人都是平等的，都有责任与其他贡献者进行建设性合作，建立一个友好的社区。这些角色是对您在IvorySQL开发中做出重大贡献的自然奖励，并为您在开发工作流中提供更多权利，以提高您的效率。同时，他们要求你承担一些额外的责任： ​ 团队荣誉：现在您已经是团队评审员/提交者/维护者的成员，意味着您代表着项目和您的团队成员。所以，请做一个好人来捍卫球队的声誉。 ​ 责任：提交者/维护者有权合并拉取请求，因此承担额外的责任来处理接受代码库或文档更改的后果。这包括在它导致问题时恢复或修复它，以及帮助发布经理解决发布前测试周期中发现的任何问题。 在贡献之前，我们需要了解下IvorySQL目前的版本以及文档的版本。目前，我们维护着5.0等版本，我们的版本紧跟PG的更新步伐，贡献之前请更新至最新版本。之后我们需要细心浏览一下贡献的样式风格，熟悉代码贡献风格、提Issue样式、拉取PR标题样式、代码注释样式、文档贡献样式、文章贡献样式，这可以帮助您尽快成为IvorySQL的贡献者奥~。 IvorySQL是在GitHub上开发的，任何希望对其作出贡献的人都必须拥有GitHub帐户，并熟悉Git工具和工作流。还建议您遵循开发人员的邮件列表，因为一些贡献可能会在那里产生更详细的讨论。 如果您有GitHub帐户，fork这个存储库至您的个人仓库中，这样您就可以拥有您的私人副本来开始hacking，并将其用作拉取请求的来源。 如果您提交的贡献是原创作品，那么您可以假设IvorySQL将作为整个IvorySQL版本的一部分发布给下游用户，该版本将遵循Apache许可证2.0版本。 ​ 如果您提交的内容不是原创作品，同样鼓励代码共享和尊重原作者的著作权，同样允许代码修改，再发布。\\*请注意需要满足如下条件\\*： （1）需要给代码的用户一份Apache许可证。 （2）如果您修改了代码，需要在被修改的文件中说明。 （3）在延伸的代码中（修改和有源代码衍生的代码中）需要带有原来代码中的协议、商标、专利声明和其他原来作者规定需要包含的说明。 （4）如果再发布的产品中包含一个Notice文件，则在Notice文件中需要带有Apache许可证。您可以在Notice中增加自己的许可，但不可以表现为对Apache许可证构成更改。 ​ 最后，请记住，从非原始的工作中删除许可标头从来都不是一个好主意。即使您使用的文件部分最初在顶部有许可标头，您也应该保留它。与往常一样，如果您不太确定您的贡献所涉及的许可问题，请随时在开发人员邮件列表中联系我们。 可以将自己修改的bug、新增的功能等代码上传至个人仓库，最后提交PR请求合并至官网：https://github.com/IvorySQL/IvorySQL 。 IvorySQL社区提供的是中英文文档。英文文档保存在…​文档存储库中，中文文档保存在i18n文档存储库中。您可以为任一文档存储库做出贡献。 GitHub: https://github.com/IvorySQL/IvorySQL ​码云：https://gitee.com/IvorySQL/ IvorySQL官网下载：https://github.com/IvorySQL/Ivory-www 邮件列表位于 官网下：https://lists.ivorysql.org/ 您可以在IvorySQL-WWW代码仓库自己提交至blog贡献，也可以联系我们IvorySQL小助理发送至邮箱ivorysql@highgo.com 。 您获得反馈和看到代码合并到项目中的机会在很大程度上取决于更改的粒度。如果您的想法发生了更大的变化，我们强烈建议您在花大量时间编写代码之前，先加入开发人员的邮件列表，并与我们分享您的建议。即使您的建议得到社区的验证，我们仍然建议您将实际工作作为一系列小型的、独立的提交来完成。这使得评审员的工作更加容易，并提高了反馈的及时性。 ​ 当谈到IvorySQL的C和C++部分时，我们尝试遵循PostgreSQL编码约定。除此之外： ​ 对于C和Perl代码，如果需要，请运行pgindent。我们建议在查看更改时使用git diff—​color，这样您提交的代码中就不会出现任何虚假的空白问题。 ​ 所有贡献给IvorySQL的新功能都应该有与其一起贡献的回归测试覆盖。如果您不确定如何测试或记录您的工作，请在ivorysql-hackers邮件列表中提出问题，社区的开发人员将尽力帮助您。 ​ 至少，您应该始终运行make install check world，以确保您没有破坏任何东西。 如果您正在进行的更改涉及PostgreSQL和IvorySQL之间的通用功能，则可能会要求您将其转发到PostgreSQL。这不仅是为了我们不断减少两个项目之间的差异，而且是为了让与PostgreSQL相关的任何变化都能从对上游PostgreSQL社区更广泛的审查中受益。一般来说，将这两个代码库都放在手边是个好主意，这样您就可以确定您的更改是否需要前移。 一旦您准备好与IvorySQL核心团队和IvorySQL社区的其他成员共享您的工作，您应该将所有提交推送到从官方IvorySQL派生的您自己的存储库中，并向我们发送请求。 假定提交的拉取请求通过验证检查，可供同行审查。同行审查是确保对IvorySQL的贡献具有高质量并与路线图和社区期望保持一致的过程。我们鼓励IvorySQL社区的每个成员审查请求并提供反馈。由于您不必成为核心团队成员就可以做到这一点，因此我们建议您向有兴趣成为IvorySQL长期贡献者的任何人提供一系列拉动式评论。 ​ 同行评审的一个结果可能是达成共识，即您需要以某些方式修改pull请求。GitHub允许您将其他提交推送到从中发送请求的分支中。这些额外的提交将对所有审阅者可见。 ​ 当同行评议收到参与者至少+1张+1和no-1张的选票时，同行评议会趋于一致。在这一点上，您应该期望核心团队成员之一将您的更改引入到项目中。 在补丁审查期间的任何时候，您都可能会因审查人员和核心团队成员的工作效率而遇到延迟。请耐心点，也不要气馁。如果您在几天内没有收到预期的反馈，请添加一条评论，要求更新pull请求本身，或者向邮件列表发送一封电子邮件。 有时，您会看到核心团队成员直接提交到存储库，而无需执行pull请求工作流。这仅适用于小的更改，我们使用的经验法则是：如果更改涉及任何可能导致测试失败的功能，那么它必须通过pull请求工作流。另一方面，如果更改发生在代码库的非功能部分（例如在注释块中修复打字错误），则核心团队成员可以决定直接提交到存储库。 1、进入 IvorySql官网：https://github.com/IvorySQL/IvorySQL ​2、点击New issue 1、bug report Title: 标题 ## Bug Report 对bug进行描述 \\### IvorySQL Version 在IvorySQL哪个版本发现的问题 \\### OS Version (uname -a) 系统版本 \\### Configuration options ( config.status --config ) 配置参数 \\### Current Behavior 当前的结果 \\### Expected behavior/code 期望的结果 \\### Step to reproduce 复现步骤 \\### Additional context that can be helpful for identifying the problem 有助于识别问题的其它信息 2、Enhancement Title: 标题 ## Enhancement 对于期望强化的功能作一个描述 3、Feature Request Title: 标题 ## Feature Request 描述你期望实现的一个功能 点击 submit new issue 按钮, 提交即可 1、打开ivorysql仓库 https://github.com/IvorySQL/IvorySQL 2、点击右上角fork按钮，等待fork完成 cd $working_dir # 将 $working_dir 替换为你想放置 repo 的目录。例如，`cd ~/Documents/GitHub` git clone git@github.com:$user/IvorySQL.git # 将 `$user` 替换为你的 GitHub ID cd $working_dir/IvorySQL git checkout -b new-branch-name 在新建的new-branch-name中修改代码。 Git add <file> Git commit -m “commit-message” Git push -u origin new-branch-name 1、打开你 Fork 的仓库： https://github.com/$user/IvorySQL（将 $user 替换为你的 GitHub ID）。 2、点击 Compare & pull request 按钮即可创建 PR。 对于提交一个PR应该保持一个功能，或者一个bug提交一次。禁止多个功能一次提交。 1、打开你 Fork 的仓库： https://github.com/$user/IvorySQL（将 $user 替换为你的 GitHub ID）。 ​ 2、点击 Compare & pull request 按钮 Fix test 功能描述 leave a comment 对该提交功能进行比较详细的描述 点击Create pull request 按钮即可提交。 （1）下载Markdown或者Typora文档编辑器。 （2）检查源仓库是否有更新，如果有更新请先更新并同步到自己的仓库。如有更新请参阅以下步骤，更新至最新版本： git remote git fetch upstream git merge upstream/main git push （3）熟悉样式风格（规范说明） IvorySQL社区提供双语文档。英文文档保存在IvorySQL/文档存储库（文档存储库）中，中文文档保存在 IvorySQL/文档-i18n存储库（文档-i18n 存储库）中。您可以为任何一方文档做出贡献，当然您也可以为两方同时做出贡献。 ​ 您可以从以下任何一项开始，以帮助改进IvorySQL网站（英文和 -i18n）上的 IvorySQL文档： ​ (1) 编写完善文档 ​ (2) 修复拼写错误或格式（标点符号、空格、缩进、代码块等） ​ (3) 修正或更新不当或过时的说明 ​ (4) 添加缺少的内容（句子、段落或新文档） ​ (5) 将文档更改从英文翻译成中文，或从中文翻译成英文。 ​ (6) 提交、回复和解决文档问题或文档-i18n问题 ​ (7) （高级）查看其他人创建的拉取请求 IvorySQL文档是用“markdown”编写的。为确保格式的质量和一致性，在修改更新文档时应遵循某些 Markdown 规则。 ​ Markdown规范 ​ 1、标题从一级开始递增使用，禁止跳级使用。例如：一级标题下面不能直接使用三级标题；二级标题下面不能直接使用四级标题。 ​ 2、标题必须统一使用 ATX 风格，即在标题前加 # 号来表示标题级别。 ​ 3、标题的引导符号 # 后必须空一格再接标题内容。 ​ 4、标题的引导符号“#”后只能空一格后再接标题内容，不能有多个空格。 ​ 5、标题必须出现在一行行首，即标题的 # 号前不能有任何空格。 ​ 6、标题末尾仅能出现中英文问号、反引号、中英文单双引号等符号。其余如冒号、逗号、句号、感叹号等符号均不能在标题末尾使用。 ​ 7、标题上面须空一行。 ​ 8、文档中不能连续出现内容重复的标题，如一级标题为 # IvorySQL 架构，紧接着的二级标题就不能是 ## IvorySQL 架构。如果不是连续的标题，则标题内容可重复。 ​ 9、文档中只能出现一个一级标题。 ​ 10、一般来说，除 TOC.md 文件可缩进 2 个空格外，其余所有 .md 文件每缩进一级，默认须缩进 4 个空格。 ​ 11、文档中（包括代码块内）禁止出现 Tab 制表符，如需缩进，必须统一用空格代替 ​ 12、禁止出现连续的空行。 ​ 13、块引用符号 > 后禁止出现多个空格，只能使用一个空格，后接引用内容。 ​ 14、使用有序列表时，必须从 1 开始，按顺序递增。 ​ 15、使用列表时，每一列表项的标识符（+、-、* 或数字）后只能空一格，后接列表内容。 ​ 16、列表（包括有序和无序列表）前后必须各空一行。 ​ 17、代码块前后必须各空一行。 ​ 18、文档中禁止出现裸露的 URL。如果希望用户能直接点击并打开该 URL，则使用一对尖括号 (<URL>) 包裹该 URL。如果由于特殊情况必须使用裸露的 URL，不需要用户通过点击打开，则使用一对反引号 (URL) 包裹该 URL。 ​ 19、使用加粗、斜体等强调效果时，在强调标识符内禁止出现多余的空格。如不能出现 加粗文本 。 ​ 20、单个反引号包裹的代码块内禁止出现多余的空格。如不能出现 ` 示例文本 `。 ​ 21、链接文本两边禁止出现多余的空格。如不能出现 [某链接](https://www.example.com/)。 ​ 22、链接必须有链接路径。如不能出现[空链接]()或[空链接](#)等情况。 1、标题从一级开始递增使用，禁止跳级使用。 # Heading 1 ### Heading 3 We skipped out a 2nd level heading in this document 2、标题必须统一使用 ATX 风格，即在标题前加 # 号来表示标题级别。 # Heading 1 ## Heading 2 ### Heading 3 #### Heading 4 ## Another Heading 2 ### Another Heading 3 3、标题的引导符号 # 后必须空一格再接标题内容。禁止#后多个空格，禁止#前出现空格 错误示范： # Heading 1 ## Heading 2 正确示范： # Heading 1 ## Heading 2 4、标题末尾仅能出现中英文问号、反引号、中英文单双引号等符号。 错误示范 # This is a heading. 正确示范 # This is a heading 5、标题上面空一行 错误示范 # Heading 1 Some text Some more text## Heading 2 正确示范 # Heading 1 Some text Some more text ## Heading 2 6、文档中不能连续出现内容重复的标题，如一级标题为 # IvorySQL 描述，紧接着的二级标题就不能是 ## IvorySQL 描述。如果不是连续的标题，则标题内容可重复。 错误示范 # Some text ## Some text 正确示范 # Some text ## Some more text 7、文档中只能出现一个一级标题。 错误示范 # Top level heading # Another top-level heading 正确示范 # Title ## Heading ## Another heading 8、一般来说，除 TOC.md 文件可缩进 2 个空格外，其余所有 .md 文件每缩进一级，默认须缩进 4 个空格。 错误示范 * List item * Nested list item indented by 3 spaces 正确示范: * List item * Nested list item indented by 4 spaces 9、文档中（包括代码块内）禁止出现 Tab 制表符，如需缩进，必须统一用空格代替 错误示范： Some text * hard tab character used to indent the list item 正确示范: Some text * Spaces used to indent the list item instead 10、禁止出现连续的空行 错误示范 Some text here Some more text here 正确示范: Some text here Some more text here 11、块引用符号 > 后禁止出现多个空格，只能使用一个空格，后接引用内容。 错误示范 > This is a blockquote with bad indentation> there should only be one. 正确示范 > This is a blockquote with correct> indentation. 12、使用有序列表时，必须从 1 开始，按顺序递增。 错误示范: 1. Do this. 1. Do that. 1. Done. 0. Do this. 1. Do that. 2. Done. 正确示范: 1. Do this. 2. Do that. 3. Done. 13、使用列表时，每一列表项的标识符（+、-、* 或数字）后只能空一格，后接列表内容。 正确示范 * Foo * Bar * Baz 1. Foo * Bar 1. Baz 14、列表（包括有序和无序列表）前后必须各空一行。 错误示范 Some text* Some* List 1. Some2. List Some text 正确示范 Some text * Some * List 1. Some 2. List Some text 15、代码块前后必须各空一行。 错误示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 正确示范 Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 16、文档中禁止出现裸露的 URL。如果希望用户能直接点击并打开该 URL，则使用一对尖括号 (<URL>) 包裹该 URL。如果由于特殊情况必须使用裸露的 URL，不需要用户通过点击打开，则使用一对反引号 (URL) 包裹该 URL。 错误示范 For more information, see https://www.example.com/. 正确示范 For more information, see <https://www.example.com/>. 17、使用加粗、斜体等强调效果时，在强调标识符内禁止出现多余的空格。如不能出现 加粗文本 。 错误示范 Here is some ** bold ** text. Here is some * italic * text. Here is some more __ bold __ text. Here is some more _ italic _ text. 正确示范: Here is some **bold** text. Here is some *italic* text. Here is some more __bold__ text. Here is some more _italic_ text. 18、单个反引号包裹的代码块内禁止出现多余的空格。如不能出现 ` 示例文本 `。 错误示范： some text some text 正确示范: some text 19、链接文本两边禁止出现多余的空格。如不能出现 [ 某链接 ](https://www.example.com/)。 错误示范 [ a link ](https://www.example.com/) 正确示范: [a link](https://www.example.com/) 20、链接必须有链接路径。如不能出现[空链接]()或[空链接](#)等情况。 错误示范 [an empty link]() [an empty fragment](#) 正确示范: [a valid link](https://example.com/) [a valid fragment](#fragment) 21文档中的代码块统一使用三个反引号进行包裹，禁止使用缩进四格风格的代码块。 错误示范： Some text. # Indented code More text. 正确示范 ```ruby # Fenced code ​``` More text. 为了测试您所做的修改是否修改，您需要准备以下环境 Node.js 安装 Antora 安装 环境安装请参考 Antora docs。 安装成功后，在终端上显示如下即为成功安装。 通过阅读之前内容，相信您已经有了充足的准备，包括 fork 我们文档中心相关的三个仓库到个人仓库中，Antora 工具的安装准备等环境的搭建。 首先，您要知道网页对应的ui的位置，如下图： 中英文的网页ui模板基本一致，因此修改时应该尽量保证同时修改两个模板，将修改过后的ui再上传至个人Github上，完成了这些，就可以考虑在本地生成您修改过后的网页了。 文档中心是由 Antora 进行搭建的，在运行 Antora 之前，记得修改对应 playbook.yml 文件 完成上述流程之后，请在终端运行命令 antora antora-playbook.yml --stacktrace，然后耐心等待，当成功运行结束后，你就可以查看自己生成的网页了。 在检查之后，你就可以开始着手上传至我们的 ivorysql_web 仓库中，提交PR的流程和之前的流程相同，感谢您对社区的贡献_，我们会在审核过后，考虑是否更新网站。 1、下载 Markdown或者 Typora文档编辑器。 ​ 2、先下载博客源码到本地，检查源仓库(https://github.com/IvorySQL/Ivory-www)是否有更新， 如果有更新请先更新并同步到自己的仓库。请参阅以下步骤，更新至最新版本： # 拉取网站源码 git clone https://github.com/IvorySQL/Ivory-www.git # 同步更新仓库 git pull ​ 3、熟悉样式风格 ([_规范说明_2]) IvorySQL社区提供双语文档。英文博客保存在源码目录<u>Ivory-www/blog</u>中，中文博客保存在源码目录<u>Ivory-www/i18n/zh-CN/docusaurus-plugin-content-blog</u>中。您可以为任何一方博客做出贡献，当然您也可以为两方同时做出贡献。 在贡献之前，让我们快速浏览一下有关IvorySQL博客维护的信息。这可以帮助您尽快成功的提交blog成为贡献者。blog提交规范 ​ （1）将代码克隆到本地仓库 git clone https://github.com/IvorySQL/Ivory-www.git ​ （2）创建一个分支 git checkout -b <branch-name> ​ （3）在博客目录中创建自己文章的目录，目录名字规则参照 ([_规范说明_2])。 # 生成英文博客目录及文件 cd Ivory-www/blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md # 生成中文博客目录及文件 cd Ivory-www/i18n/zh-CN/docusaurus-plugin-content-blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md ​ （4）在index.md编写要发布的博文，将博客中需要的图片放到和index.md同级目录。 ​ （5）提交发布博客 git add <file-path> git commit -m \"<message>\" git push origin <branch-name>:<branch-name> （1）文件夹命名格式：年-月-日-名称 示例：2022-1-28-ivorysql-arrived ​ （2）文件属性统一为index.md ​ （3）图片属性统一为 .png形式，并将需要上传的图片提前放到要提交的文件夹中。 ​ 注意：图片名字唯一，不可重复奥~。 ​ 示例：po-one.png 博客是用markdown或者Typora来编写，您可以阅读 博客 | Docusaurus来了解博客的设计方式。 （1）文章头部部署包括以下信息 --- slug: IvorySQL title: 欢迎来到IvorySQL社区 authors: [official] authorTwitter: IvorySql tags: [IvorySQL, Welcome, Database, Join Us] --- ​ 提示：您可以将以上模板复制到您的文件夹中并进行编辑。 ​ 注意：1）slug、title、authors、tags后添加文字均空一格。 ​ 2）slug每篇名字唯一且不可重复，相同文章中英文版可以相同。 （2）文本格式 ​ 正文段落标题统一用h2/“二级标题”； ​ 正文使用默认字体字号。 （3）插入照片命名形式 ​ [Hello](Hello-banner.png) （4）插入超链接命名形式 ​ [名称]（链接） ​ [Github page](https://github.com/IvorySQL/) 下载源代码或发布的软件包。 IvorySQL的文档中心 采用开源工具 Antora 进行搭建，同样的，我们的文档中心也是开源的。文档中心由三部分组成，分别是 文档文件, 静态网页文件 以及生成网页所用到的 网页模板文件 。 我们的文档中心同样欢迎每一位愿意参与到开源工作中的小伙伴的加入，记得遵守我们的行为准则_。 由于我们的文档中心全部托管于Github上，这使得任何用户都可以将我们的文档仓库 fork 到个人仓库中，然后对其进行修改，之后提交PR，由我们的开源团队的人员审核过后就可以将修改更新到我们的文档中心中。 为了更加便捷地使您达到纠正文档错误的目的，首先需要您按照想要更新的大小来建立个人仓库，如下： 如果您想修改原有的内容或者添加新的页面，仅需要 fork 文档文件 到个人仓库中。 如果您想更深度地参与到文档中心的建设工作中来，除了文档文件外，还需要 fork 静态网页文件 和 网页模板文件 到个人仓库中。 本小节将会介绍发现网页内容不适宜之后，对网页内容进行修改的流程。 在有错误内容的网页的右上角，有一个 edit this page 的按钮，点击按钮。如图： 点击之后，就会跳转到我们存放当前页面源 .adoc 文件的编辑页面，请按照 Asciidoc 格式对内容进行修改。如图： 编辑完成后，如图： 确认更新后，如图： 接下来会由开源团队的相关人员负责审核您提交的内容，审核完成后您所提交的更新就会出现在对应页面上。 本小节会介绍如何在网站上添加新的页面组件，添加新页面涉及的修改主要包括以下几种： CN/modules/ROOT/pages/vX.X 目录下的 .adoc 文件添加 CN/modules/ROOT/nav.adoc 的修改，如果修改涉及到图片的修改或者添加，请修改 images 中的图片 EN/modules/ROOT/pages/vX.X 目录下的 .adoc 文件添加 EN/modules/ROOT/nav.adoc 的修改，如果修改涉及到图片的修改或者添加，请修改 images 中的图片 首先，您需要把您 fork 的仓库，clone到本地 git clone https://github.com/$username$/ivorysql_docs.git 然后，将要添加的 .adoc 文件放至正确目录下，切记中英文的都应该准备（中英文文件应该同名），并且各自放至正确目录下，同时，修改对应的 nav.adoc 文件（修改方式可以参照文件已有内容进行修改）。 上述修改完成后，先提交至个人仓库 git add . git commit -m \"$describe your change$\" git push 之后按照如下提交PR即可 如果您不满足于简单的在网页端进行提交或者仅修改网页内容，又或者您想要修改网页ui，本小节内容将会帮助到您。 在阅读本小节内容之前，您需要确认您的Github个人仓库是否已经 fork 了网页模板文件仓库和静态网页文件仓库，如果没有请参考[_如何贡献]。 部署网页这部分工作，暂时还是由开源团队的人员负责，我们重视每一次提交和每一个issue，所以请不要担心自己对于社区的贡献被埋没_ 本章节主要介绍了如何自己主动去更新我们的文档中心，但是我们明确地知道山高路远，时不我待。因此对于没有太多时间的小伙伴来说，可以发邮件到我们的 ivorysql-docs@ivorysql.org，我们会有专门的工作人员进行处理您的每一封来信，期待您的邮件。","title":"IvorySQL社区贡献指南","component":"ivorysql-doc","version":"master","name":"issue_submission_guide","url":"/ivorysql-doc/master/contribution/issue_submission_guide.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"1.1. 说明","hash":"说明","id":2},{"text":"1.2. 行为准则","hash":"行为准则","id":3},{"text":"1.3. 管理说明","hash":"管理说明","id":4},{"text":"2. 贡献者指南","hash":"贡献者指南","id":5},{"text":"2.1. 贡献前的准备","hash":"贡献前的准备","id":6},{"text":"2.1.1. 开始","hash":"开始","id":7},{"text":"2.1.2. 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Tip","hash":"tip","id":58}]},"39":{"id":39,"text":"IvorySQL适配认证如下CPU架构： 序号 架构名称 适配品牌 全平台介质包下载 1 x86_64 Intel、AMD、兆芯、海光 deb, rpm 2 aarch64 飞腾、鲲鹏 deb, rpm 3 mips64el 龙芯3000，龙芯4000 deb, rpm 4 loongarch64 龙芯5000 deb, rpm","title":"芯片架构适配","component":"ivorysql-doc","version":"master","name":"cpu_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html","titles":[]},"40":{"id":40,"text":"IvorySQL适配认证如下操作系统： 序号 操作系统名称 操作系统简介 证书查看 1 银河麒麟高级服务器操作系统 V11 银河麒麟高级服务器操作系统V11是麒麟软件依托多年技术研发积淀与丰富应用实践经验，为企业级关键业务量身打造的新一代服务器操作系统。产品以高可靠、高可用、高安全、高性能、高扩展为核心优势，在深度融合AI技术的基础上，更以自主创新突破构建起高速网络协议 MPTCP、热补丁管理、智能故障诊断、场景化优化及安全容器镜像仓库等关键技术体系，为党政机关信息化建设、重点行业数字化转型及国家重大工程实施，筑牢安全可信的支撑底座。 2 openKylin 2.0 SP1 OpenAtom openKylin （简称“openKylin”） 是由开放原子开源基金会孵化及运营的开源项目，由基础软硬件企业、非营利性组织、社团组织、高等院校、科研机构和个人开发者共同创立，旨在以“为世界提供与人工智能技术深度融合的开源操作系统”为社区愿景，在开源、自愿、平等、协作的基础上，共同打造全球领先的智能桌面开源操作系统根社区，推动Linux开源技术及其软硬件生态繁荣发展。 3 OpenAnolis OS (龙蜥操作系统) 23 龙蜥操作系统 Anolis OS 23 是龙蜥社区（OpenAnolis）基于开源生态发展合作倡议，面向上游原生社区独立选型，持续演进并保障兼容性和稳定性的操作系统。Anolis OS 23 基于 Linux Kernel 6.6 LTS 的企业级操作系统，依托 ANCK 6.6 内核深度优化，全面支持海光、飞腾、龙芯（LoongArch）、兆芯等国产芯片及通用 x86_64/ARM64 架构。其针对虚拟化、安全特性及性能优化进行专项增强，并通过分层架构设计与智能调优工具，实现软硬协同性能最大化。同时原生支持 AI 生态组件，提供安全的 AI 容器镜像，加速模型开发与推理部署。在开发工具链方面，集成 GCC 12.3+/LLVM 17、Python 3.11、Rust 等，支持多平台高效开发。桌面生态方面兼容 GNOME、DDE 桌面环境，并通过集成玲珑包管理器满足了多样化场景需求实现生态扩展。Anolis OS 23 支持各类常见应用和国产化应用，助力企业实现高效、安全、可靠的数字化转型。 4 deppin (深度操作系统) 20 深度操作系统是一个致力于为全球用户提供美观易用、安全可靠的Linux发行版。深度操作系统 20正式版（1002）采取统一的设计风格，从桌面环境和应用进行重新设计，带来焕然一新的视觉感受。底层仓库升级到Debian 10.5，系统安装采用双内核机制（Kernel 5.4、Kernel 5.7），全面提升系统稳定性和兼容性。全新设计的启动器菜单、指纹识别、系统安全增强等，系统部分预装应用升级到最新版本，只为给你更好体验。","title":"操作系统适配","component":"ivorysql-doc","version":"master","name":"os_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html","titles":[]},"41":{"id":41,"text":"IvorySQL 作为一款兼容 Oracle 且基于 PostgreSQL 的高级开源数据库，具备强大的扩展能力，支持丰富的生态系统插件。这些插件可以帮助用户在不同场景下增强数据库功能，包括地理信息处理、向量检索、全文搜索、数据定义提取和路径规划等。以下是当前 IvorySQL 官方兼容和支持的主要插件列表： 序号 插件名称 版本 功能描述 适用场景 1 postgis 3.5.4 为 IvorySQL 提供地理空间数据支持，包括空间索引、空间函数和地理对象存储 地理信息系统（GIS）、地图服务、位置数据分析 2 pgvector 0.8.1 支持向量相似性搜索，可用于存储和检索高维向量数据 AI 应用、图像检索、推荐系统、语义搜索 3 pgddl (DDL Extractor) 0.31 提取数据库中的 DDL（数据定义语言）语句，便于版本管理和迁移 数据库版本控制、CI/CD 集成、结构比对与同步 4 pg_cron​ 1.6.0 提供数据库内部的定时任务调度功能，支持定期执行SQL语句 数据清理、定期统计、自动化维护任务 5 pgsql-http​ 1.7.0 允许在SQL中发起HTTP请求，与外部Web服务进行交互 数据采集、API集成、微服务调用 6 plpgsql_check 2.8 提供PL/pgSQL代码的静态分析功能，可在开发阶段发现潜在错误 存储过程开发、代码质量检查、调试优化 7 pgroonga 4.0.4 提供​非英语语言全文搜索功能，满足高性能应用的需求 中日韩等语言的全文搜索功能 8 pgaudit 18.0 提供细粒度的审计功能，记录数据库操作日志，便于安全审计和合规性检查 数据库安全审计、合规性检查、审计报告生成 9 pgrouting 3.8.0 提供地理空间数据的路由计算功能，支持多种算法和数据格式 地理空间分析、路径规划、物流优化 10 system_stats 3.2 提供用于访问系统级统计信息的函数 系统监控 11 pg_ai_query 0.1.1 AI驱动的自然语言转SQL扩展，支持多种大语言模型 AI辅助查询、自然语言数据库交互 12 pg_stat_monitor 2.3.1 收集性能统计数据，并通过统一视图和直方图形式直观展示查询性能指标。 性能监控 这些插件均经过 IvorySQL 团队的测试和适配，确保在 IvorySQL 环境下稳定运行。用户可以根据业务需求选择合适的插件，进一步提升数据库系统的能力和灵活性。 我们也将持续扩展和丰富 IvorySQL 的插件生态，欢迎社区开发者提交新的插件适配建议或代码贡献。如需了解更多每个插件的详细使用方法和最新兼容版本，请参阅各插件对应的文档章节。","title":"","component":"ivorysql-doc","version":"master","name":"ecosystem_overview","url":"/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html","titles":[{"text":"IvorySQL生态插件适配列表","hash":"ivorysql生态插件适配列表","id":1}]},"42":{"id":42,"text":"pg_ai_query 是一个用于 IvorySQL/PostgreSQL 的 AI 驱动自然语言转 SQL 扩展。它利用大语言模型（LLM）将用户的自然语言描述直接转换为可执行的 SQL 查询语句，支持 OpenAI、Anthropic Claude 和 Google Gemini 等多种 AI 模型。 项目地址：https://github.com/benodiwal/pg_ai_query/tree/main 开源协议：Apache-2.0 主要特性： 自然语言转 SQL：将文本语言描述转换为有效的 SQL 查询 多模型支持：支持 gpt-4o-mini、gpt-4o、gpt-5、claude-3-haiku-20240307、claude-sonnet-4-5-20250929、claude-4.5-opus 等大模型 安全保护：阻止访问 information_schema 和 pg_catalog 系统表 作用域限制：仅对用户表进行操作 可配置限制：内置行数限制强制执行 API 密钥安全：安全处理 API 凭证 下面以v0.1.1为例进行演示。 依赖要求 PostgreSQL 14+ with development headers CMake 3.16+ C++20 compatible compiler API key from OpenAI, Anthropic, or Google (Gemini) 安装依赖 sudo apt-get install libcurl4-openssl-dev 编译安装 IvorySQL 如需从源码编译 IvorySQL，可参考以下配置： ./configure \\ --prefix=$PWD/inst \\ --enable-cassert \\ --enable-debug \\ --enable-tap-tests \\ --enable-rpath \\ --enable-nls \\ --enable-injection-points \\ --with-tcl \\ --with-python \\ --with-gssapi \\ --with-pam \\ --with-ldap \\ --with-openssl \\ --with-libedit-preferred \\ --with-uuid=e2fs \\ --with-ossp-uuid \\ --with-libxml \\ --with-libxslt \\ --with-perl \\ --with-icu \\ --with-libnuma 编译安装 pg_ai_query git clone --recurse-submodules https://github.com/benodiwal/pg_ai_query.git cd pg_ai_query mkdir build && cd build export PATH=\"$HOME/works/repo/ivorysql/IvorySQL/inst/bin:$PATH\" cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/works/repo/ivorysql/IvorySQL/inst make && sudo make install 创建扩展 CREATE EXTENSION pg_ai_query; 在 home 目录下创建 ~/.pg_ai.config 配置文件： [general] log_level = \"INFO\" enable_logging = false [query] enforce_limit = true default_limit = 1000 [response] show_explanation = true show_warnings = true show_suggested_visualization = false use_formatted_response = false [anthropic] # Your Anthropic API key (if using Claude) api_key = \"******\" # Default model to use (options: claude-sonnet-4-5-20250929) default_model = \"claude-sonnet-4-5-20250929\" # Custom API endpoint (optional) - for Anthropic-compatible APIs api_endpoint = \"https://open.bigmodel.cn/api/anthropic\" [prompts] # Use file paths to read custom prompts system_prompt = /home/highgo/.pg_ai.prompts explain_system_prompt = /home/highgo/.pg_ai.explain.prompts 更多示例请参考：https://github.com/benodiwal/pg_ai_query/blob/main/docs/src/examples.md SELECT generate_query('找出所有的用户'); 输出示例： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135507cc16362d5d324ccd generate_query -------------------------------------------------------- SELECT * FROM public.users LIMIT 1000; + -- Explanation: -- Retrieves all columns and rows from the users table. + -- Warning: INFO: Applied LIMIT 1000 to prevent large result sets. Remove LIMIT if you need all data. + -- Note: Row limit was automatically applied to this query for safety (1 row) 执行查询： SELECT * FROM public.users LIMIT 1000; 输出： id | name | email | age | created_at | city ----+---------------+-------------------+-----+----------------------------+--------------- 1 | Alice Johnson | alice@example.com | 28 | 2026-02-04 15:47:55.208111 | New York 2 | Bob Smith | bob@example.com | 35 | 2026-02-04 15:47:55.208111 | San Francisco 3 | Carol Davis | carol@example.com | 31 | 2026-02-04 15:47:55.208111 | Chicago 4 | David Wilson | david@example.com | 27 | 2026-02-04 15:47:55.208111 | Seattle 5 | Eva Brown | eva@example.com | 33 | 2026-02-04 15:47:55.208111 | Boston (5 rows) 生成测试数据 SELECT generate_query('生成100条user数据，插入到users'); 输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_2026021114092101601c5650864a2d generate_query -------------------------------------------------------------------------------------------------------- INSERT INTO public.users (name, email, age, city, status) SELECT 'User_' || generate_series AS name, 'user' || generate_series || '@example.com' AS email, (18 + (generate_series % 50)) AS age, (ARRAY['Beijing','Shanghai','Guangzhou','Shenzhen','Hangzhou'])[1 + (generate_series % 5)] AS city, 'active' AS status FROM generate_series(1, 100); + -- Explanation: -- 生成100条模拟用户数据并插入到users表中。数据包括自动生成的姓名、唯一邮箱、随机年龄(18-67岁)、随机城市和默认状态。 + -- Warnings: -- 1. INFO: 依赖users表的id列有DEFAULT自增设置，未手动插入id。 -- 2. INFO: 使用generate_series函数生成序列数据，这是PostgreSQL/IvorySQL的特性。 -- 3. WARN: 确保在运行前users表为空或id序列不冲突，否则可能重复插入。 -- 4. WARN: 邮箱格式为简单模拟，实际环境中可能需要更复杂的逻辑或去重检查。 (1 row) 不区分大小写查询 SELECT generate_query('show users from beijing, beijing is non-Case insensitive'); 输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211142845878f5f1a5a2f44a7 generate_query ----------------------------------------- SELECT id, name, email, age, created_at, city, status FROM public.users WHERE LOWER(city) = LOWER('beijing') LIMIT 100; + -- Explanation: -- Selects all user details for users located in Beijing, performing a case-insensitive match on the city column. + -- Warnings: -- 1. INFO: Using LOWER() on both sides ensures case-insensitive matching but may prevent the database from using a standard index on the city column if one exists. -- 2. INFO: Row limit of 100 applied to prevent large result sets. + -- Note: Row limit was automatically applied to this query for safety (1 row) SELECT explain_query('SELECT * FROM orders WHERE user_id = 12'); 输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211175909d47a6871bcca4897 explain_query -------------------------------------------------------------------------------------------------------------- 1. 查询概述 + - 该查询旨在从 orders 表中检索 user_id 等于 12 的所有记录（SELECT *）。 - 这是一个典型的根据特定字段（user_id）筛选数据的查询。 + 2. 性能摘要 + - 总执行时间: 0.021 毫秒 - 规划时间: 0.430 毫秒 - 总成本: 18.12 - 返回行数: 0 行 (Actual Rows: 0) - 扫描行数: 0 行 (Rows Removed by Filter: 0) + 3. 执行计划分析 + - 关键步骤: 顺序扫描 - 数据库对 orders 表执行了全表扫描操作。 - 计划器预计会找到 3 行数据，但实际执行返回了 0 行。 - 过滤条件: orders.user_id = 12，这意味着数据库必须读取表中的每一行来检查这个条件。 + 4. 性能问题 + - 全表扫描风险: 虽然目前表的数据量很小（执行时间仅为 0.021ms），但使用了 Seq Scan（顺序扫描）意味着数据库没有使用索引。如果 orders 表随着时间推移增长到包含数百万行数据，这种查询方式将变得极其缓慢（高 I/O 消耗）。 - 缺失索引: 计划显示没有使用任何索引来定位 user_id = 12 的行，这表明在 user_id 列上可能缺少必要的 B-Tree 索引。 + 5. 优化建议 + - 主要建议: 在 user_id 列上创建索引以避免全表扫描。这将把查询从 O(N)（扫描所有行）转变为 O(log N)（索引查找）。 - SQL 优化示例: + CREATE INDEX idx_orders_user_id ON orders(user_id); + 6. 索引建议 + - 推荐索引: 在 orders 表的 user_id 列上创建 B-Tree 索引。 - 理由: 查询条件基于 user_id 的等值比较 (=)。创建索引后，IvorySQL (PostgreSQL) 将能够利用索引快速定位数据，显著减少查询时间和资源消耗，特别是在数据量大的情况下。 (1 row) 使用英语：虽然 AI 支持多种语言，但英语效果最佳 了解数据库结构：对数据库结构理解越深入，生成的查询越准确 迭代优化：从宽泛的开始，然后逐步添加细节以改进结果 明确指定：如果知道特定的表或列，请在提示中提及，这有助于 AI 生成精确的查询 当查询中引用的表不存在时，系统会返回错误信息： SELECT generate_query('列出所有的商品和价格'); 错误输出： [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135642777cbc5c82ca4a85 ERROR: Query generation failed: Cannot generate query. Referenced table(s) for 'products' or 'goods' do not exist in the database. Available tables: public.orders, public.student_scores, public.users, sys.dual 在这种情况下，AI 会告知可用的表列表，帮助用户调整查询。","title":"pg_ai_query","component":"ivorysql-doc","version":"master","name":"pg_ai_query","url":"/ivorysql-doc/master/ecosystem_components/pg_ai_query.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 快速开始","hash":"快速开始","id":2},{"text":"2.1. 安装","hash":"安装","id":3},{"text":"2.2. 配置","hash":"配置","id":4},{"text":"3. 使用示例","hash":"使用示例","id":5},{"text":"3.1. 基本用法","hash":"基本用法","id":6},{"text":"3.2. generate_query 示例","hash":"generate_query-示例","id":7},{"text":"3.3. explain_query 示例","hash":"explain_query-示例","id":8},{"text":"4. 最佳实践","hash":"最佳实践","id":9},{"text":"4.1. 提示词（Prompt）编写建议","hash":"提示词prompt编写建议","id":10},{"text":"4.2. 错误处理示例","hash":"错误处理示例","id":11}]},"43":{"id":43,"text":"在 PostgreSQL 中运行周期性任务，例如执行 VACUUM或删除旧数据，是一种常见需求。实现这一点的简单方法是配置 cron或其他外部守护进程，使其定期连接到数据库并运行命令。然而，随着数据库越来越多地作为托管服务或独立容器运行，配置和运行一个单独的守护进程通常变得不切实际。此外，很难让您的 cron任务感知故障转移，或者跨集群节点调度任务。 pg_cron 是 PostgreSQL 的开源定时任务扩展，允许直接在数据库内部设置 cron 风格的任务调度，用于自动化数据维护任务(清理，聚合), 数据库健康检查，执行存储过程和自定义函数等操作。它将cron任务存储在表中，周期性任务会随着 PostgreSQL 服务器自动进行故障转移。详情可以参见 pg_cron文档。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 # 拉取pg_cron源码 git clone https://github.com/citusdata/pg_cron.git cd pg_cron # 将pg_config的路径设置到PATH环境变量里，eg： export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make make install # 共享预加载扩展 shared_preload_libraries = 'pg_cron' # 指定任务元数据存储库（默认当前库） cron.database_name = 'ivorysql' # 允许的最大并发任务数 cron.max_running_jobs = 5 pg_ctl restart -D ./data -l logfile psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension pg_cron; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'pg_cron'; name | default_version | installed_version | comment ---------+-----------------+-------------------+--------------------------- pg_cron | 1.6 | |Job scheduler for PostgreSQL (1 row) SELECT cron.schedule( 'nightly-data-cleanup', -- 任务名称（唯一标识） '0 3 * * *', -- cron表达式（每天UTC 3:00） $$DELETE FROM logs WHERE created_at < now() - interval '30 days'$$ -- 执行SQL ); cron表达式速查表： 示例 含义 '0 * * * *' 每小时整点执行 '*/15 * * * *' 每15分钟执行 '0 9 * * 1-5' 工作日早9点执行 '0 1 1 * *' 每月1日凌晨1点执行 pg_cron还允许使用 '$'表示月份的最后一天。 # 查看所有任务 SELECT * FROM cron.job; # 查看任务执行历史 SELECT * FROM cron.job_run_details ORDER BY start_time DESC LIMIT 10; # 删除任务 SELECT cron.unschedule('nightly-data-cleanup'); # 暂停任务（更新状态） UPDATE cron.job SET active = false WHERE jobname = 'delete-job-run-details';","title":"pg_cron","component":"ivorysql-doc","version":"master","name":"pg_cron","url":"/ivorysql-doc/master/ecosystem_components/pg_cron.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装配置","hash":"安装配置","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"2.2. 配置文件 (ivorysql.conf)","hash":"配置文件-ivorysql-conf","id":4},{"text":"2.3. 重启服务","hash":"重启服务","id":5},{"text":"2.4. 创建Extension并确认pg_cron版本","hash":"创建extension并确认pg_cron版本","id":6},{"text":"3. 核心功能使用","hash":"核心功能使用","id":7},{"text":"3.1. 创建定时任务","hash":"创建定时任务","id":8},{"text":"3.2. 任务管理","hash":"任务管理","id":9}]},"44":{"id":44,"text":"pg_stat_monitor 是 PostgreSQL 的查询性能监控工具。它能收集性能统计数据，并通过统一视图和直方图形式直观展示查询性能指标。 该工具可帮助数据库用户全面掌握查询来源、执行情况、计划统计与详细信息，以及查询元数据。这极大地提升了可观测性，使用户能够有效调试和优化查询性能。 IvorySQL的安装包里已经集成了pg_stat_monitor插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装pg_stat_monitor。如您使用源码方式安装了IvorySQL，可以继续通过源码方式继续安装pg_stat_monitor插件，IvorySQL社区为您提供了源码安装步骤： 要从源代码构建 pg_stat_monitor，您需要以下组件: git make gcc pg_config 您可以从 GitHub 上的发布页面 下载 pg_stat_monitor 指定版本的源代码，或者使用 git 命令： git clone https://github.com/percona/pg_stat_monitor.git 编译并安装扩展程序，假如环境中已经安装了IvorySQL，安装路径为/usr/ivory-5 cd pg_stat_monitor export PG_CONFIG=/usr/ivory-5/bin/pg_config make USE_PGXS=1 make USE_PGXS=1 install 在启动时，将 pg_stat_monitor 添加到 shared_preload_libraries 配置参数中，以加载该库。这是因为 pg_stat_monitor 需要额外的共享内存。 修改 shared_preload_libraries 参数： shared_preload_libraries = 'pg_stat_monitor'; 注意：若 shared_preload_libraries 参数中已存在其他模块（例如 pg_stat_statements），需用逗号分隔全部列出。pg_stat_monitor 必须排在 pg_stat_statements 之后，例如： shared_preload_libraries = 'liboracle_parser, ivorysql_ora, pg_stat_statements, pg_stat_monitor'; 配置完成后，重启 IvorySQL实例使配置生效。 pg_ctl restart -D data 将 pg_stat_monitor 添加至 shared_preload_libraries 后，该扩展会立即开始收集所有现有数据库的统计信息。要查看监控数据，需在每个待监控的数据库中创建视图。 请使用具有超级用户或数据库所有者权限的账户执行以下操作。以超级用户身份连接至目标数据库，运行以下命令创建扩展： CREATE EXTENSION pg_stat_monitor; 完成设置后，即可查看pg_stat_monitor收集的统计信息。 以获取查询执行时间信息为例，连接数据库执行以下SQL： SELECT userid, total_exec_time, min_exec_time, max_exec_time, mean_exec_time, query FROM pg_stat_monitor; userid | total_exec_time | min_exec_time | max_exec_time | mean_exec_time | query --------+-----------------+---------------+---------------+----------------+---------------------------------------------------------------------------------------------- 10 | 1.532168 | 0.749108 | 0.78306 | 0.766084 | SELECT userid, datname, queryid, substr(query,$1, $2) AS query, calls FROM pg_stat_monitor 10 | 0.755857 | 0.755857 | 0.755857 | 0.755857 | SELECT application_name, client_ip, substr(query,$1,$2) as query FROM pg_stat_monitor 10 | 0 | 0 | 0 | 0 | SELECT userid, total_time, min_time, max_time, mean_time, query FROM pg_stat_monitor; 10 | 0 | 0 | 0 | 0 | SELECT userid, total_exec_time, min_time, max_time, mean_time, query FROM pg_stat_monitor; (4 rows) 更多关于pg_stat_monitor的使用，请参阅 pg_stat_monitor 官方文档","title":"pg_stat_monitor","component":"ivorysql-doc","version":"master","name":"pg_stat_monitor","url":"/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"3. 加载模块","hash":"加载模块","id":3},{"text":"4. 创建扩展视图","hash":"创建扩展视图","id":4},{"text":"5. 使用","hash":"使用","id":5}]},"45":{"id":45,"text":"PgAudit 是一个审计扩展，可以为 DDL、DML、DCL 等关键操作生成可追溯的日志记录。通过审计日志，数据库管理员能够满足合规要求、及时发现异常行为，并在出现问题时快速定位责任主体与影响范围。 全面审计能力：记录 SELECT、INSERT、UPDATE、DELETE、DDL 命令以及权限变更等操作，帮助构建完整的操作轨迹。 灵活的审计维度：支持全局审计、角色审计与对象审计，可按用户、角色、模式或具体操作类型进行精细化配置。 平滑集成：复用 PostgreSQL 标准日志子系统，可与 syslog、logrotate 等工具联动，兼容现有日志采集与分析方案。 合规支撑：提供结构化审计日志，便于生成符合金融、政企等行业规范的审计报告。 安全增强：通过记录和分析数据库活动，及时发现未授权访问、异常 DML 或潜在数据泄露风险。 运维优化：辅助回放操作行为、定位性能瓶颈，支撑 SQL 优化与问题排查。 已安装的 IvorySQL 数据库。 编译工具链：gcc、make、tar 等。 数据库管理员权限，用于修改 ivorysql.conf 并重启数据库实例。 以 PgAudit 18.0 为例： wget https://github.com/pgaudit/pgaudit/archive/refs/tags/18.0.tar.gz tar -xf 18.0.tar.gz cd pgaudit-18.0 make install USE_PGXS=1 PG_CONFIG=$PGHOME/bin/pg_config 上述命令依赖环境变量 PGHOME 指向安装好的 IvorySQL 主目录。安装成功后，pgaudit.so 会被放置到 IvorySQL 的扩展目录中。 修改 ivorysql.conf，启用插件并设置常用参数： shared_preload_libraries = 'pgaudit' # 需实例重启生效 pgaudit.log = 'read, write, ddl' # 审计范围示例，可按需调整 重启数据库实例，使共享库配置生效。 CREATE EXTENSION IF NOT EXISTS pgaudit; SELECT name, default_version, installed_version, comment FROM pg_available_extensions WHERE name = 'pgaudit'; 若返回的 installed_version 与期望版本一致，说明扩展安装成功。 执行如下sql示例： CREATE TABLE audit_demo(id serial PRIMARY KEY, info text); INSERT INTO audit_demo(info) VALUES ('pgaudit test'); SELECT * FROM audit_demo; UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1; DELETE FROM audit_demo WHERE id = 1; 在数据库服务器上查看审计日志： tail -f $PGDATA/log/*.log | grep 'AUDIT:' 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE TABLE,TABLE,public.audit_demo,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE INDEX,INDEX,public.audit_demo_pkey,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,ALTER SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.117 CST [11451] LOG: AUDIT: SESSION,2,1,WRITE,INSERT,,,INSERT INTO audit_demo(info) VALUES ('pgaudit test'),<not logged> 2025-10-31 15:56:32.121 CST [11451] LOG: AUDIT: SESSION,3,1,READ,SELECT,,,SELECT * FROM audit_demo,<not logged> 2025-10-31 15:56:32.122 CST [11451] LOG: AUDIT: SESSION,4,1,WRITE,UPDATE,,,UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1,<not logged> 2025-10-31 15:56:32.127 CST [11451] LOG: AUDIT: SESSION,5,1,WRITE,DELETE,,,DELETE FROM audit_demo WHERE id = 1,<not logged> 若想记录参数的值，打开`pgaudit.log_parameter = 'on'`，效果如下： ivorysql=# SHOW pgaudit.log_parameter; pgaudit.log_parameter ----------------------- on (1 row)","title":"PgAudit","component":"ivorysql-doc","version":"master","name":"pgaudit","url":"/ivorysql-doc/master/ecosystem_components/pgaudit.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 功能特点","hash":"功能特点","id":2},{"text":"3. 安装部署","hash":"安装部署","id":3},{"text":"3.1. 环境准备","hash":"环境准备","id":4},{"text":"3.2. 编译安装 PgAudit","hash":"编译安装-pgaudit","id":5},{"text":"3.3. 注册扩展前的基础配置","hash":"注册扩展前的基础配置","id":6},{"text":"3.4. 创建扩展并验证","hash":"创建扩展并验证","id":7},{"text":"4. 使用","hash":"使用","id":8}]},"46":{"id":46,"text":"pgddl 是一个专为 PostgreSQL 数据库设计的 SQL 函数扩展，它能够直接从数据库系统目录中生成清晰、格式化的 SQL DDL (数据定义语言) 脚本，例如 CREATE TABLE 或 ALTER FUNCTION。它解决了 PostgreSQL 原生缺乏类似 SHOW CREATE TABLE 命令的问题，让用户无需借助外部工具（如 pg_dump）即可在纯 SQL 环境中轻松获取对象的创建语句。 该扩展通过一组简单的 SQL 函数提供了一套完整的解决方案，其优势包括：仅需使用 SQL 查询即可操作、支持通过 WHERE 子句灵活筛选对象、并能智能处理对象之间的依赖关系，生成包含 Drop 和 Create 步骤的完整脚本。这使得它特别适用于数据库变更管理、升级脚本编写和结构审计等场景。 需要注意的是，ddlx 仍在发展中，可能尚未覆盖所有 PostgreSQL 对象类型和高级选项。生成的脚本建议始终在非生产环境中先行检查和测试，以确保其正确性与安全性。 IvorySQL的安装包里已经集成了pgddl插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装pgddl即可使用。其它安装方式可以参考下面的源码安装步骤。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 从https://github.com/lacanoid/pgddl 下载pgddl v0.31代码。 cd pgddl # 设置PG_CONFIG环境变量值为pg_config路径，eg：/usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/path/to/pg_config make PG_CONFIG=/path/to/pg_config install psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension ddlx; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'ddlx'; name | default_version | installed_version | comment ------+-----------------+-------------------+------------------------- ddlx | 0.31 | 0.31 | DDL eXtractor functions (1 row) 关于pgddl的使用，请参阅 ddlx官方文档","title":"pgddl (DDL Extractor)","component":"ivorysql-doc","version":"master","name":"pgddl","url":"/ivorysql-doc/master/ecosystem_components/pgddl.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建Extension并确认ddlx版本","hash":"创建extension并确认ddlx版本","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"47":{"id":47,"text":"PostgreSQL 内置了全文搜索功能，但在处理​​大规模数据​​、​​复杂查询​​以及​​非英语语言（特别是中日韩等 CJK 语言）​​ 时，其功能和性能可能无法满足高性能应用的需求。 PGroonga 应运而生，它是一个 PostgreSQL 的扩展插件，将 ​​Groonga​​ 这款高性能的全功能全文搜索引擎与 PostgreSQL 数据库深度融合。Groonga 本身是一个优秀的开源搜索引擎，以其极致的速度和丰富的功能著称，尤其擅长处理多语言文本。PGroonga 的使命就是将 Groonga 的强大能力无缝地带入 PostgreSQL 的世界，为用户提供远超原生全文搜索的体验。 IvorySQL的安装包里已经集成了PGroonga插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装PGroonga，直接跳过安装步骤即可。 用户可从 PGroonga package安装 页面选择适合自己的方式进行PGroonga安装。 IvorySQL社区提供了源码安装步骤，下面以PGroonga v4.0.4为例进行演示。 环境 操作系统：Ubuntu 24.04 CPU架构：x86_64 IvorySQL: v5.0 编译PGroonga，有个配置选项：HAVE_MSGPACK=1，它是用于支持WAL，使用这个选项需要安装msgpack-c 1.4.1或者更高版本。 sudo apt install libmsgpack-dev 确保Groonga >= 14.0.0 sudo apt install groonga libgroonga-dev 验证Groonga安装成功 highgo@ubuntu:~/work/IvorySQL/inst$ groonga --version Groonga 15.1.7 [Linux,x86_64,utf8,match-escalation-threshold=0,nfkc,mecab,message-pack,mruby,onigmo,zlib,lz4,zstandard,epoll,apache-arrow,xxhash,blosc,h3,simdjson,llama.cpp] wget https://packages.groonga.org/source/pgroonga/pgroonga-4.0.4.tar.gz tar xvf pgroonga-4.0.4.tar.gz cd pgroonga-4.0.4 运行make前确保`pg_config`命令的路径在`PATH`环境变量里，例如IvorySQL安装的路径是`~/work/IvorySQL/inst`，则环境变量设置如下： export PGHOME=~/work/IvorySQL/inst export PGDATA=$PGHOME/data export PATH=$PGHOME/bin:$PATH 然后执行以下命令进行编译和安装： make HAVE_MSGPACK=1 make install psql 连接到数据库的pg模式下，执行命令： postgres=# CREATE extension pgroonga; 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CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'pgrouting'; name | default_version | installed_version | comment -----------+-----------------+-------------------+--------------------- pgrouting | 3.8.0 | | pgRouting Extension (1 row) 关于pgRouting的使用，请参阅 pgRouting官方文档","title":"pgRouting","component":"ivorysql-doc","version":"master","name":"pgrouting","url":"/ivorysql-doc/master/ecosystem_components/pgrouting.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建Extension并确认ddlx版本","hash":"创建extension并确认ddlx版本","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"49":{"id":49,"text":"pgsql-http 是一个为 PostgreSQL 数据库设计的开源扩展，它允许用户直接在数据库内部发起 HTTP 请求，扮演了一个内置 Web 客户端的角色。该扩展的核心目的是打通数据库与外部 Web 服务之间的壁垒，使得通过简单的 SQL 函数调用即可与外部 Web 服务、API 端点进行交互，无需依赖外部应用程序或中间件。 借助此扩展，开发者可以在 SQL 查询、触发器或存储过程中直接获取网络数据（GET）、提交数据（POST/PUT）、更新（PATCH）或删除（DELETE）远程资源。它提供了丰富的功能，包括设置请求头、自动处理 URL 编码、发送 JSON 数据以及解析响应状态、头部和内容，极大简化了将外部数据集成到数据库操作中的流程。 其典型应用场景包括：实时获取外部数据（如汇率、天气信息）并存入表；在数据变更时通过触发器自动通知微服务；对数据库中的数据进行清洗后直接提交至外部 API 等。它为构建以数据库为中心的集成应用提供了强大而灵活的解决方案。 IvorySQL的安装包里已经集成了pgsql-http插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装pgsql-http即可使用。其它安装方式可以参考下面的源码安装步骤。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 安装依赖 对libcurl有依赖，libcurl的开发文件（例如 libcurl4-openssl-dev）需要提前安装上 #安装依赖 sudo apt install libcurl4-openssl-dev 编译安装 从https://github.com/pramsey/pgsql-http/releases/tag/v1.7.0 下载 1.7.0的源码包 pgsql-http-1.7.0.tar.gz tar xvf pgsql-http-1.7.0.tar.gz cd pgsql-http-1.7.0 # 确保pg_config在PATH里可以访问，eg: /usr/local/ivorysql/ivorysql-5/bin/pg_config make sudo make install psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension http; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'http'; name | default_version | installed_version | comment -----------+-----------------+-------------------+------------------------------------------------------------------------- http | 1.7 | 1.7 | HTTP client for PostgreSQL, allows web page retrieval inside the database. 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Oracle兼容性","hash":"oracle兼容性","id":7},{"text":"4.1. 数据类型","hash":"数据类型","id":8},{"text":"4.2. 匿名块","hash":"匿名块","id":9},{"text":"4.3. 存储过程（PROCEDURE）","hash":"存储过程procedure","id":10},{"text":"4.4. 函数（FUNCTION）","hash":"函数function","id":11}]},"51":{"id":51,"text":"在 PostgreSQL 数据库开发过程中，编写存储过程和函数时往往难以在运行前发现代码中的语法错误、类型不匹配、未定义变量等潜在问题。传统方式需要等到函数实际执行时才能发现这些错误，这不仅增加了调试成本，也可能在生产环境中引发意外故障。 plpgsql_check 是一个专为 PostgreSQL 的 PL/pgSQL 语言设计的静态代码分析工具（Linter），它能够在不实际执行函数的情况下，对存储过程和函数的源代码进行深度检查。该工具可以提前发现语法错误、类型不匹配、未使用的变量、性能问题、安全隐患等多种代码质量问题，帮助开发者在开发阶段就确保代码的正确性和健壮性。详情可以参见 plpgsql_check官方文档。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 # 从https://github.com/okbob/plpgsql_check/releases/tag/v2.8.3 下载 2.8.3的源码包 plpgsql_check-2.8.3.tar.gz tar xvf plpgsql_check-2.8.3.tar.gz cd plpgsql_check-2.8.3 # 将pg_config的路径设置到PATH环境变量里，eg： export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make USE_PGXS=1 clean make USE_PGXS=1 all sudo make USE_PGXS=1 install psql 连接到数据库，执行如下命令： ivorysql=# CREATE EXTENSION plpgsql_check; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'plpgsql_check'; name | default_version | installed_version | comment ---------------+-----------------+-------------------+--------------------------------------------------- plpgsql_check | 2.8 | 2.8 | extended check for plpgsql functions (1 row) -- 创建一个示例函数 CREATE OR REPLACE FUNCTION test_function(p_id integer) RETURNS text AS $$ DECLARE v_name text; v_unused integer; -- 未使用的变量 BEGIN SELECT name INTO v_name FROM users WHERE id = p_id; RETURN v_naem; -- 拼写错误 END; $$ LANGUAGE plpgsql; -- 使用 plpgsql_check 检查函数 SELECT * FROM plpgsql_check_function('test_function(integer)'); 检查结果示例： plpgsql_check_function -------------------------------------------------------------- error:42601:7:assignment:target variable \"v_naem\" is undefined warning:00000:4:DECLARE:unused variable \"v_unused\" (2 rows) 更多详细使用方法和高级特性，请参阅 plpgsql_check官方文档。","title":"plpgsql_check","component":"ivorysql-doc","version":"master","name":"plpgsql_check","url":"/ivorysql-doc/master/ecosystem_components/plpgsql_check.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建Extension并确认plpgsql_check版本","hash":"创建extension并确认plpgsql_check版本","id":4},{"text":"4. 使用","hash":"使用","id":5},{"text":"4.1. 检查单个函数","hash":"检查单个函数","id":6}]},"52":{"id":52,"text":"IvorySQL原生100%兼容PostgreSQL,因此，PostGIS可以完美适配IvorySQL。 根据开发环境，用户可从 PostGIS安装 页面选择适合自己的方式进行安装PostGIS。 除PostGIS社区提供的安装方式以外，IvorySQL社区也提供了源码安装方式，源码安装环境为 Ubuntu 24.04(x86_64)。 请确保环境中已安装了IvorySQL5.0及以上版本 安装依赖 sudo apt install \\ docbook-xsl-ns \\ gettext \\ libgdal-dev \\ libgeos-dev \\ libjson-c-dev \\ libproj-dev \\ libprotobuf-c-dev \\ libsfcgal-dev \\ libxml2-dev \\ libxml2-utils \\ protobuf-c-compiler \\ xsltproc 安装 PostGIS $ wget https://download.osgeo.org/postgis/source/postgis-3.5.4.tar.gz $ tar xvf postgis-3.5.4.tar.gz $ cd postgis-3.5.4 $ ./configure --with-pgconfig=/path/to/pg_config eg: /usr/ivory-5/bin/pg_config，如果ivorysql安装目录在/usr/ivory-5. $ make $ sudo make install 如出现PGXS报错, 请根据环境中IvorySQL安装路径, 修改—​with-pgconfig的参数值。 psql 连接到数据库，执行如下命令： ivorysql=# CREATE extension postgis; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'postgis'; name | default_version | installed_version | comment ---------+-----------------+-------------------+------------------------------------------------------------ postgis | 3.5.4 | 3.5.4 | PostGIS geometry and geography spatial types and functions (1 row) 关于PostGIS的使用，请参阅 PostGIS3.5官方文档","title":"PostGIS","component":"ivorysql-doc","version":"master","name":"postgis","url":"/ivorysql-doc/master/ecosystem_components/postgis.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建Extension并确认PostGIS版本","hash":"创建extension并确认postgis版本","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"53":{"id":53,"text":"system_stats 是一个 PostgreSQL 扩展，提供用于访问系统级统计信息的函数，可用于监控场景。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/local/ivorysql/ivorysql-5 # 从 https://github.com/EnterpriseDB/system_stats/releases/tag/v3.2 下载 3.2 的源码包 v3.2.zip unzip v3.2.zip cd system_stats-3.2 # 编译安装插件 make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config install psql 连接到数据库，执行如下命令： ivorysql=# CREATE EXTENSION system_stats; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'system_stats'; name | default_version | installed_version | comment --------------+-----------------+-------------------+----------------------------------------------- system_stats | 3.0 | 3.0 | EnterpriseDB system statistics for PostgreSQL (1 row) ivorysql=> select pg_sys_os_info() from dual; pg_sys_os_info -------------------------------------------------------------------------- (\"\"\"Ubuntu 24.04.1 LTS\"\" + \",\"Linux 6.14.0-29-generic\",Ubuntu,\"(none)\",5568,292,532,x86_64,,864122) (1 row) ivorysql=> select pg_sys_cpu_info() from dual; pg_sys_cpu_info ------------------------------------------------------------------------------------------------------------------------------------- (GenuineIntel,\"GenuineIntel model 154 family 6\",\"12th Gen Intel(R) Core(TM) i7-1260P\",,0,2,1,x86_64,2496000000,,,,48,32,1280,18432) (1 row) ivorysql=> select pg_sys_cpu_usage_info() from dual; pg_sys_cpu_usage_info ----------------------- (0,0,0,100,0,0,0,,,,) (1 row) ivorysql=> select pg_sys_memory_info() from dual; pg_sys_memory_info ----------------------------------------------------------------------------------- (4055482368,3911159808,144322560,4055887872,233725952,3822161920,2638602240,,,,,) (1 row) ivorysql=> select pg_sys_io_analysis_info() from dual; pg_sys_io_analysis_info --------------------------------------------------------------- (loop0,15,0,21504,0,1,0) (loop1,1362,0,27916288,0,680,0) (loop2,175,0,3354624,0,131,0) (loop3,141,0,1827840,0,83,0) (loop4,918,0,32752640,0,212,0) (loop5,156,0,2617344,0,67,0) (loop6,3707,0,131992576,0,828,0) (loop7,243,0,4321280,0,95,0) (fd0,0,0,0,0,0,0) (sda,639286,1580583,26547332096,240138948608,402702,2744653) (sda1,561,0,22966272,0,61,0) (sda2,636820,1580583,26491053056,240138948608,402316,2744653) (sr0,87,0,3219456,0,45,0) (sr1,0,0,0,0,0,0) (loop8,228,0,2491392,0,120,0) (loop10,2333,0,36571136,0,1628,0) (loop9,3730,0,66532352,0,3973,0) (loop11,87,0,709632,0,50,0) (loop12,156,0,1948672,0,52,0) (loop14,2579,0,97446912,0,2083,0) (loop13,30,0,102400,0,14,0) (loop15,82,0,1189888,0,117,0) (loop16,1357,0,28335104,0,500,0) (loop17,110,0,2187264,0,93,0) (loop18,129,0,2355200,0,28,0) (loop19,110,0,2202624,0,55,0) (26 rows) ivorysql=> select pg_sys_disk_info() from dual; pg_sys_disk_info ---------------------------------------------------------------------------------- (/,/dev/sda2,,,ext4,105086115840,50903396352,48797392896,6553600,619735,5933865) (1 row) ivorysql=> select pg_sys_load_avg_info() from dual; pg_sys_load_avg_info ---------------------- (0,0,0,) (1 row) ivorysql=> select pg_sys_process_info() from dual; pg_sys_process_info --------------------- (294,1,201,0,0) (1 row) ivorysql=> select pg_sys_network_info() from dual; pg_sys_network_info ----------------------------------------------------------------------------- (lo,127.0.0.1,1071575,5643,0,0,1071575,5643,0,0,0) (ens33,192.168.198.128,619877515,1490961,0,161,5540250106,4345449,0,0,1000) (2 rows) ivorysql=> select pg_sys_cpu_memory_by_process() from dual; pg_sys_cpu_memory_by_process ----------------------------------------------------------------------- (1,\"(systemd)\",864793,0,0.34,13840384) (2,\"(kthreadd)\",864793,0,0,0) (3,\"(pool_workqueue_release)\",864793,0,0,0) (4,\"(kworker/R-rcu_gp)\",864793,0,0,0) ......","title":"system_stats","component":"ivorysql-doc","version":"master","name":"system_stats","url":"/ivorysql-doc/master/ecosystem_components/system_stats.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"3. 创建插件并确认版本","hash":"创建插件并确认版本","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"54":{"id":54,"text":"wal2json 是一个用于 PostgreSQL 逻辑解码的输出插件，这个插件为每个事务生成一个JSON对象。 源码安装环境为 Ubuntu 24.04(x86_64)，环境中已经安装了IvorySQL5及以上版本，安装路径为/usr/ivory-5 # 从 https://github.com/eulerto/wal2json/releases/tag/wal2json_2_6 下载 2.6 的源码包 wal2json_2_6.zip unzip wal2json_2_6.zip cd wal2json_2_6 # 编译安装插件 make PG_CONFIG=/usr/ivory-5/bin/pg_config make PG_CONFIG=/usr/ivory-5/bin/pg_config install 如果出现找不到xlocale.h的错误，需要手动修改 /usr/ivory-5/include/postgresql/server/pg_config.h 删除或者注释掉 #define HAVE_XLOCALE_H 1 这一行 修改 postgresql.conf 文件 启用wal_level为logical，并设置最大复制槽数和最大wal发送进程数 wal_level = logical max_replication_slots = 10 max_wal_senders = 10 pg_hba.conf不需要修改，有以下内容即可（仅限本地链接测试）： local replication all trust host replication all 127.0.0.1/32 trust host replication all ::1/128 trust 重启数据库后配置生效。 在第一个终端中执行命令： sudo -u ivorysql /usr/ivory-5/bin/bin/pg_recvlogical -d postgres --slot wal2json_slot --create-slot -P wal2json 启动监听，实时输出变更的JSON格式 sudo -u ivorysql /usr/ivory-5/bin/bin/pg_recvlogical -d postgres --slot wal2json_slot --start -o pretty-print=1 -f - 在第二个终端中连接数据库： /usr/ivory-5/bin/psql -d postgres -p 1521 执行下面的SQL语句： CREATE TABLE test_cdc (id int primary key, name varchar(50)); INSERT INTO test_cdc VALUES (1, 'test1'); UPDATE test_cdc SET name = 'test1_update' WHERE id = 1; DELETE FROM test_cdc WHERE id = 1; DROP TABLE test_cdc; 此时在第一个终端上可以看到下面的输出： { \"change\": [ ] } { \"change\": [ { \"kind\": \"insert\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1\"] } ] } { \"change\": [ { \"kind\": \"update\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1_update\"], \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ { \"kind\": \"delete\", \"schema\": \"public\", \"table\": \"test_cdc\", \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ ] }","title":"wal2json","component":"ivorysql-doc","version":"master","name":"wal2json","url":"/ivorysql-doc/master/ecosystem_components/wal2json.html","titles":[{"text":"1. 概述","hash":"概述","id":1},{"text":"2. 安装","hash":"安装","id":2},{"text":"2.1. 源码安装","hash":"源码安装","id":3},{"text":"2.2. 修改数据库配置文件","hash":"修改数据库配置文件","id":4},{"text":"3. 使用","hash":"使用","id":5}]},"55":{"id":55,"text":"IvorySQL数据库要求周期性的清理维护。对于很多安装，让自动清理守护进程来执行清理已经足够，你也可能需要调整其中描述的自动清理参数来获得最佳结果。某些数据库管理员会希望使用手动管理的`VACUUM`命令来对后台进程的活动进行补充或者替换，这通常使用cron或任务计划程序脚本来执行。要正确地设置手动管理的清理。 IvorySQL的 VACUUM命令出于几个原因必须定期处理每一个表： 恢复或重用被已更新或已删除行所占用的磁盘空间。 更新被IvorySQL查询规划器使用的数据统计信息。 更新可见性映射，它可以加速只用索引的扫描。 保护老旧数据不会由于事务ID回卷或多事务ID回卷而丢失。 正如后续小节中解释的，每一个原因都将指示以不同的频率和范围执行`VACUUM`操作。 有两种`VACUUM`的变体：标准`VACUUM`和`VACUUM FULL`。VACUUM FULL`可以收回更多磁盘空间但是运行起来更慢。另外，标准形式的`VACUUM`可以和生产数据库操作并行运行（`SELECT、INSERT、UPDATE`和`DELETE`等命令将继续正常工作，但在清理期间你无法使用`ALTER TABLE`等命令来更新表的定义）。`VACUUM FULL`要求在其工作的表上得到一个`ACCESS EXCLUSIVE 锁，因此无法和对此表的其他使用并行。因此，通常管理员应该努力使用标准`VACUUM`并且避免`VACUUM FULL`。 `VACUUM`会产生大量I/O流量，这将导致其他活动会话性能变差。可以调整一些配置参数来后台清理活动造成的性能冲击. 在IvorySQL中，一次行的`UPDATE`或`DELETE`不会立即移除该行的旧版本。这种方法对于从多版本并发控制获益是必需的：当旧版本仍可能对其他事务可见时，它不能被删除。但是最后，任何事务都不会再对一个过时的或者被删除的行版本感兴趣。它所占用的空间必须被回收来用于新行，这样可避免磁盘空间需求的无限制增长。这通过运行`VACUUM`完成。 `VACUUM`的标准形式移除表和索引中的死亡行版本并将该空间标记为可在未来重用。不过，它将不会把该空间交还给操作系统，除非在特殊的情况中表尾部的一个或多个页面变成完全空闲并且能够很容易地得到一个排他表锁。相反，`VACUUM FULL`通过把死亡空间之外的内容写成一个完整的新版本表文件来主动紧缩表。这将最小化表的尺寸，但是要花较长的时间。它也需要额外的磁盘空间用于表的新副本，直到操作完成。 例行清理的一般目标是多做标准的`VACUUM`来避免需要`VACUUM FULL`。自动清理守护进程尝试这样工作，并且实际上永远不会发出`VACUUM FULL`。在这种方法中，其思想不是让表保持它们的最小尺寸，而是保持磁盘空间使用的稳定状态：每个表占用的空间等于其最小尺寸外加清理之间将使用的空间量。尽管`VACUUM FULL`可被用来把一个表收缩回它的最小尺寸并将该磁盘空间交还给操作系统，但是如果该表将在未来再次增长这样就没什么意义。因此，对于维护频繁被更新的表，适度运行标准`VACUUM`运行比少量运行`VACUUM FULL`要更好。 一些管理员更喜欢自己计划清理，例如在晚上负载低时做所有的工作。根据一个固定日程来做清理的难点在于，如果一个表有一次预期之外的更新活动尖峰，它可能膨胀得真正需要`VACUUM FULL`来回收空间。使用自动清理守护进程可以减轻这个问题，因为守护进程会根据更新活动动态规划清理操作。除非你的负载是完全可以预估的，完全禁用守护进程是不理智的。一种可能的折中方案是设置守护进程的参数，这样它将只对异常的大量更新活动做出反应，因而保证事情不会失控，而在负载正常时采用有计划的`VACUUM`来做批量工作。 对于那些不使用自动清理的用户，一种典型的方法是计划一个数据库范围的`VACUUM`，该操作每天在低使用量时段执行一次，并根据需要辅以在重度更新表上的更频繁的清理（一些有着极高更新率的安装会每几分钟清理一次它们的最繁忙的表）。如果你在一个集簇中有多个数据库，别忘记`VACUUM`每一个，你会用得上vacuumdb程序。 提示 当一个表因为大量更新或删除活动而包含大量死亡行版本时，纯粹的`VACUUM`可能不能令人满意。如果你有这样一个表并且你需要回收它占用的过量磁盘空间，你将需要使用`VACUUM FULL`，或者CLUSTER，或者ALTER TABLE的表重写变体之一。这些命令重写该表的一整个新拷贝并且为它构建新索引。所有这些选项都要求`ACCESS EXCLUSIVE` 锁。注意它们也临时使用大约等于该表尺寸的额外磁盘空间，因为直到新表和索引完成之前旧表和索引都不能被释放。 提示 如果你有一个表，它的整个内容会被周期性删除，考虑用TRUNCATE而不是先用`DELETE`再用`VACUUM`。`TRUNCATE`会立刻移除该表的整个内容，而不需要一次后续的`VACUUM`或`VACUUM FULL`来回收现在未被使用的磁盘空间。其缺点是会违背严格的 MVCC 语义。 IvorySQL查询规划器依赖于有关表内容的统计信息来为查询产生好的计划。这些统计信息由ANALYZE命令收集，它除了直接被调用之外还可以作为`VACUUM`的一个可选步骤被调用。拥有适度准确的统计信息很重要，否则差的计划可能降低数据库性能。 自动清理守护进程如果被启用，当一个表的内容被改变得足够多时，它将自动发出`ANALYZE`命令。不过，管理员可能更喜欢依靠手动的`ANALYZE`操作，特别是如果知道一个表上的更新活动将不会影响“感兴趣的”列的统计信息时。守护进程严格地按照一个被插入或更新行数的函数来计划`ANALYZE`，它不知道那是否将导致有意义的统计信息改变。 正如用于空间恢复的清理一样，频繁更新统计信息对重度更新的表更加有用。但即使对于一个重度更新的表，如果该数据的统计分布没有很大改变，也没有必要更新统计信息。一个简单的经验法则是考虑表中列的最大和最小值改变了多少。例如，一个包含行被更新时间的`timestamp`列将在行被增加和更新时有一直增加的最大值；这样一列将可能需要更频繁的统计更新，而一个包含一个网站上被访问页面 URL 的列则不需要。URL 列可以经常被更改，但是其值的统计分布的变化相对很慢。 可以在指定表上运行`ANALYZE`甚至在表的指定列上运行，因此如果你的应用需要，可以更加频繁地更新某些统计。但实际上，通常只分析整个数据库是最好的，因为它是一种很快的操作。`ANALYZE`对一个表的行使用一种统计的随机采样，而不是读取每一个单一行。 提示 尽管对每列的`ANALYZE`频度调整可能不是非常富有成效，你可能会发现值得为每列调整被`ANALYZE`收集统计信息的详细程度。经常在`WHERE`中被用到的列以及数据分布非常不规则的列可能需要比其他列更细粒度的数据直方图。见`ALTER TABLE SET STATISTICS`，或者使用default_statistics_target配置参数改变数据库范围的默认值。 提示 自动清理守护进程不会为外部表发出`ANALYZE`命令，因为无法确定一个合适的频度。如果你的查询需要外部表的统计信息来正确地进行规划，比较好的方式是按照一个合适的时间表在那些表上手工运行`ANALYZE`命令。 提示 autovacuum守护进程不会对分区表发出ANALYZE命令。继承性父表只有在父表本身发生变化时才会被分析—​对子表的变化不会触发对父表的自动分析。如果你的查询需要对父表进行统计以进行正确的规划，那么有必要定期对这些表运行手动ANALYZE以保持统计的最新性。 清理机制为每一个表维护着一个可见性映射，它被用来跟踪哪些页面只包含对所有活动事务（以及所有未来的事务，直到该页面被再次修改）可见的元组。这样做有两个目的。第一，清理本身可以在下一次运行时跳过这样的页面，因为其中没有什么需要被清除。 第二，这允许IvorySQL回答一些只用索引的查询，而不需要引用底层表。因为IvorySQL的索引不包含元组的可见性信息，一次普通的索引扫描会为每一个匹配的索引项获取堆元组，用来检查它是否能被当前事务所见。另一方面，一次*只用索引的扫描*会首先检查可见性映射。如果它了解到在该页面上的所有元组都是可见的，堆获取就可以被跳过。这对大数据集很有用，因为可见性映射可以防止磁盘访问。可见性映射比堆小很多，因此即使堆非常大，可见性映射也可以很容易地被缓存起来。 IvorySQL的 MVCC 事务语义依赖于能够比较事务 ID（XID）数字：如果一个行版本的插入 XID 大于当前事务的 XID，它就是“属于未来的”并且不应该对当前事务可见。但是因为事务 ID 的尺寸有限（32位），一个长时间（超过 40 亿个事务）运行的集簇会遭受到*事务 ID 回卷*问题：XID 计数器回卷到 0，并且本来属于过去的事务突然间就变成了属于未来 — 这意味着它们的输出变成不可见。简而言之，灾难性的数据丢失（实际上数据仍然在那里，但是如果你不能得到它也无济于事）。为了避免发生这种情况，有必要至少每 20 亿个事务就清理每个数据库中的每个表。 周期性的清理能够解决该问题的原因是，VACUUM会把行标记为 冻结，这表示它们是被一个在足够远的过去提交的事务所插入，这样从 MVCC 的角度来看，效果就是该插入事务对所有当前和未来事务来说当然都 是可见的。IvorySQL保留了一个特殊的 XID （FrozenTransactionId），这个 XID 并不遵循普通 XID 的比较规则 并且总是被认为比任何普通 XID 要老。普通 XID 使用模-232算 法来比较。这意味着对于每一个普通 XID都有 20 亿个 XID “更老”并且 有 20 亿个“更新”，另一种解释的方法是普通 XID 空间是没有端点的环。因此，一旦一个行版本创建时被分配了一个特定的普通 XID，该行版本将成为接下 来 20 亿个事务的“过去”（与我们谈论的具体哪个普通 XID 无关）。如果在 20 亿个事务之后该行版本仍然存在，它将突然变得好像在未来。要阻止这一切 发生，被冻结行版本会被看成其插入 XID 为`FrozenTransactionId`， 这样它们对所有普通事务来说都是“在过去”，而不管回卷问题。并且这样的行版本将一直有效直到被删除，不管它有多旧。 vacuum_freeze_min_age控制在其行版本被冻结前一个 XID 值应该有多老。如果被冻结的行将很快会被再次修改，增加这个设置可以避免不必要 的工作。但是减少这个设置会增加在表必须再次被清理之前能够流逝的事务数。 `VACUUM`通常会跳过不含有任何死亡行版本的页面，但是不会跳过那些含有带旧 XID 值的行版本的页面。要保证所有旧的行版本都已经被冻结，需要对整个表做一次扫描。vacuum_freeze_table_age控制`VACUUM`什么时候这样做：如果该表经过`vacuum_freeze_table_age`减去`vacuum_freeze_min_age`个事务还没有被完全扫描过，则会强制一次全表清扫。将这个参数设置为 0 将强制`VACUUM`总是扫描所有页面而实际上忽略可见性映射。 一个表能保持不被清理的最长时间是 20 亿个事务减去`VACUUM`上次扫描全表时的`vacuum_freeze_min_age`值。如果它超过该时间没有被清理，可能会导致数据丢失。要保证这不会发生，将在任何包含比autovacuum_freeze_max_age配置参数所指定的年龄更老的 XID 的未冻结行的表上调用自动清理（即使自动清理被禁用也会发生）。 这意味着如果一个表没有被清理，大约每`autovacuum_freeze_max_age`减去`vacuum_freeze_min_age`事务就会在该表上调用一次自动清理。对那些为了空间回收目的而被正常清理的表，这是无关紧要的。然而，对静态表（包括接收插入但没有更新或删除的表）就没有为空间回收而清理的需要，因此尝试在非常大的静态表上强制自动清理的间隔最大化会非常有用。显然我们可以通过增加`autovacuum_freeze_max_age`或减少`vacuum_freeze_min_age`来实现此目的。 vacuum_freeze_table_age的实际最大值是 0.95 * autovacuum_freeze_max_age，高于它的设置将被上限到最大值。一个高于`autovacuum_freeze_max_age`的值没有意义，因为不管怎样在那个点上都会触发一次防回卷自动清理，并且 0.95 的乘数为在防回卷自动清理发生之前运行一次手动`VACUUM`留出了一些空间。作为一种经验法则，`vacuum_freeze_table_age`应当被设置成一个低于`autovacuum_freeze_max_age`的值，留出一个足够的空间让一次被正常调度的`VACUUM`或一次被正常删除和更新活动触发的自动清理可以在这个窗口中被运行。将它设置得太接近可能导致防回卷自动清理，即使该表最近因为回收空间的目的被清理过，而较低的值将导致更频繁的全表扫描。 增加`autovacuum_freeze_max_age`（以及和它一起的`vacuum_freeze_table_age`）的唯一不足是数据库集簇的`pg_xact`和`pg_commit_ts`子目录将占据更多空间，因为它必须存储所有向后`autovacuum_freeze_max_age`范围内的所有事务的提交状态和（如果启用了`track_commit_timestamp`）时间戳。提交状态为每个事务使用两个二进制位，因此如果`autovacuum_freeze_max_age`被设置为它的最大允许值 20 亿，`pg_xact`将会增长到大约 0.5 吉字节，`pg_commit_ts`大约20GB。如果这对于你的总数据库尺寸是微小的，我们推荐设置`autovacuum_freeze_max_age`为它的最大允许值。否则，基于你想要允许`pg_xact`和`pg_commit_ts`使用的存储空间大小来设置它（默认情况下 2 亿个事务大约等于`pg_xact`的 50 MB存储空间，`pg_commit_ts`的2GB的存储空间）。 减小`vacuum_freeze_min_age`的一个不足之处是它可能导致`VACUUM`做无用的工作：如果该行在被替换成`FrozenXID`之后很快就被修改（导致该行获得一个新的 XID），那么冻结一个行版本就是浪费时间。因此该设置应该足够大，这样直到行不再可能被修改之前，它们都不会被冻结。 为了跟踪一个数据库中最老的未冻结 XID 的年龄，`VACUUM`在系统表`pg_class`和`pg_database`中存储 XID 的统计信息。特别地，一个表的`pg_class`行的`relfrozenxid`列包含被该表的上一次全表`VACUUM`所用的冻结截止 XID。该表中所有被有比这个截断 XID 老的普通 XID 的事务插入的行 都确保被冻结。相似地，一个数据库的`pg_database`行的`datfrozenxid`列是出现在该数据库中的未冻结 XID 的下界 — 它只是数据库中每一个表的`relfrozenxid`值的最小值。一种检查这些信息的方便方法是执行这样的查询： SELECT c.oid::regclass as table_name, greatest(age(c.relfrozenxid),age(t.relfrozenxid)) as age FROM pg_class c LEFT JOIN pg_class t ON c.reltoastrelid = t.oid WHERE c.relkind IN ('r', 'm'); SELECT datname, age(datfrozenxid) FROM pg_database; `age`列度量从该截断 XID 到当前事务 XID 的事务数。 `VACUUM`通常只扫描从上次清理后备修改过的页面，但是只有当全表被扫描时`relfrozenxid`才能被推进。当`relfrozenxid`比`vacuum_freeze_table_age`个事务还老时、当`VACUUM`的`FREEZE`选项被使用时或当所有页面正好要求清理来移除死亡行版本时，全表将被扫描。当`VACUUM`扫描全表时，在它被完成后，`age(relfrozenxid)`应该比被使用的`vacuum_freeze_min_age`设置略大（比在`VACUUM`开始后开始的事务数多）。如果在`autovacuum_freeze_max_age`被达到之前没有全表扫描`VACUUM`在该表上被发出，将很快为该表强制一次自动清理。 如果出于某种原因自动清理无法从一个表中清除旧的 XID，当数据库的最旧 XID 和回卷点之间达到 4 千万个事务时，系统将开始发出这样的警告消息： WARNING: database \"mydb\" must be vacuumed within 39985967 transactions HINT: To avoid a database shutdown, execute a database-wide VACUUM in that database. （如该示例所建议的，一次手动的`VACUUM`应该会修复该问题；但是注意该次`VACUUM`必须由一个超级用户来执行，否则它将无法处理系统目录并且因而不能推进数据库的`datfrozenxid`）。如果这些警告被忽略，一旦距离回卷点只剩下 3 百万个事务时，该系统将会关闭并且拒绝开始任何新的事务： ERROR: database is not accepting commands to avoid wraparound data loss in database \"mydb\" HINT: Stop the postmaster and vacuum that database in single-user mode. 这 3 百万个事务的安全余量是为了让管理员能通过手动执行所要求的`VACUUM`命令进行恢复而不丢失数据。但是，由于一旦系统进入到安全关闭模式，它将不会执行命令。做这个操作的唯一方法是停止服务器并且以单一用户启动服务器来执行`VACUUM`。单一用户模式中不会强制该关闭模式。 *Multixact ID*被用来支持被多个事务锁定的行。由于在一个元组头部 只有有限的空间可以用来存储锁信息，所以只要有多于一个事务并发地锁住一个行， 锁信息将使用一个“多个事务 ID”（或简称多事务 ID）来编码。任何特定 多事务 ID 中包括的事务 ID 的信息被独立地存储在`pg_multixact`子目 录中，并且只有多事务 ID 出现在元组头部的`xmax`域中。和事务 ID 类似，多事务 ID 也是用一个 32 位计数器实现，并且也采用了相似的存储，这些都要 求仔细的年龄管理、存储清除和回卷处理。在每个多事务中都有一个独立的存储区域 保存成员列表，它也使用一个 32 位计数器并且也应被管理。 在一次`VACUUM`表扫描（部分或者全部）期间，任何比 vacuum_multixact_freeze_min_age 要老的多事务 ID 会被替换为一个不同的值，该值可以是零值、 一个单一事务 ID 或者一个更新的多事务 ID。 对于每一个表，pg_class.relminmxid 存储了在该表任意元组中仍然存在的最老可能多事务 ID。如果这个值比 vacuum_multixact_freeze_table_age老， 将强制一次全表扫描。可以在 pg_class.relminmxid 上使用`mxid_age()`来找到它的年龄。 全表`VACUUM`扫描（不管是什么导致它们）将为表推进该值。 最后，当所有数据库中的所有表被扫描并且它们的最老多事务值被推进， 较老的多事务的磁盘存储可以被移除。 作为一种安全设备，对任何多事务年龄超过 autovacuum_multixact_freeze_max_age的表， 都将发生一次全表清理扫描。当多事务成员占用的存储超过 2GB 时，从那些具有最老多事务年龄的表开始，全表清理扫描也将逐步在所有表上进行。即使自动清理被 在名义上被禁用，也会发生这两种主动扫描。 IvorySQL有一个可选的但是被高度推荐的特性*autovacuum*，它的目的是自动执行`VACUUM`和`ANALYZE`命令。当它被启用时，自动清理会检查被大量插入、更新或删除元组的表。这些检查会利用统计信息收集功能，因此除非track_counts被设置为`true`，自动清理不能被使用。在默认配置下，自动清理是被启用的并且相关配置参数已被正确配置。 “自动清理后台进程”实际上由多个进程组成。有一个称为 自动清理启动器*的常驻后台进程， 它负责为所有数据库启动*自动清理工作者*进程。 启动器将把工作散布在一段时间上，它每隔 autovacuum_naptime秒尝试在每个数据库中启动一个工作者 （因此，如果安装中有N*个数据库，则每 autovacuum_naptime/*N秒将启动一个新的工作者）。 在同一时间只允许最多autovacuum_max_workers个工作者进程运行。如果有超过`autovacuum_max_workers` 个数据库需要被处理，下一个数据库将在第一个工作者结束后马上被处理。 每一个工作者进程将检查其数据库中的每一个表并且在需要时执行 VACUUM`和/或`ANALYZE。 可以设置log_autovacuum_min_duration来监控自动清理工作者的活动。 如果在一小段时间内多个大型表都变得可以被清理，所有的自动清理工作者可能都会被占用来在一段长的时间内清理这些表。这将会造成其他的表和数据库无法被清理，直到一个工作者变得可用。对于一个数据库中的工作者数量并没有限制，但是工作者确实会试图避免重复已经被其他工作者完成的工作。注意运行着的工作者的数量不会被计入max_connections或superuser_reserved_connections限制。 `relfrozenxid`值比autovacuum_freeze_max_age事务年龄更大的表总是会被清理（这页表示这些表的冻结最大年龄被通过表的存储参数修改过，参见后文）。否则，如果从上次`VACUUM`以来失效的元组数超过“清理阈值”，表也会被清理。清理阈值定义为： 清理阈值 = 清理基本阈值 + 清理缩放系数 * 元组数 其中清理基本阈值为autovacuum_vacuum_threshold， 清理缩放系数为autovacuum_vacuum_scale_factor， 元组数为`pg_class`.reltuples。 如果自上次清理以来插入的元组数量超过了定义的插入阈值，表也会被清理，该阈值定义为: 清理插入阈值 = 清理基础插入阈值 + 清理插入缩放系数 * 元组数 清理插入基础阈值为autovacuum_vacuum_insert_threshold，清理插入缩放系数为autovacuum_vacuum_insert_scale_factor。 这样的清理可以允许部分的表被标识为*all visible*，并且也可以允许元组被冻结，可以减小后续清理的工作需要。 对于可以接收`INSERT`操作但是不能或几乎不能`UPDATE`/DELETE`操作的表， 可能会从降低表的autovacuum_freeze_min_age中受益，因为这可能允许元组在早期清理中被冻结。 废弃元组的数量和插入元组的数量可从统计收集器中获得；它是一个半精确的计数，由每个`UPDATE、DELETE 和 INSERT 操作进行更新。 (它只是半精确的，因为一些信息可能会在重负载情况下丢失。) 如果表的`relfrozenxid`值大于`vacuum_freeze_table_age` 事务老的， 执行一个主动的清理来冻结旧的元组，并推进`relfrozenxid`;否则，只有上次清理以后修改过的页面被扫描。 对于分析，也使用了一个相似的阈值： 分析阈值 = 分析基本阈值 + 分析缩放系数 * 元组数 该阈值将与自从上次`ANALYZE`以来被插入、更新或删除的元组数进行比较。 临时表不能被自动清理访问。因此，临时表的清理和分析操作必须通过会话期间的SQL命令来执行。 默认的阈值和缩放系数都取自于`postgresql.conf`，但是可以为每一个表重写它们(和许多其他自动清理控制参数)， 详情参见Storage Parameters。 如果一个设置已经通过一个表的存储参数修改，那么在处理该表时使用该值，否则使用全局设置。 当多个工作者运行时，在所有运行着的工作者之间自动清理代价延迟参数是 “平衡的”，这样不管实际运行的工作者数量是多少， 对于系统的总体 I/O 影响总是相同的。不过，任何正在处理已经设置了每表 autovacuum_vacuum_cost_delay`或 `autovacuum_vacuum_cost_limit 存储参数的表的工作者不会被考虑在均衡算法中。 autovacuum工作进程通常不会阻止其他命令。如果某个进程尝试获取与autovacuum持有的`SHARE UPDATE EXCLUSIVE`锁冲突的锁，则锁获取将中断该autovacuum。有关冲突的锁定模式，但是，如果autovacuum正在运行以防止事务ID回卷（即在`pg_stat_activity`视图中的autovacuum查询名以`(to prevent wraparound)`结尾），则autovacuum不会被自动中断。 警告 定期运行需要获取与`SHARE UPDATE EXCLUSIVE`锁冲突的锁的命令（例如ANALYZE）可能会让autovacuum始终无法完成。 在某些情况下值得周期性地使用REINDEX命令或一系列独立重构步骤来重建索引。 已经完全变成空的B树索引页面被收回重用。但是，还是有一种低效的空间利用的可能性：如果一个页面上除少量索引键之外的全部键被删除，该页面仍然被分配。因此，在这种每个范围中大部分但不是全部键最终被删除的使用模式中，可以看到空间的使用是很差的。对于这样的使用模式，推荐使用定期重索引。 对于非B树索引可能的膨胀还没有很好地定量分析。在使用非B树索引时定期监控索引的物理尺寸是个好主意。 还有，对于B树索引，一个新建立的索引比更新了多次的索引访问起来要略快， 因为在新建立的索引上，逻辑上相邻的页面通常物理上也相邻（这样的考虑目前并不适用于非B树索引）。仅仅为了提高访问速度也值得定期重索引。 REINDEX在所有情况下都可以安全和容易地使用。 默认情况下，此命令需要一个`ACCESS EXCLUSIVE`锁，因此通常最好使用`CONCURRENTLY`选项执行它，该选项仅需要获取`SHARE UPDATE EXCLUSIVE`锁。 把数据库服务器的日志输出保存在一个地方是个好主意， 而不是仅仅通过`/dev/null`丢弃它们。 在进行问题诊断的时候，日志输出是非常宝贵的。不过，日志输出可能很庞大（特别是在比较高的调试级别上）， 因此你不会希望无休止地保存它们。你需要轮转日志文件， 这样在一段合理的时间后会开始新的日志文件并且移除旧的。 如果你简单地把`postgres`的stderr定向到一个文件中，你会得到日志输出，但是截断该日志文件的唯一方法是停止并重起服务器。这样做对于开发环境中使用的IvorySQL可能是可接受的，但是你肯定不想在生产环境上这么干。 一个更好的办法是把服务器的stderr输出发送到某种日志轮转程序里。我们有一个内建的日志轮转程序，你可以通过在 `postgresql.conf`里设置配置参数`logging_collector`为`true`的办法启用它。你也可以使用这种方法把日志数据捕捉成机器可读的CSV（逗号分隔值）格式。 另外，如果在你已经使用的其他服务器软件中有一个外部日志轮转程序，你可能更喜欢使用它。 比如，包含在Apache发布里的rotatelogs工具就可以用于IvorySQL。要做到这一点，方法之一是把服务器的stderr用管道重定向到要用的程序。 如果你用`pg_ctl`启动服务器，那么stderr已经重定向到stdout， 因此你只需要一个管道命令，比如： pg_ctl start | rotatelogs /var/log/pgsql_log 86400 您可以通过设置logrotate来收集由IvorySQL内置日志收集器生成的日志文件来组合这些方法。在这种情况下，日志收集器定义日志文件的名称和位置，而logrotate 则定期归档这些文件。启动日志轮转时，logrotate必须确保应用程序将进一步的输出发送到新文件。这通常是通过`postrotate`脚本完成的，该脚本向应用程序发送`SIGHUP`信号，使其重新打开日志文件。在IvorySQL中，您可以使用`logrotate`选项运行`pg_ctl`。服务器收到此命令后，服务器将切换到新的日志文件或重新打开现有文件，具体取决于日志记录配置。 注意 定期运行需要获取与`SHARE UPDATE EXCLUSIVE`锁冲突的锁的命令（例如ANALYZE）可能会让autovacuum始终无法完成使用静态日志文件名时，如果达到最大打开文件数限制或发生文件表溢出，则服务器可能无法重新打开日志文件。 在这种情况下，日志消息将发送到旧的日志文件，直到成功进行日志轮转为止。 如果将logrotate配置为压缩日志文件并将其删除，则服务器可能会丢失此时间范围内记录的消息。 为避免此问题，可以将日志收集器配置为动态分配日志文件名，并使用`prerotate`脚本忽略打开的日志文件。 另外一种生产级的管理日志输出的方法就是把它们发送给syslog，让syslog处理文件轮转。 要利用这个工具，我们需要设置`postgresql.conf`里的`log_destination`配置参数设置为`syslog`（记录`syslog`日志）。然后在你想强迫syslog守护进程开始写入一个新日志文件的时候， 你就可以发送一个 `SIGHUP`信号给它。 如果你想自动进行日志轮转，可以配置logrotate程序处理 来自syslog的日志文件。 不过，在很多系统上，syslog不是非常可靠，特别是在面对大量日志消息的情况下； 它可能在你最需要那些消息的时候截断或者丢弃它们。另外，在Linux，syslog会把每个消息刷写到磁盘上， 这将导致很差的性能（你可以在syslog配置文件里面的文件名开头使用一个“-”来禁用这种行为）。 请注意上面描述的所有解决方案关注的是在可配置的间隔上开始一个新的日志文件， 但它们并没有处理对旧的、不再需要的日志文件的删除。你可能还需要设置一个批处理任务来定期地删除旧日志文件。 另一种可能的方法是配置日志轮转程序，让它循环地覆盖旧的日志文件。 pgBadger 是一个外部项目，它可以进行日志文件的深度分析。check_postgres可在重要消息出现在日志文件中时向Nagios提供警告，也可以探测很多其他的特别情况。 共享磁盘故障转移避免了只使用一份数据库拷贝带来的同步开销。它使用一个由多个服务器共享的单一磁盘阵列。如果主数据库服务器失效，后备服务器则可以挂载并启动数据库，就好像它从一次数据库崩溃中恢复过来了。这是一种快速的故障转移，并且不存在数据丢失。 共享硬件功能在网络存储设备中很常见。也可以使用一个网络文件系统，但是要注意的是该文件系统应具有完全的POSIX行为。这种方法的一个重大限制是如果共享磁盘阵列失效或损坏，主要和后备服务器都会变得无法工作。另一个问题是在主要服务器运行时，后备服务器永远不能访问共享存储。 共享硬件功能的一种修改版本是文件系统复制，在其中对一个文件系统的所有改变会被镜像到位于另一台计算机上的一个文件系统。唯一的限制是该镜像过程必须能保证后备服务器有一份该文件系统的一致的拷贝 — 特别是对后备服务器的写入必须按照主控机上相同的顺序进行。DRBD是用于 Linux 的一种流行的文件系统复制方案。 温备和热备服务器能够通过读取一个预写式日志（WAL）记录的流来保持为当前状态。如果主服务器失效，后备服务器拥有主服务器的几乎所有数据，并且能够快速地被变成新的主数据库服务器。这可以是同步的或异步的，并且只能用于整个数据库服务器。 可以使用基于文件的日志传送、流复制或两者的组合来实现一个后备服务器。 逻辑复制允许数据库服务器发送数据更新流给另一台服务器。IvorySQL逻辑复制从WAL构建出逻辑数据更新流。逻辑复制允许您逐个表复制数据更改。此外，发布数据更新的服务器可以同时订阅其他服务器的更改，从而允许数据在多个方向流动。第三方扩展也能提供类似的功能。 基于触发器的复制通常会将修改数据的查询发送到指定的主服务器。它在逐个表的基础上工作，主服务器（通常）将数据更改异步发送到后备服务器。 主服务器运行时，后备服务器可以响应查询，并执行本地数据修改或写入操作。这种形式的复制通常用于减轻大数据分析型平台或者数据仓库查询负荷。 Slony-I是这种复制类型的一个例子。它使用表粒度，并且支持多个后备服务器。因为它会异步更新后备服务器（批量），在故障转移时可能会有数据丢失。 通过基于SQL的复制中间件，一个程序拦截每一个 SQL 查询并把它发送给一个或所有服务器。每一个服务器独立地操作。读写查询必须被发送给所有服务器，这样每一个服务器都能接收到任何修改。但只读查询可以只发送给一个服务器，这样允许读负载在服务器之间分布。 如果查询未经修改发送，则函数`random()`函数的随机值和`CURRENT_TIMESTAMP`函数的当前时间和序列值可能因不同服务器而异。 因为每个服务器独立运行，并且它发送 SQL 查询而没有真正的更改数据。如果这是不可接受的，那么中间件或应用程序必须从单一服务器源确定此类值，并将结果用于写入查询。 还必须注意确保所有服务器在提交或中止事务时都是相同的。这将涉及使用 两阶段提交PREPARE TRANSACTION和COMMIT PREPARED。 Pgpool-II和Continuent Tungsten就是这种复制的例子。 对于不会被定期连接或通讯链路较慢的服务器，如笔记本或远程服务器，保持服务器间的数据一致是一个挑战。通过使用异步的多主控机复制，每一个服务器独立工作并且定期与其他服务器通信来确定冲突的事务。这些冲突可以由用户或冲突解决规则来解决。Bucardo 是这种复制类型的一个例子。 在同步多主控机复制中，每一个服务器能够接受写请求，并且在每一个事务提交之前，被修改的数据会被从原始服务器传送给每一个其他服务器。繁重的写活动可能导致过多的锁定和提交延迟，进而导致很差的性能。读请求可以被发送给任意服务器。某些实现使用共享磁盘来减少通信负荷。同步多主控机复制主要对于读负载最好，尽管它的大优点是任意服务器都能接受写请求 — 没有必要在主服务器和后备服务器之间划分负载，并且因为数据修改被从一个服务器发送到另一个服务器，不会有非确定函数（如`random()`）的问题。 IvorySQL不提供这种复制类型，尽管在应用代码或中间件中可以使用 IvorySQL 的两阶段提交PREPARE TRANSACTION和COMMIT PREPARED来实现这种复制。 下表总结了上述多种方案的能力。 特性 共享磁盘 文件系统复制 预写式日志传送 逻辑复制 基于触发器的复制 SQL复制中间件 异步多主控机复制 同步多主控机复制 常用的示例 NAS DRBD 内建流复制 内建逻辑复制，pglogical Londiste，Slony pgpool-II Bucardo 通信方法 共享磁盘 磁盘块 WAL 逻辑解码 表行 SQL 表行 表行和行锁 不要求特殊硬件 • • • • • • • 允许多个主控机服务器 • • • • 无主服务器负载 • • • • 不等待多个服务器 • with sync off with sync off • • 主控机失效将永不丢失数据 • • with sync on with sync on • • 复制体接受只读查询 with hot • • • • • 每个表粒度 • • • • 不需要冲突解决 • • • • • • 有一些方案不适合上述的类别： 数据分区 数据分区将表分开成数据集。每个集合只能被一个服务器修改。例如，数据可以根据办公室划分，如伦敦和巴黎，每一个办公室有一个服务器。如果查询有必要组合伦敦和巴黎的数据，一个应用可以查询两个服务器，或者可以使用主/备复制来在每一台服务器上保持其他办公室数据的一个只读拷贝。 多服务器并行查询执行 上述的很多方案允许多个服务器来处理多个查询，但是没有一个允许一个单一查询使用多个服务器来更快完成。 这种方案允许多个服务器在一个单一查询上并发工作。 这通常通过把数据在服务器之间划分并且让每一个服务器执行该查询中属于它的部分，然后将结果返回给一个中心服务器，由它整合结果并发回给用户。 这也可以使用PL/Proxy工具集来实现这种方案。 创建主服务器和后备服务器通常是明智的，因此它们可以尽可能相似，至少从数据库服务器的角度来看是这样。特别地，与表空间相关的路径名将被未经修改地传递，因此如果该特性被使用，主、备服务器必须对表空间具有完全相同的挂载路径。记住如果CREATE TABLESPACE在主服务器上被执行，在命令被执行前，它所需要的任何新挂载点必须在主服务器和所有后备服务器上先创建好。硬件不需要完全相同，但是经验显示，在应用和系统的生命期内维护两个相同的系统比维护两个不相似的系统更容易。在任何情况下硬件架构必须相同 — 从一个 32 位系统传送到一个 64 位系统将不会工作。 通常，不能在两个运行着不同主版本IvorySQL的服务器之间传送日志。IvorySQL 全球开发组的策略是不在次版本升级中改变磁盘格式，因此在主服务器和后备服务器上运行不同次版本将会成功地工作。不过，在这方面并没有提供正式的支持，因此我们建议让主备服务器上运行的版本尽可能相同。当升级到一个新的次版本时，最安全的策略是先升级后备服务器 — 一个新的次版本发行更可能兼容从前一个次版本读取 WAL 文件。 服务器启动时，数据目录中存在 standby.signal 文件，服务器进入standby模式。 在后备模式中，服务器持续地应用从主控服务器接收到的 WAL。后备服务器可以从一个 WAL 归档restore_command或者通过一个 TCP 连接直接从主控机（流复制）读取 WAL。后备服务器将也尝试恢复在后备集簇的`pg_wal`目录中找到的 WAL。那通常在一次数据库重启后发生，那时后备机将在重启之前重播从主控机流过来的 WAL，但是你也可以在任何时候手动拷贝文件到`pg_wal`让它们被重播。 在启动时，后备机通过恢复归档位置所有可用的 WAL 来开始，这称为`restore_command`。一旦它到达那里可用的 WAL 的末尾并且`restore_command`失败，它会尝试恢复`pg_wal`目录中可用的任何 WAL。如果那也失败并且流复制已被配置，后备机会尝试连接到主服务器并且从在归档或`pg_wal`中找到的最后一个可用记录开始流式传送 WAL。如果那失败并且没有配置流复制，或者该连接后来断开，后备机会返回到步骤 1 并且尝试再次从归档里的文件恢复。这种尝试归档、`pg_wal`和流复制的循环会一直重复知道服务器停止或者一个触发器文件触发了故障转移。 当`pg_ctl promote`被运行，pg_promote()`被调用，或一个触发器文件被找到（`promote_trigger_file），后备模式会退出并且服务器会切换到普通操作。 在故障转移之前，在归档或`pg_wal`中立即可用的任何 WAL 将被恢复，但不会尝试连接到主控机。 在主服务器上设置连续归档到一个后备服务器可访问的归档目录。即使主服务器垮掉该归档位置也应当是后备服务器可访问的，即它应当位于后备服务器本身或者另一个可信赖的服务器，而不是位于主控服务器上。 如果你想要使用流复制，在主服务器上设置认证来允许来自后备服务器的复制连接。即创建一个角色并且在`pg_hba.conf`中提供一个或多个数据库域被设置为`replication`的项。还要保证在主服务器的配置文件中`max_wal_senders`被设置为足够大的值。如果要使用复制槽，请确保`max_replication_slots`也被设置得足够高。 要建立后备服务器，恢复从主服务器取得的基础备份。在后备服务器的集簇数据目录中创建一个文件standby.signal。将restore_command设置为一个从 WAL 归档中复制文件的简单命令。 如果你计划为了高可用性目的建立多个后备服务器，确认`recovery_target_timeline`被设置为`latest` (默认)来使得该后备服务器遵循发生在故障转移到另一个后备服务器之后发生的时间线改变。 注意 restore_command应该立即返回，如果必要该服务器将再次尝试该命令。 如果你想要使用流复制，在primary_conninfo中填入一个 libpq 连接字符串，其中包括主机名（或 IP 地址）和连接到主服务器所需的任何附加细节。如果主服务器需要一个口令用于认证，口令也应该被指定primary_conninfo中。 如果你正在为高性能目的建立后备服务器，像主服务器一样建立 WAL 归档、连接和认证，因为在故障转移后该后备服务器将作为一个主服务器工作。 如果你正在使用一个 WAL 归档，可以使用archive_cleanup_command参数来移除后备服务器不再需要的文件，这样可以最小化 WAL 归档的尺寸。pg_archivecleanup工具被特别设计为在典型单一后备配置下与`archive_cleanup_command`共同使用，见pg_archivecleanup。不过要注意，如果你正在为备份目的使用归档，有一些文件即使后备服务器不再需要你也需要保留它们，它们被用来从至少最后一个基础备份恢复。 配置的一个简单例子是： primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass options=''-c wal_sender_timeout=5000''' restore_command = 'cp /path/to/archive/%f %p' archive_cleanup_command = 'pg_archivecleanup /path/to/archive %r' 你可以有任意数量的后备服务器，但是如果你使用流复制，确保你在主服务器上将`max_wal_senders`设置得足够高，这样可以允许它们能同时连接。 流复制允许一台后备服务器比使用基于文件的日志传送更能保持为最新的状态。后备服务器连接到主服务器，主服务器则在 WAL 记录产生时即将它们以流式传送给后备服务器而不必等到 WAL 文件被填充。 默认情况下流复制是异步的，在这种情况下主服务器上提交一个事务与该变化在后备服务器上变得可见之间存在短暂的延迟。不过这种延迟比基于文件的日志传送方式中要小得多，在后备服务器的能力足以跟得上负载的前提下延迟通常低于一秒。在流复制中，不需要`archive_timeout`来缩减数据丢失窗口。 如果你使用的流复制没有基于文件的连续归档，该服务器可能在后备机收到 WAL 段之 前回收这些旧的 WAL 段。如果发生这种情况，后备机将需要重新从一个新的基础备 份初始化。通过设置`wal_keep_size`为一个足够高的值来确保旧 的 WAL 段不会被太早重用或者为后备机配置一个复制槽，可以避免发生这种情况。如 果设置了一个后备机可以访问的 WAL 归档，就不需要这些解决方案，因为该归档可以 为后备机保留足够的段，后备机总是可以使用该归档来追赶主控机。 要使用流复制，建立一个基于文件的日志传送后备服务器。将一个基于文件日志传送后备服务器转变成流复制后备服务器的步骤是把`recovery.conf`文件中的设置以指向主服务器。设置主服务器上的listen_addresses和认证选项（见`pg_hba.conf`），这样后备服务器可以连接到主服务器上的伪数据库`replication`。 在支持 keepalive 套接字选项的系统上，设置tcp_keepalives_idle、tcp_keepalives_interval和tcp_keepalives_count有助于主服务器迅速地注意到一个断开的连接。 设置来自后备服务器的并发连接的最大数目（详见max_wal_senders）。 当后备服务器被启动并且`primary_conninfo`被正确设置，后备服务器将在重放完归档中所有可用的 WAL 文件之后连接到主服务器。 如果连接被成功建立，你将在后备服务器中看到一个 walreceiver，并且在主服务器中有一个相应的 walsender 进程。 设置好用于复制的访问权限非常重要，这样只有受信的用户可以读取 WAL 流，因为很容易从 WAL 流中抽取出需要特权才能访问的信息。 后备服务器必须作为一个具有`REPLICATION`特权的账户或一个超级用户来向主服务器认证。 推荐为复制创建一个专用的具有`REPLICATION`和`LOGIN`特权的用户账户。 虽然`REPLICATION`特权给出了非常高的权限，但它不允许用户修改主系统上的任何数据，而`SUPERUSER`特权则可以。 复制的客户端认证由一个在*database*域中指定`replication`的`pg_hba.conf`记录控制。例如，如果后备服务器运行在主机 IP 192.168.1.100`并且用于复制的账户名为`foo，管理员可以在主服务器上向`pg_hba.conf`文件增加下列行： # 允许来自 192.168.1.100 的用户 \"foo\" 在提供了正确的口令时作为一个 # 复制后备机连接到主控机。 # # TYPE DATABASE USER ADDRESS METHOD host replication foo 192.168.1.100/32 md5 主服务器的主机名和端口号、连接用户名和口令在primary_conninfo中指定。在后备服务器上还可以在`~/.pgpass`文件中设置口令（在*database*域中指定`replication`）。例如，如果主服务器运行在主机 IP 192.168.1.50、端口`5432`上，并且口令为`foopass`，管理员可以在后备服务器的`postgresql.conf`文件中增加下列行： # 后备机要连接到的主控机运行在主机 192.168.1.50 上， # 端口号是 5432，连接所用的用户名是 \"foo\"，口令是 \"foopass\"。 primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' 流复制的一个重要健康指标是在主服务器上产生但还没有在后备服务器上应用的 WAL 记录数。你可以通过比较主服务器上的当前 WAL 写位置和后备服务器接收到的最后一个 WAL 位置来计算这个滞后量。这些位置分别可以用主服务器上的`pg_current_wal_lsn`和后备服务器上的`pg_last_wal_receive_lsn`来检索。后备服务器的最后 WAL 接收位置也被显示在 WAL 接收者进程的进程状态中，即使用`ps`命令显示的状态。 你可以通过pg_stat_replication视图检索 WAL 发送者进程的列表。 `pg_current_wal_lsn`与`sent_lsn`域之间的巨大差异表示主服务器承受着巨大的负载，而`sent_lsn`和后备服务器上`pg_last_wal_receive_lsn`之间的差异可能表示网络延迟或者后备服务器正承受着巨大的负载。 在一台热后备上，WAL接收者进程的状态可以通过pg_stat_wal_receiver`视图检索到。 `pg_last_wal_replay_lsn`和该视图的`flushed_lsn`的差别表示WAL的接收速度大于它被重放的速度。 复制槽提供了一种自动化的方法来确保主控机在所有的后备机收到 WAL 段 之前不会移除它们，并且主控机也不会移除可能导致恢复冲突的行，即使后备机断开也是如此。 作为复制槽的替代，也可以使用wal_keep_size阻止移除旧的 WAL 段，或者使用archive_command把段保存在一个归档中。 不过，这些方法常常会导致保留的 WAL 段比需要的更多，而复制槽只保留已知所需要的段。 另一方面，复制槽可以保留很多的WAL段以至于它们填满了分配给`pg_wal`的空间； max_slot_wal_keep_size限制复制槽所保留的WAL文件的大小。 类似地，hot_standby_feedback和 vacuum_defer_cleanup_age保护了相关行不被 vacuum 移除，但是前者在后备机断开期间无法提供保护，而后者则需要被设置为一个很高 的值已提供足够的保护。复制槽克服了这些缺点。 每个复制槽都有一个名字，名字可以包含小写字母、数字和下划线字符。 已有的复制槽和它们的状态可以在 pg_replication_slots视图中看到。 槽可以通过流复制协议 或者 SQL 函数创建并且移除。 你可以这样创建一个复制槽： postgres=# SELECT * FROM pg_create_physical_replication_slot('node_a_slot'); slot_name | lsn -------------+----- node_a_slot | postgres=# SELECT slot_name, slot_type, active FROM pg_replication_slots; slot_name | slot_type | active -------------+-----------+-------- node_a_slot | physical | f (1 row) 要配置后备机使用这个槽，在后备机中应该配置`primary_slot_name`。这里是一个简单的例子： primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' primary_slot_name = 'node_a_slot' 级联复制特性允许一台后备服务器接收复制连接并且把 WAL 记录流式传送给其他后备服务器，就像一个转发器一样。这可以被用来减小对于主控机的直接连接数并且使得站点间的带宽开销最小化。 一台同时扮演着接收者和发送者角色的后备服务器被称为一台级联后备服务器。“更直接”（通过更少的级联后备服务器）连接到主控机的后备服务器被称为上游服务器，而那些离得更远的后备服务器被称为下游服务器。级联复制并没有对下游服务器的数量或布置设定限制。 一台级联后备服务器不仅仅发送从主控机接收到的 WAL 记录，还要发送那些从归档中恢复的记录。因此即使某些上游连接中的复制连接被中断，只要还有新的 WAL 记录可用，下游的流复制都会继续下去。 级联复制目前是异步的。同步复制设置当前对级联复制无影响。 不管在什么样的级联布置中，热备反馈都会向上游传播。 如果一台上游后备服务器被提升为新的主控机，且下游服务器的`recovery_target_timeline`被设置成’latest'`(默认)，下游服务器将继续从新的主控机得到流。 要使用级联复制，需配置级联后备服务器以允许接收复制连接（即设置max_wal_senders和hot_standby，并且配置基于主机的认证）。你还将需要设置下游后备服务器中的`primary_conninfo`指向级联后备服务器。 PostgreSQL流复制默认是异步的。如果主服务器崩溃，则某些已被提交的事务可能还没有被复制到后备服务器，这会导致数据丢失。数据的丢失量与故障转移时的复制延迟成比例。 同步复制能够保证一个事务的所有修改都能被传送到一台或者多台同步后备服务器。这扩大了由一次事务提交所提供的标准持久化级别。在计算机科学理论中这种保护级别被称为 2-safe 复制。而当`synchronous_commit`被设置为`remote_write`时，则是 group-1-safe （group-safe 和 1-safe）。 在请求同步复制时，一个写事务的每次提交将一直等待，直到收到一个确认表明该提交在主服务器和后备服务器上都已经被写入到磁盘上的预写式日志中。数据会被丢失的唯一可能性是主服务器和后备服务器在同一时间都崩溃。这可以提供更高级别的持久性，尽管只有系统管理员要关系两台服务器的放置和管理。等待确认提高了用户对于修改不会丢失的信心，但是同时也不必要地增加了对请求事务的响应时间。最小等待时间是在主服务器和后备服务器之间的来回时间。 只读事务和事务回滚不需要等待后备服务器的回复。子事务提交也不需要等待后备服务器的响应，只有顶层提交才需要等待。长时间运行的动作（如数据载入或索引构建）不会等待最后的提交消息。所有两阶段提交动作要求提交等待，包括预备和提交。 同步后备可以是物理复制后备或者是逻辑复制订阅者。它还可以是任何其他物理或者逻辑WAL复制流的消费者，它懂得如何发送恰当的反馈消息。除内建的物理和逻辑复制系统之外，还包括`pg_receivewal`和`pg_recvlogical`之类的特殊程序，以及一些第三方复制系统和定制程序。同步复制支持的细节请查看相应的文档。 一旦流复制已经被配置，配置同步复制就只需要一个额外的配置步骤：synchronous_standby_names必须被设置为一个非空值。synchronous_commit`也必须被设置为`on，但由于这是默认值，通常不需要改变。这样的配置将导致每一次提交都等待确认消息，以保证后备服务器已经将提交记录写入到持久化存储中。`synchronous_commit`可以由个体用户设置，因此它可以在配置文件中配置、可以为特定用户或数据库配置或者由应用动态配置，这样可以在一种每事务基础上控制持久性保证。 在一个提交记录已经在主服务器上被写入到磁盘后，WAL 记录接着被发送到后备服务器。每次一批新的 WAL 数据被写入到磁盘后，后备服务器会发送回复消息，除非在后备服务器上`wal_receiver_status_interval`被设置为零。如果`synchronous_commit`被设置为`remote_apply`，当提交记录被重放时后备服务器会发送回应消息，这会让该事务变得可见。如果根据主服务器的`synchronous_standby_names`设置选中该后备服务器作为一个同步后备，将会根据来自该后备服务器和其他同步后备的回应消息来决定何时释放正在等待确认提交记录被收到的事务。这些参数允许管理员指定哪些后备服务器应该是同步后备。注意同步复制的配置主要在主控机上。命名的后备服务器必须直接连接到主控机，主控机对使用级联复制的下游后备服务器一无所知。 将`synchronous_commit`设置为`remote_write`将导致每次提交都等待后备服务器已经接收提交记录并将它写出到其自身所在的操作系统的确认，但并非等待数据都被刷出到后备服务器上的磁盘。这种设置提供了比`on`要弱一点的持久性保障：在一次操作系统崩溃事件中后备服务器可能丢失数据，尽管它不是一次IvorySQL崩溃。不过，在实际中它是一种有用的设置，因为它可以减少事务的响应时间。只有当主服务器和后备服务器都崩溃并且主服务器的数据库同时被损坏的情况下，数据丢失才会发生。 把`synchronous_commit`设置为`remote_apply`将导致每一次提交都会等待，直到当前的同步后备服务器报告说它们已经重放了该事务，这样就会使该事务对用户查询可见。在简单的情况下，这为带有因果一致性的负载均衡留出了余地。 如果请求一次快速关闭，用户将停止等待。不过，在使用异步复制时，在所有未解决的 WAL 记录被传输到当前连接的后备服务器之前，服务器将不会完全关闭。 同步复制支持一个或者更多个同步后备服务器，事务将会等待，直到所有同步后备服务器都确认收到了它们的数据为止。事务必须等待其回复的同步后备的数量由`synchronous_standby_names`指定。这个参数还指定一个后备服务器名称及方法（FIRST`和`ANY）的列表来从列出的后备中选取同步后备。 方法`FIRST`指定一种基于优先的同步复制并且让事务提交等待，直到它们的WAL记录被复制到基于优先级选中的所要求数量的同步后备上为止。在列表中出现较早的后备被给予较高的优先级，并且将被考虑为同步后备。其他在这个列表中位置靠后的后备服务器表示可能的同步后备。如果任何当前的同步后备由于任何原因断开连接，它将立刻被下一个最高优先级的后备所替代。 基于优先的多同步后备的`synchronous_standby_names`示例为： synchronous_standby_names = 'FIRST 2 (s1, s2, s3)' 在这个例子中，如果有四个后备服务器`s1`、s2、`s3`和`s4`在运行，两个后备服务器`s1`和`s2`将被选中为同步后备，因为它们出现在后备服务器名称列表的前部。`s3`是一个潜在的同步后备，当`s1`或`s2`中的任何一个失效， 它就会取而代之。`s4`则是一个异步后备因为它的名字不在列表中。 方法`ANY`指定一种基于规定数量的同步复制并且让事务提交等待，直到它们的WAL记录至少被复制到列表中所要求数量的同步后备上为止。 `synchronous_standby_names`的基于规定数量的多同步后备的例子： synchronous_standby_names = 'ANY 2 (s1, s2, s3)' 在这个例子中，如果有四台后备服务器`s1`、s2、`s3`以及`s4`正在运行，事务提交将会等待来自至少其中任意两台后备服务器的回复。`s4`是一台异步后备，因为它的名字不在该列表中。 后备服务器的同步状态可以使用`pg_stat_replication`视图查看。 同步复制通常要求仔细地规划和放置后备服务器来保证应用能令人满意地工作。等待并不利用系统资源，但是事务锁会持续保持直到传输被确认。其结果是，不小心使用同步复制将由于响应时间增加以及较高的争用率而降低数据库应用的性能。 IvorySQL允许应用开发者通过复制来指定所要求的持久性级别。这可以为整个系统指定，不过它也能够为特定的用户或连接指定，甚至还可以为单个事务指定。 例如，一个应用的载荷的组成可能是这样：10% 的改变是重要的客户详情，而 90% 的改变是不太重要的数据，即使它们丢失业务也比较容易容忍（例如用户间的聊天消息）。 通过在应用级别（在主服务器上）指定的同步复制选项，我们可以为大部分重要的改变提供同步复制，并且不会拖慢整体的载荷。应用级别选项是使高性能应用享受同步复制的一种重要和实用的工具。 你应该认为网络带宽必须比 WAL 数据的产生率高。 当`synchronous_commit`被设置为`on`、`remote_apply`或者`remote_write`时， `synchronous_standby_names`指定产生的事务提交要等待其回应的同步后备的数量和名称。如果任一同步后备崩溃，这类事务提交可能无法完成。 高可用的最佳方案是确保有所要求数量的同步后备。这可以通过使用`synchronous_standby_names`指定多个潜在后备服务器来实现。 在基于优先的同步复制中，出现在该列表前部的后备服务器将被用作同步后备。后面的后备服务器将在当前同步后备服务器失效时取而代之。 在基于规定数量的同步复制中，所有出现在该列表中的后备服务器都将被用作同步后备的候选。即使其中的一个失效，其他后备仍将继续担任候选同步后备的角色。 当一台后备服务器第一次附加到主服务器时，它将处于一种还没有正确同步的状态。这被描述为`追赶`模式。一旦后备服务器和主服务器之间的迟滞第一次变成零，我们就来到了实时的`流式`状态。在后备服务器被创建之后的很长一段时间内可能都是追赶模式。如果后备服务器被关闭，则追赶周期将被增加，增加量由后备服务器被关闭的时间长度决定。只有当后备服务器到达`流式`状态后，它才能成为一台同步后备。这种状态可以使用`pg_stat_replication`视图查看。 如果在提交正在等待确认时主服务器重启，那些正在等待的事务将在主数据库恢复时被标记为完全提交。没有办法确认所有后备服务器已经收到了在主服务器崩溃时所有还未处理的 WAL 数据。某些事务可能不会在后备服务器上显示为已提交，即使它们在主服务器上显示为已提交。我们提供的保证是：在 WAL 数据已经被所有后备服务器安全地收到之前，应用将不会收到一个事务成功提交的显式确认。 如果实在无法保持所要求数量的同步后备，那么应该减少`synchronous_standby_names`中指定的事务提交应该等待其回应的同步后备的数量（或者禁用），并且在主服务器上重载配置文件。 如果主服务器与剩下的后备服务器是隔离的，你应当故障转移到那些其他剩余后备服务器中的最佳候选者上。 如果在事务等待时你需要重建一台后备服务器，确保命令 pg_start_backup() 和 pg_stop_backup() 被运行在一个`synchronous_commit` = `off`的会话中，否则那些请求将永远等待后备服务器出现。 当在一个后备机上使用连续归档时，有两种不同的情景：WAL 归档在主服务器 和后备机之间共享，或者后备机有自己的 WAL 归档。当后备机拥有其自身的 WAL 归档时，将`archive_mode`设置为 always，后备机将在收到每个 WAL 段时调用归档命令， 不管它是从归档恢复还是使用流复制恢复。共享归档可以类似地处理，但是 `archive_command`必须测试要被归档的文件是否 已经存在，以及现有的文件是否有相同的内容。这要求 `archive_command`中有更多处理，因为它必须当心 不要覆盖具有不同内容的已有文件，但是如果完全相同的文件被归档两次时 应返回成功。并且如果两个服务器尝试同时归档同一个文件，所有这些都必须 在没有竞争情况的前提下完成。 如果`archive_mode`被设置为`on`，归档器在恢复或者后备模式中无法启用。 如果后备服务器被提升，它将在被提升后开始归档，但是它将不会归档任何不是它自身产生的 WAL或时间线历史文件。 要在归档中得到完整的一系列 WAL 文件，你必须确保所有 WAL 在到达后备机之前都被归档。 对于基于文件的日志传输来说天然就是这样，因为后备机只能恢复在归档中找到的文件，而启用了流复制时则不是这样。 当一台服务器不在恢复模式中时，在`on`和`always`模式之间没有差别。 如果主服务器失效，则后备服务器应该开始故障转移过程。 如果后备服务器失效，则不会有故障转移发生。如果后备服务器可以被重启（即使晚一点），由于可重启恢复的优势，那么恢复处理也能被立即重启。如果后备服务器不能被重启，则一个全新的后备服务器实例应该被创建。 如果主服务器失效并且后备服务器成为了新的主服务器，那么接下来旧的主服务器重启后，你必须有一种机制来通知旧的主服务器不再成为主服务器。有些时候这被称为STONITH（Shoot The Other Node In The Head，关闭其他节点），这对于避免出现两个系统都认为它们是主服务器的情况非常必要，那种情况将导致混乱并且最终导致数据丢失。 很多故障转移系统仅使用两个系统，主系统和后备系统，它们由某种心跳机制连接来持续验证两者之间的连接性和主系统的可用性。也可能会使用第三个系统（称为目击者服务器）来防止某些不当故障转移的情况，但是除非非常小心地建立它并且经过了严格地测试，额外的复杂度可能会使该工作得不偿失。 IvorySQL并不提供在主服务器上标识失败并且通知后备数据库服务器所需的系统软件。现在已有很多这样的工具并且很好地与成功的故障转移所需的操作系统功能整合在一起，例如 IP 地址迁移。 一旦发生到后备服务器的故障转移，就只有单一的一台服务器在操作。这被称为一种退化状态。之前的后备服务器现在是主服务器，但之前的主服务器处于关闭并且可能一直保持关闭。要回到正常的操作，一个后备服务器必须被重建，要么在之前的主系统起来时使用它重建，要么使用第三台（可能是全新的）服务器来重建。在大型集簇上，pg_rewind功能可以被用来加速这个过程。一旦完成，主服务器和后备服务器可以被认为是互换了角色。某些人选择使用第三台服务器来为新的主服务器提供备份，直到新的后备服务器被重建，不过显然这会使得系统配置和操作处理更复杂。 因此，从主服务器切换到后备服务器可以很快，但是要求一些时间来重新准备故障转移集群。从主服务器到后备服务器的常规切换是有用的，因为它允许每个系统有常规的关闭时间来进行维护。这也可以作为一种对故障转移机制的测试，以保证在你需要它时它真地能够工作。我们推荐写一些管理过程来做这些事情。 要触发一台日志传送后备服务器的故障转移，运行`pg_ctl promote`，调用 pg_promote()，或者创建一个触发器文件，其文件名和路径由`promote_trigger_file`设置指定。 如果你正在规划使用`pg_ctl promote`或调用`pg_promote()`以进行故障转移，`promote_trigger_file`就不是必要的。 如果你正在建立只用于从主服务器分流只读查询而不是高可用性目的的报告服务器，你不需要提升它。 术语热备用来描述处于归档恢复或后备模式中的服务器连接到服务器并运行只读查询的能力。这有助于复制目的以及以高精度恢复一个备份到一个期望的状态。术语热备也指服务器从恢复转移到正常操作而用户能继续运行查询并且保持其连接打开的能力。 在热备模式中运行查询与正常查询操作相似，尽管如下所述存在一些用法和管理上的区别。 当hot_standby参数在一台后备服务器上被设置为真时，一旦恢复将系统带到一个一致的状态它将开始接受连接。所有这些连接都被限制为只读，甚至临时表都不能被写入。 后备服务器上的数据需要一些时间从主服务器到达后备服务器，因此在主服务器和后备服务器之间将有一段可以度量的延迟。近乎同时在主服务器和后备服务器上运行相同的查询可能因此而返回不同的结果。我们说后备服务器上的数据与主服务器是*最终一致*的。一旦一个事务的提交记录在后备服务器上被重播，那个事务所作的修改将对后备服务器上所有新取得的快照可见。快照可以在每个查询或每个事务的开始时取得，这取决于当前的事务隔离级别。 在热备期间开始的事务可能发出下列命令： 查询访问: SELECT、COPY TO 游标命令: DECLARE、FETCH、CLOSE 设置: SHOW、SET、RESET 事务管理命令: BEGIN、END、ABORT、START TRANSACTION SAVEPOINT、RELEASE、ROLLBACK TO SAVEPOINT `EXCEPTION`块或其他内部子事务 LOCK TABLE，不过只在下列模式之一中明确发出： ACCESS SHARE、ROW SHARE 或 ROW EXCLUSIVE. 计划和资源: PREPARE、EXECUTE、 DEALLOCATE、DISCARD 插件和扩展: LOAD UNLISTEN 在热备期间开始的事务将不会被分配一个事务 ID 并且不能被写入到系统的预写式日志。因此，下列动作将产生错误消息： 数据操纵语言（DML）: INSERT、 UPDATE、DELETE、COPY FROM、 TRUNCATE。注意不允许在恢复期间导致一个触发器被执行的动作。这个限制甚至被应用到临时表，因为不分配事务 ID 表行就不能被读或写，而当前不可能在一个热备环境中分配事务 ID。 数据定义语言（DDL）: CREATE、 DROP、ALTER、COMMENT。这个限制甚至被应用到临时表，因为执行这些操作会要求更新系统目录表。 SELECT …​ FOR SHARE | UPDATE，因为不更新底层数据文件就无法取得行锁。 `SELECT`语句上的能产生 DML 命令的规则。 显式请求一个高于`ROW EXCLUSIVE MODE`的模式的`LOCK`。 默认短形式的`LOCK`，因为它请求`ACCESS EXCLUSIVE MODE`。 显式设置非只读状态的事务管理命令： BEGIN READ WRITE, START TRANSACTION READ WRITE SET TRANSACTION READ WRITE, SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE SET transaction_read_only = off 两阶段提交命令: PREPARE TRANSACTION、 COMMIT PREPARED、ROLLBACK PREPARED，因为即使只读事务也需要在准备阶段（两阶段提交中的第一个阶段）写 WAL。 序列更新 : nextval()、setval() LISTEN,NOTIFY 在正常操作中，“只读”事务被允许使用`LISTEN`和`NOTIFY`，因此热备会话在比普通只读会话更紧一点的限制下操作。这些限制中的某些可能会在一个未来的发行中被放松。 在热备期间，参数`transaction_read_only`总是为真并且不可以被改变。但是只要不尝试修改数据库，热备期间的连接工作起来更像其他数据库连接。如果发生故障转移或切换，该数据库将切换到正常处理模式。当服务器改变模式时会话将保持连接。一旦热备结束，它将可以发起读写事务（即使是一个在热备期间启动的会话）。 用户可以通过`SHOW in_hot_standby`来检查hot standby会话是否是活跃的 (在服务器版本 14 之前该参数`in_hot_standby`不存在。对于更早版本的服务器，可行的替代方法是 SHOW transaction_read_only。) 此外， 还有一些函数允许用户访问有关后备服务器的信息。 它们允许您编写程序来识别数据库当前的状态。用于监控恢复进度， 或者您可以编写复杂的程序将数据库恢复到特定状态。 主服务器和后备服务器在多方面都松散地连接在一起。主服务器上的动作将在后备服务器上产生效果。结果是在它们之间有潜在的负作用或冲突。最容易理解的冲突是性能：如果在主服务器上发生一次大数据量的载入，那么着将在后备服务器上产生一个相似的 WAL 记录流，因而后备服务器查询可能要竞争系统资源（例如 I/O）。 随着热备发生的还可能有其他类型的冲突。对于可能需要被取消的查询和（某些情况中）解决它们的已断开会话来说，这些冲突是硬冲突。用户可以用几种方式来处理这种冲突。冲突情况包括： 在主服务器上取得了访问排他锁（包括显式`LOCK`命令和多种DDL动作）与后备查询中的表访问冲突。 在主服务器上删除一个表空间与使用该表空间存储临时工作文件的后备查询冲突。 在主服务器上删除一个数据库与在后备服务器上连接到该数据库的会话冲突。 从 WAL 清除记录的应用与快照仍能“看见”任意要被移除的行的后备事务冲突。 从 WAL 清除记录的应用与在后备服务器上访问该目标页的查询冲突，不管要被移除的数据是否为可见。 在主服务器上，这些情况仅仅会导致等待；并且用户可以选择取消这些冲突动作中间的一个。但是，在后备服务器上则没有选择：已被 WAL 记录的动作已经在主服务器上发生，那么后备服务器不能在应用它时失败。此外，允许 WAL 应用无限等待是非常不可取的，因为后备服务器的状态将变得逐渐远远落后于主服务器的状态。因此，提供了一种机制来强制性地取消与要被应用的 WAL 记录冲突的后备查询。 该问题情形的一个例子是主服务器上的一位管理员在一个表上运行`DROP TABLE`，而该表正在后备服务器上被查询。如果`DROP TABLE`被应用在后备服务器上，很明显该后备查询不能继续。如果这种情况在主服务器上发生，`DROP TABLE`将等待直到其他查询结束。但是当`DROP TABLE`被运行在主服务器上，主服务器没有关于运行在后备服务器上查询的信息，因此它将不会等待任何这样的后备查询。WAL 改变记录在后备查询还在运行时来到后备服务器上，导致一个冲突。后备服务器必须要么延迟 WAL 记录的应用（还有它们之后的任何事情），要么取消冲突查询这样`DROP TABLE`可以被应用。 当一个冲突查询很短时，我们通常期望能延迟 WAL 应用一小会儿让它完成；但是在 WAL 应用中的一段长的延迟通常是不受欢迎的。因此取消机制有参数，max_standby_archive_delay和max_standby_streaming_delay，它们定义了在 WAL 应用中的最大允许延迟。当应用任何新收到的 WAL 数据花费了超过相关延迟设置值时，冲突查询将被取消。设立两个参数是为了对从一个归档读取 WAL 数据（即来自一个基础备份的初始恢复或者“追赶”一个已经落后很远的后备服务器）和通过流复制读取 WAL数据的两种情况指定不同的延迟值。 在一台后备服务器上这主要是为了该可用性而存在，最好把延迟参数设置得比较短，这样服务器不会由于后备查询导致的延迟落后主服务器太远。但是，如果该后备服务器是位了执行长时间运行的查询，则一个较高甚至无限的延迟值更好。但是记住一个长时间运行的查询延迟了 WAL 记录的应用，它可能导致后备服务器上的其他会话无法看到主服务器上最近的改变。 一旦`max_standby_archive_delay`或`max_standby_streaming_delay`指定的延迟被超越，冲突查询将被取消。这通常仅导致一个取消错误，尽管在重放一个`DROP DATABASE`的情况下整个冲突会话都将被中断。另外，如果冲突发生在一个被空闲事务持有的锁上，该冲突会话会被中断（这种行为可能在未来被改变）。 被取消的查询可能会立即被重试（当然是在开始一个新的事务后）。因为查询取消依赖于 WAL 记录被重放的本质，如果一个被取消的查询被再次执行，它可能会很好地成功完成。 记住延迟参数是从 WAL 数据被后备服务器收到后流逝的时间。因此，留给后备服务器上任何一个查询的宽限期从不会超过延迟参数，并且如果后备服务器已经由于等待之前的查询完成而落后或者因为过重的更新负载而无法跟上主服务器，宽限期可能会更少。 在后备查询和 WAL 重播之间发生冲突的最常见原因是“过早清除”。正常地，PostgreSQL允许在没有事务需要看到旧行版本时对它们进行清除，这样可以保证根据 MVCC 规则的正确的数据可见性。不过，这个规则只能被应用于执行在主控机上的事务。因此有可能主控机上的清除会移除对一个后备服务器事务还可见的行版本。 有经验的用户应当注意行版本清除和行版本冻结都可能与后备查询冲突。即便在一个没有被更新或被删除行的表上运行一次手工`VACUUM FREEZE`也可能导致冲突。 用户应当清楚，主服务器上被正常和重度更新的表将快速地导致后备服务器上长时间运行的查询被取消。在这样的情况下，`max_standby_archive_delay`或`max_standby_streaming_delay`的有限制设置可以被视作`statement_timeout`设置。 如果发现后备查询取消的数量不可接受，还是有补救的可能。第一种选项是设置参数 hot_standby_feedback，它阻止`VACUUM` 移除最近死亡的元组并且因此清除冲突不会产生。如果你这样做，你应当 注意这将使主服务器上的死亡元组清除被延迟，这可能会导致不希望发生 的表膨胀。不过，清除的情况不会比在主服务器上直接运行后备查询时更糟， 并且你仍然能够享受将执行分流到后备服务器的好处。如果后备服务器频繁地连接和 断开，你可能想要做些调整来处理无法提供`hot_standby_feedback` 反馈的时期。例如，考虑增加`max_standby_archive_delay`，这样 在断开连接的期间查询就不会快速地被 WAL 归档文件中的冲突取消。你也应该考虑 增加`max_standby_streaming_delay`来避免重新连接后新到达的流 WAL 项导致的快速取消。 另一个选项是增加主服务器上的vacuum_defer_cleanup_age，这样死亡行不会像平常那么快地被清理。这将允许在后备服务器上的查询能在被取消前有更多时间执行，并且不需要设置一个很高的`max_standby_streaming_delay`。但是，这种方法很难保证任何指定的执行时间窗口，因为`vacuum_defer_cleanup_age`是用主服务器上被执行的事务数来衡量的。 查询取消的数量和原因可以使用后备服务器上的`pg_stat_database_conflicts`系统视图查看。`pg_stat_database`系统视图也包含汇总信息。 当 WAL 重放由于冲突而需要比`deadlock_timeout`更长时间时，用户可以控制是否打印日志消息。由参数 log_recovery_conflict_waits控制。 如果`hot_standby`在`postgresql.conf`中被设置为`on`并且存在一个standby.signal文件，服务器将运行在热备模式。但是，可能需要一些时间来允许热备连接，因为在服务器完成足够的恢复来为查询提供一个一致的状态之前，它将不会接受连接。在此期间，尝试连接的客户端将被一个错误消息拒绝。要确认服务器已经可连接，要么循环地从应用尝试连接，要么在服务器日志中查找这些消息： LOG: entering standby mode ... then some time later ... LOG: consistent recovery state reached LOG: database system is ready to accept read only connections 在主服务器上，一旦创建一个检查点，一致性信息就被记录下来。当读取在特定时段（当在主服务器上`wal_level`没有被设置为`replica`或者`logical`的期间）产生的 WAL 时无法启用热备。在同时存在这些条件时，到达一个一致状态也会被延迟： 一个写事务有超过 64 个子事务 生存时间非常长的写事务 如果你正在运行基于文件的日志传送（“温备”），你可能需要等到下一个 WAL 文件到达，这可能和主服务器上的`archive_timeout`设置一样长。 设置几个参数可确定用于跟踪事务ID、锁和预备事务的共享内存大小。后备服务器上的设置必须大于或等于主服务器上的设置，以确保在恢复过程中不会耗尽共享内存。例如，如果主数据库正在执行预备事务，而备用数据库没有获取共享内存来跟踪预备事务，则备用数据库将无法继续恢复，直到配置更改。受影响的参数是： max_connections max_prepared_transactions max_locks_per_transaction max_wal_senders max_worker_processes 确保这不是问题的可靠方法是使备用数据库上的这些参数的值等于或大于主数据库上的值。因此，如果您想增加这些值，您应该先更改后备服务器上的设置，然后再更改主服务器上的设置。相反，如果要减小这些值，则应先更改主服务器上的设置，然后再更改后备服务器上的设置。请记住，当一个备用数据库被提升时，它会成为后续备用数据库所需参数设置的新基准。因此，最好在所有后备服务器上保持这些设置相同，这样在切换/故障转移期间就不会出现问题。 WAL 跟踪主节点上这些参数的变化。如果热备处理一个 WAL，表明主节点当前值大于备用数据库上的值，它将记录一个警告并中止恢复。例如: WARNING：由于参数设置不足，无法进行热备 详细信息：max_connections = 80 的设置低于主服务器上的设置，其值为 100。 LOG：恢复已暂停 详细信息：如果恢复未暂停，服务器将关闭。 提示：您可以在进行必要的配置更改后重新启动服务器。 此时，您应该更改备库设置并重新启动实例以继续恢复。如果备库不是热备，不兼容的参数更改将立即将其关闭而不会暂停。因为这样继续开机没有意义。 管理员为max_standby_archive_delay和max_standby_streaming_delay选择适当的设置很重要。最好的选择取决于业务的优先级。例如如果服务器主要的任务是作为高可用服务器，那么你将想要低延迟设置，甚至是零（尽管它是一个非常激进的设置）。如果后备服务器的任务是作为一个用于决策支持查询的额外服务器，那么将其最大延迟值设置为很多小时甚至 -1 （表示无限等待）可能都是可以接受的。 在主服务器上写出的事务状态 \"hint bits\" 是不被 WAL 记录的，因此后备服务器上的数据将可能重新写出该提示。这样，即使所有用户都是只读的，后备服务器仍将执行磁盘写操作；但数据值本身并没有发生改变。用户将仍写出大的排序临时文件并且重新生成 relcache 信息文件，这样在热备模式中数据库没有哪个部分是真正只读的。还要注意使用dblink模块写到远程数据库以及其他使用 PL 函数位于数据库之外的操作仍将可用，即使该事务是本地只读的。 在恢复模式期间，下列类型的管理命令是不被接受的： 数据定义语言（DDL）: e.g., CREATE INDEX 特权和所有权: GRANT, REVOKE, 维护命令: ANALYZE, VACUUM,CLUSTER, REINDEX 注意这些命令中的某些实际上在主服务器上的“只读”模式事务期间是被允许的。 结果是，你无法创建只存在于后备服务器上的额外索引以及统计信息。如果需要这些管理命令，它们应该在主服务器上被执行，并且最后那些改变将被传播到后备服务器。 `pg_cancel_backend()`和`pg_terminate_backend()`将在用户后端上工作，而不是执行恢复的 Startup 进程。`pg_stat_activity`不会为 Startup 进程显示一个项，也不会把恢复事务显示为活动。结果是在恢复期间`pg_prepared_xacts`总是为空。如果你希望解决不能确定的预备事务，查看主服务器上的`pg_prepared_xacts`并且发出命令来解决那里的事务或者在恢复结束后来解决它们。 和平常一样，pg_locks`将显示被后端持有的锁。`pg_locks`也会显示一个由 Startup 进程管理的虚拟事务，它拥有被恢复重播的事务所持有的所有`AccessExclusiveLocks。注意 Startup 进程不请求锁来做数据库更改，并且因此对于 Startup 进程除`AccessExclusiveLocks`之外的锁不显示在`pg_locks`中，它们仅被假定存在。 Nagios的插件check_pgsql将可以工作，因为它检查的简单信息是存在的。check_postgres监控脚本也将能工作，尽管某些被报告的值可能给出不同或者混乱的结果。例如，上一次清理时间将不会被维护，因为在后备服务器上不会发生清理。在主服务器上运行的清理仍会把它们的改变发送给后备服务器。 WAL 文件控制命令在恢复期间将不会工作，如`pg_start_backup`、`pg_switch_wal`等。 可动态载入的模块可以工作，包括`pg_stat_statements`。 咨询锁在恢复期间工作正常，包括死锁检测。注意咨询锁从来都不会被 WAL 记录，因此在主服务器或后备服务器上一个咨询锁不可能会与 WAL 重播发生冲突。也不可能会在主服务器上获得一个咨询锁并且在后备服务器上开始一个相似的咨询锁。咨询锁只与它们被取得的那个服务器相关。 基于触发器的复制系统（如Slony、Londiste和Bucardo）将根本不会运行在后备服务器上，然而只要改变不被发送到要被应用的后备服务器，它们将在主服务器上运行得很好。WAL 重播不是基于触发器的，因此你不能用后备服务器接替任何需要额外数据库写操作或依赖触发器使用的系统。 新的 OID 不能被分配，然而某些UUID生成器仍然能工作，只要它们不依赖于向数据库写新的状态。 当前，在只读事务期间不允许创建临时表，因此在某些情况中现有的脚本将不会正确运行。这个限制可能会在稍后的发行中被放松。这既是一个 SQL 标准符合问题也是一个技术问题。 只有在表空间为空时`DROP TABLESPACE`才能成功。某些后备服务器用户可能正在通过他们的`temp_tablespaces`参数使用该表空间。如果在该表空间中有临时文件，所有活动查询将被取消来保证临时文件被移除，这样该表空间可以被移除并且 WAL 重播可以继续。 在主服务器上运行`DROP DATABASE`或`ALTER DATABASE …​ SET TABLESPACE`将产生一个 WAL 项，它将导致所有连接到后备服务器上那个数据库的用户被强制地断开连接。这个动作会立即发生，不管`max_standby_streaming_delay`的设置是什么。注意`ALTER DATABASE …​ RENAME`不会断开用户，这在大部分情况中不会有提示，然而如果它依赖某种基于数据库名的方法，在某些情况中会导致程序混乱。 在普通（非恢复）模式中，如果你为具有登录能力的角色发出`DROP USER`或`DROP ROLE`，而该用户仍然连接着，则对已连接用户不会发生任何事情 - 他们保持连接。但是用户不能重新连接。这种行为也适用于恢复，因此在主服务器上的一次`DROP USER`不会使后备服务器上的用户断开。 在恢复期间统计收集器是活动的。所有扫描、读、阻塞、索引使用等将在后备服务器上被正常的记录。被重播的动作将不会重复它们在主服务器上的效果，因此重播一个插入将不会导致pg_stat_user_tables的 Inserts 列上的递增。在恢复的开始 stats 文件会被删除，因此来自主服务器和后备服务器的 stats 将不同；这被认为是一种特性而不是缺陷。 在恢复期间自动清理不是活动的。它将在恢复末尾正常启动。 检查点进程和后台写入进程在恢复期间是活动状态的。检查点进程将执行重启动点（与主服务器上的检查点相似），后台写入进程将执行正常的块清理活动。 这可以包括存储在后备服务器上的提示位信息的更新。在恢复期间，`CHECKPOINT`命令会被接受，然而它会执行一个重启点而不是一个新的检查点。 在主服务器上，可以使用参数wal_level和vacuum_defer_cleanup_age。在主服务器上设置max_standby_archive_delay和max_standby_streaming_delay不会产生效果。 在主服务器上，可以使用参数hot_standby、max_standby_archive_delay和max_standby_streaming_delay。只要服务器保持在后备模式vacuum_defer_cleanup_age就没有效果，然而当后备服务器变成主服务器时它将变得相关。 热备有一些限制。这些限制很可能在未来的发行中被修复： 在能够取得快照之前，需要正在运行的事务的完整知识。使用大量子事务（目前指超过 64 个）的事务将延迟只读连接的启动，直到最长的运行着的写事务完成。如果发生这种情况，说明消息将被发送到服务器日志。 主服务器上的每一个检查点将产生用于后备查询的可用启动点。如果后备服务器在主控机处于关闭状态时被关闭，就没有办法在主服务器启动之前重新进入热后备，因此它在 WAL 日志中产生一个进一步启动点。这种情况在它可能发生的大部分常见情况中不是一个问题。通常，如果主服务器被关闭并且不再可用，这可能是由于某种严重错误要求后备服务器被转变成为一个新的主服务器来操作。并且在主服务器被故意关闭的情况下，协调保证后备服务器平滑地过渡为新的主服务器也是一种标准过程。 在恢复尾声，由预备事务持有的`AccessExclusiveLocks`将要求两倍的正常锁表项。如果你计划运行大量并发的通常要求`AccessExclusiveLocks`的预备事务，或者你计划运行一个需要很多`AccessExclusiveLocks`的大型事务，我们建议你为`max_locks_per_transaction`选择一个更大的值，也许是主服务器上该参数值的两倍。如果你的`max_prepared_transactions`设置为 0，你根本不需要考虑这个问题。 可序列化事务隔离级别目前在热备中不可用。尝试在热备模式中将一个事务设置为可序列化隔离级别将产生一个错误。","title":"日常维护","component":"ivorysql-doc","version":"master","name":"daily_maintenance","url":"/ivorysql-doc/master/getting-started/daily_maintenance.html","titles":[{"text":"1. 日常清理","hash":"日常清理","id":1},{"text":"1.1. 清理的基础知识","hash":"清理的基础知识","id":2},{"text":"1.2. 恢复磁盘空间","hash":"恢复磁盘空间","id":3},{"text":"1.3. 更新规划器统计信息","hash":"更新规划器统计信息","id":4},{"text":"1.4. 更新可见性映射","hash":"更新可见性映射","id":5},{"text":"1.5. 防止事务ID回卷失败","hash":"防止事务id回卷失败","id":6},{"text":"1.6. 自动清理后台进程","hash":"自动清理后台进程","id":7},{"text":"2. 日常重建索引","hash":"日常重建索引","id":8},{"text":"3. 日志文件维护","hash":"日志文件维护","id":9},{"text":"4. 高可用、负载均衡和复制","hash":"高可用负载均衡和复制","id":10},{"text":"4.1. 不同方案的比较","hash":"不同方案的比较","id":11},{"text":"4.1.1. 共享磁盘故障转移","hash":"共享磁盘故障转移","id":12},{"text":"4.1.2. 文件系统（块设备）复制","hash":"文件系统块设备复制","id":13},{"text":"4.1.3. 预写式日志传送","hash":"预写式日志传送","id":14},{"text":"4.1.4. 逻辑复制","hash":"逻辑复制","id":15},{"text":"4.1.5. 基于触发器的主-备复制","hash":"基于触发器的主-备复制","id":16},{"text":"4.1.6. 基于SQL的复制中间件","hash":"基于sql的复制中间件","id":17},{"text":"4.1.7. 异步多主控机复制","hash":"异步多主控机复制","id":18},{"text":"4.1.8. 同步多主控机复制","hash":"同步多主控机复制","id":19},{"text":"4.2. 日志传送后备服务器","hash":"日志传送后备服务器","id":20},{"text":"4.2.1. 规划","hash":"规划","id":21},{"text":"4.2.2. 后备服务器操作","hash":"后备服务器操作","id":22},{"text":"4.2.3. 为后备服务器准备主控机","hash":"为后备服务器准备主控机","id":23},{"text":"4.2.4. 建立一个后备服务器","hash":"建立一个后备服务器","id":24},{"text":"4.2.5. 流复制","hash":"流复制","id":25},{"text":"4.2.5.1. 认证","hash":"认证","id":26},{"text":"4.2.5.2. 监控","hash":"监控","id":27},{"text":"4.2.6. 复制槽","hash":"复制槽","id":28},{"text":"4.2.6.1. 查询和操纵复制槽","hash":"查询和操纵复制槽","id":29},{"text":"4.2.6.2. 配置实例","hash":"配置实例","id":30},{"text":"4.2.7. 级联复制","hash":"级联复制","id":31},{"text":"4.2.8. 同步复制","hash":"同步复制","id":32},{"text":"4.2.8.1. 基本配置","hash":"基本配置","id":33},{"text":"4.2.8.2. 多个同步后备","hash":"多个同步后备","id":34},{"text":"4.2.8.3. 性能规划","hash":"性能规划","id":35},{"text":"4.2.8.4. 高可用性规划","hash":"高可用性规划","id":36},{"text":"4.2.9. 在后备机上连续归档","hash":"在后备机上连续归档","id":37},{"text":"4.3. 故障转移","hash":"故障转移","id":38},{"text":"4.4. 热备","hash":"热备","id":39},{"text":"4.4.1. 用户概览","hash":"用户概览","id":40},{"text":"4.4.2. 处理查询冲突","hash":"处理查询冲突","id":41},{"text":"4.4.3. 管理员概览","hash":"管理员概览","id":42},{"text":"4.4.4. 热备参数参考","hash":"热备参数参考","id":43},{"text":"4.4.5. 警告","hash":"警告","id":44}]},"56":{"id":56,"text":"在大部分 Unix 平台上，IvorySQL会修改由`ps`报告的命令标题，这样个体服务器进程可以被标识。一个显示样例 $ ps auxww | grep ^postgres postgres 15551 0.0 0.1 57536 7132 pts/0 S 18:02 0:00 postgres -i postgres 15554 0.0 0.0 57536 1184 ? Ss 18:02 0:00 postgres: background writer postgres 15555 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: checkpointer postgres 15556 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: walwriter postgres 15557 0.0 0.0 58504 2244 ? Ss 18:02 0:00 postgres: autovacuum launcher postgres 15558 0.0 0.0 17512 1068 ? Ss 18:02 0:00 postgres: stats collector postgres 15582 0.0 0.0 58772 3080 ? Ss 18:04 0:00 postgres: joe runbug 127.0.0.1 idle postgres 15606 0.0 0.0 58772 3052 ? Ss 18:07 0:00 postgres: tgl regression [local] SELECT waiting postgres 15610 0.0 0.0 58772 3056 ? Ss 18:07 0:00 postgres: tgl regression [local] idle in transaction （`ps`的调用方式随不同的平台而变，但是显示的细节都差不多。这个例子来自于一个最近的 Linux 系统）。列在这里的第一个进程是主服务器进程。为它显示的命令参数是当它被启动时使用的那些。接下来的五个进程是由主进程自动启动的后台工作者进程（如果你已经设置系统为不启动统计收集器，“统计收集器”进程将不会出现；同样“自动清理发动”进程也可以被禁用）。剩余的每一个进程都是一个处理一个客户端连接的服务器进程。每个这种进程都会把它的命令行显示设置为这种形式 postgres: user database host activity 在该客户端连接的生命期中，用户、数据库以及（客户端）主机项保持不变，但是活动指示器会改变。活动可以是`闲置`（即等待一个客户端命令）、在事务中闲置（在一个`BEGIN`块里等待客户端）或者一个命令类型名，例如`SELECT`。还有，如果服务器进程正在等待一个其它会话持有的锁， `等待中`会被追加到上述信息中。在上面的例子中，我们可以推断：进程 15606 正在等待进程 15610 完成其事务并且因此释放一些锁（进程 15610 必定是阻塞者，因为没有其他活动会话。在更复杂的情况中，可能需要查看pg_locks系统视图来决定谁阻塞了谁）。 如果配置了cluster_name，则集簇的名字 也将会显示在`ps`的输出中： $ psql -c 'SHOW cluster_name' cluster_name -------------- server1 (1 row) $ ps aux|grep server1 postgres 27093 0.0 0.0 30096 2752 ? Ss 11:34 0:00 postgres: server1: background writer ... 如果你已经关闭了update_process_title，那么活动指示器将不会被更新，进程标题仅在新进程被启动的时候设置一次。 在某些平台上这样做可以为每个命令节省可观的开销，但在其它平台上却不明显。 提示 Solaris需要特别的处理。你必需使用`/usr/ucb/ps`而不是`/bin/ps`。 你还必需使用两个`w`标志，而不是一个。另外，你对`postgres`命令的最初调用必须用一个比服务器进程提供的短的`ps`状态显示。如果你没有满足全部三个要求，每个服务器进程的`ps`输出将是原始的`postgres`命令行。 IvorySQL的 统计收集器 是一个支持收集和报告服务器活动信息的子系统。 目前，这个收集器可以对表和索引的访问计数，计数可以按磁盘块和个体行来进行。它还跟踪每个表中的总行数、每个表的清理和分析动作的信息。它也统计调用用户定义函数的次数以及在每次调用中花费的总时间。 IvorySQL也支持报告有关系统正在干什么的 动态信息，例如当前正在被其他服务器进程执行的命令以及系统中存在哪些其他连接。 这个功能是独立于收集器进程存在的。 因为统计收集给查询执行增加了一些负荷，系统可以被配置为收集或不收集信息。这由配置参数控制，它们通常在`postgresql.conf`中设置。 参数track_activities允许监控当前被任意服务器进程执行的命令。 参数track_counts控制是否收集关于表和索引访问的统计信息。 参数track_functions启用对用户定义函数使用的跟踪。 参数track_io_timing启用对块读写次数的监控。 通常这些参数被设置在`postgresql.conf`中，这样它们会应用于所有服务器进程，但是可以在单个会话中使用SET命令打开或关闭它们（为了阻止普通用户对管理员隐藏他们的活动，只有超级用户被允许使用`SET`来改变这些参数）。 统计收集器通过临时文件将收集到的信息传送给其他IvorySQL进程。这些文件被存储在名字由stats_temp_directory参数指定的目录中，默认是`pg_stat_tmp`。为了得到更好的性能，`stats_temp_directory`可以被指向一个基于 RAM 的文件系统来降低物理 I/O 需求。当服务器被干净地关闭时，一份统计数据的永久拷贝被存储在`pg_stat`子目录中，这样在服务器重启后统计信息能被保持。当在服务器启动时执行恢复时（例如立即关闭、服务器崩溃以及时间点恢复之后），所有统计计数器会被重置。 表 1 中列出了一些预定义视图 可以用来显示系统的当前状态。 表 2 中列出了另一些视图可以 显示统计收集的结果。你也可以使用底层统计函数来建立自定义的视图。 在使用统计信息监控收集到的数据时，你必须了解这些信息并非是实时更新的。每个独立的服务器进程只在进入闲置状态之前才向收集器传送新的统计计数；因此正在进行的查询或事务并不影响显示出来的总数。同样，收集器本身也最多每`PGSTAT_STAT_INTERVAL`毫秒（缺省为 500ms，除非在编译服务器的时候修改过）发送一 次新的报告。因此显示的信息总是落后于实际活动。但是由`track_activities`收集的当前查询信息总是最新的。 另一个重点是当一个服务器进程被要求显示任何这些统计信息时，它首先取得收集器进程最近发出的报告并且接着为所有统计视图和函数使用这个快照，直到它的当前事务的结尾。因此只要你继续当前事务，统计数据将会一直显示静态信息。相似地，当任何关于所有会话的当前查询的信息在一个事务中第一次被请求时，这样的信息将被收集。并且在整个事务期间将显示相同的信息。这是一种特性而非缺陷，因为它允许你在该统计信息上执行多个查询并且关联结果而不用担心那些数字会在你不知情的情况下改变。但是如果你希望用每个查询都看到新结果，要确保在任何事务块之外做那些查询。或者，你可以调用`pg_stat_clear_snapshot`()，那将丢弃当前事务的统计快照（如果有）。下一次对统计性信息的使用将导致获取一个新的快照。 一个事务也可以在视图`pg_stat_xact_all_tables`、pg_stat_xact_sys_tables、`pg_stat_xact_user_tables`和`pg_stat_xact_user_functions`中看到它自己的统计信息（还没有被传送给收集器）。这些数字并不像上面所述的那样行动，相反它们在事务期间持续被更新。 表 1中显示的动态统计视图中的一些信息是有安全限制的。 普通用户只能看到关于他们自己的会话的所有信息（属于他们是成员的角色的会话）。 在关于其他会话的行中，许多列将为空。 但是，请注意，一个会话的存在和它的一般属性，例如会话用户和数据库，对所有用户都是可见的。 超级用户和内置角色`pg_read_all_stats`的成员可以看到所有会话的所有信息。 表1.动态统计视图 视图名称 描述 pg_stat_activity 每个服务器进程一行，显示与那个进程的当前活动相关的信息，例如状态和当前查询。 pg_stat_replication 每一个 WAL 发送进程一行，显示有关到该发送进程连接的后备服务器的复制的统计信息。 pg_stat_wal_receiver 只有一行，显示来自 WAL 接收器所连接服务器的有关该接收器的统计信息。 pg_stat_subscription 每个订阅至少一行，显示有关该订阅的工作者的信息。 pg_stat_ssl 每个连接（常规的或者复制）一行，显示在这个连接上使用的SSL的信息。 pg_stat_gssapi 每个连接（常规和复制）有一行，显示关于GSSAPI验证和加密的信息。 pg_stat_progress_analyze 每个运行`ANALYZE`的后端(包括自动清理工作者进程)的行，显示当前进度。 pg_stat_progress_create_index 每个后台运行`CREATE INDEX`或`REINDEX`的后端都有一行，显示当前的进度。 pg_stat_progress_vacuum 每个运行着`VACUUM`的后端（包括autovacuum工作者进程）一行，显示当前的进度。 pg_stat_progress_cluster 每个运行着`CLUSTER`或`VACUUM FULL`的后端一行，显示当前进度。 pg_stat_progress_basebackup 每一个WAL发送者进程的行显示一个基础备份，显示当前进度。 表2.已收集统计信息的视图 视图名称 描述 pg_stat_archiver 只有一行，显示有关 WAL 归档进程活动的统计信息。 pg_stat_bgwriter 只有一行，显示有关后台写进程的活动的统计信息。 pg_stat_database 每个数据库一行，显示数据库范围的统计信息。 pg_stat_database_conflicts 每个数据库一行，显示数据库范围的统计信息， 这些信息的内容是关于由于与后备服务器的恢复过程 发生冲突而被取消的查询。 pg_stat_all_tables 当前数据库中每个表一行，显示有关访问指定表的统计信息。 pg_stat_sys_tables 和`pg_stat_all_tables`一样，但只显示系统表。 pg_stat_user_tables 和`pg_stat_all_tables`一样，但只显示用户表。 pg_stat_xact_all_tables 和`pg_stat_all_tables`相似，但计数动作只在当前事务内发生，用于生存和死亡行数量的列以及清理和分析动作在此视图中不出现。 pg_stat_xact_sys_tables 和`pg_stat_xact_all_tables`一样，但只显示系统表。 pg_stat_xact_user_tables 和`pg_stat_xact_all_tables`一样，但只显示用户表。 pg_stat_all_indexes 当前数据库中的每个索引一行，显示：表OID、索引OID、模式名、表名、索引名、 使用了该索引的索引扫描总数、索引扫描返回的索引记录数、使用该索引的简 单索引扫描抓取的活表(livetable)中数据行数。 当前数据库中的每个索引一行，显示与访问指定索引有关的统计信息。 pg_stat_sys_indexes 和`pg_stat_all_indexes`一样，但只显示系统表上的索引。 pg_stat_user_indexes 和`pg_stat_all_indexes`一样，但只显示用户表上的索引。 pg_statio_all_tables 当前数据库中每个表一行(包括TOAST表)，显示：表OID、模式名、表名、 从该表中读取的磁盘块总数、缓冲区命中次数、该表上所有索引的磁盘块读取总数、 该表上所有索引的缓冲区命中总数、在该表的辅助TOAST表(如果存在)上的磁盘块读取总数、 在该表的辅助TOAST表(如果存在)上的缓冲区命中总数、TOAST表的索引的磁盘块读 取总数、TOAST表的索引的缓冲区命中总数。 当前数据库中的每个表一行，显示有关在指定表上 I/O 的统计信息。 pg_statio_sys_tables 和`pg_statio_all_tables`一样，但只显示系统表。 pg_statio_user_tables 和`pg_statio_all_tables`一样，但只显示用户表。 pg_statio_all_indexes 当前数据库中每个索引一行，显示：表OID、索引OID、模式名、 表名、索引名、该索引的磁盘块读取总数、该索引的缓冲区命中总数。 当前数据库中的每个索引一行，显示与指定索引上的 I/O 有关的统计信息。 pg_statio_sys_indexes 和`pg_statio_all_indexes`一样，但只显示系统表上的索引。 pg_statio_user_indexes 和`pg_statio_all_indexes`一样，但只显示用户表上的索引。 pg_statio_all_sequences 当前数据库中每个序列对象一行，显示：序列OID、模式名、序列名、序列的磁盘读取总数、序列的缓冲区命中总数。 当前数据库中的每个序列一行，显示与指定序列上的 I/O 有关的统计信息。 pg_statio_sys_sequences 和`pg_statio_all_sequences`一样，但只显示系统序列（目前没有定义系统序列，因此这个视图总是为空）。 pg_statio_user_sequences 和`pg_statio_all_sequences`一样，但只显示用户序列。 pg_stat_user_functions 对于所有跟踪功能，函数的OID，模式，名称，数量 通话总时间，和自我的时间。自我时间是 在函数本身所花费的时间量，总时间包括 它调用函数所花费的时间。时间值以毫秒为单位。 每一个被跟踪的函数一行，显示与执行该函数有关的统计信息。 pg_stat_xact_user_functions 和`pg_stat_user_functions`相似，但是只统计在当前事务期间的调用 pg_stat_slru 每个SLRU一行, 显示操作的统计信息。 针对每个索引的统计信息对于判断哪个索引正被使用以及它们的效果特别有用。 `pg_statio_`系列视图主要用于判断缓冲区的效果。当实际磁盘读取数远小于缓冲区命中时，这个缓冲能满足大部分读请求而无需进行内核调用。但是，这些统计信息并没有给出所有的事情：由于IvorySQL处理磁盘 I/O 的方式，不在IvorySQL缓冲区中的数据库仍然驻留在内核的 I/O 缓存中，并且因此可以被再次读取而不需要物理磁盘读取。我们建议希望了解IvorySQL I/O 行为更多细节的用户将IvorySQL统计收集器和操作系统中允许观察内核处理 I/O 的工具一起使用。 `pg_stat_activity`视图每个服务器进程将有一行，显示与该进程当前活动相关的信息。 表3.pg_stat_activity 视图 列 类型 描述 datid oid 这个后端连接到的数据库的OID datname name 这个后端连接到的数据库的名称 pid integer 这个后端的进程 ID leader_pid integer 并行组组长的进程ID，如果该进程是并行查询工作者。如果该进程是一个并行组的组长或不参与并行查询，则为`NULL`。 usesysid oid 登录到这个后端的用户的 OID usename name 登录到这个后端的用户的 名称 application_name text 连接到这个后端的应用的名称 client_addr inet 连接到这个后端的客户端的 IP 地址。如果这个字段为空，它表示客户端通过服务器机器上的一个 Unix 套接字连接或者这是一个内部进程，如自动清理。 client_hostname text 已连接的客户端的主机名，由 client_addr 的反向 DNS 查找报告。 这个字段将只对 IP 连接非空，并且只有log_hostname被启用时才会非空。 client_port integer 客户端用于与此后端通信的TCP端口号，如果使用Unix套接字，则为`-1`。如果该字段为空，它表示这是一个内部服务器进程。 backend_start timestamp with time zone 这个进程被启动的时间。对客户端后端来说，这就是客户端连接到服务器的时间。 xact_start timestamp with time zone 这个进程的当前事务被启动的时间，如果没有活动事务则为空。 如果当前查询是它的第一个事务，这一列等于 query_start 列。 query_start timestamp with time zone 当前活动查询被开始的时间，如果 state 不是 active，则为上一个查询开始的时间 state_change timestamp with time zone state 上一次被改变的时间 wait_event_type text 后端等待的事件类型，如果有的话;否则NULL。 wait_event text 如果后端当前正在等待，则等待事件名称，否则为NULL。 state text 这个后端的当前总体状态。可能的值为：active: 后端正在执行一个查询。idle: 后端正在等待一个新的客户端命令。idle in transaction: 后端在一个事务中，但是当前没有正在执行一个查询。idle in transaction (aborted): 这个状态与 idle in transaction 相似，除了在该事务中的一个语句导致了一个错误。fastpath function call: 后端正在执行一个 fast-path 函数。disabled: 如果在这个后端中track_activities被禁用，则报告这个状态。 backend_xid xid 这个后端的顶层事务标识符，如果存在。 backend_xmin xid 当前后端的 xmin 范围。 query text 这个后端最近查询的文本。如果 state 为 active，这个字段显示当前正在执行的查询。 在所有其他状态下，它显示上一个被执行的查询。默认情况下，查询文本会被截断至1024个字节，这个值可以通过参数track_activity_query_size更改。 backend_type text 当前后端的类型。可能的类型为 autovacuum launcher, autovacuum worker, logical replication launcher, logical replication worker, parallel worker, background writer, client backend, checkpointer, startup, walreceiver, walsender and walwriter. 除此以外，由扩展注册的后台Worker可能有额外的类型。 wait_event`和`state`列是独立的。如果一个后端处于`active`状态，它可能是也可能不是某个事件上的`waiting。如果状态是`active`并且`wait_event`为非空，它意味着一个查询正在被执行，但是它被阻塞在系统中某处。 表4.等待事件类型 等待事件类型 描述 Activity 服务器进程空闲。此事件类型表示在其主处理循环中等待活动的进程。 `wait_event`将识别特定的等待点。 BufferPin 服务器进程正在等待对数据缓冲的独占访问。 如果另一个进程持有一个打开的游标，该游标最后一次从相关缓冲区读取数据，则缓冲区销等待可能是漫长的。 Client 服务器进程正在等待连接到用户应用程序的套接字上的活动。 因此，服务器预计发生一些独立于其内部进程的事情。`wait_event`将识别特定的等待点。 Extension 服务器进程正在等待扩展模块定义的某个条件。 IO 服务器进程正在等待一个I/O操作完成。`wait_event`将识别特定的等待点。 IPC 服务器进程正在等待与另一个服务器进程进行交互。`wait_event`将识别特定的等待点。 Lock 服务器进程正在等待一个重量级锁。重量级锁，也称为锁管理器锁或简单锁，主要保护表等SQL可见对象。 然而，它们也用于确保某些内部操作的互斥，例如关系扩展。`wait_event`将识别等待的锁的类型。 LWLock 服务器进程正在等待一个轻量级锁。大多数这样的锁保护共享内存中的特定数据结构。 `wait_event`将包含标识轻量级锁用途的名称。 (有些锁有特定的名称；其他锁是一组锁的一部分，每个锁具有类似的目的。) Timeout 服务器进程正在等待超时过期。`wait_event`将识别特定的等待点。 表5.`Activity`类型的等待事件 Activity 等待事件 描述 ArchiverMain 在归档进程的主循环中等待。 AutoVacuumMain 在自动清理启动过程的主循环中等待。 BgWriterHibernate 在后台写进程中等待，休眠状态。 BgWriterMain 在后台写进程主循环中等待。 CheckpointerMain 在校验指针进程的主循环中等待。 LogicalApplyMain 在逻辑复制应用进程的主循环中等待。 LogicalLauncherMain 在逻辑复制启动器进程的主循环中等待。 PgStatMain 在统计收集器进程的主循环中等待。 RecoveryWalStream 流恢复期间，在启动进程主循环等待WAL到达。 SysLoggerMain 在syslogger进程的主循环中等待。 WalReceiverMain 在WAL接收器进程的主循环中等待。 WalSenderMain 在WAL发送者进程的主循环中等待。 WalWriterMain 在WAL写入进程的主循环中等待。 表6.`BufferPin`类型的等待事件 BufferPin 等待事件 描述 BufferPin 等待获得缓冲区上的独占销。 表7.`Client`类型的等待事件 Client 等待事件 描述 ClientRead 等待从客户端读取数据。 ClientWrite 等待写入数据到客户端。 GSSOpenServer 在建立GSSAPI会话时等待从客户端读取数据。 LibPQWalReceiverConnect 在WAL接收器等待与远程服务器建立连接。 LibPQWalReceiverReceive 在WAL接收器中等待从远程服务器接收数据。 SSLOpenServer 在尝试连接时等待SSL。 WalReceiverWaitStart 等待启动进程发送用于流复制的初始数据。 WalSenderWaitForWAL 在WAL发送器进程中等待WAL被刷新。 WalSenderWriteData 在WAL发送器进程中处理WAL接收器的回复时，等待任何活动。 表8.`Extension`类型的等待事件 Extension 等待事件 描述 Extension 在扩展中等待。 表9.`IO`类型的等待事件 IO 等待事件 描述 BufFileRead 等待从缓冲文件中读取。 BufFileWrite 等待对缓冲文件的写入。 ControlFileRead 等待读取`pg_control`文件。 ControlFileSync 等待`pg_control`文件到达持久存储。 ControlFileSyncUpdate 等待更新`pg_control`文件以达到持久存储。 ControlFileWrite 等待写入`pg_control`文件。 ControlFileWriteUpdate 等待写入更新`pg_control`文件。 CopyFileRead 在文件复制操作期间等待读取。 CopyFileWrite 在文件拷贝操作期间等待写入。 DSMFillZeroWrite 等待用零填充动态共享内存备份(backing)文件。 DataFileExtend 等待关系数据文件被扩展。 DataFileFlush 等待关系数据文件达到持久存储。 DataFileImmediateSync 等待关系数据文件到持久存储的立即同步。 DataFilePrefetch 等待关系数据文件的异步预取。 DataFileRead 等待对关系数据文件的读取。 DataFileSync 等待对关系数据文件的更改达到持久存储。 DataFileTruncate 等待关系数据文件被截断。 DataFileWrite 等待对关系数据文件的写入。 LockFileAddToDataDirRead 在向数据目录锁文件中添加一行时等待读取。 LockFileAddToDataDirSync 等待数据到达持久存储，同时向数据目录锁文件添加一行。 LockFileAddToDataDirWrite 在向数据目录锁文件中添加一行时等待写操作。 LockFileCreateRead 创建数据目录锁文件时等待读取。 LockFileCreateSync 在创建数据目录锁文件时等待数据到达持久存储。 LockFileCreateWrite 在创建数据目录锁文件时等待写操作。 LockFileReCheckDataDirRead 在重新检查数据目录锁文件期间等待读取。 LogicalRewriteCheckpointSync 等待逻辑重写映射到在检查点到达持久存储。 LogicalRewriteMappingSync 在逻辑重写期间等待映射数据到达持久存储 LogicalRewriteMappingWrite 在逻辑重写期间等待映射数据的写入。 LogicalRewriteSync 等待逻辑重写映射到达持久存储。 LogicalRewriteTruncate 等待在逻辑重写期间截断映射数据。 LogicalRewriteWrite 等待逻辑重写映射的写入。 RelationMapRead 等待关系映射文件的读取。 RelationMapSync 等待关系映射文件到达持久存储。 RelationMapWrite 等待对关系映射文件的写入。 ReorderBufferRead 在重新排序缓冲区管理期间等待读取。 ReorderBufferWrite 在重新排序缓冲区管理期间等待写操作。 ReorderLogicalMappingRead 在重新排序缓冲区管理期间等待读取逻辑映射。 ReplicationSlotRead 等待从复制槽位控制文件读取。 ReplicationSlotRestoreSync 等待复制槽控制文件到达持久存储，同时将其恢复到内存中。 ReplicationSlotSync 等待复制槽控制文件到达持久存储。 ReplicationSlotWrite 等待对复制槽控制文件的写入。 SLRUFlushSync 在检查点或数据库关闭期间等待SLRU数据到达持久存储。 SLRURead 等待读取SLRU页面。 SLRUSync 在写页面后等待SLRU数据到达持久存储。 SLRUWrite 等待SLRU页面的写入。 SnapbuildRead 等待读取序列化的历史目录快照。 SnapbuildSync 等待序列化历史目录快照到达持久存储。 SnapbuildWrite 等待串行历史目录快照的写入。 TimelineHistoryFileSync 等待通过流复制接收的时间线历史文件到达持久存储。 TimelineHistoryFileWrite 等待通过流复制接收的时间线历史文件的写入。 TimelineHistoryRead 等待读取时间线历史文件。 TimelineHistorySync 等待新创建的时间线历史文件到达持久存储。 TimelineHistoryWrite 等待写入新创建的时间线历史文件。 TwophaseFileRead 等待读取两阶段状态文件。 TwophaseFileSync 等待两阶段状态文件到达持久存储。 TwophaseFileWrite 等待对两阶段状态文件的写入。 WALBootstrapSync 在引导过程中等待WAL达到持久存储。 WALBootstrapWrite 在引导过程中等待WAL页面的写入。 WALCopyRead 通过复制一个已有WAL段来创建一个新的WAL段时等待读取。 WALCopySync 等待通过复制一个已有WAL段到持久存储来创建一个新的WAL段。 WALCopyWrite 通过复制一个已有WAL段来创建一个新的WAL段时等待写入。 WALInitSync 等待一个新初始化的WAL文件到持久存储。 WALInitWrite 在初始化一个新的WAL文件时等待写入。 WALRead 等待WAL文件的读取。 WALSenderTimelineHistoryRead 在walsender时间线命令期间等待从时间线历史文件读取。 WALSync 等待WAL文件到达持久存储。 WALSyncMethodAssign 等待数据到达持久存储，同时分配一个新的WAL同步方法。 WALWrite 等待写入WAL文件。 表10.`IPC`类型的等待事件 IPC 等待事件 描述 BackupWaitWalArchive 等待备份所需的WAL文件成功存档。 BgWorkerShutdown 等待后台工作者关闭。 BgWorkerStartup 等待后台工作者启动。 BtreePage 正等待继续并行B-树扫描所需的页号变得可用。 CheckpointDone 等待检查点完成。 CheckpointStart 等待检查点开始。 ExecuteGather 在执行`Gather` 计划节点时，等待子进程的活动。 HashBatchAllocate 等待一个选定的并行哈希参与者分配哈希表。 HashBatchElect 等待选择一个并行哈希参与者来分配哈希表。 HashBatchLoad 等待其他并行哈希参与者完成哈希表的加载。 HashBuildAllocate 等待一个选定的并行哈希参与者分配初始哈希表。 HashBuildElect 等待选择一个并行哈希参与者来分配初始哈希表。 HashBuildHashInner 等待其他并行哈希参与者完成内部关系的散列。 HashBuildHashOuter 等待其他Parallel 哈希参与者完成对外部关系的分区。 HashGrowBatchesAllocate 等待选定的并行哈希参与者分配更多批处理。 HashGrowBatchesDecide 等待选择一个并行哈希参与者来决定未来的批处理增长。 HashGrowBatchesElect 等待选择一个Parallel 哈希参与者来分配更多批处理。 HashGrowBatchesFinish 等待一个选定的并行哈希参与者决定未来的批量增长。 HashGrowBatchesRepartition 等待一个选定的并行哈希参与者决定未来的批处理增长。 HashGrowBucketsAllocate 等待选定的并行哈希参与者完成更多bucket的分配。 HashGrowBucketsElect 等待选择一个并行哈希参与者来分配更多的buckets。 HashGrowBucketsReinsert 等待其他Parallel 哈希参与者完成将元组插入到新buckets中。 LogicalSyncData 等待逻辑复制远程服务器发送用于初始表同步的数据。 LogicalSyncStateChange 等待逻辑复制远程服务器更改状态。 MessageQueueInternal 等待另一个进程附加到共享消息队列。 MessageQueuePutMessage 等待将协议消息写入共享消息队列。 MessageQueueReceive 等待从共享消息队列接收字节。 MessageQueueSend 等待将字节发送到共享消息队列。 ParallelBitmapScan 等待并行位图扫描被初始化。 ParallelCreateIndexScan 等待并行`CREATE INDEX` 工作者完成堆扫描。 ParallelFinish 等待并行工作人员完成计算。 ProcArrayGroupUpdate 等待组领导在并行操作结束时清除事务ID。 ProcSignalBarrier 等待屏障事件被所有后端处理。 Promote 等待备用系统提升。 RecoveryConflictSnapshot 等待vacuum清理的恢复冲突解决。 RecoveryConflictTablespace 等待恢复冲突解决删除表空间。 RecoveryPause 等待恢复继续进行。 ReplicationOriginDrop 等待复制源变为非活动状态，以便可以删除它。 ReplicationSlotDrop 等待复制槽变为非活动状态，以便可以删除它。 SafeSnapshot 等待获取`READ ONLY DEFERRABLE`事务的有效快照。 SyncRep 在同步复制期间等待远程服务器的确认。 XactGroupUpdate 等待分组组长在并行操作结束时更新事务状态。 表11.`Lock`类型的等待事件 Lock 等待事件 描述 advisory 等待获得一个建议用户锁。 extend 等待扩展一个关系。 frozenid 等待升级 pg_database.datfrozenxid 和 pg_database.datminmxid. object 等待获取非关系数据库对象上的锁。 page 等待获取一个关系页面上的锁。 relation 等待获得一个关系的锁。 spectoken 等待获取推测的插入锁。 transactionid 等待事务完成。 tuple 等待获取元组上的锁。 userlock 等待获取用户锁。 virtualxid 等待获取虚拟事务ID锁。 表12.`LWLock`类型的等待事件 LWLock 等待事件 描述 AddinShmemInit 等待管理共享内存中的扩展空间分配。 AutoFile 等待更新`postgresql.auto.conf`文件。 Autovacuum 等待读取或更新自动清理工作者的当前状态。 AutovacuumSchedule 等待确保选择为自动清理的表仍然需要清理。 BackgroundWorker 等待读取或更新后台工作者状态。 BtreeVacuum 等待读取或更新b-树索引的清理相关信息。 BufferContent 等待访问内存中的数据页。 BufferIO 等待数据页上的I/O。 BufferMapping 等待将数据块与缓冲池中的缓冲区关联。 Checkpoint 等待开始一个检查点。 CheckpointerComm 等待管理fsync请求。 CommitTs 等待读取或更新事务提交时间戳的最后一个值集。 CommitTsBuffer 在提交时间戳SLRU缓冲区上等待I/O。 CommitTsSLRU 等待访问提交时间戳SLRU缓存。 ControlFile 等待读取或更新`pg_control`文件或创建一个新的WAL文件。 DynamicSharedMemoryControl 等待读取或更新动态共享内存分配信息。 LockFastPath 等待读取或更新进程的快速路径锁信息。 LockManager 等待读取或更新关于“heavyweight”锁。 LogicalRepWorker 等待读取或更新逻辑复制工作器的状态。 MultiXactGen 等待读取或更新共享的multixact状态。 MultiXactMemberBuffer 在multixact成员SLRU缓冲区上等待I/O。 MultiXactMemberSLRU 等待访问multixact成员SLRU缓存。 MultiXactOffsetBuffer 在multixact 偏移 SLRU缓冲区上等待I/O。 MultiXactOffsetSLRU 等待访问multixact 偏移 SLRU缓存。 MultiXactTruncation 等待读取或截断multixact信息。 NotifyBuffer 在`NOTIFY` 消息 SLRU缓冲区上等待I/O。 NotifyQueue 等待读取或更新`NOTIFY` 消息。 NotifyQueueTail 等待`NOTIFY`消息存储上的更新限制。 NotifySLRU 等待访问`NOTIFY`消息SLRU缓存。 OidGen 等待分配一个新的OID。 OldSnapshotTimeMap 等待读取或更新旧的快照控制信息。 ParallelAppend 在并行附加计划执行期间等待选择下一个子计划。 ParallelHashJoin 在并行哈希连接计划执行期间等待同步工作器。 ParallelQueryDSA 等待并行查询动态共享内存分配。 PerSessionDSA 等待并行查询动态共享内存分配。 PerSessionRecordType 等待访问有关复合类型的并行查询信息。 PerSessionRecordTypmod 等待访问有关标识匿名记录类型的类型修饰符的并行查询信息。 PerXactPredicateList 在并行查询期间等待访问当前可序列化事务持有的谓词锁列表。 PredicateLockManager 等待访问可序列化事务使用的谓词锁信息。 ProcArray 等待访问每个进程共享的数据结构(通常情况，是获取快照或报告会话的事务ID)。 RelationMapping 等待读取或更新`pg_filenode.map`文件(用于跟踪某些系统目录的文件节点分配)。 RelCacheInit 等待读取或更新`pg_internal.init`关系缓存初始化文件。 ReplicationOrigin 等待创建、删除或使用复制源。 ReplicationOriginState 等待读取或更新一个复制源的进度。 ReplicationSlotAllocation 等待分配或释放复制槽。 ReplicationSlotControl 等待读取或更新复制槽状态。 ReplicationSlotIO 在复制槽位上等待I/O。 SerialBuffer 在可串行事务冲突的SLRU缓冲区上等待I/O。 SerializableFinishedList 等待访问已完成的可序列化事务列表。 SerializablePredicateList 等待访问可序列化事务持有的谓词锁列表。 SerializableXactHash 等待读取或更新关于可序列化事务的信息。 SerialSLRU 等待访问可序列化事务冲突SLRU缓存。 SharedTidBitmap 在并行位图索引扫描期间等待访问共享的TID位图。 SharedTupleStore 在并行查询期间等待访问共享元组存储。 ShmemIndex 等待在共享内存中找到或分配空间。 SInvalRead 等待从共享目录失效队列中检索消息。 SInvalWrite 等待向共享编目失效队列添加消息。 SubtransBuffer 在子事务SLRU缓冲区上等待I/O。 SubtransSLRU 等待访问子事务SLRU缓存。 SyncRep 等待读取或更新有关同步复制状态的信息。 SyncScan 等待选择同步表扫描的起始位置。 TablespaceCreate 等待创建或删除表空间。 TwoPhaseState 等待读取或更新已准备事务的状态。 WALBufMapping 等待在WAL缓冲区中替换一个页面。 WALInsert 等待将WAL数据插入内存缓冲区。 WALWrite 等待WAL缓冲区写入磁盘。 WrapLimitsVacuum 等待更新事务id和multixact消费的限制。 XactBuffer 在事务状态的SLRU缓冲区上等待I/O。 XactSLRU 等待访问事务状态的SLRU缓存。 XactTruncation 等待执行`pg_xact_status`或更新它可用的最早的事务ID。 XidGen 等待分配新的事务ID。 表13.`Timeout`类型的等待事件 Timeout 等待事件 描述 BaseBackupThrottle 当有限流活动时在基础备份期间等待。 PgSleep 由于调用`pg_sleep`或同类函数而等待。 RecoveryApplyDelay 由于延迟设置，在恢复期间等待应用WAL。 RecoveryRetrieveRetryInterval 当WAL数据无法从任何来源(pg_wal，存档或流)获得时，在恢复期间等待。 VacuumDelay 在一个基于代价的清理延迟点。 下面的例子展示了如何查看等待事件： SELECT pid, wait_event_type, wait_event FROM pg_stat_activity WHERE wait_event is NOT NULL; pid | wait_event_type | wait_event ------+-----------------+------------ 2540 | Lock | relation 6644 | LWLock | ProcArray (2 rows) `pg_stat_replication`视图将在每个WAL发送方进程中包含一行，显示关于复制到发送方连接的后备服务器的统计信息。 只有直接连接的备用设备被列出;没有关于下游后备服务器的信息。 表14.pg_stat_replication 视图 列 类型 描述 pid integer 一个 WAL 发送进程的进程 ID usesysid oid 登录到这个 WAL 发送进程的用户的 OID usename name 登录到这个 WAL 发送进程的用户的名称 application_name text 连接到这个 WAL 发送进程的应用的名称 client_addr inet 连接到这个 WAL 发送进程的客户端的 IP 地址。 如果这个域为空，它表示该客户端通过服务器机器上的一个Unix 套接字连接。 client_hostname text 连接上的客户端的主机名，由一次对`client_addr`的逆向 DNS 查找报告。 这个域将只对 IP 连接非空，并且只有在 log_hostname被启用时非空。 client_port integer 客户端用来与这个 WAL 发送进程通讯的 TCP 端口号，如果使用 Unix 套接字则为`-1` backend_start timestamp with time zone 这个进程开始的时间，即客户端是何时连接到这个WAL 发送进程的。 backend_xmin xid 由hot_standby_feedback报告的这个后备机的`xmin`水平线。 state text 当前的 WAL 发送进程状态。 可能的值是：startup: 这个WAL发送器正在启动。catchup: 这个WAL发送者连接的后备服务器正在赶上主服务器。streaming: 在其连接的后备服务器赶上主服务器之后，这个WAL发送方正在流化变化。backup: 这个WAL发送器正在发送一个备份。stopping: 这个WAL发送器正在停止。 sent_lsn pg_lsn 在这个连接上发送的最后一个预写式日志的位置 write_lsn pg_lsn 被这个后备服务器写入到磁盘的最后一个预写式日志的位置 flush_lsn pg_lsn 被这个后备服务器刷入到磁盘的最后一个预写式日志的位置 replay_lsn pg_lsn 被重放到这个后备服务器上的数据库中的最后一个预写式日志的位置 write_lag interval 从本地刷新近期的WAL与接收到此后备服务器已写入WAL的通知(但尚未刷新或应用它)之间的时间经过。 如果将此服务器配置为同步后备服务器，则可以使用此参数来衡量在提交时`synchronous_commit`级别`remote_write`所导致的延迟。 flush_lag interval 在本地刷写近期的WAL与接收到后备服务器已经写入并且刷写它（但还没有应用）的通知之间流逝的时间。 如果这台服务器被配置为一个同步后备，这可以用来计量在提交时`synchronous_commit`的级别`on`所导致的延迟。 replay_lag interval 在本地刷写近期的WAL与接收到后备服务器已经写入它、刷写它并且应用它的通知之间流逝的时间。 如果这台服务器被配置为一个同步后备，这可以用来计量在提交时`synchronous_commit`的级别`remote_apply`所导致的延迟。 sync_priority integer 在基于优先的同步复制中，这台后备服务器被选为同步后备的优先级。在基于规定数量的同步复制中，这个值没有效果。 sync_state text 这一台后备服务器的同步状态。 可能的值是：async: 这台后备服务器是异步的。potential: 这台后备服务器现在是异步的，但可能在当前的同步后备失效时变成同步的。sync: 这台后备服务器是同步的。quorum: 这台后备服务器被当做规定数量后备服务器的候选。 reply_time 带时区的时间戳 从后备服务器收到的最后一条回复信息的发送时间 `pg_stat_replication`视图中报告的滞后时间近期的WAL被写入、刷写并且重放以及发送器知道这一切所花的时间的度量。如果远程服务器被配置为一台同步后备，这些时间表示由每一种同步提交级别所带来（或者是可能带来）的提交延迟。对于一台异步后备，`replay_lag`列是最近的事务变得对查询可见的延迟时间的近似值。如果后备服务器已经完全追上了发送服务器并且没有WAL活动，在短时间内将继续显示最近测到的滞后时间，再然后就会显示为NULL。 对于物理复制会自动测量滞后时间。逻辑解码插件可能会选择性地发出跟踪消息，如果它们没有这样做，跟踪机制将把滞后显示为NULL。 注意 报告的滞后时间并非按照当前的重放速率该后备还有多久才能追上发送服务器的预测。在新的WAL被生成期间，这样一种系统将显示类似的时间，但是当发送器变为闲置时会显示不同的值。特别是当后备服务器完全追上时，`pg_stat_replication`显示的是写入、刷写及重放最近报告的WAL位置所花的时间而不是一些用户可能预期的零。这种做法与为近期的写事务测量同步提交和事务可见性延迟的目的一致。为了降低用户预期一种不同的滞后模型带来的混淆，在一个完全重放完的闲置系统上，lag列会在一段比较短的时间后回复成NULL。监控系统应该选择将这种情况表示为缺失数据、零或者继续显示最近的已知值。 `pg_stat_wal_receiver`事务只包含一行，它显示了从 WAL 接收器所连接的服务器得到的有关该接收器的统计信息。 表15.pg_stat_wal_receiver 视图 列 类型 描述 pid integer WAL接收器进程的进程ID status text WAL接收进程的活动状态 receive_start_lsn pg_lsn WAL接收器启动时使用的第一个写前日志位置 receive_start_tli integer WAL接收器启动时使用的第一个时间线数字 written_lsn pg_lsn 已经接收并写入磁盘的最后一个预写式日志位置，但没有刷入。这不能用于数据完整性检查。 flushed_lsn pg_lsn 已经接收并刷入到磁盘的最后一个预写式日志位置，该字段的初始值是启动WAL接收器时使用的第一个日志位置 received_tli integer 接收并刷入到磁盘的最后一个预写式日志位置的时间线数字，该字段的初始值为启动WAL接收器时使用的第一个日志位置的时间线数字 last_msg_send_time timestamp with time zone 从源头WAL发送器收到的最后一条信息的发送时间 last_msg_receipt_time timestamp with time zone 从源头WAL发送器收到的最后一条信息的接收时间 latest_end_lsn pg_lsn 向源头WAL发送器报告的最后的预写式日志位置 latest_end_time timestamp with time zone 向源头WAL发送方报告的最后一次写前日志位置的时间 slot_name text 这个WAL接收器使用的复制槽的名称 sender_host text 这个WAL接收器连接到的IvorySQL实例的主机。这可以是主机名、IP地址，或者目录路径，如果连接是通过Unix套接字进行的。(路径的情况可以区分，因为它总是以/开头的绝对路径。) sender_port integer 这个WAL接收器连接的IvorySQL实例的端口号。 conninfo text 这个WAL接收器使用的连接字符串，对安全敏感的字段进行了模糊处理。 每一个订阅的主工作者都在`pg_stat_subscription`视图中有一行（如果工作者没有运行则PID为空），处理被订阅表的初始数据拷贝操作的工作者还会有额外的行。 表16.pg_stat_subscription 视图 列 类型 描述 subid oid 订阅的OID subname name 订阅的名称 pid integer 订阅工作者进程的进程ID relid oid 工作器正在同步的关系的OID;Null用于主应用工作器 received_lsn pg_lsn 接收到的最后一个预写式日志位置，该字段的初始值为0 last_msg_send_time timestamp with time zone 从WAL发送器收到的最后一条信息的发送时间 last_msg_receipt_time timestamp with time zone 从WAL发送器收到的最后一条信息的接收时间 latest_end_lsn pg_lsn 向WAL发送器报告的最后预写式日志位置 latest_end_time timestamp with time zone 向WAL发送器报告的最后一次预写式日志位置的时间 `pg_stat_ssl`视图将为每一个后端或者 WAL 发送进程包含一行，用来显示这个连接上的 SSL 使用情况。 可以把它与`pg_stat_activity`或者`pg_stat_replication`通过`pid`列连接来得到更多有关该连接的细节。 表17.pg_stat_ssl 视图 列 类型 描述 pid integer 后端或WAL发送器进程ID ssl boolean 如果在此连接上使用SSL，则为真 version text 使用SSL的版本，如果此连接上没有使用SSL则为NULL cipher text 正在使用的SSL密码的名称，如果此连接上没有使用SSL则为NULL bits integer 使用的加密算法中的位数，如果此连接上没有使用SSL则为NULL compression boolean 如果使用SSL压缩则为真，否则为假，如果此连接未使用SSL则为NULL client_dn text 区别名称(DN，Distinguished Name)字段与使用的客户端证书，如果没有提供客户端证书或在此连接上没有使用SSL，则为NULL。 如果DN字段长于NAMEDATALEN(标准构建中为64个字符)，则该字段将被截断。 client_serial numeric 客户端证书的序列号，如果没有提供客户端证书或在此连接上没有使用SSL，则为NULL。 证书序列号和证书颁发者的组合唯一标识一个证书(除非颁发者错误地重用序列号)。 issuer_dn text 客户端证书颁发者的区别名称(DN，Distinguished Name)，如果没有提供客户端证书或在此连接上没有使用SSL，则为NULL。该字段像client_dn一样被截断。 `pg_stat_gssapi`视图将包含每一个后端一个行，显示该连接上的GSSAPI使用情况。 它可以加入到`pg_stat_activity`或`pg_stat_replication`上的`pid`列，获取更多关于连接的详细信息。 表18.pg_stat_gssapi 视图 列 类型 描述 pid integer 后端进程ID gss_authenticated boolean 如果此连接使用了GSSAPI身份验证，则为True principal text 用于验证此连接的主体，如果未使用GSSAPI对此连接进行身份验证，则为NULL。 如果主体长度超过`NAMEDATALEN`(标准构建中为64个字符)，则该字段被截断。 encrypted boolean 如果在此连接上使用了GSSAPI加密，则为真 `pg_stat_archiver`视图总是有一行，其中包含关于集群的存档进程的数据。 表19.pg_stat_archiver 视图 列 类型 描述 archived_count bigint 已成功存档的WAL文件数 last_archived_wal text 最后一个成功存档的WAL文件的名称 last_archived_time timestamp with time zone 最后一次成功存档操作的时间 failed_count bigint 记录WAL文件归档失败次数 last_failed_wal text 最后一次失败的存档操作的WAL文件的名称 last_failed_time timestamp with time zone 上次存档操作失败的时间 stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 `pg_stat_bgwriter`视图始终只有一行，其中包含集群的全局数据。 表20.pg_stat_bgwriter 视图 列 类型 描述 checkpoints_timed bigint 已执行的预定检查点数 checkpoints_req bigint 请求已执行的检查点数 checkpoint_write_time double precision 检查点处理中将文件写入磁盘的部分所花费的总时间，以毫秒为单位 checkpoint_sync_time double precision 检查点处理中将文件同步到磁盘的部分所花费的总时间，以毫秒为单位 buffers_checkpoint bigint 检查点期间写入的缓冲区数 buffers_clean bigint 后台写入器写入的缓冲区数 maxwritten_clean bigint 后台写入器因为写入太多缓冲区而停止清理扫描的次数 buffers_backend bigint 后端直接写入的缓冲区数 buffers_backend_fsync bigint 后端必须执行自己的 fsync 调用的次数（通常后台写入器处理这些，即使后端执行自己的写入） buffers_alloc bigint 分配的缓冲区数 stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 `pg_stat_database`视图将包含一行用于集群中的每个数据库，加一行用于共享对象，显示数据库范围的统计信息。 表21. pg_stat_database 视图 列 类型 描述 datid oid 该数据库的 OID，属于共享关系的对象为 0 datname name 这个数据库的名称，或者共享对象为 NULL numbackends integer 当前连接到此数据库的后端数，对于共享对象则为 NULL。这是该视图中唯一返回反映当前状态的值的列；所有其他列返回自上次重置以来累积的值 xact_commit bigint 此数据库中已提交的事务数 xact_rollback bigint 该数据库中已回滚的事务数 blks_read bigint 在该数据库中读取的磁盘块数 blks_hit bigint 在缓存中发现磁盘块的次数，因此读取不是必需的（这只包括在 IvorySQL 缓存中，而不是在操作系统的文件系统缓存中） tup_returned bigint 这个数据库中查询返回的行数 tup_fetched bigint 这个数据库中查询获取的行数 tup_inserted bigint 查询在该数据库中插入的行数 tup_updated bigint 这个数据库中查询更新的行数 tup_deleted bigint 这个数据库中被查询删除的行数 conflicts bigint 由于与此数据库中的恢复冲突而取消的查询数（冲突只发生在后备服务器上） temp_files bigint 这个数据库中查询创建的临时文件的数量。所有临时文件都将被计数，而不顾及临时文件为什么被创建（例如，排序或散列），也不考虑 log_temp_files 设置 temp_bytes bigint 这个数据库中的查询写入临时文件的数据总量。所有临时文件都将被计数，而不考虑临时文件为什么被创建，也不考虑 log_temp_files 设置 deadlocks bigint 在此数据库中检测到的死锁数 checksum_failures bigint 在此数据库（或共享对象）中检测到的数据页校验码失败数，如果没有启用数据校验码则为 NULL checksum_last_failure timestamp with time zone 在此数据库（或共享对象）中检测到最后一个数据页校验码失败的时间，如果没有启用数据校验码则为 NULL blk_read_time double precision 在这个数据库中通过后端读取数据文件块所花费的时间，以毫秒为单位（如果启用了 track_io_timing，否则为零） blk_write_time double precision 在这个数据库中通过后端写数据文件块所花费的时间，以毫秒为单位（如果启用了 track_io_timing，否则为零） stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 `pg_stat_database_conflicts`视图为每一个数据库包含一行， 用来显示数据库范围内由于与后备服务器上的恢复过程冲突而被取消的查询的统计信息。 这个视图将只包含后备服务器上的信息，因为冲突会不发生在主服务器上。 表22.pg_stat_database_conflicts 视图 列 类型 描述 datid oid 数据库的OID datname name 数据库的名称 confl_tablespace bigint 这个数据库中由于删除表空间而取消的查询的数量 confl_lock bigint 此数据库中由于锁定超时而被取消的查询数 confl_snapshot bigint 此数据库中由于旧快照而取消的查询数 confl_bufferpin bigint 此数据库中由于固定缓冲区而被取消的查询数 confl_deadlock bigint 此数据库中由于死锁而被取消的查询数 `pg_stat_all_tables`视图将为当前数据库中的每一个表（包括 TOAST 表）包含一行，该行显示与对该表的访问相关的统计信息。 `pg_stat_user_tables`和`pg_stat_sys_tables`视图包含相同的信息，但是被过滤得分别只显示用户和系统表。 表23.pg_stat_all_tables 视图 列 类型 描述 relid oid 表的OID schemaname name 该表所在的模式的名称 relname name 这个表的名称 seq_scan bigint 在此表上启动的顺序扫描数 seq_tup_read bigint 连续扫描获取的实时行数 idx_scan bigint 对这个表发起的索引扫描数 idx_tup_fetch bigint 索引扫描获取的实时行数 n_tup_ins bigint 插入的行数 n_tup_upd bigint 更新的行数（包括HOT更新的行） n_tup_del bigint 删除的行数 n_tup_hot_upd bigint HOT更新的行数（即不需要单独的索引更新） n_live_tup bigint 活的行的估计数量 n_dead_tup bigint 僵死行的估计数量 n_mod_since_analyze bigint 自上次分析此表以来修改的行的估计数量 n_ins_since_vacuum bigint 自上次清空此表以来插入的行的估计数量 last_vacuum timestamp with time zone 最后一次手动清理这个表（不包括VACUUM FULL） last_autovacuum timestamp with time zone 这个表最后一次被自动清理守护进程清理的时间 last_analyze timestamp with time zone 上一次手动分析这个表 last_autoanalyze timestamp with time zone 自动清理守护进程最后一次分析这个表 vacuum_count bigint 这个表被手动清理的次数（VACUUM FULL不计数） autovacuum_count bigint 这个表被autovacuum守护进程清理的次数 analyze_count bigint 手动分析这个表的次数 autoanalyze_count bigint 这个表被autovacuum守护进程分析的次数 `pg_stat_all_indexes`视图将为当前数据库中的每个索引包含一行，该行显示关于对该索引访问的统计信息。`pg_stat_user_indexes`和`pg_stat_sys_indexes`视图包含相同的信息，但是被过滤得只分别显示用户和系统索引。 表24.pg_stat_all_indexes 视图 列 类型 描述 relid oid 这个索引所在表的OID indexrelid oid 这个索引的OID schemaname name 这个索引所在的模式名称 relname name 这个索引所在表的名称 indexrelname name 这个索引的名称 idx_scan bigint 在这个索引上开启的索引扫描的数量 idx_tup_read bigint 扫描此索引返回的索引项数 idx_tup_fetch bigint 使用此索引进行简单索引扫描获取的活动表行数 索引可以被简单索引扫描、“位图”索引扫描以及优化器使用。在一次位图扫描中，多个索引的输出可以被通过 AND 或 OR 规则组合，因此当使用一次位图扫描时难以将取得的个体堆行与特定的索引关联起来。因此，一次位图扫描会增加它使用的索引的`pg_stat_all_indexes`.idx_tup_read`计数，并且为每个表增加`pg_stat_all_tables.idx_tup_fetch`计数，但是它不影响`pg_stat_all_indexes.idx_tup_fetch。如果所提供的常量值不在优化器统计信息记录的范围之内，优化器也会访问索引来检查，因为优化器统计信息可能已经“不新鲜”了。 注意 即使不用位图扫描，`idx_tup_read`和`idx_tup_fetch`计数也可能不同，因为`idx_tup_read`统计从该索引取得的索引项而`idx_tup_fetch`统计从表取得的活着的行。如果使用该索引取得了任何死亡行或还未提交的行，或者如果通过一次只用索引扫描的方式避免了任何堆获取，后者将较小。 `pg_statio_all_tables`视图将为当前数据库中的每个表（包括 TOAST 表）包含一行，该行显示指定表上有关 I/O 的统计信息。`pg_statio_user_tables`和`pg_statio_sys_tables`视图包含相同的信息，但是被过滤得分别只显示用户表和系统表。 表25.pg_statio_all_tables 视图 列 类型 描述 relid oid 表的OID schemaname name 该表所在的模式名 relname name 这个表的名称 heap_blks_read bigint 从该表中读取的磁盘块的数量 heap_blks_hit bigint 该表中的缓冲区命中数 idx_blks_read bigint 从这个表上所有索引读取的磁盘块数 idx_blks_hit bigint 这个表上所有索引中的缓冲区命中数 toast_blks_read bigint 从这个表的TOAST表中读取的磁盘块的数量（如果有的话） toast_blks_hit bigint 这个表的TOAST表中的缓冲区命中数（如果有的话） tidx_blks_read bigint 从这个表的TOAST表索引中读取的磁盘块的数量（如果有的话） tidx_blks_hit bigint 这个表的TOAST表索引中的缓冲区命中数（如果有的话） `pg_statio_all_indexes`视图将为当前数据库中的每个索引包含一行，该行显示指定索引上有关 I/O 的统计信息。 `pg_statio_user_indexes`和`pg_statio_sys_indexes`视图包含相同的信息，但是被过滤得分别只显示用户索引和系统索引。 表26.pg_statio_all_indexes 视图 列 类型 描述 relid oid 这个索引所在表的OID indexrelid oid 这个索引的OID schemaname name 索引所在的模式名称 relname name 这个索引所在表的名称 indexrelname name 这个索引的名称 idx_blks_read bigint 从这个索引中读取的磁盘块数量 idx_blks_hit bigint 这个索引中的缓冲区命中数 `pg_statio_all_sequences`视图将为当前数据库中的每个序列包含一行，该行显示在指定序列上有关 I/O 的统计信息。 表27.pg_statio_all_sequences 视图 列 类型 描述 relid oid 序列的OID schemaname name 此序列所在的模式的名称 relname name 此序列的名称 blks_read bigint 从这个序列中读取的磁盘块的数量 blks_hit bigint 在此序列中的缓冲区命中数 `pg_stat_user_functions`视图将为每一个被追踪的函数包含一行，该行显示有关该函数执行的统计信息。 track_functions参数控制到底哪些函数被跟踪。 表28.pg_stat_user_functions 视图 列 类型 描述 funcid oid 函数的OID schemaname name 这个函数所在的模式的名称 funcname name 这个函数的名称 calls bigint 这个函数已经被调用的次数 total_time double precision 在这个函数以及它所调用的其他函数中花费的总时间，以毫秒计 self_time double precision 在这个函数本身花费的总时间，不包括被它调用的其他函数，以毫秒计 IvorySQL通过*SLRU*(simple least-recently-used，简单的最近-最少-使用)缓存访问某些磁盘上的信息。 `pg_stat_slru`视图将为每个被跟踪的SLRU缓存包含一行，显示关于访问缓存页面的统计信息。 表29.pg_stat_slru 视图 列 类型 描述 name text SLRU的名称 blks_zeroed bigint 初始化期间被置零的块数 blks_hit bigint 已经在SLRU中的磁盘块被发现的次数，因此不需要读取（这只包括SLRU中的命中，而不是操作系统的文件系统缓存） blks_read bigint 为这个SLRU读取的磁盘块数 blks_written bigint 为这个SLRU写入的磁盘块数 blks_exists bigint 为这个SLRU检查是否存在的块数 flushes bigint 此SLRU的脏数据刷新数 truncates bigint 这个SLRU的截断数 stats_reset timestamp with time zone 这些统计数据最后一次重置的时间 其他查看统计信息的方法是直接使用查询，这些查询使用上述标准视图用到的底层统计信息访问函数。 如要了解如函数名等细节，可参考标准视图的定义（例如，在psql中你可以发出`\\d+ pg_stat_activity`）。 针对每一个数据库统计信息的访问函数把一个数据库 OID 作为参数来标识要报告哪个数据库。而针对每个表和每个索引的函数要求表或索引 OID。 针对每个函数统计信息的函数用一个函数 OID。注意只有在当前数据库中的表、索引和函数才能被这些函数看到。 更多统计集合的函数列在 表 30 中。 表30.Additional Statistics Functions 函数 描述 pg_backend_pid () → integer 返回附加到当前会话的服务器进程的进程ID。 pg_stat_get_activity ( integer ) → setof record 使用指定的进程ID返回有关后端信息的记录，如果指定了NULL，则返回系统中每个活动后端的一条记录。返回的字段是pg_stat_activity视图中字段的子集。 pg_stat_get_snapshot_timestamp () → timestamp with time zone 返回当前统计快照的时间戳。 pg_stat_clear_snapshot () → void 丢弃当前的统计快照。 pg_stat_reset () → void 将当前数据库的所有统计计数器重置为零。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_shared ( text ) → void 根据参数的不同，将一些集群范围的统计计数器重置为零。参数可以是bgwriter来重置pg_stat_bgwriter视图中显示的所有计数器，或者archiver来重置pg_stat_archiver视图中显示的所有计数器。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_single_table_counters ( oid ) → void 将当前数据库中单个表或索引的统计信息重置为零。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_single_function_counters ( oid ) → void 将当前数据库中单个函数的统计信息重置为零。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 pg_stat_reset_slru ( text ) → void 将单个SLRU缓存或集群中所有SLRU的统计信息重置为零。如果该参数为NULL，则所有SLRU缓存的pg_stat_slru视图中显示的计数器将被重置。参数可以是CommitTs、MultiXactMember、MultiXactOffset、Notify、Serial、Subtrans或Xact中的一个，以便只重置该条目的计数器。如果参数是other（或实际上，任何无法识别的名称），那么所有其他SLRU缓存的计数器，如扩展定义的缓存，将被重置。默认情况下该函数仅限于超级用户，但是其他用户可以被授予EXECUTE来运行此函数。 `pg_stat_get_activity`是`pg_stat_activity`视图的底层函数， 它返回一个行集合，其中包含有关每个后端进程所有可用的信息。有时只获得该信息的一个子集可能会更方便。 在那些情况中，可以使用一组更老的针对每个后端的统计访问函数，这些显示在 表 31中。 这些访问函数使用一个后端 ID 号，范围从 1 到当前活动后端数目。 函数`pg_stat_get_backend_idset`提供了一种方便的方法为每个活动后端产生一行来调用这些函数。 例如，要显示PID以及所有后端当前的查询： SELECT pg_stat_get_backend_pid(s.backendid) AS pid, pg_stat_get_backend_activity(s.backendid) AS query FROM (SELECT pg_stat_get_backend_idset() AS backendid) AS s; 表31.Per-Backend Statistics Functions 函数 描述 pg_stat_get_backend_idset () → setof integer 返回当前活动后端ID号的集合(从1到活动后端数)。 pg_stat_get_backend_activity ( integer ) → text 返回此后端最近查询的文本。 pg_stat_get_backend_activity_start ( integer ) → timestamp with time zone 返回后端最近一次查询开始的时间。 pg_stat_get_backend_client_addr ( integer ) → inet 返回连接到此后端的客户端的IP地址。 pg_stat_get_backend_client_port ( integer ) → integer 返回客户端用于通信的TCP端口号。 pg_stat_get_backend_dbid ( integer ) → oid 返回此后端连接的数据库的OID。 pg_stat_get_backend_pid ( integer ) → integer 返回此后端进程ID。 pg_stat_get_backend_start ( integer ) → timestamp with time zone 返回该进程开始的时间。 pg_stat_get_backend_userid ( integer ) → oid 返回登录到此后端的用户的OID。 pg_stat_get_backend_wait_event_type ( integer ) → text 如果后端当前正在等待，返回等待事件类型名称，否则返回NULL。 pg_stat_get_backend_wait_event ( integer ) → text 如果后端当前正在等待，则返回等待事件名称，否则为NULL。 pg_stat_get_backend_xact_start ( integer ) → timestamp with time zone 返回后端当前事务开始的时间。 监控数据库活动的另外一个有用的工具是`pg_locks`系统表。这样就允许数据库管理员查看在锁管理器里面未解决的锁的信息。例如，这个功能可以被用于： 查看当前所有未解决的锁、在一个特定数据库中的关系上所有的锁、在一个特定关系上所有的锁，或者由一个特定IvorySQL会话持有的所有的锁。 判断当前数据库中带有最多未授予锁的关系（它很可能是数据库客户端的竞争源）。 判断锁竞争给数据库总体性能带来的影响，以及锁竞争随着整个数据库流量的变化范围。 IvorySQL具有在命令执行过程中报告某些命令进度的能力。 目前，支持进度报告的命令只有`ANALYZE`,CLUSTER,CREATE INDEX, VACUUM, 和 BASE_BACKUP例如 pg_basebackup发出的进行基础备份的复制命令。未来可能还会扩展。 每当`ANALYZE`运行时，`pg_stat_progress_analyze`视图将包含当前运行该命令的每个后端的一行。 下面的表描述了将要报告的信息，并提供了关于如何解释它们的信息。 表32.pg_stat_progress_analyze 视图 列 类型 描述 pid integer 后端的进程ID。 datid oid 后端连接到的数据库的OID。 datname name 后端连接到的数据库的名称。 relid oid 被分析的表的OID。 phase text 当前处理阶段。参见 表 33。 sample_blks_total bigint 将被采样的堆块的总数。 sample_blks_scanned bigint 扫描的堆块数量。 ext_stats_total bigint 扩展统计信息的数量。 ext_stats_computed bigint 已经计算的扩展统计的数量。此计数器仅在 computing extended statistics 阶段增进。 child_tables_total bigint 子表的数量。 child_tables_done bigint 扫描的子表数。此计数器只有在 acquiring inherited sample rows 阶段才会增进。 current_child_table_relid oid 当前正在扫描的子表的 OID。此字段仅在 acquiring inherited sample rows 时有效。 表33.ANALYZE phases 阶段 描述 initializing 命令正在准备开始扫描堆。这个阶段预计会非常短暂。 acquiring sample rows 该命令当前正在扫描`relid`给出的表以获得示例行。 acquiring inherited sample rows 该命令当前正在扫描子表以获得示例行。列`child_tables_total`,child_tables_done, 和`current_child_table_relid`包含此阶段的进度信息。 computing statistics 该命令从表扫描期间获得的样例行计算统计信息。 computing extended statistics 该命令从表扫描期间获得的样例行计算扩展统计信息。 finalizing analyze 该命令在更新`pg_class`。当此阶段完成时，ANALYZE 将结束。 注意 当在分区表上运行`ANALYZE`时，它的所有分区也会被递归分析，如在ANALYZE中曾提到过。 在这种情况下，首先报告父表的`ANALYZE`进度，收集它的继承统计信息，然后是每个分区的(继承统计信息)。 每当运行`CREATE INDEX`或`REINDEX`时，`pg_stat_progress_create_index`视图将包含当前正在创建索引的每个后端的一行。 下面的表描述了将要报告的信息，并提供了关于如何解释它的信息。 表34.pg_stat_progress_create_index 视图 列 类型 描述 pid integer 后端的进程ID。 datid oid 后端连接到的数据库的OID。 datname name 后端连接到的数据库的名称。 relid oid 正在创建索引的表的OID。 index_relid oid 正在创建或重建索引的OID。在非并发 CREATE INDEX 的时候，此为 0。 command text 在运行的命令：CREATE INDEX、CREATE INDEX CONCURRENTLY、REINDEX 或 REINDEX CONCURRENTLY。 phase text 索引创建的当前处理阶段。参见 [表 35](http://www.postgresql.org/docs/17/progress-reporting.html#CREATE-INDEX-PHASES)。 lockers_total bigint 在适用的情况下，需要等待的储物柜总数。 lockers_done bigint 已经等待的储物柜数量。 current_locker_pid bigint 目前正在等待的储物柜的进程ID。 blocks_total bigint 本阶段要处理的区块总数。 blocks_done bigint 当前阶段已经处理的区块数量。 tuples_total bigint 当前阶段要处理的元组总数。 tuples_done bigint 在当前阶段已经处理的元组数量。 partitions_total bigint 在分区表上创建索引时，该列被设置为要在其上创建索引的分区总数。 partitions_done bigint 当在分区表上创建索引时，该列被设置为在其上完成索引的分区数。 表35.CREATE INDEX 的阶段 阶段 描述 初始化 `CREATE INDEX`或`REINDEX`正在准备创建索引。 这个阶段预计会非常短暂。 构建前等待读写器 CREATE INDEX CONCURRENTLY`或`REINDEX CONCURRENTLY`正在等待有可能看到表的写锁的事务完成。 当不在并发模式时，这个阶段会被跳过。`lockers_total、 lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 新建索引 索引是由访问方法专用代码建立的。 在这一阶段，支持进度报告的访问方法填写自己的进度数据，子阶段在这一栏中表示。 通常情况下，blocks_total`和`blocks_done`将包含进度数据，也可能包含`tuples_total`和`tuples_done。 在验证前等待读写器 CREATE INDEX CONCURRENTLY`或`REINDEX CONCURRENTLY`正在等待有可能写入表的事务完成写锁的事务。当不在并发模式时，这个阶段会被跳过。`lockers_total、 lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 索引验证：扫描索引 CREATE INDEX CONCURRENTLY`正在扫描索引，搜索需要验证的图元组。如果不是在并发模式下，这个阶段会被跳过。列 `blocks_total（设置为索引的总大小）和 `blocks_done`包含了这个阶段的进度信息。 指数验证：排序元组 `CREATE INDEX CONCURRENTLY`正在对索引扫描阶段的输出进行排序。 索引验证：扫描表 `CREATE INDEX CONCURRENTLY`正在扫描表，以验证前两个阶段收集的索引图元。当不在并发模式时，这个阶段被跳过。`blocks_total`列（设置为表的总大小）和`blocks_done`列包含这个阶段的进度信息。 等待旧照 CREATE INDEX CONCURRENTLY`或`REINDEX CONCURRENTLY`正在等待可能看到表的事务释放快照。 当不处于并发模式时，这个阶段会被跳过。 `lockers_total、lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 标记 dead之前等待readers REINDEX CONCURRENTLY`等待表上有读锁的事务完成后，再将旧索引标记为死索引。当不在并发模式时，这个阶段被跳过。`lockers_total、lockers_done 和 current_locker_pid 列包含了这个阶段的进度信息。 在 dropping之前等待readers REINDEX CONCURRENTLY`等待表上有读锁的事务完成后，再丢弃旧索引。当不在并发模式时，这个阶段被跳过。列 `lockers_total、lockers_done 和 `current_locker_pid`包含了这个阶段的进度信息。 只要`VACUUM`正在运行，每一个当前正在清理的后端（包括autovacuum工作者进程）在`pg_stat_progress_vacuum`视图中都会有一行。下面的表描述了将被报告的信息并且提供了如何解释它们的信息。`VACUUM FULL`命令的进度是通过`pg_stat_progress_cluster`报告的，因为`VACUUM FULL`和`CLUSTER`都是重写表，而普通的`VACUUM`只是原地修改表。 表36.pg_stat_progress_vacuum 视图 列 类型 描述 pid integer 后端的进程ID。 datid oid 这个后端连接的数据库的OID。 datname name 这个后端连接的数据库的名称。 relid oid 被vacuum的表的OID。 phase text vacuum的当前处理阶段。 heap_blks_total bigint 该表中堆块的总数。这个数字在扫描开始时报告，之后增加的块将不会（并且不需要）被这个VACUUM访问。 heap_blks_scanned bigint 被扫描的堆块数量。由于visibility map被用来优化扫描，一些块将被跳过而不做检查，被跳过的块会被包括在这个总数中，因此当清理完成时这个数字最终将会等于heap_blks_total。仅当处于扫描堆阶段时这个计数器才会前进。 heap_blks_vacuumed bigint 被清理的堆块数量。除非表没有索引，这个计数器仅在处于清理堆阶段时才会前进。不包含死亡元组的块会被跳过，因此这个计数器可能有时会向前跳跃一个比较大的增量。 index_vacuum_count bigint 已完成的索引清理周期数。 max_dead_tuples bigint 在需要执行一个索引清理周期之前我们可以存储的死亡元组数，取决于maintenance_work_mem。 num_dead_tuples bigint 从上一个索引清理周期以来收集的死亡元组数。 表37.VACUUM的阶段 阶段 描述 初始化 `VACUUM`正在准备开始扫描堆。这个阶段应该很简短。 扫描堆 `VACUUM`正在扫描堆。如果需要，它将会对每个页面进行修建以及碎片整理，并且可能会执行冻结动作。`heap_blks_scanned`列可以用来监控扫描的进度。 清理索引 `VACUUM`当前正在清理索引。如果一个表拥有索引，那么每次清理时这个阶段会在堆扫描完成后至少发生一次。如果maintenance_work_mem不足以存放找到的死亡元组，则每次清理时会多次清理索引。 清理堆 VACUUM`当前正在清理堆。清理堆与扫描堆不是同一个概念，清理堆发生在每一次清理索引的实例之后。如果`heap_blks_scanned`小于`heap_blks_total，系统将在这个阶段完成之后回去扫描堆；否则，系统将在这个阶段完成后开始清理索引。 清除索引 `VACUUM`当前正在清除索引。这个阶段发生在堆被完全扫描并且对堆和索引的所有清理都已经完成以后。 截断堆 `VACUUM`正在截断堆，以便把关系尾部的空页面返还给操作系统。这个阶段发生在清除完索引之后。 执行最后的清除 `VACUUM`在执行最终的清除。在这个阶段中，`VACUUM`将清理空闲空间映射、更新`pg_class`中的统计信息并且将统计信息报告给统计收集器。当这个阶段完成时，`VACUUM`也就结束了。 每当`CLUSTER`或`VACUUM FULL`运行时，`pg_stat_progress_cluster`视图将包含当前正在运行的每一个后台的记录。下面的表格描述了将被报告的信息，并提供了关于如何解释这些信息的信息。 表38.pg_stat_progress_cluster 视图 列 类型 描述 pid integer 后台的进程ID。 datid oid 该后端连接的数据库的OID。 datname name 与此后端连接的数据库的名称。 relid oid 被集群的表的OID。 command text 正在运行的命令。CLUSTER`或`VACUUM FULL。 phase text 当前处理阶段。 cluster_index_relid oid 如果正在使用索引对表进行扫描，这就是正在使用的索引的OID；否则为0。 heap_tuples_scanned bigint 扫描的堆元组数。这个计数器只有在阶段为`seq scanning heap`、`index scanning heap`或`writing new heap`时才会增进。 heap_tuples_written bigint 写入的堆元组的数量。这个计数器只有在阶段为`seq scanning heap`、`index scanning heap`或`writing new heap`时才会前进。 heap_blks_total bigint 表中的堆块总数。这个数字是在`seq scanning heap`的开始时报告的。 heap_blks_scanned bigint 扫描的堆块数量。这个计数器只有在阶段为`seq scanning heap`时才会增进。 index_rebuild_count bigint 重建的索引数。该计数器仅在`重建索引`阶段时才会增进。 表39.CLUSTER 和 VACUUM FULL 阶段 阶段 描述 初始化 该命令准备开始扫描堆栈。 这个阶段预计会非常短暂。 seq扫描堆 该命令目前采用顺序扫描的方式对表进行扫描。 索引扫描堆 `CLUSTER`目前正在使用索引扫描表。 元组排序 `CLUSTER`目前正在对元组进行排序。 新写入堆 `CLUSTER`目前正在编写新的堆。 交换关系文件 目前，该命令正在将新建立的文件调换到位。 重建索引 该命令目前正在重建一个索引。 清理 该命令正在执行最后的清理工作。 当此阶段完成后，`CLUSTER`或`VACUUM FULL`将结束。 每当像pg_basebackup这样的应用程序进行基本备份时， `pg_stat_progress_basebackup`视图将包含当前运行`BASE_BACKUP`复制命令和流备份的每个WAL发送进程的一行。 下面的表描述了将要报告的信息，并提供了关于如何解释它的信息。 表40.pg_stat_progress_basebackup 视图 列 类型 描述 pid integer WAL发送方进程ID。 phase text 目前的处理阶段。 backup_total bigint 将被流输送的数据总量。这是在streaming database files阶段开始时的估计和报告。注意，这只是一个近似值，因为在streaming database files阶段，数据库可能会改变，而WAL日志可能会在稍后的备份中包含。一旦流数据量超过了估计的总大小，该值始终与backup_streamed相同。如果在pg_basebackup中禁用估算（也就是说，指定了—​no-estimate-size选项），这为NULL。 backup_streamed bigint 数据流的总量。这个计数器只在streaming database files阶段或transferring wal files时增进。 tablespaces_total bigint 要流输送的表空间总数。 tablespaces_streamed bigint 流输送的表空间数。此计数器仅在streaming database files阶段增进。 表41.基础备份阶段 阶段 描述 initializing WAL发送器进程正在准备开始备份。这个阶段预计会非常短暂。 waiting for checkpoint to finish WAL发送器进程目前正在执行`pg_start_backup`以准备进行基础备份，并等待启动备份检查点完成。 estimating backup size WAL发送程序目前正在估计将作为基础备份流传输的数据库文件的总量。 streaming database files WAL发送器当前正在流数据库文件作为基础备份。 waiting for wal archiving to finish WAL发送方进程目前正在执行`pg_stop_backup`以完成备份，并等待基础备份所需的所有WAL文件成功存档。 如果在pg_basebackup中指定了`--wal-method=none`或`--wal-method=stream`，则备份将在此阶段完成后结束。 transferring wal files WAL发送器进程正在传输备份过程中产生的所有WAL日志。 如果pg_basebackup中指定了`--wal-method=fetch`， 则该阶段发生在`waiting for wal archiving to finish`阶段之后。当此阶段完成时备份将结束。 IvorySQL提供了功能来支持数据库服务器的动态追踪。这样就允许在代码中的特定点上调用外部工具来追踪执行过程。 一些探针或追踪点已经被插入在源代码中。这些探针的目的是被数据库开发者和管理员使用。默认情况下，探针不被编译到IvorySQL中；用户需要显式地告诉配置脚本使得探针可用。 目前，DTrace已被支持，它在 Solaris、macOS、FreeBSD、NetBSD 和 Oracle Linux 上可用。 Linux 的SystemTap项目提供了一种可用的 DTrace 等价物。支持其他动态追踪工具在理论上可以通过改变`src/include/utils/probes.h`中的宏定义实现。 默认情况下，探针是不可用的，因此你将需要显式地告诉配置脚本让探针在IvorySQL中可用。要包括 DTrace 支持，在配置时指定`--enable-dtrace`。 如表 42所示，源代码中提供了一些标准探针。表 43显示了在探针中使用的类型。当然，可以增加更多探针来增强IvorySQL的可观测性。 表42.内建 DTrace 探针 名称 参数 描述 transaction-start (LocalTransactionId) 在一个新事务开始时触发的探针。arg0 是事务 ID。 transaction-commit (LocalTransactionId) 在一个事务成功完成时触发的探针。arg0 是事务 ID。 transaction-abort (LocalTransactionId) 当一个事务失败完成时触发的探针。arg0 是事务 ID。 query-start (const char *) 当一个查询的处理被开始时触发的探针。arg0 是查询字符串。 query-done (const char *) 当一个查询的处理完成时触发的探针。arg0 是查询字符串。 query-parse-start (const char *) 当一个查询的解析被开始时触发的探针。arg0 是查询字符串。 query-parse-done (const char *) 当一个查询的解析完成时触发的探针。arg0 是查询字符串。 query-rewrite-start (const char *) 当一个查询的重写被开始时触发的探针。arg0 是查询字符串。 query-rewrite-done (const char *) 当一个查询的重写完成时触发的探针。arg0 是查询字符串。 query-plan-start () 当一个查询的规划被开始时触发的探针。 query-plan-done () 当一个查询的规划完成时触发的探针。 query-execute-start () 当一个查询的执行被开始时触发的探针。 query-execute-done () 当一个查询的执行完成时触发的探针。 statement-status (const char *) 任何时候当服务器进程更新它的`pg_stat_activity`.`status`时触发的探针。arg0 是新的状态字符串。 checkpoint-start (int) 当一个检查点被开始时触发的探针。arg0 传递位标志来区分不同的检查点类型，例如关闭（shutdown）、立即（immediate）或强制（force）。 checkpoint-done (int, int, int, int, int) 当一个检查点完成时触发的探针（检查点处理过程中序列中列出的下一个触发的探针）。arg0 是要写的缓冲区数量。arg1 是缓冲区的总数。arg2、arg3 和 arg4 分别包含了增加、删除和循环回收的 WAL 文件的数量。 clog-checkpoint-start (bool) 当一个检查点的 CLOG 部分被开始时触发的探针。arg0 为真表示正常检查点，为假表示关闭检查点。 clog-checkpoint-done (bool) 当一个检查点的 CLOG 部分完成时触发的探针。arg0 的含义与`clog-checkpoint-start`中相同。 subtrans-checkpoint-start (bool) 当一个检查点的 SUBTRANS 部分被开始时触发的探针。arg0 为真表示正常检查点，为假表示关闭检查点。 subtrans-checkpoint-done (bool) 当一个检查点的 SUBTRANS 部分完成时触发的探针。arg0 的含义与`subtrans-checkpoint-start`中相同。 multixact-checkpoint-start (bool) 当一个检查点的 MultiXact 部分被开始时触发的探针。arg0 为真表示正常检查点，为假表示关闭检查点。 multixact-checkpoint-done (bool) 当一个检查点的 MultiXact 部分完成时触发的探针。arg0 的含义与`multixact-checkpoint-start`中相同。 buffer-checkpoint-start (int) 当一个检查点的写缓冲区部分被开始时触发的探针。arg0 传递位标志来区分不同的检查点类型，例如关闭（shutdown）、立即（immediate）或强制（force）。 buffer-sync-start (int, int) 当我们在检查点期间开始写脏缓冲区时（在标识哪些缓冲区必须被写之后）触发的探针。arg0 是缓冲区总数，arg1 是当前为脏并且需要被写的缓冲区数量。 buffer-sync-written (int) 在检查点期间当每个缓冲区被写完之后触发的探针。arg0 是缓冲区的 ID。 buffer-sync-done (int, int, int) 当所有脏缓冲区被写之后触发的探针。arg0 是缓冲区总数。arg1 是检查点进程实际写的缓冲区数量。arg2 是期望写的数目（`buffer-sync-start`的 arg1）；arg1 和 arg2 的任何的不同反映在该检查点期间有其他进程刷写了缓冲区。 buffer-checkpoint-sync-start () 在脏缓冲区被写入到内核之后并且在开始发出 fsync 请求之前触发的探针。 buffer-checkpoint-done () 当同步缓冲区到磁盘完成时触发的探针。 twophase-checkpoint-start () 当一个检查点的两阶段部分被开始时触发的探针。 twophase-checkpoint-done () 当一个检查点的两阶段部分完成时触发的探针。 buffer-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool) 当一次缓冲区读被开始时触发的探针。arg0 和 arg1 包含该页的分叉号和块号（如果这是一次关系扩展请求，arg1 为 -1）。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是 InvalidBackendId（-1）。arg6 为真表示一次关系扩展请求，为假表示正常读。 buffer-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool, bool) 当一次缓冲区读完成时触发的探针。arg0 和 arg1 包含该页的分叉号和块号（如果这是一次关系扩展请求，arg1 现在包含新增加块的块号）。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是 InvalidBackendId（-1）。arg6 为真表示一次关系扩展请求，为假表示正常读。arg7 为真表示在池中找到该缓冲区，为假表示没有找到。 buffer-flush-start (ForkNumber, BlockNumber, Oid, Oid, Oid) 在发出对一个共享缓冲区的任意写请求之前触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。 buffer-flush-done (ForkNumber, BlockNumber, Oid, Oid, Oid) 当一个写请求完成时触发的探针（注意这只反映传递数据给内核的时间，它通常并没有实际地被写入到磁盘）。参数和`buffer-flush-start`的相同。 buffer-write-dirty-start (ForkNumber, BlockNumber, Oid, Oid, Oid) 当一个服务器进程开始写一个脏缓冲区时触发的探针（如果这经常发生，表示shared_buffers太小，或需要调整后台写入器的控制参数）。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。 buffer-write-dirty-done (ForkNumber, BlockNumber, Oid, Oid, Oid) 当一次脏缓冲区写完成时触发的探针。参数与`buffer-write-dirty-start`相同。 wal-buffer-write-dirty-start () 当一个服务器进程因为没有可用 WAL 缓冲区空间开始写一个脏 WAL 缓冲区时触发的探针（如果这经常发生，表示wal_buffers太小）。 wal-buffer-write-dirty-done () 当一次脏 WAL 缓冲区完成时触发的探针。 wal-insert (unsigned char, unsigned char) 当一个 WAL 记录被插入时触发的探针。arg0 是该记录的资源管理者（rmid）。arg1 包含 info 标志。 wal-switch () 当请求一次 WAL 段切换时触发的探针。 smgr-md-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) 当开始从一个关系读取一块时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。 smgr-md-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) 当一次块读取完成时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。arg6 是实际读取的字节数，而 arg7 是请求读取的字节数（如果两者不同就意味着麻烦）。 smgr-md-write-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) 当开始向一个关系中写入一个块时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3 和 arg4 包含表空间、数据库和关系 OID 用以识别该关系。对一个本地缓冲区，arg5 是创建临时关系的后端的 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。 smgr-md-write-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) 当一个块写操作完成时触发的探针。arg0 和 arg1 包含该页的分叉号和块号。arg2、arg3和arg4 包含表空间、数据库和关系 OID来标识该关系。对于一个本地缓冲区，arg5 是创建临时关系的后端 ID；对于一个共享缓冲区，arg5 是`InvalidBackendId`（-1）。arg6 是实际写的字节数，而 arg7 是要求写的字节数（如果这两者不同，则意味着麻烦）。 sort-start (int, bool, int, int, bool, int) 当一次排序操作开始时触发的探针。arg0 指示是堆排序、索引排序或数据排序。arg1 为真表示唯一值强制。arg2 是键列的数目。arg3 是允许使用的工作内存数（以千字节计）。如果要求随机访问排序结果，那么 arg4 为真。arg5为`0`时表示串行，为`1`时表示并行工作者，为`2`时表示并行领袖。 sort-done (bool, long) 当一次排序完成时触发的探针。arg0 为真表示外排序，为假表示内排序。arg1 是用于一次外排序的磁盘块的数目，或用于一次内排序的以千字节计的内存。 lwlock-acquire (char *, LWLockMode) 当成功获得一个 LWLock 时触发的探针。 arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lwlock-release (char *) 当一个 LWLock 被释放时（但是注意还没有唤醒任何一个被释放的等待者）触发的探针。 arg0 是该 LWLock 所在的切片（Tranche）。 lwlock-wait-start (char *, LWLockMode) 当一个 LWLock不是当即可用并且一个服务器进程因此开始等待该锁变为可用时触发的探针。 arg0 是该 LWLock 所在的切片（Tranche）。 arg1 请求的锁模式，是排他或共享。 lwlock-wait-done (char *, LWLockMode) 当一个进程从对一个 LWLock 的等待中被释放时（它实际还没有得到该锁）时触发的探针。arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lwlock-condacquire (char *, LWLockMode) 当调用者指定无需等待而成功获得一个 LWLock 时触发的探针。arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lwlock-condacquire-fail (char *, LWLockMode) 当调用者指定无需等待而没有成功获得一个 LWLock 时触发的探针。arg0 是该 LWLock 所在的切片（Tranche）。 arg1 所请求的锁模式，是排他或共享。 lock-wait-start (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) 当一个重量级锁（lmgr锁）的请求由于锁不可用开始等待时触发的探针。arg0 到 arg3 是标识被锁定对象的标签域。arg4 指示被锁对象的类型。arg5 表示被请求的锁类型。 lock-wait-done (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) 当一个重量级锁（lmgr 锁）的请求结束等待时（即已经得到锁）触发的探针。参数与`lock-wait-start`一样。 deadlock-found () 当死锁检测器发现死锁时触发的探针。 表43.定义用在探针参数中的类型 类型 定义 LocalTransactionId unsigned int LWLockMode int LOCKMODE int BlockNumber unsigned int Oid unsigned int ForkNumber int bool unsigned char 下面的例子展示了一个分析系统中事务计数的 DTrace 脚本，可以用来代替一次性能测试之前和之后的`pg_stat_database`快照： #!/usr/sbin/dtrace -qs postgresql$1:::transaction-start { @start[\"Start\"] = count(); self->ts = timestamp; } postgresql$1:::transaction-abort { @abort[\"Abort\"] = count(); } postgresql$1:::transaction-commit /self->ts/ { @commit[\"Commit\"] = count(); @time[\"Total time (ns)\"] = sum(timestamp - self->ts); self->ts=0; } 当被执行时，该例子 D 脚本给出这样的输出： # ./txn_count.d `pgrep -n postgres` or ./txn_count.d <PID> ^C Start 71 Commit 70 Total time (ns) 2312105013 注意 SystemTap 为追踪脚本使用一个不同于 DTrace 的标记，但是底层的探针是兼容的。值得注意的是，在这样写的时候，SystemTap 脚本必须使用双下划线代替连字符来引用探针名。在未来的 SystemTap 发行中这很可能会被修复。你应该记住，DTrace 脚本需要细心地编写和调试，否则被收集的追踪信息可能会毫无意义。在大部分发现问题的情况中，它就是发生问题的部件，而不是底层系统。当讨论使用动态追踪发现的信息时，一定要附上使用的脚本以便其也被检查和讨论。 开发者可以在代码中任意位置定义新的探针，当然这要重新编译之后才能生效。下面是插入新探针的步骤： 决定探针名称以及探针可用的数据 把该探针定义加入到`src/backend/utils/probes.d` 如果`pg_trace.h`还不存在于包含该探针点的模块中，包括它，并且在源代码中期望的位置插入`TRACE_POSTGRESQL`探针宏 重新编译并验证新探针是可用的 例子：. 这里是一个如何增加一个探针来用事务 ID 追踪所有新事务的例子。 决定探针将被命名为`transaction-start`并且需要一个`LocalTransactionId`类型的参数 将该探针定义加入到`src/backend/utils/probes.d`： ``` probe transaction__start(LocalTransactionId); ``` 注意探针名字中双下划线的使用。在一个使用探针的 DTrace 脚本中，双下划线需要被替换为一个连字符，因此 ，对用户而言`transaction-start`是文档名。 在编译时，transaction__start`被转换成一个宏调用`TRACE_POSTGRESQL_TRANSACTION_START（注意这里是单下划线），可以通过包括头文件`pg_trace.h`获得。将宏调用加入到源代码中的合适位置。在这种情况下，看起来类似： ``` TRACE_POSTGRESQL_TRANSACTION_START(vxid.localTransactionId); ``` 在重新编译和运行新的二进制文件之后，通过运行下面的 DTrace 命令来检查新增的探针是否可用。你应该看到类似下面的输出： ``` # dtrace -ln transaction-start ID PROVIDER MODULE FUNCTION NAME 18705 postgresql49878 postgres StartTransactionCommand transaction-start 18755 postgresql49877 postgres StartTransactionCommand transaction-start 18805 postgresql49876 postgres StartTransactionCommand transaction-start 18855 postgresql49875 postgres StartTransactionCommand transaction-start 18986 postgresql49873 postgres StartTransactionCommand transaction-start ``` 向C代码中添加追踪宏时，有一些事情需要注意： 需要小心的是，为探针参数指定的数据类型要匹配宏中使用的变量的数据类型，否则会发生编译错误。 在大多数平台上，如果用`--enable-dtrace`编译了IvorySQL，无论何时当控制经过一个追踪宏时，都会评估该宏的参数，即使没有进行追踪也会这样做。通常不需要担心你是否只在报告一些局部变量的值。但是要注意不要将开销大的函数调用放入参数中。如果你需要这样做，考虑通过检查追踪是否真的被启用来保护该宏： ``` if (TRACE_POSTGRESQL_TRANSACTION_START_ENABLED()) TRACE_POSTGRESQL_TRANSACTION_START(some_function(...)); ``` 每个追踪宏有一个对应的`ENABLED`宏。 每个表都有一个主要的堆磁盘文件，大多数数据都存储在其中。如果一个表有着可能会很宽（尺寸大）的列， 则另外还有一个TOAST文件与这个表相关联， 它用于存储因为太宽而不能存储在主表里面的值。如果有这个附属文件，那么TOAST表上会有一个可用的索引。 当然，同时还可能有索引和基表关联。每个表和索引都存放在单独的磁盘文件里 — 如果文件超过 1G 字节，甚至可能多于一个文件。 你可以以三种方式监视磁盘空间：使用oid2name模块或者人工观察系统目录。SQL函数是最容易使用的方法，同时也是我们通常推荐的方法。本节剩余的部分将展示如何通过观察系统目录来监视磁盘空间。 在一个最近清理过或者分析过的数据库上使用psql，你可以发出查询来查看任意表的磁盘用量： SELECT pg_relation_filepath(oid), relpages FROM pg_class WHERE relname = 'customer'; pg_relation_filepath | relpages ----------------------+---------- base/16384/16806 | 60 (1 row) 每个页通常都是 8K 字节（记住，relpages`只会由`VACUUM、`ANALYZE`和少数几个 DDL 命令如`CREATE INDEX`所更新）。如果你想直接检查表的磁盘文件，那么文件路径名应该有用。 要显示TOAST表使用的空间，我们可以使用一个类似下面这样的查询： SELECT relname, relpages FROM pg_class, (SELECT reltoastrelid FROM pg_class WHERE relname = 'customer') AS ss WHERE oid = ss.reltoastrelid OR oid = (SELECT indexrelid FROM pg_index WHERE indrelid = ss.reltoastrelid) ORDER BY relname; relname | relpages ----------------------+---------- pg_toast_16806 | 0 pg_toast_16806_index | 1 你也可以很容易地显示索引的尺寸： SELECT c2.relname, c2.relpages FROM pg_class c, pg_class c2, pg_index i WHERE c.relname = 'customer' AND c.oid = i.indrelid AND c2.oid = i.indexrelid ORDER BY c2.relname; relname | relpages -------------------+---------- customer_id_index | 26 我们很容易用下面的信息找出最大的表和索引： SELECT relname, relpages FROM pg_class ORDER BY relpages DESC; relname | relpages ----------------------+---------- bigtable | 3290 customer | 3144 一个数据库管理员最重要的磁盘监控任务就是确保磁盘不会写满。一个写满了的数据磁盘可能不会导致数据的崩溃，但它肯定会让系统变得不可用。如果保存 WAL 文件的磁盘变满，会发生数据库服务器致命错误并且可能发生关闭。 如果你不能通过删除一些其他的东西来释放一些磁盘空间，那么你可以通过使用表空间把一些数据库文件移动到其他文件系统上去。 提示 有些文件系统在快满的时候性能会急剧恶化，因此不要等到磁盘完全满的时候才采取行动。 如果你的系统支持每用户的磁盘份额，那么数据库将自然地受制于用户所处的服务器给他的份额限制。超过份额的负面影响和完全用光磁盘是完全一样的。","title":"日常监控","component":"ivorysql-doc","version":"master","name":"daily_monitoring","url":"/ivorysql-doc/master/getting-started/daily_monitoring.html","titles":[{"text":"1. 监控数据活动","hash":"监控数据活动","id":1},{"text":"1.1. 标准Unix工具","hash":"标准unix工具","id":2},{"text":"1.2. 统计收集器","hash":"统计收集器","id":3},{"text":"1.2.1. 统计收集配置","hash":"统计收集配置","id":4},{"text":"1.2.2. 查看统计信息","hash":"查看统计信息","id":5},{"text":"1.3. pg_stat_activity","hash":"pg_stat_activity","id":6},{"text":"1.3.1. pg_stat_replication","hash":"pg_stat_replication","id":7},{"text":"1.3.2. pg_stat_wal_receiver","hash":"pg_stat_wal_receiver","id":8},{"text":"1.3.3. pg_stat_subscription","hash":"pg_stat_subscription","id":9},{"text":"1.3.4. pg_stat_ssl","hash":"pg_stat_ssl","id":10},{"text":"1.3.5. pg_stat_gssapi","hash":"pg_stat_gssapi","id":11},{"text":"1.3.6. pg_stat_archiver","hash":"pg_stat_archiver","id":12},{"text":"1.3.7. pg_stat_bgwriter","hash":"pg_stat_bgwriter","id":13},{"text":"1.3.8. pg_stat_database","hash":"pg_stat_database","id":14},{"text":"1.3.9. pg_stat_database_conflicts","hash":"pg_stat_database_conflicts","id":15},{"text":"1.3.10. pg_stat_all_tables","hash":"pg_stat_all_tables","id":16},{"text":"1.3.11. pg_stat_all_indexes","hash":"pg_stat_all_indexes","id":17},{"text":"1.3.12. pg_statio_all_tables","hash":"pg_statio_all_tables","id":18},{"text":"1.3.13. pg_statio_all_indexes","hash":"pg_statio_all_indexes","id":19},{"text":"1.3.14. pg_statio_all_sequences","hash":"pg_statio_all_sequences","id":20},{"text":"1.3.15. pg_stat_user_functions","hash":"pg_stat_user_functions","id":21},{"text":"1.3.16. pg_stat_slru","hash":"pg_stat_slru","id":22},{"text":"1.3.17. Statistics Functions","hash":"statistics-functions","id":23},{"text":"2. 查看锁","hash":"查看锁","id":24},{"text":"3. Progress Reporting","hash":"progress-reporting","id":25},{"text":"3.1. ANALYZE Progress Reporting","hash":"analyze-progress-reporting","id":26},{"text":"3.2. CREATE INDEX Progress Reporting","hash":"create-index-progress-reporting","id":27},{"text":"3.3. VACUUM进度报告","hash":"vacuum进度报告","id":28},{"text":"3.4. CLUSTER进度报告","hash":"cluster进度报告","id":29},{"text":"3.5. 基础备份进度报告","hash":"基础备份进度报告","id":30},{"text":"4. 动态追踪","hash":"动态追踪","id":31},{"text":"4.1. 动态追踪的编译","hash":"动态追踪的编译","id":32},{"text":"4.2. 内建探针","hash":"内建探针","id":33},{"text":"4.3. 使用探针","hash":"使用探针","id":34},{"text":"4.4. 定义新探针","hash":"定义新探针","id":35},{"text":"5. 监控磁盘的使用","hash":"监控磁盘的使用","id":36},{"text":"5.1. 判断磁盘用量","hash":"判断磁盘用量","id":37},{"text":"5.2. 磁盘满失败","hash":"磁盘满失败","id":38}]},"57":{"id":57,"text":"硬件要求 配置参数 最低配置 推荐配置 CPU 4核 16核 内存 4GB 64GB 存储 800MB，机械硬盘 5GB以上，SSD或NvMe 网络 千兆网络 万兆网络 软件要求 IvorySQL数据库目前支持的操作系统包括但不限于CentOS 8.X、CentOS Stream 9以及Ubuntu系统。 快速开始示例所使用的操作系统为CentOS Stream 9。 安装前准备 安装前请先创建一个用户，并赋予其root权限，安装和使用均以该用户执行。这里以ivorysql用户为例。 如何创建sudo用户 下载安装 创建或编辑IvorySQL yum源配置文件/etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 保存退出后，安装IvorySQL5 $ sudo dnf install -y ivorysql5-5.0 正确安装后，数据库将被安装在/usr/ivory-5/路径下的IvorySQL-version(如:IvorySQL-5.0)文件夹内 执行以下命令为ivorysql用户赋权： $ sudo chown -R ivorysql:ivorysql /usr/ivory-5 配置环境变量 将以下配置写入~/.bash_profile文件并使用source命令该文件使环境变量生效： PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile 数据库初始化 $ initdb -D /usr/ivory-5/data 其中-D参数用来指定数据库的数据目录。更多参数使用方法，请使用initdb --help命令获取。 启动数据库服务 $ pg_ctl -D /usr/ivory-5/data -l ivory.log start 其中-D参数用来指定数据库的数据目录，如果[配置环境变量] 配置了PGDATA，则该参数可以省略。-l参数用来指定日志文件。更多参数使用方法，请使用pg_ctl --help命令获取。 查看确认数据库启动成功： $ ps -ef | grep postgres ivorysql 3214 1 0 20:35 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 3215 3214 0 20:35 ? 00:00:00 postgres: checkpointer ivorysql 3216 3214 0 20:35 ? 00:00:00 postgres: background writer ivorysql 3218 3214 0 20:35 ? 00:00:00 postgres: walwriter ivorysql 3219 3214 0 20:35 ? 00:00:00 postgres: autovacuum launcher ivorysql 3220 3214 0 20:35 ? 00:00:00 postgres: logical replication launcher ivorysql 3238 1551 0 20:35 pts/0 00:00:00 grep --color=auto postgres 从Docker Hub上获取IvorySQL镜像 $ docker pull ivorysql/ivorysql:5.0-ubi8 运行IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 查看IvorySQL容器运行是否成功 $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql psql连接数据库： $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# 其中-d参数用来指定想要连接到的数据库名称。IvorySQL默认使用ivorysql数据库，但较低版本的IvorySQL首次使用时需用户先连接postgres数据库，然后自己创建ivorysql数据库。较高版本的IvorySQL则已为用户创建好ivorysql数据库，可以直接连接。 更多参数使用方法，请使用psql --help命令获取。 Docker运行IvorySQL时，需要添加额外参数，如 psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 现在可以开始使用IvorySQL啦！就是这么简单！ 想要获得更多安装方式，请参考安装指南","title":"快速开始","component":"ivorysql-doc","version":"master","name":"quick_start","url":"/ivorysql-doc/master/getting-started/quick_start.html","titles":[{"text":"1. 环境要求","hash":"环境要求","id":1},{"text":"2. 快速安装","hash":"快速安装","id":2},{"text":"2.1. 从yum源安装IvorySQL数据库","hash":"从yum源安装ivorysql数据库","id":3},{"text":"2.2. docker方式运行","hash":"docker方式运行","id":4},{"text":"3. 数据库连接","hash":"数据库连接","id":5}]},"58":{"id":58,"text":"IvorySQL提供兼容Oracle内置函数 SYS_CONTEXT('namespace', 'parameter' [, length ]) , 返回当前时刻与给定上下文关联参数的值，可以在SQL和PLSQL语言中使用。 提供以下命名空间： USERENV​ - 用于描述当前会话。 SYS_SESSION_ROLES​ - 指示某个特定角色在当前会话中是否已启用。 SYS_CONTEXT 的实现原理是通过动态查询系统表与postgres的内置函数，确保结果的实时性， 使用 SECURITY INVOKER 确保函数以调用者的权限执行，避免权限提升问题。 该方法的实现逻辑如下： CREATE OR REPLACE FUNCTION sys.sys_context(a varchar2, b varchar2) RETURNS varchar2 AS $$ DECLARE res varchar2; BEGIN IF upper(a) = 'USERENV' THEN CASE upper(b) WHEN 'CURRENT_SCHEMA' THEN SELECT current_schema() INTO res; WHEN 'LANG' THEN SELECT sys.get_lang() INTO res; ... ELSE RAISE EXCEPTION 'invalid USERENV parameter: %', b; END CASE; ELSIF upper(a) = 'SYS_SESSION_ROLES' THEN CASE upper(b) WHEN 'LOGIN' THEN SELECT CASE WHEN rolcanlogin = 't' THEN 'TRUE' ELSE 'FALSE' END INTO res FROM pg_roles WHERE oid = current_user::regrole::oid; ... ELSE RAISE EXCEPTION 'invalid SYS_SESSION_ROLES parameter: %', b; END CASE; ELSE SELECT current_setting(a||'.'||b, true) INTO res; END IF; RETURN res; END; $$ LANGUAGE plisql SECURITY INVOKER; 参数名称 返回值 CURRENT_SCHEMA 当前活动默认模式的名称。在会话期间，可以通过 ALTER SESSION SET CURRENT_SCHEMA 语句更改该值。该值也可能在会话期间发生变化，以反映任何活动定义者权限对象的所有者。如果直接在视图定义的正文中使用，那么将返回在执行使用视图的游标时使用的默认模式。 CURRENT_SCHEMAID 当前活动的默认模式的标识符。 SESSION_USER 当前session的用户 会话用户（登录用户）的名称。在数据库会话期间，随着 Real Application Security 会话的附加或分离，该名称可能会发生变化。对于企业用户，返回模式。对于其他用户，返回数据库用户名。如果数据库会话当前附加了 Real Application Security 会话，则返回用户 XS$NULL。 SESSION_USERID 当前session的用户ID。 PROXY_USER 代表 SESSION_USER 打开当前会话的数据库用户名称。 PROXY_USERID 代表 SESSION_USER 打开当前会话的数据库用户的标识符。 CURRENT_USER 当前会话中执行操作的用户。 CURRENT_USERID 当前会话中执行操作的用户标识符。 CURRENT_EDITION_NAME 当前版本的名字。 CLIENT_PROGRAM_NAME 客户端程序的名称。 参数名称 返回值 IP_ADDRESS 客户端的IP地址。 HOST 客户端主机的名称。 ISDBA 如果当前用户是数据库管理员，则返回TRUE。 LANG 语言的缩写名称，比现有的 “LANGUAGE ”参数更简短。 LANGUAGE 当前会话的使用的语言和地区，以及数据库字符集，格式为：language_territory.characterset。 NLS_DATE_FORMAT 当前session的日期格式。 PLATFORM_SLASH 在用户的平台上用作文件路径分隔符的斜线字符（Unix或Linux是’/’，Windows是’\\’）。 DB_NAME 当前连接的CDB的名称。 SID 当前会话的系统标识符。 SESSIONID 审计会话标识符。不能在分布式 SQL 语句中使用此属性。 CLIENT_INFO 返回最多 64 字节的用户会话信息，应用程序可使用 DBMS_APPLICATION_INFO 包存储这些信息。 ENTRYID 当前审计条目编号。审计条目编号序列在细粒度审计记录和常规审计记录之间共享。不能在分布式 SQL 语句中使用此属性。只有通过标准或细粒度审计的审计处理程序才能看到正确的审计条目标识符。 TERMINAL 当前会话客户端的操作系统标识符。在分布式 SQL 语句中，该属性返回本地会话的标识符。在分布式环境中，仅支持远程 SELECT 语句，不支持远程 INSERT、UPDATE 或 DELETE 操作。(此参数的返回长度可能因操作系统而异)。 参数名称 返回值 DBA 如果当前用户是数据库管理员，则返回TRUE。 LOGIN 如果当前用户是登录角色，则返回TRUE。 CREATEROLE 如果当前session的用户具有创建角色的权限，则返回TRUE。 CREATEDB 如果当前session的用户具有创建数据库的权限，则返回TRUE。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"sys_context","url":"/ivorysql-doc/master/oracle_builtin_functions/sys_context.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"2. 命名空间USERENV支持的参数","hash":"命名空间userenv支持的参数","id":2},{"text":"3. 命名空间SYS_SESSION_ROLES支持的参数","hash":"命名空间sys_session_roles支持的参数","id":3}]},"59":{"id":59,"text":"IvorySQL提供兼容Oracle内置函数 USERENV('parameter') ,用于返回当前会话的相关信息。 这是一个遗留函数，建议使用 SYS_CONTEXT 函数及其内置的 USERENV 命名空间来获取当前功能。 USERENV 通过Bison规则解析函数调用，检查参数合法性并映射到对应的具体SQL函数。 解析规则在 ora_gram.y 中实现，逻辑如下： USERENV '(' Sconst ')' { char *normalized_param = downcase_identifier($3, strlen($3), true, true); #define CHECK_AND_CALL(param, func_name) \\ if (strcmp(normalized_param, param) == 0) \\ $$ = (Node *) makeFuncCall(OracleSystemFuncName(func_name), NIL, COERCE_EXPLICIT_CALL, @1); CHECK_AND_CALL(\"client_info\", \"get_client_info\") else CHECK_AND_CALL(\"entryid\", \"get_entryid\") else CHECK_AND_CALL(\"terminal\", \"get_terminal\") else CHECK_AND_CALL(\"isdba\", \"get_isdba\") else CHECK_AND_CALL(\"lang\", \"get_lang\") else CHECK_AND_CALL(\"language\", \"get_language\") else CHECK_AND_CALL(\"sessionid\", \"get_sessionid\") else CHECK_AND_CALL(\"sid\", \"get_sid\") else ereport(ERROR, (errcode(ERRCODE_FEATURE_NOT_SUPPORTED), errmsg(\"invalid USERENV parameter: \\\"%s\\\".\", $3))); #undef CHECK_AND_CALL } 具体功能则是在 builtin_functions—​1.0.sql 中实现。例如： CREATE OR REPLACE FUNCTION sys.get_language() RETURNS varchar2 AS $$ SELECT (regexp_split_to_array(current_setting('lc_monetary'), '\\.'))[1]||'.'||pg_client_encoding(); $$ LANGUAGE sql STRICT; 参数名称 返回值 sid 当前session的用户ID。 sessionid 返回审计会话标识符。 language 返回数据库当前会话的语言、地域和字符集。 lang 返回ISO缩写语言名称。 isdba 如果用户已经被认证为管理员;或者是通过操作系统或口令文件具有管理员特权的，返回“TRUE\"，否则返回\"FALSE\"。","title":"功能概述","component":"ivorysql-doc","version":"master","name":"userenv","url":"/ivorysql-doc/master/oracle_builtin_functions/userenv.html","titles":[{"text":"1. 实现原理","hash":"实现原理","id":1},{"text":"2. USERENV支持的参数","hash":"userenv支持的参数","id":2}]},"60":{"id":60,"text":"本文档是PLSQL匿名块（anonymous block）兼容Oracle语法功能的设计文档，目的是可以在IvorySQL中兼容Oracle的匿名块语句。 匿名块是能够动态地创建和执行过程代码的PLSQL结构，而不需要以持久化的方式将代码作为数据库对象储存在系统目录中。本次实现中IvorySQL主要兼容的是PLSQL匿名块的语法格式，我们主要处理的部分包括客户端工具psql、主服务器和PSQL端支持。 declare i integer := 10; begin raise notice '%', i; end; / NOTICE: 10 DECLARE grade CHAR(1); BEGIN grade := 'B'; CASE grade WHEN 'A' THEN raise notice 'Excellent'; WHEN 'B' THEN raise notice 'Very Good'; END CASE; EXCEPTION WHEN CASE_NOT_FOUND THEN raise notice 'No such grade'; END; / NOTICE: Very Good","title":"兼容Oracle匿名块","component":"ivorysql-doc","version":"master","name":"anonymous_block","url":"/ivorysql-doc/master/oracle_compatibility/anonymous_block.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"61":{"id":61,"text":"char varchar varchar2 number binary_float binary_double date timestamp timestamp with time zone timestamp with local time zone interval year to month interval day to second raw long sysdate systimestamp add_months last_day next_day months_between current_date current_timestamp new_time tz_offset trunc instr instrb substr substrb trim ltrim rtrim length lengthb rawtohex replace regexp_replace regexp_substr regexp_instr regexp_like to_number to_char to_date to_timestamp to_timestamp_tz to_yminterval to_dsinterval numtodsinterval numtoyminterval localtimestamp from_tz sys_extract_utc sessiontimezone hextoraw uid USERENV asciistr to_multi_byte to_single_byte compose decompose sys_context 功能：查看对应的日期与时间，测试用例如下： 查询当前系统的日期： select sysdate() from dual; sysdate ------------ 2023-07-06 (1 row) 查询往前推1天的日期： select sysdate()-1 from dual; ?column? ------------ 2023-07-05 (1 row) 功能：返回本机数据库上当前系统日期和时间（包括微秒和时区），测试用例如下： 查询当前日期的日期和时间： select systimestamp() from dual; systimestamp ----------------------------------- 2023-07-06 10:18:31.674322 +08:00 (1 row) 功能：函数将一个月数(n)添加一个日期，并返回相隔n月的同一天，支持参数：date, number；测试用例如下： 查询当前日期（七月六日）的下个月的同一天： select add_months(sysdate(),1) from dual; add_months ------------ 2023-08-06 (1 row) 查询当前日期的上个月的同一天： select add_months(sysdate(),-1) from dual; add_months ------------ 2023-06-06 (1 row) 功能：返回指定日期所在月份的最后一天，支持参数：date,测试用例如下： 查询当天所在月份的最后一天： select last_day(sysdate())from dual; last_day ------------ 2023-07-31 (1 row) 查询某一天所在月份的最后一天： select last_day(to_date('2019-09-01'))from dual; last_day ------------ 2019-09-30 (1 row) 功能：返回指定日期的下一个日期。支持参数:date, integer /date ,text, 说明：当函数中第二个参数传的星期数比现有星期数小时，会返回下一个星期的日期；当函数中第二个参数所传的日期比现有星期数大，会返回本周相应星期日期。测试用例如下： 查询当前日期的下一天： select next_day(sysdate(),1) from dual; next_day ------------ 2023-07-07 (1 row) 查询当前日期的下个星期五： select next_day(sysdate(),'FRIDAY') from dual; next_day ------------ 2023-07-07 (1 row) 功能：返回日期类型的date1和date2之间相差的月份，支持参数：date,date，说明：如果date1晚于date2，返回正数；如果date1早于date2返回负数；如果date1和date2是某月里的同一天，返回结果为整数；如果不是同一天，会在每月31天的基础上返回带有小数部分的结果。测试用例如下： 查询不同月份同一天之间相差的月份： select months_between(to_date('2023-07-06'),to_date('2023-08-06')) from dual; months_between ---------------- -1 (1 row) 查询不同月份不同日期之间相差的月份： select months_between(to_date('2023-07-06'),to_date('2023-08-05')) from dual; months_between -------------------- -0.967741935483871 (1 row) 功能：返回当前时区的当前日期，测试用例如下： 查询当前时区的当前日期： select current_date from dual; current_date -------------- 2023-07-06 (1 row) 功能：返回当前时区的当前日期与当前时间，包含当前时区信息。支持参数：integer, 说明：返回的时间可调整精度。测试用例如下： 查询当前时区的当前日期与时间： select current_timestamp from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:01.440600 +08:00 (1 row) 查询当前时区的当前日期与时间(精度调整为前三位小数)： select current_timestamp(3) from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:14.182000 +08:00 (1 row) 功能：返回某个时间在某时区所对应的在另一个时区的日期，支持参数：date, text, text ,测试用例如下： 返回当前日期在另一个时区对应的日期： select sysdate() bj_time,new_time(sysdate(),'PDT','GMT')los_angles from dual; bj_time | los_angles ------------+------------ 2023-07-06 | 2023-07-06 (1 row) 功能：返回给定时区与标准时区的偏移量，支持参数：text,测试用例如下： 返回给定时区与标准时区偏移量： select tz_offset('US/Eastern') from dual; tz_offset ----------- -04:00 (1 row) 功能：可以截取日期，得到想要的数值，如年，月，日，时，分，支持参数：date/date,text,测试用例如下： 截取当前日期： select trunc(sysdate()) from dual; trunc ------------ 2023-07-06 (1 row) 截取年，返回值只有年是正确的，月和日不是准确值： select trunc(sysdate(),'yyyy') from dual; trunc ------------ 2023-01-01 (1 row) 截取月，返回值只有月是正确的，年和日不是准确值： select trunc(sysdate(),'mm') from dual; trunc ------------ 2023-07-01 (1 row) 功能：字符串查找函数，用于判断源字符串中是否包含目标字符串，并返回匹配位置。支持参数形式如下： instr(string, str) instr(string, str, start_position, nth_appearance) 以下为测试用例： 默认情况下返回第一次匹配的位置： SELECT INSTR('database administration', 'data') FROM DUAL; instr ------- 1 (1 row) 指定起始位置和匹配序号： SELECT INSTR('database administration', 'i', 1, 2) FROM DUAL; instr ------- 15 (1 row) 支持从尾部开始的反向搜索： SELECT INSTR('mississippi river', 's', -5, 2) FROM DUAL; instr ------- 6 (1 row) 当未匹配到目标字符串时返回0： SELECT INSTR('database administration', 'z') FROM DUAL; instr ------- 0 (1 row) instr 还能像 like 一样用于模糊查询： select * from tableName where instr(name,'helloworld')>0; 函数实现说明： 查找一个字符串在另一个字符串中的位置，采用经典的暴力匹配算法。 复用了 text_instring 函数。 通过 isByte 参数判断当前数据库编码是否为多字节，并区分单字节与多字节处理逻辑。 根据 position 的正负确定正向或反向搜索，并设置循环的起止位置和步长。 在循环中逐字节移动并比较，与模式字符串匹配成功即返回相应位置。 功能：字符串查找函数，返回字符串的位置，支持参数： varchar2, text, number DEFAULT 1, number DEFAULT 1,以下为测试用例： 返回CORPORATE FLOOR中默认第一次出现OR时字符串的位置： SELECT INSTRB('CORPORATE FLOOR','OR') \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 2 (1 row) 返回corporate floor中从第五个字符开始查询，第二次出现or时字符串的位置： SELECT INSTRB('CORPORATE FLOOR','OR',5,2) \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 14 (1 row) 功能：截取字符串函数，以字符为单位截取，支持参数：text, integer, 测试用例如下： 截取’今天天气很好’中从第五个字符开始，往后的字符串： SELECT SUBSTR('今天天气很好',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- 很好 (1 row) 功能：截取字符串函数，以字节为单位截取，支持参数：varchar2, number/varchar2, number，number,测试用例如下： 截取’今天天气很好’中从第五个字节开始，往后的字符串： SELECT SUBSTRB('今天天气很好',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- 天气很好 (1 row) 截取’今天天气很好’中从第五个字节开始，第八个字节结束的字符串： SELECT SUBSTRB('今天天气很好',5，8) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- 天气 (1 row) 功能：去除指定字符串的左右空格或对应数据，支持参数：varchar2 /varchar2，varchar2，测试用例如下： 去除' aaa bbb ccc '的左右空格： select trim(' aaa bbb ccc ')trim from dual; trim ------------- aaa bbb ccc (1 row) 去除’aaa bbb ccc’中的aaa： select trim('aaa bbb ccc','aaa')trim from dual; trim ---------- bbb ccc (1 row) 功能：去除指定字符串的左侧空格或对应数据，支持参数：varchar2 /varchar2,varchar2,测试用例如下： 去除' abcdefg '的左侧空格： select ltrim(' abcdefg ')ltrim from dual; ltrim ------------ abcdefg (1 row) 从’abcdefg’左侧开始遍历，一旦存在某字符出现在’fegab’中就去除，不存在则返回结果： select ltrim('abcdefg','fegab')ltrim from dual; ltrim ------- cdefg (1 row) 功能：去除指定字符串的右侧空格，测试用例如下： 去除' abcdefg '的右侧空格： select rtrim(' abcdefg ')rtrim from dual; rtrim ---------------- abcdefg (1 row) 从’abcdefg’右侧开始遍历，一旦存在某字符出现在’fegab’中就去除，不存在则返回结果： select rtrim('abcdefg','fegab')rtrim from dual; rtrim ------- abcd (1 row) 功能：求取指定字符串字符的长度，支持参数：char/integer/varchar2测试用例如下： 查询223的字符长度： select length(223) from dual; length -------- 3 (1 row) 查询'223’的字符长度： select length('223') from dual; length -------- 3 (1 row) 查询’ivorysql数据库’的字符长度： select length('ivorysql数据库') from dual; length -------- 11 (1 row) 功能：求取指定字符串字节的长度，支持参数：char/bytea/varchar2测试用例如下： 查询’ivorysql’的字节长度: select lengthb('ivorysq'::char) from dual; lengthb --------- 1 (1 row) 查询'0x2C’的字节长度: select lengthb('0x2C'::bytea) from dual; lengthb --------- 4 (1 row) 查询’ivorysql数据库’的字节长度： select lengthb('ivorysql数据库'::varchar2) from dual; lengthb --------- 17 (1 row) 功能：替换指定字符串中的字符或删除字符，支持参数：text, text, text/varchar2, varchar2, varchar2 DEFAULT NULL::varchar2, 测试用例如下： 替换’jack and jue’中的’j’为’bl'： select replace('jack and jue','j','bl') from dual; replace ---------------- black and blue (1 row) 删除’jack and jue’中的’j': select replace('jack and jue','j') from dual; replace ------------ ack and ue (1 row) 此函数为`replace` 函数的扩展。功能：用于通过正则表达式来进行匹配替换。支持参数：text, text, text /text, text, text, integer/varchar2, varchar2/varchar2, varchar2 varchar2, 测试用例如下： 将匹配到的数字替换为*#： select regexp_replace('01234abcd56789','[0-9]','*#')from dual; regexp_replace -------------------------- *#*#*#*#*#abcd*#*#*#*#*# (1 row) 从第二个数开始将匹配到的数字替换为*#： select regexp_replace('01234abcd56789','[0-9]','*#',2)from dual; regexp_replace ------------------------- 0*#*#*#*#abcd*#*#*#*#*# 删除'01234abcd56789’中的'01'： select regexp_replace('01234abcd56789','01')from dual; regexp_replace ---------------- 234abcd56789 (1 row) 用’xxx’替换01234abcd56789’中的012： select regexp_replace('01234abcd56789','012','xxx')from dual; regexp_replace ---------------- xxx34abcd56789 (1 row) 功能：拾取符合正则表达式描述的字符子串，支持参数：text, text,integer /text, text, integer, integer/ text, text, integer, integer, text /varchar2 ,varchar2,测试用例如下： 查询'012ab34’中从第一个数开始的012字串： select regexp_substr('012ab34', '012',1) from dual; regexp_substr --------------- 012 (1 row) 查询'012ab34’中从第一个数第一组开始的012字串： select regexp_substr('012ab34', '012',1,1) from dual; regexp_substr --------------- 012 (1 row) 查询'012a012Ab34’中从第一个数第一组开始不区分大小写的012字串： select regexp_substr('012a012Ab34', '012A',1,1,'i') from dual; regexp_substr --------------- 012a (1 row) 查询'012a012Ab34’中从第一个数第一组开始区分大小写的012字串： select regexp_substr('012a012Ab34', '012A',1,1,'c') from dual; regexp_substr --------------- 012A (1 row) 查询’数据库’中 '数据’子串： select regexp_substr('数据库', '数据') from dual; regexp_substr --------------- 数据 (1 row) 功能：用于标定符合正则表达式的字符子串的开始位置，支持参数：text, text,integer /text, text, integer, integer/ text, text, integer, integer, text/text, text, integer, integer, text, integer/ varchar2, varchar2,测试用例如下： 查询’abcaBcabc’中从第一个字符开始，出现abc子串的位置： SELECT regexp_instr('abcaBcabc', 'abc', 1); regexp_instr -------------- 1 (1 row) 查询’abcaBcabc’中从第一个字符开始，第三次出现abc子串的位置： SELECT regexp_instr('abcaBcabc', 'abc', 1, 3); regexp_instr -------------- 7 (1 row) 查询’abcabcabc’中从第一个字符开始，第二次出现abc子串后发生的位置： SELECT regexp_instr('abcaBcabc', 'abc', 1, 2,1); regexp_instr -------------- 7 (1 row) 查询’abcaBcabc’中从第一个字符开始，第一次出现abc子串后发生的位置（区分大小写）： SELECT regexp_instr('abcaBcabc', 'abc',1,2,1,'c'); regexp_instr -------------- 7 (1 row) 查询’数据库’中’库’出现的位置： SELECT regexp_instr('数据库', '库'); regexp_instr -------------- 3 (1 row) 功能：与like类似，用于模糊查询。支持参数：varchar2, varchar2 /varchar2, varchar2 varchar2, 首先创建一个regexp_like表用于测试用例查询： create table t_regexp_like ( id varchar(4), value varchar(10) ); insert into t_regexp_like values ('1','1234560'); insert into t_regexp_like values ('2','1234560'); insert into t_regexp_like values ('3','1b3b560'); insert into t_regexp_like values ('4','abc'); insert into t_regexp_like values ('5','abcde'); insert into t_regexp_like values ('6','ADREasx'); insert into t_regexp_like values ('7','123 45'); insert into t_regexp_like values ('8','adc de'); insert into t_regexp_like values ('9','adc,.de'); insert into t_regexp_like values ('10','abcbvbnb'); insert into t_regexp_like values ('11','11114560'); 测试用例如下： 查询t_regexp_like表中带有abc的列： select * from t_regexp_like where regexp_like(value,'abc'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) 查询t_regexp_like表中带有ABC的列(不区分大小写)： select * from t_regexp_like where regexp_like(value,'ABC','i'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) 功能：是将一些处理过的按一定格式编排过的字符串变回数值型的格式，支持参数：text/text,text测试用例如下： 将字符串'-34,338,492’转换为数值型格式： SELECT to_number('34,338,492', '99,999,999') from dual; to_number ----------- -34338492 (1 row) 将字符串'5.01-'转换为数值型格式： SELECT to_number('5.01-', '9.99S'); to_number ----------- -5.01 (1 row) 功能：将数字或日期转换为字符类型，支持参数：date/date,text/timestamp/timestamp,text测试用例如下： 将当前系统日期转换为字符格式： select to_char(sysdate()) from dual; to_char ------------ 2023-07-10 (1 row) 将当前系统日期转换为月份/日期/年字符格式： select to_char(sysdate(),'mm/dd/yyyy') from dual; to_char ------------ 07/10/2023 (1 row) 将当前日期的timestamp格式转换为字符格式： SELECT to_char(sysdate()::timestamp); to_char ---------------------------- 2023-07-10 09:46:44.000000 将当前日期的timestamp格式转换为月份/日期/年字符格式： SELECT to_char(sysdate()::timestamp,'MM-YYYY-DD'); to_char ------------ 07-2023-10 (1 row) 功能：将字符类型转换为日期类型，支持参数：text/text,text测试用例如下： 将'2023/07/06’转换为日期类型： select to_date('20230706') from dual; to_date ------------ 2023-07-06 (1 row) 将'-44-02-01’转换为日期类型： SELECT to_date('-44,0201','YYYY-MM-DD'); to_date ------------ 0044-02-01 (1 row) 功能：可以存储年、月、日、小时、分钟、秒，同时还可以存储秒的小数部分。支持参数：text/text,text测试用例如下： 查询'2018-11-02 12:34:56.025’以日期形式输出： SELECT to_timestamp('20181102.12.34.56.025'); to_timestamp ---------------------------- 2018-11-02 12:34:56.025000 (1 row) 查询’2011,12,18 11:38’以日期形式输出： SELECT to_timestamp('2011,12,18 11:38 ', 'YYYY-MM-DD HH24:MI:SS'); to_timestamp ---------------------------- 2011-12-18 11:38:00.000000 (1 row) 功能：根据时间查询，时间字符串有T,Z并有毫秒，时区。测试用例如下： 查询'2016-10-9 14:10:10.123000’以日期形式输出： SELECT to_timestamp_tz('2016-10-9 14:10:10.123000') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-10-09 14:10:10.123000 +08:00 (1 row) 查询'10-9-2016 14:10:10.123000 +8:30’以日期形式输出： SELECT to_timestamp_tz('10-9-2016 14:10:10.123000 +8:30', 'DD-MM-YYYY HH24:MI:SS.FF TZH:TZM') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-09-10 13:40:10.123000 +08:00 (1 row) 功能：将一个字符串类型转化为年和月的时间差类型，支持参数:text, 测试用例如下： 查询'20110101’以后两个年零八个月后的日期： select to_date('20110101','yyyymmdd')+to_yminterval('02-08') from dual; ?column? ------------ 2013-09-01 (1 row) 功能：将一个日期加上一定的小时或者天数变成另外一个日期,支持参数:text,测试用例如下： 查询当前系统时间加上9个半小时后的日期（当前为2023-07-06，18：00）： select sysdate()+to_dsinterval('0 09:30:00')as newdate from dual; newdate ------------ 2023-07-07 (1 row) 功能：将数字转换成时间间隔类型的数据。支持参数:double precision, text测试用例如下： 转换100.00个小时为时间间隔类型数据： SELECT NUMTODSINTERVAL(100.00, 'hour'); numtodsinterval ------------------------------- +000000004 04:00:00.000000000 (1 row) 转换100分钟为时间间隔类型数据： SELECT NUMTODSINTERVAL(100, 'minute'); numtodsinterval ------------------------------- +000000000 01:40:00.000000000 (1 row) 功能：将数字转换成日期间隔类型的数据。 支持参数：double precision,text,测试用例如下： 转换1.00，year为日期间隔： SELECT NUMTOYMINTERVAL(1.00,'year'); numtoyminterval ----------------- +000000001-00 (1 row) 转换1，mouth为日期间隔： SELECT NUMTOYMINTERVAL(1,'month'); numtoyminterval ----------------- +000000000-01 (1 row) 功能：返回会话中的日期和时间，支持参数：integer, 函数中增加参数为精度，测试用例如下： 返回当前会话中的日期和时间： select localtimestamp from dual; localtimestamp ---------------------------- 2023-07-07 09:18:15.896472 (1 row) 返回当前会话中的日期和时间（精度为1）： select localtimestamp(1) from dual; localtimestamp ---------------------------- 2023-07-07 09:18:16.100000 (1 row) 功能：将时间从一个时区转换为另一个时区，支持参数;timestamp, text ,测试用例如下： 将'2000-03-28 08:00:00', '3:00’转换为当前时区： SELECT FROM_TZ(TIMESTAMP '2000-03-28 08:00:00', '3:00') FROM DUAL; from_tz ----------------------------------- 2000-03-28 13:00:00.000000 +08:00 (1 row) 功能：将一个timestamptz转换为UTC时区时间。支持参数:timestamp with time zone 测试用例如下： 查询转换timestamp '2000-03-28 11:30:00.00 -8:00’为UTC时区后的时间： select sys_extract_utc(timestamp '2000-03-28 11:30:00.00 -8:00') from dual; sys_extract_utc ---------------------------- 2000-03-28 19:30:00.000000 (1 row) 功能：查看时区详细信息，测试用例如下： 查看当前时区的详细信息： select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Shanghai (1 row) 修改timezone后，查看时区详细信息： set timezone = 'Asia/Hong_Kong'; SET select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Hong_Kong (1 row) 功能：将字符串表示的二进制数值转换为一个raw数值。支持参数：text,测试用例如下： 将字符串’abcdef’转换为raw数值： select hextoraw('abcdef')from dual; hextoraw ---------- \\xabcdef (1 row) 功能：获取数据库的实例名。测试用例如下： 获取当前数据库的实例名： select uid() from dual; uid ----- 10 (1 row) 功能：返回当前用户环境的信息，测试用例如下： 查看当前用户是否是dba，如果是返回true： select userenv('isdba')from dual; get_isdba ----------- TRUE (1 row) 查看会话标志： select userenv('sessionid')from dual; get_sessionid --------------- 1 (1 row) 功能：传入字符串，返回对应的ASCII字符，测试用例如下： 只有ASCII字符： select asciistr('Hello, World!') from dual; asciistr --------------- Hello, World! (1 row) 非ASCII字符： select asciistr('你好') from dual; asciistr ------------ \\4F60\\597D 同时包含ASCII字符和非ASCII字符： select asciistr('ABÄCDE') from dual; asciistr ------------ AB\\00C4CDE (1 row) 功能：将字符串中的半角字符转换为全角字符： 输入半角字符，转换为全角字符： select to_multi_byte('1.2'::text) ; to_multi_byte --------------- １．２ 功能：将字符串中的半角字符转换为全角字符 输入全角字符，转换为半角字符： select to_single_byte('１．２'); to_single_byte ---------------- 1.2 功能：将基本字符和组合标记组合一个复合Unicode字符: 输入基本字符a和组合标记768, 返回法语à select compose('a'||chr(768)) from dual; compose --------- à (1 row) 功能：将复合Unicode字符（如带有重音或特殊符号的字符）分解为其基本字符和组合标记 输入法语é,返回基本字符e和组合标记301： select asciistr(decompose('é')) from dual; asciistr ---------- e\\0301 功能：返回当前时刻与给定上下文关联的参数的值，可以在SQL和PLSQL语言中使用。 ivorysql=# select sys_context('USERENV','DB_NAME'); sys_context ------------- ivorysql (1 row)","title":"内置数据类型与内置函数","component":"ivorysql-doc","version":"master","name":"builtin_types_functions","url":"/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html","titles":[{"text":"1. 内置数据类型","hash":"内置数据类型","id":1},{"text":"2. 内置函数类型","hash":"内置函数类型","id":2},{"text":"3. 内置函数说明","hash":"内置函数说明","id":3},{"text":"3.1. sysdate 函数","hash":"sysdate-函数","id":4},{"text":"3.2. systimestamp 函数","hash":"systimestamp-函数","id":5},{"text":"3.3. add_months 函数","hash":"add_months-函数","id":6},{"text":"3.4. last_day 函数","hash":"last_day-函数","id":7},{"text":"3.5. next_day 函数","hash":"next_day-函数","id":8},{"text":"3.6. months_between 函数","hash":"months_between-函数","id":9},{"text":"3.7. current_date 函数","hash":"current_date-函数","id":10},{"text":"3.8. current_timestamp 函数","hash":"current_timestamp-函数","id":11},{"text":"3.9. new_time 函数","hash":"new_time-函数","id":12},{"text":"3.10. tz_offset 函数","hash":"tz_offset-函数","id":13},{"text":"3.11. trunc 函数","hash":"trunc-函数","id":14},{"text":"3.12. instr 函数","hash":"instr-函数","id":15},{"text":"3.13. instrb 函数","hash":"instrb-函数","id":16},{"text":"3.14. substr 函数","hash":"substr-函数","id":17},{"text":"3.15. substrb 函数","hash":"substrb-函数","id":18},{"text":"3.16. trim 函数","hash":"trim-函数","id":19},{"text":"3.17. ltrim 函数","hash":"ltrim-函数","id":20},{"text":"3.18. rtrim 函数","hash":"rtrim-函数","id":21},{"text":"3.19. length 函数","hash":"length-函数","id":22},{"text":"3.20. lengthb 函数","hash":"lengthb-函数","id":23},{"text":"3.21. replace 函数","hash":"replace-函数","id":24},{"text":"3.22. regexp_replace 函数","hash":"regexp_replace-函数","id":25},{"text":"3.23. regexp_substr 函数","hash":"regexp_substr-函数","id":26},{"text":"3.24. regexp_instr 函数","hash":"regexp_instr-函数","id":27},{"text":"3.25. regexp_like 函数","hash":"regexp_like-函数","id":28},{"text":"3.26. to_number 函数","hash":"to_number-函数","id":29},{"text":"3.27. to_char 函数","hash":"to_char-函数","id":30},{"text":"3.28. to_date 函数","hash":"to_date-函数","id":31},{"text":"3.29. to_timestamp 函数","hash":"to_timestamp-函数","id":32},{"text":"3.30. to_timestamp_tz 函数","hash":"to_timestamp_tz-函数","id":33},{"text":"3.31. to_yminterval 函数","hash":"to_yminterval-函数","id":34},{"text":"3.32. to_dsinterval 函数","hash":"to_dsinterval-函数","id":35},{"text":"3.33. numtodsinterval 函数","hash":"numtodsinterval-函数","id":36},{"text":"3.34. numtoyminterval 函数","hash":"numtoyminterval-函数","id":37},{"text":"3.35. localtimestamp 函数","hash":"localtimestamp-函数","id":38},{"text":"3.36. from_tz 函数","hash":"from_tz-函数","id":39},{"text":"3.37. sys_extract_utc 函数","hash":"sys_extract_utc-函数","id":40},{"text":"3.38. sessiontimezone 函数","hash":"sessiontimezone-函数","id":41},{"text":"3.39. hextoraw 函数","hash":"hextoraw-函数","id":42},{"text":"3.40. uid 函数","hash":"uid-函数","id":43},{"text":"3.41. USERENV 函数","hash":"userenv-函数","id":44},{"text":"3.42. ASCIISTR 函数","hash":"asciistr-函数","id":45},{"text":"3.43. TO_MULTI_BYTE 函数","hash":"to_multi_byte-函数","id":46},{"text":"3.44. TO_SINGLE_BYTE 函数","hash":"to_single_byte-函数","id":47},{"text":"3.45. COMPOSE 函数","hash":"compose-函数","id":48},{"text":"3.46. DECOMPOSE 函数","hash":"decompose-函数","id":49},{"text":"3.47. SYS_CONTEXT 函数","hash":"sys_context-函数","id":50}]},"62":{"id":62,"text":"IvorySQL中的CALL语句支持调用单独的函数存储过程，也可以是在包或对象类型中的函数和存储过程。CALL调用函数增加INTO子句语法，插入的目标是一个host variable。 CALL支持调用单独的或定义在包中函数和存储过程。 CALL调用函数时，增加INTO子句语法，插入的目标是一个host variable。 CALL调用无参或全取默认值的函数/存储过程不能省略空括号。 CALL在调用函数和存储过程时的参数或INTO子句中支持引用绑定变量。 CALL语句中OUT参数对应的绑定变量支持对精度和数据类型的验证。 输出绑定变量不允许被重复绑定。 -- 创建函数 ivorysql=# create or replace function f_defs(a number default 1314) ivorysql-# return number ivorysql-# is ivorysql-# begin ivorysql-# raise notice '%', a; ivorysql-# return a; ivorysql-# end; ivorysql-# / CREATE FUNCTION -- 声明绑定变量 ivorysql=# variable x number -- 调用函数并获取返回值 ivorysql=# call f_defs() into :x; NOTICE: 1314 Call completed. ivorysql=# print x X ------ 1314 ivorysql=# create table tb1(c1 int); CREATE TABLE -- 创建包规范 ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# procedure test_p ; ivorysql-# end; ivorysql-# / CREATE PACKAGE -- 创建包体 ivorysql=# create or replace package body pkg is ivorysql-# procedure test_p is ivorysql-# begin ivorysql-# insert into tb1 values(1); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 2; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY -- 调用包中的过程 ivorysql=# call pkg.test_p(); CALL ivorysql=# select * from tb1; c1 ----- 1 (1 row) -- 创建带默认参数函数 ivorysql=# CREATE OR REPLACE FUNCTION default_arg_func(p_num NUMBER DEFAULT 100) RETURN NUMBER AS ivorysql-# BEGIN ivorysql-# RETURN p_num + 5; ivorysql-# END; ivorysql-# / CREATE FUNCTION -- 正确调用默认参数函数（必须带括号） ivorysql=# VARIABLE default_result NUMBER; ivorysql=# CALL default_arg_func() INTO :default_result; -- 使用默认值100 Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 105 -- 带参数调用 ivorysql=# CALL default_arg_func(200) INTO :default_result; Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 205 -- 创建独立函数 stand_alone_func CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER) RETURN NUMBER IS BEGIN RETURN p_input * 2; END; / -- 设置输入绑定变量 ivorysql=# VARIABLE input_num NUMBER = 7; -- 使用绑定变量作为参数调用函数 ivorysql=# VARIABLE func_result NUMBER; ivorysql=# CALL stand_alone_func(:input_num) INTO :func_result; Call completed. ivorysql=# PRINT func_result; FUNC_RESULT ------------- 14 -- 创建带OUT参数的过程 ivorysql=# CREATE OR REPLACE PROCEDURE out_param_proc( ivorysql(# p_in IN VARCHAR2, ivorysql(# p_out OUT VARCHAR2, ivorysql(# p_num_out OUT NUMBER ivorysql(# ) AS ivorysql-# BEGIN ivorysql-# p_out := p_in || ' processed'; ivorysql-# p_num_out := LENGTH(p_in); ivorysql-# END; ivorysql-# / CREATE PROCEDURE -- 测试OUT参数类型匹配 ivorysql=# VARIABLE out_var VARCHAR2(50); ivorysql=# VARIABLE num_var NUMBER; ivorysql=# CALL out_param_proc('Test input', :out_var, :num_var); Call completed. ivorysql=# PRINT out_var; OUT_VAR ---------------------- Test input processed ivorysql=# PRINT num_var; NUM_VAR --------- 10 -- 测试OUT参数精度不足（会截断） ivorysql=# VARIABLE short_out VARCHAR2(5); ivorysql=# CALL out_param_proc('Long input string', :short_out, :num_var); Call completed. ivorysql=# PRINT short_out; SHORT_OUT ----------- Long -- 测试类型不匹配（会报错） ivorysql=# VARIABLE wrong_type NUMBER; ivorysql=# CALL out_param_proc('Test', :wrong_type, :num_var); ERROR: invalid input syntax for type numeric: \"Test processed\" -- 准备绑定变量 ivorysql=# VARIABLE dup_var VARCHAR2(100); -- 尝试重复绑定（会报错） ivorysql=# CALL out_param_func('Test', :dup_var) INTO :dup_var; ERROR: output parameter cannot be a duplicate bind -- 正确做法：使用不同的绑定变量 ivorysql=# VARIABLE out1 VARCHAR2(100); ivorysql=# CALL out_param_proc('Correct usage', :out1, :num_var); Call completed. ivorysql=# PRINT out1; OUT1 ------------------------- Correct usage processed","title":"兼容Oracle的CALL INTO","component":"ivorysql-doc","version":"master","name":"compat_call_into","url":"/ivorysql-doc/master/oracle_compatibility/compat_call_into.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3},{"text":"3.1. call into调用函数，并插入host variable","hash":"call-into调用函数并插入host-variable","id":4},{"text":"3.2. call调用包中的存储过程","hash":"call调用包中的存储过程","id":5},{"text":"3.3. 无参和默认参数调用","hash":"无参和默认参数调用","id":6},{"text":"3.4. 在参数或INTO子句中引用绑定变量","hash":"在参数或into子句中引用绑定变量","id":7},{"text":"3.5. CALL语句中OUT参数支持精度和数据类型验证","hash":"call语句中out参数支持精度和数据类型验证","id":8},{"text":"3.6. 输出绑定变量不允许重复绑定","hash":"输出绑定变量不允许重复绑定","id":9}]},"63":{"id":63,"text":"为了满足PG和Oracle的引用标识符大小写兼容，ivorysql设计了三种引用标识符的大小写转换模式。通过guc参数“identifier_case_switch”选择转换模式; 如果 guc参数“identifier_case_switch”值为“normal” 1). 保持双引号所引用的标识符中的字母大小写不变。 如果 guc参数“identifier_case_switch”值为“interchange”： 1). 如果双引号所引用的标识符中的字母全部为大写，则将大写转换为小写。 2). 如果双引号所引用的标识符中的字母全部为小写，则将小写转换为大写。 3). 如果用双引号引起来的标识符中的字母是大小写混合的，则保持标识符不变。 如果 guc参数“identifier_case_switch”值为“lowercase” 1). 如果双引号所引用的标识符中的字母全部为大写，则将大写转换为小写。 2). 如果用双引号引起来的标识符中的字母是大小写混合的，则保持标识符不变。 在initdb程序中加入 -C选项设置大小写转换模式，-C对应的值为： \"normal\" ------ \"0\"同义 \"interchange\" ------ \"1\"同义 \"lowercase\" ------ \"2\"同义 在初始化数据库集簇的过程中，将大小写转换模式保存到data目录的global/pg_control文件中。 normal ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = normal; SET ivorysql=# CREATE TABLE \"NORMAL_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Normal_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"normal_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"NORMAL_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Normal_1\"; ERROR: relation \"Normal_1\" does not exist LINE 1: select * from \"Normal_1\"; ivorysql=# select * from \"normal_1\"; ERROR: relation \"normal\" does not exist LINE 1: select * from \"normal\"; ivorysql=# select * from NORMAL_1; ERROR: relation \"normal_1\" does not exist LINE 1: select * from NORMAL_1; ivorysql=# select * from \"Normal_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_2\"; ERROR: relation \"NORMAL_2\" does not exist LINE 1: select * from \"NORMAL_2\"; ivorysql=# select * from \"normal_2\"; ERROR: relation \"normal_2\" does not exist LINE 1: select * from \"normal_2\"; ivorysql=# select * from Normal_2; ERROR: relation \"normal_2\" does not exist LINE 1: select * from Normal_2; ivorysql=# select * from \"normal_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_3\"; ERROR: relation \"NORMAL_3\" does not exist LINE 1: select * from \"NORMAL_3\"; ivorysql=# select * from \"Normal_3\"; ERROR: relation \"Normal_3\" does not exist LINE 1: select * from \"Normal_3\"; ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE interchange ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = interchange; SET ivorysql=# CREATE TABLE \"INTER_CHANGE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Inter_Change_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"inter_change_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"INTER_CHANGE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_1\"; ERROR: relation \"Inter_Change_1\" does not exist LINE 1: select * from \"Inter_Change_1\"; ivorysql=# select * from \"inter_change_1\"; ERROR: relation \"INTER_CHANGE_1\" does not exist LINE 1: select * from \"inter_change_1\"; ivorysql=# select * from INTER_CHANGE_1; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_2\"; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from \"INTER_CHANGE_2\"; ivorysql=# select * from \"inter_change_2\"; ERROR: relation \"INTER_CHANGE_2\" does not exist LINE 1: select * from \"inter_change_2\"; ivorysql=# select * from Inter_Change_2; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from Inter_Change_2; ivorysql=# select * from \"inter_change_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_3\"; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# select * from \"Inter_Change_3\"; ERROR: relation \"Inter_Change_3\" does not exist LINE 1: select * from \"Inter_Change_3\"; ivorysql=# select * from inter_change_3; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# drop table \"INTER_CHANGE_1\"; DROP TABLE ivorysql=# drop table \"Inter_Change_2\"; DROP TABLE ivorysql=# drop table \"inter_change_3\"; DROP TABLE lowercase ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = lowercase; SET ivorysql=# CREATE TABLE \"LOWER_CASE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Lower_Case_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"lower_case_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"LOWER_CASE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_1\"; ERROR: relation \"Lower_Case_1\" does not exist LINE 1: select * from \"Lower_Case_1\"; ivorysql=# select * from \"lower_case_1\"; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from LOWER_CASE_1; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from \"Lower_Case_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"LOWER_CASE_2\"; ivorysql=# select * from \"lower_case_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"lower_case_2\"; ivorysql=# select * from Lower_Case_2; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from Lower_Case_2; ivorysql=# select * from \"lower_case_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_3\"; ERROR: relation \"Lower_Case_3\" does not exist LINE 1: select * from \"Lower_Case_3\"; ivorysql=# select * from LOWER_CASE_3; c1 | c2 ----+---- (0 行记录) ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE","title":"引用标识符的大小写转换设计","component":"ivorysql-doc","version":"master","name":"compat_case_conversion","url":"/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"2.1. 大小写转换的三种模式（默认为interchange）","hash":"大小写转换的三种模式默认为interchange","id":3},{"text":"2.2. 初始化数据库集簇时","hash":"初始化数据库集簇时","id":4},{"text":"2.3. 经典用例","hash":"经典用例","id":5}]},"64":{"id":64,"text":"在 IvorySQL 的 Oracle 兼容模式下,支持将空字符串转换成NULL进行存储,提供与Oracle数据库一致的行为。 Oracle兼容模式下支持将空字符串转换成NULL进行存储。 通过参数 ivorysql.enable_emptystring_to_null 控制该特性,默认值为 on。 当该参数开启时,插入空字符串会自动转换为NULL值存储。 可以通过 IS NULL 条件查询到转换后的NULL值。 -- 创建测试表 ivorysql=# create table abc (id int); CREATE TABLE -- 查看空字符串转NULL参数状态 ivorysql=# show ivorysql.enable_emptystring_to_null; ivorysql.enable_emptystring_to_NULL ------------------------------------- on (1 row) -- 插入空字符串 ivorysql=# insert into abc values(''); INSERT 0 1 -- 查询表数据,显示为NULL ivorysql=# select * from abc; id \\---- (1 row) -- 使用IS NULL条件查询 ivorysql=# select * from abc where id is null; id \\---- (1 row)","title":"兼容Oracle 空字符串转NULL","component":"ivorysql-doc","version":"master","name":"compat_empty_string_to_null","url":"/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"65":{"id":65,"text":"本文档解释 IvorySQL 中 Force View 的用途，帮助用户在依赖对象尚未就绪时仍可创建强制视图占位并保持与 Oracle 行为一致。 Force View 可支持在依赖表尚未准备好的情况下进行视图迁移，在依赖满足后通过自动或显式编译快速恢复为普通视图。 CREATE [OR REPLACE] FORCE VIEW：即使查询引用了不存在的表或函数，也会创建视图占位对象并保存原始 SQL，提示“View created with compilation errors”。 自动编译：访问 Force View 时会尝试重新编译；成功后变为普通视图，失败则报错 view \"<schema>.<name>\" has errors。 依赖失效回退：当普通视图因依赖对象被删除或结构变更而失效时，系统会自动将其转化为 Force View，保留最后一次有效定义以便后续恢复。 -- 引用不存在的基础表，验证可成功创建占位 CREATE FORCE VIEW fv_customer AS SELECT c_id, c_name FROM missing_customer; -- 期望输出：WARNING: View created with compilation errors -- 先补齐依赖对象 CREATE TABLE missing_customer( c_id int primary key, c_name text ); INSERT INTO missing_customer VALUES (1, 'Alice'); -- 访问 Force View 时自动尝试编译 SELECT * FROM fv_customer; -- 成功时视图转换为普通视图并返回数据 -- 再次让视图失效：重建定义为 Force View CREATE OR REPLACE FORCE VIEW fv_customer AS SELECT c_id, upper(c_name) AS c_name FROM missing_customer_v2; -- 显式编译：依赖仍缺少，保持 Force View 状态并输出 WARNING ALTER VIEW fv_customer COMPILE; -- 期望输出：WARNING: View altered with compilation errors -- 补齐依赖后再次编译 CREATE TABLE missing_customer_v2( c_id int, c_name text ); ALTER VIEW fv_customer COMPILE; -- 期望输出：ALTER VIEW 成功，视图恢复为普通视图","title":"Force View","component":"ivorysql-doc","version":"master","name":"compat_force_view","url":"/ivorysql-doc/master/oracle_compatibility/compat_force_view.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3},{"text":"3.1. 创建缺失依赖的 Force View","hash":"创建缺失依赖的-force-view","id":4},{"text":"3.2. 自动编译并恢复为普通视图","hash":"自动编译并恢复为普通视图","id":5},{"text":"3.3. 显式编译与失败回退","hash":"显式编译与失败回退","id":6}]},"66":{"id":66,"text":"PostgreSQL支持函数(FUNCTION)和存储过程(PROCEDURE)，但是在语法上和Oracle有差异，为了让Oracle的PLSQL语句可以在IvorySQL上执行，也就是“语法兼容”，IvorySQL采取这样的解决方案：如果Oracle的子句在IvorySQL中存在相同功能的子句，则直接映射为对应的IvorySQL子句，否则只实现其语法，不实现其功能。 具体的Oracle兼容支持包括以下几个方面： CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer AS BEGIN RETURN 1; END; / CREATE or replace NONEDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / configure --help --with-max-funarg=MAXFUNARG CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE OR REPLACE FUNCTION ora_func (num1 IN int, num2 IN int) RETURN int AS num3 int :=10; num4 int :=10; num5 int; BEGIN num3 := num1 + num2; num4 := num1 * num2; num5 := num3 * num4; RETURN num5; END; / CREATE FUNCTION select ora_func(5,9)from dual; ora_func ---------- 630 (1 row) CREATE OR REPLACE FUNCTION test_nocopy(a IN int, b OUT NOCOPY int, c IN OUT NOCOPY int) RETURN int IS BEGIN b := a; c := a; return 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID CURRENT_USER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON () IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING pg_catalog.int4 IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED TABLE POLYMORPHIC USING pg_catalog.int4 SQL_MACRO IS BEGIN RETURN 1; END; / alter function public.test_func noneditionable; alter function test_func compile; alter function test_func compile debug; alter function test_func compile debug sd = mv; alter function test_func compile debug reuse settings; CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE NONEDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = NONE IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID DEFINER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS p integer := 20; begin raise notice '%', p; end; / alter procedure test_proc editionable; alter procedure public.test_proc noneditionable; alter procedure test_proc compile; alter procedure test_proc compile debug; alter procedure test_proc compile debug sd = mv; alter procedure test_proc compile debug reuse settings; create or replace function f_noparentheses return int is begin return 11; end; / select f_noparentheses from dual; create or replace procedure protest as begin raise notice 'protest'; end; / CALL protest(); 相关视图位于文件 contrib/ivorysql_ora/src/sysview/sysview--1.0.sql 中，包括DBA_PROCEDURES，ALL_PROCEDURES，USER_PROCEDURES，DBA_SOURCE，ALL_SOURCE，USER_SOURCE，DBA_ARGUMENTS，ALL_ARGUMENTS，USER_ARGUMENTS等。","title":"PL/iSQL函数与存储过程","component":"ivorysql-doc","version":"master","name":"compat_function_procedure","url":"/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 兼容支持","hash":"兼容支持","id":2},{"text":"2.1. CREATE FUNCTION 语法支持 EDITIONABLE/NONEDITIONABLE","hash":"create-function-语法支持-editionablenoneditionable","id":3},{"text":"2.2. CREATE FUNCTION 语法支持 RETURN/IS 关键字，无需指定语言","hash":"create-function-语法支持-returnis-关键字无需指定语言","id":4},{"text":"2.3. CREATE FUNCTION 语法函数没有参数，函数名后面不带()","hash":"create-function-语法函数没有参数函数名后面不带","id":5},{"text":"2.4. CREATE FUNCTION 参数个数增加，最大值可在configure时指定","hash":"create-function-参数个数增加最大值可在configure时指定","id":6},{"text":"2.5. CREATE FUNCTION 语法中 END; 在psql中以/结束","hash":"create-function-语法中-end-在psql中以结束","id":7},{"text":"2.6. CREATE FUNCTION 语法变量声明前面没有 DECLARE 关键字","hash":"create-function-语法变量声明前面没有-declare-关键字","id":8},{"text":"2.7. CREATE FUNCTION 语法支持 OUT 参数 NOCOPY 功能","hash":"create-function-语法支持-out-参数-nocopy-功能","id":9},{"text":"2.8. CREATE FUNCTION 语法支持 sharing 子句","hash":"create-function-语法支持-sharing-子句","id":10},{"text":"2.9. CREATE FUNCTION 语法支持 invoker_rights (AUTHID) 子句，默认权限改成 DR（DEFINER）","hash":"create-function-语法支持-invoker_rights-authid-子句默认权限改成-drdefiner","id":11},{"text":"2.10. CREATE FUNCTION 语法支持 ACCESSIBLE BY","hash":"create-function-语法支持-accessible-by","id":12},{"text":"2.11. CREATE FUNCTION 语法支持 DEFAULT COLLATION","hash":"create-function-语法支持-default-collation","id":13},{"text":"2.12. CREATE FUNCTION 语法支持 deterministic 子句，功能与 IvorySQL 的 IMMUTABLE 相同","hash":"create-function-语法支持-deterministic-子句功能与-ivorysql-的-immutable-相同","id":14},{"text":"2.13. CREATE FUNCTION 语法支持 parallel_enable 子句，功能与 IvorySQL 的 PARALLEL SAFE 相同","hash":"create-function-语法支持-parallel_enable-子句功能与-ivorysql-的-parallel-safe-相同","id":15},{"text":"2.14. CREATE FUNCTION 语法支持 result_cache 子句","hash":"create-function-语法支持-result_cache-子句","id":16},{"text":"2.15. CREATE FUNCTION 语法支持 aggregate 子句","hash":"create-function-语法支持-aggregate-子句","id":17},{"text":"2.16. CREATE FUNCTION 语法支持 pipelined 子句","hash":"create-function-语法支持-pipelined-子句","id":18},{"text":"2.17. CREATE FUNCTION 语法支持 sql_macro 子句","hash":"create-function-语法支持-sql_macro-子句","id":19},{"text":"2.18. ALTER FUNCTION 语法兼容","hash":"alter-function-语法兼容","id":20},{"text":"2.19. CREATE PROCEDURE 语法支持 EDITIONABLE / NONEDITIONABLE","hash":"create-procedure-语法支持-editionable-noneditionable","id":21},{"text":"2.20. CREATE PROCEDURE 语法函数没有参数，函数名后面不带()","hash":"create-procedure-语法函数没有参数函数名后面不带","id":22},{"text":"2.21. CREATE PROCEDURE 语法中 END; 在psql中以 / 结束","hash":"create-procedure-语法中-end-在psql中以-结束","id":23},{"text":"2.22. CREATE PROCEDURE 语法支持 sharing 子句","hash":"create-procedure-语法支持-sharing-子句","id":24},{"text":"2.23. CREATE PROCEDURE 语法支持 DEFAULT COLLATION 子句","hash":"create-procedure-语法支持-default-collation-子句","id":25},{"text":"2.24. CREATE PROCEDURE 语法支持 invoker_rights (AUTHID) 子句","hash":"create-procedure-语法支持-invoker_rights-authid-子句","id":26},{"text":"2.25. CREATE PROCEDURE 语法支持 ACCESSIBLE BY 子句","hash":"create-procedure-语法支持-accessible-by-子句","id":27},{"text":"2.26. ALTER PROCEDURE 语法兼容","hash":"alter-procedure-语法兼容","id":28},{"text":"2.27. 存储过程和函数没有参数，SELECT调用支持不带 ()","hash":"存储过程和函数没有参数select调用支持不带","id":29},{"text":"2.28. 函数和存储过程相关的视图","hash":"函数和存储过程相关的视图","id":30},{"text":"2.29. 支持—​和/**/两种注释方法","hash":"支持和两种注释方法","id":31},{"text":"2.30. pg_dump备份SQL脚本文件的备份格式时，在函数的定义最后增加一个斜线 /","hash":"pg_dump备份sql脚本文件的备份格式时在函数的定义最后增加一个斜线","id":32}]},"67":{"id":67,"text":"本文档意在为使用 like 模糊查询的人员提供一个深入了解兼容Oracle 的模糊查询like实现的过程，是like兼容的实现文档。 数据库名称 like模糊查询 oracle oracle的字符串类型是varchar2，支持对数字、日期、字符串字段类型的列用Like关键字配合通配符来实现模糊查询 IvorySQL IvorySQL的字符串基本类型是text，所以like是以text为基础上，其他IvorySQL的类型能隐式转换成text，这样不用创建operator就能自动转换 create table t_ora_like (id int ,str1 varchar(8), date1 timestamp with time zone, date2 time with time zone, num int, str2 varchar(8)); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123456,'test1','2022-09-26 16:39:20','2022-09-26 16:39:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123457,'test2','2022-09-26 16:40:20','2022-09-26 16:40:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (223456,'test3','2022-09-26 16:41:20','2022-09-26 16:41:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123458,'test4','2022-09-26 16:42:20','2022-09-26 16:42:20'); select * from t_ora_like where str1 like 'test%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date1 like '2022%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date2 like '16%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where id like '123%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (3 rows) select * from t_ora_like where id like null; id | str1 | date1 | date2 | num | str2 ----+------+-------+-------+-----+------ (0 rows)","title":"兼容Oracle like","component":"ivorysql-doc","version":"master","name":"compat_like_operator","url":"/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"68":{"id":68,"text":"在 IvorySQL 中使用 Oracle 风格嵌套子函数。 支持在匿名块、函数或过程内部声明与调用子函数/子过程，作用域限定在父块内。 子函数可读取及更新父级变量，也可定义自身局部变量；父级无法直接访问子函数内部状态。 支持重载解析机制，按参数个数、类型或命名区分同名子程序。 DO $$ DECLARE v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; RETURN p_value * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; END; $$ LANGUAGE plisql; DO $$ DECLARE v_base_multiplier integer := 20; v_audit_counter integer := 0; v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; v_audit_counter := v_audit_counter + 1; RETURN v_base_multiplier * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; RAISE NOTICE 'v_audit_counter=%', v_audit_counter; END; $$ LANGUAGE plisql; -- Polymorphic nested function specializing on argument type DO $$ DECLARE v_last_notice text := 'none'; FUNCTION describe_value(p_input anyelement) RETURN text IS BEGIN v_last_notice := format('polymorphic dispatch with %s', pg_typeof(p_input)); RETURN v_last_notice; END; FUNCTION describe_value(p_input anyarray, p_element anyelement) RETURN text IS BEGIN v_last_notice := format('array dispatch with %s', pg_typeof(p_input)::text); RETURN v_last_notice; END; BEGIN RAISE NOTICE '%', describe_value(100); RAISE NOTICE '%', describe_value('IvorySQL'::text); -- explicit cast avoids ambiguous literal RAISE NOTICE '%', describe_value(ARRAY[1,2,3], NULL::int); -- extra arg guides anyarray resolution RAISE NOTICE 'last notice=%', v_last_notice; END; $$ LANGUAGE plisql;","title":"兼容Oracle 嵌套子函数","component":"ivorysql-doc","version":"master","name":"compat_nested_function","url":"/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"69":{"id":69,"text":"NLS 是 National Language Support（国家语言支持）的缩写，指的是 Oracle 提供的本地化支持功能。 IvorySQL提供兼容Oracle的NLS参数功能。 包含如下参数： 参数名称 功能描述 ivorysql.datetime_ignore_nls_mask 表示日期格式是否忽略NLS参数影响，默认为0。 nls_length_semantics 兼容Oracle的同名参数，表示char/varchar/varchar2的类型修饰符的大小单位是字节还是字符。 nls_date_format 表示默认的日期格式，可以通过show命令查看，默认为‘YYYY-MM-DD’。 nls_timestamp_format 兼容Oracle的同名参数，控制带时间的日期格式。 nls_timestamp_tz_format 兼容Oracle的同名参数，控制带时区的日期时间格式。 nls_territory 兼容Oracle的同名参数，指定数据库的默认区域。 nls_iso_currency 兼容Oracle的同名参数，指定国家和区域对应的唯一货币符。 nls_currency 兼容Oracle的同名参数，指定显示本地货币的符号，对应数字字符串格式中占位符L。 示例： ivorysql=# set ivorysql.datetime_ignore_nls_mask = 0; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ERROR: datetime format picture ends before converting entire input string LINE 1: select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ^ ivorysql=# set ivorysql.datetime_ignore_nls_mask = 2; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) 示例： ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) ivorysql=# set nls_timestamp_format=\"pg\"; SET ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; ERROR: date format not recognized LINE 1: select '2025-10-15 11:00:00.102030 '::oratimestamp ; ^ IvorySQL使用nls_length_semantics参数的值来决定长度类型，有byte和char两种值，默认为byte。 示例： vorysql=# alter session set nls_length_semantics = char; SET ivorysql=# create table character_tb(char_c char(6), char_b varchar2(6 byte), char_v varchar(6)); CREATE TABLE ivorysql=# insert into character_tb values('中文测试数据', '123456', '成功'); INSERT 0 1 ivorysql=# select * from character_tb ; char_c | char_b | char_v --------------+--------+-------- 中文测试数据 | 123456 | 成功 (1 row) ivorysql=# select length(char_b), length(char_c), length(char_v) from character_tb; length | length | length --------+--------+-------- 6 | 6 | 2 (1 row) 示例： ivorysql=# show ivorysql.identifier_case_switch; ivorysql.identifier_case_switch --------------------------------- interchange (1 row) ivorysql=# set nls_currency to \"CHINA\"; SET ivorysql=# show nls_currency; nls_currency -------------- CHINA (1 row) ivorysql=# set nls_currency to \"China\"; SET ivorysql=# show nls_currency; nls_currency -------------- China (1 row) nls_currency和nls_iso_currency的行为，受兼容oracle大小写特性 identifier_case_switch 影响","title":"NLS 参数","component":"ivorysql-doc","version":"master","name":"compat_nls_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. NLS 日期掩码设置","hash":"nls-日期掩码设置","id":3},{"text":"2.2. 禁用NLS日期/时间戳参数","hash":"禁用nls日期时间戳参数","id":4},{"text":"2.3. 设置nls_length_semantics","hash":"设置nls_length_semantics","id":5},{"text":"2.4. 设置NLS货币符号","hash":"设置nls货币符号","id":6}]},"70":{"id":70,"text":"IvorySQL提供了兼容Oracle的out参数功能，包括含有out参数的函数与过程、匿名块支持out参数、libpq支持out参数。 本文档旨在为使用人员介绍out参数的功能。 IvorySQL提供了兼容Oracle的out参数功能，包括如下内容。 创建语法如下： CREATE [ OR REPLACE ] FUNCTION name ( [ [ argmode ] [ argname ] argtype [ { DEFAULT | = } default_expr ] [, ...] ] ) [ RETURNS rettype | RETURNS TABLE ( column_name column_type [, ...] ) ] { LANGUAGE lang_name | TRANSFORM { FOR TYPE type_name } [, ... ] | WINDOW | { IMMUTABLE | STABLE | VOLATILE } | [ NOT ] LEAKPROOF | { CALLED ON NULL INPUT | RETURNS NULL ON NULL INPUT | STRICT } | { [ EXTERNAL ] SECURITY INVOKER | [ EXTERNAL ] SECURITY DEFINER } | PARALLEL { UNSAFE | RESTRICTED | SAFE } | COST execution_cost | ROWS result_rows | SUPPORT support_function | SET configuration_parameter { TO value | = value | FROM CURRENT } | AS 'definition' | AS 'obj_file', 'link_symbol' | sql_body } ... 其中argmode可以是IN，OUT，INOUT（IN OUT），VARIADIC。如果不指定默认是IN。argmode也可以写在argname的后面。 不同于原生PG，兼容Oracle的out参数和返回值数据类型没有关联。IN OUT模式和OUT模式都不能有默认值。如果有out参数，则函数体中必须有RETURN语句。 支持冒号占位符形式的绑定变量，例如: 1，:name。 新增DO+USING语法： DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, …​] 支持在libpq中按位置和按参数名字绑定变量，提供系统函数get_parameter_descr，该函数根据SQL语句，返回变量名字与位置的关系。 libpq接口端提供准备、绑定、执行函数，这些函数与OCI相应函数类似。 使用流程为： 使用IvyHandleAlloc分配语句句柄和错误句柄。 调用IvyStmtPrepare准备语句。 调用IvyBindByPos或IvyBindByName 绑定参数。 调用IvyStmtExecute 执行，可重复执行。 调用IvyFreeHandle 释放语句句柄和错误句柄。 另外还实现了Ivyconnectdb，Ivystatus，Ivyexec，IvyresultStatus，IvyCreatePreparedStatement，IvybindOutParameterByPos，IvyexecPreparedStatement，IvyexecPreparedStatement2，Ivynfields，Ivyntuples，Ivyclear等一系列接口函数。 在源代码中 src/interfaces/libpq/ivytest 目录里可以找到示例程序。 out参数和返回值数据类型没有关联 ``` ivorysql=# create or replace function test_return_out(id integer,price out integer,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000; ivorysql-# name := 'test a char out'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / CREATE FUNCTION ``` IN OUT模式和OUT模式都不能有默认值 ``` ivorysql=# create or replace function test_return_inout(id integer,price in out integer default 100,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000 + price; ivorysql-# name := 'this is a test'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / ERROR: IN OUT formal parameters may have no default expressions ``` 如果有out参数，并且函数返回类型不是void，则函数体中必须有RETURN语句 ``` --if function's return type is non-void, the function body must has RETURN statement --if there is no RETURN statement, the function can be created, but when it is called, --an error is raised ivorysql=# create or replace function f2(id integer,price out integer) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 2; ivorysql-# end; ivorysql-# / CREATE FUNCTION ivorysql=# declare ivorysql-# a varchar(20); ivorysql-# b int; ivorysql-# begin ivorysql-# a := f2(1, b); ivorysql-# end; ivorysql-# / ERROR: Function returned without value CONTEXT: PL/iSQL function f2(pg_catalog.int4,pg_catalog.int4) line 0 at RETURN PL/iSQL function inline_code_block line 5 at assignment ``` 支持冒号占位符形式的绑定变量，新增DO+USING语法 ``` ivorysql=# do $$ ivorysql$# declare ivorysql$# a int; ivorysql$# begin ivorysql$# :x := 1; ivorysql$# :y := 2; ivorysql$# end; $$ using out, out; $1 | $2 ----+---- 1 | 2 (1 row) ``` 系统函数 get_parameter_descr ``` ivorysql=# select * from get_parameter_description('insert into t values(:x,:y)'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows) ```","title":"out参数","component":"ivorysql-doc","version":"master","name":"compat_out_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. 含有out参数的函数","hash":"含有out参数的函数","id":3},{"text":"2.2. 匿名块支持out参数","hash":"匿名块支持out参数","id":4},{"text":"2.3. libpq中调用含out参数的函数","hash":"libpq中调用含out参数的函数","id":5},{"text":"3. 测试用例","hash":"测试用例","id":6},{"text":"3.1. 含有out参数的函数","hash":"含有out参数的函数-2","id":7},{"text":"3.2. 匿名块支持out参数","hash":"匿名块支持out参数-2","id":8}]},"71":{"id":71,"text":"IvorySQL提供了兼容Oracle RowID的功能。RowID是一种伪列，在创建表时由数据库自动生成，对于数据库中的每一行，RowID 伪列返回该行的地址。 RowID 应当具备以下特性： 1. 逻辑地标识每一行，且值唯一 2. 可以通过ROWID快速查询和修改表的其他列，自身不能被插入和修改 3. 用户可以控制是否开启此功能 IvorySQL提供了多种方式来开启RowID功能。 在IvorySQL 的兼容Oracle 模式下，可以通过 set ivorysql.default_with_rowids to on 来开启RowID，这个参数默认值为off。打开后创建的表，默认就带有了RowID列，可以通过 \\d+ 表名 来查看。 ivorysql=# show ivorysql.default_with_rowids; ivorysql.default_with_rowids ------------------------------ off (1 row) ivorysql=# create table t(a int); CREATE TABLE ivorysql=# \\d+ t Table \"public.t\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap 用户可以选择在需要的表上带有这个选项，没有WITH ROWID选项，将会创建一个普通的表。 ivorysql=# create table t2(a int) with rowid; CREATE TABLE ivorysql=# \\d+ t2 Table \"public.t2\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Indexes: \"t2_16432_rowid_idx\" btree (rowid) Access method: heap Has ROWID: yes 这种方式允许当一个普通表需要使用 ROWID 时，通过ATLER 命令去添加ROWID 列。 也可以通过ALTER TABLE … SET WITHOUT ROWID命令去除RowID。 ivorysql=# create table t3(a int); CREATE TABLE ivorysql=# alter table t3 set with rowid; ALTER TABLE ivorysql=# \\d+ t3; Table \"public.t3\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap Has ROWID: yes","title":"RowID","component":"ivorysql-doc","version":"master","name":"compat_rowid","url":"/ivorysql-doc/master/oracle_compatibility/compat_rowid.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能开启","hash":"功能开启","id":2},{"text":"2.1. 通过GUC参数开启","hash":"通过guc参数开启","id":3},{"text":"2.2. 通过建表语句中增加 WITH ROWID 选项开启","hash":"通过建表语句中增加-with-rowid-选项开启","id":4},{"text":"2.3. 通过对现有表ALTER TABLE … SET WITH ROWID开启","hash":"通过对现有表alter-table-set-with-rowid开启","id":5}]},"72":{"id":72,"text":"IvorySQL的sys_guid() 是一个强大的随机数产生函数，它产生并返回一个由16个字节组成的数据库级别唯一的标识符(原始值)。 ivorysql=# select sys_guid() from dual; sys_guid ------------------------------------ \\x3ed9426c8a093442a38bea09a74f44a1 (1 row) create table student ( student_id raw(16) default sys_guid() primary key, student_name varchar2(100) not null ); insert into student(student_name) values ('Steven');","title":"sys_guid() 函数","component":"ivorysql-doc","version":"master","name":"compat_sys_guid","url":"/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 使用示例","hash":"使用示例","id":2},{"text":"3. sys_guid函数在建表时可以作为主键","hash":"sys_guid函数在建表时可以作为主键","id":3},{"text":"4. 新增数据时自动填充主键","hash":"新增数据时自动填充主键","id":4}]},"73":{"id":73,"text":"IvorySQL提供了兼容Oracle的plsql数据类型功能，包括%TYPE、%ROWTYPE。 本文档旨在为使用人员介绍%TYPE、%ROWTYPE的功能。 IvorySQL提供了兼容Oracle的%TYPE、%ROWTYPE功能，包括如下内容。 创建表以及函数。 CREATE TABLE t1(id int, name varchar(20)); --function's parameter datatype is tablename.columnname%TYPE CREATE OR REPLACE FUNCTION fun1(v t1.id%TYPE) RETURN varchar AS BEGIN RETURN v; END; / 函数状态是valid，并且执行函数能够成功。 SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) SELECT fun1(1) FROM dual; fun1 ------ 1 (1 row) 修改引用的先前定义的声明，函数状态变为invalid，但是函数能够执行成功。 ALTER TABLE t1 ALTER COLUMN id TYPE varchar(20); SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --n prostatus ----------- n (1 row) --after changing the column id type from int to varchar, call the function again SELECT fun1('a') FROM dual; --successfully fun1 ------ a (1 row) 重新编译函数，状态重新变成valid。 ALTER FUNCTION fun1 COMPILE; SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) 示例： --the following testcase will fail DECLARE name VARCHAR(25) NOT NULL := 'Niu'; surname name%TYPE ; BEGIN raise notice 'name=%' ,name; raise notice 'surname=%' ,surname; END; / ERROR: variable \"surname\" must have a default value, since it's declared NOT NULL LINE 3: surname name%TYPE ; ^ 示例： CREATE TABLE employees(first_name varchar(20) not null, last_name varchar(20) not null, phone_number varchar(50)); INSERT INTO employees VALUES ('Steven','Niu','1-650-555-1234'); CREATE OR REPLACE PROCEDURE p0(v employees%ROWTYPE) AS BEGIN raise notice 'v.first_name = %, v.last_name = %, v.phone_number = %', v.first_name, v.last_name, v.phone_number; END; / DECLARE a employees%ROWTYPE; BEGIN select * into a from employees ; raise notice 'a=%', a; call p0(a); END; / NOTICE: a=(Steven,Niu,1-650-555-1234) NOTICE: v.first_name = Steven, v.last_name = Niu, v.phone_number = 1-650-555-1234 \\df p0 List of functions Schema | Name | Result data type | Argument data types | Type --------+------+------------------+------------------------+------ public | p0 | | IN v employees%ROWTYPE | proc (1 row) INSERT语句增强支持把一个%ROWTYPE声明的变量插入表中。 语法为： INSERT INTO table_name VALUES row_variable ; 示例： CREATE TABLE t1(id int, name varchar(20)); DECLARE v1 t1%ROWTYPE; BEGIN FOR i IN 1 .. 5 LOOP v1.id := i; v1.name := 'a' || i; INSERT INTO t1 VALUES v1; END LOOP; END; / SELECT * FROM t1; id | name ----+------ 1 | a1 2 | a2 3 | a3 4 | a4 5 | a5 (5 rows) 示例： CREATE TABLE t1(id int, name varchar(20)); DELETE FROM t1; DECLARE v1 t1%ROWTYPE; v2 t1%ROWTYPE; BEGIN v1.id := 11; v1.name := 'abc'; INSERT INTO t1 VALUES v1; v2.id := 22; v2.name := 'new'; UPDATE t1 SET ROW = v2; END; / SELECT * FROM t1; id | name ----+------ 22 | new (1 row)","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"compat_type_rowtype","url":"/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. 引用发生改变，%TYPE或%ROWTYPE声明的变量也相应改变。","hash":"引用发生改变type或rowtype声明的变量也相应改变","id":3},{"text":"2.2. %TYPE声明的变量继承引用变量的约束","hash":"type声明的变量继承引用变量的约束","id":4},{"text":"2.3. 表名%ROWTYPE或视图名%ROWTYPE作为函数或存储过程的参数类型和函数返回值类型","hash":"表名rowtype或视图名rowtype作为函数或存储过程的参数类型和函数返回值类型","id":5},{"text":"2.4. INSERT语句增强","hash":"insert语句增强","id":6},{"text":"2.5. UPDATE语句增强","hash":"update语句增强","id":7}]},"74":{"id":74,"text":"本功能的引入是为了兼容Oracle的不可见列功能，将使数据库设计更加简洁，数据管理的灵活性得到提高，用户可以更好地控制列的可见性。 在应用迁移过程中，不可见列也很有用。使新列不可见意味着它们不会被任何现有应用程序看到，但仍然可以被任何新应用程序引用，从而使应用程序的在线迁移更加简单。 不可见列功能使用户能够在执行诸如SELECT * 等操作时隐藏特定列。可以使用不可见列对表进行更改，而不会干扰使用该表的应用程序。对表的任何通用访问都不会显示不可见的列，必须通过列名显式引用不可见列，查询和其他操作才能访问不可见列。 在Oracle模式下，psql的扩展描述命令（\\d+）增加了对不可见列的支持。 ivorysql=# CREATE TABLE mytable (a INT, b INT INVISIBLE, c INT); CREATE TABLE 只有在INSERT语句中显式指定不可见列时，才能向不可见列插入值。向包含不可见列的表插入值时不能省略列列表，否则会报错。 ivorysql=# INSERT INTO mytable (a, b, c) VALUES (1,2,3); INSERT 0 1 ivorysql=# INSERT INTO mytable VALUES (4,5,6); ERROR: INSERT has more expressions than target columns LINE 1: INSERT INTO mytable VALUES (4,5,6); 使用SELECT语句查询时，如果要显示不可见列信息，必须显式指定该不可见列，否则将不显示该不可见列。 ivorysql=# select * from mytable; a | c ---+--- 1 | 3 (1 row) ivorysql=# select a,b,c from mytable; a | b | c ---+---+--- 1 | 2 | 3 (1 row) ivorysql=# \\d+ mytable Table \"public.mytable\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | b | pg_catalog.int4 | | | | invisible | plain | | | c | pg_catalog.int4 | | | | | plain | | | Access method: heap 目前不支持modify column，将在未来版本实现； 在Oracle中，不可见列和列顺序有特别的注意事项。当一个表包含一个或多个不可见列时，这些不可见列不会包含在表的列顺序中。目前我们尚未对这块做处理。","title":"不可见列","component":"ivorysql-doc","version":"master","name":"invisible_column","url":"/ivorysql-doc/master/oracle_compatibility/invisible_column.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3},{"text":"3.1. 创建包含不可见列的表","hash":"创建包含不可见列的表","id":4},{"text":"3.2. 向不可见列插入值","hash":"向不可见列插入值","id":5},{"text":"3.3. 显示不可见列的输出","hash":"显示不可见列的输出","id":6},{"text":"3.4. 在Oracle模式下psql扩展描述命令（\\d+）对不可见列的支持","hash":"在oracle模式下psql扩展描述命令d对不可见列的支持","id":7},{"text":"4. 限制","hash":"限制","id":8}]},"75":{"id":75,"text":"IvorySQL提供了兼容Oracle自定义包的功能。包（package）是一个封装的相关程序对象集合，这些对象存储在数据库中。程序对象包括过程、函数、变量、常量、游标和异常。 本文档旨在为使用人员提供一个深入了解自定义包实现的过程。 IvorySQL提供了兼容Oracle自定义包的功能，包括包和包体的创建、修改和描述，在PostgreSQL交互终端（psql）中增加了与包相关的命令，新增了 \\dk 命令。 使用CREATE OR REPLACE PACKAGE语句创建或替换一个包的规范。包是相关的存储过程、函数、和其他程序对象的集合，在数据库中作为一个单元存储。包规范声明这些对象，在包体中定义这些对象。 在自己的模式下创建或替换一个包规范，必须有CREATE PROCEDURE系统权限，如果在其他用户的模式下创建或替换一个包，则需要有CREATE ANY PROCEDURE系统权限。 使用CREATE OR REPLACE PACKAGE BODY语句创建或替换一个包体。创建包体需要有与创建包规范一样的权限，要求包体与包规范在同一个模式下，且包规范必须存在，该语句定义包规范中声明的对象。 当包规范中有cursor或子过程时，那么必须拥有一个包体去定义它，否则包体是可选的。 使用ALTER PACKAGE语句来改变包的属性。执行ALTER PACKAGE语句的权限要求：必须是包的所有者，或者具有ALTER ANY PROCEDURE权限才能修改其他用户的包。 DROP PACKAGE语句删除数据库中的一个存储包。这个语句会同时删除包体和包规范。DROP PACKAGE BODY则只删除包体。 不能使用此语句删除包中的一个单独对象。权限要求：包必须在用户模式下，或用户有DROP ANY PROCEDURE系统权限。 DISCARD PACKAGE功能是为了兼容PostgreSQL的DISCARD功能而做的。 psql中支持 \\dk[+] 命令查看包与包体的定义信息。 命令 描述 \\dk[+] 列出当前能看到的包信息 \\dk[+] xxx 列出xxx包的包规范和包体内容 ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# var2 integer; ivorysql-# function test_f(id integer) return integer; ivorysql-# procedure test_p(id integer); ivorysql-# end; ivorysql-# / CREATE PACKAGE ivorysql=# create or replace package body pkg is ivorysql-# var3 integer; ivorysql-# function test_f(id integer) return integer is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg test_f'); ivorysql-# return id; ivorysql-# end; ivorysql-# procedure test_p(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg proc'); ivorysql-# end; ivorysql-# --privite function ivorysql-# function test_piv1(id integer) return integer is ivorysql-# begin ivorysql-# return id; ivorysql-# end; ivorysql-# --privite procedure ivorysql-# procedure test_piv2(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('privite proc'); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 1; ivorysql-# var2 := 2; ivorysql-# var3 := 4; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY ivorysql=# alter package pkg noneditionable; ALTER PACKAGE ivorysql=# Drop package pkg; DROP PACKAGE ivorysql=# Drop package body pkg; DROP PACKAGE BODY ivorysql=# discard package; DISCARD PACKAGES ivorysql=# \\dk List of packages Schema | Name | Owner --------+----------+---------- public | pkg | ivorysql public | test_pkg | ivorysql (2 rows) ivorysql=# \\dk pkg List of packages Schema | Name | Owner --------+------+---------- public | pkg | ivorysql (1 row) ivorysql=# \\dk pkg1 Did not find any package named \"pkg1\". ivorysql=# \\dk+ List of packages Schema | Name | Owner | Security | Editionable | Use Collation | Specification | Package Body --------+----------+----------+----------+-------------+---------------+-----------------------------------------------+----------------------------------------------------- --------- public | pkg | ivorysql | definer | Editionable | default | var1 integer; +| | | | | | | var2 integer; +| | | | | | | function test_f(id integer) return integer; +| | | | | | | procedure test_p(id integer); +| | | | | | | end | public | test_pkg | ivorysql | definer | Editionable | default | var1 integer; +| FUNCTION test_f(id integer) RETURN integer IS + | | | | | | FUNCTION test_f(id integer) RETURN integer;+| BEGIN + | | | | | | end | dbms_output.put_line('invoke function test_pkg.t est_f');+ | | | | | | | RETURN 23; + | | | | | | | end; + | | | | | | | BEGIN + | | | | | | | var1 := 23; + | | | | | | | end (2 rows)","title":"包","component":"ivorysql-doc","version":"master","name":"package","url":"/ivorysql-doc/master/oracle_compatibility/package.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"2.1. 创建包规范","hash":"创建包规范","id":3},{"text":"2.2. 创建包体","hash":"创建包体","id":4},{"text":"2.3. 更新包","hash":"更新包","id":5},{"text":"2.4. 删除包和包体","hash":"删除包和包体","id":6},{"text":"2.5. 丢弃包","hash":"丢弃包","id":7},{"text":"2.6. 在psql中使用 \\dk[+] 查看包与包体定义","hash":"在psql中使用-dk-查看包与包体定义","id":8},{"text":"3. 测试用例","hash":"测试用例","id":9},{"text":"3.1. 创建包规范","hash":"创建包规范-2","id":10},{"text":"3.2. 创建包体","hash":"创建包体-2","id":11},{"text":"3.3. 更新包","hash":"更新包-2","id":12},{"text":"3.4. 删除包和包体","hash":"删除包和包体-2","id":13},{"text":"3.5. 丢弃包","hash":"丢弃包-2","id":14},{"text":"3.6. 在psql中使用 \\dk[+] 查看包与包体定义","hash":"在psql中使用-dk-查看包与包体定义-2","id":15}]},"76":{"id":76,"text":"为了将Oracle端口、IP与PG的端口IP进行区分。现需要增加对ORAPORT和ORAHOST的处理; 新增ivoryhost：需要在连接时新增参数ivoryhost即可指定，其功能类似 host; 新增ivoryport：相较于host，port的功能相对复杂一些。其中涉及到可以在configure阶段配置，连接阶段指定端口; ./configure --with-oraport=5555 ./initdb .... ./pg_ctl -D ../data start ./pg_ctl -o \"-p 5433 -o 1522\" -D ../data","title":"新增Oracle兼容模式的端口与IP","component":"ivorysql-doc","version":"master","name":"port_ip","url":"/ivorysql-doc/master/oracle_compatibility/port_ip.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能","hash":"功能","id":2},{"text":"3. 测试用例：","hash":"测试用例","id":3}]},"77":{"id":77,"text":"本文档是IvorySQL兼容Oracle sequence文档，目的是可以在IvorySQL中使用Oracle的sequence语句。 序列是一个数据库对象，与表和视图类似，它表示可以由全局数据库命名空间中的任何表和视图使用的整数序列。可以使用NEXTVAL和CURRVAL访问序列值。序列可以是升序或降序 ivorysql=# CREATE sequence seq; CREATE SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 1 (1 row) ivorysql=# ALTER sequence seq restart start with 10; ALTER SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 10 (1 row) ivorysql=# DROP SEQUENCE seq; DROP SEQUENCE","title":"兼容Oracle sequence","component":"ivorysql-doc","version":"master","name":"sequence","url":"/ivorysql-doc/master/oracle_compatibility/sequence.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 功能说明","hash":"功能说明","id":2},{"text":"3. 测试用例","hash":"测试用例","id":3}]},"78":{"id":78,"text":"在Oracle中，常会出现带有XML函数的SQL代码，IvorySQL在PostgreSQL的基础上，实现与Oracle XML函数的高度兼容，确保了从Oracle迁移到IvorySQL后的数据格式和结构的一致性。这种兼容性意味着用户无需对现有的XML处理逻辑进行大规模修改，从而保证了数据的完整性和准确性。此外，IvorySQL的这种跨平台的兼容特性，也降低了因格式差异带来的额外用户维护和升级成本，使得数据处理和管理更加高效、可靠和灵活。 XML（eXtended Markup Language可扩展标记语言）是一种基于文本的，用于结构化任何可标记文档的格式语言。它是一种轻便的，可扩展的，标准的且简单易懂的保存数据的语言。 IvorySQL在实现与Oracle 12c中11个常用XML SQL函数的兼容性时，与PostgreSQL保持了一致，底层处理函数同样采用了libxml2库提供的接口。这些XML函数作为ivorysql_ora插件的一个子插件提供，确保了与PostgreSQL数据库在XML处理方面的兼容性和一致性。 由于Oracle的xml函数要求某些参数类型是 XMLType，比如下面这个existsnode()函数： 原型： EXISTSNODE(XMLType_instance, XPath_string [, namespace_string ]) 示例： SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a') from dual; 因此为了兼容，添加了一个 XMLType 数据类型，作用是把用户提供的字符串转换成内部的xmltype类型，让用户的SQL语句从Oracle迁移到IvorySQL不用做改动 另外，为了避免与PostgreSQL中已有的同名关键字“extract”产生混淆，IvorySQL将原有的同名关键字重命名为“PGEXTRACT”，以确保函数调用的清晰性和准确性。 在实现这11个Oracle兼容的XML函数时，IvorySQL采用了两种不同的实现方式。其中，除了UPDATEXML函数外，其他10个函数均通过SQL函数的方式进行实现。由于UPDATEXML函数的参数数量是不确定的，因此采用了表达式方式来实现，这要求为其编写专门的语法分析代码和执行器代码，以确保其功能的正确性和灵活性。 序号 函数名 功能简介 1 extract(XML) 该函数用于返回XML节点路径下的相应内容, 其中参数XMLType_instance用于指定XMLType实例，Xpath_string用于指定XML节点路径 2 extractvalue 该函数提供对XML内容的检索功能，extractvalue只能返回一个节点的一个值。 3 existsnode 该函数用于检查XML内容是否符合指定的路径表达式。 4 deletexml 该函数用于删除指定路径的XML节点 5 appendchildxml 该函数用于在XML对象中插入子节点。 它接受一个XML对象和一个XML片段作为参数，并将XML片段作为子节点插入到XML对象中 6 updatexml 该函数用于更新特定XML相应的节点路径的内容 7 insertxmlbefore 该函数用于在XML中的指定路径前插入子节点 8 insertxmlafter 该函数用于在XML中的指定路径后插入子节点 9 insertchildxml 该函数用于向指定的XML路径中插入子节点 10 insertchildxmlbefore 该函数用于向指定的XML路径之前插入子节点 11 insertchildxmlafter 该函数用于向指定的XML路径之后插入子节点 12 xmlisvalid 该函数用于检查XML格式是否正确 ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# create table inaf(a int, b xmltype); CREATE TABLE ivorysql=# insert into inaf values(1,xmltype('<a><b>100</b></a>')); INSERT 0 1 ivorysql=# insert into inaf values(2, ''); INSERT 0 1 ivorysql=# select * from inaf; a | b ---+------------------- 1 | <a><b>100</b></a> 2 | (2 rows) ivorysql=# create table xmltest(id int, data XMLType); CREATE TABLE ivorysql=# insert into xmltest values(1, '<value>one</value>'); INSERT 0 1 ivorysql=# insert into xmltest values(2, '<value>two</value>'); INSERT 0 1 ivorysql=# select * from xmltest; id | data ----+-------------------- 1 | <value>one</value> 2 | <value>two</value> (2 rows) ivorysql=# create table xmlnstest(id int, data xmltype); CREATE TABLE ivorysql=# INSERT INTO xmlnstest VALUES(1, xmltype('<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:typ=\"http://www.def.com\" xmlns:web=\"http://www.abc.com\"><soapenv:Body><web:BBB><typ:EEE>41</typ:EEE><typ:FFF>42</typ:FFF></web:BBB></soapenv:Body></soapenv:Envelope>')); INSERT 0 1 ivorysql# SELECT extract(XMLType('<AA><ID>1</ID></AA>'), '/AA/ID') from dual; extract ------------ <ID>1</ID> (1 row) ivorysql# SELECT extractvalue(XMLType('<a><b>100</b></a>'),'/a/b') from dual; extractvalue -------------- 100 (1 row) ivorysql=# SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a/b') from dual; existsnode ------------ 1 (1 row) ivorysql=# SELECT deletexml(XMLType('<test><value>oldnode</value><value>oldnode</value></test>'), '/test/value') from dual; deletexml ----------- <test/> (1 row) ivorysql=# SELECT appendchildxml(XMLType('<test><value></value><value></value></test>'), '/test/value', XMLTYPE('<name>newnode</name>')) from dual; appendchildxml -------------------------- <test> + <value> + <name>newnode</name>+ </value> + <value> + <name>newnode</name>+ </value> + </test> (1 row) ivorysql=# SELECT updatexml(xmltype('<value>one</value>'), '/value', xmltype('<newvalue>1111</newvalue>')) FROM dual; updatexml --------------------------- <newvalue>1111</newvalue> (1 row) ivorysql=# SELECT insertxmlbefore(XMLType('<a>222<b>100</b><b>200</b></a>'), '/a/b', XMLTYPE('<c>88</c>')) from dual; insertxmlbefore -------------------------------------------------- <a>222<c>88</c><b>100</b><c>88</c><b>200</b></a> (1 row) ivorysql=# SELECT insertxmlafter(XMLType('<a><b>100</b></a>'),'/a/b',XMLType('<c>88</c>')) from dual; insertxmlafter ---------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT insertchildxml(XMLType('<a>one<b></b>three<b></b></a>'), '//b', 'name', XMLTYPE('<name>newnode</name>')) from dual; insertchildxml ----------------------------------------------------------------------- <a>one<b><name>newnode</name></b>three<b><name>newnode</name></b></a> (1 row) ivorysql=# SELECT insertchildxmlbefore(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlbefore ---------------------- <a> + <c>88</c> + <b>100</b> + </a> (1 row) ivorysql=# SELECT insertchildxmlafter(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlafter --------------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a>')); xmlisvalid ------------ f (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a/>')); xmlisvalid ------------ t (1 row)","title":"XML","component":"ivorysql-doc","version":"master","name":"xml_functions","url":"/ivorysql-doc/master/oracle_compatibility/xml_functions.html","titles":[{"text":"1. 目的","hash":"目的","id":1},{"text":"2. 实现原理","hash":"实现原理","id":2},{"text":"3. 兼容函数如下","hash":"兼容函数如下","id":3},{"text":"4. XML函数使用示例","hash":"xml函数使用示例","id":4},{"text":"4.1. 准备表与数据","hash":"准备表与数据","id":5},{"text":"4.2. extract(XML)","hash":"extract(XML)","id":6},{"text":"4.3. extractvalue","hash":"extractvalue","id":7},{"text":"4.4. existsnode","hash":"existsnode","id":8},{"text":"4.5. deletexml","hash":"deletexml","id":9},{"text":"4.6. appendchildxml","hash":"appendchildxml","id":10},{"text":"4.7. updatexml","hash":"updatexml","id":11},{"text":"4.8. insertxmlbefore","hash":"insertxmlbefore","id":12},{"text":"4.9. insertxmlafter","hash":"insertxmlafter","id":13},{"text":"4.10. insertchildxml","hash":"insertchildxml","id":14},{"text":"4.11. insertchildxmlbefore","hash":"insertchildxmlbefore","id":15},{"text":"4.12. insertchildxmlafter","hash":"insertchildxmlafter","id":16},{"text":"4.13. xmlisvalid","hash":"xmlisvalid","id":17}]},"79":{"id":79,"text":"函数名称 current_catalog 参数数据类型 无输入参数 函数返回值数据类型 Name 函数含义 获得当前的database 名称。 注意：在SQL标准中，databases被称之为catalogs。 使用举例 postgres=# select current_catalog; current_catalog ----------------- postgres (1 row) postgres=# 函数名称 current_database() 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得当前的database 名称。 使用举例 postgres=# select current_database(); current_database ------------------ postgres (1 row) postgres=# 函数名称 current_query() 参数数据类型 无输入参数 函数返回值数据类型 text 函数含义 获得client端提交的当前执行的SQL语句的文本 使用举例 postgres=# BEGIN; BEGIN postgres=*# select 1,current_query(); ?column? | current_query ----------+--------------------------- 1 | select 1,current_query(); (1 row) postgres=*# select 2,current_query(); ?column? | current_query ----------+--------------------------- 2 | select 2,current_query(); (1 row) postgres=*# ROLLBACK; ROLLBACK postgres=# 函数名称 current_role 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 与current_user作用相同，获得当前执行上下文的username 使用举例 postgres=# select current_role; current_role -------------- pg131 (1 row) postgres=# 函数名称 current_schema 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得search path中的第一个schema名称（当search path为空时，返回空值）。当表或者其他对象建立不带schema名称时，本函数返回的schema就是该表或者该对象的schema 使用举例 postgres=# select current_schema; current_schema ---------------- public (1 row) postgres=# 函数名称 current_schema() 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 作用与current_schema函数相同：获得search path中的第一个schema名称（当search path为空时，返回空值）。当表或者其他对象建立不带schema名称时，本函数返回的schema就是该表或者该对象的schema 使用举例 postgres=# select current_schema(); current_schema ---------------- public (1 row) postgres=# 函数名称 current_schemas(include_implicit boolean) 参数数据类型 bool 函数返回值数据类型 name 函数含义 以优先级顺序返回有效search_path中当前所有模式名称，如果入参为true，那么在函数返回结果中会包括隐式搜索的系统模式pg_catalog 使用举例 postgres=# select current_schemas(false); current_schemas ----------------- {public} (1 row) postgres=# select current_schemas(true); current_schemas --------------------- {pg_catalog,public} (1 row) postgres=# 函数名称 current_user 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得当前执行上下文的username 使用举例 postgres=# select current_user; current_user -------------- pg131 (1 row) postgres=# 函数名称 inet_client_addr() 参数数据类型 无输入参数 函数返回值数据类型 inet 函数含义 获得当前client的ip地址。如果返回值为空，表示当前连接是经过Unix-domain socket的 使用举例 postgres=# select inet_client_addr(); inet_client_addr ------------------ (1 row) postgres=# 函数名称 inet_client_port() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 获得当前client的端口号。如果返回值为空，表示当前连接是经过Unix-domain socket的 使用举例 postgres=# select inet_client_port(); inet_client_port ------------------ (1 row) postgres=# 函数名称 inet_server_addr() 参数数据类型 无输入参数 函数返回值数据类型 inet 函数含义 获得当前连接的pg数据库服务器的ip地址。如果返回值为空，表示当前连接是经过Unix-domain socket的。 使用举例 postgres=# select inet_server_port(); inet_server_port ------------------ (1 row) postgres=# 函数名称 inet_server_port() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 获得当前连接的pg数据库服务器的端口号。如果返回值为空，表示当前连接是经过Unix-domain socket的。 使用举例 postgres=# select inet_server_port(); inet_server_port ------------------ (1 row) postgres=# 函数名称 pg_backend_pid() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 获得附加到当前session的backend进程的进程ID 使用举例 postgres=# select pg_backend_pid(); pg_backend_pid ---------------- 4068 (1 row) postgres=# 函数名称 pg_blocking_pids(integer) 参数数据类型 Integer 函数返回值数据类型 Integer[] 函数含义 返回入参pid的blocking pid(即:阻塞者pid)若是本函数返回空值，表示不存在阻塞者若是本函数返回值是零，那么表示阻塞者是prepared transacion，见下面的输出： postgres=# select pg_blocking_pids(12947); pg_blocking_pids ------------------ {0} (1 row) postgres=# 频繁调用本函数会对数据库性能有影响，因为本函数在短时间内对lock manager’s shared state进行排他访问(exclusive accesss) 使用举例 Session1： postgres=# SELECT pg_backend_pid(); pg_backend_pid ---------------- 32262 (1 row) postgres=# CREATE TABLE tbl_students(rno int, name character varying(10)); CREATE TABLE postgres=# BEGIN TRANSACTION; BEGIN postgres=*# LOCK tbl_students IN ACCESS EXCLUSIVE MODE; LOCK TABLE postgres=*# session2： postgres=# SELECT pg_backend_pid(); pg_backend_pid ---------------- 32439 (1 row) postgres=# INSERT INTO tbl_students VALUES (1,'Anvesh'); session3: postgres=# SELECT pg_blocking_pids(32439); pg_blocking_pids ------------------ {32262} (1 row) postgres=# 函数名称 pg_conf_load_time() 参数数据类型 无输入参数 函数返回值数据类型 timestamp with time zone 函数含义 返回PG配置参数文件最后一次被reload的时间 使用举例 postgres=# select pg_conf_load_time(); pg_conf_load_time ------------------------------- 2021-03-22 12:46:18.962643+08 (1 row) postgres=# 函数名称 pg_current_logfile([text]); 参数数据类型 无输入参数或者text型输入参数 (csvlog、stderr) 函数返回值数据类型 Text 函数含义 返回正在被logging collector进程使用的log file的path(是log_directory的值)和文件名。当logging collector是被禁用时，本函数返回结果是null。当有不同格式的多种log files存在时，不带任何参数的pg_current_logfile()函数返回有序列表(stderr、csvlog)中找到的第一种格式的文件path和文件名。当没有这两个格式的文件时，本函数返回NULL 使用举例 postgres=# select pg_current_logfile(); pg_current_logfile -------------------------------------- log/postgresql-2021-03-22_124618.log (1 row) postgres=# select pg_current_logfile('stderr'); pg_current_logfile -------------------------------------- log/postgresql-2021-03-22_124618.log (1 row) postgres=# select pg_current_logfile('csvlog'); pg_current_logfile -------------------- (1 row) postgres=# show log_destination ; log_destination ----------------- stderr (1 row) postgres=# 函数名称 pg_my_temp_schema() 参数数据类型 无输入参数 函数返回值数据类型 oid 函数含义 返回当前会话的临时schema的OID，如果没有临时schema，本函数返回0（即：数字零） 使用举例 postgres=# select pg_my_temp_schema(); pg_my_temp_schema ------------------- 0 (1 row) postgres=# 函数名称 pg_is_other_temp_schema(oid) 参数数据类型 oid 函数返回值数据类型 boolean 函数含义 当入参的oid是其他session中临时schema的oid时，本函数返回true。 使用举例 postgres=# select pg_is_other_temp_schema(16426); pg_is_other_temp_schema ------------------------- f (1 row) postgres=# 函数名称 pg_jit_available() 参数数据类型 无输入参数 函数返回值数据类型 boolean 函数含义 如果jit编译器扩展是可用的并且jit配置参数设置为on的话，本函数返回true 使用举例 postgres=# select pg_jit_available(); pg_jit_available ------------------ f (1 row) postgres=# select * from pg_config where name='CONFIGURE' AND SETTING LIKE '%--with-llvm%'; name | setting ------+--------- (0 rows) postgres=# show jit; jit ----- on (1 row) postgres=# 函数名称 pg_listening_channels() 参数数据类型 无输入参数 函数返回值数据类型 setof text 函数含义 当前session正在监听的异步通知通道的名称 使用举例 postgres=# select pg_listening_channels(); pg_listening_channels ----------------------- (0 rows) postgres=# 函数名称 pg_notification_queue_usage() 参数数据类型 无输入参数 函数返回值数据类型 double precision 函数含义 返回被正在等待处理的通知占据的异步通知队列最大大小的比例。 使用举例 postgres=# select pg_notification_queue_usage(); pg_notification_queue_usage ----------------------------- 0 (1 row) postgres=# 函数名称 pg_postmaster_start_time() 参数数据类型 无输入参数 函数返回值数据类型 timestamp with time zone 函数含义 返回PG intance 启动的时间点 使用举例 postgres=# select pg_postmaster_start_time(); pg_postmaster_start_time ------------------------------- 2021-03-23 14:01:33.352011+08 (1 row) postgres=# 函数名称 pg_safe_snapshot_blocking_pids(integer) 参数数据类型 Integer 函数返回值数据类型 integer[] 函数含义 返回阻止指定服务器进程ID获取安全快照的进程ID若是没有阻塞，则返回空array。一个运行SERIALIZABLE事务的session阻塞了一个SERIALIZABLE READ ONLY DEFERRABLE事务，防止后者获得snapshot，直到后者确认可以安全的避免获取任何谓词锁。频繁调用本函数会对数据库性能有影响，因为它在短时间内需要访问谓词锁管理器的共享状态(predicate lock manager’s shared state) 使用举例 postgres=# select pg_safe_snapshot_blocking_pids(1234); pg_safe_snapshot_blocking_pids -------------------------------- {} (1 row) postgres=# 函数名称 pg_trigger_depth() 参数数据类型 无输入参数 函数返回值数据类型 integer 函数含义 返回PostgreSQL触发器的当前嵌套级别(如果没有从触发器内部直接或间接调用，则为返回值为0) 使用举例 postgres=# select pg_trigger_depth(); pg_trigger_depth ------------------ 0 (1 row) postgres=# 函数名称 user 参数数据类型 无输入参数 函数返回值数据类型 name 函数含义 获得当前执行上下文的username，等同于current_user函数 使用举例 postgres=# select user; user ------- pg131 (1 row) postgres=# 函数名称 Version() 参数数据类型 无输入参数 函数返回值数据类型 text 函数含义 返回带有数据库版本号的字符串，您也可以查询server_version配置参数获得数据库版本号。对于机器可读的版本，请使用server_version_num配置参数。在软件开发过程中应该使用server_version_num配置参数或者PqserverVersion，而不是去解析文本的版本号(见下面的使用举例) 使用举例 postgres=# select version(); version --------------------------------------------------------------------------------------------------------- PostgreSQL 13.1 on x86_64-pc-linux-gnu, compiled by gcc (GCC) 4.8.5 20150623 (Red Hat 4.8.5-44), 64-bit (1 row) postgres=# 函数名称 has_any_column_privilege(user, table, privilege) 参数数据类型 user, table, privilege或者table, privilege 函数返回值数据类型 boolean 函数含义 确定一个user是否对当前PG database中的某个table任一列有列权限确定当前user是否对当前PG database中的某个table任一列有列权限权限类型是SELECT, INSERT, UPDATE,REFERENCES的组合。 使用举例 [pg131@VM-0-8-centos ~]$ psql -d cwbase3 -U lc0039999 psql (13.1) Type \"help\" for help. cwbase3=> \\d List of relations Schema | Name | Type | Owner --------+-------+-------+----------- public | test1 | table | lc0039999 (1 row) cwbase3=> select has_any_column_privilege('lc0039999','test1','select'); has_any_column_privilege -------------------------- t (1 row) cwbase3=> cwbase3=> select has_any_column_privilege('test1','select,insert'); has_any_column_privilege -------------------------- t (1 row) cwbase3=> cwbase3=> select has_any_column_privilege('test1','select'); has_any_column_privilege -------------------------- t (1 row) cwbase3=> 函数名称 has_column_privilege(user, table, column, privilege); 参数数据类型 user, table, column, privilege或者table, column, privilege 函数返回值数据类型 boolean 函数含义 特定user对特定表的某一列是否具有某种权限当前user对特定表的某一列是否具有某种权限权限类型是SELECT, INSERT, UPDATE,REFERENCES的组合。 使用举例 cwbase3=> \\d test1 Table \"public.test1\" Column | Type | Collation | Nullable | Default --------+---------+-----------+----------+--------- id | integer | | | cwbase3=> cwbase3=> select has_column_privilege('lc0039999','test1','id','select'); has_column_privilege ---------------------- t (1 row) cwbase3=> cwbase3=> select has_column_privilege('test1','id','select'); has_column_privilege ---------------------- t (1 row) cwbase3=> 函数名称 has_database_privilege 参数数据类型 user, database, privilege或者database, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定database有某种权限当前user是否对特定database有某种权限权限类型是CREATE, CONNECT, TEMPORARY, or TEMP的组合。 使用举例 postgres=# select has_database_privilege('lc0039999','cwbase3','create,connect,temp'); has_database_privilege ------------------------ t (1 row) postgres=# select has_database_privilege('lc0019999','cwbase3','create,connect,temp'); has_database_privilege ------------------------ t (1 row) postgres=# 函数名称 has_foreign_data_wrapper_privilege 参数数据类型 user, fdw, privilege或者fdw, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定fdw有usage权限当前user是否对特定fdw有usage权限此处的权限只能指定usage 使用举例 postgres=# select has_foreign_data_wrapper_privilege('lc0019999','file_fdw','usage'); has_foreign_data_wrapper_privilege ------------------------------------ f (1 row) postgres=# postgres=# select has_foreign_data_wrapper_privilege('file_fdw','usage'); has_foreign_data_wrapper_privilege ------------------------------------ t (1 row) postgres=# 函数名称 has_function_privilege 参数数据类型 user, function, privilege或者function, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定function有EXECUTE权限当前user是否对特定function有EXECUTE权限此处的权限只能是EXECUTE 使用举例 postgres=# select has_function_privilege('lc0039999','version()','execute'); has_function_privilege ------------------------ t (1 row) postgres=# postgres=# select has_function_privilege('version()','execute'); has_function_privilege ------------------------ t (1 row) postgres=# 函数名称 has_language_privilege 参数数据类型 user, language, privilege或者language, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定procedural language有usage权限当前user是否对特定procedural language有usage权限此处的权限必须是usage 使用举例 postgres=# select has_language_privilege('lc0019999','sql','usage'); has_language_privilege ------------------------ t (1 row) postgres=# select has_language_privilege('plpgsql','usage'); has_language_privilege ------------------------ t (1 row) postgres=# 注意：此处的language可以从pg_language系统表查询到。 函数名称 has_schema_privilege 参数数据类型 user, schema, privilege或者schema, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定schema有usage或者create权限当前user是否对特定schema有usage或者create权限此处的权限必须是create或者usage的组合 使用举例 postgres=# \\dnS List of schemas Name | Owner --------------------+------- information_schema | pg131 pg_catalog | pg131 pg_toast | pg131 public | pg131 s_abc | pg131 (5 rows) postgres=# select has_schema_privilege('lc0019999','s_abc','usage'); has_schema_privilege ---------------------- f (1 row) postgres=# select has_schema_privilege('lc0019999','s_abc','usage,create'); has_schema_privilege ---------------------- f (1 row) postgres=# 函数名称 has_sequence_privilege 参数数据类型 user, sequence, privilege或者sequence, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定sequence有特定权限当前user是否对特定sequence有特定权限此处的权限是USAGE, SELECT, UPDATE的组合。 使用举例 postgres=# \\ds+ List of relations Schema | Name | Type | Owner | Persistence | Size | Description --------+--------------+----------+-------+-------------+------------+------------- public | gen_y_c1_seq | sequence | pg131 | permanent | 8192 bytes | (1 row) postgres=# select has_sequence_privilege('pg131','gen_y_c1_seq','usage'); has_sequence_privilege ------------------------ t (1 row) postgres=# select has_sequence_privilege('lc0039999','gen_y_c1_seq','usage'); has_sequence_privilege ------------------------ f (1 row) postgres=# 函数名称 has_server_privilege 参数数据类型 user, server, privilege或者server, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定foreign server有usage权限特定user是否对特定foreign server有usage权限此处的权限必须是usage 使用举例 postgres=# select has_server_privilege('s1','usage'); has_server_privilege ---------------------- t (1 row) postgres=# select has_server_privilege('s1','USAGE'); has_server_privilege ---------------------- t (1 row) postgres=# select has_server_privilege('pg123','s1','USAGE'); has_server_privilege ---------------------- t (1 row) postgres=# 函数名称 has_table_privilege 参数数据类型 user, table, privilege或者table, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定table有特定权限当前user是否对特定table有特定权限此处的权限是指如下的组合： SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, TRIGGER，还可以带有WITH GRANT OPTION用来测试privilege是否带有grant option。当权限字符串有多个值(比如’insert, select')时,只要有任何一个权限是满足的，本函数会返回t有关对权限字符串的说明：权限字符串不区分大小写权限字符串的前后可以有一个或者多个空格（权限名字内部不能有空格） 使用举例 postgres=# select has_table_privilege('t1',' select, insert '); has_table_privilege --------------------- t (1 row) postgres=# [pg131@VM-0-8-centos ~]$ psql -d cwbase3 -U lc0039999 psql (13.1) Type \"help\" for help. cwbase3=> cwbase3=> grant select on test1 to lc0019999; GRANT cwbase3=> exit [pg131@VM-0-8-centos ~]$ psql -d cwbase3 -U lc0019999 psql (13.1) Type \"help\" for help. cwbase3=> select * from test1; id ---- 1 2 3 4 5 (6 rows) cwbase3=> select has_table_privilege('test1','insert, select '); has_table_privilege --------------------- t (1 row) cwbase3=> select has_table_privilege('test1','insert'); has_table_privilege --------------------- f (1 row) cwbase3=> select has_table_privilege('test1',' insert '); has_table_privilege --------------------- f (1 row) cwbase3=> insert into test1 values(6); ERROR: permission denied for table test1 cwbase3=> 函数名称 has_tablespace_privilege 参数数据类型 user, tablespace, privilege或者tablespace, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定tablesapce有特定权限当前user是否对特定tablespace有特定权限此处的权限必须是create 使用举例 postgres=# select has_tablespace_privilege('ts1','create '); has_tablespace_privilege -------------------------- t (1 row) postgres=# select has_tablespace_privilege('lc0019999','ts1','create '); has_tablespace_privilege -------------------------- f (1 row) postgres=# 函数名称 has_type_privilege 参数数据类型 user, type, privilege或者type, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定type有特定权限当前user是否对特定type有特定权限特定type可以使用type的name，也可以使用oid::regtype的形式此处的权限必须是USAGE 使用举例 postgres=# select has_type_privilege('bytea','usage'); has_type_privilege -------------------- t (1 row) postgres=# select has_type_privilege('lc0019999','bytea','usage'); has_type_privilege -------------------- t (1 row) postgres=# select has_type_privilege('lc0019999','17:regtype','usage'); ERROR: syntax error at or near \"17\" CONTEXT: invalid type name \"17:regtype\" postgres=# select has_type_privilege('lc0019999',17::regtype,'usage'); has_type_privilege -------------------- t (1 row) postgres=# select has_type_privilege('lc0019999','17::regtype','usage'); ERROR: syntax error at or near \"17\" CONTEXT: invalid type name \"17::regtype\" postgres=# 函数名称 pg_has_role 参数数据类型 user, role, privilege或者role, privilege 函数返回值数据类型 boolean 函数含义 特定user是否对特定role group有特定权限当前user是否对特定role group有特定权限此处的权限必须是member或者usage当权限是member时，表示特定user是否是特定role group的member当权限是usage时，表示在不执行set role时，特定user是否有特定role的权限 使用举例 postgres=# create role editors; CREATE ROLE postgres=# create user maxwell; CREATE ROLE postgres=# create user ernest; CREATE ROLE postgres=# grant authors to editors; --editors can do what authors can do GRANT ROLE postgres=# grant editors to maxwell; --maxwell is an editor GRANT ROLE postgres=# grant authors to ernest; --ernest is an author GRANT ROLE postgres=# select pg_has_role('maxwll','editors','member'); ERROR: role \"maxwll\" does not exist postgres=# select pg_has_role('maxwell','editors','member'); pg_has_role ------------- t (1 row) postgres=# select pg_has_role('maxwell','editors','usage'); pg_has_role ------------- t (1 row) postgres=# select pg_has_role('maxwell','ernest','usage'); pg_has_role ------------- f (1 row) postgres=# select pg_has_role('maxwell','ernest','member'); pg_has_role ------------- f (1 row) postgres=# 函数名称 row_security_active 参数数据类型 Table，可以是table name，也可以是table的oid 函数返回值数据类型 boolean 函数含义 在current_user的上下文中，特定table是否已开启row security 使用举例 postgres=# select oid from pg_class where relname='test'; oid ------- 16860 (1 row) postgres=# select row_security_active('test'); row_security_active --------------------- f (1 row) postgres=# select row_security_active(16860); row_security_active --------------------- f (1 row) postgres=# 函数名称 acldefault 参数数据类型 type, ownerId 函数返回值数据类型 aclitem[] 函数含义 返回属于某个user oid对象的默认访问权限。当一个对象的ACL条目为null时这些访问权限是该对象的默认权限Type参数的取值如下：'c' for COLUMN’r' for TABLE 以及类似对象’s' for SEQUENCE’d' for DATABASE’f' for FUNCTION 或者PROCEDURE’l' for LANGUAGE’L' for LARGE OBJECT’n' for SCHEMA’t' for TABLESPACE’F' for FOREIGN DATA WRAPPER’S' for FOREIGN SERVER’T' for TYPE or DOMAIN 使用举例 postgres=# select acldefault('f',16877); acldefault -------------------------------------- {=X/lc0039999,lc0039999=X/lc0039999} (1 row) postgres=# 函数名称 aclexplode 参数数据类型 aclitem[] 函数返回值数据类型 setof record 函数含义 解析 acl 权限 使用举例 postgres=# select aclexplode('{=X/lc0039999,lc0039999=X/lc0039999}'); aclexplode ------------------------- (16877,0,EXECUTE,f) (16877,16877,EXECUTE,f) (2 rows) postgres=# postgres=# select \\* from aclexplode('{=X/lc0039999,lc0039999=X/lc0039999}'); grantor | grantee | privilege_type | is_grantable ---------+---------+----------------+-------------- 16877 | 0 | EXECUTE | f 16877 | 16877 | EXECUTE | f (2 rows) postgres=# 函数名称 makeaclitem 参数数据类型 grantee, grantor, privilege, grantable 函数返回值数据类型 aclitem 函数含义 构建aclitem 使用举例 postgres=# select \\* from makeaclitem(16877,16877,'EXECUTE',false); makeaclitem ----------------------- lc0039999=X/lc0039999 (1 row) postgres=# 函数名称 pg_collation_is_visible 参数数据类型 collation_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定collation是否可见。 使用举例 postgres=# select * from pg_collation where collname = 'zh_CN'; oid | collname | collnamespace | collowner | collprovider | collisdeterministic | collencoding | collcollate | collctype | collversion -------+----------+---------------+-----------+--------------+---------------------+--------------+-------------+------------+------------- 13086 | zh_CN | 11 | 10 | c | t | 2 | zh_CN | zh_CN | 2.17 13243 | zh_CN | 11 | 10 | c | t | 6 | zh_CN.utf8 | zh_CN.utf8 | 2.17 (2 rows) postgres=# select pg_collation_is_visible(13086); pg_collation_is_visible ------------------------- f (1 row) postgres=# select pg_collation_is_visible(13243); pg_collation_is_visible ------------------------- t (1 row) postgres=# postgres=# select pg_collation_is_visible('\"zh_CN\"'::regcollation); pg_collation_is_visible ------------------------- t (1 row) postgres=# 函数名称 pg_conversion_is_visible 参数数据类型 conversion_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定conversion是否可见 使用举例 postgres=# select * from pg_conversion where conname like '%cn%'; oid | conname | connamespace | conowner | conforencoding | contoencoding | conproc | condefault ------+----------------+--------------+----------+----------------+---------------+----------------+------------ 4422 | euc_cn_to_mic | 11 | 10 | 2 | 7 | euc_cn_to_mic | t 4423 | mic_to_euc_cn | 11 | 10 | 7 | 2 | mic_to_euc_cn | t 4480 | euc_cn_to_utf8 | 11 | 10 | 2 | 6 | euc_cn_to_utf8 | t 4481 | utf8_to_euc_cn | 11 | 10 | 6 | 2 | utf8_to_euc_cn | t (4 rows) postgres=# select pg_conversion_is_visible(4480); pg_conversion_is_visible -------------------------- t (1 row) postgres=# 函数名称 pg_function_is_visible 参数数据类型 function_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定function、procedures、aggregates是否可见。对于函数，如果路径前面没有相同名称和参数数据类型的对象，则搜索路径中的对象可见 使用举例 postgres=# select oid from pg_proc where proname='version'; oid ----- 89 (1 row) postgres=# select pg_function_is_visible(89); pg_function_is_visible ------------------------ t (1 row) postgres=# postgres=# select pg_function_is_visible('version'::regproc); pg_function_is_visible ------------------------ t (1 row) postgres=# select pg_function_is_visible('version()'::regprocedure); pg_function_is_visible ------------------------ t (1 row) postgres=# 函数名称 pg_opclass_is_visible 参数数据类型 opclass_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator class是否可见考虑因素是operator class name和相关的索引访问方法。 使用举例 postgres=# select oid from pg_opclass where opcname='array_ops'; oid ------- 10000 10001 10063 (3 rows) postgres=# select pg_opclass_is_visible(10000); pg_opclass_is_visible ----------------------- t (1 row) postgres=# 函数名称 pg_operator_is_visible 参数数据类型 operator_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator 是否可见对于运算符，如果路径前面没有相同名称和参数数据类型的对象，则搜索路径中的对象可见 使用举例 postgres=# select oprname from pg_operator where oid=15; oprname --------- = (1 row) postgres=# select pg_operator_is_visible(15); pg_operator_is_visible ------------------------ t (1 row) postgres=# postgres=# select pg_operator_is_visible('=(integer,integer)'::regoperator); pg_operator_is_visible ------------------------ t (1 row) postgres=# 函数名称 pg_opfamily_is_visible 参数数据类型 opclass_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator family是否可见 使用举例 postgres=# select * from pg_opfamily where oid='397'; oid | opfmethod | opfname | opfnamespace | opfowner -----+-----------+-----------+--------------+---------- 397 | 403 | array_ops | 11 | 10 (1 row) postgres=# select pg_opfamily_is_visible(397); pg_opfamily_is_visible ------------------------ t (1 row) postgres=# 函数名称 pg_statistics_obj_is_visible 参数数据类型 stat_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定operator class是否可见 使用举例 函数名称 pg_table_is_visible 参数数据类型 table_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定的table、views、materialized views、 indexes、 sequences、foreign tables是否可见 使用举例 postgres=# select oid from pg_class where relname='data1'; oid ------- 16384 (1 row) postgres=# select pg_table_is_visible(16384); pg_table_is_visible --------------------- t (1 row) postgres=# postgres=# SELECT pg_table_is_visible('public.data1'::regclass); pg_table_is_visible --------------------- t (1 row) postgres=# 函数名称 pg_ts_config_is_visible 参数数据类型 config_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search configuration是否可见 使用举例 postgres=# select oid from pg_ts_config where cfgname='greek'; oid ------- 13861 (1 row) postgres=# select pg_ts_config_is_visible(13861); pg_ts_config_is_visible ------------------------- t (1 row) postgres=# postgres=# select pg_ts_config_is_visible('greek'::regconfig); pg_ts_config_is_visible ------------------------- t (1 row) postgres=# 函数名称 pg_ts_dict_is_visible 参数数据类型 dict_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search dictionary是否可见 使用举例 postgres=# select oid,dictname,dictinitoption from pg_ts_dict where dictname='greek_stem'; oid | dictname | dictinitoption -------+------------+-------------------- 13860 | greek_stem | language = 'greek' (1 row) postgres=# select pg_ts_dict_is_visible(13860); pg_ts_dict_is_visible ----------------------- t (1 row) postgres=# postgres=# select pg_ts_dict_is_visible('greek_stem'::regdictionary); pg_ts_dict_is_visible ----------------------- t (1 row) postgres=# 函数名称 pg_ts_parser_is_visible 参数数据类型 parser_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search parser是否可见 使用举例 postgres=# select * from pg_ts_parser ; oid | prsname | prsnamespace | prsstart | prstoken | prsend | prsheadline | prslextype ------+---------+--------------+------------+----------------+----------+---------------+-------------- 3722 | default | 11 | prsd_start | prsd_nexttoken | prsd_end | prsd_headline | prsd_lextype (1 row) postgres=# select pg_ts_parser_is_visible(3722); pg_ts_parser_is_visible ------------------------- t (1 row) postgres=# 函数名称 pg_ts_template_is_visible 参数数据类型 template_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定text search template是否可见 使用举例 postgres=# select * from pg_ts_template ; oid | tmplname | tmplnamespace | tmplinit | tmpllexize -------+-----------+---------------+----------------+------------------ 3727 | simple | 11 | dsimple_init | dsimple_lexize 3730 | synonym | 11 | dsynonym_init | dsynonym_lexize 3733 | ispell | 11 | dispell_init | dispell_lexize 3742 | thesaurus | 11 | thesaurus_init | thesaurus_lexize 13845 | snowball | 11 | dsnowball_init | dsnowball_lexize (5 rows) postgres=# select pg_ts_temp postgres=# select pg_ts_template_is_visible(3730); pg_ts_template_is_visible --------------------------- t (1 row) postgres=# 函数名称 pg_type_is_visible 参数数据类型 type_oid 函数返回值数据类型 boolean 函数含义 在当前schema search path中，特定type、domain是否可见 使用举例 postgres=# select oid,typname from pg_type where typname='bool'; oid | typname -----+--------- 16 | bool (1 row) postgres=# select pg_type_is_visible(16); pg_type_is_visible -------------------- t (1 row) postgres=# postgres=# SELECT pg_type_is_visible('pg_catalog.bool'::regtype); pg_type_is_visible -------------------- t (1 row) postgres=# 函数名称 format_type 参数数据类型 type_oid, typemod 函数返回值数据类型 Text 函数含义 得到一个data type的SQL Name 使用举例 postgres=# select * from pg_type where typname='bool'; -[ RECORD 1 ]--+--------- oid | 16 typname | bool typnamespace | 11 typowner | 10 typlen | 1 typbyval | t typtype | b typcategory | B typispreferred | t typisdefined | t typdelim | , typrelid | 0 typelem | 0 typarray | 1000 typinput | boolin typoutput | boolout typreceive | boolrecv typsend | boolsend typmodin | - typmodout | - typanalyze | - typalign | c typstorage | p typnotnull | f typbasetype | 0 typtypmod | -1 typndims | 0 typcollation | 0 typdefaultbin | typdefault | typacl | postgres=# postgres=# select format_type(16,0); format_type ------------- boolean (1 row) postgres=# 函数名称 pg_get_constraintdef 参数数据类型 constraint_oid或者constraint_oid, pretty_bool 函数返回值数据类型 text 函数含义 得到一个constraint的定义 使用举例 postgres=# select pg_get_constraintdef(16918); pg_get_constraintdef ---------------------- PRIMARY KEY (c1) (1 row) postgres=# 函数名称 pg_get_expr 参数数据类型 pg_node_tree, relation_oid,或者 pg_node_tree, relation_oid, pretty_bool 函数返回值数据类型 Text 函数含义 反编译指定expression的内部格式，假设：其中的任何变量引用第二个参数指示的relation 使用举例 postgres=# CREATE TABLE tab1 (a int, b int) PARTITION BY RANGE(a); CREATE TABLE postgres=# CREATE TABLE tab1_p1 PARTITION OF tab1 FOR VALUES FROM (0) TO (100); CREATE TABLE postgres=# CREATE TABLE tab1_p2 PARTITION OF tab1 FOR VALUES FROM (100) TO (200); CREATE TABLE postgres=# select relname, pg_get_expr(relpartbound, oid) from pg_class where relispartition and relname ~ 'tab1' order by relname; relname | pg_get_expr ---------+-------------------------------- tab1_p1 | FOR VALUES FROM (0) TO (100) tab1_p2 | FOR VALUES FROM (100) TO (200) (2 rows) postgres=# 函数名称 pg_get_functiondef 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得function或者procedure的定义 使用举例 postgres=# select pg_get_functiondef(89); pg_get_functiondef ------------------------------------------------- CREATE OR REPLACE FUNCTION pg_catalog.version()+ RETURNS text + LANGUAGE internal + STABLE PARALLEL SAFE STRICT + AS $function$pgsql_version$function$ (1 row) postgres=# 函数名称 pg_get_function_arguments 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得function或者procedure的传入参数清单 使用举例 postgres=# select oid from pg_proc where proname='has_database_privilege'; -[ RECORD 1 ] oid | 2250 -[ RECORD 2 ] oid | 2251 -[ RECORD 3 ] oid | 2252 -[ RECORD 4 ] oid | 2253 -[ RECORD 5 ] oid | 2254 -[ RECORD 6 ] oid | 2255 postgres=# select pg_get_function_arguments(2250); -[ RECORD 1 ]-------------+----------------- pg_get_function_arguments | name, text, text postgres=# \\x Expanded display is off. postgres=# select pg_get_function_arguments(2250); pg_get_function_arguments --------------------------- name, text, text (1 row) postgres=# 函数名称 pg_get_function_identity_arguments 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得参数清单以标识出function或者procedure 使用举例 postgres=# select pg_get_function_identity_arguments(2250); pg_get_function_identity_arguments ------------------------------------ name, text, text (1 row) postgres=# 函数名称 pg_get_function_result 参数数据类型 func_oid 函数返回值数据类型 text 函数含义 获得function，若是procedure，返回null 使用举例 postgres=# select pg_get_function_result(89); pg_get_function_result ------------------------ text (1 row) postgres=# 函数名称 pg_get_indexdef 参数数据类型 index_oid或者index_oid, column_no, pretty_bool 函数返回值数据类型 text 函数含义 获得索引的定义语句获得某个索引上第n个索引列的名称 使用举例 postgres=# select oid from pg_class where relname='idx_2'; oid ------- 16427 (1 row) postgres=# select pg_get_indexdef(16427); pg_get_indexdef --------------------------------------------------------- CREATE INDEX idx_2 ON public.data1 USING btree (c1, c3) (1 row) postgres=# postgres=# select pg_get_indexdef(16427,2,true); pg_get_indexdef ----------------- c3 (1 row) postgres=# select pg_get_indexdef(16427,1,true); pg_get_indexdef ----------------- c1 (1 row) postgres=# 函数名称 pg_get_keywords 参数数据类型 无输入参数 函数返回值数据类型 setof record 函数含义 返回PostgreSQL数据库中的SQL关键字清单返回结果中有3列(以逗号分隔)，第一列是关键字名称，第二列catcode是category code U for unreserved C for column name T for type or function name R for reserved第三列catdesc是描述 使用举例 postgres=# select pg_get_keywords(); pg_get_keywords ------------------------------------------------------------------ (abort,U,unreserved) (absolute,U,unreserved) (access,U,unreserved) (action,U,unreserved) (add,U,unreserved) (admin,U,unreserved) (after,U,unreserved) (aggregate,U,unreserved) (all,R,reserved) (also,U,unreserved) (alter,U,unreserved) (always,U,unreserved) (analyse,R,reserved) (analyze,R,reserved) (and,R,reserved) 限于篇幅，本函数的输出仅摘录到此处 函数名称 pg_get_ruledef 参数数据类型 rule_oid或者rule_oid, pretty_bool 函数返回值数据类型 Text 函数含义 获得rule的create rule语句 使用举例 postgres=# select oid ,rulename from pg_rewrite where rulename='pg_settings_u'; oid | rulename -------+--------------- 12168 | pg_settings_u (1 row) postgres=# select pg_get_ruledef(12168); pg_get_ruledef --------------------------------------------------------------------------------------------------- CREATE RULE pg_settings_u AS + ON UPDATE TO pg_catalog.pg_settings + WHERE (new.name = old.name) DO SELECT set_config(old.name, new.setting, false) AS set_config; (1 row) postgres=# 函数名称 pg_get_serial_sequence 参数数据类型 table_name, column_name 函数返回值数据类型 text 函数含义 获得自增列或者标识列使用的sequence的name 使用举例 postgres=# select pg_get_serial_sequence('gen_y','c1'); pg_get_serial_sequence ------------------------ public.gen_y_c1_seq (1 row) postgres=# 函数名称 pg_get_statisticsobjdef 参数数据类型 statobj_oid 函数返回值数据类型 text 函数含义 获得extended statistics object的CREATE STATISTICS语句。 使用举例 函数名称 pg_get_triggerdef 参数数据类型 trigger_oid 或者trigger_oid, pretty_bool 函数返回值数据类型 text 函数含义 获得trigger的create trigger语句 使用举例 postgres=# select pg_get_triggerdef(16992); pg_get_triggerdef ------------------------------------------------------------------------------------------------------------ CREATE TRIGGER example_trigger AFTER INSERT ON public.company FOR EACH ROW EXECUTE FUNCTION auditlogfunc() (1 row) postgres=# 函数名称 pg_get_userbyid 参数数据类型 role_oid 函数返回值数据类型 name 函数含义 获得特定role oid的role name 使用举例 postgres=# select pg_get_userbyid(16426); pg_get_userbyid ----------------- lc0029999 (1 row) postgres=# 函数名称 pg_get_viewdef 参数数据类型 view_name 或者 view_name, pretty_bool或者 view_oid或者 view_oid, pretty_bool或者view_oid, wrap_column_int 函数返回值数据类型 text 函数含义 获得view的定义 使用举例 postgres=# select pg_get_viewdef('company_view'); pg_get_viewdef --------------------- SELECT company.id,+ company.name, + company.age + FROM company; (1 row) postgres=# 函数名称 pg_index_column_has_property 参数数据类型 index regclass, column integer, property text 函数返回值数据类型 boolean 函数含义 测试索引列是否有特定的索引列属性。索引列属性见下： asc 索引列升序 desc 索引列降序 nulls_first null值在前 nulls_last null值在后 orderable 列是否具有任何定义的排序顺序？ distance_orderable 列是否可以被\"distance\" operator 顺序扫描到, 比如 ORDER BY col <→ constant ? returnable index-only扫描是否可以返回列值 search_array 列是否原生支持 col = ANY(array)搜索 search_nulls 列是否支持IS NULL 和IS NOT NULL 搜索 注意：extension的访问方法可以为它们的indexes定义额外的属性名称。 本函数在如下情况下会返回NULL：属性名称未知(not known)或者不适用于特定的对象Oid或者column number不能标识出一个有效的对象。 使用举例 postgres=# select pg_index_column_has_property('idx_2'::regclass,1,'desc'); pg_index_column_has_property ------------------------------ f (1 row) postgres=# select pg_index_column_has_property('idx_2'::regclass,1,'asc'); pg_index_column_has_property ------------------------------ t (1 row) postgres=# 函数名称 pg_index_has_property 参数数据类型 index regclass, property text 函数返回值数据类型 boolean 函数含义 测试一个index是否具有特定的索引属性。如下是索引属性的清单 clusterable 在cluster命令中是否可以用到index index_scan index 是否支持 plain (non-bitmap) scans? bitmap_scan index 是否支持 bitmap scans? backward_scan Can the scan direction be changed in mid-scan (to support FETCH BACKWARD on a cursor without needing materialization)? 注意：extension的访问方法可以为它们的indexes定义额外的属性名称。 本函数在如下情况下会返回NULL：属性名称未知(not known)或者不适用于特定的对象oid不能标识出一个有效的对象。 使用举例 postgres=# select pg_index_has_property('idx_2'::regclass,'index_scan'); pg_index_has_property ----------------------- t (1 row) postgres=# 函数名称 pg_indexam_has_property 参数数据类型 am oid, property text 函数返回值数据类型 boolean 函数含义 测试一个index访问方法是否具有特定的属性。如下是索引访问方法属性清单： can_order 访问方法是否支持 ASC, DESC 以及CREATE INDEX的related keywords can_unique 访问方法是否支持unique indexes? can_multi_col 访问方法是否支持多列indexes? can_exclude 访问方法是否支持exclusion constraints? can_include 访问方法是否支持CREATE INDEX的 INCLUDE 子句 本函数在如下情况下会返回NULL：属性名称未知(not known)或者不适用于特定的对象 oid不能标识出一个有效的对象。 使用举例 postgres=# select * from pg_am; oid | amname | amhandler | amtype ------+--------+----------------------+-------- 2 | heap | heap_tableam_handler | t 403 | btree | bthandler | i 405 | hash | hashhandler | i 783 | gist | gisthandler | i 2742 | gin | ginhandler | i 4000 | spgist | spghandler | i 3580 | brin | brinhandler | i (7 rows) postgres=# postgres=# select pg_indexam_has_property(4000,'can_multi_col'); pg_indexam_has_property ------------------------- f (1 row) postgres=# postgres=# select pg_indexam_has_property(403,'can_unique'); pg_indexam_has_property ------------------------- t (1 row) postgres=# 函数名称 pg_options_to_table 参数数据类型 options_array text[] 函数返回值数据类型 setof record ( option_name text, option_value text ) 函数含义 返回pg_class.reloptions或者pg_attribute.attoptions表示的存储选项 使用举例 postgres=# select pg_options_to_table(reloptions) from pg_class where oid='16384'; pg_options_to_table --------------------------------------------- (autovacuum_vacuum_insert_threshold,10000) (autovacuum_vacuum_insert_scale_factor,0.1) (2 rows) postgres=# 函数名称 pg_tablespace_databases 参数数据类型 tablespace oid 函数返回值数据类型 setof oid 函数含义 返回特定tablespace中保存的object的database的oid，如果本函数返回任何值，那么就表示tablespace不为空并且不能被drop掉。若想查出在该表空间内有哪些对象，你需要连接到本函数返回的database中，并查询pg_class获得这些对象。 使用举例 postgres=# select pg_tablespace_databases(1663); pg_tablespace_databases ------------------------- 16424 1 16878 14174 14173 (5 rows) postgres=# 函数名称 pg_tablespace_location 参数数据类型 tablespace oid 函数返回值数据类型 Text 函数含义 返回指定tablespace的文件系统路径 使用举例 postgres=# select pg_tablespace_location(16391); pg_tablespace_location ------------------------ /home/pg131/ts1 (1 row) postgres=# 函数名称 pg_typeof 参数数据类型 any 函数返回值数据类型 regtype 函数含义 指定值的数据类型的oid 使用举例 postgres=# SELECT pg_typeof(33.339999); -[ RECORD 1 ]------ pg_typeof | numeric postgres=# SELECT typlen FROM pg_type WHERE oid = pg_typeof(33.339999); -[ RECORD 1 ] typlen | -1 postgres=# 函数名称 COLLATION FOR 参数数据类型 \"any\" 函数返回值数据类型 text 函数含义 返回指定值的collation的name，本函数的返回值是被双引号引起来的，如果入参表达式不能派生出collation，则返回null如果传入的参数不是可以collatable的data type，则会抛出错误 使用举例 postgres=# select collation for(datname) from pg_database; pg_collation_for ------------------ \"C\" \"C\" \"C\" \"C\" \"C\" (5 rows) postgres=# 函数名称 to_regclass 参数数据类型 text 函数返回值数据类型 Regclass 函数含义 将文本形式的relation name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL 使用举例 postgres=# select to_regclass('public.data1'); -[ RECORD 1 ]------ to_regclass | data1 postgres=# \\x Expanded display is off. postgres=# select oid, relname from pg_class where oid=to_regclass('public.data1'); oid | relname -------+--------- 16384 | data1 (1 row) postgres=# 函数名称 to_regcollation 参数数据类型 Text 函数返回值数据类型 regcollation 函数含义 将文本形式的collation name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL 使用举例 postgres=# select to_regcollation('\"POSIX\"'); to_regcollation ----------------- \"POSIX\" (1 行记录) postgres=# select * from pg_collation where oid=to_regcollation('\"POSIX\"'); oid | collname | collnamespace | collowner | collprovider | collisdeterministic | collencoding | collcollate | collctype | collversion -----+----------+---------------+-----------+--------------+---------------------+--------------+-------------+-----------+------------- 951 | POSIX | 11 | 10 | c | t | -1 | POSIX | POSIX | (1 行记录) postgres=# select oid,collname from pg_collation where oid=to_regcollation('\"POSIX\"'); oid | collname -----+---------- 951 | POSIX (1 行记录) postgres=# 函数名称 to_regnamespace 参数数据类型 text 函数返回值数据类型 regnamespace 函数含义 将文本形式的schema name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL 使用举例 postgres=# select to_regnamespace('public'); to_regnamespace ----------------- public (1 行记录) postgres=# 函数名称 to_regoper 参数数据类型 text 函数返回值数据类型 regoper 函数含义 将文本形式的operator name转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name或者含义不明确，本函数会返回NULL 使用举例 postgres=# select oid, oprname from pg_operator where oid=to_regoper('!'); oid | oprname -----+--------- 388 | ! (1 row) postgres=# select to_regoper('!'); to_regoper ------------ ! (1 row) postgres=# 函数名称 to_regoperator 参数数据类型 text 函数返回值数据类型 regoperator 函数含义 将文本形式的operator name(带参数类型)转换为oid，本函数的输入参数不可以是数字形式的oid。如果本函数未找到输入参数指定的name，本函数会返回NULL. 使用举例 postgres=# select oid, oprname from pg_operator where oid=to_regoperator('+(integer,integer)'); oid | oprname -----+--------- 551 | + (1 row) postgres=# select to_regoperator('+(int4,int4)'); to_regoperator -------------------- +(integer,integer) (1 row) postgres=# 函数名称 to_regproc 参数数据类型 text 函数返回值数据类型 regproc 函数含义 将文本的function或者procedure转换为其oid。如果本函数未找到输入参数指定的name或者含义不明确，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select to_regproc('pg_stat_statements'); to_regproc -------------------- pg_stat_statements (1 row) postgres=# select oid,proname from pg_proc where oid=to_regproc('pg_stat_statements'); oid | proname -------+-------------------- 16401 | pg_stat_statements (1 row) postgres=# 函数名称 to_regprocedure 参数数据类型 text 函数返回值数据类型 regprocedure 函数含义 将文本的function或者procedure(带参数)转换为其oid。如果本函数未找到输入参数指定的name，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select to_regprocedure('pg_get_viewdef(text)'); to_regprocedure ---------------------- pg_get_viewdef(text) (1 row) postgres=# select oid, proname from pg_proc where oid= to_regprocedure('pg_get_viewdef(text)'); oid | proname ------+---------------- 1640 | pg_get_viewdef (1 row) postgres=# 函数名称 to_regrole 参数数据类型 text 函数返回值数据类型 regrole 函数含义 将文本的role name转换为其oid。如果本函数未找到输入参数指定的name，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select oid,rolname from pg_roles where oid=to_regrole('lc0039999'); oid | rolname -------+----------- 16877 | lc0039999 (1 row) postgres=# select to_regrole('lc0039999'); to_regrole ------------ lc0039999 (1 row) postgres=# 函数名称 to_regtype 参数数据类型 text 函数返回值数据类型 regtype 函数含义 将文本的type name转换为其oid。如果本函数未找到输入参数指定的name，本函数会返回NULL。本函数的输入参数不可以是数字形式的oid。 使用举例 postgres=# select to_regtype('int4'); to_regtype ------------ integer (1 row) postgres=# select oid, typname from pg_type where oid=to_regtype('int4'); oid | typname -----+--------- 23 | int4 (1 row) postgres=# 函数名称 pg_describe_object 参数数据类型 classid oid, objid oid, objsubid integer 函数返回值数据类型 text 函数含义 用于描述pg_depend系统表中保存的依赖对象。 使用举例 postgres=# select oid,relname from pg_class where oid in (select distinct classid from pg_depend) order by oid; oid | relname ------+------------------------- 1247 | pg_type 1255 | pg_proc 1259 | pg_class 2328 | pg_foreign_data_wrapper 2602 | pg_amop 2603 | pg_amproc 2606 | pg_constraint 2612 | pg_language 2616 | pg_opclass 2617 | pg_operator 2618 | pg_rewrite 2620 | pg_trigger 2753 | pg_opfamily 3079 | pg_extension 3600 | pg_ts_dict 3602 | pg_ts_config 3764 | pg_ts_template (17 rows) postgres=# select * from pg_foreign_data_wrapper ; oid | fdwname | fdwowner | fdwhandler | fdwvalidator | fdwacl | fdwoptions -------+----------+----------+------------+--------------+--------+------------ 16954 | file_fdw | 10 | 16952 | 16953 | | (1 row) postgres=# select pg_describe_object(2328,16954,0); pg_describe_object ------------------------------- foreign-data wrapper file_fdw (1 row) postgres=# 函数名称 pg_identify_object 参数数据类型 classid oid, objid oid, objsubid integer 函数返回值数据类型 record ( type text, schema text, name text, identity text ) 函数含义 返回一个数据库对象的详细信息 使用举例 postgres=# select pg_identify_object(2328,16954,0); pg_identify_object --------------------------------------------- (\"foreign-data wrapper\",,file_fdw,file_fdw) (1 row) postgres=# 函数名称 pg_identify_object_as_address 参数数据类型 classid oid, objid oid, objsubid integer 函数返回值数据类型 record ( type text, object_names text[], object_args text[] ) 函数含义 返回一个数据库对象的足够信息以标识出数据库对象 使用举例 postgres=# select pg_identify_object_as_address(2328,16954,0); pg_identify_object_as_address ---------------------------------------- (\"foreign-data wrapper\",{file_fdw},{}) (1 row) postgres=# 函数名称 pg_get_object_address 参数数据类型 type text, object_names text[], object_args text[] 函数返回值数据类型 record ( classid oid, objid oid, objsubid integer ) 函数含义 本函数是pg_identify_object_as_address的反函数 使用举例 postgres=# select pg_get_object_address('foreign-data wrapper','{file_fdw}','{}'); pg_get_object_address ----------------------- (2328,16954,0) (1 row) postgres=# 函数名称 col_description 参数数据类型 table oid, column integer 函数返回值数据类型 text 函数含义 获取某个表的某个列的comment 使用举例 postgres=# create table t_comment(c1 int ); CREATE TABLE postgres=# comment on column t_comment.c1 is 'this is c1 comment!'; COMMENT postgres=# select oid from pg_class where oid=to_regclass('t_comment') postgres-# ; oid ------- 17001 (1 row) postgres=# select col_description(17001,1); col_description --------------------- this is c1 comment! (1 row) postgres=# postgres=# select col_description(to_regclass('t_comment'),1); col_description --------------------- this is c1 comment! (1 row) postgres=# 函数名称 obj_description 参数数据类型 object oid, catalog name 或者object oid 函数返回值数据类型 text 函数含义 返回特定数据库对象的注释信息 使用举例 postgres=# comment on table t_comment is ' table-level test comment'; COMMENT postgres=# \\dt+ data1 List of relations Schema | Name | Type | Owner | Persistence | Size | Description --------+-------+-------+-------+-------------+-------+------------- public | data1 | table | pg131 | permanent | 42 MB | (1 row) postgres=# \\dt+ t_comment; List of relations Schema | Name | Type | Owner | Persistence | Size | Description --------+-----------+-------+-------+-------------+---------+--------------------------- public | t_comment | table | pg131 | permanent | 0 bytes | table-level test comment (1 row) postgres=# select obj_description(17001); obj_description --------------------------- table-level test comment (1 row) postgres=# 函数名称 shobj_description 参数数据类型 object oid, catalog name 函数返回值数据类型 text 函数含义 返回特定共享数据库对象(如databases, roles, and tablespaces)的注释信息. 使用举例 postgres=# select shobj_description(14174,'pg_database'); shobj_description -------------------------------------------- default administrative connection database (1 row) postgres=# 函数名称 pg_current_xact_id 参数数据类型 无输入参数 函数返回值数据类型 xid8 函数含义 返回当前的事务id，若是没有当前事务id，系统会自动指派一个新的事务id 使用举例 postgres=# select pg_current_xact_id(); pg_current_xact_id -------------------- 720 (1 row) postgres=# 函数名称 pg_current_xact_id_if_assigned() 参数数据类型 无输入参数 函数返回值数据类型 xid8 函数含义 返回当前的current transaction’s ID，若是没有transaction’s ID被指派，本函数返回NULL。如果事务可能是只读的，最好使用本函数，以避免不必要的XID消耗。 使用举例 postgres=*# select pg_current_xact_id_if_assigned(); pg_current_xact_id_if_assigned -------------------------------- (1 row) postgres=*# update data1 set c2='data1_1' where c1=1; UPDATE 1 postgres=*# select pg_current_xact_id_if_assigned(); pg_current_xact_id_if_assigned -------------------------------- 722 (1 row) postgres=*# 函数名称 pg_xact_status 参数数据类型 xid8 函数返回值数据类型 text 函数含义 返回近期事务的提交状态。本函数的执行结果是下面三个中的一个:in progress、commited、aborted。当事务足够老而没有在PostgreSQL中保存下来时，commit status会被丢弃，此时本函数返回结果为NULL。请注意，针对prepared transactions，本函数的返回结果为in progress,应用程序必须检查pg_prepared_xacts以确定一个transaction ID是否属于prepared transaction 使用举例 postgres=# select pg_xact_status(pg_current_xact_id()); pg_xact_status ---------------- in progress (1 row) postgres=# postgres=# select pg_xact_status(722::text::xid8); pg_xact_status ---------------- committed (1 row) postgres=# 函数名称 pg_current_snapshot 参数数据类型 无输入参数 函数返回值数据类型 pg_snapshot 函数含义 返回当前的snapshot,这是一种显示哪个transaction id是正处于in-progress状态的数据结构。注意：有关pg_snapshot数据类型： pg_snapshot数据类型用于存储在一个特定时间点上的transaction ID visibility信息。 pg_snapshot数据类型的表示方法是xmin:xmax:xip_list这个xmin:xmax:xip_list就是pg_snapshot数据类型组成。见下： xmin:处于active状态的最小的transaction id，所有小于xmin的transaction id要么是committed (visible),要么是rolled back(dead) xmax:大于或者等于本xmax值的transaction id是在snapshot结束时未完成的，因此，xmax是不可见的。 xip_list:在生成snapshot时，处于in progress状态的transactions。在xmin ⇐ X < xmax范围内并且不在本xip_list中的transaction ID 表示在snapshot生成时是已经完成的transaction ID,因此，根据commit status，要么是visible的要么是dead，本列表中不包括subtransactions的transaction IDs 使用举例 postgres=# select pg_current_snapshot(); pg_current_snapshot --------------------- 727:727: (1 row) postgres=# 函数名称 pg_snapshot_xip 参数数据类型 pg_snapshot 函数返回值数据类型 setof xid8 函数含义 返回snapshot中处于in-progress状态的transaction id 使用举例 postgres=# select pg_snapshot_xip('10:20:10,14,15'); pg_snapshot_xip ----------------- 10 14 15 (3 rows) postgres=# 函数名称 pg_snapshot_xmax 参数数据类型 pg_snapshot 函数返回值数据类型 xid8 函数含义 返回snapshot的xmax 使用举例 postgres=# select pg_snapshot_xmax('10:20:10,14,15'); pg_snapshot_xmax ------------------ 20 (1 row) postgres=# 函数名称 pg_snapshot_xmin 参数数据类型 pg_snapshot 函数返回值数据类型 xid8 函数含义 返回snapshot的xmin 使用举例 postgres=# select pg_snapshot_xmin('10:20:10,14,15'); pg_snapshot_xmin ------------------ 10 (1 row) postgres=# 函数名称 pg_visible_in_snapshot 参数数据类型 xid8, pg_snapshot 函数返回值数据类型 bool 函数含义 给定的事务ID是否对snapshot可见，即：给定的事务ID是否在生成snapshot之前完成。本函数对subtransaction ID不能给出正确的结果。 使用举例 postgres=# select pg_visible_in_snapshot(722::text::xid8,'10:20:10,14,15'); pg_visible_in_snapshot ------------------------ f (1 row) postgres=# select pg_visible_in_snapshot(12::text::xid8,'10:20:10,14,15'); pg_visible_in_snapshot ------------------------ t (1 row) postgres=# 函数名称 txid_current 参数数据类型 无输入参数 函数返回值数据类型 bigint 函数含义 返回当前的事务id，若是没有当前事务id，系统会自动指派一个新的事务id。本函数（返回值是bigint数据类型）的作用与pg_current_xact_id(返回值是xid8数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_current(); txid_current -------------- 735 (1 row) postgres=# 函数名称 txid_current_if_assigned() 参数数据类型 无输入参数 函数返回值数据类型 bigint 函数含义 本函数（注意：返回值是bigint数据类型）的作用与pg_current_xact_id_if_assigned(注意：返回值是xid8数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 [pg131@VM-0-8-centos ~]$ psql -d postgres psql (13.1) Type \"help\" for help. postgres=# select txid_current_if_assigned(); txid_current_if_assigned -------------------------- (1 row) postgres=# begin; BEGIN postgres=*# select txid_current_if_assigned(); txid_current_if_assigned -------------------------- (1 row) postgres=*# select txid_current(); txid_current -------------- 738 (1 row) postgres=*# select txid_current_if_assigned(); txid_current_if_assigned -------------------------- 738 (1 row) 函数名称 txid_current_snapshot() 参数数据类型 无输入参数 函数返回值数据类型 txid_snapshot 函数含义 返回当前的snapshot,这是一种显示哪个transaction id是正处于in-progress状态的数据结构。本函数（注意：返回值是txid_snapshot数据类型）的作用与pg_current_snapshot (注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_current_snapshot(); txid_current_snapshot ----------------------- 741:741: (1 row) postgres=# \\gdesc Column | Type -----------------------+--------------- txid_current_snapshot | txid_snapshot (1 row) postgres=# 函数名称 txid_snapshot_xip(txid_snapshot) 参数数据类型 txid_snapshot 函数返回值数据类型 setof bigint 函数含义 返回snapshot中处于in-progress状态的transaction id。本函数（注意：返回值是txid_snapshot数据类型）的作用与pg_snapshot_xip (注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_snapshot_xip('10:20:10,14,15'); txid_snapshot_xip ------------------- 10 14 15 (3 rows) postgres=# \\gdesc Column | Type -------------------+-------- txid_snapshot_xip | bigint (1 row) 函数名称 txid_snapshot_xmax 参数数据类型 txid_snapshot 函数返回值数据类型 bigint 函数含义 返回snapshot的xmax。本函数（注意：返回值是bigint数据类型）的作用与pg_snapshot_xmax (注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_snapshot_xmax('10:20:10,14,15'); txid_snapshot_xmax -------------------- 20 (1 row) postgres=# \\gdesc Column | Type --------------------+-------- txid_snapshot_xmax | bigint (1 row) postgres=# 函数名称 txid_snapshot_xmin 参数数据类型 txid_snapshot 函数返回值数据类型 bigint 函数含义 返回snapshot的xmin。本函数（注意：返回值是bigint数据类型）的作用与pg_snapshot_xmin(注意：返回值是pg_snapshot数据类型)的作用是类似的，只是两个函数的返回值数据类型不同。 使用举例 postgres=# select txid_snapshot_xmin('10:20:10,14,15'); txid_snapshot_xmin -------------------- 10 (1 row) postgres=# \\gdesc Column | Type --------------------+-------- txid_snapshot_xmin | bigint (1 row) postgres=# 函数名称 txid_visible_in_snapshot 参数数据类型 bigint, txid_snapshot 函数返回值数据类型 boolean 函数含义 给定的事务ID是否对snapshot可见，即：给定的事务ID是否在生成snapshot之前完成。本函数对subtransaction ID不能给出正确的结果。 使用举例 postgres=# select txid_visible_in_snapshot(722,'10:20:10,14,15'); txid_visible_in_snapshot -------------------------- f (1 row) postgres=# select txid_visible_in_snapshot(12,'10:20:10,14,15'); txid_visible_in_snapshot -------------------------- t (1 row) postgres=# 函数名称 txid_status 参数数据类型 bigint 函数返回值数据类型 text 函数含义 返回近期事务的提交状态。本函数的执行结果是下面三个中的一个:in progress、commited、aborted。当事务足够老而没有在PostgreSQL中保存下来时，commit status会被丢弃，此时本函数返回结果为NULL。请注意，针对prepared transactions，本函数的返回结果为in progress,应用程序必须检查pg_prepared_xacts以确定一个transaction ID是否属于prepared transaction. 使用举例 postgres=# select txid_status(txid_current()); txid_status ------------- in progress (1 row) postgres=# select txid_current(); txid_current -------------- 742 (1 row) postgres=# select txid_status(722); txid_status ------------- committed (1 row) postgres=# 函数名称 pg_xact_commit_timestamp 参数数据类型 xid 函数返回值数据类型 timestamp with time zone 函数含义 返回一个事务的提交时间。本函数能正常使用的前提就是配置参数track_commit_timestamp设置为on，否会有如下提示： postgres=# select pg_xact_commit_timestamp(722::text::xid); ERROR: could not get commit timestamp data HINT: Make sure the configuration parameter \"track_commit_timestamp\" is set. postgres=# 使用举例 postgres=# select txid_current(); txid_current -------------- 743 (1 row) postgres=# select pg_xact_commit_timestamp(743::text::xid); pg_xact_commit_timestamp ------------------------------ 2021-04-29 16:52:37.19133+08 (1 row) postgres=# 函数名称 pg_last_committed_xact 参数数据类型 无输入数据类型 函数返回值数据类型 record ( xid xid, timestamp timestamp with time zone ) 函数含义 返回最后一个提交事务的transaction id和commit timestamp 使用举例 postgres=# select pg_last_committed_xact(); pg_last_committed_xact -------------------------------------- (743,\"2021-04-29 16:52:37.19133+08\") (1 row) postgres=# 函数名称 pg_control_checkpoint 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回当前checkpoint状态的相关信息 使用举例 函数名称 pg_control_system 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回当前控制文件状态的相关信息。 使用举例 postgres=# select pg_control_system(); pg_control_system --------------------------------------------------------------- (1300,202007201,6908228805274066467,\"2021-04-29 16:57:56+08\") (1 row) postgres=# 函数名称 pg_control_init 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回cluster初始化时的相关信息 使用举例 postgres=# select pg_control_init(); pg_control_init --------------------------------------------------- (8,8192,131072,8192,16777216,64,32,1996,2048,t,0) (1 row) postgres=# 函数名称 pg_control_recovery 参数数据类型 无输入数据类型 函数返回值数据类型 record 函数含义 本函数是从PostgreSQL控制文件中获取数据。本函数返回恢复状态的信息。 使用举例 postgres=# select pg_control_recovery(); pg_control_recovery --------------------- (0/0,0,0/0,0/0,f) (1 row) postgres=# 函数名称 gen_random_uuid 参数数据类型 无输入参数 函数返回值数据类型 uuid 函数含义 本函数返回version 4版本的uuid 使用举例 postgres=# select gen_random_uuid(); gen_random_uuid -------------------------------------- 39158a63-f6d0-4883-8765-8b8e9c9cae62 (1 row) postgres=# 函数名称 array_to_tsvector 参数数据类型 text[] 函数返回值数据类型 tsvector 函数含义 将array of lexemes转换为tsvector 使用举例 postgres=# select array_to_tsvector('{fat,cat,rat}'::text[]); array_to_tsvector ------------------- 'cat' 'fat' 'rat' (1 row) postgres=# 函数名称 get_current_ts_config 参数数据类型 无输入参数 函数返回值数据类型 regconfig 函数含义 返回当前默认的text search配置的oid（参见配置参数default_text_search_config） 使用举例 postgres=# select get_current_ts_config(); get_current_ts_config ----------------------- english (1 row) postgres=# 函数名称 length 参数数据类型 tsvector 函数返回值数据类型 integer 函数含义 返回lexemes的数量 使用举例 postgres=# select length('fat:2,4 cat:3 rat:5A'::tsvector); length -------- 3 (1 row) postgres=# 函数名称 numnode 参数数据类型 tsquery 函数返回值数据类型 integer 函数含义 返回lexemes加operator的数量 使用举例 postgres=# select numnode('(fat & rat) | cat'::tsquery) ; numnode --------- 5 (1 row) postgres=# 函数名称 plainto_tsquery 参数数据类型 [config regconfig,] query text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery。其中，字符串中的任何标点都将被忽略 使用举例 postgres=# select plainto_tsquery('english', 'The Fat Rats'); plainto_tsquery ----------------- 'fat' & 'rat' (1 row) postgres=# 函数名称 phraseto_tsquery 参数数据类型 [config regconfig,] query text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery。其中，字符串中的任何标点都将被忽略 使用举例 postgres=# select phraseto_tsquery('english', 'The Fat Rats') ; phraseto_tsquery ------------------ 'fat' <-> 'rat' (1 row) postgres=# 函数名称 websearch_to_tsquery 参数数据类型 [ config regconfig, ] query text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery。引用的单词序列被转换成短语测试。\"or\"一词被理解为产生or运算符，破折号产生NOT运算符，忽略其他标点符号 使用举例 postgres=# select websearch_to_tsquery('english', '\"fat rat\" or cat dog'); websearch_to_tsquery --------------------------------- 'fat' <-> 'rat' | 'cat' & 'dog' (1 row) postgres=# 函数名称 querytree 参数数据类型 tsquery 函数返回值数据类型 text 函数含义 返回一个tsquery中的可索引部分。当返回结果为空或者T时，表示这是一个不可索引的query。 使用举例 postgres=# select querytree('foo & ! bar'::tsquery); querytree ----------- 'foo' (1 row) postgres=# 函数名称 setweight 参数数据类型 vector tsvector, weight \"char\"或者vector tsvector, weight \"char\", lexemes text[] 函数返回值数据类型 tsvector 函数含义 为vector中的每个元素指定权重或者为在lexemes之内的vector中的每个元素指定权重 使用举例 postgres=# select setweight('fat:2,4 cat:3 rat:5B'::tsvector, 'A'); setweight ------------------------------- 'cat':3A 'fat':2A,4A 'rat':5A (1 row) postgres=# postgres=# select setweight('fat:2,4 cat:3 rat:5,6B'::tsvector, 'A', '{cat,rat}'); setweight -------------------------------- 'cat':3A 'fat':2,4 'rat':5A,6A (1 row) postgres=# 函数名称 strip 参数数据类型 tsvector 函数返回值数据类型 tsvector 函数含义 从tsvector中抹掉position和weight 使用举例 postgres=# select strip('fat:2,4 cat:3 rat:5A'::tsvector); strip ------------------- 'cat' 'fat' 'rat' (1 row) postgres=# 函数名称 to_tsquery 参数数据类型 regconfig, text 或者text 函数返回值数据类型 tsquery 函数含义 将text转换为tsquery，本函数的返回结果必须由有效的tsquery operator进行连接。 使用举例 postgres=# select to_tsquery('english', 'The & Fat & Rats'); to_tsquery --------------- 'fat' & 'rat' (1 row) postgres=# select to_tsquery( 'The & Fat & Rats'); to_tsquery --------------- 'fat' & 'rat' (1 row) postgres=# 函数名称 to_tsvector 参数数据类型 regconfig,text 函数返回值数据类型 tsvector 函数含义 将text转换为tsvector ，结果中包括位置信息 使用举例 postgres=# select to_tsvector('english', 'The Fat Rats'); to_tsvector ----------------- 'fat':2 'rat':3 (1 row) postgres=# 函数名称 to_tsvector 参数数据类型 regconfig,json或者regconfig,jsonb 函数返回值数据类型 tsvector 函数含义 将json文档中的每个字符串值转换为tsvector，结果值是按照文档顺序进行连接后输出的，当输入参数数据类型为jsonb时，json对象的字段的文档顺序是与实现相关的 使用举例 postgres=# select to_tsvector('english', '{\"aa\": \"The Fat Rats\", \"b\": \"dog\"}'::json); to_tsvector ------------------------- 'dog':5 'fat':2 'rat':3 (1 row) postgres=# postgres=# select to_tsvector('english', '{\"aa\": \"The Fat Rats\", \"b\": \"dog\"}'::jsonb); to_tsvector ------------------------- 'dog':1 'fat':4 'rat':5 (1 row) postgres=# 函数名称 json_to_tsvector 参数数据类型 [config regconfig,] json, jsonb 函数返回值数据类型 tsvector 函数含义 将json文档中满足filter条件的每个item转换为一个tsvector。结果值是按照文档顺序进行连接后输出的，当输入参数数据类型为jsonb时，json对象的字段的文档顺序是与实现相关的。Filter条件必须是一个包括另个或者多个关键字的json 数组：string代表包括所有字符串值numeric代表包括所有数字值boolean代表包括所有boolean值key代表包括所有keyall代表包括上述所有。作为一个简单的例子，filter条件不可以是一个简单的并且是上述关键字之一的json值。 使用举例 postgres=# select json_to_tsvector('english', '{\"a\": \"The Fat Rats\", \"b\": 123}'::json, '[\"string\", \"numeric\"]'); json_to_tsvector ------------------------- '123':5 'fat':2 'rat':3 (1 row) postgres=# 函数名称 jsonb_to_tsvector 参数数据类型 [config regconfig,] jsonb,jsonb 函数返回值数据类型 tsvector 函数含义 将json文档中满足filter条件的每个item转换为一个tsvector。结果值是按照文档顺序进行连接后输出的，当输入参数数据类型为jsonb时，json对象的字段的文档顺序是与实现相关的。Filter条件必须是一个包括另个或者多个关键字的json 数组：string代表包括所有字符串值numeric代表包括所有数字值boolean代表包括所有boolean值key代表包括所有keyall代表包括上述所有。作为一个简单的例子，filter条件不可以是一个简单的并且是上述关键字之一的json值。 使用举例 postgres=# select jsonb_to_tsvector('english', '{\"a\": \"The Fat Rats\", \"b\": 123}'::jsonb, '[\"string\", \"numeric\"]'); jsonb_to_tsvector ------------------------- '123':5 'fat':2 'rat':3 (1 row) postgres=# 函数名称 ts_delete 参数数据类型 tsvector,text 函数返回值数据类型 tsvector 函数含义 从vector中删除掉指定的lexeme 使用举例 postgres=# select ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, 'fat'); ts_delete ------------------ 'cat':3 'rat':5A (1 row) postgres=# 函数名称 ts_delete 参数数据类型 tsvector,text[] 函数返回值数据类型 tsvector 函数含义 从vector中删除掉指定的lexeme 使用举例 postgres=# select ts_delete('fat:2,4 cat:3 rat:5A'::tsvector, ARRAY['fat','rat']); ts_delete ----------- 'cat':3 (1 row) postgres=# 函数名称 ts_filter 参数数据类型 tsvector, \"char\"[] 函数返回值数据类型 tsvector 函数含义 从vector中筛选出带有指定weights的元素 使用举例 postgres=# select ts_filter('fat:2,4 cat:3b,7c rat:5A'::tsvector, '{a,b}'); ts_filter ------------------- 'cat':3B 'rat':5A (1 row) postgres=# 函数名称 ts_headline 参数数据类型 [config regconfig,] document text, query tsquery [, options text] 函数返回值数据类型 text 函数含义 以简写的形式显示在document匹配到的query 使用举例 postgres=# select ts_headline('The fat cat ate the rat.', 'cat'); ts_headline --------------------------------- The fat <b>cat</b> ate the rat. (1 row) postgres=# 函数名称 ts_headline 参数数据类型 [config regconfig,] document json, query tsquery [, options text] 函数返回值数据类型 json 函数含义 以缩写的形式显示在json document匹配到的query 使用举例 postgres=# select ts_headline('{\"cat\":\"raining cats and dogs\"}'::json, 'cat'); ts_headline ---------------------------------------- {\"cat\":\"raining <b>cats</b> and dogs\"} (1 row) 函数名称 ts_headline 参数数据类型 [config regconfig,] document jsonb, query tsquery [, options text] 函数返回值数据类型 jsonb 函数含义 以缩写的形式显示在json document匹配到的query 使用举例 postgres=# select ts_headline('{\"cat\":\"raining cats and dogs\"}'::jsonb, 'cat'); ts_headline ----------------------------------------- {\"cat\": \"raining <b>cats</b> and dogs\"} (1 row) postgres=# 函数名称 ts_rank 参数数据类型 [weights real[], ] vector tsvector, query tsquery [, normalization integer] 函数返回值数据类型 real 函数含义 计算query与vector的匹配程度 使用举例 postgres=# SELECT ts_rank(to_tsvector('The quick brown fox jumps over the lazy dog.'),to_tsquery('fox & dog')); ts_rank ------------ 0.09148999 (1 row) postgres=# 函数名称 ts_rank_cd 参数数据类型 [weights real[], ] vector tsvector, query tsquery [, normalization integer] 函数返回值数据类型 real 函数含义 使用覆盖密度算法来计算query与vector的匹配程度 使用举例 postgres=# SELECT ts_rank_cd('{0.1, 0.2, 0.4, 1.0}', to_tsvector('PostgreSQL full text search is a wonderful method'),to_tsquery('wonderful')); ts_rank_cd ------------ 0.1 (1 row) postgres=# 函数名称 ts_rewrite 参数数据类型 query tsquery, target tsquery, substitute tsquery 函数返回值数据类型 tsquery 函数含义 用substitute替换掉query中的target 使用举例 postgres=# SELECT ts_rewrite('c & b'::tsquery, 'b'::tsquery, 'bad|bat'::tsquery); ts_rewrite ------------------------- ( 'bad' | 'bat' ) & 'c' (1 row) postgres=# 函数名称 ts_rewrite 参数数据类型 query tsquery, select text 函数返回值数据类型 tsquery 函数含义 使用举例 postgres=# CREATE TABLE test (col1 tsquery PRIMARY KEY, COL2 tsquery); CREATE TABLE postgres=# INSERT INTO test values ('p','q'); INSERT 0 1 postgres=# SELECT ts_rewrite('r & q'::tsquery, 'SELECT col1,col2 FROM test'); ts_rewrite ------------ 'r' & 'q' (1 row) postgres=# 函数名称 tsquery_phrase 参数数据类型 query1 tsquery, query2 tsquery 函数返回值数据类型 tsquery 函数含义 构造一个phrase query用于在连续的词位匹配query1和query2(同<→操作符) 使用举例 postgres=# select tsquery_phrase(to_tsquery('fat'), to_tsquery('cat')); tsquery_phrase ----------------- 'fat' <-> 'cat' (1 row) postgres=# 函数名称 tsquery_phrase 参数数据类型 query1 tsquery, query2 tsquery, distance integer 函数返回值数据类型 tsquery 函数含义 构造一个词组查询，该词组搜索匹配query1且query2完全distance分开词素的匹配项 使用举例 postgres=# select tsquery_phrase(to_tsquery('fat'), to_tsquery('cat'), 10); tsquery_phrase ------------------ 'fat' <10> 'cat' (1 row) postgres=# 函数名称 tsvector_to_array 参数数据类型 tsvector 函数返回值数据类型 text[] 函数含义 将tsvector转换为词素数组 使用举例 postgres=# select tsvector_to_array('fat:2,4 cat:3 rat:5A'::tsvector) ; tsvector_to_array ------------------- {cat,fat,rat} (1 row) postgres=# 函数名称 unnest 参数数据类型 tsvector 函数返回值数据类型 setof record ( lexeme text, positions smallint[], weights text ) 函数含义 将tsvector扩展为一行一个词素 使用举例 postgres=# select * from unnest('cat:3 fat:2,4 rat:5A'::tsvector); lexeme | positions | weights --------+-----------+--------- cat | {3} | {D} fat | {2,4} | {D,D} rat | {5} | {A} (3 rows) postgres=# 函数名称 abbrev 参数数据类型 inet 函数返回值数据类型 text 函数含义 将inet转换为text的缩写显示格式 使用举例 postgres=# select abbrev(inet '10.1.1.3'); abbrev ---------- 10.1.1.3 (1 row) postgres=# 函数名称 abbrev 参数数据类型 cidr 函数返回值数据类型 text 函数含义 将cidr转换为text的缩写显示格式，返回结果中去掉了netmask中右边的零 使用举例 postgres=# select abbrev(cidr '10.1.0.0/16'); abbrev --------- 10.1/16 (1 row) postgres=# 函数名称 broadcast 参数数据类型 inet 函数返回值数据类型 inet 函数含义 计算一个ip地址的广播地址 使用举例 postgres=# select broadcast(inet '192.168.1.5/24'); broadcast ------------------ 192.168.1.255/24 (1 row) postgres=# 函数名称 family 参数数据类型 inet 函数返回值数据类型 integer 函数含义 返回一个地址是IPV4（返回4）还是IPV6（返回6） 使用举例 postgres=# select family('fe80::5054:ff:fe99:540c') ; family -------- 6 (1 row) postgres=# select family(inet '172.1.0.23') ; family -------- 4 (1 row) postgres=# 函数名称 host 参数数据类型 inet 函数返回值数据类型 text 函数含义 返回IP地址的text形式，且忽略掉netmask 使用举例 postgres=# select host(inet '192.168.1.0/24'); host ------------- 192.168.1.0 (1 row) postgres=# 函数名称 hostmask 参数数据类型 Inet 函数返回值数据类型 Inet 函数含义 返回ip地址的host mask 使用举例 postgres=# select hostmask(inet '192.168.23.20/30') ; hostmask ---------- 0.0.0.3 (1 row) postgres=# 函数名称 inet_merge 参数数据类型 inet, inet 函数返回值数据类型 cidr 函数含义 返回包含两个给定网络的最小网络 使用举例 postgres=# select inet_merge(inet '192.168.1.5/24', inet '192.168.2.5/24'); inet_merge ---------------- 192.168.0.0/22 (1 row) postgres=# 函数名称 inet_same_family 参数数据类型 inet，inet 函数返回值数据类型 bool 函数含义 返回两个ip地址是否属于同一个family 使用举例 postgres=# select inet_same_family(inet '192.168.1.5/24', inet '::1'); inet_same_family ------------------ f (1 row) postgres=# 函数名称 masklen 参数数据类型 inet 函数返回值数据类型 integer 函数含义 返回以位为单位的网络掩码长度 使用举例 postgres=# select masklen(inet '192.168.1.5/24'); masklen --------- 24 (1 row) postgres=# 函数名称 netmask 参数数据类型 inet 函数返回值数据类型 inet 函数含义 返回网络地址的网络掩码 使用举例 postgres=# select netmask(inet '192.168.1.5/24'); netmask --------------- 255.255.255.0 (1 row) postgres=# 函数名称 network 参数数据类型 inet 函数返回值数据类型 inet 函数含义 返回地址的网络部分，将网络掩码右边的内容清零 使用举例 postgres=# select network(inet '192.168.1.5/24') ; network ---------------- 192.168.1.0/24 (1 row) postgres=# 函数名称 set_masklen 参数数据类型 inet, integer 函数返回值数据类型 inet 函数含义 为inet设置netmask长度，网络地址部分保持不变 使用举例 postgres=# select set_masklen(inet '192.168.1.5/24', 16); set_masklen ---------------- 192.168.1.5/16 (1 row) postgres=# 函数名称 set_masklen 参数数据类型 cidr, integer 函数返回值数据类型 cidr 函数含义 为cidr设置netmask长度，网络地址右边的设置为零 使用举例 postgres=# select set_masklen(cidr '192.168.1.0/24', 16); set_masklen ---------------- 192.168.0.0/16 (1 row) postgres=# 函数名称 text 参数数据类型 inet 函数返回值数据类型 text 函数含义 返回ip地址及其掩码的text形式 使用举例 postgres=# select text(inet '192.168.1.5'); text ---------------- 192.168.1.5/32 (1 row) postgres=# 函数名称 trunc 参数数据类型 macaddr 函数返回值数据类型 macaddr 函数含义 将mac地址的最后三个字节清零，前缀可以与特定的网卡制造商关联 使用举例 postgres=# select trunc(macaddr '12:34:56:78:90:ab') ; trunc ------------------- 12:34:56:00:00:00 (1 row) postgres=# 函数名称 trunc 参数数据类型 macaddr8 函数返回值数据类型 macaddr8 函数含义 将mac地址的最后五个字节清零，前缀可以与特定的网卡制造商关联 使用举例 postgres=# select trunc(macaddr8 '12:34:56:78:90:ab:cd:ef') ; trunc ------------------------- 12:34:56:00:00:00:00:00 (1 row) postgres=# 函数名称 macaddr8_set7bit 参数数据类型 macaddr8 函数返回值数据类型 macaddr8 函数含义 将地址的第7位设置为1，从而创建被称为修改后的EUI-64，以包含在IPv6地址中。 使用举例 postgres=# select macaddr8_set7bit(macaddr8 '00:34:56:ab:cd:ef'); macaddr8_set7bit ------------------------- 02:34:56:ff:fe:ab:cd:ef (1 row) postgres=# 函数名称 area 参数数据类型 geometric_type 函数返回值数据类型 double precision 函数含义 计算面积，针对box, path, circle适用，path必须是能closed，否则会返回NULL 使用举例 postgres=# select area(box '(2,2),(0,0)'); area ------ 4 (1 row) postgres=# 函数名称 center 参数数据类型 geometric_type 函数返回值数据类型 point 函数含义 计算中点，针对box, circle适用 使用举例 postgres=# select center(box '(1,2),(0,0)') ; center --------- (0.5,1) (1 row) postgres=# 函数名称 diagonal 参数数据类型 box 函数返回值数据类型 lseg 函数含义 获取box的对角线作为一个line segment，作用与lseg(box)一样 使用举例 postgres=# select diagonal(box '(1,2),(0,0)'); diagonal --------------- [(1,2),(0,0)] (1 row) postgres=# 函数名称 diameter 参数数据类型 circle 函数返回值数据类型 double precision 函数含义 计算circle的直径 使用举例 postgres=# select diameter(circle '<(0,0),2>') ; diameter ---------- 4 (1 row) postgres=# 函数名称 height 参数数据类型 box 函数返回值数据类型 double precision 函数含义 计算box的垂直大小 使用举例 postgres=# select height(box '(1,2),(0,0)'); height -------- 2 (1 row) postgres=# 函数名称 isclosed 参数数据类型 path 函数返回值数据类型 bool 函数含义 path是否是closed的 使用举例 postgres=# select isclosed(path '((0,0),(1,1),(2,0))'); isclosed ---------- t (1 row) postgres=# 函数名称 isopen 参数数据类型 path 函数返回值数据类型 bool 函数含义 Path是否是open的 使用举例 postgres=# select isopen(path '[(0,0),(1,1),(2,0)]'); isopen -------- t (1 row) postgres=# 函数名称 length 参数数据类型 geometric_type 函数返回值数据类型 double precision 函数含义 计算总长度，对lseg以及path适用 使用举例 postgres=# select length(path '((-1,0),(1,0))') ; length -------- 4 (1 row) postgres=# 函数名称 npoints 参数数据类型 geometric_type 函数返回值数据类型 integer 函数含义 返回point的数量，对path以及polygon适用 使用举例 postgres=# select npoints(path '[(0,0),(1,1),(2,0)]'); npoints --------- 3 (1 row) postgres=# 函数名称 pclose 参数数据类型 path 函数返回值数据类型 path 函数含义 将patch转换为closed的形式 使用举例 postgres=# select pclose(path '[(0,0),(1,1),(2,0)]'); pclose --------------------- ((0,0),(1,1),(2,0)) (1 row) postgres=# 函数名称 popen 参数数据类型 path 函数返回值数据类型 path 函数含义 将patch转换为open的形式 使用举例 postgres=# select popen(path '((0,0),(1,1),(2,0))'); popen --------------------- [(0,0),(1,1),(2,0)] (1 row) postgres=# 函数名称 radius 参数数据类型 circle 函数返回值数据类型 double precision 函数含义 求一个圆形的半径 使用举例 postgres=# select radius(circle '<(0,0),2>'); radius -------- 2 (1 row) postgres=# 函数名称 slope 参数数据类型 point,point 函数返回值数据类型 double precision 函数含义 计算通过两个点的直线的斜率 使用举例 postgres=# select slope(point '(0,0)', point '(2,1)'); slope ------- 0.5 (1 row) postgres=# 函数名称 width 参数数据类型 double precision 函数返回值数据类型 box 函数含义 计算box的horizontal size 使用举例 postgres=# select width(box '(1,2),(0,0)') ; 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track_commit_timestamp 参数值数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 记录transaction的提交时间。注意：只有当这个参数是on时，函数pg_xact_commit_timestamp(xid)、pg_last_committed_xact()才能获得已提交事务的时间值。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 transform_null_equals 参数值数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当本参数值为on时，PG会将expr=NULL(或者NULL=expr)视为expr IS NULL.本参数仅仅影响=NULL这个形式，不会影响其他形式（如in） 是否可session级修改 可以，superuser和普通用户均可以在session级修改 修改后何时生效 Reload即可生效Session级修改时会立即在session级生效 参数名称 max_files_per_process 参数值数据类型 integer 默认值 1000 取值范围 64到2147483647 参数单位 参数含义 本参数用于设置每个server subprocess允许同时打开的files的最大数量。如果遇到 \"Too many open files\"的提示，请尝试减少本参数值。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 cluster_name 参数值数据类型 string 默认值 空值 取值范围 参数单位 参数含义 本参数用于设置PGCluster的名字，可以用操作系统命令ps -ef| grep postgres查看该名字。该名字会出现在所有server process的开头位置，另外，对于standby连接来说，该名字还是默认的application name. 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 update_process_title 参数值数据类型 Bool 默认值 on（除windows之外的平台）；off（Windows平台） 取值范围 on和off 参数单位 参数含义 每当服务器接收到一个新的 SQL 命令时都更新process title 是否可session级修改 是，此时仅限于superuser可以在session级修改 修改后何时生效 Reload即可生效Session级修改时会立即在session级生效 参数名称 synchronous_standby_names 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为primary角色库参数。本参数值区分大小写。本参数指定了支持同步复制的standby server清单。standby server的name是standby连接信息中的application_name,在物理复制standby中，standby server的name是在primary_conninfo中设置的，如果cluster_name配置参数的配置过了，那么standby server的name默认是cluster_name参数值，否则是walreceiver对于逻辑复制，可以在subscription中设置连接信息，默认值是subscription name本参数使用如下语法指定了一系列的standby servers的列表：[FIRST] num_sync ( standby_name [, …​] )ANY num_sync ( standby_name [, …​] )standby_name [, …​] num_sync代表同步standby的个数举例，FIRST 3 (s1, s2, s3, s4)的含义:s1, s2, s3这三个standby是同步standbys4这个standby是潜在的同步standby(potential synchronous standbys)如果当前同步standby不论什么原因断开，它的位置会立即被next-highest-priority standby取代。关键字FIRST是可选的。FIRST 3 (s1, s2, s3, s4)中，优先级由大到小为s1>s2>s3>s4 举例， ANY 3 (s1, s2, s3, s4) 的含义：s1, s2, s3, s4这四个standby 中的任意3个standby server应用wal变化之后，primary端的事务才能提交请务必注意：FIRST 和ANY关键字是大小写敏感的。如果这两个关键字被用于standby server的name，那么standby_name需要用双引号引起来。在PostgreSQL 9.6之前版本中使用的第三种语法已经受到支持。该语法与\"FIRST 1\"是相同作用。比如， FIRST 1 (s1, s2) 和 s1, s2 有相同的含义：要么s1要么s2被选为同步standby 表示匹配任何standby name 没有机制来确保standby name的唯一性。在重复发生时，匹配到的standby 会被视为更高的优先级。尽管确切地说哪个是不确定的注意：每个standby_name应该是有效地SQL 标识符，除非是。如果需要,你可以使用双引号。但是请注意：standby_names与standby application_name 比较时，是不区分大小写的，不论有无双引号。 如果synchronous_standby_names参数不指定值，同步复制不会启用。即使同步复制被启用，通过设置synchronous_commit配置参数为local或者off，单个事务可以被配置为不等待standby端的wal被应用。 本参数可以在postgresql.conf文件或者在the server command line设置 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 vacuum_defer_cleanup_age 参数值数据类型 integer 默认值 0 取值范围 0到1000000 参数单位 参数含义 该参数指定了在primary库上vacuum和HOT updates 会延迟清理dead row versions的事务的数量。默认值是零个事务，零个事务的含义是dead row versions会尽可能快的被removed掉。尽可能快是指dead row versions不会被任何open transaction可见。你可以在hot standby环境中的primary端为此参数设置为非零值，这允许standby端的query运行更长的时间以便完成，而不会遇到过早的dead row versions cleanup而导致冲突。由于本参数值是根据在primary上发生的写入事务的数量来衡量的，因此，很难预测会为standby query增加多长时间。 本参数可以在postgresql.conf文件或者在the server command line设置 你可以考虑在standby 端使用hot_standby_feedback配置参数作为vacuum_defer_cleanup_age的替代。本参数不会防止那些已经达到age（由old_snapshot_threshold配置参数指定）的dead rows的清理。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 max_logical_replication_workers 参数值数据类型 integer 默认值 4 取值范围 0到262143 参数单位 参数含义 本参数设置Logical replication workers进程的最大数量，此处的worker进程包括两种：apply workers 和table synchronization workers。Logical replication workers进程的数量从max_worker_processes配置参数定义的池中获取。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 max_sync_workers_per_subscription 参数值数据类型 integer 默认值 2 取值范围 0到262143 参数单位 参数含义 指定了每个subscription中synchronization workers的最大数量。本参数控制在subscription初始化 或者 新表加入时的initial data copy的并行度。目前，每个table只能指定一个synchronization workersynchronization workers从max_logical_replication_workers配置参数定义的池中取得。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 restore_command 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 恢复时指定archive wal的位置 restore_command = 'cp /mnt/server/archivedir/%f \"%p\"' restore_command = 'copy \"C:\\\\server\\\\archivedir\\\\%f\" \"%p\"' # Windows restore_command对应的命令执行成功之后，restore_command返回零， %f代表archived wal文件名 %p代表copy destination path name %r代表含有最后一个有效的resart point的file的文件名 %%代表一个%字符(当需要写入%字符的情况下使用) 本参数需要在pg instance启动之前进行设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 archive_cleanup_command 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数定义了在每个restart point时所执行的shell命令。 archive_cleanup_command参数的目的是提供一个清理不再被standby server所需要的老的archived wal file的机制。 %r代表最后一个有效的restart point的wal file。该wal file是最早一个必须保留的文件，以便允许restore操作可以被restart。注意： restart point 是一个 point ，该point用于standby server重启recovery操作。因此，所有早于%r的文件可以被安全的清理掉。本信息可以用来truncate掉archive wal file，以便满足从当前restore可以restart的最小需求。 pg_archivecleanup实用命令常被用在单个standby配置的archive_cleanup_command参数中 archive_cleanup_command = 'pg_archivecleanup /mnt/server/archivedir %r' %%代表一个%字符(当需要写入%字符的情况下使用)如果命令返回非零值，一个警告日志消息会被写入。一个例外是：当命令被一个signal终止或者shell中有错误时，一个fatal error会被抛出。 本参数可以在postgresql.conf设置，也可以在server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 recovery_end_command 参数值数据类型 String 默认值 空字符串 取值范围 参数单位 参数含义 本参数用于定义在recovery结束时候的需要执行的shell命令。如果命令返回非零值，一个警告日志消息会被写入，然后数据库会继续启动。一个例外是：当命令被一个signal终止或者shell中错误时，数据库不会继续启动。%r代表最后一个有效的restart point的wal file 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 exit_on_error 参数值数据类型 Bool 默认值 off 取值范围 on和off 参数单位 参数含义 当设置为on时，任何error均会导致session被终止。默认情况下，本参数值为off，只有FATAL error才会终止session。 postgres=# set exit_on_error=on; SET postgres=# show exit_on_error ; -[RECORD 1]-+--- exit_on_error | on postgres=# begin; BEGIN postgres=# select \\ form t1; FATAL: syntax error at or near \"form\" LINE 1: select \\* form t1; ^ server closed the connection unexpectedly This probably means the server terminated abnormally before or while processing the request. The connection to the server was lost. Attempting reset: Succeeded.postgres=# 是否可session级修改 是 修改后何时生效 Reload即可生效Session级别修改可以立即生效。 参数名称 restart_after_crash 参数值数据类型 bool 默认值 on 取值范围 参数单位 参数含义 当设置为on时，PostgreSQL会在backend crash之后重新初始化(即:restart)。在某些情况下，比如PostgreSQL被Clusterware调用时，需要禁用PostgreSQL的restart(将该参数值设置为off)以便clusterware获得PostreSQL的控制权并采取适当的操作。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 data_sync_retry 参数值数据类型 bool 默认值 off 取值范围 off 和on 参数单位 参数含义 如果设置为off(默认值),当PostgreSQL flush数据到文件系统上遇到failure时，PostgreSQL会抛出一个panic级别的error。这会导致数据库服务crash掉。本参数只能在数据库服务启动之前设置。在某些操作系统中，当回写(write-back)失败之后，kernel的page cache中的数据状态是未知的(unknown).在一些情况下，它可能会被完全忘记，设置为尝试是不安全的，在实际上数据已经丢失的情况下，第二次尝试可能会报告成功，在这些场景中，避免数据损失的唯一方法是解决掉硬件问题之后从wal中恢复。当设置为on时，PostgreSQL会报告一个error并会继续运行，data flushing操作会在稍后的checkpoint中尝试。注意：仅在调查操作系统对buffered data的处理以防回写失败的情况下，才将其设置为on。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 archive_mode 参数值数据类型 enum 默认值 off 取值范围 off,on,always 参数单位 参数含义 指定归档模式。on表示启用归档模式，当wal_level配置参数值为minimal时, archive_mode 不能设置为on。当本参数值设置为always时，standby 实例会为收到的每个wal segments调用归档命令,也就是standby实例上也会进行wal的归档操作。本参数仅仅能在pg启动时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 archive_command 参数值数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 定义对wal进行归档的命令。当archive_mode配置参数启用并且archive_command配置参数是空字符串时，wal archiving暂时被禁用，但是pg会继续积累wal segment文件。archive_command参数值设置为/bin/true会禁用归档，但这样会导致wal文件归档中断，归档中断是无法进行归档恢复的，请注意这一点。使用举例如下： archive_command = 'test ! -f /mnt/server/archivedir/%f && cp %p /mnt/server/archivedir/%f' # Unix archive_command = 'copy \"%p\" \"C:\\\\server\\\\archivedir\\\\%f\"' # Windows 退出码是零代表本命令正常运行。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 archive_timeout 参数值数据类型 integer 默认值 0 取值范围 0到1073741823 参数单位 若是不指定单位，那默认的单位是秒 参数含义 强制进行wal segments归档的时长。使用本参数归档出来的wal segment与正常的wal segment是相同的大小。因此，本参数值设置太小会对存储空间有浪费。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 bgwriter_delay 参数值数据类型 integer 默认值 200 取值范围 10到10000 参数单位 若是不指定单位，那默认的单位是毫秒(ms) 参数含义 在每一轮循环中，background writer都会为一定数量的脏缓冲区发出写操作，然后background writer进行睡眠，睡眠的时间为bgwriter_delay参数值，然后再唤醒，然后重复。设置bgwriter_delay为不是10的倍数的值可能与将其设置为10的下一个更高的倍数具有相同的结果。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 bgwriter_lru_maxpages 参数值数据类型 integer 默认值 100 取值范围 0到1073741823 参数单位 参数含义 每轮bgwriter进程写入LRU Page的最大数量。本参数设置为零会禁用background writing行为，但是background writer进程依然会存在。本参数可以在postgresql.conf或者server command line设置 是否可session级修改 否 修改后何时生效 Reload即可 参数名称 bgwriter_lru_multiplier 参数值数据类型 real 默认值 2.0 取值范围 0到10 参数单位 参数含义 每轮写dirty buffer的数量基于服务器进程(即:backend process)在最近几轮中所需要的新buffer数量。最近的平均需要量乘以本参数值就是下一轮需要的新buffers数量的估计值。Dirty buffers会一直写入，直到有那么多干净的、可重用的buffers为止。因此，本参数值为1.0表示准确预计了需要的buffers数量。较大的本参数值提供了一些缓冲，以防需求激增，而较小的本参数值会有意将写dirty buffers操作留给服务器进程(即:backend process)完成。本参数可以在postgresql.conf或者server command line设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 bgwriter_flush_after 参数值数据类型 integer 默认值 Linux平台下是64，也就是512KB；其他平台下是0 取值范围 源码中BLCKSZ 符号常量为8KB时，取值范围是0到256；源码中BLCKSZ 符号常量不为8KB时，等比例计算即可。 参数单位 8KB 参数含义 当background writer写入的数据量超过本参数值之后，尝试强制操作系统发送这些写入的数据到底层存储中。这么做会限制kernel中page cache的dirty data的数量，减少在checkpoint末尾发生fsync时发生卡顿的可能性。本参数可以在postgresql.conf或者server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_statement_stats 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入系统性能统计信息。本参数on时，其他参数（log_parser_stats 、log_planner_stats、log_executor_stats）必须设置为off在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 09:55:21.621 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,6,\"\",2021-03-31 09:51:32 CST,3/0,0,LOG,00000,\"QUERY STATISTICS\",\"! system usage stats: ! 0.000657 s user, 0.000000 s system, 0.000657 s elapsed ! [0.003464 s user, 0.001403 s system total] ! 6288 kB max resident size ! 0/8 [0/24] filesystem blocks in/out ! 0/128 [0/1295] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [11/2] voluntary/involuntary context switches\",,,,,\"select \\*from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是 修改后何时生效 Session级修改立即生效；非session级修改reload生效，仅仅superuser能修改本参数 参数名称 log_parser_stats 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入解析统计信息。在本参数设置为on的情况下，log_statement_stats配置参数无法设置为on，设置为on时会有如下的错误： postgres=# alter system set log_statement_stats='on'; ERROR: invalid value for parameter \"log_statement_stats\": 1 DETAIL: Cannot enable \"log_statement_stats\" when \"log_parser_stats\", \"log_planner_stats\", or \"log_executor_stats\" is true. postgres=#在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,41,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"PARSER STATISTICS\",\"! system usage stats: ! 0.000019 s user, 0.000000 s system, 0.000019 s elapsed ! [0.015481 s user, 0.001403 s system total] ! 7720 kB max resident size ! 0/0 [0/56] filesystem blocks in/out ! 0/0 [0/1673] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent ! 0/0 [39/6] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,42,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"statement: select \\* from pg_database;\",,,,,,,,,\"psql\",\"client backend\" 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,43,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"PARSE ANALYSIS STATISTICS\",\"! system usage stats: ! 0.000031 s user, 0.000000 s system, 0.000030 s elapsed ! [0.015565 s user, 0.001403 s system total] ! 7720 kB max resident size ! 0/0 [0/56] filesystem blocks in/out ! 0/0 [0/1673] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent ! 0/0 [39/6] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 2021-03-31 10:16:25.962 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,44,\"\",2021-03-31 09:51:32 CST,3/3486,0,LOG,00000,\"REWRITER STATISTICS\",\"! system usage stats: ! 0.000003 s user, 0.000000 s system, 0.000003 s elapsed ! [0.015582 s user, 0.001403 s system total] ! 7720 kB max resident size ! 0/0 [0/56] filesystem blocks in/out ! 0/0 [0/1673] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [39/6] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是，但是不能在log_statement_stats=on时设置本参数为on 修改后何时生效 Session级修改立即生效非session级修改reload生效，仅仅superuser能修改本参数 参数名称 log_planner_stats 参数值数据类型 boolean 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入规划器统计信息。在本参数设置为on的情况下，log_statement_stats配置参数无法设置为on，设置为on时会有如下的错误： postgres=# alter system set log_statement_stats='on'; ERROR: invalid value for parameter \"log_statement_stats\": 1 DETAIL: Cannot enable \"log_statement_stats\" when \"log_parser_stats\", \"log_planner_stats\", or \"log_executor_stats\" is true. postgres=#在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 10:24:39.323 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,63,\"\",2021-03-31 09:51:32 CST,3/3491,0,LOG,00000,\"PLANNER STATISTICS\",\"! system usage stats: ! 0.000041 s user, 0.000006 s system, 0.000045 s elapsed ! [0.017384 s user, 0.002173 s system total] ! 7740 kB max resident size ! 0/0 [0/80] filesystem blocks in/out ! 0/0 [0/1674] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [55/10] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是，但是不能在log_statement_stats=on时设置本参数为on 修改后何时生效 Session级修改立即生效;非session级修改reload生效，仅仅superuser能修改本参数 参数名称 log_executor_stats 参数值数据类型 Bool 默认值 off 取值范围 off和on 参数单位 参数含义 在本参数设置为on的情况下，pg会向运行日志中写入执行器统计信息。在本参数设置为on的情况下，log_statement_stats配置参数无法设置为on，设置为on时会有如下的错误： postgres=# alter system set log_statement_stats='on'; ERROR: invalid value for parameter \"log_statement_stats\": 1 DETAIL: Cannot enable \"log_statement_stats\" when \"log_parser_stats\", \"log_planner_stats\", or \"log_executor_stats\" is true. postgres=#在本参数设置为on的情况下，当前台发出SQL语句时（如下以psql中执行select \\* from pg_database为例子），在pg的运行日志中，会有如下的输出： 2021-03-31 10:27:59.858 CST,\"pg131\",\"postgres\",8896,\"[local]\",6063d5a4.22c0,71,\"\",2021-03-31 09:51:32 CST,3/3496,0,LOG,00000,\"EXECUTOR STATISTICS\",\"! system usage stats: ! 0.000031 s user, 0.000000 s system, 0.000030 s elapsed ! [0.018581 s user, 0.003320 s system total] ! 7744 kB max resident size ! 0/0 [0/104] filesystem blocks in/out ! 0/0 [0/1676] page faults/reclaims, 0 [0] swaps ! 0 [0] signals rcvd, 0/0 [0/0] messages rcvd/sent! 0/0 [71/14] voluntary/involuntary context switches\",,,,,\"select \\* from pg_database;\",,,\"psql\",\"client backend\" 是否可session级修改 是，但是不能在log_statement_stats=on时设置本参数为on 修改后何时生效 Session级修改立即生效非session级修改reload生效，仅仅superuser能修改本参数 参数名称 jit_provider 数据类型 string 默认值 llvmjit 取值范围 参数单位 参数含义 本参数值指定了jit provider library的名称，本参数仅仅可以在pg启动时设置。若是指定了不存在的library，则jit不可用，但是不会有报错抛出。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 local_preload_libraries 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数指定在连接启动时被预加载的一个或多个共享库。多个库名称以逗号分隔，其中每个库名的解释与load命令相同。库名之间的空格会被忽略，若是需要在库名中包括空格或者逗号，请用双引号将库名引起来。参数值仅仅在连接开始时生效，后续更改无效。如果找不到指定的库，则连接会将失败(即:连接将无法登陆到数据库中)。本参数任何user都可以设置。因此，被加载的共享库仅仅限于在$libdir/plugins目录下。数据库管理员有责任确保该目录下的共享库的安全性。本参数的参数值可以明确指定目录，比如$libdir/plugins/mylib，或者仅仅指定library名称，比如mylib，mylib的作用等同于$libdir/plugins/mylib本参数的目的是允许非授权用户加载debugging或者performance-measurement 库到特定session中，而无需load命令。为此，可以在客户端上使用设置了本参数的PGOPTIONS环境变量或者使用alter role set 是否可session级修改 是 修改后何时生效 Session级修改立即生效非session级修改reload生效 参数名称 session_preload_libraries 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数值指定了在连接启动时被预加载的一个或者多个共享库。多个库名称以逗号分隔，其中每个库名的解释与load命令相同。库名之间的空格会被忽略，若是需要在库名中包括空格或者逗号，请用双引号将库名引起来。参数值仅仅在连接开始时生效，后续更改无效。如果找不到指定的库，则连接会将失败(即:连接将无法登陆到数据库中)。本参数的目的是允许加载debugging或者performance-measurement 库到特定session中，而无需load命令。比如，使用alter role set 语句设置本参数可以将auto_explain针对所有session生效。与shared_preload_libraries不同，在会话开始时（而不是在首次使用时）加载库没有太大的性能优势。但是，使用连接池时有一些优点。 是否可session级修改 可以，只有superuser可以改变本参数值 修改后何时生效 Session级修改立即生效非session级修改reload生效，reload之后，仅仅对新启动的session生效。 参数名称 shared_preload_libraries 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数值指定了在PostgreSQL数据库启动时被预加载的一个或者多个共享库。多个库名称以逗号分隔，其中每个库名的解释与load命令相同。库名之间的空格会被忽略，若是需要在库名中包括空格或者逗号，请用双引号将库名引起来。该参数只能在PostgreSQL数据库启动时设置，若是找不到指定的库，PostgreSQL数据库启动会失败。设置了本参数之后，新的backend process的启动时间会略有延长，即使这个backedn process不用这个共享库。因此，本参数推荐用于哪些在绝大多数session中使用的共享库。另外，由于本参数需要重启PostgreSQL数据库生效，因此，若是用于短期的debugging目的，请使用session_preload_libraries参数。请注意，在Windows主机上，在服务器启动时预加载库不会减少启动每个新服务器进程所需的时间。每个服务器进程将重新加载所有预加载库。但是，对于需要在postmaster启动时执行操作的库，本参数在windows主机上是很有用的。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 deadlock_timeout 数据类型 string 默认值 1000 取值范围 1到2147483647 参数单位 毫秒 参数含义 在检查是否存在deadlock条件之前，等待一个lock的时间长度。死锁检查是相对昂贵的，因此，pg不会在每次等待lock时都运行死锁检测。PostgreSQL会乐观的认为死锁在生产应用程序中并不常见，只需要等待一段时间后再检查死锁。这是您在实践中想要设置的最小值，在负载很重的pg中，可能需要提高本参数值。在理想情况下，本参数值应该超过典型的transaction 时间，以提高在PostgreSQL决定检查死锁之前将释放锁的几率。仅仅superuser可以修改本参数值。在log_lock_waits参数启用的情况下，deadlock_timeout参数值也决定了一个有关lock wait的 log message被写入运行日志之前的等待时间。如果你正在尝试调查锁定延迟(locking delays)，你可能希望设置比正常deadlock_timeout参数值更短的时间。 是否可session级修改 是(仅仅superuser可修改) 修改后何时生效 Session级修改立即生效(仅仅superuser)；非session级别修改Reload即可生效 参数名称 max_locks_per_transaction 数据类型 integer 默认值 64 取值范围 10到2147483647 参数单位 参数含义 共享锁表(shared lock table)追踪max_locks_per_transaction \\* (max_connections + max_prepared_transactions) 个lock objects，因此，一次只能锁定这么多不同的对象。本参数控制分配给每个事务的object lock的平均数量。单个事务可以锁定更多的对象，只要所有事务的锁都在锁表(lock table)中. 本参数值不是可以锁定的行数；可以锁定的行数是无限的。max_locks_per_transaction的默认值是64在历史上是被证明足够的，但是如果在多个事务中有涉及多个不同的表的SQL语句(比如一个有多个子表的父表的SQL语句),则可能提高该数值。本参数只能在PostgreSQL数据库启动时设置。当存在standby server时，standby server上的本参数值要大于或者等于master server上的本参数值，否则，查询语句在standby server上是不被允许的。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_pred_locks_per_transaction 数据类型 Integer 默认值 64 取值范围 10到2147483647 参数单位 参数含义 共享谓词锁表(shared predicate lock table)追踪max_pred_locks_per_transaction \\* (max_connections + max_prepared_transactions) 个lock objects，因此，一次只能锁定这么多不同的对象。本参数控制分配给每个事务的object lock的平均数量。单个事务可以锁定更多的对象，只要所有事务的锁都在锁表(lock table)中. 本参数值不是可以锁定的行数；可以锁定的行数是无限的。本参数默认值64在测试环境中是足够的，但是如果你的client在单个串行化事务中用到了多个不同的table，你可能需要加大本参数值。本参数只能在PostgreSQL数据库启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_pred_locks_per_relation 数据类型 Integer 默认值 -2 取值范围 -2147483648到2147483647 参数单位 参数含义 本参数控制在将锁提升为覆盖整个relation之前，单个relation中能有多少pages或者tuples能被predicate-locked 。本参数值大于等于零时表示绝对限制，本参数值为负值时表示max_pred_locks_per_transaction/本参数值的绝对值。本参数可以在postgresql.conf设置也可以在server command line设置。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 max_pred_locks_per_page 数据类型 integer 默认值 2 取值范围 0到2147483647 参数单位 参数含义 本参数控制在将锁提升为覆盖整个page之前，单个page中能有多少tuples能被predicate-locked。本参数可以在postgresql.conf设置也可以在server command line设置。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 vacuum_cost_delay 数据类型 Real 默认值 0，零代表禁用了基于成本的vacuum delay特性。 取值范围 0到100 参数单位 毫秒 参数含义 vacuum_cost_limit参数值是一个累积vacuum io操作的成本值，当vacuum操作达到vacuum_cost_limit参数值之后，vacuum操作会停下来休眠一段时间，该段时间是vacuum_cost_delay参数值指定的，然后，PostgreSQL会重置计数器并继续vacuum操作。本参数的参数值建议尽量小，大值是没有帮助的。 是否可session级修改 是 修改后何时生效 session修改会立即生效；非session级修改reload即可生效。 参数名称 vacuum_cost_page_hit 数据类型 Integer 默认值 1 取值范围 0到10000 参数单位 参数含义 对shared buffer中发现的一个buffer进行vacuum的估计成本，本成本代表：锁定buffer pool的成本+查找shared hash table的成本+扫描page中内容的成本。 是否可session级修改 是 修改后何时生效 session修改会立即生效;非session级修改reload即可生效。 参数名称 vacuum_cost_page_miss 数据类型 Integer 默认值 10 取值范围 0到10000 参数单位 参数含义 对不在shared buffer中的一个buffer(只能从disk读取)进行vacuum的估计成本,本成本代表：锁定buffer pool的成本+查找shared hash table的成本+从disk中读取该block到buffer的成本+扫描page中内容的成本。 是否可session级修改 是 修改后何时生效 session修改会立即生效;非session级修改reload即可生效。 参数名称 vacuum_cost_page_dirty 数据类型 Integer 默认值 20 取值范围 0到10000 参数单位 参数含义 Vacuum改变一个之前是干净的block的估计成本。本成本代表：额外的io操作以便将dirty block刷新到disk中。 是否可session级修改 是 修改后何时生效 session修改会立即生效；非session级修改reload即可生效。 参数名称 vacuum_cost_limit 数据类型 Integer 默认值 200 取值范围 1到10000 参数单位 参数含义 本参数值是一个累积vacuum io操作的成本值，当vacuum操作达到本参数值之后，vacuum操作会停下来休眠一段时间。 是否可session级修改 是 修改后何时生效 session修改会立即生效；非session级修改reload即可生效。 参数名称 max_wal_senders 数据类型 Integer 默认值 10 取值范围 0到262143，0表示复制被禁用 参数单位 参数含义 同时运行wal sender process的最大数量。运行wal sender process的场景包括流复制和以streaming模式进行base backup(译者注：其实还包括逻辑复制使用逻辑复制槽的场景，但是在PostgreSQL文档中并没有写明这一点)。本参数仅能在PostgreSQL启动时进行设置。配置参数waL_level必须设置为replica或者更高的值以便允许来自standby server的连接。在流复制环境中，在流复制备库上的该参数值至少要等于或者大于流复制主库上的该参数值，否则在备库上的query是不允许的。 是否可session级修改 否 修改后何时生效 重启PG instanc生效 参数名称 max_replication_slots 数据类型 Integer 默认值 10 取值范围 0到262143 参数单位 参数含义 设置PostgreSQL能支持的复制槽的最大数量，本参数仅能在PostgreSQL启动时进行设置。当本参数值小于当前已经存在的replicat slot时，会导致PG instance无法启动。配置参数waL_level必须设置为replica或者更高的值以便允许replication slot被用到。 是否可session级修改 否 修改后何时生效 重启PG instanc生效 参数名称 wal_keep_size 数据类型 Integer 默认值 0，0表示PostgreSQL不会为流复制环境保留额外的wal segments， 取值范围 0到2147483647 参数单位 MB，若是设置时不带单位，则默认为MB 参数含义 指定在pg_wal目录中保留的wal segment file的最小大小，以便流复制环境中的standby server 从主库中fetch wal file。如果连接到发送服务器的standby server超过本参数值的大小，发送服务器可能会删除掉standby server仍然需要的wal segments，此时，复制连接会终止，下游的连接最终也会因此失败，若是wal进行了归档，standby server可以从归档中进行fetch segments，以便恢复流复制。本参数值仅仅设置保留在pg_wal中的wal segments的最小大小。系统可能需要保留更多的wal segments以便进行wal 归档或者从检查点中恢复。本参数值仅仅在postgresql.conf中进行设置或者在server command line中进行设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 max_slot_wal_keep_size 数据类型 Integer 默认值 -1，-1表示复制槽保留不限数量的wal files。 取值范围 -1到2147483647 参数单位 MB 参数含义 用来指定复制槽保留pg_wal目录中wal file的最大大小。当一个复制槽的restart_lsn落后于current_lsn超过本参数值时，使用了复制槽的standby server不能再继续流复制，因为已经删除掉了需要的wal files 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_sender_timeout 数据类型 Integer 默认值 60000 取值范围 0到2147483647，0代表禁用timeout机制。 参数单位 毫秒，若是指定时不带单位，默认为为毫秒数 参数含义 当处于inactive状态多长时间(多长时间由本参数指定)之后，终止掉复制连接。这对于发送服务器检测standby crash或者网络中断很有用。当主备节点分布于不同的地理位置时，不同地理位置的节点使用不同的本参数值会带来管理上的灵活性。对于低延迟网络连接，较小的本参数值有助于快速的连接故障检测；对于地理位置较远的高延迟网络连接，较高的本参数值有助于判断standby server的健康状况。 是否可session级修改 是 修改后何时生效 Session修改立即生效，允许非superuser修改本参数值非session级修改reload生效。 参数名称 authentication_timeout 数据类型 Integer 默认值 60 取值范围 1到600 参数单位 秒，如果指定本参数值时不带单位，默认为秒 参数含义 允许完成连接认证的最长时间。如果客户端没有在本参数值指定的时间内完成连接认证，PostgreSQL数据库会关闭此次连接。本参数用于防止大量的客户端无限制的占用连接。本参数仅仅可以在postgresql.conf设置或者在server command line设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 password_encryption 数据类型 enum 默认值 md5 取值范围 md5和scram-sha-256，写on也可以，on与md5有相同的作用。 参数单位 参数含义 本参数决定了create role或者alter role语句中指定的密码的加密算法。请注意，老的客户端版本不支持SCRAM-SHA-256密码加密算法，具体请见下面的描述。有两个关键的标准去确定是否支持SCRAM密码加密算法。 正在运行PostgreSQL 10及其更高版本 你用来连接到PostgreSQL数据库的驱动有SCRAM兼容性。PostgreSQL社区已经提供了一个驱动清单(https://wiki.postgresql.org/wiki/List_of_drivers#Drivers)如果您的系统满足上述两个标准，您可以使用SCRAM密码加密算法。请注意：要把现有的环境从md5升级到scram-sha-256，可以在确保所有在用的客户端已经足以支持SCRAM之后，在postgresql.conf中设置password_encryption = 'scram-sha-256'，然后让所有用户设置新口令并且在pg_hba.conf中将认证方法说明改为scram-sha-256。 是否可session级修改 是 修改后何时生效 Session级修改立即生效；非session级修改reload生效 参数名称 krb_server_keyfile 数据类型 string 默认值 FILE:<sysconfdir>/krb5.keytab，注意,sysconfdir代表sysconfdir目录，该目录可以用pg_config --sysconfdir操作系统命令获得。 取值范围 参数单位 参数含义 设置服务器Kerberos key file的位置。如果本参数设置为空字符串(empty string),PostgreSQL会忽略这个空字符串，此时系统默认值会被使用。本参数值在postgresql.conf或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 krb_caseins_users 数据类型 bool 默认值 off，off代表大小写敏感 取值范围 参数单位 参数含义 本参数用于控制GSSAPI user names是否被视为大小写不敏感，本参数值仅能在postgresql.conf或者server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 db_user_namespace 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 若本参数值设置为on，那么创建的用户名的格式是username@dbname，当client传递username时，@符以及database name会被附加到username的末尾，并且PostgreSQL数据库查询用户是查找username@dbname这个格式的用户。当你在SQL环境中使用username@dbname这个格式的用户名时，请使用引号引起来username@dbname。当本参数值设置为on时，你依然可以建立普通的全局users，在客户端指定username时加上@即可，当PostgreSQL查找username之前，@符号会被去掉。 本参数会导致客户端和服务器端的用户名称表示方法不同。认证始终检查服务器端的username。因此，认证方法必须被配置为服务器端的username。因为md5密码加密算法在客户端和服务器端均使用username作为salt，因此，md5密码加密算法不能与本参数值一起使用。 本参数值仅能在postgresql.conf或者server command line中设置。请注意，本特性的目的是在一个完整的解决方案找到之前作为临时措施，到那时，本参数会被remove掉。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 track_activities 数据类型 boolean 默认值 on 取值范围 on 和off 参数单位 参数含义 当设置为on时，当每个session中执行SQL语句或者命令时，PostgreSQL会收集信息（包括命令执行的时间信息）。收集的信息仅仅对superuser和执行命令所在session的owner可见。 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效;非session级修改reload生效 参数名称 track_activity_query_size 数据类型 Integer 默认值 1024 取值范围 100到1048576 参数单位 Byte，当指定本参数不带单位时，默认是Bytes 参数含义 为每个active的session保留当前执行命令的文本的大小，该文本对应pg_stat_activity.query列。本参数仅仅能在PostgreSQL start时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 track_counts 数据类型 Bool 默认值 on 取值范围 on和off 参数单位 参数含义 当设置为on时，针对database activity收集统计信息。Autovacuum进程需要这些收集的信息。 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效；非session级修改reload生效 参数名称 track_io_timing 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 是否为database中的 io call/activity启用计时（即:io操作消耗了多少时间）。io计时信息体现在如下方面： pg_stat_database中 explain 带buffer时的输出中 pg_stat_statements中当设置为on时,PostgreSQL会重复查询操作系统的当前时间，这个可能会导致在某些平台下有大量开销。您可以使用pg_test_timing工具(该工具在bin目录下)来衡量系统的计时开销。仅superuser可以更改本参数值 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效非session级修改reload生效 参数名称 track_functions 数据类型 enum 默认值 none，none的含义是禁用本功能 取值范围 none,pl,all 参数单位 参数含义 启用追踪函数调用计数和消耗的时间。参数值pl的含义是仅仅对procedural-language函数进行追踪，参数值all的含义是追踪SQL和C语言函数。仅superuser可以更改本参数值 是否可session级修改 是，仅限于superuser可进行session级修改 修改后何时生效 Session级修改立即生效；非session级修改reload生效 参数名称 stats_temp_directory 数据类型 string 默认值 pg_stat_tmp 取值范围 参数单位 参数含义 本参数值用于指定存储临时统计信息的目录。本参数值可以是绝对路径，也可以是相对于data directory的相对路径本参数值指定为基于RAM的文件系统会减少物理IO并提升性能。本参数仅能在postgresql.conf中设置或者在server command line设置。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 data_directory 数据类型 string 默认值 取值范围 参数单位 参数含义 本参数值表示PostgreSQL的数据目录的位置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 config_file 数据类型 string 默认值 在数据目录中 取值范围 参数单位 参数含义 本参数值指定了postgresql.conf的位置本参数值仅仅可以在postgres command line设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 hba_file 数据类型 string 默认值 在数据目录中 取值范围 参数单位 参数含义 本参数值指定了pg_hba.conf的位置；本参数值仅仅可以在PostgreSQL启动时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 ident_file 数据类型 string 默认值 在数据目录中 取值范围 参数单位 参数含义 本参数值指定了pg_ident.conf的位置本参数值仅仅可以在PostgreSQL启动时设置 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 external_pid_file 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 指定了将postmaster的pid写入哪个文件本参数值仅仅可以在PostgreSQL启动时设置。 postgres=# alter system set external_pid_file='/home/pg131/postmasterpid.txt'; ALTER SYSTEM postgres=# exit [pg131@VM-0-8-centos ~]$ pg_ctl restart waiting for server to shut down…​. done server stopped waiting for server to start…​.2021-04-09 10:09:21.776 CST [5977] LOG: redirecting log output to logging collector process 2021-04-09 10:09:21.776 CST [5977] HINT: Future log output will appear in directory \"log\". done server started [pg131@VM-0-8-centos ~]$ cd [pg131@VM-0-8-centos ~]$ ll total 136260 -rw-rw-r-- 1 pg131 pg131 0 Mar 10 20:47 12345aa drwxrwxr-x 2 pg131 pg131 4096 Apr 9 10:09 archive drwx------ 20 pg131 pg131 4096 Apr 9 10:09 data -rw-rw-r-- 1 pg131 pg131 72 Mar 23 10:34 para.sql drwxrwxr-x 6 pg131 pg131 4096 Dec 20 14:32 postgresql-13.1 -rw-r—​r-- 1 pg131 pg131 139499520 Dec 20 14:07 postgresql-13.1.tar -rw-r—​r-- 1 pg131 pg131 5 Apr 9 10:09 postmasterpid.txt drwxrwxr-x 7 pg131 pg131 4096 Dec 20 14:45 soft drwx------ 3 pg131 pg131 4096 Dec 20 16:04 ts1 [pg131@VM-0-8-centos ~]$ cat postmasterpid.txt 5977 [pg131@VM-0-8-centos ~]$ pg_ctl status pg_ctl: server is running (PID: 5977) /home/pg131/soft/bin/postgres[pg131@VM-0-8-centos ~]$ 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 log_min_messages 数据类型 enum 默认值 warning 取值范围 debug5,debug4,debug3,debug2,debug1,info,notice,warning,error,log,fatal,panic 参数单位 参数含义 控制哪种message level写入系统日志。取值范围见上。在上面的取值范围中，前一个取值包括其后的取值。在上面的取值范围中，级别越在后边，写入系统日志中的messages就越少。注意: 本参数的参数值log的级别与client_min_messages配置参数的参数值log的级别不同。仅允许superuser可以修改本参数值。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效；其他级别修改reload生效 参数名称 log_min_error_statement 数据类型 enum 默认值 error，表示那些导致了errors、log messages、fatal errors、panics的SQL语句会被记录到日志中。 取值范围 debug5,debug4,debug3,debug2,debug1,info,notice,warning,error,log,fatal,panic 参数单位 参数含义 控制导致何种级别错误的SQL语句内容被记录到日志中。为了关闭对错误SQL语句的记录，请设置被参数值为panic仅允许superuser可以修改本参数值。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_min_duration_statement 数据类型 integer 默认值 -1，-1表示禁用本功能。 取值范围 -1到2147483647 参数单位 毫秒，若是不指定单位的话。 参数含义 所有运行超过本参数值的SQL语句会打印到系统日志中。本参数对追踪未优化的SQL语句有很大帮助。本参数值为零表示打印所有的SQL语句。仅允许superuser可以修改本参数值。本参数会覆盖log_min_duration_sample参数，这意味持续时间超过本参数值的SQL语句将不进行采样，而是始终记录下来。对于使用扩展查询协议的客户端，Parse、Bind、Execute阶段会被各自独立记录下来。本参数与log_statement参数合用时，由于log_statement参数起作用而记录到log中的SQL文本不会被重复记录。在不使用syslog时，推荐使用log_line_prefix参数，以便记录PID或者Session ID。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_min_duration_sample 数据类型 Integer 默认值 -1，-1表示禁用本功能。 取值范围 -1到2147483647，参数值为零时，记录所有语句的sample 参数单位 毫秒 参数含义 举例说明，如果本参数值设置为100ms，那么运行时间超过100ms的SQL语句会被考虑进行采样。仅允许superuser可以修改本参数值。当流量太大而无法记录所有查询时，启用此参数会很有帮助。本参数的优先级比log_min_duration_statement参数优先级要低。这意味着持续时间超过log_min_duration_statement参数值的SQL语句不受采样限制，并且总是被记录下来。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_statement_sample_rate 数据类型 Real 默认值 1 取值范围 0到1 参数单位 参数含义 确定将记录的持续时间超过log_min_duration_sample参数值的的语句的比率。例如0.5意味着统计上每两个给定语句中就有一个被记录的机会。默认值为1.0，意味着记录所有采样语句。本参数值设置为0表示禁用sampled statement-duration logging。仅允许superuser可以修改本参数值。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 log_transaction_sample_rate 数据类型 Real 默认值 0，参数值为0表示不记录任何其他事务的语句 取值范围 0到1，参数值为1表示记录所有事物的所有语句 参数单位 参数含义 设置除其他原因记录的语句之外，所有记录语句的事务的百分比，本参数适用于每个新事务，而不考虑其语句的持续时间。采样是随机的，例如0.1意味着在统计上，每十个交易中有一个机会记录任何给定的事务。本参数值有助于构建一个事务的采样。仅允许superuser可以修改本参数值。另外请注意：与所有statement-logging选项一样，本参数会显著增加系统负载。 是否可session级修改 是，此时仅限于superuser在session级别修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 checkpoint_timeout 数据类型 Integer 默认值 300 取值范围 30到86400 参数单位 秒 参数含义 设置两次automatic WAL checkpoints的时间间隔。增加本参数值会导致进行crash recovery的时间长度增大。本参数值仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 checkpoint_completion_target 数据类型 Real 默认值 0.5 取值范围 0到1 参数单位 参数含义 指定了checkpoint 完成的目标，即:checkpoint需要在checkpoint_timeout\\* checkpoint_completion_target的时间长度内完成。本参数值仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 checkpoint_flush_after 数据类型 Integer 默认值 32，注意：linux平台下本参数默认值为32，Windows平台下本参数默认值为0 取值范围 0到256，如果BLCKSZ符号常量值不是8KB，请根据比例进行修改。 参数单位 8KB，若是指定参数值时不带单位，默认是BLCKSZ bytes，在不修改BLCKSZ符号常量值的情况下，该符号常量值为8KB。 参数含义 每当在执行检查点时写入的数据量超过本参数值时，会尝试强制操作系统向底层存储发出这些写入操作。这样会限制kernel page cache中dirty data的总量，减少了在checkpoint的末尾发出fsync命令时，或者当操作系统在后台以更大的批写入数据时，PostgreSQL暂停的可能性。这通常会大大减少事务延迟，但也有一些情况，特别是在工作负载大于shared_buffers，但小于操作系统page cache的情况下，性能可能会降低。本参数值仅可以在postgresql.conf中设置或者在server command line进行设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 checkpoint_warning 数据类型 integer 默认值 30 取值范围 0到2147483647，0会禁用warning。 参数单位 秒。若是指定本参数值不带单位，默认是秒 参数含义 如果由于填充WAL段文件而导致的检查点发生的时间间隔比本参数值更小（这表明应该加大max_wal_size配置参数值），则向服务器日志写入一条消息。当checkpoint_timeout参数值小于checkpoint_warning参数值时，warning不会产生。本参数值仅可以在postgresql.conf中设置或者在server command line进行设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 max_wal_size 数据类型 Integer 默认值 1024 取值范围 2到2147483647 参数单位 MB 参数含义 触发automatic checkpoints的最大wal 大小。这是软限制，wal size可以在如下情况下超过本参数值的限制：重负载，失败的archive_command命令，以及高的wal_keep_size参数值设置。增加本参数值会导致进行crash recovery的时间长度增大。本参数值仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 min_wal_size 数据类型 Integer 默认值 80 取值范围 2到2147483647 参数单位 MB 参数含义 只要WAL Disk的使用低于本参数值，旧的wal文件总是在checkpoint发生时被循环使用，而不是删除wal文件。这可以用来确保保留足够的WAL空间来处理WAL使用中的峰值，例如在运行大型批处理作业时。此参数只能在postgresql.conf文件文件或在server command line上。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 dynamic_library_path 数据类型 string 默认值 $libdir, $libdir代表的绝对路径请见pg_config --pkglibdir命令的输出 取值范围 参数单位 参数含义 如果需要打开可动态加载的模块，并且create function 或LOAD命令中指定的文件名没有目录组件（即名称不包含斜杠），系统将在该路径中搜索所需的文件。本参数值必须是由冒号分隔(针对Linux平台)或者分号分隔(针对Windows平台)的绝对路径列表。如果本参数值以特殊字符串$libdir开头，那么编译后的PostgreSQL package library directory会取代$libdir, PostgreSQL package library directory请见pg_config --pkglibdir命令的结果。若是本参数值被设置为空字符串，表示自动path search功能会被关闭。superuser可以在运行时更改此参数，但这样做的设置只会持续到客户端连接结束，因此应将此方法保留用于开发目的。 是否可session级修改 是，此时仅限于superuser在session级别修改。 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 gin_fuzzy_search_limit 数据类型 integer 默认值 0，0表示无限制 取值范围 0到2147483647 参数单位 参数含义 GIN索引扫描返回的结果集大小的软上限。开发GIN索引的主要目的是在PostgreSQL中创建对高度可伸缩的全文搜索的支持，而且通常情况下，全文搜索会返回非常大的结果集。此外，当查询包含非常频繁的单词时，这种情况经常发生，因此大的结果集甚至没有用处。因为从磁盘读取许多元组并对它们进行排序可能需要花费大量时间，这对于生产来说是不可接受的。（请注意，索引搜索本身非常快。）为了便于此类查询的受控执行，GIN对返回的行数有一个可配置的软上限，这就是gin_fuzzy_search_limit配置参数。默认设置为0（表示无限制）。如果设置了非零限制，则返回的集是随机选择的整个结果集的子集。\"软\"意味着返回结果的实际数量可能与指定的限制有所不同，这取决于查询和系统随机数生成器的质量。从经验来看，以千为单位的本参数值（例如，5000-20000）工作正常。 是否可session级修改 否 修改后何时生效 Reload生效 参数名称 tcp_keepalives_count 数据类型 默认值 0，0表示取操作系统的默认值， 取值范围 0到2147483647 参数单位 参数含义 指定在服务器与客户端的连接被视为已断开之前可以被丢弃的TCP keepalive messages.在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为零。本参数在Windows操作系统上不被支持，因此，在Windows操作系统上，本参数值必须为0。本参数仅仅支持符合如下条件的操作系统：那些支持TCP_KEEPCNT或者等价socket option的操作系统。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 tcp_keepalives_idle 数据类型 integer 默认值 0，0表示取操作系统的默认值 取值范围 0到2147483647 参数单位 秒 参数含义 指定在没有网络活动的情况下，经过多长时间之后，操作系统应该向Client端发送TCP keepalive message.本参数仅仅支持符合如下条件的操作系统：那些支持TCP_KEEPIDLE或者等价socket option的操作系统。因此，在Windows以及其他操作系统中，本参数值必须为0，在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为0。在Windows操作系统上，本参数值为0时，本参数值将会被视为2小时，因为，Windows不提供读取系统默认值的方法。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 tcp_keepalives_interval 数据类型 Integer 默认值 0，0表示取操作系统的默认值 取值范围 0到2147483647 参数单位 秒 参数含义 指定TCP keepalive message重传的时间间隔。本参数仅仅支持符合如下条件的操作系统：那些支持TCP_KEEPINTVL或者等价socket option的操作系统。因此，在Windows以及其他操作系统中，本参数值必须为零，在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为零。在Windows操作系统上，本参数值为零时，本参数值将会被视为1秒，因为，Windows不提供读取系统默认值的方法。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 tcp_user_timeout 数据类型 Integer 默认值 0，0表示取操作系统的默认值 取值范围 0到2147483647 参数单位 毫秒 参数含义 指定在强制关闭TCP连接之前，传输的数据可能保持未确认状态的时间长度本参数仅仅支持符合如下条件的操作系统：那些支持TCP_USER_TIMEOUT或者等价socket option的操作系统。因此，在其他操作系统中，本参数值必须为零，在一个基于Unix-domain socket的session中，本参数会被忽略，此时本参数值被视为零。在Windows操作系统上，本参数不受支持，此时本参数值必须设置为零。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或者禁用genetic query optimization，生产环境中不应该将本参数设置为off。配置参数geqo_threshold提供了对GEQO更细粒度的控制。遗传查询优化器（GEQO）是一种使用启发式搜索进行查询计划的算法。这减少了用于复杂查询（那些包含许多关系的查询）的计划时间，但所产生的计划的成本有时不如普通穷举搜索算法所找到的计划。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_threshold 数据类型 integer 默认值 12 取值范围 2到2147483647 参数单位 参数含义 本参数是启用geqo的阈值，即:当from之后的关系数量至少有本参数值指定的个数时，才启用geqo。注意一个FULL OUTER JOIN只被计为一个FROM项。对于简单的SQL语句，通常最好使用常规的穷举搜索planner，但是对于具有许多表的查询，穷举搜索花费的时间太长，通常比执行次优计划的代价要长，因此，本阈值是管理geqo的一种方法。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_effort 数据类型 Integer 默认值 5 取值范围 1到10 参数单位 参数含义 控制GEQO中计划时间和查询计划质量之间的权衡。此变量必须是1到10之间的整数。默认值为5。较大的值会增加生成查询计划所花费的时间，但也会增加选择高效查询计划的可能性。本参数实际上并不直接做任何事情；它只用于计算影响geqo行为的其他变量的默认值。如果愿意，可以手动设置其他参数。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_pool_size 数据类型 Integer 默认值 0，0表示PostgreSQL会根据SQL语句中表的数量以及geqo_effort参数值选择一个合适的值。 取值范围 0到2147483647 参数单位 参数含义 控制GEQO使用的pool size，即:遗传群体中的个体数，至少是2，有用的值通常是100到1000. 参数值为0表示PostgreSQL会根据SQL语句中表的数量以及geqo_effort参数值选择一个合适的值。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_generations 数据类型 Integer 默认值 0 取值范围 0到2147483647 参数单位 参数含义 控制GEQO使用的generations，即算法的迭代次数。它必须至少是一个，并且有用的值通常是100到1000。如果设置为零（默认设置），则PostgreSQL会根据geqo_pool_size选择合适的值。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_selection_bias 数据类型 real 默认值 2 取值范围 1.5到2 参数单位 参数含义 控制GEQO使用的选择偏差。选择偏差是population内部的选择压力 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 geqo_seed 数据类型 real 默认值 0 取值范围 0到1 参数单位 参数含义 控制GEQO用于通过连接顺序搜索空间选择随机路径的随机数生成器的初始值，更改该值将更改所探索的连接路径集。并可能导致找到更好或更差的最佳路径 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 recovery_target 数据类型 string 默认值 空串 取值范围 immediate 参数单位 参数含义 此参数指定恢复应在达到一致状态后尽快结束，即尽早结束。从联机备份还原时，这意味着在备份结束的时间点结束恢复。 是否可session级修改 否 修改后何时生效 本参数不支持修改。 参数名称 recovery_target_name 数据类型 string 默认值 空串 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的命名还原点,该还原点由函数pg_create_restore_point()创建。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_time 数据类型 string 默认值 空串 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的time stamp，精确的停止点受到recovery_target_inclusive配置参数影响。此参数的值是一个时间戳，其格式与timestamp with time zone数据类型所接受的格式相同，只是您不能使用时区缩写（除非在配置文件的前面设置了timezone_abbreviations变量）。首选样式是使用UTC的数字偏移量，或者您可以编写完整的时区名称，例如，Europe/Helsinki，而不是EEST 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_xid 数据类型 string 默认值 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的trasaction ID。请记住，虽然事务ID在事务开始时按顺序分配，但事务可以按不同的数字顺序完成。要恢复的事务是在指定事务之前提交的事务（也可以选择包括指定事务），精确的停止点受到recovery_target_inclusive配置参数影响。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_lsn 数据类型 string 默认值 空串 取值范围 参数单位 参数含义 此参数指定将继续恢复(recovery)到的LSN，精确的停止点受到recovery_target_inclusive配置参数影响。本参数使用系统数据类型pg_lsn解析。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_inclusive 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当本参数设置为on时指定到达指定的recovery target之后停止当本参数设置为off时指定到达指定的recovery target之前停止本参数与如下参数配合使用：recovery_target_lsn、recovery_target_time、recovery_target_xid。本参数控制含有精确recovery target(恢复目标)的LSN、commit time、或者trasaction id是否被包括恢复(recovery)之内。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_timeline 数据类型 string 默认值 latest 取值范围 参数单位 参数含义 指定recovery进入一个特定的timeline，本参数值可以是一个数字的timeline id或者一个特定的值，值current的含义：recover到做base backup时的那个timeline；值latest表示recover到在archive 中找到的最新的timeline，值latest适用于standby server。您通常只需要在复杂的重新恢复情况下设置本参数，在这种情况下，您需要返回到在时间点恢复之后达到的状态。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 recovery_target_action 数据类型 enum 默认值 pause 取值范围 pause,promote,shutdown 参数单位 参数含义 指定了一旦达到recovery target，PostgreSQL该采取的操作。默认值pause表示恢复暂停(pause)，值promote表示恢复过程完成并且PostgreSQL将会启动以接受外部连接，值shutdown表示在达到recovery target之后会stop PostgreSQL。pause值的预期用途是允许对数据库执行查询，以检查此恢复目标是否是最理想的恢复点,pg_wal_replay_resume()函数表示paused state resumed，这意味着恢复走到了终点。若是recovery target不是期望的那个stopping point，那么请关闭PostgreSQL，改变recovery target到更为之后的一个target，然后重启recovery即可。 是否可session级修改 否 修改后何时生效 重启PG Instance生效 参数名称 default_statistics_target 数据类型 integer 默认值 100 取值范围 1到10000 参数单位 参数含义 为没有通过ALTER TABLE SET STATISTICS设置column-specific target的表列设置默认统计目标。较大的本参数值会增加analyze的时间，但也会提升planner估计的质量。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 constraint_exclusion 数据类型 enum 默认值 partition 取值范围 partition,on,off on表示检查所有表的约束 off表示不检查约束partition表示仅仅对继承的子表以及union all子查询检查约束。 参数单位 参数含义 控制planner是否使用constraints优化查询,如果表的约束保证没有与查询匹配的行，table scan会被跳过。本参数经常与传统的继承树一起使用以提高性能，比如如下的例子： CREATE TABLE parent(key integer, …​); CREATE TABLE child1000(check (key between 1000 and 1999)) INHERITS(parent); CREATE TABLE child2000(check (key between 2000 and 2999)) INHERITS(parent); …​ SELECT \\* FROM parent WHERE key = 2400;当本参数启用时，select语句就不会扫描child1000这个表。目前，默认情况下仅对经常用于通过继承树实现表分区的情况启用约束排除。为所有表启用它会带来额外的计划开销，这在简单查询中非常明显，而且对于简单查询通常不会产生任何好处。如果没有使用传统继承进行分区的表，则可能希望完全关闭它。（请注意，分区表的等效功能由单独的配置参数enable_partition_pruning控制。） 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 cursor_tuple_fraction 数据类型 real 默认值 0.1 取值范围 0到1 参数单位 参数含义 设置planner’s估计，本参数设置较少的值时，会使planner倾向于对cursors使用\"fast start\" plan，这会快速的检索前几行，而获取所有行可能需要很长时间。较大的值更强调总的估计时间。在最大设置为1.0时，游标的规划与常规查询完全相同，只考虑总的估计时间，而不考虑第一行的交付时间。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 from_collapse_limit 数据类型 integer 默认值 8 取值范围 1到2147483647 参数单位 参数含义 如果生成的FROM列表包含的项目不超过本参数值，那么planner将会把子查询合并到上面的查询中。较小的值会减少计划时间，但可能会产生较差的查询计划。设置本参数值为geqo_threshold参数值或者更大可能会触发GEQO Planner的使用，进而导致非优化的执行计划。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 jit 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 确定jit编译是否被PostgreSQL使用。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 join_collapse_limit 数据类型 integer 默认值 8 取值范围 1到2147483647 参数单位 参数含义 每当生成的items列表不超过本参数值时,planner就会将explicit join结构（FULL JOINs除外）重写为FROM items列表。较小的值会减少计划时间，但可能会产生较差的查询计划.默认情况下，此参数值设置为与from_collapse_limit参数值相同，这适用于大多数用途。将其设置为1可防止explicit join的任何重新排序。因此，查询中指定的显式连接顺序将是连接关系的实际顺序。因为查询规划器并不总是选择最佳的连接顺序，所以高级用户可以选择将此参数值临时设置为1，然后显式指定所需的连接顺序。设置本参数值为geqo_threshold参数值或者更大可能会触发GEQO Planner的使用，进而导致非优化的执行计划。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 parallel_leader_participation 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 允许leader进程在Gather和Gather Merge node下执行查询计划，而不是等待worker进程。将此值设置为off可降低worker进程被阻塞的可能性，因为leader进程读取元组的速度不够快，但需要leader进程等待工作进程启动，然后才能生成第一个元组。Leader进程能够帮助或阻碍性能的程度取决于计划类型、worker数量和查询持续时间。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 force_parallel_mode 数据类型 enum 默认值 off 取值范围 off,on,regress off表示仅仅当预期能提升性能时，才使用parallel mode on表示对被视为safe的查询语句强制使用并行查询。regress表示除了有本参数值设置为\"on\"的相同的所有效果之外，还有一些旨在促进自动回归测试的附加效果 参数单位 参数含义 是否使用parallel query功能。更具体地说，将此值设置为on将在任何查询计划的顶部添加一个Gather节点，这看起来是安全的，因此查询在并行worker进程中运行，即使并行worker进程不可用或无法使用，也将禁止在并行查询上下文中禁止的操作（如启动子事务），除非planner认为这将导致查询失败，如果设置此选项时出现故障或意外结果，则查询使用的某些函数可能需要标记为PARALLEL UNSAFE（或者，可能是PARALLEL RESTRICTED）通常，来自并行worker进程的消息包含一个context line，但是设置为regress会抑制该context line，以便输出与非并行执行中的输出相同。此外，通过此设置，添加到计划中的Gather nodes将隐藏在EXPLAIN的输出中，以便当本参数值设置为off时，输出与获得的结果相匹配。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 plan_cache_mode 数据类型 enum 默认值 auto 取值范围 auto,force_generic_plan,force_custom_plan 参数单位 参数含义 可以使用自定义或generic的plan执行prepared statements（explicitly prepared或implicitly generated，例如通过PL/pgSQL），针对每一次的执行，自定义plan使用特定的一组参数值被重新生成，generic plans不依赖本plan_cache_mode参数值，可以被多次的执行重复使用。此外，使用generic plans会节省 planning time，但是，如果理想的plan强烈依靠本参数，那么，generic plan可能效率低下。这些选项之间的选择通常是自动进行的，但它可以被plan_cache_mode配置参数覆盖。在执行缓存的计划时，而不是在准备计划时，会考虑本参数值 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 array_nulls 数据类型 bool 默认值 on，本参数值为on时表示允许输入包括NULL的数组值 取值范围 on和off 参数单位 参数含义 本参数控制数组输入解析器是否将未加引号的NULL识别为指定NULL数组元素。8.2版本之前的PostgreSQL不支持数组中的null值，因此将null视为使用字符串值\"null\"指定普通数组元素。为了向后兼容需要旧行为的应用程序，可以关闭此参数。请注意，即使本参数值是off，也可以创建包含NULL的数组值 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 backslash_quote 数据类型 enum 默认值 safe_encoding 取值范围 safe_encoding,on,off on表示始终允许\\' off表示始终拒绝'safe_encoding表示仅当客户端编码不允许在多字节字符中使用ASCII\\时才允许. 参数单位 参数含义 本参数用于设置字符串的引用符是否是'符号。SQL标准中是'',而PostgreSQL历史上也接受了'符号，但是，使用'会带来安全风险，这是因为，在一些客户端字符集编码中，存在多字节字符，其中最后一个字节在数字上等同于ASCII \\.如果客户端没有正确的转义，那么SQL注入攻击是可能的。这种风险可以通过使PostgreSQL数据库拒绝引用符被backslash反斜杠转义的查询来防止。请注意，在标准一致性字符串文本中，无论如何，\\仅仅表示\\。此参数仅影响对非标准一致性文本的处理，包括转义字符串语法（E'…'） 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 escape_string_warning 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当设置为on时，表示当\\出现在一个普通的字符串('…'这样的)中并且standard_conforming_strings参数值为off时，一个warning会发出。希望使用反斜杠作为转义的应用程序应该修改为使用转义字符串语法（E'…'）. 因为根据SQL标准，普通字符串的默认行为现在是将反斜杠视为普通字符. 可以启用本参数来帮助定位需要更改的代码 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 lo_compat_privileges 数据类型 bool 默认值 off 取值范围 参数单位 参数含义 对large object，为权限检查启用面向backward的兼容性模式。在PostgreSQL9.0之前，large object并不具有访问权限，因此，large object总是能被所有的user进行读和写。设置本参数值为on将会禁用新权限的检查，这会与PostgreSQL9.0之前的版本保持兼容。 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 operator_precedence_warning 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 启用此选项后，解析器将对自PostgreSQL 9.4以来由于运算符优先级的更改而可能已更改含义的任何构造发出警告。对于审核应用程序以查看优先级更改是否破坏了任何内容非常有用；但这并不意味着在生产环境中保持打开状态，因为它将警告某些完全有效、符合标准的SQL代码 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 quote_all_identifiers 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当数据库生成SQL语句时，即使标识符不是关键字，也会将标识符引用起来。这会影响explain的输出以及诸如pg_get_viewdef函数的结果，比如下面的例子： postgres=# select pg_get_viewdef('company_view'); -[RECORD 1]--+-------------------- pg_get_viewdef SELECT company.id,+ company.name, company.age FROM company; postgres=# set quote_all_identifiers=on; SET postgres=# select pg_get_viewdef('company_view'); -[RECORD 1]--+------------------------ pg_get_viewdef SELECT \"company\".\"id\",+ \"company\".\"name\", \"company\".\"age\" FROM \"company\"; postgres=# 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 standard_conforming_strings 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数控制普通字符串文本（'…'）是否按SQL标准中的指定按字面含义处理反斜杠.从PostgreSQL9.1开始，默认值为on。此参数的存在也可以被视为表示支持转义字符串语法（E'…'）如果应用程序希望反斜杠被视为转义字符，则应使用转义字符串语法 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 synchronize_seqscans 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 这允许对大表的顺序扫描彼此同步，以便并发扫描在大约相同的时间读取相同的块，从而共享I/O工作负载。启用本参数后，扫描可能从表的中间开始，然后\"环绕\"结尾以覆盖所有行，以便与正在进行的扫描活动同步。这可能导致没有ORDERBY子句的查询返回的行顺序发生不可预知的更改。将此参数设置为off可确保与PostgreSQL 8.3之前的行为，即顺序扫描始终从表的开头开始 是否可session级修改 是 修改后何时生效 Session级别修改立即生效，其他级别修改reload生效 参数名称 listen_addresses 数据类型 string 默认值 localhost 取值范围 参数单位 参数含义 设置数据库服务器监听的hostname或者IP地址，监听多个hostname或者IP地址时，请用逗号隔开。 \\*号表示监听所有可用的IP地址， 0:0:0:0监听代表所有IPV4的地址， ::代表监听所有IPV6的地址。当本参数值为空时，PostgreSQL不会监听任何的IP，此时，只有Unix-domain sockets才可以用于连接到数据库中。本参数控制哪些IP地址上接受连接尝试，这可以帮助防止在不安全的IP地址上重复出现恶意连接请求。该参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 port 数据类型 integer 默认值 5432 取值范围 1到65535 参数单位 参数含义 PostgreSQL监听的TCP端口。请注意，PostgreSQL监听的所有IP地址都使用相同的端口号，本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_connections 数据类型 integer 默认值 100 取值范围 1到262143 参数单位 参数含义 本参数值指定了PostgreSQL数据库的最大连接数。本参数只能在PostgreSQL启动时设置。在流复制备库上，必须将本参数值设置为与主库相同或者比主库参数值大,否则，后备服务器将不允许查询操作。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 superuser_reserved_connections 数据类型 integer 默认值 3 取值范围 0到262143 参数单位 参数含义 为superusers保留的connection \"slots\"的数量，当PostgreSQL的活跃的并发连接的数量等于max_connections参数值减去superuser_reserved_connections参数值之后，仅仅可以面向superuser建立新的连接，并且，replication connection也是建立不了的。本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 unix_socket_directories 数据类型 string 默认值 在Linux中默认值为/tmp；在Windows中默认值为空 取值范围 参数单位 参数含义 本参数指定了PostgreSQL监听客户端连接的Unix-domain socket(s)目录，多个目录可以用逗号隔开，两个目录之间的空格会被忽略，如果你想在目录名中包括逗号或者空格，请使用双引号引起来。本参数值为空时表示不监听任何的Unix-domain socket(s)目录，这意味着只有TCP/IP sockets可以被用来连接到PostgreSQL中。本参数只能在PostgreSQL启动时设置。在本参数值指定的目录下，会有名为s.PGSQL.nnnn的socket file，nnnn是PostgreSQL使用的端口号，在该目录下，还有一个名为.s.PGSQL.nnnn.lock的文件。这两个文件请不要手工删除。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 unix_socket_group 数据类型 string 默认值 空字符串，空字符串表示使用启动PostgreSQL 的user的默认组。 取值范围 参数单位 参数含义 设置Unix-domain socket(s).的owning group(请注意：socket的owning user总是启动PostgreSQL的user)。本参数与unix_socket_permissions结合使用可以将其作为Unix-domain connections的额外访问控制机制。本参数只能在PostgreSQL启动时设置。本参数不支持在Windows上使用，在Windows上，本参数取任何值都会被忽略。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 unix_socket_permissions 数据类型 integer 默认值 0777，0777表示任何人都可以连接 取值范围 0到511 参数单位 参数含义 本参数用于设置Unix-domain socket(s)的访问权限。Unix-domain socket使用通常的Unix文件系统权限集。参数值预计是被chmod和umask系统调用接受的数字模式。（要使用惯用的八进制格式，数字必须以0（零）开头。）合理的参数值是0770（仅用户和组，另请参见unix_socket_group）和0700（仅用户）。请注意，对于Unix-domain socket，仅写许可权是重要的，因此设置或撤消读或执行许可权没有意义。本参数只能在PostgreSQL启动时设置。在完全忽略socket permissions的操作系统上（特别是自solaris10起的Solaris），此参数是不相关的。在此类操作系统上，可以通过将unix目录指向一个搜索权限仅限于所需访问用户的目录来实现类似的效果。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 bonjour 数据类型 boolean 默认值 off 取值范围 off和on 参数单位 参数含义 本参数控制是否通过Bonjour机制通告PostgreSQL的存在。本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 bonjour_name 数据类型 string 默认值 空字符串。空字符串表示使用计算机名 取值范围 参数单位 参数含义 本参数用于设置Bonjour service name。当PostgreSQL软件没有编译Bonjour support时，本参数会被忽略。本参数只能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效。 参数名称 effective_io_concurrency 数据类型 integer 默认值 1 取值范围 0到1000，0表示禁止发布异步I/O请求 参数单位 参数含义 有效利用disk子系统的并发disk io的数量。目前，本参数仅仅影响bitmap heap scans。对于HDD(机械硬盘)，本参数的起始值应该是PostgreSQL database用到的、组成raid 0 条带或者raid 1镜像的单独 disk drive的个数。但是，如果PostgreSQL数据库经常忙于并发会话中发出的多个查询，则较低的值可能足以使磁盘阵列保持繁忙。高于使磁盘保持忙碌的本参数值只会导致额外的CPU开销，固态盘(SSD)以及其他基于内存的存储可以经常处理很多并发请求，因此，此时最佳的值可能是几百。异步IO请求取决于一个有效的posix_fadvise函数，该函数在有些操作系统上是缺少的。在该函数缺少时，设置本参数为非零值会导致错误发生。在有些操作系统上(比如Solaris),本函数存在但是并没有实际的执行任何操作。在受到支持的操作系统上，本参数值的默认值为1，否则为零。本参数值可以被tablespace级别的同名参数值所覆盖。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 maintenance_io_concurrency 数据类型 integer 默认值 10 取值范围 0到1000 参数单位 参数含义 本参数类似于effective_io_concurrency参数，但本参数用于代表许多客户端会话执行的维护工作。在受到支持的操作系统上，本参数值的默认值为10，否则为零。本参数值可以被tablespace级别的同名参数值所覆盖。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 max_worker_processes 数据类型 integer 默认值 8 取值范围 0到262143 参数单位 参数含义 本参数值指定了PostgreSQL所能支持的并发worker进程的最大数量。本参数值仅能在PostgreSQL启动时设置。当运行在standby server环境中时，在standby server上的本参数值必须大于或者等于在master server上的本参数值。否则，query是不能运行在standby server上。当改变本参数值时，请考虑调整max_parallel_workers,参数，max_parallel_maintenance_workers参数，max_parallel_workers_per_gather参数 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_parallel_workers_per_gather 数据类型 integer 默认值 2 取值范围 0到1024，0表示禁用parallel query execution 参数单位 参数含义 本参数指定了可以被单个Gather或者Gather Merge Node启动的worker进程的最大数量。Parallel Workers进程从由max_worker_processes配置参数建立的进程池中获取，受限于max_parallel_workers配置参数。请注意，请求的workers的数量在运行时并不一定实际可用，如果这个情况发生，执行计划会以少于期望个数的workers去运行，这可能是低效的。请注意，parallel queries可能比非parallel queries消耗更多的资源，因为每个工作进程是一个完全独立的进程，其对系统的影响与附加用户会话大致相同。在为此设置选择值时，以及在配置控制资源利用率的其他设置（如work_mem）时，都应考虑到这一点。诸如work_mem之类的资源限制单独应用于每个worker，这意味着所有进程的总利用率可能比任何单个进程的正常利用率高得多。例如，一个使用4个worker的并行查询可能会使用多达5倍的CPU时间、内存、I/O带宽。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 max_parallel_maintenance_workers 数据类型 integer 默认值 2 取值范围 0到1024，0表示禁用由实用命令使用的parallel workers 参数单位 参数含义 可以被单个实用命令启动的parallel worker的最大数量。当前，支持parallel workers的并行实用命令有如下几种： create index 建立B-Tree时； vacuum 不带full选项时； Parallel Workers进程从由max_worker_processes配置参数建立的进程池中获取，受限于max_parallel_workers配置参数。请注意，请求的workers的数量在运行时并不一定实际可用，如果这个情况发生，执行计划会以少于期望个数的workers去运行，这可能是低效的。注意，并行实用程序命令不应该比等效的非并行操作消耗更多的内存，这种策略不同于并行查询，在并行查询中资源限制通常适用于每个worker进程。并行实用命令将资源限制maintenance_work_mem视为应用于整个实用命令的限制，而不考虑并行工作进程的数量。然而，并行实用命令仍然可能消耗更多的CPU资源和I/O带宽。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 max_parallel_workers 数据类型 integer 默认值 8 取值范围 0到1024 参数单位 参数含义 本参数值指定了PostgreSQL支持的parallel操作的worker的最大数量。当增加或者减少本参数值时，请考虑同时调整max_parallel_maintenance_workers 配置参数和 max_parallel_workers_per_gather配置参数。请注意：当本参数值高于max_worker_processes参数值时不会有任何作用。因为，因为parallel workers是从max_worker_processes配置参数所建立的工作进程池中获取的。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 backend_flush_after 数据类型 integer 默认值 0，0表示禁用强制writeback 取值范围 0到256 参数单位 8KB 参数含义 每当单个backend进程写入的数据超过本参数值时，尝试强制操作系统向底层存储发出这些写入操作。这样做将限制内核page cache中脏数据的数量，减少在检查点结束时发出fsync时，或者当操作系统在后台以更大的批写入数据时暂停的可能性。这通常会大大减少事务延迟，但也有一些情况，特别是在工作负载大于共享缓冲区，但小于操作系统page cache的情况下，性能可能会降低。此参数可能在某些平台下无效。如果指定该值时没有单位，则将其作为块，即源码中的符号常量BLCKSZ个字节，通常为8KB。如果BLCKSZ不是8KB，则最大值与之成比例缩放。 是否可session级修改 是 修改后何时生效 Session级别修改立即生效非session级修改reload生效 参数名称 old_snapshot_threshold 数据类型 intger 默认值 -1，-1代表禁用本特性，表示snapshot age 是无限制的。 取值范围 -1到86400 参数单位 分钟 参数含义 设置在使用snapshot时可以使用查询快照而不会出现\"snapshot too old\"错误的最短时间。处于dead状态的且超过本参数值的数据可以被vacuum掉。这有助于防止snapshot长期使用时出现表膨胀。为了防止由于清除快照可见的数据而产生不正确的结果，当快照早于本参数值并且快照用于读取自创建快照以来已修改的页面时，将生成一个错误。对生产环境有用的值可能从几个小时到几天不等。只允许使用较小的值（如0或1min），因为它们有时可能对测试有用。虽然允许高达60d的设置，但请注意，在许多工作负载中，在更短的时间内可能会出现极端膨胀或事务ID环绕(transaction ID wraparound)。启用本特性后，relation末尾释放的空间将无法释放给操作系统，因为这可能会删除检测\"snapshot too old\"条件所需的信息。分配给relation的所有空间都与该relation保持关联，以便仅在该relation中重用，除非显式释放（例如，使用VACUUM FULL）。此参数并不试图保证在任何特定情况下都会生成错误。事实上，如果正确的结果可以从一个已经物化的结果集中生成，则即使引用表中的底层行已被vacuum，也不会生成错误。某些表不能安全地提前vacuum，因此不受此设置的影响，例如系统目录(system catalogs)。对于这样的表，此设置既不会减少膨胀，也不会在扫描时产生\"snapshot too old\"错误的可能性。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 primary_conninfo 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数值指定了连接到sending server的连接字符串(连接信息）.在本连接字符串中缺少的option，PostgreSQL会到环境变量中取寻找。若是环境变量中也没有，那么就用默认值。连接字符串应该指定如下信息： Sending Server的主机名或者IP地址、 Sending Server的端口号（若是跟standby server端口号不同） Username(该用户在sending-server端有合适的权限) Password：请注意，密码可以在primary_conninfo中提供，也可以在standby server上的~/.pgpass文件中提供（此时，是用replication作为database name）。请不要在primary_conninfo中指定database的名字本参数仅能在postgresql.conf文件或者server command line中设置。当在wal receiver process正在运行的情况下修改本参数值，wal receiver process会被发信号通知关闭然后再以新参数值去启动，除非primary_conninfo配置参数为空字符串。当PostgreSQL没有处于standby mode的情况下，本参数无效。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 primary_slot_name 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数指定了当要连接到sending-server端时，要使用的sending-server端的已经存在的replication slot，用来控制sending-server端的wal removal。本参数仅能在postgresql.conf文件或者server command line中设置。当在wal receiver process正在运行的情况下修改本参数值，wal receiver process会被发信号通知关闭然后再以新参数值去启动，本参数在如下情况下没有作用：PostgreSQL没有处于standby mode或者primary_conninfo参数值没有设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 promote_trigger_file 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数值指定了一个trigger file，该文件用于standby server结束recovery状态进行角色提升。本参数值没有设置时，你可以使用pg_ctl promote或者使用函数pg_promote()进行角色提升。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 hot_standby 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数控制standby server是否允许运行只读查询语句。本参数仅能在server start时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 max_standby_archive_delay 数据类型 integer 默认值 30000 取值范围 -1到2147483647，-1表示允许standby server一直在等待直到冲突的query执行完毕。 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。当hot standby在被启用的状态下，本参数决定了standby server在取消掉standby中运行的且与WAL日志应用有冲突的查询语句之前的等待时间。本参数适用于从wal 归档读取wal data的情况。当不指定单位时，本参数的单位是毫秒。本参数仅能在postgresql.conf文件或者server command line中设置。 本参数值与查询语句在取消之前可以运行的最长时间不同。相反，本参数值是：一旦standby server从sending-server收到wal data，所允许的应用wal data的最长时间。因此，如果一个查询在一个wal segment之前导致了显著的延迟，那么在standby server再次赶上之前，后续冲突查询的容忍时间(宽限时间)将少得多。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 max_standby_streaming_delay 数据类型 integer 默认值 30000 取值范围 -1到2147483647，-1表示允许standby server一直在等待直到冲突的query执行完毕。 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。当hot standby在被启用的状态下，本参数决定了standby server在取消掉standby中运行的且与WAL日志应用有冲突的查询语句之前的等待时间。本参数适用于wal data通过流复制被接收的情况。当不指定单位时，本参数的单位是毫秒。本参数仅能在postgresql.conf文件或者server command line中设置。本参数值与查询语句在取消之前可以运行的最长时间不同。相反，本参数值是从主服务器接收到WAL数据后允许应用该数据的最长总时间，因此，如果一个查询导致了显著的延迟，那么在后备服务器再次赶上之前，后续冲突查询的容忍时间(宽限时间)将少得多 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_receiver_create_temp_slot 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数用于指定当永久的复制槽没有配置(复制槽配置请见配置参数primary_slot_name)的情况下，wal receiver是否在远程PG Cluster上创建一个临时的复制槽。本参数仅能在postgresql.conf文件或者server command line中设置。当wal receiver进程正在运行的同时本参数被改变时，wal receiver进程会关闭并重启以读取到新值。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_receiver_status_interval 数据类型 integer 默认值 10 取值范围 0到2147483，0表示完全禁止状态更新。 参数单位 秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数指定了备库上的wal receiver 进程向 primary库或者sending-server库发送复制进度(replication progress)的最短时间频率。备库将报告它写到的wal的位置，它flush 到disk的位置以及它应用wal的位置。本参数值是两次报告之间的最大时间间隔。每次写入(write)或刷新(flush)位置更改时都会发送更新，或者至少与本参数指定的频率相同。因此，应用的位置可能稍微落后于真实位置。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 hot_standby_feedback 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。是否允许一个hot standby的备库发送关于正在备库执行的查询的feedback给primary库或者upstream库。本参数值可以消除由于cleanup records导致的query cancels，但是会导致primary库上发生膨胀。feedback信息的发送频率不会超过配置参数wal_receiver_status_interval。当是级联复制环境时，feedback会通过upstream(即:中间节点)会最终到达primary库。Standby节点除了向upstream反馈信息外，没有其他用途。本参数不会覆盖primary端的配置参数old_snapshot_threshold的行为。Standby端上超过primary端age阈值的snapshot会变为invalid，这导致standby端上的查询被取消。这是因为配置参数old_snapshot_threshold的目的是在时间上对dead row导致膨胀提供绝对的限制，否则会因为standby的配置违反该限制。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_receiver_timeout 数据类型 integer 默认值 60000 取值范围 0到2147483647，0代表禁用本timeout检测机制。 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。当本备端与sending-server端或者primary端的inactive状态超过本参数值时，终止掉复制连接。本参数对receiving standby server检测primary node crash或者网络故障很有用。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_retrieve_retry_interval 数据类型 integer 默认值 5000 取值范围 1到2147483647 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。本参数控制standby server遇到从所有源头(streaming replication、本地pg_wal目录、WAL Archive位置)获取wal而获取不到时的等待时间。本参数对处于in recovery的节点需要控制等待新的wal data可用的时间很有用。比如，在archive recovery中，通过降低本参数值，在检测一个新wal log file时，可以使recovery更快响应。在一个wal file产生很少的PostgreSQL中，增加本参数值会减少访问wal archive的请求次数。这在那些基础设施的访问次数会被考虑在内的云环境中非常有用。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 recovery_min_apply_delay 数据类型 integer 默认值 0，0代表无延迟。 取值范围 0到2147483647 参数单位 毫秒 参数含义 本参数为备库参数，本参数在sending-server端会被忽略。默认情况下，standby server会及时的restore 来自于sending server端的WAL 记录。数据的延迟copy可能很有用，它提供了纠正数据丢失错误的机会。本参数值指定了允许你指定延迟recovery的时间长度。比如，你设置本参数值为5分钟，那么只有当standby端的系统时间比master端报告的提交时间至少晚5分钟时，standby端才会重放每个事物的提交。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 seq_page_cost 数据类型 real 默认值 1 取值范围 0到1.79769e+308 参数单位 参数含义 本参数设置了顺序读取磁盘上的page的成本。在table以及index上的表空间级别的同名本参数可以覆盖配置参数中的该参数值。增大本参数值会使索引扫描看起来相对更昂贵 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 random_page_cost 数据类型 real 默认值 4 取值范围 0到1.79769e+308 参数单位 参数含义 本参数设置了非顺序读取磁盘上的page的成本。在table以及index上的表空间级别的同名本参数可以覆盖配置参数中的该参数值。相对于seq_page_cost减少本参数值会使PostgreSQL更倾向于使用索引扫描。随机访问机械硬盘要比四次顺序访问还要昂贵。但是，使用较低的默认值(4.0)，因为大多数对磁盘的随机访问(如索引读取)都假定在cache中。默认值可以认为是将随机访问模型比顺序访问慢40倍，同时，期望90%的随机读取都是在cache中。如果您认为90%的缓存比率对于您的工作负载来说是一个不正确的假设，那么你可以增加本参数值以反映随机存储读取的真实成本。相应的，如果你的数据可能完全在cache中，比如在database大小小于服务器内存，减少本参数值是合适的。那些相对于顺序读具有低随机读成本的存储（比如固态驱动器），也可以使用较低本参数值（比如1.1） 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 cpu_tuple_cost 数据类型 real 默认值 0.01 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置在执行一个SQL语句时planner预计的处理每行数据的成本 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 cpu_index_tuple_cost 数据类型 real 默认值 0.005 取值范围 0到1.79769e+308 参数单位 参数含义 本参数用于设置在索引扫描期间planner预计的处理每个index entry的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 cpu_operator_cost 数据类型 real 默认值 0.0025 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置在执行一个SQL语句时planner预计的处理执行的每个operator或者function的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 parallel_setup_cost 数据类型 real 默认值 1000 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置planner预估的启动parallel worker processes的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 parallel_tuple_cost 数据类型 real 默认值 0.1 取值范围 0到1.79769e+308 参数单位 参数含义 本参数值设置了从一个parallel worker进程将一个tuple传输到另外一个parallel worker进程的成本。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 min_parallel_table_scan_size 数据类型 integer 默认值 1024，默认情况下是8MB 取值范围 0到715827882 参数单位 8KB 参数含义 本参数设置了考虑使用parallel scan的table的数据(data)的最小大小，对于并行顺序扫描，table的数据(data)的最小大小总是等于table的大小。但是当index被用到时，table的数据(data)的最小大小通常会笑。如果本参数不带单位，取的是源码中符号常量BLCKSZ的值，通常为8KB。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 min_parallel_index_scan_size 数据类型 integer 默认值 64，默认情况下是512KB 取值范围 0到715827882 参数单位 8KB 参数含义 本参数设置了考虑使用parallel scan的index的数据(data)的最小大小，请注意：一个并行索引扫通常不会扫过整个index。本参数是planner认为扫描实际会涉及的页数，本参数也用来决定一个特定的索引是否可以参与并行vacuum。如果本参数不带单位，取的是源码中符号常量BLCKSZ的值。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 effective_cache_size 数据类型 integer 默认值 524288 取值范围 1到2147483647 参数单位 8KB 参数含义 本参数设置了planner对单个SQL语句可用的disk cache的有效大小。这被纳入使用索引的成本估算中。值越大，使用索引扫描的可能性越大；值越小，使用顺序扫描的可能性越大。设置本参数时，应该同时考虑PostgreSQL的shared buffers和kernel的page cache用于PostgreSQL数据文件的部分，虽然这两个位置都可能存在一些数据。另外，还要考虑在不同表上预计的并发SQL语句数量，因为他们必须共享可用空间。本参数不影响PostgreSQL分配的shared buffers大小，也不会保留kernel的page cache。它仅用于估算目的。系统也不坚定在两次查询之间数据扔保留在磁盘cache中。如果本参数不带单位，取的是源码中符号常量BLCKSZ的值。通常为8KB。如果BLCKSZ不是8KB，则默认值按照定比例缩放。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 jit_above_cost 数据类型 real 默认值 100000 取值范围 -1到1.79769e+308。-1表示禁用JIT编译 参数单位 参数含义 当SQL语句的查询成本高于本参数值时，JIT编译会被激活，若是JIT编译被enable的情况下。执行JIT会消耗planning time但是可以加速查询执行。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 jit_inline_above_cost 数据类型 real 默认值 500000 取值范围 -1到1.79769e+308，-1表示禁用inline 参数单位 参数含义 当SQL语句的查询成本高于本参数值时，JIT编译会尝试inline functions and operators。Inlining会增加planning time，但是会提升执行速度。将本参数值设置为低于jit_above_cost是没有意义的。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 jit_optimize_above_cost 数据类型 real 默认值 500000 取值范围 -1到1.79769e+308，-1表示禁用expensive optimizations 参数单位 参数含义 当SQL语句的查询成本高于本参数值时，JIT编译会尝试进行expensive optimizations。这种优化会增加planning time，但是会提升执行速度。将本参数值设置为低于jit_above_cost是没有意义的，将本参数值设置为高于jit_inline_above_cost也是没有益处的。 是否可session级修改 是 修改后何时生效 Session修改立即生效；其他修改Reload即可生效 参数名称 autovacuum 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 控制PostgreSQL是否允许autovacuum launcher daemon.autovacuum进程能正常工作的前提条件是配置参数track_counts设置为启用。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。即使本参数被禁用，若是需要防止事务id wraparound，PostgreSQL会启动autovacuum进程。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_autovacuum_min_duration 数据类型 integer 默认值 0，0表示记录所有autovacuum操作到PostgreSQL运行日志中 取值范围 -1到2147483647，-1表示禁止autovacuum进程执行信息打印到PostgreSQL运行日志中。 参数单位 毫秒 参数含义 当autovacuum进程运行的时间长度大于等于本参数值时，autovacuum进程执行时的每个动作会被记录到PostgreSQL运行日志中。举例，若是本参数值设置为250ms，那么所有运行时间长度大于等于250ms的automatic vacuum 和analyze操作会记录到PostgreSQL运行日志中。当本参数值被设置为-1之外的值时，若是autovacumm由于冲突所或者并发dropped relation的原因被skipped掉，一条信息会打印到PostgreSQL运行日志中。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_max_workers 数据类型 integer 默认值 3 取值范围 1到262143 参数单位 参数含义 本参数设置了同时可以运行的autovacuum worker进程的最大个数。本参数仅仅可以在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 autovacuum_naptime 数据类型 integer 默认值 60 取值范围 1·到2147483 参数单位 秒 参数含义 本参数指定了两次autovacuum进程运行的最小时间间隔。在每次autovacuum进程运行时，autovacuum守护进程检查数据库并针对需要的表发出vacuum和analyze命令。本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_threshold 数据类型 integer 默认值 50 取值范围 0到2147483647 参数单位 参数含义 本参数用于设置作为vacuum操作候选表的条件，即:更新或者删除掉多少条记录可以被作为vacuum操作的候选表。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_insert_threshold 数据类型 integer 默认值 1000 取值范围 -1到2147483647，-1表示即使有本参数值数量的insert记录，autovacuum守护进程也不会针对该表触发vacuum操作。 参数单位 参数含义 本参数用于设置作为vacuum操作候选表的条件，即:插入多少条记录可以触发针对该表的vacuum。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_analyze_threshold 数据类型 integer 默认值 50 取值范围 0到2147483647 参数单位 参数含义 本参数用于设置作为analyze操作候选表的条件，即:插入、更新、删除多少条记录可以触发针对该表的analyze。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_scale_factor 数据类型 real 默认值 0.2，0.2表示表大小的20% 取值范围 0到100 参数单位 参数含义 本参数设置了表大小的一个比率，当决定一个表是否触发vacuum操作时，该比率会被配置参数autovacuum_vacuum_threshold考虑在内。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_insert_scale_factor 数据类型 real 默认值 0.2，0.2表示表大小的20% 取值范围 0到100 参数单位 参数含义 本参数设置了表大小的一个比率，当决定一个表是否触发vacuum操作时，该比率会被配置参数autovacuum_vacuum_insert_threshold考虑在内。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_analyze_scale_factor 数据类型 real 默认值 0.1 取值范围 0到100 参数单位 参数含义 本参数设置了表大小的一个比率，当决定一个表是否触发vacuum操作时，该比率会被配置参数autovacuum_analyze_threshold考虑在内。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_freeze_max_age 数据类型 integer 默认值 200000000 取值范围 100000到2000000000 参数单位 参数含义 指定表的pg_class.relfrozenxid字段在强制执行vacuum操作以防止事务ID wraparound之前可以达到的最大年龄，请注意，即使autovacuum进程被禁用，PostgreSQL也会为了防止wraparound而启动autovacuum进程。Vacuum还允许从pg_xact子目录中删除旧文件，这就是为什么默认值是相对较低的2亿个事务的原因。此参数只能在服务器启动时设置，但是可以通过更改表存储参数来减少单个表的设置值。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 autovacuum_multixact_freeze_max_age 数据类型 integer 默认值 400000000 取值范围 10000到2000000000 参数单位 参数含义 指定表的pg_class. relminmxid字段在强制执行vacuum操作以防止multixact ID wraparound之前可以达到的最大年龄，请注意，即使autovacuum进程被禁用，PostgreSQL也会为了防止wraparound而启动autovacuum进程。Vacuuming multixacts还允许从pg_multixact/members 和pg_multixact/offsets子目录中删除旧文件，这就是为什么默认值是相对较低的4亿个事务的原因。此参数只能在服务器启动时设置，但是可以通过更改表存储参数来减少单个表的设置值。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 autovacuum_vacuum_cost_delay 数据类型 real 默认值 2 取值范围 -1到100，-1表示使用配置参数vacuum_cost_delay的参数值。 参数单位 毫秒 参数含义 本参数是在autovacuum中vacuum操作用到的cost delay值。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 autovacuum_vacuum_cost_limit 数据类型 integer 默认值 -1，-1表示使用配置参数vacuum_cost_limit的参数值。 取值范围 -1到10000 参数单位 参数含义 本参数是在autovacuum中vacuum操作用到的cost limit值。请注意，如果有多个autovacuum workers，则本参数值按比例分布在运行中的autovacuum workers之间，以便每个autovacuum worker该值的总和不超过本参数值。本参数仅能在postgresql.conf文件或者server command line中设置。本参数可以在表级设置，在表级设置的优先级高于配置参数中的优先级。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 是否启用ssl连接 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_ca_file 数据类型 string 默认值 空字符串，空字符串表示没有CA File被加载，并且Client身份验证不会被执行。 取值范围 参数单位 参数含义 本参数指定了包括SSL server certificate authority (CA)的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_cert_file 数据类型 string 默认值 server.crt 取值范围 参数单位 参数含义 本参数指定了包括SSL server certificate的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_crl_file 数据类型 string 默认值 空字符串，空字符串表示没有CRL文件被加载。 取值范围 参数单位 参数含义 本参数指定了包括SSL server certificate revocation list (CRL)的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_key_file 数据类型 string 默认值 server.key 取值范围 参数单位 参数含义 本参数指定了包括SSL server private key的文件名。本参数值是相对于data directory的相对路径，本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_ciphers 数据类型 string 默认值 HIGH:MEDIUM:+3DES:!aNULL 取值范围 参数单位 参数含义 设置允许被SSL连接使用的SSL ciphers suites的清单。有关此设置的语法和支持值的列表，请参阅OpenSSL package中的ciphers manual page。有使用TLS版本1.2及更低版本的连接才会受到影响。当前没有控制TLS版本1.3连接使用的密码选择的设置。默认值通常是一个合理的选择，除非您有特定的安全要求。本参数仅能在postgresql.conf文件或者server command line中设置。下面解释一下默认值的含义： HIGH：使用HIGH Group的Cipher suites（例如，AES、Camellia、3DES）MEDIUM：使用MEDIUM Group的Cipher suites（例如，RC4, SEED） +3DES：针对HIGH的OpenSSL默认排序是存在问题的，因为3DES排序高于AES128.这是错误的，因为3DES比ASE128提供了少的安全性，并且3DES更慢。在所有除了HIGH和MEDIUM的ciphers外，+3DES进行了重新排序 !aNULL:禁用掉没有经过身份验证的匿名cipher suites。此类cipher suites易于遭受中间人攻击，因此不应该使用。可用的cipher suites在不同OpenSSL版本中的细节差异很大。使用操作系统命令openssl ciphers -v 'HIGH:MEDIUM:+3DES:!aNULL’可以看到当前安装的OpenSSL库的实际细节。请注意：这个list会在运行时基于server key type被过滤 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_prefer_server_ciphers 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 指定是否优先使用server端的SSL cipher，还是使用client端的。本参数仅能在postgresql.conf文件或者server command line中设置。更老的PostgreSQL版本没有本参数，更老的PostgreSQL版本总是优先使用client端的SSL cipher。本参数仅能在postgresql.conf文件或者server command line中设置。本参数的目的是用于向后兼容性。优先使用server端通常更好，因为更可能的是server端的配置是适当的 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_ecdh_curve 数据类型 string 默认值 prime256v1 取值范围 参数单位 参数含义 指定要在ECDH密钥交换中使用的曲线的名称。它需要所有连接的客户端支持。它不需要与服务器的椭圆曲线密钥使用的曲线相同。本参数仅能在postgresql.conf文件或者server command line中设置。最常见曲线的OpenSSL名称是：prime256v1（NIST P-256）、secp384r1（NIST P-384）、secp521r1（NIST P-521）。可用曲线的完整列表可以通过操作系统命令openssl ecparam -list_curves显示。但并不是所有这些都可以在TLS中使用。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_min_protocol_version 数据类型 enum 默认值 TLSv1.2 取值范围 {TLSv1,TLSv1.1,TLSv1.2,TLSv1.3} 参数单位 参数含义 设置使用的SSL/TLS协议的最小版本。OpenSSLLibrary的更老的版本不支持所有的值。如果选择了不支持的设置，将引发错误。TLS 1.0之前的协议版本（即SSL version 2 和3）始终处于禁用状态。默认值是TLSv1.2，该默认值是满足本文档编写时的工业最佳实践。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_max_protocol_version 数据类型 enum 默认值 空字符串，空字符串意味着运行任何的协议版本。 取值范围 {\"\",TLSv1,TLSv1.1,TLSv1.2,TLSv1.3} 参数单位 参数含义 设置使用的SSL/TLS协议的最大版本，这对测试或者有些组件使用新版本协议出现问题是有用的。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_dh_params_file 数据类型 string 默认值 空字符串，空字符串意味着使用默认DH参数编译。 取值范围 参数单位 参数含义 指定包含Diffie-Hellman参数的文件名，该参数用于所谓的临时DH SSL密码族。如果攻击者设法破解众所周知的编译在DH中的参数，那么使用自定义DH参数可以减少暴露。您可以使用操作系统命令openssl dhparam-out dhparams.pem 2048创建自己的DH参数文件本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_passphrase_command 数据类型 string 默认值 空字符串，空字符串表示使用内置的提示机制。 取值范围 参数单位 参数含义 设置需要获取用于解密SSL文件（如私钥）的密码短语时要调用的外部命令。命令必须将密码短语打印到标准输出，并以代码0退出。在参数值中，%p替换为提示字符串(写%%表示文本值%)。请注意提示字符串可能包含空格，因此请确保引用足够的引号。命令实际上不必提示用户输入密码短语，它可以从一个文件中读取它，从一个keychain工具中获取它，或者类似的。由用户来确定所选机制是否足够安全本参数仅能在postgresql.conf文件或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 ssl_passphrase_command_supports_reload 数据类型 bool 默认值 off，off表示在reload时，ssl_passphrase_command配置参数会被忽略，并且在passphrase被需要时，SSL configuration不会被reload 取值范围 on和off 参数单位 参数含义 当一个key file需要passphrase时，本参数决定由ssl_passphrase_command配置参数设置的passphrase command在configuration reload期间是否被调用本参数仅能在postgresql.conf文件或者server command line中设置。该设置适用于需要TTY进行提示的命令，该命令在PostgreSQL运行时可能不可用。例如，如果密码短语是从文件获取的，则将本参数设置为on可能是合适的 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 shared_buffers 数据类型 integer 默认值 16384，即：128MB 取值范围 16到1073741823 参数单位 8KB，若是指定本参数值时不带单位，则取自源码中的符号常量BLCKSZ大小，默认是8KB。非默认的BLCKSZ大小会改变本参数的最小值。本参数值仅仅能在PostgreSQL启动时设置。建议将OS物理内存的25%分配给本参数值。因为PostgreSQL依赖于操作系统的cache（也就是文件系统的page cache），因此将超过40%的RAM分配给本参数值的情况不太可能比分配较小的缓冲区效果更好。较大的本参数值通常需要相应的增加max_wal_size, 以便在较长时间内分散写入大量新数据或更改数据的过程。 参数含义 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 huge_pages 数据类型 enum 默认值 try，try的含义是PostgreSQL会尝试使用huge pages，如果尝试失败将返回默认值。on的含义是请求huge pages失败将阻止PostgreSQL启动，off的含义是不会启用huge pages。目前，本参数值仅支持Linux和Windows平台。当在其他平台下本参数值设置为try时，本参数值会被忽略。 huge pages的使用导致了更小的page tables以及耗费在内存管理方面的更小的CPU时间，这提高了性能。在Windows平台上，huge pages被称之为large pages。开启large pages的步骤： 0.安装PostgreSQL 数据库 1.关闭windows uac控制面板—​系统和安全—​更改用户账户控制设置—​改为’从不通知'，点击确定。 2.在windows组策略编辑器，赋予运行PostgreSQL的操作系统用户’锁定内存页’的权力（Lock Pages in Memory）计算机配置\\Windows 设置\\安全设置\\本地策略\\用户权利分配\\下的\"锁定内存页\"这个策略。 3.将windows服务中的postgresql-x64-11这个服务改为手动启动。 4.重新启动OS 5.设置postgresql.conf的配置参数huge_pages=on 6.设置postgresql.conf的配置参数shared_buffers=2048MB 7.以管理员身份打开cmd窗口，执行pg_ctl start -D e:\\postgresql\\11\\data启动PostgreSQL 8.验证是否启用请注意：当启用large pages时，不能用Windows服务中的PostgreSQL服务启动postgresql。请注意，本参数设置仅影响main shared memory area. Linux、FreeBSD和Illumos等操作系统也可以自动使用huge pages（也称为\"super\" pages 或者 \"large\" pages）进行正常内存分配，而无需PostgreSQL的显式请求。在Linux上，这被称为\"transparent huge pages\"（THP）。众所周知，对于某些Linux版本的某些用户来说，该特性会导致PostgreSQL的性能下降，因此目前不鼓励使用它（与显式使用huge_pages）。 取值范围 {off,on,try} 参数单位 参数含义 控制shared_buffers是否使用huge pages。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 temp_buffers 数据类型 integer 默认值 1024 取值范围 100到1073741823 参数单位 8KB 参数含义 设置每个session使用的temporary buffers的最大值，供临时表使用。指定本参数值时若是不带单位，以blocks为单位，取自源码中符号常量BLCKSZ,通常为8KB。若是BLCKSZ不为8KB，则本参数按照等比例变化。本参数值可以在单个session中更改，但只能在session中首次使用临时表之前更改；随后更改该值的尝试不会对该session产生任何影响。session将根据需要分配临时缓冲区，直到到达本参数值指定的限制。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 max_prepared_transactions 数据类型 integer 默认值 100 取值范围 0到262143，0代表禁用prepared-transaction特性。 参数单位 设置同时可以处于prepared状态的最大的事务个数。若是你不打算使用prepared transaction，请将本参数值设置为零以防止偶然建立prepared transaction。如果你使用prepared transaction，你可能希望本参数值与max_connections一样大。这样每个session都可以有一个prepared transaction pending。当有流复制环境是，你必须确保在备库上该参数值要大于等于在主库上的该参数值，否则，不能在备库上运行查询语句。 参数含义 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 work_mem 数据类型 integer 默认值 4096 取值范围 64到2147483647 参数单位 KB 参数含义 在写入磁盘临时文件之前，被查询操作（比如sort或者hash table）使用的内存最大值。若是指定本参数值时不带单位，那么是以KB为单位。请注意，针对复杂的查询，sort或者hash操作可能以parallel去运行，在开始向磁盘的临时文件写入数据之前，每个操作被允许最多使用本参数值对应的内存大小。同事，正在运行的多个session会并发执行这些操作。因此，使用的所有内存可以是本参数值的若干倍，当设置本参数值时，需要明确记住这个事实。Sort操作被如下使用：order by、distinct、merge joins；hash table被如下使用：hash joins、hash-base aggregation、在in子句中的基于hash的处理。 基于哈希的操作通常比基于排序的等效操作对内存可用性更加敏感。哈希表可用的内存是通过work_mem*hash_mem_multiplier来计算的。这使得基于哈希的操作使用的内存量可能超过通常的work_mem数量。 是否可session级修改 是 修改后何时生效 Session级修改立即生效其他修改Reload即可生效 参数名称 hash_mem_multiplier 数据类型 real 默认值 1 取值范围 1到1000 参数单位 参数含义 本参数值用于计算hash-base操作可以使用的最大内存。该最大内存是由work_mem*hash_mem_multiplier决定的。在经常有大量查询操作的环境中，考虑增加本参数值，特别是当简单的增加work_mem会导致内存压力时（内存压力通常以间歇性内存不足的形式出现）。设置我1.5或者2.0可能对混合负载有效。在work_mem参数值已经增加到40MB或者更多时，2.0-8.0或者更大范文内的更高设置可能会有效。 是否可session级修改 是 修改后何时生效 Session级修改立即生效其他修改Reload即可生效 参数名称 maintenance_work_mem 数据类型 integer 默认值 65536 取值范围 1024到2147483647 参数单位 KB 参数含义 指定用于维护操作(maintenance operations)的最大可用的内存。维护操作包括：vacuum,create index, ALTER TABLE ADD FOREIGN KEY.若指定本参数值时不带单位，那默认的单位是KB。较大的本参数值有助于提升vacuuming以及restore database时的性能。请注意：当autovacuum运行时，最多有本参数值\\* autovacuum_max_workers的内存被使用，因此，请小心设置本参数值，不要设置太高。可以使用配置参数autovacuum_work_mem来单独控制autovacuum时的内存使用。 是否可session级修改 是 修改后何时生效 Session级修改立即生效其他修改Reload即可生效 参数名称 autovacuum_work_mem 数据类型 integer 默认值 -1，-1表示使用maintenance_work_mem配置参数值 取值范围 -1到2147483647 参数单位 KB 参数含义 本参数用于设置每个autovacuum worker process使用的最大内存大小。若指定本参数值时不带单位，那默认的单位是KB。当vacuum的行为运行在其他上下文环境中时，本参数不生效。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 logical_decoding_work_mem 数据类型 integer 默认值 65536，即64MB 取值范围 64到2147483647 参数单位 KB 参数含义 本参数用于设置在解码后的变化写入到本地disk之前，逻辑解码使用的最大内存大小。本参数限制了逻辑流复制连接使用的内存大小。由于每个复制连接只使用此大小的单个buffer，并且通常不会有许多这样的连接(受到max_wal_senders配置参数限制)。因此可以将本参数值设置为比work_mem高，从而减少写入磁盘的解码变化量。 是否可session级修改 是 修改后何时生效 session级修改立即生效其他修改Reload即可生效 参数名称 max_stack_depth 数据类型 integer 默认值 2048 取值范围 100到2147483647 参数单位 KB 参数含义 本参数值指定PostgreSQL execution stack的最大安全深度。本参数值的理想设置是kernel执行的实际stack size limit（可以用操作系统命令ulimit -s或者ulimit -a进行查看）减去大约1MB的安全裕度。所以需要安全裕度，是因为并不是在服务器中的每个例程中都检查stack depth，而是只在可能递归的关键例程中检查stack depth。若指定本参数值时不带单位，那默认的单位是KB。本参数默认值为2MB，该值相对保守，不太可能有crash的风险。但是，它可能太小，无法执行复杂的函数。只有superuser才能更改此设置。本参数值设置为高于kernel的限制将意味着一个失控的递归函数可以crash掉一个单独的backend process。在PostgreSQL可以确定内核限制的平台上，服务器将不允许把本参数值设置为不安全的值。但是并非所有平台都提供该信息。因此建议谨慎选择本参数值。 是否可session级修改 是，仅限于superuser可进行session级修改。 修改后何时生效 session级修改立即生效其他修改Reload即可生效 参数名称 shared_memory_type 数据类型 enum 默认值 mmap 取值范围 {sysv,mmap,windows}, sysv,mmap适用于Linux平台；windows适用于windows平台。 参数单位 参数含义 指定shared memory的实现方式。 mmap是指使用mmap分配的anonymous shared memory； sysv是指通过shmget分配的 System V shared memory ；windows是指Windows shared memory。通常不鼓励使用sysv值，因为它通常需要非默认的内核设置来允许较大的分配，而sysv值在任何平台上都不是默认的。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 dynamic_shared_memory_type 数据类型 enum 默认值 posix 取值范围 {posix,sysv,mmap,windows} 其中posix,sysv,mmap适用于Linux平台；windows适用于Windows平台 参数单位 参数含义 指定PostgreSQL使用的dynamic shared memory的实现方式。 posix是指使用shm_open分配的POSIX shared memory; sysv是指使用shmget分配的System V shared memory； windows是指Windows shared memory； mmap是指使用存在的data directory中的memory-mapped files模拟的shared memory。通常不鼓励使用mmap值，这在任何平台上都不是默认值，因为操作系统可能会反复将修改过的页面写回磁盘，从而增加系统I/O负载；但是，当pg_dynshmem子目录存储在RAM磁盘上，或者其他共享内存工具不可用时，它可能对调试有用。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 log_destination 数据类型 string 默认值 stderr 取值范围 Linux平台下的取值有：stderr、csvlog、syslogWindows平台下的取值有：stderr、csvlog、eventlog 参数单位 参数含义 PostgreSQL支持多种记录PostgreSQL消息的方法。包括stderr、csvlog、syslog，在windows平台下还有eventlog。当本参数值是一个列表时，请使用逗号分隔每个值。本参数仅能在postgresql.conf中设置或者在server command line中设置。当本参数值中含有csvlog时，日志条目被输出为csv格式（comma separated value），这便于程序加载log内容。需要将配置参数logging_collector设置为on才可以生成csv格式的log。当本参数值含有stderr或者csvlog时，current_logfiles文件被建立用来记录当前正在被logging collector进程使用的logfile的位置。这提供了一个查找当前正在使用的log的简便方法。 current_logfiles文件的内容如下（举例）: stderr log/postgresql.log csvlog log/postgresql.csv当新的logfile被建立或者log_destination被加载时，current_logfiles文件会被重建。当本参数值不含有stderr或者csvlog，以及logging collector被禁用时，current_logfiles文件会被删除掉。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 logging_collector 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 本参数会启用logging collector进程，该进程是一个background process，用来捕获发送到stderr的log messages并重定向这些信息到log file中。这个方法比记录到syslog中要有用。因为有些类型的messages不会显示在syslog的输出中（一个例子是dynamic-linker failure messages，另外一个是脚本产生的error messages，比如archive_command）.本参数仅仅可以在PostgreSQL启动时设置。logging collector设计为永不丢失消息。这意味着在负载极高的情况下，当logging collector落后时，服务器进程可能会在尝试发送额外日志消息时被阻止。相反，syslog更喜欢在无法写入消息时丢弃消息，这意味着在这种情况下它可能无法记录某些消息，但不会阻塞系统的其余部分。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 log_directory 数据类型 string 默认值 log 取值范围 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数用于确定log file被建立在哪个目录下。本参数值可以是绝对路径，也可以是相对于data directory的相对路径。本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_filename 数据类型 string 默认值 postgresql-%Y-%m-%d_%H%M%S.log 取值范围 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数用于确定所创建的log file的文件名。本参数值被视为strftime pattern，因此可以使用%-escapes指定随时间变化的文件名(请注意，如果存在任何依赖于时区的%-转义，则计算将在配置参数log_timezone指定的时区中完成。）支持的%-转义与开放组的strftime规范中列出的那些类似。请注意，系统的strftime不是直接使用的，因此特定于平台（非标准）的扩展不起作用。如果本参数值中不带转义符，你应使用log rotation 工具以避免日志填充满整个磁盘。若是在log_destination配置参数中启用了csv格式的输出，那么会将.CSV附加到带时间戳的日志文件名. 以创建CSV格式输出的文件名(如果日志文件名以.log结尾，则替换后缀。）本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_file_mode 数据类型 string 默认值 0600， 0600表示只有PostgreSQL的owner可以读取或者写入log files 取值范围 0到511 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，在Unix/Linux系统中本参数用于设置logfile的权限（在Microsoft Windows平台上，本参数被忽略）。 参数值应该是以chmod和umask系统调用接受的格式指定的数字模式。 0600表示只有PostgreSQL的owner可以读取或者写入log files.其他通常有用的设置是0640，0640允许owner的group的成员去读取log files。请注意，要使用这样的设置，您需要更改log_directory目录以将文件存储在data directory目录之外的某个位置。无论如何，让日志文件具有广泛的可读性是不明智的，因为它们可能包含敏感数据本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_rotation_age 数据类型 integer 默认值 1440 取值范围 0到35791394，0表示禁用掉基于time的new log file创建。 参数单位 分钟 参数含义 在logging_collector配置参数设置为on的情况下，本参数确定了一个单独的log file能使用的最长时间。在将此数量的数据发送到日志文件后，将创建一个新的日志文件。若指定本参数值时不带单位，那默认的单位是分钟。默认值是24小时。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_rotation_size 数据类型 integer 默认值 10240 取值范围 0到2097151，0表示禁用掉基于size的new log file创建 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数确定了一个单独的log file能使用的最大大小。将此数量的数据发送到日志文件后，将创建一个新的日志文件。若指定本参数值时不带单位，那默认的单位是KB。默认值是10MB。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_truncate_on_rotation 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 在logging_collector配置参数设置为on的情况下，本参数将导致PostgreSQL截断（覆盖）而不是附加到任何同名的现有日志文件。请注意，只有在由于基于time(时间)的rotate而打开新文件时才会发生截断，而不是在服务器启动或基于size的rotate期间。当本参数值设置off时，在所有情况下都会附加到已有文件中。将本参数值与日志文件名（如postgresql-%H.log）结合使用会生成24个每小时一次的日志文件，然后周期性地覆盖它们。本参数仅能在postgresql.conf中设置或者在server command line中设置。举例：为了达到如下目的：保留7天的log，每天的log文件的文件名是server_log.Mon,server_log.Tue等等，然后用本周的log自动覆盖上周的log。该需求需要设置log_filename为server_log.%a, log_truncate_on_rotation为on，log_rotation_age为1440举例：为了达到如下目的：保留24小时的log，每小时一个log file，如果log file大小超过1GB时也需要很快进行rotate。该需求需要设置log_filename为server_log.%H%M, log_truncate_on_rotation为on，log_rotation_age为60，log_rotation_size为1000000.在log_filename中包括%M允许任何基于size的且可能在同一个小时内有多个log file的rotate 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_facility 数据类型 enum 默认值 local0 取值范围 {local0,local1,local2,local3,local4,local5,local6,local7} 参数单位 参数含义 当配置参数log_destination被设置为syslog时，本参数用于确定使用的syslog facility。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_ident 数据类型 string 默认值 postgres 取值范围 参数单位 参数含义 本参数设置了在操作系统syslog日志中用于标识PostgreSQL消息的关键字。本参数仅仅适用于Linux平台，因为只有Linux平台才有syslog.虽然在Windows平台下也能修改本参数值，但是该修改没有任何意义。本参数仅能在postgresql.conf中设置或者在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_sequence_numbers 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当配置参数log_destination被设置为syslog并且本参数值设置为on时，PostgreSQL会在每条消息的前面加一个依次增大的顺序号，同一个命令的多条输出会以[2-1],[2-2]之类的次序标出：（如下截取自linux的/var/log/messages）image::PostgreSQL13%E6%96%87%E6%A1%A3%E4%B9%8BPG%E5%8F%82%E6%95%B0%E5%8F%82%E8%80%83%E6%89%8B%E5%86%8C_html_b376f05a85e6462b.png[\"\",357,140] 本参数设置为off的效果如下：（如下截取自linux的/var/log/messages）image::PostgreSQL13%E6%96%87%E6%A1%A3%E4%B9%8BPG%E5%8F%82%E6%95%B0%E5%8F%82%E8%80%83%E6%89%8B%E5%86%8C_html_340e68e94edcda77.png[\"\",361,131]本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 syslog_split_messages 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当配置参数log_destination被设置为syslog并且本参数值设置为on时，消息被按行分割，长的行会被分割，以便填满1024Bytes，这是传统syslog实现上的典型大小限制。当设置为off时，PostgreSQL日志消息将按照原样传递给syslog服务，并由syslog服务来处理可能非常庞大的消息。如果syslog最终记录到一个文本文件中，那么无论哪种方式，效果都是一样的，最好保持该设置处于on状态，因为大多数syslog实现要么不能处理大型消息，要么需要专门配置来处理它们。但是，如果syslog最终要写入其他介质，那么将消息逻辑地保持在一起可能是必要的或更有用的。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 event_source 数据类型 bool 默认值 PostgreSQL 取值范围 参数单位 参数含义 本参数仅仅适用于Windows平台，因为只有Windows平台才有event log，本参数值会体现在Windows操作系统的事件查看器的\"Windows日志\"-\"应用程序\"-\"来源\"一列。虽然在Linux平台下也能修改本参数值，但是该修改没有任何意义。本参数仅能在postgresql.conf中设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 block_size 数据类型 Integer 默认值 8192 取值范围 8192 参数单位 参数含义 数据库使用的disk block的大小，该参数也影响其他的配置参数，比如shared_buffers，该参数通过编译时./configure命令的—​with-blocksize选项确定 是否可session级修改 否，不可修改。 修改后何时生效 不可修改。 参数名称 data_checksums 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 显示data checksums是否启用。本参数值通过initdb时的-k选项或者—​data-checksums选项确定。本参数值可以使用pg_checksums进行修改，pg_checksums命令具体用法请见pg_checksums --help 是否可session级修改 否 修改后何时生效 立即生效 参数名称 data_directory_mode 数据类型 integer 默认值 0700 取值范围 0到511 参数单位 参数含义 在linux/unix中显示data_directory参数值对应的目录的权限；在windows系统中，本参数值总是显示为0700 是否可session级修改 否 修改后何时生效 不可修改 参数名称 debug_assertions 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 显示PG在编译时是否启用assertion。本参数值通过编译时./configure命令的—​enable-cassert选项确定。 是否可session级修改 否 修改后何时生效 不可修改 参数名称 integer_datetimes 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 支持64bit整型的date和time。本参数通过编译时./configure命令确定。注意：configure的—​disable-integer-datetimes选项，是\"obsolete option, no longer supported\" 是否可session级修改 否 修改后何时生效 不可修改 参数名称 lc_collate 数据类型 string 默认值 en_US.UTF-8(根据操作系统环境变量不同而不同，见os命令locale的返回结果) 取值范围 参数单位 参数含义 字符串的排序规则(String sort order)。本参数值通过initdb时的—​lc-collate确定，若是不指定该选项，initdb会取环境变量 是否可session级修改 否 修改后何时生效 在PG Database级别确定，确定后不可修改，只读参数 参数名称 lc_ctype 数据类型 string 默认值 en_US.UTF-8(根据操作系统环境变量不同而不同，见os命令locale的返回结果) 取值范围 参数单位 参数含义 字符分类(是否区分大小写，什么是字符等)。本参数通过initdb时的—​lc-ctype确定，若是不指定该选项，initdb会取环境变量 是否可session级修改 否 修改后何时生效 在PG Database级别确定，确定后不可修改，只读参数。 参数名称 max_function_args 数据类型 integer 默认值 100 取值范围 参数单位 参数含义 返回函数参数的最大个数， 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 max_identifier_length 数据类型 integer 默认值 63 取值范围 参数单位 参数含义 返回标识符的最大长度 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 max_index_keys 数据类型 integer 默认值 32 取值范围 参数单位 参数含义 一个index最多能含有多少列 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 segment_size 数据类型 integer 默认值 131072 取值范围 参数单位 8KB 参数含义 本参数值确定了每个disk file中的page的数量。本参数值通过编译时./configure命令执行的—​with-segsize=SEGSIZE选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 server_encoding 数据类型 string 默认值 UTF8 取值范围 参数单位 参数含义 数据库的编码 (即:字符集),本参数通过initdb时的-E或者—​encoding参数确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 server_version 数据类型 string 默认值 默认值是当前PostgreSQL版本，格式为主版本号.次版本号 取值范围 参数单位 参数含义 返回PostgreSQL的版本号 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 server_version_num 数据类型 integer 默认值 130000，返回主版本号 取值范围 参数单位 参数含义 返回PostgreSQL的版本号 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 ssl_library 数据类型 string 默认值 OpenSSL 取值范围 参数单位 参数含义 返回PostgreSQL软件编译时的SSL library。本参数值通过./configure的—​with-openssl选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 wal_block_size 数据类型 integer 默认值 8192 取值范围 参数单位 参数含义 返回WAL中disk block的大小。本参数值通过./configure的—​with-wal-blocksize选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 wal_segment_size 数据类型 string 默认值 16777216 取值范围 参数单位 Byte 参数含义 返回WAL Segments的大小。本参数值通过initdb的—​wal-segsize选项确定。 是否可session级修改 否 修改后何时生效 不可修改，只读参数 参数名称 DateStyle 数据类型 string 默认值 ISO, MDY 取值范围 参数单位 参数含义 本参数用于设置date和time的显示格式，以及解释不明确日期输入值的规则。由于历史原因，本参数值包括两个部分：第一部分：output format specification (ISO, Postgres, SQL, or German)；第二部分：the input/output specification for year/month/day ordering (DMY, MDY, or YMD)。上述两者可以单独设置，也可以一起设置。请注意，关键词Euro和European是DMY的同义词；关键词US和NonEnro是MDY的同义词；Initdb生成配置参数文件时，会根据os的locale命令的lc_time结果来设置本参数值。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 IntervalStyle 数据类型 enum 默认值 postgres 取值范围 postgres,postgres_verbose,sql_standard,iso_8601 参数单位 参数含义 本参数用于设置interval values的显示格式。参数值sql_standard表示产生匹配SQL标准interval常量的输出；参数值postgres表示当DateStyle参数值设置为ISO时，产生匹配PostgreSQL8.4版本之前的输出；参数值postgres_verbose表示当DateStyle参数值设置为非ISO时，表示当DateStyle参数值设置为ISO时，参数值iso_8601表示产生匹配在ISO 8601标准的4.4.3.2章节中定义的时间间隔带\"format with designators\"匹配的输出。本参数值也会影响模糊的interval输入的解释。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 TimeZone 数据类型 String 默认值 内置的默认值是GMT，一般情况下，本参数值依据initdb时的操作系统时区而定，即：initdb时的操作系统时区是什么，initdb后postgresql.conf中的本参数值就是什么。注意：CentoOS7/8的操作系统当前时区请使用操作系统命令timedatectl status查询。 取值范围 参数单位 参数含义 为显示以及翻译timestamp设置时区。可用的时区名称可以通过pg_timezone_names视图进行查询。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 timezone_abbreviations 数据类型 string 默认值 Default 取值范围 参数单位 参数含义 被PostgreSQL接受的datetime输入的时区简写。默认值Default在世界上大部分地区都有效。另外的有效值是Australia和India。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 extra_float_digits 数据类型 string 默认值 1 取值范围 -15到3 参数单位 参数含义 设置浮点值(float4,float8以及地理数据类型)的显示的位数。extra_float_digits大于０时，原有精度最多增加3位， 小于0时， 精度最多减掉相应数值， 等于０时， float4精度为6位， float8精度为15 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 client_encoding 数据类型 string 默认值 取自Database encoding 取值范围 参数单位 参数含义 本参数用于设置客户端字符集。 是否可session级修改 是 修改后何时生效 Reload即可生效。 参数名称 lc_messages 数据类型 string 默认值 与系统locale设置有关 取值范围 参数单位 参数含义 本参数用于设置消息显示的语言。可接受的参数值是跟系统相关的。本参数值会影响发送到服务器运行日志以及客户端的消息，不正确的本参数值会影响服务器日志的可读性。 是否可session级修改 是 修改后何时生效 Reload即可生效，且只有超级用户才能更改此设置。 参数名称 lc_monetary 数据类型 string 默认值 与系统locale设置有关 取值范围 参数单位 参数含义 设置用于格式化货币金额的语言环境，可接受的参数值是跟系统相关的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 lc_numeric 数据类型 string 默认值 与系统locale设置有关 取值范围 参数单位 参数含义 本参数值用于设置格式化数字。可接受的参数值是跟系统相关的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 lc_time 数据类型 string 默认值 取值范围 参数单位 参数含义 本参数值用于设置格式化的日期和时间。可接受的参数值是跟系统相关的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_text_search_config 数据类型 string 默认值 依据initdb时的lc_ctype对应的设置而定 取值范围 参数单位 参数含义 本参数值用于设置当全文检索函数中不明确指定配置项时所用的全文检索配置项。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 wal_level 数据类型 enum 默认值 replica 取值范围 {minimal,replica,logical} 参数单位 参数含义 本参数定义了有多少信息被写入到WAL日志中。参数值replica表示WAL日志中写入了足够的信息以支持WAL Archiving和replicatin（包括在standby server上的read-only查询），参数值minimal表示WAL日志中移除了除了需要从crash或者immeidate shutdown进行recovery之外的信息。参数值logical添加了必要信息以支持逻辑解码(logical decoding)，在本参数使用logical参数值的情况下，会使得WAL日志的数量变大，特别是很多表被配置为replica identity full并且很多delete和update被执行的情况下。本参数仅仅可以在PostgreSQL Start时修改。每个更高级别的参数值会包括更低级别参数值中记录的所有内容。但是最少的WAL不能包含足够的信息来从基本备份和WAL日志中重建数据，因此必须使用replica参数值来提供更多的信息以启用WAL归档（archive_mode）和流复制.在9.6之前的版本中，此参数值还可以是archive和hot_standby。在PG13版本中，这些仍被接受，但已映射到replica参数值上。当本参数设置为minimal时，如下操作会更快，因为如下操作在创建或重写它们的事务的其余部分中，没有记录永久关系的信息： ALTER …​ SET TABLESPACE CLUSTER CREATE TABLE REFRESH MATERIALIZED VIEW（无CONCURRENTLY） REINDEX TRUNCATE 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 fsync 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当本参数值为on时，通过发出fsync系统调用或者各种等价的方法(参见wal_sync_method配置参数)，PostgreSQL会尝试确保更新被物理的写入disk。这就保证了PostgreSQL Cluster可以在操作系统或者硬件crash之后能recover(恢复)到一个一致的状态。当本参数值为off时，会带来性能优势，在电源故障或者系统crash时，这会导致不可恢复的数据损坏。仅仅当数据库的全部数据来源自外部数据的情况下，才建议将本参数值设置为off关闭fsync的场景包括：从备份文件中加载数据到PG中；使用PG进行数据的批处理，然后数据库会被废弃并重建；一个经常被重建的只读数据库克隆，并且不会用于failover.高质量的硬件不足以让本参数值设置为off基于可靠恢复的原因将本参数值从off改为on时，需要强制将kernel中修改过的buffer刷新到持久存储中，可以通过如下方法达到本目的： PostgreSQL Cluster关闭；当fsync参数值为on时运行initdb –sync-only,运行sync操作系统命令，unmount掉文件系统或者重启服务器操作系统。在很多场景中，针对非关键事务将synchronous_commit参数值设置为off可以提供比将fsync参数值设置为off更大的潜在优势，不会造成数据损坏等风险。本参数值只能在postgresql.conf文件或server command line中设置。如果关闭此参数，请考虑关闭full_page_writes。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 synchronous_commit 数据类型 enum 默认值 on 取值范围 {local,remote_write,remote_apply,on,off} 参数单位 参数含义 本参数值指定了在PostgreSQL数据库返回一个成功的提示给客户端之前，必须处理多少WAL数据。如果synchronous_standby_names是空，那么本参数有意义的参数值为on和off；remote_apply、remote_write、和local提供了与on参数值一样的本地同步级别。所有非off模式的本地行为是等待本地WAL刷新到Disk中。在off模式下，没有等待，因此存在一个延迟，这个延迟是给client报告成功与以后保证事务安全以防server crash之间的延迟（最大的延迟是三倍的wal_writer_delay参数值）。与fsync参数值不同，将此参数设置为off不会造成数据库不一致的任何风险。操作系统或者数据库的crash可能会导致一些最近据称已提交的事务丢失，但数据库状态将与这些事务完全中止时的状态相同，因此，当性能比事务持久性的精确确定性更重要时，将本参数设置为off是一个有用的选择。如果synchronous_standby_names是非空，本参数值用来控制事务提交是否等待在standby server上的WAL Record被处理。 当本参数值为remote_apply时,commit会等待直到从当前的同步standby指示它们已经收到事务的commit record并应用( applied),因此，它可以在standby 端被query可见，并且在standby端被写入持久性的存储中。这会导致比之前设置的更大的提交延迟，因为primary端会等standby端的WAL replay。 当本参数值为on时，commit会等待直到当前同步standby指示已经收到事务的commit record并且已经flush到持久性存储中。这确保了事务不会丢失,除非primary端和所有同步standby端的存储都发生损坏。 当本参数值为remote_write时,commit会等待直到当前同步standby指示已经收到事务的commit record并且已经写入到它们的文件系统中。本参数确保了在一个PostgreSQL 实例crash之后的数据持久，但是不针对standby端的操作系统级别的crash，因为，数据在standby端可能没有flush到持久性存储中。当参数值为local时，会导致commits一直在等待直到local flush到disk，而不是复制。当同步复制使用时，通常不需要这么做，但是为了完整而提供了这种方法。 此参数可随时更改；任何一个事务的行为都由提交时生效的设置决定。因此，让一些事务同步提交，而其他事务异步提交是可能的，也是有用的。例如，要在默认值相反时使单个多语句事务异步提交，请在事务中发出SET LOCAL synchronous_commit TO OFF 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 wal_sync_method 数据类型 enum 默认值 Linux和FreeBSD下默认值是fdatasyncWindows下默认值是open_datasync 取值范围 {fsync,fdatasync,open_sync,open_datasync} 参数单位 参数含义 本参数值指定了强制将WAL写入Disk时使用的方法。当fsync参数值为off时，本参数值无关紧要。 open_datasync：使用open()的O_DSYNC选项写WAL Files fdatasync:每次commit时调用fdatasync() fsync:每次commit时调用fsync() fsync_writethrough:每次commit时调用fsync(),对disk write cache强制write-through open_sync：使用open()的O_SYNC选项写WAL Files open_*选项使用O_DIRECT(若是可用)，不是上述所有选项都在所有平台下可用。默认值是在该平台下支持的上述列表中的第一个方法，除了在Linux平台和FreeBSD平台下fdatasync是默认值。默认值不一定理想，可能有必要更次此设置或系统配置的其他方面，以创建crash的安全配置或实现最佳性能。本参数值只能在postgresql.conf文件或server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 full_page_writes 数据类型 bool 默认值 on 取值范围 参数单位 参数含义 当本参数值为on时，在一个checkpoint发生之后，当第一个修改发生时，将full page（即：该修改所在的disk page中的全部内容）写入WAL日志中。这是需要的，因为在操作系统crash期间正在进行的page write可能仅部分完成，从而导致磁盘上的页包含新旧数据的混合。通常存储在WAL中的行级更改数据不足以在crash之后的恢复中完全还原一个page。存储full page image可以保证页面可以正确还原，但代价是增加必须写入WAL的数据量(因为WAL replay总是从检查点开始，所以在检查点之后的每个页面的第一次更改期间这样做就足够了。因此，降低整页写入成本的一种方法是增加checkpoint interval参数。）关闭此参数可加快正常操作，但在系统发生故障后，可能会导致不可恢复的数据损坏或静默数据损坏。风险类似于关闭fsync，虽然较小，但应该仅基于为fsync参数建议的相同情况关闭本参数。关闭此参数不会影响将WAL存档用于时间点恢复（PITR）。此参数只能在postgresql.conf文件或server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_log_hints 数据类型 bool 默认值 off 取值范围 参数单位 参数含义 当本参数值设置为on时，在一个checkpoint发生之后，当第一个修改发生时，将full page（即：该修改所在的disk page中的全部内容）写入WAL日志中。即使对于所谓的提示位的非关键修改也是如此。如果data ckecksums被启用，hint bit updates总是被记录在wal中，此时本参数值被忽略。你可以使用本参数值来测试当数据库cluster启用checksum之后的会生成多少额外的wal-logging.本参数仅能在server start时设置。 是否可session级修改 否 修改后何时生效 PG instance重启生效 参数名称 wal_compression 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当本参数值为on时，在如下情况下PostgreSQL压缩一个全部的page image并写入到WAL中：full_page_writes参数值为on或者执行一次base backup。压缩的page image会在wal replay期间进行解压。启用本参数可以降低WAL日志的产生量而不会增加不可恢复的数据损坏的风险，但是在wal压缩以及解压过程中会有额外的cpu消耗。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，非session修改relaod生效 参数名称 wal_init_zero 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当本参数值为on时，会导致新的wal file以零填充，在某些文件系统中，这确保了在需要写入wal records之前的空间已被分配。但是，Copy-On-Write (COW)文件系统不会从本参数中获益，因此可以选择跳过不必要的工作。如果设置为off，则在创建文件时仅写入最后一个字节，以使其具有预期的大小。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，非session修改relaod生效 参数名称 wal_recycle 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数值为on时，会导致wal file被循环使用(通过renaming wal file的方式)，避免了创建新wal file的需要。在COW文件系统中，本参数可能让create new wal file更快。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，非session修改relaod生效 参数名称 wal_buffers 数据类型 integer 默认值 -1，即:shared_buffers配置参数值的1/32大小，不小于64KB也不大于一个wal segment大小。请注意这是默认值的取值范围，不是手工设置本参数值的取值范围。 取值范围 -1到262143 参数单位 8KB 参数含义 分配给尚未写入到disk中的wal data的共享内存大小；如果自动选择太大或太小，则可以手动设置本参数值，但是任何小于32 KB的正数参数值.将被视为32 KB.如果指定的该值不带单位，则单位是WAL块的个数，即源码中符号常量XLOG_BLCKSZ，通常为8KB. 本参数值只能在服务器启动时设置。当每次事务提交时，wal buffer中的内容都会被写入disk中，因此，极大的本参数值不可能带来显著的益处。 是否可session级修改 否 修改后何时生效 PG instance重启生效 参数名称 wal_writer_delay 数据类型 integer 默认值 200 取值范围 1到10000 参数单位 毫秒 参数含义 指定了wal writer进程写wal buffer到disk中的频率，以时间去衡量，当wal writer进程flush wal之后，wal writer进程会休眠本参数值一段时间，除非被一个异步提交事务很快唤醒。如果最后一次flush在小于wal_writer_delay参数值之前发生 以及 小于wal_writer_flush_after参数值对应的WAL生成量，那么WAL被写入操作系统而不是flush到disk中。请注意，在很多操作系统中，sleep delays的有效精度是10毫秒， 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_writer_flush_after 数据类型 Integer 默认值 128，即128*8KB=1MB 取值范围 0到2147483647 参数单位 8KB 参数含义 本参数值指定了wal writer进程flush wal的频率，以wal的大小来衡量。如果最后一次flush在小于wal_writer_delay参数值之前发生 以及 小于wal_writer_flush_after参数值对应的WAL生成量，那么WAL被写入操作系统而不是flush到disk中。本参数值为0代表wal data总是被立即被flush到disk中。如果指定的该值不带单位，则单位是WAL块的个数，即源码中符号常量XLOG_BLCKSZ，通常为8KB. 本参数值只能在postgresql.conf或者server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 wal_skip_threshold 数据类型 Integer 默认值 2048 取值范围 0到2147483647 参数单位 KB 参数含义 当wal_level参数值为minimal并且在create或者rewriting一个永久关系之后进行事务提交，本参数控制怎么持久化新的数据。如果数据小于本参数值，PostgreSQL会将这些数据写入wal log，否则，PostgreSQL会针对受影响的文件使用一个fsync。取决于你的存储属性，如果这些commit正在拖慢并发事务，增大或者降低本参数值可能会有帮助. 本参数值不带单位时，默认以KB为单位。 是否可session级修改 是 修改后何时生效 Reload即可生效。 参数名称 commit_delay 数据类型 Integer 默认值 0，零表示没有延迟。 取值范围 0到100000 参数单位 毫秒 参数含义 设置本参数值会增加在wal flush启动之前的延迟。如果系统负载足够高以至于在给定的时间间隔内准备好提交其他事务，则这可以通过允许经由单个 WAL flush以提交大量事务来提高组提交吞吐量。但是，每次wal flush会增加延迟，最大延迟是本参数指指定的时间。如果没有其他事务会变成准备提交状态，延迟的时间是被浪费的，因此，如果当wal flush被初始化时，至少有commit_siblings个其他事务是active的情况下，延迟才被执行。同样，如果fsync被关闭，delay是不会被执行的。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，其他修改reload生效 参数名称 commit_siblings 数据类型 Integer 默认值 5 取值范围 0到1000 参数单位 参数含义 在执行配置参数commit_delay指定的延迟之前，需要的最小的并发打开的事务数。值越大，在延迟间隔期间至少有一个其他事务准备好提交的可能性就越大 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 allow_system_table_mods 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 允许修改系统表的结构以及对系统表进行某些其他有风险的操作。否则即使对于超级用户也是不允许的。不明智地使用此设置可能会导致无法挽回的数据丢失或严重损坏数据库系统。只有超级用户才能更改此设置。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，其他修改reload生效 参数名称 backtrace_functions 数据类型 string 默认值 空字符串 取值范围 参数单位 参数含义 本参数值是以逗号分隔的C函数名称。如果一个错误被抛出并且错误发生地方的内部C函数名称与本参数值list中的值相匹配，然后backtrace与error message会被写入server log。不是所有的平台都支持backtrace功能，backtrace的质量取决于编译选项。 是否可session级修改 是，仅限于superuser 修改后何时生效 Session级修改立即生效，其他修改reload生效 参数名称 ignore_system_indexes 数据类型 boolean 默认值 off 取值范围 off和on 参数单位 参数含义 当读取system table时忽略system index（但是当修改表时，依然会更新索引）。本参数用于从损坏的索引中恢复的场景中。本参数当session启动后无法改变 是否可session级修改 否 修改后何时生效 Reload即可生效，但是仅仅针对reload之后的新session生效。 参数名称 post_auth_delay 数据类型 Integer 默认值 0，零表示禁用延迟。 取值范围 0到2147 参数单位 秒 参数含义 在PostgreSQL构建认证阶段之后，当一个新server process被fork出来时的延迟的时间。本参数值用来给开发人员一个用debugger attach到server process的机会。 是否可session级修改 否 修改后何时生效 Reload即可生效，但是仅仅针对reload之后的新session生效。 参数名称 pre_auth_delay 数据类型 Integer 默认值 0，零表示禁用延迟。 取值范围 0到60 参数单位 秒 参数含义 在PostgreSQL构建认证阶段之前，在一个新server process被fork出来之后的延迟的时间。本参数值用来给开发人员一个使用debugger attach到server process以跟踪在认证阶段异常行为的机会。本参数只能在postgresql.conf中或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 trace_notify 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 为LISTEN和NOTIFY命令生成大量的debugging输出。配置参数client_min_messages或者log_min_messages必须设置为DEBUG1或者更低才能发送该输出到client或者server log 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 trace_recovery_messages 数据类型 enum 默认值 log，log不会影响log决策 取值范围 {debug5,debug4,debug3,debug2,debug1,log,notice,warning,error} 参数单位 参数含义 启用记录恢复相关的调试输出，否则将不会被记录。此参数允许用户覆盖log_min_messages的正常设置，但仅限于特定消息。本参数的目的是调试hot standby。其他值导致该优先级或更高优先级的与恢复相关的调试消息被记录，就好像它们具有LOG优先级一样。针对配置参数log_min_messages的通用设置会导致无条件发送messages到server log中。本参数只能在postgresql.conf中或者server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 trace_sort 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当本参数值为on时，在sort操作期间，会发出有关资源使用的信息。本参数仅仅针对PostgreSQL编译时TRACE_SORT macro被定义时才有效（默认情况下，TRACE_SORT是被定义的） 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 trace_locks 数据类型 bool 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_lwlocks 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_userlocks 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_lock_oidmin 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 trace_lock_table 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 debug_deadlocks 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 log_btree_build_stats 数据类型 默认值 取值范围 参数单位 参数含义 是否可session级修改 修改后何时生效 参数名称 wal_consistency_checking 数据类型 String 默认值 空串，空串意味着禁用本特性。 取值范围 参数单位 参数含义 本参数的目的是检查WAL redo routine中的错误。当启用本参数时，与WAL记录一起修改的任何buffer的full page image都会添加到记录中。如果该记录随后被重放，系统将首先应用每个记录，然后测试由该记录修改的buffer是否与存储的image匹配。在某些情况下（如提示位），可以接受较小的变化，并将被忽略。任何意外的差异都将导致致命错误，从而终止恢复。本参数值可以设置为all，all表示检查所有记录，本参数值也可以设置为逗号分隔的resource manager列表，受到支持的resource manager 是heap、heap2、btree、hash、gin、gist、sequence、spgist、brin、generic 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效 参数名称 wal_debug 数据类型 bool 默认值 取值范围 参数单位 参数含义 当设置为on时，发出与wal相关的debugging out。本参数仅仅当PostgreSQL编译时定义了WAL_DEBUG macro的情况下才适用。 是否可session级修改 修改后何时生效 参数名称 ignore_checksum_failure 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 本参数仅仅在data checksums启用的情况下才生效。在读取过程中检查到checksum失败通常会导致PostgreSQL报告一个error，从而中止当前事务。本参数值设置为on会导致PostgreSQL忽略故障（但是依然会报告警告）并继续处理。此行为可能导致crash、传播或隐藏corruption或其他严重问题。但是，如果block header是正常的，可能允许您越过错误并检索可能仍然存在于表中的未损坏的tuple。如果block header已损坏，那么即使启用本选项，也会报告错误。 是否可session级修改 是，仅限于superuser在session级修改 修改后何时生效 Reload即可生效 参数名称 zero_damaged_pages 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 检测到一个损坏的page header通常会导致PostgreSQL报错一个error，并终止当前事务。设置本参数值为on会导致系统报告warning，将内存中损坏的页面清零，然后继续处理。这种行为会破坏数据，即：损坏页面上的所有行。但是，本参数确实允许您克服错误并从表中可能存在的任何未损坏的页面中检索行。如果由于硬件或软件错误而发生损坏，它对于恢复数据很有用。在您放弃从表的损坏页恢复数据的希望之前，您通常不应设置本参数。。清零页不会被强制写入磁盘，因此建议在再次关闭此参数之前重新创建表或索引。本参数值只能由超级用户更改。 是否可session级修改 是，仅限于superuser 修改后何时生效 Reload即可生效 参数名称 ignore_invalid_pages 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 当本参数值是off时，在recovery期间检测wal record中存在invalid pages之后，PostgreSQL会发出一个PANIC-level的error，进而会终止recovery。当本参数值设置为on时会导致PostgreSQL忽略wal record中的invalid pages（但是依然会报warning）继续进行recovery。本行为可能会导致crash，data loss，传播或者隐藏corruption，或者其他严重问题。但是，本参数会允许你绕过的PANIC-level的error，进而完成recovery，进而启动PostgreSQL。本参数仅能在PostgreSQL启动时设置。本参数仅仅在recovery或者standby mode下生效。 是否可session级修改 否 修改后何时生效 重启PG instance生效 参数名称 jit_debugging_support 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 注册JIT编译函数给debugger使用，本参数仅能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 jit_dump_bitcode 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 写出LLVM的bitcode以便利于JIT debugging， 是否可session级修改 是，仅限于superuser 修改后何时生效 Reload即可生效 参数名称 jit_expressions 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当 JIT 编译被启用时，本参数用于确定表达式是否被 JIT 编译 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 jit_profiling_support 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 如果 LLVM 具有所需的功能，则发出允许perf分析 JIT 生成的函数所需的数据。这会将文件写入$HOME/.debug/jit/下; 用户负责在需要时执行清理。默认设置是off。该参数只能在服务器启动时设置。本参数仅能在PostgreSQL启动时设置。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 jit_tuple_deforming 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当JIT编译被启用的状态下，本参数用于确定允许不允许tuple deforming进行jit 编译 是否可session级修改 是 修改后何时生效 Reload接口生效 参数名称 enable_bitmapscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对位图扫描计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_gathermerge 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对gather merge计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_hashagg 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对hashed aggregation计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_hashjoin 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对hash-join计划类型的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_incremental_sort 数据类型 bool 默认值 on 取值范围 on和off 数单位 参数含义 启用或禁用query planner对incremental sort steps的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_indexscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对index-scan的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_indexonlyscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对index-only-scan的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_material 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对materialization的使用，完全抑制物化是不可能的，但是关闭本参数可以防止query planner插入物化节点，除非在需要正确性的情况下。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_mergejoin 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对merge-join的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_nestloop 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对nested-loop的使用。完全抑制nested-loop连接是不可能的，但是如果有其他方法可用，关闭本参数会阻止query planner使用nested-loop。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_parallel_append 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对parallel-aware append的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_parallel_hash 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对parall hash join的使用，在hash-join被禁用的情况下，本参数值无效。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_partition_pruning 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner的partitioned table中消除partition。这也控制计划器生成查询计划的能力。允许查询执行器在查询执行期间删除（忽略）分区 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_partitionwise_join 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对partitionwise join的使用，这允许在partitioned table上的join在匹配的partition上执行join。Partitionwise 目前仅适用于连接条件包括所有分区键的情况，这些分区键必须是相同的数据类型并且具有一对一匹配的子分区集。由于分区连接规划在规划期间可以使用明显更多的 CPU 时间和内存，本参数默认值为off 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_partitionwise_aggregate 数据类型 bool 默认值 off 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对partitionwise grouping or aggregation的使用，这允许在partitioned table上的partitionwise grouping or aggregation会在每个partition上单独执行。如果GROUP BY子句不包含分区键，则只能在每个分区的基础上执行部分聚合，并且必须稍后执行最终确定。由于partitionwise grouping or aggregation在规划期间可能会使用更多的 CPU 时间和内存，本参数值为off 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_seqscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对sequential scan的使用。完全抑制顺序扫描是不可能的，但是如果有其他方法可用，关闭本参数会阻止query planner使用sequential scan。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_sort 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对explicit sort steps的使用。完全抑制显式排序是不可能的，但是如果有其他方法可用，关闭本参数会阻止query planner使用显式排序。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 enable_tidscan 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 启用或禁用query planner对TID scan的使用 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 application_name 数据类型 string 默认值 psql 取值范围 参数单位 参数含义 本参数值的长度小于NAMEDATALEN值（NAMEDATALEN是源码中的符号常量，默认是64个字符）。本参数被连接到服务器上的应用程序设置，本参数值会显示在pg_stat_activity视图中，也会显示在csv log中。本参数值也可以被包括在配置参数log_line_prefix的参数值中。只有可打印的ASCII字符才可以用于本参数值。其他字符会被问号(?)取代。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_print_parse 数据类型 bool 默认值 取值范围 参数单位 参数含义 在运行日志中记录query的parse tree。本消息在LOG消息级别的情况下被发出，因此，默认情况下，本消息会出现在运行日志中，但不会发给client。您可以通过调整client_min_messages配置参数、log_min_messages配置参数让消息发送给client。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_print_rewritten 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在运行日志中记录query的query rewriter output,。本消息在LOG消息级别的情况下被发出，因此，默认情况下，本消息会出现在运行日志中，但不会发给client。您可以通过调整client_min_messages配置参数、log_min_messages配置参数让消息发送给client。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_print_plan 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 在运行日志中记录query的execution plan。本消息在LOG消息级别的情况下被发出，因此，默认情况下，本消息会出现在运行日志中，但不会发给client。您可以通过调整client_min_messages配置参数、log_min_messages配置参数让消息发送给client。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 debug_pretty_print 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数设置为on时，会将debug_print_parse, debug_print_rewritten, or debug_print_plan配置参数产生的消息进行缩进，这会导致比设置为off时的compact格式更具有可读性但输出会更长。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 log_checkpoints 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 将checkpoints 和restartpoints记录到运行日志中，一些统计信息被包括到消息中，如写入buffer的数量以及写buffer的耗时等。本参数仅能在postgresql.conf设置或者在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_connections 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当本参数值为on时，每一次尝试连接到PostgreSQL数据库以及客户端成功完成身份认证都会被记录到运行日志中。请注意：有些客户端程序(如psql)会连接两次以确定password是否需要，在该情况下，会有重复的\"connection received\"消息出现，该消息不一定表示有问题。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 log_disconnections 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当本参数值为on时， session终止的信息会被记录到运行日志中。日志输出类似log_connections配置参数的输出信息，以及session的持续时间。 是否可session级修改 否 修改后何时生效 Reload即可对新的connection生效 参数名称 log_error_verbosity 数据类型 enum 默认值 default 取值范围 {terse,default,verbose} 参数单位 参数含义 设置日志消息的详细程度。控制在PG运行日志中为记录的每条消息写入的详细程度。Terse排除了DETAIL、HINT、QUERY、和CONTEXT 级别的信息。Verbose输出包括SQLSTATE错误代码以及源码文件名称、函数名称、生成错误的行号。仅有superuser能修改本参数值 是否可session级修改 是，仅限于superuser 修改后何时生效 Reload即可生效 参数名称 log_hostname 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 默认情况下，在运行日志中显示连接主机的 IP 地址。打开此参数也会导致记录主机名。请注意，根据您的主机名解析设置，这可能会造成不可忽视的性能损失。此参数只能在postgresql.conf文件中或在server command line中设置。 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 log_line_prefix 数据类型 string 默认值 %m [%p] 取值范围 参数单位 参数含义 控制运行日志中每行开头的输出内容。 是否可session级修改 否 修改后何时生效 Reload即可生效。 参数名称 log_lock_waits 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当一个session的等待时间长于配置参数deadlock_timeout参数值以获得锁时是否生成日志消息。这在锁定等待是否导致性能不佳时非常有用。只有superuser才能更改本设置。 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效。 参数名称 log_parameter_max_length 数据类型 integer 默认值 -1 取值范围 -1到1073741823 参数单位 Byte 参数含义 如果本参数值大于零，则使用非错误语句日志消息记录的每个绑定参数值将被修剪为本参数值个字节。零禁用非错误语句日志的绑定参数的日志记录。-1（默认）允许完整记录绑定参数。如果此值未指定单位，则将其视为字节。只有超级用户才能更改此设置。 此设置仅影响由于log_statement、log_duration和相关设置而打印的日志消息。此设置的非零值会增加一些开销，特别是如果参数以二进制形式发送，则需要转换为文本。 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效 参数名称 log_parameter_max_length_on_error 数据类型 integer 默认值 0 取值范围 -1到1073741823 参数单位 Byte 参数含义 如果大于零，错误消息中报告的每个绑定参数值都会被修剪到本参数值个字节。零（默认值）禁用在错误消息中包含绑定参数。-1允许完整打印绑定参数。如果此值未指定单位，则将其视为字节此设置的非零值会增加开销，因为PostgreSQL需要在每个语句开始时将参数值的文本表示存储在内存中，无论最终是否发生错误。以二进制形式发送绑定参数时的开销比以文本形式发送时要大，因为前者需要数据转换，而后者只需要复制字符串 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 log_statement 数据类型 enum 默认值 all 取值范围 {none,ddl,mod,all} none是off的意思， ddl的含义是记录所有数据定义语句，如create、alter、drop; mod的含义是记录DDL语句之外，还会记录数据修改语句，如insert、update、delete、truncate以及copy from语句。PREPARE、EXECUTE和EXPLAIN ANALYZE语句所包含的命令的类型适当时，也会记录这些语句。all的含义是记录所有语句。 参数单位 参数含义 控制在PG的运行日志中写入哪些类型的SQL语句。对于使用扩展查询协议的客户端来说，记录到运行日志是从一个执行消息被收到开始的。绑定参数值也会被记录。请注意，即使是本参数值设置为all，运行日志中也不会显示那些包含简单语法错误的SQL语句。因为仅仅在基本的解析完成并确定SQL语句的type之后，log message才会被发出。在扩展查询协议的情况下，本参数值同样不会记录在执行阶段之前(即:在parser分析或planner期间)失败的语句。设置log_min_error_statement为error(或者更低)以记录此类语句 是否可session级修改 是，仅限于superuser进行修改 修改后何时生效 Reload即可生效 参数名称 log_replication_commands 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 是否将每个复制命令记录到PG运行日志中 是否可session级修改 是，仅限于superuser修改 修改后何时生效 Reload即可生效 参数名称 log_temp_files 数据类型 integer 默认值 -1，代表禁用本log功能 取值范围 -1到2147483647 参数单位 KB 参数含义 控制是否记录本临时文件的文件名和大小。临时文件被创建用以排序、hash以及临时查询结果。当本参数值被启用时，当临时文件被删除时，一个日志条目会被发出。本参数值为0时会记录所有的临时文件信息。本参数值为正数时表示仅仅会记录那些临时文件大小大于等于本参数值的临时文件。本参数值不指定单位时，默认值以KB为单位。本参数仅仅superuser可以修改。 是否可session级修改 是，仅限于superuser进行修改。 修改后何时生效 Reload即可生效 参数名称 log_timezone 数据类型 String 默认值 内置默认值是GMT，但该内置默认值通常在postgresql.conf中被覆盖; initdb将在会设置本参数值以便与系统环境相对应。 取值范围 参数单位 参数含义 设置在log message中使用的time zone。与TimeZone配置参数不同，此值是PostgreSQL Cluster范围的，因此所有会话将一致地报告时间戳。此参数只能在postgresql.conf文件中或在server command line中设置 是否可session级修改 否 修改后何时生效 Reload即可生效 参数名称 client_min_messages 数据类型 enum 默认值 notice 取值范围 {debug5,debug4,debug3,debug2,debug1,log,notice,warning,error} 参数单位 参数含义 控制将哪些消息级别发送到客户端。有效值为DEBUG5，DEBUG4，DEBUG3，DEBUG2，DEBUG1，LOG，NOTICE，WARNING和ERROR。每个级别包括其后的所有级别。级别越靠后，发送的消息就越少。请注意，本参数值log的级别与log_min_messages配置参数值log的级别是不同的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 search_path 数据类型 string 默认值 \"$user\", public 取值范围 参数单位 参数含义 当一个object(table, data type, function等等)不带前面的schema name时，本参数用来指定哪个schema name会被搜索到。当在不同的schema name中有相同的object name时，在search_path中第一个匹配的object的那个schema会被使用。若是一个object没有被search_path中的任何schema匹配到，那么，这个object使用时需要带schema name(即:前面带上 \"schema.\").本参数值需要以逗号分隔，当本参数值包括不存在schema name 或者user没有对该schema name的usage权限时，此类参数值会被忽略。本参数值中出现的$user，其含义是current_user，若是存在与该user同名的schema name并且该user有对该schema name的usage权限（若不是这样，$user会被忽略）不论在本参数值中包括不包括pg_catalog，pg_catalog总会被搜索到，若是在本参数值中包括pg_catalog，那么会按照在本参数值中出现的顺序去搜，若是在本参数值中不包括pg_catalog,那么，PostgreSQL会在本参数值之前去搜索pg_catalog.类似的，当前的session临时表schema pg_temp_nnn 若是存在的话，会总被搜索到。可以在本参数值中使用pg_temp来明确指定要对pg_temp_nnn进行搜索。若是在本参数值中不明确列出，那么，PostgreSQL会首先搜索pg_temp_nnn（在pg_catalog之前）。临时schema name仅仅被用来搜索relation(表、视图、序列等等)和data type name，临时schema name不能用来搜索function name或者operator name当一个object被建立时其前面不指定任何schema name时，该object的schema name是在本参数值中搜索到的第一个有效的schema。当本参数值为空时，一个错误会被抛出。 本参数的默认值是\"$user\", public，该默认值支持对一个数据库的共享使用（即:没有user有私有的schemas，并且都对public共享使用）、每个user schema的私有使用以及混合使用。其他的影响可以通过alter语句修改本参数值来获得，这个修改可以是全局的也可以每个user的。可以通过函数current_schemas来检查当前使用的有效的schema name，这与search_path的参数值并不完全一致，因为函数current_schemas显示了在本参数值中出现的item是如何解析的。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 row_security 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 本参数控制是否抛出错误以代替应用行安全策略。on代表正常应用行安全策略，off代表查询失败除非应用了至少行安全策略。在限制了行可见性导致错误的结果的情况下，可以将本参数值改为off。在默认情况下，pg_dump会进行该修改(即:修改本参数值为off)。本参数对可以绕过行安全策略的roles是无效的，即:superuser和那些带有bypassrls属性的role。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_table_access_method 数据类型 string 默认值 heap 取值范围 参数单位 参数含义 本参数指定了create table或者create materialized views不带访问方法时table或者materialized views的访问方法，以及select …into语句使用时用到的访问方法。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_tablespace 数据类型 string 默认值 空字符串，空字符串代表当前database的默认tablespace 取值范围 参数单位 参数含义 在create object(table和index)语句不明确指定tablespace情况下，本参数值指定了该object对应的tablespace。如果指定的本参数值与现有的任何tablespace不匹配，那么PostgreSQL会自动使用当前database的默认tablespace。如果一个非默认表空间被指定，那么user必须带有对该非默认表空间的create的权限，否则创建会失败本参数不适用于临时表。临时表对应的配置参数是temp_tablespaces。本参数也不适用于create database语句。默认情况下，一个新的database从template数据库中继承了tablespace。若是本参数值是非空字符串，然后partitioned table被创建，那么，该partitioned table的表空间被设置为该非空字符串，该非空字符串也是将来partition被建立的默认表空间，即使本配置参数值之后被更改。 是否可session级修改 是 修改后何时生效 Reload即可生效。 参数名称 temp_tablespaces 数据类型 string 默认值 空字符串，空字符串代表所有的temporary objects都会被创建在当前数据库的默认表空间中。 取值范围 参数单位 参数含义 在create temporary object(temp tables 和temp tables 上的indexes)语句不明确指定tablespace情况下，本参数值指定了该object对应的temporary tablespace。诸如排序生成的临时文件也被建立到这个temp_tablespaces指定的临时表空间中。本参数值是一系列的tablespace name的清单，当有多个tablespace name的情况下，每次创建临时对象时，PostgreSQL 都会从列表中随机选择一个tablespace name。除了在事务中，连续创建的临时对象被放置在列表中的连续表空间中。如果列表中选定的是空字符串，PostgreSQL会自动使用当前数据库的默认表空间。当temp_tablespaces以交互方式设置时，指定不存在的表空间会报错，指定用户没有CREATE权限的表空间也会报错。但是，当使用先前设置的值时，将忽略不存在的表空间，以及用户缺乏CREATE权限的表空间。此规则适用于在postgresql.conf设置本参数值的情况下 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 check_function_bodies 数据类型 bool 默认值 on 取值范围 on和off 参数单位 参数含义 当设置为off时，它会在CREATE FUNCTION期间禁用函数体字符串的验证。禁用验证可避免验证过程的副作用，并避免由于前向引用等问题导致的误报。在代表其他用户加载函数之前，将此参数设置为off；pg_dump会自动执行此操作 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_transaction_isolation 数据类型 enum 默认值 read committed 取值范围 {serializable,\"repeatable read\",\"read committed\",\"read uncommitted\"} 参数单位 参数含义 本参数用来控制新事务的隔离级别 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_transaction_read_only 数据类型 bool 默认值 off，off的意思是read/write 取值范围 off和on 参数单位 参数含义 本参数用来控制每个新事务的默认只读状态。一个只读事务不能改变(alter)一个非临时表. 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 default_transaction_deferrable 数据类型 bool 默认值 off 取值范围 off和on 参数单位 参数含义 当运行在serializable隔离级别中时，一个deferrable的只读事务在它被允许继续处理之前可能会被延迟。然而，一旦它开始执行，它就不会产生任何确保可串行化所需的开销；所以序列化代码没有理由因为并发更新而强制中止该只读事务，这确保了对长时间运行的只读事务是很合适的。本参数控制每个新事务的默认的deferrable状态。当前，本参数对read-write事务是无效的，对隔离级别低于serializable的事务也是无效的 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 session_replication_role 数据类型 enum 默认值 origin 取值范围 {origin,replica,local} 参数单位 参数含义 控制针对当前session的与复制相关的triggers和rules的触发(firing),设置本参数需要superuser权限，并且会导致之前cache起来的查询计划被丢弃掉。本参数的用意是逻辑复制系统在应用复制的更改时将本参数值设置为replica，这样做的结果是，触发器和规则（没有从默认配置更改）不会在复制副本上触发。详细信息，请参阅ALTER TABLE子句ENABLE TRIGGER和ENABLE RULE PostgreSQL在内部将参数值origin和local视为相同的值，第三方复制软件可能把这两个参数用于他们的目的，比如，使用local来指示一个session的变化不会被复制的session。因为外键是通过trigger实现的，因此，设置本参数为replica也会disable掉所有的外键约束检查。 是否可session级修改 是，仅限于superuser可进行修改。 修改后何时生效 Reload即可生效 参数名称 statement_timeout 数据类型 integer 默认值 0，零代表禁用超时机制 取值范围 0到2147483647 参数单位 毫秒 参数含义 终止掉运行时间超过本参数值时间长度的SQL语句。如果log_min_error_statement配置参数被设置为error或者更低级别，超时的SQL语句也会被记录下来。超时时间是如下计算的：从SQL语句到PostgreSQL数据库服务器端开始算，直到该语句被服务器处理完成。如果单个简单查询消息(single simple-Query message)中出现多个SQL语句，则将分别对每条语句应用超时.( PostgreSQL13之前的版本通常将超时处理为应用于整个查询字符串)，在扩展查询协议中，当任何与查询相关的消息（Parse、Bind、Execute、descripe）到达PostgreSQL数据库时，超时就开始计时，并通过完成Execute或Sync消息而取消。在postgresql.conf中设置本参数值是不推荐的，因为这会影响所有session 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 lock_timeout 数据类型 integer 默认值 0，零代表禁用超时机制 取值范围 0到2147483647 参数单位 毫秒 参数含义 终止掉在尝试获取表、索引、行或其他数据库对象上的锁的等待时间超过本参数值的任何语句。时间限制分别适用于每次锁获取尝试，该限制既适用于显式锁定请求（如LOCK TABLE，或SELECT FOR UPDATE without NOWAIT），也适用于隐式获取的锁。如果指定的值没有单位，则以毫秒为单位。值为零（默认值）将禁用超时。与statement_timeout配置参数不同，本超时只能在锁等待时发生。请注意，如果statement_timeout非零，设置lock_timeout为相同或更大的值是毫无意义的，因为语句超时总是首先触发。如果log_min_error_statement设置为ERROR或更低，则将记录超时的语句。在postgresql.conf中设置本参数值是不推荐的，因为这会影响所有session 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 idle_in_transaction_session_timeout 数据类型 integer 默认值 0，零代表禁用超时机制 取值范围 0到2147483647 参数单位 毫秒 参数含义 终止掉那些处于open transaction状态的并且idle时间长度超过本参数值的任何会话。这允许释放该会话持有的任何锁，并重用连接槽；它还允许对仅对此事务可见的元组进行vacuum。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 vacuum_freeze_table_age 数据类型 integer 默认值 150000000 取值范围 0到2000000000 参数单位 参数含义 如果表的pg_class.relfrozenxid字段值已经达到本参数值，那么vacuum会执行一次侵略性的扫描（aggressive scan）。侵略性的扫描（aggressive scan）不同于常规vacuum(regular VACUUM), 侵略性的扫描（aggressive scan）会查看每个page以确定page中是否包括未冻结的xids或者mxids，而不是仅仅查看page中是否包括dead tuples。尽管用户可以将此值设置为 0 到 20 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_freeze_max_age 的95% ，以便有机会定期在为表启动反环绕自动清理(anti-wraparound autovacuum)之前手工运行VACUUM。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 vacuum_freeze_min_age 数据类型 integer 默认值 50000000 取值范围 0到1000000000 参数单位 参数含义 本参数用于指定VACUUM在扫描表时用于决定是否冻结行版本的截止年龄(cutoff age，以事物计算), 尽管用户可以将此值设置为 0 到 10 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_freeze_max_age参数值的一半，这样强制 autovacuum之间的时间不会过短。 是否可session级修改 修改后何时生效 参数名称 vacuum_multixact_freeze_table_age 数据类型 integer 默认值 150000000 取值范围 0到2000000000 参数单位 参数含义 如果表的pg_class. relminmxid字段值已经达到本参数值，那么vacuum会执行一次侵略性的扫描（aggressive scan）。侵略性的扫描（aggressive scan）不同于常规vacuum(regular VACUUM), 侵略性的扫描（aggressive scan）会查看每个page以确定page中是否包括未冻结的xids或者mxids，而不是仅仅查看page中是否包括dead tuples。尽管用户可以将此值设置为 0 到 20 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_multixact_freeze_max_age的95% ，以便有机会定期在为表启动反环绕自动清理(anti-wraparound autovacuum)之前手工运行VACUUM。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 vacuum_multixact_freeze_min_age 数据类型 integer 默认值 5000000 取值范围 0到1000000000 参数单位 参数含义 本参数用于指定VACUUM在扫描表时用于决定是否冻结行版本的multixacts截止年龄(cutoff age), 尽管用户可以将此值设置为 0 到 10 亿之间的任何值，但VACUUM会默默地将有效值限制为autovacuum_multixact_freeze_max_age参数值的一半，这样强制 autovacuum之间的时间不会过短。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 bytea_output 数据类型 enum 默认值 hex 取值范围 {escape,hex} 参数单位 参数含义 本配置参数定义了对数据类型bytea的输出格式。 bytea数据类型在输入时对两种格式(escape和hex)均接受。escape代表传统的PostgreSQL格式。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 xmlbinary 数据类型 enum 默认值 base64 取值范围 {base64,hex} 参数单位 参数含义 设置二进制值在 XML 中的编码方式。例如，这适用于通过函数xmlelement或xmlforest将bytea值转换为XML的情况。可能的值为base64和hex，它们都在 XML 模式标准中定义。 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 xmloption 数据类型 enum 默认值 content 取值范围 {content,document} 参数单位 参数含义 本参数用于设置在XML和字符串值之间转换时DOCUMENT或CONTENT是隐式的。在PostgreSQL中，也可以使用下面的SQL标准来设置:SET XML OPTION { DOCUMENT | CONTENT }; 是否可session级修改 是 修改后何时生效 Reload即可生效 参数名称 gin_pending_list_limit 数据类型 integer 默认值 4096 取值范围 64到2147483647 参数单位 KB 参数含义 设置GIN索引的挂起列表(pending list)的最大大小，该列表在启用fastupdate时使用。如果列表的大小超过此最大值，则通过将列表中的条目批量移动到索引的主数据结构来清除列表。如果指定此值时没有单位，则将其作为千字节。默认值为4MB。通过更改索引存储参数，可以为单个GIN索引重写此设置. 是否可session级修改 是 修改后何时生效 Reload即可生效","title":"","component":"ivorysql-doc","version":"master","name":"pg_parameters_reference","url":"/ivorysql-doc/master/pg_reference/pg_parameters_reference.html","titles":[{"text":"1. 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Replication / Master Server","hash":"replication-master-server","id":13},{"text":"1.6.1. synchronous_standby_names","hash":"synchronous_standby_names","id":14},{"text":"1.6.2. vacuum_defer_cleanup_age","hash":"vacuum_defer_cleanup_age","id":15},{"text":"1.7. Replication / Subscribers","hash":"replication-subscribers","id":16},{"text":"1.7.1. max_logical_replication_workers","hash":"max_logical_replication_workers","id":17},{"text":"1.7.2. max_sync_workers_per_subscription","hash":"max_sync_workers_per_subscription","id":18},{"text":"1.8. Write-Ahead Log / Archive Recovery","hash":"write-ahead-log-archive-recovery","id":19},{"text":"1.8.1. restore_command","hash":"restore_command","id":20},{"text":"1.8.2. archive_cleanup_command","hash":"archive_cleanup_command","id":21},{"text":"1.8.3. recovery_end_command","hash":"recovery_end_command","id":22},{"text":"1.9. Error Handling","hash":"error-handling","id":23},{"text":"1.9.1. exit_on_error","hash":"exit_on_error","id":24},{"text":"1.9.2. restart_after_crash","hash":"restart_after_crash","id":25},{"text":"1.9.3. data_sync_retry","hash":"data_sync_retry","id":26},{"text":"1.10. Write-Ahead Log / Archiving","hash":"write-ahead-log-archiving","id":27},{"text":"1.10.1. archive_mode","hash":"archive_mode","id":28},{"text":"1.10.2. archive_command","hash":"archive_command","id":29},{"text":"1.10.3. archive_timeout","hash":"archive_timeout","id":30},{"text":"1.11. Resource Usage / Background Writer","hash":"resource-usage-background-writer","id":31},{"text":"1.11.1. bgwriter_delay","hash":"bgwriter_delay","id":32},{"text":"1.11.2. bgwriter_lru_maxpages","hash":"bgwriter_lru_maxpages","id":33},{"text":"1.11.3. bgwriter_lru_multiplier","hash":"bgwriter_lru_multiplier","id":34},{"text":"1.11.4. bgwriter_flush_after","hash":"bgwriter_flush_after","id":35},{"text":"1.12. Statistics / Monitoring","hash":"statistics-monitoring","id":36},{"text":"1.12.1. log_statement_stats","hash":"log_statement_stats","id":37},{"text":"1.12.2. log_parser_stats","hash":"log_parser_stats","id":38},{"text":"1.12.3. log_planner_stats","hash":"log_planner_stats","id":39},{"text":"1.12.4. log_executor_stats","hash":"log_executor_stats","id":40},{"text":"1.13. Client Connection Defaults / Shared Library Preloading","hash":"client-connection-defaults-shared-library-preloading","id":41},{"text":"1.13.1. jit_provider","hash":"jit_provider","id":42},{"text":"1.13.2. local_preload_libraries","hash":"local_preload_libraries","id":43},{"text":"1.13.3. session_preload_libraries","hash":"session_preload_libraries","id":44},{"text":"1.13.4. shared_preload_libraries","hash":"shared_preload_libraries","id":45},{"text":"1.14. Lock Management","hash":"lock-management","id":46},{"text":"1.14.1. deadlock_timeout","hash":"deadlock_timeout","id":47},{"text":"1.14.2. max_locks_per_transaction","hash":"max_locks_per_transaction","id":48},{"text":"1.14.3. max_pred_locks_per_transaction","hash":"max_pred_locks_per_transaction","id":49},{"text":"1.14.4. max_pred_locks_per_relation","hash":"max_pred_locks_per_relation","id":50},{"text":"1.14.5. max_pred_locks_per_page","hash":"max_pred_locks_per_page","id":51},{"text":"1.15. Resource Usage / Cost-Based Vacuum Delay","hash":"resource-usage-cost-based-vacuum-delay","id":52},{"text":"1.15.1. vacuum_cost_delay","hash":"vacuum_cost_delay","id":53},{"text":"1.15.2. vacuum_cost_page_hit","hash":"vacuum_cost_page_hit","id":54},{"text":"1.15.3. vacuum_cost_page_miss","hash":"vacuum_cost_page_miss","id":55},{"text":"1.15.4. vacuum_cost_page_dirty","hash":"vacuum_cost_page_dirty","id":56},{"text":"1.15.5. vacuum_cost_limit","hash":"vacuum_cost_limit","id":57},{"text":"1.16. Replication / Sending Servers","hash":"replication-sending-servers","id":58},{"text":"1.16.1. max_wal_senders","hash":"max_wal_senders","id":59},{"text":"1.16.2. max_replication_slots","hash":"max_replication_slots","id":60},{"text":"1.16.3. wal_keep_size","hash":"wal_keep_size","id":61},{"text":"1.16.4. max_slot_wal_keep_size","hash":"max_slot_wal_keep_size","id":62},{"text":"1.16.5. wal_sender_timeout","hash":"wal_sender_timeout","id":63},{"text":"1.17. Connections and Authentication / Authentication","hash":"connections-and-authentication-authentication","id":64},{"text":"1.17.1. authentication_timeout","hash":"authentication_timeout","id":65},{"text":"1.17.2. password_encryption","hash":"password_encryption","id":66},{"text":"1.17.3. krb_server_keyfile","hash":"krb_server_keyfile","id":67},{"text":"1.17.4. krb_caseins_users","hash":"krb_caseins_users","id":68},{"text":"1.17.5. db_user_namespace","hash":"db_user_namespace","id":69},{"text":"1.18. Statistics / Query and Index Statistics Collector","hash":"statistics-query-and-index-statistics-collector","id":70},{"text":"1.18.1. track_activities","hash":"track_activities","id":71},{"text":"1.18.2. track_activity_query_size","hash":"track_activity_query_size","id":72},{"text":"1.18.3. track_counts","hash":"track_counts","id":73},{"text":"1.18.4. track_io_timing","hash":"track_io_timing","id":74},{"text":"1.18.5. track_functions","hash":"track_functions","id":75},{"text":"1.18.6. stats_temp_directory","hash":"stats_temp_directory","id":76},{"text":"1.19. File Locations","hash":"file-locations","id":77},{"text":"1.19.1. data_directory","hash":"data_directory","id":78},{"text":"1.19.2. config_file","hash":"config_file","id":79},{"text":"1.19.3. hba_file","hash":"hba_file","id":80},{"text":"1.19.4. ident_file","hash":"ident_file","id":81},{"text":"1.19.5. external_pid_file","hash":"external_pid_file","id":82},{"text":"1.20. Reporting and Logging / When to Log","hash":"reporting-and-logging-when-to-log","id":83},{"text":"1.20.1. log_min_messages","hash":"log_min_messages","id":84},{"text":"1.20.2. log_min_error_statement","hash":"log_min_error_statement","id":85},{"text":"1.20.3. log_min_duration_statement","hash":"log_min_duration_statement","id":86},{"text":"1.20.4. log_min_duration_sample","hash":"log_min_duration_sample","id":87},{"text":"1.20.5. log_statement_sample_rate","hash":"log_statement_sample_rate","id":88},{"text":"1.20.6. log_transaction_sample_rate","hash":"log_transaction_sample_rate","id":89},{"text":"1.21. Checkpoints","hash":"checkpoints","id":90},{"text":"1.21.1. checkpoint_timeout","hash":"checkpoint_timeout","id":91},{"text":"1.21.2. checkpoint_completion_target","hash":"checkpoint_completion_target","id":92},{"text":"1.21.3. checkpoint_flush_after","hash":"checkpoint_flush_after","id":93},{"text":"1.21.4. checkpoint_warning","hash":"checkpoint_warning","id":94},{"text":"1.21.5. max_wal_size","hash":"max_wal_size","id":95},{"text":"1.21.6. min_wal_size","hash":"min_wal_size","id":96},{"text":"1.22. dynamic_library_path","hash":"dynamic_library_path","id":97},{"text":"1.23. gin_fuzzy_search_limit","hash":"gin_fuzzy_search_limit","id":98},{"text":"1.24. tcp_keepalives_count","hash":"tcp_keepalives_count","id":99},{"text":"1.25. tcp_keepalives_idle","hash":"tcp_keepalives_idle","id":100},{"text":"1.26. tcp_keepalives_interval","hash":"tcp_keepalives_interval","id":101},{"text":"1.27. tcp_user_timeout","hash":"tcp_user_timeout","id":102},{"text":"1.28. Query Tuning / Genetic Query Optimizer","hash":"query-tuning-genetic-query-optimizer","id":103},{"text":"1.28.1. geqo","hash":"geqo","id":104},{"text":"1.28.2. geqo_threshold","hash":"geqo_threshold","id":105},{"text":"1.28.3. geqo_effort","hash":"geqo_effort","id":106},{"text":"1.28.4. geqo_pool_size","hash":"geqo_pool_size","id":107},{"text":"1.28.5. geqo_generations","hash":"geqo_generations","id":108},{"text":"1.28.6. geqo_selection_bias","hash":"geqo_selection_bias","id":109},{"text":"1.28.7. geqo_seed","hash":"geqo_seed","id":110},{"text":"1.29. Write-Ahead Log / Recovery Target","hash":"write-ahead-log-recovery-target","id":111},{"text":"1.29.1. recovery_target","hash":"recovery_target","id":112},{"text":"1.29.2. recovery_target_name","hash":"recovery_target_name","id":113},{"text":"1.29.3. recovery_target_time","hash":"recovery_target_time","id":114},{"text":"1.29.4. recovery_target_xid","hash":"recovery_target_xid","id":115},{"text":"1.29.5. recovery_target_lsn","hash":"recovery_target_lsn","id":116},{"text":"1.29.6. recovery_target_inclusive","hash":"recovery_target_inclusive","id":117},{"text":"1.29.7. recovery_target_timeline","hash":"recovery_target_timeline","id":118},{"text":"1.29.8. recovery_target_action","hash":"recovery_target_action","id":119},{"text":"1.30. Query Tuning / Other Planner Options","hash":"query-tuning-other-planner-options","id":120},{"text":"1.30.1. default_statistics_target","hash":"default_statistics_target","id":121},{"text":"1.30.2. constraint_exclusion","hash":"constraint_exclusion","id":122},{"text":"1.30.3. cursor_tuple_fraction","hash":"cursor_tuple_fraction","id":123},{"text":"1.30.4. from_collapse_limit","hash":"from_collapse_limit","id":124},{"text":"1.30.5. jit","hash":"jit","id":125},{"text":"1.30.6. join_collapse_limit","hash":"join_collapse_limit","id":126},{"text":"1.30.7. parallel_leader_participation","hash":"parallel_leader_participation","id":127},{"text":"1.30.8. force_parallel_mode","hash":"force_parallel_mode","id":128},{"text":"1.30.9. plan_cache_mode","hash":"plan_cache_mode","id":129},{"text":"1.31. 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Connections and Authentication / Connection Settings","hash":"connections-and-authentication-connection-settings","id":139},{"text":"1.32.1. listen_addresses","hash":"listen_addresses","id":140},{"text":"1.32.2. port","hash":"port","id":141},{"text":"1.32.3. max_connections","hash":"max_connections","id":142},{"text":"1.32.4. superuser_reserved_connections","hash":"superuser_reserved_connections","id":143},{"text":"1.32.5. unix_socket_directories","hash":"unix_socket_directories","id":144},{"text":"1.32.6. unix_socket_group","hash":"unix_socket_group","id":145},{"text":"1.32.7. unix_socket_permissions","hash":"unix_socket_permissions","id":146},{"text":"1.32.8. bonjour","hash":"bonjour","id":147},{"text":"1.32.9. bonjour_name","hash":"bonjour_name","id":148},{"text":"1.33. Resource Usage / Asynchronous Behavior","hash":"resource-usage-asynchronous-behavior","id":149},{"text":"1.33.1. effective_io_concurrency","hash":"effective_io_concurrency","id":150},{"text":"1.33.2. maintenance_io_concurrency","hash":"maintenance_io_concurrency","id":151},{"text":"1.33.3. max_worker_processes","hash":"max_worker_processes","id":152},{"text":"1.33.4. max_parallel_workers_per_gather","hash":"max_parallel_workers_per_gather","id":153},{"text":"1.33.5. max_parallel_maintenance_workers","hash":"max_parallel_maintenance_workers","id":154},{"text":"1.33.6. max_parallel_workers","hash":"max_parallel_workers","id":155},{"text":"1.33.7. backend_flush_after","hash":"backend_flush_after","id":156},{"text":"1.33.8. old_snapshot_threshold","hash":"old_snapshot_threshold","id":157},{"text":"1.34. Replication / Standby Servers","hash":"replication-standby-servers","id":158},{"text":"1.34.1. primary_conninfo","hash":"primary_conninfo","id":159},{"text":"1.34.2. primary_slot_name","hash":"primary_slot_name","id":160},{"text":"1.34.3. promote_trigger_file","hash":"promote_trigger_file","id":161},{"text":"1.34.4. hot_standby","hash":"hot_standby","id":162},{"text":"1.34.5. max_standby_archive_delay","hash":"max_standby_archive_delay","id":163},{"text":"1.34.6. max_standby_streaming_delay","hash":"max_standby_streaming_delay","id":164},{"text":"1.34.7. wal_receiver_create_temp_slot","hash":"wal_receiver_create_temp_slot","id":165},{"text":"1.34.8. wal_receiver_status_interval","hash":"wal_receiver_status_interval","id":166},{"text":"1.34.9. hot_standby_feedback","hash":"hot_standby_feedback","id":167},{"text":"1.34.10. wal_receiver_timeout","hash":"wal_receiver_timeout","id":168},{"text":"1.34.11. wal_retrieve_retry_interval","hash":"wal_retrieve_retry_interval","id":169},{"text":"1.34.12. recovery_min_apply_delay","hash":"recovery_min_apply_delay","id":170},{"text":"1.35. Query Tuning / Planner Cost Constants","hash":"query-tuning-planner-cost-constants","id":171},{"text":"1.35.1. seq_page_cost","hash":"seq_page_cost","id":172},{"text":"1.35.2. random_page_cost","hash":"random_page_cost","id":173},{"text":"1.35.3. cpu_tuple_cost","hash":"cpu_tuple_cost","id":174},{"text":"1.35.4. cpu_index_tuple_cost","hash":"cpu_index_tuple_cost","id":175},{"text":"1.35.5. cpu_operator_cost","hash":"cpu_operator_cost","id":176},{"text":"1.35.6. parallel_setup_cost","hash":"parallel_setup_cost","id":177},{"text":"1.35.7. parallel_tuple_cost","hash":"parallel_tuple_cost","id":178},{"text":"1.35.8. min_parallel_table_scan_size","hash":"min_parallel_table_scan_size","id":179},{"text":"1.35.9. min_parallel_index_scan_size","hash":"min_parallel_index_scan_size","id":180},{"text":"1.35.10. effective_cache_size","hash":"effective_cache_size","id":181},{"text":"1.35.11. jit_above_cost","hash":"jit_above_cost","id":182},{"text":"1.35.12. jit_inline_above_cost","hash":"jit_inline_above_cost","id":183},{"text":"1.35.13. jit_optimize_above_cost","hash":"jit_optimize_above_cost","id":184},{"text":"1.36. autovacuum","hash":"autovacuum","id":185},{"text":"1.36.1. autovacuum","hash":"autovacuum-2","id":186},{"text":"1.36.2. log_autovacuum_min_duration","hash":"log_autovacuum_min_duration","id":187},{"text":"1.36.3. autovacuum_max_workers","hash":"autovacuum_max_workers","id":188},{"text":"1.36.4. autovacuum_naptime","hash":"autovacuum_naptime","id":189},{"text":"1.36.5. autovacuum_vacuum_threshold","hash":"autovacuum_vacuum_threshold","id":190},{"text":"1.36.6. autovacuum_vacuum_insert_threshold","hash":"autovacuum_vacuum_insert_threshold","id":191},{"text":"1.36.7. autovacuum_analyze_threshold","hash":"autovacuum_analyze_threshold","id":192},{"text":"1.36.8. autovacuum_vacuum_scale_factor","hash":"autovacuum_vacuum_scale_factor","id":193},{"text":"1.36.9. autovacuum_vacuum_insert_scale_factor","hash":"autovacuum_vacuum_insert_scale_factor","id":194},{"text":"1.36.10. autovacuum_analyze_scale_factor","hash":"autovacuum_analyze_scale_factor","id":195},{"text":"1.36.11. autovacuum_freeze_max_age","hash":"autovacuum_freeze_max_age","id":196},{"text":"1.36.12. autovacuum_multixact_freeze_max_age","hash":"autovacuum_multixact_freeze_max_age","id":197},{"text":"1.36.13. autovacuum_vacuum_cost_delay","hash":"autovacuum_vacuum_cost_delay","id":198},{"text":"1.36.14. autovacuum_vacuum_cost_limit","hash":"autovacuum_vacuum_cost_limit","id":199},{"text":"1.37. Connections and Authentication / SSL","hash":"connections-and-authentication-ssl","id":200},{"text":"1.37.1. ssl","hash":"ssl","id":201},{"text":"1.37.2. ssl_ca_file","hash":"ssl_ca_file","id":202},{"text":"1.37.3. ssl_cert_file","hash":"ssl_cert_file","id":203},{"text":"1.37.4. ssl_crl_file","hash":"ssl_crl_file","id":204},{"text":"1.37.5. ssl_key_file","hash":"ssl_key_file","id":205},{"text":"1.37.6. ssl_ciphers","hash":"ssl_ciphers","id":206},{"text":"1.37.7. ssl_prefer_server_ciphers","hash":"ssl_prefer_server_ciphers","id":207},{"text":"1.37.8. ssl_ecdh_curve","hash":"ssl_ecdh_curve","id":208},{"text":"1.37.9. ssl_min_protocol_version","hash":"ssl_min_protocol_version","id":209},{"text":"1.37.10. ssl_max_protocol_version","hash":"ssl_max_protocol_version","id":210},{"text":"1.37.11. ssl_dh_params_file","hash":"ssl_dh_params_file","id":211},{"text":"1.37.12. ssl_passphrase_command","hash":"ssl_passphrase_command","id":212},{"text":"1.37.13. ssl_passphrase_command_supports_reload","hash":"ssl_passphrase_command_supports_reload","id":213},{"text":"1.38. Resource Usage / Memory","hash":"resource-usage-memory","id":214},{"text":"1.38.1. shared_buffers","hash":"shared_buffers","id":215},{"text":"1.38.2. huge_pages","hash":"huge_pages","id":216},{"text":"1.38.3. temp_buffers","hash":"temp_buffers","id":217},{"text":"1.38.4. max_prepared_transactions","hash":"max_prepared_transactions","id":218},{"text":"1.38.5. work_mem","hash":"work_mem","id":219},{"text":"1.38.6. hash_mem_multiplier","hash":"hash_mem_multiplier","id":220},{"text":"1.38.7. maintenance_work_mem","hash":"maintenance_work_mem","id":221},{"text":"1.38.8. autovacuum_work_mem","hash":"autovacuum_work_mem","id":222},{"text":"1.38.9. logical_decoding_work_mem","hash":"logical_decoding_work_mem","id":223},{"text":"1.38.10. max_stack_depth","hash":"max_stack_depth","id":224},{"text":"1.38.11. shared_memory_type","hash":"shared_memory_type","id":225},{"text":"1.38.12. dynamic_shared_memory_type","hash":"dynamic_shared_memory_type","id":226},{"text":"1.39. Reporting and Logging / Where to Log","hash":"reporting-and-logging-where-to-log","id":227},{"text":"1.39.1. log_destination","hash":"log_destination","id":228},{"text":"1.39.2. logging_collector","hash":"logging_collector","id":229},{"text":"1.39.3. log_directory","hash":"log_directory","id":230},{"text":"1.39.4. log_filename","hash":"log_filename","id":231},{"text":"1.39.5. log_file_mode","hash":"log_file_mode","id":232},{"text":"1.39.6. log_rotation_age","hash":"log_rotation_age","id":233},{"text":"1.39.7. log_rotation_size","hash":"log_rotation_size","id":234},{"text":"1.39.8. log_truncate_on_rotation","hash":"log_truncate_on_rotation","id":235},{"text":"1.39.9. syslog_facility","hash":"syslog_facility","id":236},{"text":"1.39.10. syslog_ident","hash":"syslog_ident","id":237},{"text":"1.39.11. syslog_sequence_numbers","hash":"syslog_sequence_numbers","id":238},{"text":"1.39.12. syslog_split_messages","hash":"syslog_split_messages","id":239},{"text":"1.39.13. event_source","hash":"event_source","id":240},{"text":"1.40. 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+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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diff --git a/docs/en/ivorysql-doc/master/compatibility_features_design/nls_parameter.html b/docs/en/ivorysql-doc/master/compatibility_features_design/nls_parameter.html
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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diff --git a/docs/en/ivorysql-doc/master/containerization/docker_podman_deployment.html b/docs/en/ivorysql-doc/master/containerization/docker_podman_deployment.html
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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diff --git a/docs/en/ivorysql-doc/master/containerization/k8s_deployment.html b/docs/en/ivorysql-doc/master/containerization/k8s_deployment.html
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+  <li class="nav-item" data-depth="3">
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 </ul>
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 </ul>
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 </ul>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html b/docs/en/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
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 </ul>
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 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html b/docs/en/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -502,6 +505,13 @@ <h2 id="ivorysql-ecosystem-plugin-compatibility-list" class="discrete">IvorySQL
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 </tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><strong>12</strong></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><a href="pg_stat_monitor.html" class="xref page">pg_stat_monitor</a></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">2.3.1</p></td>
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+<td class="tableblock halign-left valign-top"><p class="tableblock">Performance monitoring</p></td>
+</tr>
 </tbody>
 </table>
 <div class="paragraph">
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+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
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+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
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+    <a class="nav-link" href="../oracle_compatibility/compat_function_procedure.html">4、functions and stored procedures</a>
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+    <a class="nav-link" href="../oracle_compatibility/builtin_types_functions.html">5、Built-in data types and built-in functions</a>
+  </li>
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+    <a class="nav-link" href="../oracle_compatibility/package.html">9、Package</a>
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+    <a class="nav-link" href="../oracle_compatibility/invisible_column.html">10、Invisible Columns</a>
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+    <a class="nav-link" href="../oracle_compatibility/compat_rowid.html">11、RowID Column</a>
+  </li>
+  <li class="nav-item" data-depth="2">
+    <a class="nav-link" href="../oracle_compatibility/compat_out_parameter.html">12、OUT Parameter</a>
+  </li>
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+    <a class="nav-link" href="../oracle_compatibility/compat_type_rowtype.html">13、%Type &amp; %Rowtype</a>
+  </li>
+  <li class="nav-item" data-depth="2">
+    <a class="nav-link" href="../oracle_compatibility/compat_nls_parameter.html">14、NLS Parameters</a>
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+    <a class="nav-link" href="../oracle_compatibility/compat_force_view.html">15、Force View</a>
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+    <a class="nav-link" href="../oracle_compatibility/compat_nested_function.html">16、Nested Subfunctions</a>
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+    <a class="nav-link" href="../oracle_compatibility/compat_sys_guid.html">17、sys_guid Function</a>
+  </li>
+  <li class="nav-item" data-depth="2">
+    <a class="nav-link" href="../oracle_compatibility/compat_empty_string_to_null.html">18、Empty String to NULL</a>
+  </li>
+  <li class="nav-item" data-depth="2">
+    <a class="nav-link" href="../oracle_compatibility/compat_call_into.html">19、CALL INTO</a>
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+    <li>IvorySQL Ecosystem</li>
+    <li>Eco Component Adaption</li>
+    <li><a href="pg_stat_monitor.html">pg_stat_monitor</a></li>
+  </ul>
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+  <div class="edit-this-page"><a href="https://github.com/IvorySQL/ivorysql_docs/edit/master/EN/modules/ROOT/pages/master/ecosystem_components/pg_stat_monitor.adoc">Edit this Page</a></div>
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+<article class="doc">
+<h1 class="page">pg_stat_monitor</h1>
+<div class="sect1">
+<h2 id="overview"><a class="anchor" href="#overview"></a>1. Overview</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>pg_stat_monitor is a Query Performance Monitoring tool for PostgreSQL. pg_stat_monitor collects performance statistics and provides query performance insights in a single view and graphically in histogram.</p>
+</div>
+<div class="paragraph">
+<p>These insights allow database users to understand query origins, execution, planning statistics and details, query information, and metadata. This significantly improves observability, enabling users to debug and tune query performance.</p>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="installation"><a class="anchor" href="#installation"></a>2. Installation</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>The IvorySQL installation package already includes the pg_stat_monitor plugin. If you installed IvorySQL using the installation package, you typically don&#8217;t need to manually install pg_stat_monitor. If you installed IvorySQL from source code, you can proceed to install the pg_stat_monitor plugin also from source. The IvorySQL community provides step-by-step instructions for source code installation：</p>
+</div>
+<div class="paragraph">
+<p>To build pg_stat_monitor from source code, you require the following:</p>
+</div>
+<div class="ulist">
+<ul>
+<li>
+<p>git</p>
+</li>
+<li>
+<p>make</p>
+</li>
+<li>
+<p>gcc</p>
+</li>
+<li>
+<p>pg_config</p>
+</li>
+</ul>
+</div>
+<div class="paragraph">
+<p>You can download the source code of the latest release of pg_stat_monitor from <a href="https://github.com/Percona/pg_stat_monitor/releases">the releases page on GitHub</a> or using git:</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">git clone https://github.com/percona/pg_stat_monitor.git</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>Compile and install the extension. Assuming IvorySQL is already installed in the environment with the installation path at /usr/ivory-5.</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">cd pg_stat_monitor
+export PG_CONFIG=/usr/ivory-5/bin/pg_config
+make USE_PGXS=1
+make USE_PGXS=1 install</code></pre>
+</div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="load-the-module"><a class="anchor" href="#load-the-module"></a>3. Load the module</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>Load pg_stat_monitor at the start time by adding it to the shared_preload_libraries configuration parameter. This is because pg_stat_monitor requires additional shared memory.</p>
+</div>
+<div class="paragraph">
+<p>modify the shared_preload_libraries parameter：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-conf hljs" data-lang="conf">shared_preload_libraries = 'pg_stat_monitor';</code></pre>
+</div>
+</div>
+<div class="admonitionblock note">
+<table>
+<tr>
+<td class="icon">
+<i class="fa icon-note" title="Note"></i>
+</td>
+<td class="content">
+If you’ve added other modules to the shared_preload_libraries parameter (for example, pg_stat_statements), list all of them separated by commas for the ALTER SYSTEM command.
+</td>
+</tr>
+</table>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-conf hljs" data-lang="conf">shared_preload_libraries = 'liboracle_parser, ivorysql_ora, pg_stat_statements, pg_stat_monitor';</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>Start or restart the IvorySQL instance to apply the changes.</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">pg_ctl restart -D data</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>After you have added pg_stat_monitor to the shared_preload_libraries, it starts collecting statistics data for all existing databases. To access this data, you need to create the view on every database that you wish to monitor.</p>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="create-the-extension-view"><a class="anchor" href="#create-the-extension-view"></a>4. Create the extension view</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>Create the extension view with the user that has the privileges of a superuser or a database owner. Connect to psql as a superuser for a database and run the CREATE EXTENSION command:</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">CREATE EXTENSION pg_stat_monitor;</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>After the setup is complete, you can see the stats collected by pg_stat_monitor.</p>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="usage"><a class="anchor" href="#usage"></a>5. Usage</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p>To obtain query execution time information as an example, connect to the database and execute the following SQL：</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">SELECT  userid,  total_exec_time, min_exec_time, max_exec_time, mean_exec_time, query FROM pg_stat_monitor;</code></pre>
+</div>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-text hljs" data-lang="text">userid | total_exec_time | min_exec_time | max_exec_time | mean_exec_time |                                            query
+--------+-----------------+---------------+---------------+----------------+----------------------------------------------------------------------------------------------
+     10 |        1.532168 |      0.749108 |       0.78306 |       0.766084 | SELECT userid,  datname, queryid, substr(query,$1, $2) AS query, calls FROM pg_stat_monitor
+     10 |        0.755857 |      0.755857 |      0.755857 |       0.755857 | SELECT application_name, client_ip, substr(query,$1,$2) as query FROM pg_stat_monitor
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_exec_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+(4 rows)</code></pre>
+</div>
+</div>
+<div class="paragraph">
+<p>For more information on using pg_stat_monitor, please refer to pg_stat_monitor <a href="https://docs.percona.com/pg-stat-monitor/user_guide.html"> Official documentation</a></p>
+</div>
+</div>
+</div>
+<nav class="pagination">
+  <span class="prev"><a href="wal2json.html">wal2json</a></span>
+  <span class="next"><a href="../architecture/dual_parser.html">Dual Parser</a></span>
+</nav>
+</article>
+  </div>
+</main>
+</div>
+<footer class="footer">
+
+</footer>
+<script id="site-script" src="../../../_/js/site.js" data-ui-root-path="../../../_"></script>
+<script async src="../../../_/js/vendor/highlight.js"></script>
+<script src="../../../_/js/vendor/lunr.js"></script>
+<script src="../../../_/js/search-ui.js" id="search-ui-script" data-site-root-path="../../.." data-snippet-length="100" data-stylesheet="../../../_/css/search.css"></script>
+<script async src="../../../search-index.js"></script>
+  </body>
+</html>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html
index bf642253..898b65e5 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html
index f08d6699..ceca9516 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html
index e76706a6..655c6731 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html
index aa8d4c88..e1f9f97e 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html
index 1aa0d5b9..723cc5ae 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html
index ecdc0f20..b24dfe14 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html b/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html
index bd5581a0..20f2e6a1 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html b/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html
index 7661d120..5d418f15 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html b/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html
index 828e1fc9..fdf13462 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html b/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html
index 65220866..23f4f93b 100644
--- a/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html
+++ b/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html
@@ -6,7 +6,7 @@
     <title>wal2json :: IvorySQL Document Site</title>
     <link rel="canonical" href="https://docs.ivorysql.org/ivorysql-doc/master/ecosystem_components/wal2json.html">
     <link rel="prev" href="system_stats.html">
-    <link rel="next" href="../architecture/dual_parser.html">
+    <link rel="next" href="pg_stat_monitor.html">
     <meta name="generator" content="Antora 3.1.7">
     <link rel="stylesheet" href="../../../_/css/site.css">
     <script>var uiRootPath = '../../../_'</script>
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item is-current-page" data-depth="3">
     <a class="nav-link" href="wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -578,7 +581,7 @@ <h2 id="use"><a class="anchor" href="#use"></a>3. Use</h2>
 </div>
 <nav class="pagination">
   <span class="prev"><a href="system_stats.html">system_stats</a></span>
-  <span class="next"><a href="../architecture/dual_parser.html">Dual Parser</a></span>
+  <span class="next"><a href="pg_stat_monitor.html">pg_stat_monitor</a></span>
 </nav>
 </article>
   </div>
diff --git a/docs/en/ivorysql-doc/master/gb18030.html b/docs/en/ivorysql-doc/master/gb18030.html
index 7add9944..9267aa5c 100644
--- a/docs/en/ivorysql-doc/master/gb18030.html
+++ b/docs/en/ivorysql-doc/master/gb18030.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html b/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html
index 8b0e3582..87bcf4f7 100644
--- a/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html
+++ b/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html b/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html
index 3179bf6c..b7ad2585 100644
--- a/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html
+++ b/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/getting-started/quick_start.html b/docs/en/ivorysql-doc/master/getting-started/quick_start.html
index b14e0e14..acd36b64 100644
--- a/docs/en/ivorysql-doc/master/getting-started/quick_start.html
+++ b/docs/en/ivorysql-doc/master/getting-started/quick_start.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/installation_guide.html b/docs/en/ivorysql-doc/master/installation_guide.html
index 6f79ac5c..6466a44c 100644
--- a/docs/en/ivorysql-doc/master/installation_guide.html
+++ b/docs/en/ivorysql-doc/master/installation_guide.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/migration_guide.html b/docs/en/ivorysql-doc/master/migration_guide.html
index e8083920..14ef774b 100644
--- a/docs/en/ivorysql-doc/master/migration_guide.html
+++ b/docs/en/ivorysql-doc/master/migration_guide.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/operation_guide.html b/docs/en/ivorysql-doc/master/operation_guide.html
index c33563d4..e781fe0d 100644
--- a/docs/en/ivorysql-doc/master/operation_guide.html
+++ b/docs/en/ivorysql-doc/master/operation_guide.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -671,7 +674,211 @@ <h3 id="upgrade-data-by-copying"><a class="anchor" href="#upgrade-data-by-copyin
 </div>
 </div>
 <div class="sect1">
-<h2 id="managing-ivorysql-versions"><a class="anchor" href="#managing-ivorysql-versions"></a>2. Managing IvorySQL Versions</h2>
+<h2 id="use-pg_upgrade-to-upgrade-postgresql-to-ivorysql"><a class="anchor" href="#use-pg_upgrade-to-upgrade-postgresql-to-ivorysql"></a>2. Use pg_upgrade to upgrade PostgreSQL to IvorySQL</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p><code>pg_upgrade</code> supports upgrading native PostgreSQL clusters to IvorySQL. When the source cluster is native PostgreSQL and the target is an IvorySQL cluster, the <code>-g</code>（<code>--using-ora-pg</code>）parameter must be used.</p>
+</div>
+<div class="paragraph">
+<p>Upgrading from PostgreSQL to IvorySQL is considered a "cross-product" migration. It is recommended to complete comprehensive testing and validation before performing the operation in a production environment, and to develop a detailed rollback plan.</p>
+</div>
+<div class="sect2">
+<h3 id="parameter-description"><a class="anchor" href="#parameter-description"></a>2.1. Parameter description</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 25%;">
+<col style="width: 75%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">parameter</th>
+<th class="tableblock halign-left valign-top">description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code> / <code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Indicates that the source cluster is PostgreSQL and the target is an IvorySQL cluster. When this option is enabled, <code>pg_upgrade</code> uniformly uses the PostgreSQL standard port when connecting to both clusters, ensuring proper handling of the upgrade.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="upgrade-steps"><a class="anchor" href="#upgrade-steps"></a>2.2. Upgrade steps</h3>
+<div class="sect3">
+<h4 id="step-1-initialize-the-new-ivorysql-cluster"><a class="anchor" href="#step-1-initialize-the-new-ivorysql-cluster"></a>2.2.1. Step 1: Initialize the new IvorySQL cluster</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash"># Use IvorySQL's initdb to initialize the new cluster
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-2-stop-the-source-postgresql-cluster"><a class="anchor" href="#step-2-stop-the-source-postgresql-cluster"></a>2.2.2. Step 2: Stop the source PostgreSQL cluster</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-3-run-the-upgrade-check-optional"><a class="anchor" href="#step-3-run-the-upgrade-check-optional"></a>2.2.3. Step 3: Run the upgrade check (optional)</h4>
+<div class="paragraph">
+<p>Use <code>-c</code> to perform compatibility checks only without modifying any data:</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # Source PG executable directory
+  -B /opt/ivorysql/bin \            # Target IvorySQL executable directory
+  -d /data/pg/data \                # Source PG data directory
+  -D /data/ivorysql/data \          # Target IvorySQL data directory
+  -g \                              # Source is PostgreSQL, target is IvorySQL
+  -c                                # Check only, do not upgrade</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-4-perform-the-upgrade"><a class="anchor" href="#step-4-perform-the-upgrade"></a>2.2.4. Step 4: Perform the upgrade</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-5-start-ivorysql-and-verify"><a class="anchor" href="#step-5-start-ivorysql-and-verify"></a>2.2.5. Step 5: Start IvorySQL and verify</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# Verify if the database is functioning properly
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-6-clean-up-the-old-cluster"><a class="anchor" href="#step-6-clean-up-the-old-cluster"></a>2.2.6. Step 6: Clean up the old cluster</h4>
+<div class="paragraph">
+<p>After the upgrade is completed,<code>pg_upgrade</code> will generate a cleanup script:</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">./delete_old_cluster.sh</code></pre>
+</div>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="quick-reference-for-common-parameters"><a class="anchor" href="#quick-reference-for-common-parameters"></a>2.3. Quick reference for common parameters</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 20%;">
+<col style="width: 40%;">
+<col style="width: 40%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">Parameters</th>
+<th class="tableblock halign-left valign-top">Long options</th>
+<th class="tableblock halign-left valign-top">Description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-b</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster executable directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-B</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster executable directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-d</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster data directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-D</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster data directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Upgrade from PostgreSQL to IvorySQL</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-p</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-P</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster PG port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster Oracle port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-Q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster Oracle port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-j</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--jobs</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Number of parallel processes</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-k</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--link</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Use hard links instead of file copying</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-c</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--check</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Check compatibility only, do not perform the upgrade</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-v</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--verbose</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Output detailed logs</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="precautions"><a class="anchor" href="#precautions"></a>2.4. Precautions</h3>
+<div class="ulist">
+<ul>
+<li>
+<p>The <code>-g</code> parameter is only used when the <strong>source is a pure PostgreSQL cluster</strong>、and the target is <strong>IvorySQL</strong> . If the source cluster is already IvorySQL, this parameter is not needed.</p>
+</li>
+<li>
+<p>Both the source and target clusters must be in a stopped state during the upgrade.</p>
+</li>
+<li>
+<p>Be sure to perform a complete backup of the source cluster before upgrading.</p>
+</li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="managing-ivorysql-versions"><a class="anchor" href="#managing-ivorysql-versions"></a>3. Managing IvorySQL Versions</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL is based on PostgreSQL and is updated at the same frequency as PostgreSQL, with one major release per year and one minor release per quarter. IvorySQL 5.0 is based on PostgreSQL 18.0, and all versions of IvorySQL are backward compatible.The relevant version features can be viewed by looking at <a href="https://www.ivorysql.org/en/releases-page/">Official Website</a>。</p>
@@ -679,7 +886,7 @@ <h2 id="managing-ivorysql-versions"><a class="anchor" href="#managing-ivorysql-v
 </div>
 </div>
 <div class="sect1">
-<h2 id="managing-ivorysql-database-access"><a class="anchor" href="#managing-ivorysql-database-access"></a>3. Managing IvorySQL database access</h2>
+<h2 id="managing-ivorysql-database-access"><a class="anchor" href="#managing-ivorysql-database-access"></a>4. Managing IvorySQL database access</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL uses the concept of <strong>Roles</strong>  to manage database access rights.  A role can be thought of as a database user or a group of database users, depending on how the role is set. Roles can own database objects (for example, tables and functions) and can delegate permissions on those objects to other roles to control who can access which objects. In addition, <strong>membership</strong> in one role can be granted to another role, allowing member roles to use the permissions granted to another role.</p>
@@ -747,21 +954,21 @@ <h2 id="managing-ivorysql-database-access"><a class="anchor" href="#managing-ivo
 </div>
 </div>
 <div class="sect1">
-<h2 id="defining-data-objects"><a class="anchor" href="#defining-data-objects"></a>4. Defining Data Objects</h2>
+<h2 id="defining-data-objects"><a class="anchor" href="#defining-data-objects"></a>5. Defining Data Objects</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL is based on PostgreSQL and has a full SQL with defined data objects that can be referred to <a href="https://www.postgresql.org/docs/current/ddl.html">doc</a>.On top of that, IvorySQL has done some Oracle proprietary data object compatibility for Oracle compatibility.</p>
 </div>
 <div class="sect2">
-<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>4.1. VARCHAR2</h3>
+<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>5.1. VARCHAR2</h3>
 <div class="sect3">
-<h4 id="overview"><a class="anchor" href="#overview"></a>4.1.1. Overview</h4>
+<h4 id="overview"><a class="anchor" href="#overview"></a>5.1.1. Overview</h4>
 <div class="paragraph">
 <p>Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="grammar"><a class="anchor" href="#grammar"></a>4.1.2. Grammar</h4>
+<h4 id="grammar"><a class="anchor" href="#grammar"></a>5.1.2. Grammar</h4>
 <div class="literalblock">
 <div class="content">
 <pre>VARCHAR2(size)</pre>
@@ -769,7 +976,7 @@ <h4 id="grammar"><a class="anchor" href="#grammar"></a>4.1.2. Grammar</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="use-cases"><a class="anchor" href="#use-cases"></a>4.1.3. Use Cases</h4>
+<h4 id="use-cases"><a class="anchor" href="#use-cases"></a>5.1.3. Use Cases</h4>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">create table test(a varchar2(5));
@@ -793,7 +1000,7 @@ <h4 id="use-cases"><a class="anchor" href="#use-cases"></a>4.1.3. Use Cases</h4>
 </div>
 </div>
 <div class="sect1">
-<h2 id="search-data"><a class="anchor" href="#search-data"></a>5. Search Data</h2>
+<h2 id="search-data"><a class="anchor" href="#search-data"></a>6. Search Data</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL is developed based on PostgreSQL, with full SQL, query data specific operations can be referred to <a href="https://www.postgresql.org/docs/current/queries.html">doc</a>.</p>
@@ -801,7 +1008,7 @@ <h2 id="search-data"><a class="anchor" href="#search-data"></a>5. Search Data</h
 </div>
 </div>
 <div class="sect1">
-<h2 id="use-of-foreign-data"><a class="anchor" href="#use-of-foreign-data"></a>6. Use of foreign data</h2>
+<h2 id="use-of-foreign-data"><a class="anchor" href="#use-of-foreign-data"></a>7. Use of foreign data</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL implements portions of the SQL/MED specification, allowing you to access data that resides outside IvorySQL using regular SQL queries. Such data is referred to as <strong>foreign data</strong>. (Note that this usage is not to be confused with foreign keys, which are a type of constraint within the database.)</p>
@@ -818,7 +1025,7 @@ <h2 id="use-of-foreign-data"><a class="anchor" href="#use-of-foreign-data"></a>6
 </div>
 </div>
 <div class="sect1">
-<h2 id="backup-and-restore"><a class="anchor" href="#backup-and-restore"></a>7. Backup and Restore</h2>
+<h2 id="backup-and-restore"><a class="anchor" href="#backup-and-restore"></a>8. Backup and Restore</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>As with everything that contains valuable data, IvorySQL databases should be backed up regularly. While the procedure is essentially simple, it is important to have a clear understanding of the underlying techniques and assumptions.</p>
@@ -840,7 +1047,7 @@ <h2 id="backup-and-restore"><a class="anchor" href="#backup-and-restore"></a>7.
 </ul>
 </div>
 <div class="sect2">
-<h3 id="sql-dump"><a class="anchor" href="#sql-dump"></a>7.1. SQL Dump</h3>
+<h3 id="sql-dump"><a class="anchor" href="#sql-dump"></a>8.1. SQL Dump</h3>
 <div class="paragraph">
 <p>The idea behind this dump method is to generate a file with SQL commands that, when fed back to the server, will recreate the database in the same state as it was at the time of the dump. IvorySQL provides the utility program pg_dumpfor this purpose. The basic usage of this command is:</p>
 </div>
@@ -868,7 +1075,7 @@ <h3 id="sql-dump"><a class="anchor" href="#sql-dump"></a>7.1. SQL Dump</h3>
 <p>​    Dumps created by pg_dump are internally consistent, meaning, the dump represents a snapshot of the database at the time pg_dump began running. pg_dump does not block other operations on the database while it is working. (Exceptions are those operations that need to operate with an exclusive lock, such as most forms of <code>ALTER TABLE</code>.)</p>
 </div>
 <div class="sect3">
-<h4 id="restoring-the-dump"><a class="anchor" href="#restoring-the-dump"></a>7.1.1. Restoring the Dump</h4>
+<h4 id="restoring-the-dump"><a class="anchor" href="#restoring-the-dump"></a>8.1.1. Restoring the Dump</h4>
 <div class="paragraph">
 <p>Text files created by pg_dump are intended to be read in by the psql program. The general command form to restore a dump is</p>
 </div>
@@ -910,7 +1117,7 @@ <h4 id="restoring-the-dump"><a class="anchor" href="#restoring-the-dump"></a>7.1
 </div>
 </div>
 <div class="sect3">
-<h4 id="using-pg_dumpall"><a class="anchor" href="#using-pg_dumpall"></a>7.1.2. Using pg_dumpall</h4>
+<h4 id="using-pg_dumpall"><a class="anchor" href="#using-pg_dumpall"></a>8.1.2. Using pg_dumpall</h4>
 <div class="paragraph">
 <p>pg_dump dumps only a single database at a time, and it does not dump information about roles or tablespaces (because those are cluster-wide rather than per-database). To support convenient dumping of the entire contents of a database cluster, the <a href="https://www.postgresql.org/docs/current/app-pg-dumpall.html">pg_dumpall</a> program is provided. pg_dumpall backs up each database in a given cluster, and also preserves cluster-wide data such as role and tablespace definitions. The basic usage of this command is:</p>
 </div>
@@ -938,7 +1145,7 @@ <h4 id="using-pg_dumpall"><a class="anchor" href="#using-pg_dumpall"></a>7.1.2.
 </div>
 </div>
 <div class="sect3">
-<h4 id="handling-large-databases"><a class="anchor" href="#handling-large-databases"></a>7.1.3. Handling Large Databases</h4>
+<h4 id="handling-large-databases"><a class="anchor" href="#handling-large-databases"></a>8.1.3. Handling Large Databases</h4>
 <div class="paragraph">
 <p>Some operating systems have maximum file size limits that cause problems when creating large pg_dump output files. Fortunately, pg_dump can write to the standard output, so you can use standard Unix tools to work around this potential problem. There are several possible methods:</p>
 </div>
@@ -1029,7 +1236,7 @@ <h4 id="handling-large-databases"><a class="anchor" href="#handling-large-databa
 </div>
 </div>
 <div class="sect2">
-<h3 id="file-system-level-backup"><a class="anchor" href="#file-system-level-backup"></a>7.2. File System Level Backup</h3>
+<h3 id="file-system-level-backup"><a class="anchor" href="#file-system-level-backup"></a>8.2. File System Level Backup</h3>
 <div class="literalblock">
 <div class="content">
 <pre>An alternative backup strategy is to directly copy the files that IvorySQL uses to store the data in the database，You can use whatever method you prefer for doing file system backups; for example:</pre>
@@ -1070,7 +1277,7 @@ <h3 id="file-system-level-backup"><a class="anchor" href="#file-system-level-bac
 </div>
 </div>
 <div class="sect2">
-<h3 id="continuous-archiving-and-point-in-time-recovery-pitr"><a class="anchor" href="#continuous-archiving-and-point-in-time-recovery-pitr"></a>7.3. Continuous Archiving and Point-in-Time Recovery (PITR)</h3>
+<h3 id="continuous-archiving-and-point-in-time-recovery-pitr"><a class="anchor" href="#continuous-archiving-and-point-in-time-recovery-pitr"></a>8.3. Continuous Archiving and Point-in-Time Recovery (PITR)</h3>
 <div class="paragraph">
 <p>At all times,IvorySQLmaintains a <strong>write ahead log</strong> (WAL) in the <code>pg_wal/</code> subdirectory of the cluster&#8217;s data directory. The log records every change made to the database&#8217;s data files. This log exists primarily for crash-safety purposes: if the system crashes, the database can be restored to consistency by “replaying” the log entries made since the last checkpoint. However, the existence of the log makes it possible to use a third strategy for backing up databases: we can combine a file-system-level backup with backup of the WAL files. If recovery is needed, we restore the file system backup and then replay from the backed-up WAL files to bring the system to a current state. This approach is more complex to administer than either of the previous approaches, but it has some significant benefits:</p>
 </div>
@@ -1103,7 +1310,7 @@ <h3 id="continuous-archiving-and-point-in-time-recovery-pitr"><a class="anchor"
 </div>
 </div>
 <div class="sect1">
-<h2 id="loading-and-unloading-data"><a class="anchor" href="#loading-and-unloading-data"></a>8. Loading and unloading data</h2>
+<h2 id="loading-and-unloading-data"><a class="anchor" href="#loading-and-unloading-data"></a>9. Loading and unloading data</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p><code>COPY</code> moves data between IvorySQL tables and standard file-system files. <code>COPY TO</code> copies the contents of a table <strong>to</strong> a file, while <code>COPY FROM</code> copies data <strong>from</strong> a file to a table (appending the data to whatever is in the table already). <code>COPY TO</code> can also copy the results of a <code>SELECT</code> query.</p>
@@ -1118,7 +1325,7 @@ <h2 id="loading-and-unloading-data"><a class="anchor" href="#loading-and-unloadi
 <p>​    Each backend running <code>COPY</code> will report its progress in the <code>pg_stat_progress_copy</code> view.</p>
 </div>
 <div class="sect2">
-<h3 id="synopsis"><a class="anchor" href="#synopsis"></a>8.1. Synopsis</h3>
+<h3 id="synopsis"><a class="anchor" href="#synopsis"></a>9.1. Synopsis</h3>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">COPY table_name [ ( column_name [, ...] ) ]
@@ -1150,7 +1357,7 @@ <h3 id="synopsis"><a class="anchor" href="#synopsis"></a>8.1. Synopsis</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="outputs"><a class="anchor" href="#outputs"></a>8.2. Outputs</h3>
+<h3 id="outputs"><a class="anchor" href="#outputs"></a>9.2. Outputs</h3>
 <div class="paragraph">
 <p>On successful completion, a <code>COPY</code> command returns a command tag of the form</p>
 </div>
@@ -1167,7 +1374,7 @@ <h3 id="outputs"><a class="anchor" href="#outputs"></a>8.2. Outputs</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="notes"><a class="anchor" href="#notes"></a>8.3. Notes</h3>
+<h3 id="notes"><a class="anchor" href="#notes"></a>9.3. Notes</h3>
 <div class="paragraph">
 <p><code>COPY TO</code> can be used only with plain tables, not views, and does not copy rows from child tables or child partitions. For example, <code>COPY <strong>`table</code></strong> TO` copies the same rows as <code>SELECT * FROM ONLY <strong>`table</code></strong><code>. The syntax `COPY (SELECT * FROM <strong>`table</code></strong>) TO &#8230;&#8203;` can be used to dump all of the rows in an inheritance hierarchy, partitioned table, or view.</p>
 </div>
@@ -1212,9 +1419,9 @@ <h3 id="notes"><a class="anchor" href="#notes"></a>8.3. Notes</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="file-formats"><a class="anchor" href="#file-formats"></a>8.4. File Formats</h3>
+<h3 id="file-formats"><a class="anchor" href="#file-formats"></a>9.4. File Formats</h3>
 <div class="sect3">
-<h4 id="text-format"><a class="anchor" href="#text-format"></a>8.4.1. Text Format</h4>
+<h4 id="text-format"><a class="anchor" href="#text-format"></a>9.4.1. Text Format</h4>
 <div class="paragraph">
 <p>When the <code>text</code> format is used, the data read or written is a text file with one line per table row. Columns in a row are separated by the delimiter character. The column values themselves are strings generated by the output function, or acceptable to the input function, of each attribute&#8217;s data type. The specified null string is used in place of columns that are null. <code>COPY FROM</code> will raise an error if any line of the input file contains more or fewer columns than are expected.</p>
 </div>
@@ -1291,7 +1498,7 @@ <h4 id="text-format"><a class="anchor" href="#text-format"></a>8.4.1. Text Forma
 </div>
 </div>
 <div class="sect3">
-<h4 id="csv-format"><a class="anchor" href="#csv-format"></a>8.4.2. CSV Format</h4>
+<h4 id="csv-format"><a class="anchor" href="#csv-format"></a>9.4.2. CSV Format</h4>
 <div class="paragraph">
 <p>This format option is used for importing and exporting the Comma Separated Value (<code>CSV</code>) file format used by many other programs, such as spreadsheets. Instead of the escaping rules used by IvorySQL&#8217;s standard text format, it produces and recognizes the common CSV escaping mechanism.</p>
 </div>
@@ -1330,7 +1537,7 @@ <h4 id="csv-format"><a class="anchor" href="#csv-format"></a>8.4.2. CSV Format</
 </div>
 </div>
 <div class="sect3">
-<h4 id="binary-format"><a class="anchor" href="#binary-format"></a>8.4.3. Binary Format</h4>
+<h4 id="binary-format"><a class="anchor" href="#binary-format"></a>9.4.3. Binary Format</h4>
 <div class="paragraph">
 <p>The <code>binary</code> format option causes all data to be stored/read as binary format rather than as text. It is somewhat faster than the text and <code>CSV</code> formats, but a binary-format file is less portable across machine architectures and IvorySQL versions. Also, the binary format is very data type specific; for example it will not work to output binary data from a <code>smallint</code> column and read it into an <code>integer</code> column, even though that would work fine in text format.</p>
 </div>
@@ -1396,7 +1603,7 @@ <h4 id="binary-format"><a class="anchor" href="#binary-format"></a>8.4.3. Binary
 </div>
 </div>
 <div class="sect3">
-<h4 id="file-trailer"><a class="anchor" href="#file-trailer"></a>8.4.4. File Trailer</h4>
+<h4 id="file-trailer"><a class="anchor" href="#file-trailer"></a>9.4.4. File Trailer</h4>
 <div class="paragraph">
 <p>The file trailer consists of a 16-bit integer word containing -1. This is easily distinguished from a tuple&#8217;s field-count word.</p>
 </div>
@@ -1406,7 +1613,7 @@ <h4 id="file-trailer"><a class="anchor" href="#file-trailer"></a>8.4.4. File Tra
 </div>
 </div>
 <div class="sect2">
-<h3 id="examples"><a class="anchor" href="#examples"></a>8.5. Examples</h3>
+<h3 id="examples"><a class="anchor" href="#examples"></a>9.5. Examples</h3>
 <div class="paragraph">
 <p>The following example copies a table to the client using the vertical bar (<code>|</code>) as the field delimiter:</p>
 </div>
@@ -1483,13 +1690,13 @@ <h3 id="examples"><a class="anchor" href="#examples"></a>8.5. Examples</h3>
 </div>
 </div>
 <div class="sect1">
-<h2 id="performance-tips"><a class="anchor" href="#performance-tips"></a>9. Performance Tips</h2>
+<h2 id="performance-tips"><a class="anchor" href="#performance-tips"></a>10. Performance Tips</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>Query performance can be affected by a variety of factors. Some of these factors can be controlled by the user, while others are fundamentals of the system&#8217;s lower-level design.</p>
 </div>
 <div class="sect2">
-<h3 id="using-explain"><a class="anchor" href="#using-explain"></a>9.1. Using EXPLAIN</h3>
+<h3 id="using-explain"><a class="anchor" href="#using-explain"></a>10.1. Using EXPLAIN</h3>
 <div class="paragraph">
 <p>IvorySQL devises a <strong>query plan</strong> for each query it receives. Choosing the right plan to match the query structure and the properties of the data is absolutely critical for good performance, so the system includes a complex <strong>planner</strong> that tries to choose good plans. You can use the <a href="https://www.postgresql.org/docs/17/sql-explain.html"><code>EXPLAIN</code></a> command to see what query plan the planner creates for any query. Plan-reading is an art that requires some experience to master, but this section attempts to cover the basics.</p>
 </div>
@@ -1497,7 +1704,7 @@ <h3 id="using-explain"><a class="anchor" href="#using-explain"></a>9.1. Using EX
 <p>​    The examples use `EXPLAIN&#8217;s default “text” output format, which is compact and convenient for humans to read. If you want to feed `EXPLAIN&#8217;s output to a program for further analysis, you should use one of its machine-readable output formats (XML, JSON, or YAML) instead.</p>
 </div>
 <div class="sect3">
-<h4 id="explain-basics"><a class="anchor" href="#explain-basics"></a>9.1.1. EXPLAIN Basics</h4>
+<h4 id="explain-basics"><a class="anchor" href="#explain-basics"></a>10.1.1. EXPLAIN Basics</h4>
 <div class="paragraph">
 <p>The structure of a query plan is a tree of <strong>plan nodes</strong>. Nodes at the bottom level of the tree are scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. There are also non-table row sources, such as <code>VALUES</code> clauses and set-returning functions in <code>FROM</code>, which have their own scan node types. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes above the scan nodes to perform these operations. Again, there is usually more than one possible way to do these operations, so different node types can appear here too. The output of <code>EXPLAIN</code> has one line for each node in the plan tree, showing the basic node type plus the cost estimates that the planner made for the execution of that plan node. Additional lines might appear, indented from the node&#8217;s summary line, to show additional properties of the node. The very first line (the summary line for the topmost node) has the estimated total execution cost for the plan; it is this number that the planner seeks to minimize.</p>
 </div>
@@ -1834,7 +2041,7 @@ <h4 id="explain-basics"><a class="anchor" href="#explain-basics"></a>9.1.1. EXPL
 </div>
 </div>
 <div class="sect3">
-<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EXPLAIN ANALYZE</h4>
+<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>10.1.2. EXPLAIN ANALYZE</h4>
 <div class="paragraph">
 <p>It is possible to check the accuracy of the planner&#8217;s estimates by using <code>EXPLAIN&#8217;s `ANALYZE</code> option. With this option, <code>EXPLAIN</code> actually executes the query, and then displays the true row counts and true run time accumulated within each plan node, along with the same estimates that a plain <code>EXPLAIN</code> shows. For example, we might get a result like this:</p>
 </div>
@@ -2044,7 +2251,7 @@ <h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EX
 </div>
 </div>
 <div class="sect3">
-<h4 id="caveats"><a class="anchor" href="#caveats"></a>9.1.3. Caveats</h4>
+<h4 id="caveats"><a class="anchor" href="#caveats"></a>10.1.3. Caveats</h4>
 <div class="paragraph">
 <p>There are two significant ways in which run times measured by <code>EXPLAIN ANALYZE</code> can deviate from normal execution of the same query. First, since no output rows are delivered to the client, network transmission costs and I/O conversion costs are not included. Second, the measurement overhead added by <code>EXPLAIN ANALYZE</code> can be significant, especially on machines with slow <code>gettimeofday()</code> operating-system calls. You can use the <a href="https://www.postgresql.org/docs/17/pgtesttiming.html">pg_test_timing</a> tool to measure the overhead of timing on your system.</p>
 </div>
@@ -2084,9 +2291,9 @@ <h4 id="caveats"><a class="anchor" href="#caveats"></a>9.1.3. Caveats</h4>
 </div>
 </div>
 <div class="sect2">
-<h3 id="statistics-used-by-the-planner"><a class="anchor" href="#statistics-used-by-the-planner"></a>9.2. Statistics Used by the Planner</h3>
+<h3 id="statistics-used-by-the-planner"><a class="anchor" href="#statistics-used-by-the-planner"></a>10.2. Statistics Used by the Planner</h3>
 <div class="sect3">
-<h4 id="single-column-statistics"><a class="anchor" href="#single-column-statistics"></a>9.2.1. Single-Column Statistics</h4>
+<h4 id="single-column-statistics"><a class="anchor" href="#single-column-statistics"></a>10.2.1. Single-Column Statistics</h4>
 <div class="paragraph">
 <p>As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates.</p>
 </div>
@@ -2154,7 +2361,7 @@ <h4 id="single-column-statistics"><a class="anchor" href="#single-column-statist
 </div>
 </div>
 <div class="sect3">
-<h4 id="extended-statistics"><a class="anchor" href="#extended-statistics"></a>9.2.2. Extended Statistics</h4>
+<h4 id="extended-statistics"><a class="anchor" href="#extended-statistics"></a>10.2.2. Extended Statistics</h4>
 <div class="paragraph">
 <p>It is common to see slow queries running bad execution plans because multiple columns used in the query clauses are correlated. The planner normally assumes that multiple conditions are independent of each other, an assumption that does not hold when column values are correlated. Regular statistics, because of their per-individual-column nature, cannot capture any knowledge about cross-column correlation. However, IvorySQL has the ability to compute <strong>multivariate statistics</strong>, which can capture such information.</p>
 </div>
@@ -2171,7 +2378,7 @@ <h4 id="extended-statistics"><a class="anchor" href="#extended-statistics"></a>9
 <p>The following subsections describe the kinds of extended statistics that are currently supported.</p>
 </div>
 <div class="sect4">
-<h5 id="functional-dependencies"><a class="anchor" href="#functional-dependencies"></a>9.2.2.1. Functional Dependencies</h5>
+<h5 id="functional-dependencies"><a class="anchor" href="#functional-dependencies"></a>10.2.2.1. Functional Dependencies</h5>
 <div class="paragraph">
 <p>The simplest kind of extended statistics tracks <strong>functional dependencies</strong>, a concept used in definitions of database normal forms. We say that column <code>b</code> is functionally dependent on column <code>a</code> if knowledge of the value of <code>a</code> is sufficient to determine the value of <code>b</code>, that is there are no two rows having the same value of <code>a</code> but different values of <code>b</code>. In a fully normalized database, functional dependencies should exist only on primary keys and superkeys. However, in practice many data sets are not fully normalized for various reasons; intentional denormalization for performance reasons is a common example. Even in a fully normalized database, there may be partial correlation between some columns, which can be expressed as partial functional dependency.</p>
 </div>
@@ -2206,7 +2413,7 @@ <h5 id="functional-dependencies"><a class="anchor" href="#functional-dependencie
 <p>When computing the selectivity for a query involving functionally dependent columns, the planner adjusts the per-condition selectivity estimates using the dependency coefficients so as not to produce an underestimate.</p>
 </div>
 <div class="sect5">
-<h6 id="limitations-of-functional-dependencies"><a class="anchor" href="#limitations-of-functional-dependencies"></a>9.2.2.1.1. Limitations of Functional Dependencies</h6>
+<h6 id="limitations-of-functional-dependencies"><a class="anchor" href="#limitations-of-functional-dependencies"></a>10.2.2.1.1. Limitations of Functional Dependencies</h6>
 <div class="paragraph">
 <p>Functional dependencies are currently only applied when considering simple equality conditions that compare columns to constant values, and <code>IN</code> clauses with constant values. They are not used to improve estimates for equality conditions comparing two columns or comparing a column to an expression, nor for range clauses, <code>LIKE</code> or any other type of condition.</p>
 </div>
@@ -2235,7 +2442,7 @@ <h6 id="limitations-of-functional-dependencies"><a class="anchor" href="#limitat
 </div>
 </div>
 <div class="sect4">
-<h5 id="multivariate-n-distinct-counts"><a class="anchor" href="#multivariate-n-distinct-counts"></a>9.2.2.2. Multivariate N-Distinct Counts</h5>
+<h5 id="multivariate-n-distinct-counts"><a class="anchor" href="#multivariate-n-distinct-counts"></a>10.2.2.2. Multivariate N-Distinct Counts</h5>
 <div class="paragraph">
 <p>Single-column statistics store the number of distinct values in each column. Estimates of the number of distinct values when combining more than one column (for example, for <code>GROUP BY a, b</code>) are frequently wrong when the planner only has single-column statistical data, causing it to select bad plans.</p>
 </div>
@@ -2268,7 +2475,7 @@ <h5 id="multivariate-n-distinct-counts"><a class="anchor" href="#multivariate-n-
 </div>
 </div>
 <div class="sect4">
-<h5 id="multivariate-mcv-lists"><a class="anchor" href="#multivariate-mcv-lists"></a>9.2.2.3. Multivariate MCV Lists</h5>
+<h5 id="multivariate-mcv-lists"><a class="anchor" href="#multivariate-mcv-lists"></a>10.2.2.3. Multivariate MCV Lists</h5>
 <div class="paragraph">
 <p>Another type of statistic stored for each column are most-common value lists. This allows very accurate estimates for individual columns, but may result in significant misestimates for queries with conditions on multiple columns.</p>
 </div>
@@ -2313,7 +2520,7 @@ <h5 id="multivariate-mcv-lists"><a class="anchor" href="#multivariate-mcv-lists"
 </div>
 </div>
 <div class="sect2">
-<h3 id="controlling-the-planner-with-explicit-join-clauses"><a class="anchor" href="#controlling-the-planner-with-explicit-join-clauses"></a>9.3. Controlling the Planner with Explicit <code>JOIN</code> Clauses</h3>
+<h3 id="controlling-the-planner-with-explicit-join-clauses"><a class="anchor" href="#controlling-the-planner-with-explicit-join-clauses"></a>10.3. Controlling the Planner with Explicit <code>JOIN</code> Clauses</h3>
 <div class="paragraph">
 <p>It is possible to control the query planner to some extent by using the explicit <code>JOIN</code> syntax. To see why this matters, we first need some background.</p>
 </div>
@@ -2410,18 +2617,18 @@ <h3 id="controlling-the-planner-with-explicit-join-clauses"><a class="anchor" hr
 </div>
 </div>
 <div class="sect2">
-<h3 id="populating-a-database"><a class="anchor" href="#populating-a-database"></a>9.4. Populating a Database</h3>
+<h3 id="populating-a-database"><a class="anchor" href="#populating-a-database"></a>10.4. Populating a Database</h3>
 <div class="paragraph">
 <p>One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible.</p>
 </div>
 <div class="sect3">
-<h4 id="disable-autocommit"><a class="anchor" href="#disable-autocommit"></a>9.4.1. Disable Autocommit</h4>
+<h4 id="disable-autocommit"><a class="anchor" href="#disable-autocommit"></a>10.4.1. Disable Autocommit</h4>
 <div class="paragraph">
 <p>When using multiple <code>INSERT`s, turn off autocommit and just do one commit at the end. (In plain SQL, this means issuing `BEGIN</code> at the start and <code>COMMIT</code> at the end. Some client libraries might do this behind your back, in which case you need to make sure the library does it when you want it done.) If you allow each insertion to be committed separately, IvorySQL is doing a lot of work for each row that is added. An additional benefit of doing all insertions in one transaction is that if the insertion of one row were to fail then the insertion of all rows inserted up to that point would be rolled back, so you won&#8217;t be stuck with partially loaded data.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="use-copy"><a class="anchor" href="#use-copy"></a>9.4.2. Use COPY</h4>
+<h4 id="use-copy"><a class="anchor" href="#use-copy"></a>10.4.2. Use COPY</h4>
 <div class="paragraph">
 <p>Use <a href="https://www.postgresql.org/docs/17/sql-copy.html"><code>COPY</code></a> to load all the rows in one command, instead of using a series of <code>INSERT</code> commands. The <code>COPY</code> command is optimized for loading large numbers of rows; it is less flexible than <code>INSERT</code>, but incurs significantly less overhead for large data loads. Since <code>COPY</code> is a single command, there is no need to disable autocommit if you use this method to populate a table.</p>
 </div>
@@ -2436,7 +2643,7 @@ <h4 id="use-copy"><a class="anchor" href="#use-copy"></a>9.4.2. Use COPY</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="remove-indexes"><a class="anchor" href="#remove-indexes"></a>9.4.3. Remove Indexes</h4>
+<h4 id="remove-indexes"><a class="anchor" href="#remove-indexes"></a>10.4.3. Remove Indexes</h4>
 <div class="paragraph">
 <p>If you are loading a freshly created table, the fastest method is to create the table, bulk load the table&#8217;s data using <code>COPY</code>, then create any indexes needed for the table. Creating an index on pre-existing data is quicker than updating it incrementally as each row is loaded.</p>
 </div>
@@ -2445,7 +2652,7 @@ <h4 id="remove-indexes"><a class="anchor" href="#remove-indexes"></a>9.4.3. Remo
 </div>
 </div>
 <div class="sect3">
-<h4 id="remove-foreign-key-constraints"><a class="anchor" href="#remove-foreign-key-constraints"></a>9.4.4. Remove Foreign Key Constraints</h4>
+<h4 id="remove-foreign-key-constraints"><a class="anchor" href="#remove-foreign-key-constraints"></a>10.4.4. Remove Foreign Key Constraints</h4>
 <div class="paragraph">
 <p>Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing.</p>
 </div>
@@ -2454,19 +2661,19 @@ <h4 id="remove-foreign-key-constraints"><a class="anchor" href="#remove-foreign-
 </div>
 </div>
 <div class="sect3">
-<h4 id="increase-maintenance_work_mem"><a class="anchor" href="#increase-maintenance_work_mem"></a>9.4.5. Increase maintenance_work_mem</h4>
+<h4 id="increase-maintenance_work_mem"><a class="anchor" href="#increase-maintenance_work_mem"></a>10.4.5. Increase maintenance_work_mem</h4>
 <div class="paragraph">
 <p>Temporarily increasing the <a href="https://www.postgresql.org/docs/17/runtime-config-resource.html#GUC-MAINTENANCE-WORK-MEM">maintenance_work_mem</a> configuration variable when loading large amounts of data can lead to improved performance. This will help to speed up <code>CREATE INDEX</code> commands and <code>ALTER TABLE ADD FOREIGN KEY</code> commands. It won&#8217;t do much for <code>COPY</code> itself, so this advice is only useful when you are using one or both of the above techniques.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="increase-max_wal_size"><a class="anchor" href="#increase-max_wal_size"></a>9.4.6. Increase max_wal_size</h4>
+<h4 id="increase-max_wal_size"><a class="anchor" href="#increase-max_wal_size"></a>10.4.6. Increase max_wal_size</h4>
 <div class="paragraph">
 <p>Temporarily increasing the <a href="https://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-MAX-WAL-SIZE">max_wal_size</a> configuration variable can also make large data loads faster. This is because loading a large amount of data into IvorySQL will cause checkpoints to occur more often than the normal checkpoint frequency (specified by the <code>checkpoint_timeout</code> configuration variable). Whenever a checkpoint occurs, all dirty pages must be flushed to disk. By increasing <code>max_wal_size</code> temporarily during bulk data loads, the number of checkpoints that are required can be reduced.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="disable-wal-archival-and-streaming-replication"><a class="anchor" href="#disable-wal-archival-and-streaming-replication"></a>9.4.7. Disable WAL Archival and Streaming Replication</h4>
+<h4 id="disable-wal-archival-and-streaming-replication"><a class="anchor" href="#disable-wal-archival-and-streaming-replication"></a>10.4.7. Disable WAL Archival and Streaming Replication</h4>
 <div class="paragraph">
 <p>When loading large amounts of data into an installation that uses WAL archiving or streaming replication, it might be faster to take a new base backup after the load has completed than to process a large amount of incremental WAL data. To prevent incremental WAL logging while loading, disable archiving and streaming replication, by setting <a href="https://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-WAL-LEVEL">wal_level</a> to <code>minimal</code>, <a href="https://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-ARCHIVE-MODE">archive_mode</a> to <code>off</code>, and <a href="https://www.postgresql.org/docs/17/runtime-config-replication.html#GUC-MAX-WAL-SENDERS">max_wal_senders</a> to zero. But note that changing these settings requires a server restart, and makes any base backups taken before unavailable for archive recovery and standby server, which may lead to data loss.</p>
 </div>
@@ -2475,13 +2682,13 @@ <h4 id="disable-wal-archival-and-streaming-replication"><a class="anchor" href="
 </div>
 </div>
 <div class="sect3">
-<h4 id="run-analyze-afterwards"><a class="anchor" href="#run-analyze-afterwards"></a>9.4.8. Run ANALYZE Afterwards</h4>
+<h4 id="run-analyze-afterwards"><a class="anchor" href="#run-analyze-afterwards"></a>10.4.8. Run ANALYZE Afterwards</h4>
 <div class="paragraph">
 <p>Whenever you have significantly altered the distribution of data within a table, running <a href="https://www.postgresql.org/docs/17/sql-analyze.html"><code>ANALYZE</code></a> is strongly recommended. This includes bulk loading large amounts of data into the table. Running <code>ANALYZE</code> (or <code>VACUUM ANALYZE</code>) ensures that the planner has up-to-date statistics about the table. With no statistics or obsolete statistics, the planner might make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics. Note that if the autovacuum daemon is enabled, it might run <code>ANALYZE</code> automatically.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="some-notes-about-pg_dump"><a class="anchor" href="#some-notes-about-pg_dump"></a>9.4.9. Some Notes about pg_dump</h4>
+<h4 id="some-notes-about-pg_dump"><a class="anchor" href="#some-notes-about-pg_dump"></a>10.4.9. Some Notes about pg_dump</h4>
 <div class="paragraph">
 <p>Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To restore a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while <strong>restoring</strong> a dump, not while <strong>creating</strong> it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.)</p>
 </div>
@@ -2513,7 +2720,7 @@ <h4 id="some-notes-about-pg_dump"><a class="anchor" href="#some-notes-about-pg_d
 </div>
 </div>
 <div class="sect2">
-<h3 id="non-durable-settings"><a class="anchor" href="#non-durable-settings"></a>9.5. Non-Durable Settings</h3>
+<h3 id="non-durable-settings"><a class="anchor" href="#non-durable-settings"></a>10.5. Non-Durable Settings</h3>
 <div class="paragraph">
 <p>Durability is a database feature that guarantees the recording of committed transactions even if the server crashes or loses power. However, durability adds significant database overhead, so if your site does not require such a guarantee, IvorySQL can be configured to run much faster. The following are configuration changes you can make to improve performance in such cases. Except as noted below, durability is still guaranteed in case of a crash of the database software; only an abrupt operating system crash creates a risk of data loss or corruption when these settings are used.</p>
 </div>
diff --git a/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html b/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html
index a86caa32..74f14129 100644
--- a/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html
+++ b/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html b/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html
index 413d7f85..e61799f2 100644
--- a/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html
+++ b/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html b/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html
index d6c7d61f..f9ea4bd7 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html b/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html
index 04ed4b06..fe80cadd 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html
index 888bfec8..17f84e19 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
@@ -546,7 +549,15 @@ <h3 id="call-with-no-parameters-or-default-parameters"><a class="anchor" href="#
 <h3 id="reference-bind-variables-in-parameters-or-into-clauses"><a class="anchor" href="#reference-bind-variables-in-parameters-or-into-clauses"></a>3.4. Reference bind variables in parameters or INTO clauses</h3>
 <div class="listingblock">
 <div class="content">
-<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- Set input bind variables
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- Create functions stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- Set input bind variables
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- Call a function using bind variables as parameters
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
index ba4ba6c9..f38d4bc6 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
index 99d6be0a..e25b68e9 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
index 734d5db8..db5791ed 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
index ec01add9..9baeba4a 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
index 751c7d9c..c68df1ec 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
index 0db00e4c..ca46a1e6 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
index 4b8f9bfb..2a4ef517 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
index c73c9e05..e2b6042c 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
index 399a2fa4..58b6fb4a 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
index 4711d472..e30afd50 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
index 30bf2a18..caa0ac8f 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html b/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html
index 924f0d9a..17a971af 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/package.html b/docs/en/ivorysql-doc/master/oracle_compatibility/package.html
index 0b1edf33..bbcbc915 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/package.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/package.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html b/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html
index 60ea33ec..fa1d1a9b 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html b/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html
index eb3e2e9b..a7089239 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html b/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html
index 92e135c6..ce20d368 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="../welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="../ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="../ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/release_notes.html b/docs/en/ivorysql-doc/master/release_notes.html
index c03b7f94..469e1d57 100644
--- a/docs/en/ivorysql-doc/master/release_notes.html
+++ b/docs/en/ivorysql-doc/master/release_notes.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/tools_reference.html b/docs/en/ivorysql-doc/master/tools_reference.html
index f6cce37d..371ebde6 100644
--- a/docs/en/ivorysql-doc/master/tools_reference.html
+++ b/docs/en/ivorysql-doc/master/tools_reference.html
@@ -172,6 +172,9 @@ <h3 class="title"><a href="welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/master/welcome.html b/docs/en/ivorysql-doc/master/welcome.html
index 2914bfdf..e94cf2e2 100644
--- a/docs/en/ivorysql-doc/master/welcome.html
+++ b/docs/en/ivorysql-doc/master/welcome.html
@@ -171,6 +171,9 @@ <h3 class="title"><a href="welcome.html">IvorySQL</a></h3>
   <li class="nav-item" data-depth="3">
     <a class="nav-link" href="ecosystem_components/wal2json.html">wal2json</a>
   </li>
+  <li class="nav-item" data-depth="3">
+    <a class="nav-link" href="ecosystem_components/pg_stat_monitor.html">pg_stat_monitor</a>
+  </li>
 </ul>
   </li>
 </ul>
diff --git a/docs/en/ivorysql-doc/v5.3/4.4.html b/docs/en/ivorysql-doc/v5.3/4.4.html
index 83f83191..0599b2a4 100644
--- a/docs/en/ivorysql-doc/v5.3/4.4.html
+++ b/docs/en/ivorysql-doc/v5.3/4.4.html
@@ -698,7 +698,211 @@ <h3 id="upgrade-data-by-copying"><a class="anchor" href="#upgrade-data-by-copyin
 </div>
 </div>
 <div class="sect1">
-<h2 id="managing-ivorysql-versions"><a class="anchor" href="#managing-ivorysql-versions"></a>2. Managing IvorySQL Versions</h2>
+<h2 id="use-pg_upgrade-to-upgrade-postgresql-to-ivorysql"><a class="anchor" href="#use-pg_upgrade-to-upgrade-postgresql-to-ivorysql"></a>2. Use pg_upgrade to upgrade PostgreSQL to IvorySQL</h2>
+<div class="sectionbody">
+<div class="paragraph">
+<p><code>pg_upgrade</code> supports upgrading native PostgreSQL clusters to IvorySQL. When the source cluster is native PostgreSQL and the target is an IvorySQL cluster, the <code>-g</code>（<code>--using-ora-pg</code>）parameter must be used.</p>
+</div>
+<div class="paragraph">
+<p>Upgrading from PostgreSQL to IvorySQL is considered a "cross-product" migration. It is recommended to complete comprehensive testing and validation before performing the operation in a production environment, and to develop a detailed rollback plan.</p>
+</div>
+<div class="sect2">
+<h3 id="parameter-description"><a class="anchor" href="#parameter-description"></a>2.1. Parameter description</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 25%;">
+<col style="width: 75%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">parameter</th>
+<th class="tableblock halign-left valign-top">description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code> / <code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Indicates that the source cluster is PostgreSQL and the target is an IvorySQL cluster. When this option is enabled, <code>pg_upgrade</code> uniformly uses the PostgreSQL standard port when connecting to both clusters, ensuring proper handling of the upgrade.</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="upgrade-steps"><a class="anchor" href="#upgrade-steps"></a>2.2. Upgrade steps</h3>
+<div class="sect3">
+<h4 id="step-1-initialize-the-new-ivorysql-cluster"><a class="anchor" href="#step-1-initialize-the-new-ivorysql-cluster"></a>2.2.1. Step 1: Initialize the new IvorySQL cluster</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash"># Use IvorySQL's initdb to initialize the new cluster
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-2-stop-the-source-postgresql-cluster"><a class="anchor" href="#step-2-stop-the-source-postgresql-cluster"></a>2.2.2. Step 2: Stop the source PostgreSQL cluster</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-3-run-the-upgrade-check-optional"><a class="anchor" href="#step-3-run-the-upgrade-check-optional"></a>2.2.3. Step 3: Run the upgrade check (optional)</h4>
+<div class="paragraph">
+<p>Use <code>-c</code> to perform compatibility checks only without modifying any data:</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # Source PG executable directory
+  -B /opt/ivorysql/bin \            # Target IvorySQL executable directory
+  -d /data/pg/data \                # Source PG data directory
+  -D /data/ivorysql/data \          # Target IvorySQL data directory
+  -g \                              # Source is PostgreSQL, target is IvorySQL
+  -c                                # Check only, do not upgrade</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-4-perform-the-upgrade"><a class="anchor" href="#step-4-perform-the-upgrade"></a>2.2.4. Step 4: Perform the upgrade</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-5-start-ivorysql-and-verify"><a class="anchor" href="#step-5-start-ivorysql-and-verify"></a>2.2.5. Step 5: Start IvorySQL and verify</h4>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">/opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# Verify if the database is functioning properly
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"</code></pre>
+</div>
+</div>
+</div>
+<div class="sect3">
+<h4 id="step-6-clean-up-the-old-cluster"><a class="anchor" href="#step-6-clean-up-the-old-cluster"></a>2.2.6. Step 6: Clean up the old cluster</h4>
+<div class="paragraph">
+<p>After the upgrade is completed,<code>pg_upgrade</code> will generate a cleanup script:</p>
+</div>
+<div class="listingblock">
+<div class="content">
+<pre class="highlightjs highlight"><code class="language-bash hljs" data-lang="bash">./delete_old_cluster.sh</code></pre>
+</div>
+</div>
+</div>
+</div>
+<div class="sect2">
+<h3 id="quick-reference-for-common-parameters"><a class="anchor" href="#quick-reference-for-common-parameters"></a>2.3. Quick reference for common parameters</h3>
+<table class="tableblock frame-all grid-all stretch">
+<colgroup>
+<col style="width: 20%;">
+<col style="width: 40%;">
+<col style="width: 40%;">
+</colgroup>
+<thead>
+<tr>
+<th class="tableblock halign-left valign-top">Parameters</th>
+<th class="tableblock halign-left valign-top">Long options</th>
+<th class="tableblock halign-left valign-top">Description</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-b</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster executable directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-B</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-bindir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster executable directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-d</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster data directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-D</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-datadir</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster data directory</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-g</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--using-ora-pg</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Upgrade from PostgreSQL to IvorySQL</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-p</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-P</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-port</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster PG port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--old-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Source cluster Oracle port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-Q</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--new-oraport</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Target cluster Oracle port</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-j</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--jobs</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Number of parallel processes</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-k</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--link</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Use hard links instead of file copying</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-c</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--check</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Check compatibility only, do not perform the upgrade</p></td>
+</tr>
+<tr>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>-v</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock"><code>--verbose</code></p></td>
+<td class="tableblock halign-left valign-top"><p class="tableblock">Output detailed logs</p></td>
+</tr>
+</tbody>
+</table>
+</div>
+<div class="sect2">
+<h3 id="precautions"><a class="anchor" href="#precautions"></a>2.4. Precautions</h3>
+<div class="ulist">
+<ul>
+<li>
+<p>The <code>-g</code> parameter is only used when the <strong>source is a pure PostgreSQL cluster</strong>、and the target is <strong>IvorySQL</strong> . If the source cluster is already IvorySQL, this parameter is not needed.</p>
+</li>
+<li>
+<p>Both the source and target clusters must be in a stopped state during the upgrade.</p>
+</li>
+<li>
+<p>Be sure to perform a complete backup of the source cluster before upgrading.</p>
+</li>
+</ul>
+</div>
+</div>
+</div>
+</div>
+<div class="sect1">
+<h2 id="managing-ivorysql-versions"><a class="anchor" href="#managing-ivorysql-versions"></a>3. Managing IvorySQL Versions</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL is based on PostgreSQL and is updated at the same frequency as PostgreSQL, with one major release per year and one minor release per quarter. IvorySQL 5.3 is based on PostgreSQL 18.3, and all versions of IvorySQL are backward compatible.The relevant version features can be viewed by looking at <a href="https://www.ivorysql.org/en/releases-page/">Official Website</a>。</p>
@@ -706,7 +910,7 @@ <h2 id="managing-ivorysql-versions"><a class="anchor" href="#managing-ivorysql-v
 </div>
 </div>
 <div class="sect1">
-<h2 id="managing-ivorysql-database-access"><a class="anchor" href="#managing-ivorysql-database-access"></a>3. Managing IvorySQL database access</h2>
+<h2 id="managing-ivorysql-database-access"><a class="anchor" href="#managing-ivorysql-database-access"></a>4. Managing IvorySQL database access</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL uses the concept of <strong>Roles</strong>  to manage database access rights.  A role can be thought of as a database user or a group of database users, depending on how the role is set. Roles can own database objects (for example, tables and functions) and can delegate permissions on those objects to other roles to control who can access which objects. In addition, <strong>membership</strong> in one role can be granted to another role, allowing member roles to use the permissions granted to another role.</p>
@@ -774,21 +978,21 @@ <h2 id="managing-ivorysql-database-access"><a class="anchor" href="#managing-ivo
 </div>
 </div>
 <div class="sect1">
-<h2 id="defining-data-objects"><a class="anchor" href="#defining-data-objects"></a>4. Defining Data Objects</h2>
+<h2 id="defining-data-objects"><a class="anchor" href="#defining-data-objects"></a>5. Defining Data Objects</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL is based on PostgreSQL and has a full SQL with defined data objects that can be referred to <a href="https://www.postgresql.org/docs/current/ddl.html">doc</a>.On top of that, IvorySQL has done some Oracle proprietary data object compatibility for Oracle compatibility.</p>
 </div>
 <div class="sect2">
-<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>4.1. VARCHAR2</h3>
+<h3 id="varchar2"><a class="anchor" href="#varchar2"></a>5.1. VARCHAR2</h3>
 <div class="sect3">
-<h4 id="overview"><a class="anchor" href="#overview"></a>4.1.1. Overview</h4>
+<h4 id="overview"><a class="anchor" href="#overview"></a>5.1.1. Overview</h4>
 <div class="paragraph">
 <p>Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="grammar"><a class="anchor" href="#grammar"></a>4.1.2. Grammar</h4>
+<h4 id="grammar"><a class="anchor" href="#grammar"></a>5.1.2. Grammar</h4>
 <div class="literalblock">
 <div class="content">
 <pre>VARCHAR2(size)</pre>
@@ -796,7 +1000,7 @@ <h4 id="grammar"><a class="anchor" href="#grammar"></a>4.1.2. Grammar</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="use-cases"><a class="anchor" href="#use-cases"></a>4.1.3. Use Cases</h4>
+<h4 id="use-cases"><a class="anchor" href="#use-cases"></a>5.1.3. Use Cases</h4>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">create table test(a varchar2(5));
@@ -820,7 +1024,7 @@ <h4 id="use-cases"><a class="anchor" href="#use-cases"></a>4.1.3. Use Cases</h4>
 </div>
 </div>
 <div class="sect1">
-<h2 id="search-data"><a class="anchor" href="#search-data"></a>5. Search Data</h2>
+<h2 id="search-data"><a class="anchor" href="#search-data"></a>6. Search Data</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL is developed based on PostgreSQL, with full SQL, query data specific operations can be referred to <a href="https://www.postgresql.org/docs/current/queries.html">doc</a>.</p>
@@ -828,7 +1032,7 @@ <h2 id="search-data"><a class="anchor" href="#search-data"></a>5. Search Data</h
 </div>
 </div>
 <div class="sect1">
-<h2 id="use-of-foreign-data"><a class="anchor" href="#use-of-foreign-data"></a>6. Use of foreign data</h2>
+<h2 id="use-of-foreign-data"><a class="anchor" href="#use-of-foreign-data"></a>7. Use of foreign data</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>IvorySQL implements portions of the SQL/MED specification, allowing you to access data that resides outside IvorySQL using regular SQL queries. Such data is referred to as <strong>foreign data</strong>. (Note that this usage is not to be confused with foreign keys, which are a type of constraint within the database.)</p>
@@ -845,7 +1049,7 @@ <h2 id="use-of-foreign-data"><a class="anchor" href="#use-of-foreign-data"></a>6
 </div>
 </div>
 <div class="sect1">
-<h2 id="backup-and-restore"><a class="anchor" href="#backup-and-restore"></a>7. Backup and Restore</h2>
+<h2 id="backup-and-restore"><a class="anchor" href="#backup-and-restore"></a>8. Backup and Restore</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>As with everything that contains valuable data, IvorySQL databases should be backed up regularly. While the procedure is essentially simple, it is important to have a clear understanding of the underlying techniques and assumptions.</p>
@@ -867,7 +1071,7 @@ <h2 id="backup-and-restore"><a class="anchor" href="#backup-and-restore"></a>7.
 </ul>
 </div>
 <div class="sect2">
-<h3 id="sql-dump"><a class="anchor" href="#sql-dump"></a>7.1. SQL Dump</h3>
+<h3 id="sql-dump"><a class="anchor" href="#sql-dump"></a>8.1. SQL Dump</h3>
 <div class="paragraph">
 <p>The idea behind this dump method is to generate a file with SQL commands that, when fed back to the server, will recreate the database in the same state as it was at the time of the dump. IvorySQL provides the utility program pg_dumpfor this purpose. The basic usage of this command is:</p>
 </div>
@@ -895,7 +1099,7 @@ <h3 id="sql-dump"><a class="anchor" href="#sql-dump"></a>7.1. SQL Dump</h3>
 <p>​    Dumps created by pg_dump are internally consistent, meaning, the dump represents a snapshot of the database at the time pg_dump began running. pg_dump does not block other operations on the database while it is working. (Exceptions are those operations that need to operate with an exclusive lock, such as most forms of <code>ALTER TABLE</code>.)</p>
 </div>
 <div class="sect3">
-<h4 id="restoring-the-dump"><a class="anchor" href="#restoring-the-dump"></a>7.1.1. Restoring the Dump</h4>
+<h4 id="restoring-the-dump"><a class="anchor" href="#restoring-the-dump"></a>8.1.1. Restoring the Dump</h4>
 <div class="paragraph">
 <p>Text files created by pg_dump are intended to be read in by the psql program. The general command form to restore a dump is</p>
 </div>
@@ -937,7 +1141,7 @@ <h4 id="restoring-the-dump"><a class="anchor" href="#restoring-the-dump"></a>7.1
 </div>
 </div>
 <div class="sect3">
-<h4 id="using-pg_dumpall"><a class="anchor" href="#using-pg_dumpall"></a>7.1.2. Using pg_dumpall</h4>
+<h4 id="using-pg_dumpall"><a class="anchor" href="#using-pg_dumpall"></a>8.1.2. Using pg_dumpall</h4>
 <div class="paragraph">
 <p>pg_dump dumps only a single database at a time, and it does not dump information about roles or tablespaces (because those are cluster-wide rather than per-database). To support convenient dumping of the entire contents of a database cluster, the <a href="https://www.postgresql.org/docs/current/app-pg-dumpall.html">pg_dumpall</a> program is provided. pg_dumpall backs up each database in a given cluster, and also preserves cluster-wide data such as role and tablespace definitions. The basic usage of this command is:</p>
 </div>
@@ -965,7 +1169,7 @@ <h4 id="using-pg_dumpall"><a class="anchor" href="#using-pg_dumpall"></a>7.1.2.
 </div>
 </div>
 <div class="sect3">
-<h4 id="handling-large-databases"><a class="anchor" href="#handling-large-databases"></a>7.1.3. Handling Large Databases</h4>
+<h4 id="handling-large-databases"><a class="anchor" href="#handling-large-databases"></a>8.1.3. Handling Large Databases</h4>
 <div class="paragraph">
 <p>Some operating systems have maximum file size limits that cause problems when creating large pg_dump output files. Fortunately, pg_dump can write to the standard output, so you can use standard Unix tools to work around this potential problem. There are several possible methods:</p>
 </div>
@@ -1056,7 +1260,7 @@ <h4 id="handling-large-databases"><a class="anchor" href="#handling-large-databa
 </div>
 </div>
 <div class="sect2">
-<h3 id="file-system-level-backup"><a class="anchor" href="#file-system-level-backup"></a>7.2. File System Level Backup</h3>
+<h3 id="file-system-level-backup"><a class="anchor" href="#file-system-level-backup"></a>8.2. File System Level Backup</h3>
 <div class="literalblock">
 <div class="content">
 <pre>An alternative backup strategy is to directly copy the files that IvorySQL uses to store the data in the database，You can use whatever method you prefer for doing file system backups; for example:</pre>
@@ -1097,7 +1301,7 @@ <h3 id="file-system-level-backup"><a class="anchor" href="#file-system-level-bac
 </div>
 </div>
 <div class="sect2">
-<h3 id="continuous-archiving-and-point-in-time-recovery-pitr"><a class="anchor" href="#continuous-archiving-and-point-in-time-recovery-pitr"></a>7.3. Continuous Archiving and Point-in-Time Recovery (PITR)</h3>
+<h3 id="continuous-archiving-and-point-in-time-recovery-pitr"><a class="anchor" href="#continuous-archiving-and-point-in-time-recovery-pitr"></a>8.3. Continuous Archiving and Point-in-Time Recovery (PITR)</h3>
 <div class="paragraph">
 <p>At all times,IvorySQLmaintains a <strong>write ahead log</strong> (WAL) in the <code>pg_wal/</code> subdirectory of the cluster&#8217;s data directory. The log records every change made to the database&#8217;s data files. This log exists primarily for crash-safety purposes: if the system crashes, the database can be restored to consistency by “replaying” the log entries made since the last checkpoint. However, the existence of the log makes it possible to use a third strategy for backing up databases: we can combine a file-system-level backup with backup of the WAL files. If recovery is needed, we restore the file system backup and then replay from the backed-up WAL files to bring the system to a current state. This approach is more complex to administer than either of the previous approaches, but it has some significant benefits:</p>
 </div>
@@ -1130,7 +1334,7 @@ <h3 id="continuous-archiving-and-point-in-time-recovery-pitr"><a class="anchor"
 </div>
 </div>
 <div class="sect1">
-<h2 id="loading-and-unloading-data"><a class="anchor" href="#loading-and-unloading-data"></a>8. Loading and unloading data</h2>
+<h2 id="loading-and-unloading-data"><a class="anchor" href="#loading-and-unloading-data"></a>9. Loading and unloading data</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p><code>COPY</code> moves data between IvorySQL tables and standard file-system files. <code>COPY TO</code> copies the contents of a table <strong>to</strong> a file, while <code>COPY FROM</code> copies data <strong>from</strong> a file to a table (appending the data to whatever is in the table already). <code>COPY TO</code> can also copy the results of a <code>SELECT</code> query.</p>
@@ -1145,7 +1349,7 @@ <h2 id="loading-and-unloading-data"><a class="anchor" href="#loading-and-unloadi
 <p>​    Each backend running <code>COPY</code> will report its progress in the <code>pg_stat_progress_copy</code> view.</p>
 </div>
 <div class="sect2">
-<h3 id="synopsis"><a class="anchor" href="#synopsis"></a>8.1. Synopsis</h3>
+<h3 id="synopsis"><a class="anchor" href="#synopsis"></a>9.1. Synopsis</h3>
 <div class="listingblock">
 <div class="content">
 <pre class="highlightjs highlight"><code class="language-asciidoc hljs" data-lang="asciidoc">COPY table_name [ ( column_name [, ...] ) ]
@@ -1177,7 +1381,7 @@ <h3 id="synopsis"><a class="anchor" href="#synopsis"></a>8.1. Synopsis</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="outputs"><a class="anchor" href="#outputs"></a>8.2. Outputs</h3>
+<h3 id="outputs"><a class="anchor" href="#outputs"></a>9.2. Outputs</h3>
 <div class="paragraph">
 <p>On successful completion, a <code>COPY</code> command returns a command tag of the form</p>
 </div>
@@ -1194,7 +1398,7 @@ <h3 id="outputs"><a class="anchor" href="#outputs"></a>8.2. Outputs</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="notes"><a class="anchor" href="#notes"></a>8.3. Notes</h3>
+<h3 id="notes"><a class="anchor" href="#notes"></a>9.3. Notes</h3>
 <div class="paragraph">
 <p><code>COPY TO</code> can be used only with plain tables, not views, and does not copy rows from child tables or child partitions. For example, <code>COPY <strong>`table</code></strong> TO` copies the same rows as <code>SELECT * FROM ONLY <strong>`table</code></strong><code>. The syntax `COPY (SELECT * FROM <strong>`table</code></strong>) TO &#8230;&#8203;` can be used to dump all of the rows in an inheritance hierarchy, partitioned table, or view.</p>
 </div>
@@ -1239,9 +1443,9 @@ <h3 id="notes"><a class="anchor" href="#notes"></a>8.3. Notes</h3>
 </div>
 </div>
 <div class="sect2">
-<h3 id="file-formats"><a class="anchor" href="#file-formats"></a>8.4. File Formats</h3>
+<h3 id="file-formats"><a class="anchor" href="#file-formats"></a>9.4. File Formats</h3>
 <div class="sect3">
-<h4 id="text-format"><a class="anchor" href="#text-format"></a>8.4.1. Text Format</h4>
+<h4 id="text-format"><a class="anchor" href="#text-format"></a>9.4.1. Text Format</h4>
 <div class="paragraph">
 <p>When the <code>text</code> format is used, the data read or written is a text file with one line per table row. Columns in a row are separated by the delimiter character. The column values themselves are strings generated by the output function, or acceptable to the input function, of each attribute&#8217;s data type. The specified null string is used in place of columns that are null. <code>COPY FROM</code> will raise an error if any line of the input file contains more or fewer columns than are expected.</p>
 </div>
@@ -1318,7 +1522,7 @@ <h4 id="text-format"><a class="anchor" href="#text-format"></a>8.4.1. Text Forma
 </div>
 </div>
 <div class="sect3">
-<h4 id="csv-format"><a class="anchor" href="#csv-format"></a>8.4.2. CSV Format</h4>
+<h4 id="csv-format"><a class="anchor" href="#csv-format"></a>9.4.2. CSV Format</h4>
 <div class="paragraph">
 <p>This format option is used for importing and exporting the Comma Separated Value (<code>CSV</code>) file format used by many other programs, such as spreadsheets. Instead of the escaping rules used by IvorySQL&#8217;s standard text format, it produces and recognizes the common CSV escaping mechanism.</p>
 </div>
@@ -1357,7 +1561,7 @@ <h4 id="csv-format"><a class="anchor" href="#csv-format"></a>8.4.2. CSV Format</
 </div>
 </div>
 <div class="sect3">
-<h4 id="binary-format"><a class="anchor" href="#binary-format"></a>8.4.3. Binary Format</h4>
+<h4 id="binary-format"><a class="anchor" href="#binary-format"></a>9.4.3. Binary Format</h4>
 <div class="paragraph">
 <p>The <code>binary</code> format option causes all data to be stored/read as binary format rather than as text. It is somewhat faster than the text and <code>CSV</code> formats, but a binary-format file is less portable across machine architectures and IvorySQL versions. Also, the binary format is very data type specific; for example it will not work to output binary data from a <code>smallint</code> column and read it into an <code>integer</code> column, even though that would work fine in text format.</p>
 </div>
@@ -1423,7 +1627,7 @@ <h4 id="binary-format"><a class="anchor" href="#binary-format"></a>8.4.3. Binary
 </div>
 </div>
 <div class="sect3">
-<h4 id="file-trailer"><a class="anchor" href="#file-trailer"></a>8.4.4. File Trailer</h4>
+<h4 id="file-trailer"><a class="anchor" href="#file-trailer"></a>9.4.4. File Trailer</h4>
 <div class="paragraph">
 <p>The file trailer consists of a 16-bit integer word containing -1. This is easily distinguished from a tuple&#8217;s field-count word.</p>
 </div>
@@ -1433,7 +1637,7 @@ <h4 id="file-trailer"><a class="anchor" href="#file-trailer"></a>8.4.4. File Tra
 </div>
 </div>
 <div class="sect2">
-<h3 id="examples"><a class="anchor" href="#examples"></a>8.5. Examples</h3>
+<h3 id="examples"><a class="anchor" href="#examples"></a>9.5. Examples</h3>
 <div class="paragraph">
 <p>The following example copies a table to the client using the vertical bar (<code>|</code>) as the field delimiter:</p>
 </div>
@@ -1510,13 +1714,13 @@ <h3 id="examples"><a class="anchor" href="#examples"></a>8.5. Examples</h3>
 </div>
 </div>
 <div class="sect1">
-<h2 id="performance-tips"><a class="anchor" href="#performance-tips"></a>9. Performance Tips</h2>
+<h2 id="performance-tips"><a class="anchor" href="#performance-tips"></a>10. Performance Tips</h2>
 <div class="sectionbody">
 <div class="paragraph">
 <p>Query performance can be affected by a variety of factors. Some of these factors can be controlled by the user, while others are fundamentals of the system&#8217;s lower-level design.</p>
 </div>
 <div class="sect2">
-<h3 id="using-explain"><a class="anchor" href="#using-explain"></a>9.1. Using EXPLAIN</h3>
+<h3 id="using-explain"><a class="anchor" href="#using-explain"></a>10.1. Using EXPLAIN</h3>
 <div class="paragraph">
 <p>IvorySQL devises a <strong>query plan</strong> for each query it receives. Choosing the right plan to match the query structure and the properties of the data is absolutely critical for good performance, so the system includes a complex <strong>planner</strong> that tries to choose good plans. You can use the <a href="https://www.postgresql.org/docs/17/sql-explain.html"><code>EXPLAIN</code></a> command to see what query plan the planner creates for any query. Plan-reading is an art that requires some experience to master, but this section attempts to cover the basics.</p>
 </div>
@@ -1524,7 +1728,7 @@ <h3 id="using-explain"><a class="anchor" href="#using-explain"></a>9.1. Using EX
 <p>​    The examples use `EXPLAIN&#8217;s default “text” output format, which is compact and convenient for humans to read. If you want to feed `EXPLAIN&#8217;s output to a program for further analysis, you should use one of its machine-readable output formats (XML, JSON, or YAML) instead.</p>
 </div>
 <div class="sect3">
-<h4 id="explain-basics"><a class="anchor" href="#explain-basics"></a>9.1.1. EXPLAIN Basics</h4>
+<h4 id="explain-basics"><a class="anchor" href="#explain-basics"></a>10.1.1. EXPLAIN Basics</h4>
 <div class="paragraph">
 <p>The structure of a query plan is a tree of <strong>plan nodes</strong>. Nodes at the bottom level of the tree are scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. There are also non-table row sources, such as <code>VALUES</code> clauses and set-returning functions in <code>FROM</code>, which have their own scan node types. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes above the scan nodes to perform these operations. Again, there is usually more than one possible way to do these operations, so different node types can appear here too. The output of <code>EXPLAIN</code> has one line for each node in the plan tree, showing the basic node type plus the cost estimates that the planner made for the execution of that plan node. Additional lines might appear, indented from the node&#8217;s summary line, to show additional properties of the node. The very first line (the summary line for the topmost node) has the estimated total execution cost for the plan; it is this number that the planner seeks to minimize.</p>
 </div>
@@ -1861,7 +2065,7 @@ <h4 id="explain-basics"><a class="anchor" href="#explain-basics"></a>9.1.1. EXPL
 </div>
 </div>
 <div class="sect3">
-<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EXPLAIN ANALYZE</h4>
+<h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>10.1.2. EXPLAIN ANALYZE</h4>
 <div class="paragraph">
 <p>It is possible to check the accuracy of the planner&#8217;s estimates by using <code>EXPLAIN&#8217;s `ANALYZE</code> option. With this option, <code>EXPLAIN</code> actually executes the query, and then displays the true row counts and true run time accumulated within each plan node, along with the same estimates that a plain <code>EXPLAIN</code> shows. For example, we might get a result like this:</p>
 </div>
@@ -2071,7 +2275,7 @@ <h4 id="explain-analyze"><a class="anchor" href="#explain-analyze"></a>9.1.2. EX
 </div>
 </div>
 <div class="sect3">
-<h4 id="caveats"><a class="anchor" href="#caveats"></a>9.1.3. Caveats</h4>
+<h4 id="caveats"><a class="anchor" href="#caveats"></a>10.1.3. Caveats</h4>
 <div class="paragraph">
 <p>There are two significant ways in which run times measured by <code>EXPLAIN ANALYZE</code> can deviate from normal execution of the same query. First, since no output rows are delivered to the client, network transmission costs and I/O conversion costs are not included. Second, the measurement overhead added by <code>EXPLAIN ANALYZE</code> can be significant, especially on machines with slow <code>gettimeofday()</code> operating-system calls. You can use the <a href="https://www.postgresql.org/docs/17/pgtesttiming.html">pg_test_timing</a> tool to measure the overhead of timing on your system.</p>
 </div>
@@ -2111,9 +2315,9 @@ <h4 id="caveats"><a class="anchor" href="#caveats"></a>9.1.3. Caveats</h4>
 </div>
 </div>
 <div class="sect2">
-<h3 id="statistics-used-by-the-planner"><a class="anchor" href="#statistics-used-by-the-planner"></a>9.2. Statistics Used by the Planner</h3>
+<h3 id="statistics-used-by-the-planner"><a class="anchor" href="#statistics-used-by-the-planner"></a>10.2. Statistics Used by the Planner</h3>
 <div class="sect3">
-<h4 id="single-column-statistics"><a class="anchor" href="#single-column-statistics"></a>9.2.1. Single-Column Statistics</h4>
+<h4 id="single-column-statistics"><a class="anchor" href="#single-column-statistics"></a>10.2.1. Single-Column Statistics</h4>
 <div class="paragraph">
 <p>As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates.</p>
 </div>
@@ -2181,7 +2385,7 @@ <h4 id="single-column-statistics"><a class="anchor" href="#single-column-statist
 </div>
 </div>
 <div class="sect3">
-<h4 id="extended-statistics"><a class="anchor" href="#extended-statistics"></a>9.2.2. Extended Statistics</h4>
+<h4 id="extended-statistics"><a class="anchor" href="#extended-statistics"></a>10.2.2. Extended Statistics</h4>
 <div class="paragraph">
 <p>It is common to see slow queries running bad execution plans because multiple columns used in the query clauses are correlated. The planner normally assumes that multiple conditions are independent of each other, an assumption that does not hold when column values are correlated. Regular statistics, because of their per-individual-column nature, cannot capture any knowledge about cross-column correlation. However, IvorySQL has the ability to compute <strong>multivariate statistics</strong>, which can capture such information.</p>
 </div>
@@ -2198,7 +2402,7 @@ <h4 id="extended-statistics"><a class="anchor" href="#extended-statistics"></a>9
 <p>The following subsections describe the kinds of extended statistics that are currently supported.</p>
 </div>
 <div class="sect4">
-<h5 id="functional-dependencies"><a class="anchor" href="#functional-dependencies"></a>9.2.2.1. Functional Dependencies</h5>
+<h5 id="functional-dependencies"><a class="anchor" href="#functional-dependencies"></a>10.2.2.1. Functional Dependencies</h5>
 <div class="paragraph">
 <p>The simplest kind of extended statistics tracks <strong>functional dependencies</strong>, a concept used in definitions of database normal forms. We say that column <code>b</code> is functionally dependent on column <code>a</code> if knowledge of the value of <code>a</code> is sufficient to determine the value of <code>b</code>, that is there are no two rows having the same value of <code>a</code> but different values of <code>b</code>. In a fully normalized database, functional dependencies should exist only on primary keys and superkeys. However, in practice many data sets are not fully normalized for various reasons; intentional denormalization for performance reasons is a common example. Even in a fully normalized database, there may be partial correlation between some columns, which can be expressed as partial functional dependency.</p>
 </div>
@@ -2233,7 +2437,7 @@ <h5 id="functional-dependencies"><a class="anchor" href="#functional-dependencie
 <p>When computing the selectivity for a query involving functionally dependent columns, the planner adjusts the per-condition selectivity estimates using the dependency coefficients so as not to produce an underestimate.</p>
 </div>
 <div class="sect5">
-<h6 id="limitations-of-functional-dependencies"><a class="anchor" href="#limitations-of-functional-dependencies"></a>9.2.2.1.1. Limitations of Functional Dependencies</h6>
+<h6 id="limitations-of-functional-dependencies"><a class="anchor" href="#limitations-of-functional-dependencies"></a>10.2.2.1.1. Limitations of Functional Dependencies</h6>
 <div class="paragraph">
 <p>Functional dependencies are currently only applied when considering simple equality conditions that compare columns to constant values, and <code>IN</code> clauses with constant values. They are not used to improve estimates for equality conditions comparing two columns or comparing a column to an expression, nor for range clauses, <code>LIKE</code> or any other type of condition.</p>
 </div>
@@ -2262,7 +2466,7 @@ <h6 id="limitations-of-functional-dependencies"><a class="anchor" href="#limitat
 </div>
 </div>
 <div class="sect4">
-<h5 id="multivariate-n-distinct-counts"><a class="anchor" href="#multivariate-n-distinct-counts"></a>9.2.2.2. Multivariate N-Distinct Counts</h5>
+<h5 id="multivariate-n-distinct-counts"><a class="anchor" href="#multivariate-n-distinct-counts"></a>10.2.2.2. Multivariate N-Distinct Counts</h5>
 <div class="paragraph">
 <p>Single-column statistics store the number of distinct values in each column. Estimates of the number of distinct values when combining more than one column (for example, for <code>GROUP BY a, b</code>) are frequently wrong when the planner only has single-column statistical data, causing it to select bad plans.</p>
 </div>
@@ -2295,7 +2499,7 @@ <h5 id="multivariate-n-distinct-counts"><a class="anchor" href="#multivariate-n-
 </div>
 </div>
 <div class="sect4">
-<h5 id="multivariate-mcv-lists"><a class="anchor" href="#multivariate-mcv-lists"></a>9.2.2.3. Multivariate MCV Lists</h5>
+<h5 id="multivariate-mcv-lists"><a class="anchor" href="#multivariate-mcv-lists"></a>10.2.2.3. Multivariate MCV Lists</h5>
 <div class="paragraph">
 <p>Another type of statistic stored for each column are most-common value lists. This allows very accurate estimates for individual columns, but may result in significant misestimates for queries with conditions on multiple columns.</p>
 </div>
@@ -2340,7 +2544,7 @@ <h5 id="multivariate-mcv-lists"><a class="anchor" href="#multivariate-mcv-lists"
 </div>
 </div>
 <div class="sect2">
-<h3 id="controlling-the-planner-with-explicit-join-clauses"><a class="anchor" href="#controlling-the-planner-with-explicit-join-clauses"></a>9.3. Controlling the Planner with Explicit <code>JOIN</code> Clauses</h3>
+<h3 id="controlling-the-planner-with-explicit-join-clauses"><a class="anchor" href="#controlling-the-planner-with-explicit-join-clauses"></a>10.3. Controlling the Planner with Explicit <code>JOIN</code> Clauses</h3>
 <div class="paragraph">
 <p>It is possible to control the query planner to some extent by using the explicit <code>JOIN</code> syntax. To see why this matters, we first need some background.</p>
 </div>
@@ -2437,18 +2641,18 @@ <h3 id="controlling-the-planner-with-explicit-join-clauses"><a class="anchor" hr
 </div>
 </div>
 <div class="sect2">
-<h3 id="populating-a-database"><a class="anchor" href="#populating-a-database"></a>9.4. Populating a Database</h3>
+<h3 id="populating-a-database"><a class="anchor" href="#populating-a-database"></a>10.4. Populating a Database</h3>
 <div class="paragraph">
 <p>One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible.</p>
 </div>
 <div class="sect3">
-<h4 id="disable-autocommit"><a class="anchor" href="#disable-autocommit"></a>9.4.1. Disable Autocommit</h4>
+<h4 id="disable-autocommit"><a class="anchor" href="#disable-autocommit"></a>10.4.1. Disable Autocommit</h4>
 <div class="paragraph">
 <p>When using multiple <code>INSERT`s, turn off autocommit and just do one commit at the end. (In plain SQL, this means issuing `BEGIN</code> at the start and <code>COMMIT</code> at the end. Some client libraries might do this behind your back, in which case you need to make sure the library does it when you want it done.) If you allow each insertion to be committed separately, IvorySQL is doing a lot of work for each row that is added. An additional benefit of doing all insertions in one transaction is that if the insertion of one row were to fail then the insertion of all rows inserted up to that point would be rolled back, so you won&#8217;t be stuck with partially loaded data.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="use-copy"><a class="anchor" href="#use-copy"></a>9.4.2. Use COPY</h4>
+<h4 id="use-copy"><a class="anchor" href="#use-copy"></a>10.4.2. Use COPY</h4>
 <div class="paragraph">
 <p>Use <a href="https://www.postgresql.org/docs/17/sql-copy.html"><code>COPY</code></a> to load all the rows in one command, instead of using a series of <code>INSERT</code> commands. The <code>COPY</code> command is optimized for loading large numbers of rows; it is less flexible than <code>INSERT</code>, but incurs significantly less overhead for large data loads. Since <code>COPY</code> is a single command, there is no need to disable autocommit if you use this method to populate a table.</p>
 </div>
@@ -2463,7 +2667,7 @@ <h4 id="use-copy"><a class="anchor" href="#use-copy"></a>9.4.2. Use COPY</h4>
 </div>
 </div>
 <div class="sect3">
-<h4 id="remove-indexes"><a class="anchor" href="#remove-indexes"></a>9.4.3. Remove Indexes</h4>
+<h4 id="remove-indexes"><a class="anchor" href="#remove-indexes"></a>10.4.3. Remove Indexes</h4>
 <div class="paragraph">
 <p>If you are loading a freshly created table, the fastest method is to create the table, bulk load the table&#8217;s data using <code>COPY</code>, then create any indexes needed for the table. Creating an index on pre-existing data is quicker than updating it incrementally as each row is loaded.</p>
 </div>
@@ -2472,7 +2676,7 @@ <h4 id="remove-indexes"><a class="anchor" href="#remove-indexes"></a>9.4.3. Remo
 </div>
 </div>
 <div class="sect3">
-<h4 id="remove-foreign-key-constraints"><a class="anchor" href="#remove-foreign-key-constraints"></a>9.4.4. Remove Foreign Key Constraints</h4>
+<h4 id="remove-foreign-key-constraints"><a class="anchor" href="#remove-foreign-key-constraints"></a>10.4.4. Remove Foreign Key Constraints</h4>
 <div class="paragraph">
 <p>Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing.</p>
 </div>
@@ -2481,19 +2685,19 @@ <h4 id="remove-foreign-key-constraints"><a class="anchor" href="#remove-foreign-
 </div>
 </div>
 <div class="sect3">
-<h4 id="increase-maintenance_work_mem"><a class="anchor" href="#increase-maintenance_work_mem"></a>9.4.5. Increase maintenance_work_mem</h4>
+<h4 id="increase-maintenance_work_mem"><a class="anchor" href="#increase-maintenance_work_mem"></a>10.4.5. Increase maintenance_work_mem</h4>
 <div class="paragraph">
 <p>Temporarily increasing the <a href="https://www.postgresql.org/docs/17/runtime-config-resource.html#GUC-MAINTENANCE-WORK-MEM">maintenance_work_mem</a> configuration variable when loading large amounts of data can lead to improved performance. This will help to speed up <code>CREATE INDEX</code> commands and <code>ALTER TABLE ADD FOREIGN KEY</code> commands. It won&#8217;t do much for <code>COPY</code> itself, so this advice is only useful when you are using one or both of the above techniques.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="increase-max_wal_size"><a class="anchor" href="#increase-max_wal_size"></a>9.4.6. Increase max_wal_size</h4>
+<h4 id="increase-max_wal_size"><a class="anchor" href="#increase-max_wal_size"></a>10.4.6. Increase max_wal_size</h4>
 <div class="paragraph">
 <p>Temporarily increasing the <a href="https://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-MAX-WAL-SIZE">max_wal_size</a> configuration variable can also make large data loads faster. This is because loading a large amount of data into IvorySQL will cause checkpoints to occur more often than the normal checkpoint frequency (specified by the <code>checkpoint_timeout</code> configuration variable). Whenever a checkpoint occurs, all dirty pages must be flushed to disk. By increasing <code>max_wal_size</code> temporarily during bulk data loads, the number of checkpoints that are required can be reduced.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="disable-wal-archival-and-streaming-replication"><a class="anchor" href="#disable-wal-archival-and-streaming-replication"></a>9.4.7. Disable WAL Archival and Streaming Replication</h4>
+<h4 id="disable-wal-archival-and-streaming-replication"><a class="anchor" href="#disable-wal-archival-and-streaming-replication"></a>10.4.7. Disable WAL Archival and Streaming Replication</h4>
 <div class="paragraph">
 <p>When loading large amounts of data into an installation that uses WAL archiving or streaming replication, it might be faster to take a new base backup after the load has completed than to process a large amount of incremental WAL data. To prevent incremental WAL logging while loading, disable archiving and streaming replication, by setting <a href="https://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-WAL-LEVEL">wal_level</a> to <code>minimal</code>, <a href="https://www.postgresql.org/docs/17/runtime-config-wal.html#GUC-ARCHIVE-MODE">archive_mode</a> to <code>off</code>, and <a href="https://www.postgresql.org/docs/17/runtime-config-replication.html#GUC-MAX-WAL-SENDERS">max_wal_senders</a> to zero. But note that changing these settings requires a server restart, and makes any base backups taken before unavailable for archive recovery and standby server, which may lead to data loss.</p>
 </div>
@@ -2502,13 +2706,13 @@ <h4 id="disable-wal-archival-and-streaming-replication"><a class="anchor" href="
 </div>
 </div>
 <div class="sect3">
-<h4 id="run-analyze-afterwards"><a class="anchor" href="#run-analyze-afterwards"></a>9.4.8. Run ANALYZE Afterwards</h4>
+<h4 id="run-analyze-afterwards"><a class="anchor" href="#run-analyze-afterwards"></a>10.4.8. Run ANALYZE Afterwards</h4>
 <div class="paragraph">
 <p>Whenever you have significantly altered the distribution of data within a table, running <a href="https://www.postgresql.org/docs/17/sql-analyze.html"><code>ANALYZE</code></a> is strongly recommended. This includes bulk loading large amounts of data into the table. Running <code>ANALYZE</code> (or <code>VACUUM ANALYZE</code>) ensures that the planner has up-to-date statistics about the table. With no statistics or obsolete statistics, the planner might make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics. Note that if the autovacuum daemon is enabled, it might run <code>ANALYZE</code> automatically.</p>
 </div>
 </div>
 <div class="sect3">
-<h4 id="some-notes-about-pg_dump"><a class="anchor" href="#some-notes-about-pg_dump"></a>9.4.9. Some Notes about pg_dump</h4>
+<h4 id="some-notes-about-pg_dump"><a class="anchor" href="#some-notes-about-pg_dump"></a>10.4.9. Some Notes about pg_dump</h4>
 <div class="paragraph">
 <p>Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To restore a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while <strong>restoring</strong> a dump, not while <strong>creating</strong> it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.)</p>
 </div>
@@ -2540,7 +2744,7 @@ <h4 id="some-notes-about-pg_dump"><a class="anchor" href="#some-notes-about-pg_d
 </div>
 </div>
 <div class="sect2">
-<h3 id="non-durable-settings"><a class="anchor" href="#non-durable-settings"></a>9.5. Non-Durable Settings</h3>
+<h3 id="non-durable-settings"><a class="anchor" href="#non-durable-settings"></a>10.5. Non-Durable Settings</h3>
 <div class="paragraph">
 <p>Durability is a database feature that guarantees the recording of committed transactions even if the server crashes or loses power. However, durability adds significant database overhead, so if your site does not require such a guarantee, IvorySQL can be configured to run much faster. The following are configuration changes you can make to improve performance in such cases. Except as noted below, durability is still guaranteed in case of a crash of the database software; only an abrupt operating system crash creates a risk of data loss or corruption when these settings are used.</p>
 </div>
diff --git a/docs/en/ivorysql-doc/v5.3/7.22.html b/docs/en/ivorysql-doc/v5.3/7.22.html
index a937f6d9..12141aec 100644
--- a/docs/en/ivorysql-doc/v5.3/7.22.html
+++ b/docs/en/ivorysql-doc/v5.3/7.22.html
@@ -573,7 +573,15 @@ <h3 id="call-with-no-parameters-or-default-parameters"><a class="anchor" href="#
 <h3 id="reference-bind-variables-in-parameters-or-into-clauses"><a class="anchor" href="#reference-bind-variables-in-parameters-or-into-clauses"></a>3.4. Reference bind variables in parameters or INTO clauses</h3>
 <div class="listingblock">
 <div class="content">
-<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- Set input bind variables
+<pre class="highlightjs highlight"><code class="language-sql hljs" data-lang="sql">-- Create functions stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- Set input bind variables
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- Call a function using bind variables as parameters
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/en/ivorysql-doc/v5.3/_exports/index.pdf b/docs/en/ivorysql-doc/v5.3/_exports/index.pdf
index 646ce65fc28ddf42bcd44108deff29841b4f2c83..286e3fbcbfd4ed7be6ad3fe15f50a7a894736e29 100644
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diff --git a/docs/en/ivorysql-doc/v5.3/ivorysql.pdf b/docs/en/ivorysql-doc/v5.3/ivorysql.pdf
index 646ce65fc28ddf42bcd44108deff29841b4f2c83..286e3fbcbfd4ed7be6ad3fe15f50a7a894736e29 100644
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diff --git a/docs/en/search-index.js b/docs/en/search-index.js
index f455e25c..17b71a60 100644
--- a/docs/en/search-index.js
+++ b/docs/en/search-index.js
@@ -1 +1 @@
-antoraSearch.initSearch(lunr, 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At the same time, IvorySQL implements Oracle-compatible functions by adding plug-ins ivorysql_ora bound to the kernel, and the functions currently implemented include built-in functions, data types, system views, merges, and the addition of GUC parameters, and will continue to implement new compatible functions in the form of plug-ins bound to the kernel in the future. We are committed to the principles of the open source approach and we strongly believe in building a healthy and inclusive community. We insist that good ideas can come from anywhere. Only by including different perspectives can we make the best decisions. While the first release of IvorySQL focuses on Oracle-compatible features, the future roadmap and feature set will be defined by the community in an open source manner. IvorySQL is developed based on PostgreSQL database and is compatible with Oracle database for strong compatibility. Suitable for PostgreSQL database and Oracle database scenarios. Core Open Source: IvorySQL’s core code including compatible features are all open under the open source protocol, with no vendor restrictions. It is also used in Hankook database company instances and has an active developer community. Oracle compatible: Oracle databases can be migrated to IvorySQL. Customizable: Simply download the code and customize it the way you want. Easy to use: For system administrators, IvorySQL dramatically reduces the cost of administration and maintenance. For developers, IvorySQL provides a simple interface, a minimalist solution, and seamless integration with third-party tools. For data analysis professionals, IvorySQL provides easy access to data. Hemco Support: Powered by the leading PostgreSQL database provider, Hemco. IvorySQL is based on PostgreSQL, with complete SQL, rock-solid reliability and a huge ecosystem. Ivory database’s main application scenarios. Enterprise database For example, ERP, transaction system, financial system involves funds, customers and other information, data cannot be lost and business logic is complex. Choosing IvorySQL as the underlying data storage can help you provide high availability under the premise of data consistency, and you can implement complex business logic with simple programming. Applications with LBS Large-scale games, O2O and other applications need to support world map, nearby businesses, distance between two points and other capabilities. PostGIS adds support for geographic objects, allowing you to run location queries in SQL without complex coding, helping you to rationalize your logic more easily, implement LBS more conveniently, and improve user stickiness. Data Warehousing and Big Data With more data types and powerful computing power, IvorySQL makes it easier for you to build a database warehouse or big data analytics platform to enhance your business operations. Website or App Building IvorySQL's good performance and powerful features can effectively improve website performance and reduce development difficulty. Database Migration If you need to migrate Oracle database to PostgreSQL database, you can directly use IvorySQL database for migration. IvorySQL is a powerful open source object-relational database management system (ORDBMS). Used to store data securely, support best practices, and allow them to be retrieved when requests are processed. In addition, it is also compatible with Oracle’s syntax, which is suitable for scenarios where Oracle is used. Case conversion LIKE operator anonymous block functions and stored procedures Built-in data types and built-in functions ports and IP XML Function sequence Package Invisible Columns RowID Column OUT Parameter %Type & %Rowtype NLS Parameters Force View Nested Subfunctions sys_guid Function Empty String to NULL CALL INTO","title":"","component":"ivorysql-doc","version":"master","name":"about_ivorysql","url":"/ivorysql-doc/master/about_ivorysql.html","titles":[{"text":"1. Introduction to IvorySQL","hash":"introduction-to-ivorysql","id":1},{"text":"1.1. Overview","hash":"overview","id":2},{"text":"1.2. Product Goals and Scope","hash":"product-goals-and-scope","id":3},{"text":"1.3. Core Features","hash":"core-features","id":4},{"text":"1.4. Competitive Advantages","hash":"competitive-advantages","id":5},{"text":"1.5. Technical Ecology","hash":"technical-ecology","id":6},{"text":"1.6. Core Application Scenarios","hash":"core-application-scenarios","id":7},{"text":"2. Main, Basic Features","hash":"main-basic-features","id":8},{"text":"3. Compatibility with Oracle","hash":"compatibility-with-oracle","id":9}]},"2":{"id":2,"text":"For quick database installation by yum, please refer to Quick installation。 For more installation options, please refer to Installation。 The master node database needs to be installed and started. Stop firewall for all the nodes in the cluster to ensure the communication: $ sudo systemctl stop firewalld To create the streaming replication, we need configure the postgresql.conf and pg_hba.conf files on the primary node. postgresql.conf Append the following contents to the end of postgresql.conf: listen_addresses = '*' max_connections = 100 wal_level = replica max_wal_senders = 5 hot_standby = on pg_hba.conf Append the following contents to the end of pg_hba.conf: host all all 0.0.0.0/0 trust host replication all 0.0.0.0/0 trust The configuration of pg_hba in the example is only for demo purposes and testing. This configuration will result in invalidation of the database password. Please configure according to the actual environment. $ pg_ctl restart For quick database installation by yum, please refer to Quick installation。 For more installation options, please refer to Installation。 The standby node database only needs to be installed and not started. Stop firewall for all the nodes in the cluster to ensure the communication: $ sudo systemctl stop firewalld Run below command on the standby node to take base backups of the primary, that is, to build a streaming replication: $ sudo pg_basebackup -F p -P -X fetch -R -h <primary_ip> -p <primary_port> -U ivorysql -D /usr/local/ivorysql/ivorysql-5/data Specifies the host name of the machine on which the server is running; Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults is 5432; User name to connect as; Directory to write the output to. pg_basebackup will create the directory and any parent directories if necessary. The directory may already exist, but it is an error if the directory already exists and is not empty. For more options, refer to pg_basebackup --help. Add below contents in ~/.bash_profile file: PATH=/usr/local/ivorysql/ivorysql-5/bin:$PATH export PATH PGDATA=/usr/local/ivorysql/ivorysql-5/data export PGDATA Source to make it effective: $ source ~/.bash_profile $ sudo pg_ctl -D /usr/local/ivorysql/ivorysql-5/data start Run below command on the primary node, you will see walsender: $ ps -ef |grep postgres ... ivorysql 11176 8067 0 21:54 ? 00:00:00 postgres: walsender ivorysql 192.168.31.102(53416) streaming 0/7000060... while it is walreceiver on standby: $ ps -ef | grep postgres ... ivorysql 6567 6139 0 21:54 ? 00:00:00 postgres: walreceiver streaming 0/7000060 ... On the primary node, connect to IvorySQL and show the status: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# select * from pg_stat_replication; pid | usesysid | usename | application_name | client_addr | client_hostname | client_port | backend_start | backend_ xmin | state | sent_lsn | write_lsn | flush_lsn | replay_lsn | write_lag | flush_lag | replay_lag | sync_priority | sync_state | reply_time -------+----------+----------+------------------+----------------+-----------------+-------------+-------------------------------+--------- -----+-----------+-----------+-----------+-----------+------------+-----------+-----------+------------+---------------+------------+------ ------------------------- 11176 | 10 | ivorysql | walreceiver | 192.168.31.102 | | 53416 | 2024-12-18 21:54:52.041847-05 | | streaming | 0/7000148 | 0/7000148 | 0/7000148 | 0/7000148 | | | | 0 | async | 2024- 12-18 22:52:07.325111-05 (1 row) Here 192.168.31.102 is the ip address of the standby node, and async means the data synchronization method is asynchronous. All writing operations are performed on the primary node, while reading can be on both primary and standby. The data on primary is synchronized to standby through streaming replication. The writing result can be queried on all the nodes in the cluster. Below is an example. Create a new database test on primary and query: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# create database test; CREATE DATABASE ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows) Query on the standby node: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows)","title":"Building an IvorySQL cluster","component":"ivorysql-doc","version":"master","name":"cluster_setup","url":"/ivorysql-doc/master/cluster_setup.html","titles":[{"text":"1. Primary node","hash":"primary-node","id":1},{"text":"1.1. Installing and start database","hash":"installing-and-start-database","id":2},{"text":"1.2. Stopping firewall","hash":"stopping-firewall","id":3},{"text":"1.3. Setting environment variables","hash":"setting-environment-variables","id":4},{"text":"1.4. Restarting IvorySQL sevice","hash":"restarting-ivorysql-sevice","id":5},{"text":"2. Standby node","hash":"standby-node","id":6},{"text":"2.1. Installing database","hash":"installing-database","id":7},{"text":"2.2. Stopping firewall","hash":"stopping-firewall-2","id":8},{"text":"2.3. Building streaming replication","hash":"building-streaming-replication","id":9},{"text":"2.4. Setting environment variables","hash":"setting-environment-variables-2","id":10},{"text":"2.5. Starting IvorySQL sevice","hash":"starting-ivorysql-sevice","id":11},{"text":"3. Experience the IvorySQL cluster","hash":"experience-the-ivorysql-cluster","id":12},{"text":"3.1. Checking cluster status","hash":"checking-cluster-status","id":13},{"text":"3.2. Using the cluster","hash":"using-the-cluster","id":14}]},"3":{"id":3,"text":"lvorySQL provides unique additional functionality on top of the open source PostgreSQL database. IvorySQL is committed to delivering value to its end-users through innovation and building on top of open source based database solutions. Our goal is to deliver a solution with high performance，scalability，reliability，and ease of use for small medium and large-scale enterprises. The extended functionality provided by IvorySQL will enable users to build highly performant and scalable PostgreSQL database clusters with better database compatibility and administration.This simplifies the process of migration to PostgreSQLfrom other DBMS with enhanced database administration experiences. The IvorySQL follows the same general architecture of PostgreSQL with some additions,but it does not deviate from its core philosophy.Thediagram below depicts essentially how IvorySQL operates. The yellow color in the diagram shows the new modules added by IvorySQL on top of existing PostgreSQL while IvorySQL has also made changes to existing modules and logical structures as well. The most noteworthy of those modules that received updates for supporting oracle compatibility are backend parser and system catalogs. The following diagram depicts the changes made to PostgreSQL’s existing directories and the additions that have been made. The first test to see whether you can access the database server is to try to create a database. A running IvorySQL server can manage many databases. Typically, a separate database is used for each project or for each user. Possibly, your site administrator has already created a database for your use. In that case you can omit this step and skip ahead to the next section. To create a new database, in this example named mydb, you use the following command: $ createdb mydb If this produces no response then this step was successful and you can skip over the remainder of this section. If you see a message similar to: createdb: command not found then IvorySQL was not installed properly. Either it was not installed at all or your shell’s search path was not set to include it. Try calling the command with an absolute path instead: $ /usr/local/pgsql/bin/createdb mydb The path at your site might be different. Contact your site administrator or check the installation instructions to correct the situation. Another response could be this: createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory Is the server running locally and accepting connections on that socket? This means that the server was not started, or it is not listening where createdb expects to contact it. Again, check the installation instructions or consult the administrator. Another response could be this: createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: FATAL: role \"joe\" does not exist where your own login name is mentioned. This will happen if the administrator has not created a IvorySQL user account for you. (IvorySQL user accounts are distinct from operating system user accounts.) If you are the administrator, You will need to become the operating system user under which IvorySQL was installed (usually postgres) to create the first user account. It could also be that you were assigned a IvorySQL user name that is different from your operating system user name; in that case you need to use the -U switch or set the PGUSER environment variable to specify your IvorySQL user name. If you have a user account but it does not have the privileges required to create a database, you will see the following: createdb: error: database creation failed: ERROR: permission denied to create database Not every user has authorization to create new databases. If IvorySQL refuses to create databases for you then the site administrator needs to grant you permission to create databases. Consult your site administrator if this occurs. If you installed IvorySQL yourself then you should log in for the purposes of this tutorial under the user account that you started the server as. [1] You can also create databases with other names. IvorySQL allows you to create any number of databases at a given site. Database names must have an alphabetic first character and are limited to 63 bytes in length. A convenient choice is to create a database with the same name as your current user name. Many tools assume that database name as the default, so it can save you some typing. To create that database, simply type: $ createdb If you do not want to use your database anymore you can remove it. For example, if you are the owner (creator) of the database mydb, you can destroy it using the following command: $ dropdb mydb (For this command, the database name does not default to the user account name. You always need to specify it.) This action physically removes all files associated with the database and cannot be undone, so this should only be done with a great deal of forethought. More about createdb and dropdb can be found in createdb and dropdb respectively. You can create a new table by specifying the table name, along with all column names and their types: CREATE TABLE weather ( city varchar(80), temp_lo int, -- low temperature temp_hi int, -- high temperature prcp real, -- precipitation date date ); You can enter this into psql with the line breaks. psql will recognize that the command is not terminated until the semicolon. White space (i.e., spaces, tabs, and newlines) can be used freely in SQL commands. That means you can type the command aligned differently than above, or even all on one line. Two dashes (“--”) introduce comments. Whatever follows them is ignored up to the end of the line. SQL is case insensitive about key words and identifiers, except when identifiers are double-quoted to preserve the case (not done above). varchar(80) specifies a data type that can store arbitrary character strings up to 80 characters in length. int is the normal integer type. real is a type for storing single precision floating-point numbers. date should be self-explanatory. (Yes, the column of type date is also named date. This might be convenient or confusing — you choose.) IvorySQL supports the standard SQL types int, smallint, real, double precision, char(`N), `varchar(`N), `date, time, timestamp, and interval, as well as other types of general utility and a rich set of geometric types. IvorySQL can be customized with an arbitrary number of user-defined data types. Consequently, type names are not key words in the syntax, except where required to support special cases in the SQL standard. The second example will store cities and their associated geographical location: CREATE TABLE cities ( name varchar(80), location point ); The point type is an example of a IvorySQL-specific data type. Finally, it should be mentioned that if you don’t need a table any longer or want to recreate it differently you can remove it using the following command: DROP TABLE tablename; When a table is created, it contains no data. The first thing to do before a database can be of much use is to insert data. Data is inserted one row at a time. You can also insert more than one row in a single command, but it is not possible to insert something that is not a complete row. Even if you know only some column values, a complete row must be created. To create a new row, use the INSERT command. The command requires the table name and column values. CREATE TABLE products ( product_no integer, name text, price numeric ); CREATE TABLE new_products ( product_no int , name varchar(255), price DECIMAL(10, 2), release_date DATE ); An example command to insert a row would be: INSERT INTO products VALUES (1, 'Cheese', 9.99); The data values are listed in the order in which the columns appear in the table, separated by commas. Usually, the data values will be literals (constants), but scalar expressions are also allowed. The above syntax has the drawback that you need to know the order of the columns in the table. To avoid this you can also list the columns explicitly. For example, both of the following commands have the same effect as the one above: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99); INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1); Many users consider it good practice to always list the column names. If you don’t have values for all the columns, you can omit some of them. In that case, the columns will be filled with their default values. For example: INSERT INTO products (product_no, name) VALUES (1, 'Cheese'); INSERT INTO products VALUES (1, 'Cheese'); The second form is a IvorySQL extension. It fills the columns from the left with as many values as are given, and the rest will be defaulted. For clarity, you can also request default values explicitly, for individual columns or for the entire row: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', DEFAULT); INSERT INTO products DEFAULT VALUES; You can insert multiple rows in a single command: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99), (2, 'Bread', 1.99), (3, 'Milk', 2.99); INSERT INTO new_products (product_no, name, price, release_date) VALUES (1, 'A', 100.00, '2025-05-29'), (2, 'B', 150.50, '2024-11-20'), (3, 'C', 200.75, '2025-05-29'); It is also possible to insert the result of a query (which might be no rows, one row, or many rows): INSERT INTO products (product_no, name, price) SELECT product_no, name, price FROM new_products WHERE release_date = '2025-05-29'; This provides the full power of the SQL query mechanism for computing the rows to be inserted. Tip When inserting a lot of data at the same time, consider using the COPY command. It is not as flexible as the INSERT command, but is more efficient. The results of two queries can be combined using the set operations union, intersection, and difference. The syntax is query1 UNION [ALL] query2 query1 INTERSECT [ALL] query2 query1 EXCEPT [ALL] query2 where query1 and query2 are queries that can use any of the features discussed up to this point. UNION effectively appends the result of query2 to the result of query1 (although there is no guarantee that this is the order in which the rows are actually returned). Furthermore, it eliminates duplicate rows from its result, in the same way as DISTINCT, unless UNION ALL is used. INTERSECT returns all rows that are both in the result of query1 and in the result of query2. Duplicate rows are eliminated unless INTERSECT ALL is used. EXCEPT returns all rows that are in the result of query1 but not in the result of query2. (This is sometimes called the difference between two queries.) Again, duplicates are eliminated unless EXCEPT ALL is used. In order to calculate the union, intersection, or difference of two queries, the two queries must be “union compatible”, which means that they return the same number of columns and the corresponding columns have compatible data types. Set operations can be combined, for example query1 UNION query2 EXCEPT query3 which is equivalent to (query1 UNION query2) EXCEPT query3 As shown here, you can use parentheses to control the order of evaluation. Without parentheses, UNION and EXCEPT associate left-to-right, but INTERSECT binds more tightly than those two operators. Thus query1 UNION query2 INTERSECT query3 means query1 UNION (query2 INTERSECT query3) You can also surround an individual query with parentheses. This is important if the query needs to use any of the clauses discussed in following sections, such as LIMIT. Without parentheses, you’ll get a syntax error, or else the clause will be understood as applying to the output of the set operation rather than one of its inputs. For example, SELECT a FROM b UNION SELECT x FROM y LIMIT 10 is accepted, but it means (SELECT a FROM b UNION SELECT x FROM y) LIMIT 10 not SELECT a FROM b UNION (SELECT x FROM y LIMIT 10) When the optimizer determines that parallel query is the fastest execution strategy for a particular query, it will create a query plan that includes a Gather or Gather Merge node. Here is a simple example: EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%'; QUERY PLAN ------------------------------------------------------------------------------------- Gather (cost=1000.00..217018.43 rows=1 width=97) Workers Planned: 2 -> Parallel Seq Scan on pgbench_accounts (cost=0.00..216018.33 rows=1 width=97) Filter: (filler ~~ '%x%'::text) (4 rows) In all cases, the Gather or Gather Merge node will have exactly one child plan, which is the portion of the plan that will be executed in parallel. If the Gather or Gather Merge node is at the very top of the plan tree, then the entire query will execute in parallel. If it is somewhere else in the plan tree, then only the portion of the plan below it will run in parallel. In the example above, the query accesses only one table, so there is only one plan node other than the Gather node itself; since that plan node is a child of the Gather node, it will run in parallel. Using EXPLAIN, you can see the number of workers chosen by the planner. When the Gather node is reached during query execution, the process that is implementing the user’s session will request a number of background worker processes equal to the number of workers chosen by the planner. The number of background workers that the planner will consider using is limited to at most max_parallel_workers_per_gather. The total number of background workers that can exist at any one time is limited by both max_worker_processes and max_parallel_workers. Therefore, it is possible for a parallel query to run with fewer workers than planned, or even with no workers at all. The optimal plan may depend on the number of workers that are available, so this can result in poor query performance. If this occurrence is frequent, consider increasing max_worker_processes and max_parallel_workers so that more workers can be run simultaneously or alternatively reducing max_parallel_workers_per_gather so that the planner requests fewer workers. Every background worker process that is successfully started for a given parallel query will execute the parallel portion of the plan. The leader will also execute that portion of the plan, but it has an additional responsibility: it must also read all of the tuples generated by the workers. When the parallel portion of the plan generates only a small number of tuples, the leader will often behave very much like an additional worker, speeding up query execution. Conversely, when the parallel portion of the plan generates a large number of tuples, the leader may be almost entirely occupied with reading the tuples generated by the workers and performing any further processing steps that are required by plan nodes above the level of the Gather node or Gather Merge node. In such cases, the leader will do very little of the work of executing the parallel portion of the plan. When the node at the top of the parallel portion of the plan is Gather Merge rather than Gather, it indicates that each process executing the parallel portion of the plan is producing tuples in sorted order, and that the leader is performing an order-preserving merge. In contrast, Gather reads tuples from the workers in whatever order is convenient, destroying any sort order that may have existed. There are several settings that can cause the query planner not to generate a parallel query plan under any circumstances. In order for any parallel query plans whatsoever to be generated, the following settings must be configured as indicated. max_parallel_workers_per_gather must be set to a value that is greater than zero. This is a special case of the more general principle that no more workers should be used than the number configured via max_parallel_workers_per_gather. In addition, the system must not be running in single-user mode. Since the entire database system is running as a single process in this situation, no background workers will be available. Even when it is in general possible for parallel query plans to be generated, the planner will not generate them for a given query if any of the following are true: The query writes any data or locks any database rows. If a query contains a data-modifying operation either at the top level or within a CTE, no parallel plans for that query will be generated. As an exception, the following commands, which create a new table and populate it, can use a parallel plan for the underlying SELECT part of the query: CREATE TABLE …​ AS SELECT INTO CREATE MATERIALIZED VIEW REFRESH MATERIALIZED VIEW The query might be suspended during execution. In any situation in which the system thinks that partial or incremental execution might occur, no parallel plan is generated. For example, a cursor created using DECLARE CURSOR will never use a parallel plan. Similarly, a PL/pgSQL loop of the form FOR x IN query LOOP .. END LOOP will never use a parallel plan, because the parallel query system is unable to verify that the code in the loop is safe to execute while parallel query is active. The query uses any function marked PARALLEL UNSAFE. Most system-defined functions are PARALLEL SAFE, but user-defined functions are marked PARALLEL UNSAFE by default. The query is running inside of another query that is already parallel. For example, if a function called by a parallel query issues an SQL query itself, that query will never use a parallel plan. This is a limitation of the current implementation, but it may not be desirable to remove this limitation, since it could result in a single query using a very large number of processes. Even when parallel query plan is generated for a particular query, there are several circumstances under which it will be impossible to execute that plan in parallel at execution time. If this occurs, the leader will execute the portion of the plan below the Gather node entirely by itself, almost as if the Gather node were not present. This will happen if any of the following conditions are met: No background workers can be obtained because of the limitation that the total number of background workers cannot exceed max_worker_processes. No background workers can be obtained because of the limitation that the total number of background workers launched for purposes of parallel query cannot exceed max_parallel_workers. The client sends an Execute message with a non-zero fetch count. See the discussion of the extended query protocol. Since libpq currently provides no way to send such a message, this can only occur when using a client that does not rely on libpq. If this is a frequent occurrence, it may be a good idea to set max_parallel_workers_per_gather to zero in sessions where it is likely, so as to avoid generating query plans that may be suboptimal when run serially. Because each worker executes the parallel portion of the plan to completion, it is not possible to simply take an ordinary query plan and run it using multiple workers. Each worker would produce a full copy of the output result set, so the query would not run any faster than normal but would produce incorrect results. Instead, the parallel portion of the plan must be what is known internally to the query optimizer as a partial plan; that is, it must be constructed so that each process that executes the plan will generate only a subset of the output rows in such a way that each required output row is guaranteed to be generated by exactly one of the cooperating processes. Generally, this means that the scan on the driving table of the query must be a parallel-aware scan. The following types of parallel-aware table scans are currently supported. In a parallel sequential scan, the table’s blocks will be divided into ranges and shared among the cooperating processes. Each worker process will complete the scanning of its given range of blocks before requesting an additional range of blocks. In a parallel bitmap heap scan, one process is chosen as the leader. That process performs a scan of one or more indexes and builds a bitmap indicating which table blocks need to be visited. These blocks are then divided among the cooperating processes as in a parallel sequential scan. In other words, the heap scan is performed in parallel, but the underlying index scan is not. In a parallel index scan or parallel index-only scan, the cooperating processes take turns reading data from the index. Currently, parallel index scans are supported only for btree indexes. Each process will claim a single index block and will scan and return all tuples referenced by that block; other processes can at the same time be returning tuples from a different index block. The results of a parallel btree scan are returned in sorted order within each worker process. Other scan types, such as scans of non-btree indexes, may support parallel scans in the future. Just as in a non-parallel plan, the driving table may be joined to one or more other tables using a nested loop, hash join, or merge join. The inner side of the join may be any kind of non-parallel plan that is otherwise supported by the planner provided that it is safe to run within a parallel worker. Depending on the join type, the inner side may also be a parallel plan. In a nested loop join, the inner side is always non-parallel. Although it is executed in full, this is efficient if the inner side is an index scan, because the outer tuples and thus the loops that look up values in the index are divided over the cooperating processes. In a merge join, the inner side is always a non-parallel plan and therefore executed in full. This may be inefficient, especially if a sort must be performed, because the work and resulting data are duplicated in every cooperating process. In a hash join (without the \"parallel\" prefix), the inner side is executed in full by every cooperating process to build identical copies of the hash table. This may be inefficient if the hash table is large or the plan is expensive. In a parallel hash join, the inner side is a parallel hash that divides the work of building a shared hash table over the cooperating processes. IvorySQL supports parallel aggregation by aggregating in two stages. First, each process participating in the parallel portion of the query performs an aggregation step, producing a partial result for each group of which that process is aware. This is reflected in the plan as a Partial Aggregate node. Second, the partial results are transferred to the leader via Gather or Gather Merge. Finally, the leader re-aggregates the results across all workers in order to produce the final result. This is reflected in the plan as a Finalize Aggregate node. Because the Finalize Aggregate node runs on the leader process, queries that produce a relatively large number of groups in comparison to the number of input rows will appear less favorable to the query planner. For example, in the worst-case scenario the number of groups seen by the Finalize Aggregate node could be as many as the number of input rows that were seen by all worker processes in the Partial Aggregate stage. For such cases, there is clearly going to be no performance benefit to using parallel aggregation. The query planner takes this into account during the planning process and is unlikely to choose parallel aggregate in this scenario. Parallel aggregation is not supported in all situations. Each aggregate must be safe for parallelism and must have a combine function. If the aggregate has a transition state of type internal, it must have serialization and deserialization functions. See CREATE AGGREGATE for more details. Parallel aggregation is not supported if any aggregate function call contains DISTINCT or ORDER BY clause and is also not supported for ordered set aggregates or when the query involves GROUPING SETS. It can only be used when all joins involved in the query are also part of the parallel portion of the plan. Whenever IvorySQL needs to combine rows from multiple sources into a single result set, it uses an Append or MergeAppend plan node. This commonly happens when implementing UNION ALL or when scanning a partitioned table. Such nodes can be used in parallel plans just as they can in any other plan. However, in a parallel plan, the planner may instead use a Parallel Append node. When an Append node is used in a parallel plan, each process will execute the child plans in the order in which they appear, so that all participating processes cooperate to execute the first child plan until it is complete and then move to the second plan at around the same time. When a Parallel Append is used instead, the executor will instead spread out the participating processes as evenly as possible across its child plans, so that multiple child plans are executed simultaneously. This avoids contention, and also avoids paying the startup cost of a child plan in those processes that never execute it. Also, unlike a regular Append node, which can only have partial children when used within a parallel plan, a Parallel Append node can have both partial and non-partial child plans. Non-partial children will be scanned by only a single process, since scanning them more than once would produce duplicate results. Plans that involve appending multiple results sets can therefore achieve coarse-grained parallelism even when efficient partial plans are not available. For example, consider a query against a partitioned table that can only be implemented efficiently by using an index that does not support parallel scans. The planner might choose a Parallel Append of regular Index Scan plans; each individual index scan would have to be executed to completion by a single process, but different scans could be performed at the same time by different processes. enable_parallel_append can be used to disable this feature. If a query that is expected to do so does not produce a parallel plan, you can try reducing parallel_setup_cost or parallel_tuple_cost. Of course, this plan may turn out to be slower than the serial plan that the planner preferred, but this will not always be the case. If you don’t get a parallel plan even with very small values of these settings (e.g., after setting them both to zero), there may be some reason why the query planner is unable to generate a parallel plan for your query. When executing a parallel plan, you can use EXPLAIN (ANALYZE, VERBOSE) to display per-worker statistics for each plan node. This may be useful in determining whether the work is being evenly distributed between all plan nodes and more generally in understanding the performance characteristics of the plan. ABORT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ABORT rolls back the current transaction and causes all the updates made by the transaction to be discarded. This command is identical in behavior to the standard SQL command ROLLBACK, and is present only for historical reasons. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use COMMIT to successfully terminate a transaction. Issuing ABORT outside of a transaction block emits a warning and otherwise has no effect. To abort all changes: ABORT; This command is a IvorySQL extension present for historical reasons. ROLLBACK is the equivalent standard SQL command. BEGIN [ WORK | TRANSACTION ] [ transaction_mode [, ...] ] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE BEGIN initiates a transaction block, that is, all statements after a BEGIN command will be executed in a single transaction until an explicit COMMIT or ROLLBACK is given. By default (without BEGIN), IvorySQL executes transactions in “autocommit” mode, that is, each statement is executed in its own transaction and a commit is implicitly performed at the end of the statement (if execution was successful, otherwise a rollback is done). Statements are executed more quickly in a transaction block, because transaction start/commit requires significant CPU and disk activity. Execution of multiple statements inside a transaction is also useful to ensure consistency when making several related changes: other sessions will be unable to see the intermediate states wherein not all the related updates have been done. If the isolation level, read/write mode, or deferrable mode is specified, the new transaction has those characteristics, as if SET TRANSACTION was executed. WORK TRANSACTION Optional key words. They have no effect. Refer to SET TRANSACTION for information on the meaning of the other parameters to this statement. START TRANSACTION has the same functionality as BEGIN. Use COMMIT or ROLLBACK to terminate a transaction block. Issuing BEGIN when already inside a transaction block will provoke a warning message. The state of the transaction is not affected. To nest transactions within a transaction block, use savepoints (see SAVEPOINT). For reasons of backwards compatibility, the commas between successive transaction_modes can be omitted. To begin a transaction block: BEGIN; BEGIN is a IvorySQL language extension. It is equivalent to the SQL-standard command START TRANSACTION, whose reference page contains additional compatibility information. The DEFERRABLE transaction_mode is a IvorySQL language extension. Incidentally, the BEGIN key word is used for a different purpose in embedded SQL. You are advised to be careful about the transaction semantics when porting database applications. COMMIT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] COMMIT commits the current transaction. All changes made by the transaction become visible to others and are guaranteed to be durable if a crash occurs. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use ROLLBACK to abort a transaction. Issuing COMMIT when not inside a transaction does no harm, but it will provoke a warning message. COMMIT AND CHAIN when not inside a transaction is an error. To commit the current transaction and make all changes permanent: COMMIT; The command COMMIT conforms to the SQL standard. The form COMMIT TRANSACTION is a IvorySQL extension. COMMIT PREPARED transaction_id COMMIT PREPARED commits a transaction that is in prepared state. transaction_id The transaction identifier of the transaction that is to be committed. To commit a prepared transaction, you must be either the same user that executed the transaction originally, or a superuser. But you do not have to be in the same session that executed the transaction. This command cannot be executed inside a transaction block. The prepared transaction is committed immediately. All currently available prepared transactions are listed in the pg_prepared_xacts system view. Commit the transaction identified by the transaction identifier foobar: COMMIT PREPARED 'foobar'; COMMIT PREPARED is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. END [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] END commits the current transaction. All changes made by the transaction become visible to others and are guaranteed to be durable if a crash occurs. This command is a IvorySQL extension that is equivalent to COMMIT. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use ROLLBACK to abort a transaction. Issuing END when not inside a transaction does no harm, but it will provoke a warning message. To commit the current transaction and make all changes permanent: END; END is a IvorySQL extension that provides functionality equivalent to COMMIT, which is specified in the SQL standard. PREPARE TRANSACTION transaction_id PREPARE TRANSACTION prepares the current transaction for two-phase commit. After this command, the transaction is no longer associated with the current session; instead, its state is fully stored on disk, and there is a very high probability that it can be committed successfully, even if a database crash occurs before the commit is requested. Once prepared, a transaction can later be committed or rolled back with COMMIT PREPARED or ROLLBACK PREPARED, respectively. Those commands can be issued from any session, not only the one that executed the original transaction. From the point of view of the issuing session, PREPARE TRANSACTION is not unlike a ROLLBACK command: after executing it, there is no active current transaction, and the effects of the prepared transaction are no longer visible. (The effects will become visible again if the transaction is committed.) If the PREPARE TRANSACTION command fails for any reason, it becomes a ROLLBACK: the current transaction is canceled. transaction_id An arbitrary identifier that later identifies this transaction for COMMIT PREPARED or ROLLBACK PREPARED. The identifier must be written as a string literal, and must be less than 200 bytes long. It must not be the same as the identifier used for any currently prepared transaction. PREPARE TRANSACTION is not intended for use in applications or interactive sessions. Its purpose is to allow an external transaction manager to perform atomic global transactions across multiple databases or other transactional resources. Unless you’re writing a transaction manager, you probably shouldn’t be using PREPARE TRANSACTION. This command must be used inside a transaction block. Use BEGIN to start one. It is not currently allowed to PREPARE a transaction that has executed any operations involving temporary tables or the session’s temporary namespace, created any cursors WITH HOLD, or executed LISTEN, UNLISTEN, or NOTIFY. Those features are too tightly tied to the current session to be useful in a transaction to be prepared. If the transaction modified any run-time parameters with SET (without the LOCAL option), those effects persist after PREPARE TRANSACTION, and will not be affected by any later COMMIT PREPARED or ROLLBACK PREPARED. Thus, in this one respect PREPARE TRANSACTION acts more like COMMIT than ROLLBACK. All currently available prepared transactions are listed in the pg_prepared_xacts system view. It is unwise to leave transactions in the prepared state for a long time. This will interfere with the ability of VACUUM to reclaim storage, and in extreme cases could cause the database to shut down to prevent transaction ID wraparound (see Section 25.1.5). Keep in mind also that the transaction continues to hold whatever locks it held. The intended usage of the feature is that a prepared transaction will normally be committed or rolled back as soon as an external transaction manager has verified that other databases are also prepared to commit. If you have not set up an external transaction manager to track prepared transactions and ensure they get closed out promptly, it is best to keep the prepared-transaction feature disabled by setting max_prepared_transactions to zero. This will prevent accidental creation of prepared transactions that might then be forgotten and eventually cause problems. Prepare the current transaction for two-phase commit, using foobar as the transaction identifier: PREPARE TRANSACTION 'foobar'; PREPARE TRANSACTION is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. ROLLBACK [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ROLLBACK rolls back the current transaction and causes all the updates made by the transaction to be discarded. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use COMMIT to successfully terminate a transaction. Issuing ROLLBACK outside of a transaction block emits a warning and otherwise has no effect. ROLLBACK AND CHAIN outside of a transaction block is an error. To abort all changes: ROLLBACK; The command ROLLBACK conforms to the SQL standard. The form ROLLBACK TRANSACTION is a IvorySQL extension. ROLLBACK PREPARED transaction_id ROLLBACK PREPARED rolls back a transaction that is in prepared state. transaction_id The transaction identifier of the transaction that is to be rolled back. To roll back a prepared transaction, you must be either the same user that executed the transaction originally, or a superuser. But you do not have to be in the same session that executed the transaction. This command cannot be executed inside a transaction block. The prepared transaction is rolled back immediately. All currently available prepared transactions are listed in the pg_prepared_xacts system view. Roll back the transaction identified by the transaction identifier foobar: ROLLBACK PREPARED 'foobar'; ROLLBACK PREPARED is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. SAVEPOINT savepoint_name SAVEPOINT establishes a new savepoint within the current transaction. A savepoint is a special mark inside a transaction that allows all commands that are executed after it was established to be rolled back, restoring the transaction state to what it was at the time of the savepoint. savepoint_name The name to give to the new savepoint. If savepoints with the same name already exist, they will be inaccessible until newer identically-named savepoints are released. Use ROLLBACK TO to rollback to a savepoint. Use RELEASE SAVEPOINT to destroy a savepoint, keeping the effects of commands executed after it was established. Savepoints can only be established when inside a transaction block. There can be multiple savepoints defined within a transaction. To establish a savepoint and later undo the effects of all commands executed after it was established: BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); ROLLBACK TO SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); COMMIT; The above transaction will insert the values 1 and 3, but not 2. To establish and later destroy a savepoint: BEGIN; INSERT INTO table1 VALUES (3); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (4); RELEASE SAVEPOINT my_savepoint; COMMIT; The above transaction will insert both 3 and 4. To use a single savepoint name: BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); -- rollback to the second savepoint ROLLBACK TO SAVEPOINT my_savepoint; SELECT * FROM table1; -- shows rows 1 and 2 -- release the second savepoint RELEASE SAVEPOINT my_savepoint; -- rollback to the first savepoint ROLLBACK TO SAVEPOINT my_savepoint; SELECT * FROM table1; -- shows only row 1 COMMIT; The above transaction shows row 3 being rolled back first, then row 2. SQL requires a savepoint to be destroyed automatically when another savepoint with the same name is established. In IvorySQL, the old savepoint is kept, though only the more recent one will be used when rolling back or releasing. (Releasing the newer savepoint with RELEASE SAVEPOINT will cause the older one to again become accessible to ROLLBACK TO SAVEPOINT and RELEASE SAVEPOINT.) Otherwise, SAVEPOINT is fully SQL conforming. SET CONSTRAINTS { ALL | name [, ...] } { DEFERRED | IMMEDIATE } SET CONSTRAINTS sets the behavior of constraint checking within the current transaction. IMMEDIATE constraints are checked at the end of each statement. DEFERRED constraints are not checked until transaction commit. Each constraint has its own IMMEDIATE or DEFERRED mode. Upon creation, a constraint is given one of three characteristics: DEFERRABLE INITIALLY DEFERRED, DEFERRABLE INITIALLY IMMEDIATE, or NOT DEFERRABLE. The third class is always IMMEDIATE and is not affected by the SET CONSTRAINTS command. The first two classes start every transaction in the indicated mode, but their behavior can be changed within a transaction by SET CONSTRAINTS. SET CONSTRAINTS with a list of constraint names changes the mode of just those constraints (which must all be deferrable). Each constraint name can be schema-qualified. The current schema search path is used to find the first matching name if no schema name is specified. SET CONSTRAINTS ALL changes the mode of all deferrable constraints. When SET CONSTRAINTS changes the mode of a constraint from DEFERRED to IMMEDIATE, the new mode takes effect retroactively: any outstanding data modifications that would have been checked at the end of the transaction are instead checked during the execution of the SET CONSTRAINTS command. If any such constraint is violated, the SET CONSTRAINTS fails (and does not change the constraint mode). Thus, SET CONSTRAINTS can be used to force checking of constraints to occur at a specific point in a transaction. Currently, only UNIQUE, PRIMARY KEY, REFERENCES (foreign key), and EXCLUDE constraints are affected by this setting. NOT NULL and CHECK constraints are always checked immediately when a row is inserted or modified (not at the end of the statement). Uniqueness and exclusion constraints that have not been declared DEFERRABLE are also checked immediately. The firing of triggers that are declared as “constraint triggers” is also controlled by this setting — they fire at the same time that the associated constraint should be checked. Because IvorySQL does not require constraint names to be unique within a schema (but only per-table), it is possible that there is more than one match for a specified constraint name. In this case SET CONSTRAINTS will act on all matches. For a non-schema-qualified name, once a match or matches have been found in some schema in the search path, schemas appearing later in the path are not searched. This command only alters the behavior of constraints within the current transaction. Issuing this outside of a transaction block emits a warning and otherwise has no effect. This command complies with the behavior defined in the SQL standard, except for the limitation that, in IvorySQL, it does not apply to NOT NULL and CHECK constraints. Also, IvorySQL checks non-deferrable uniqueness constraints immediately, not at end of statement as the standard would suggest. SET TRANSACTION transaction_mode [, ...] SET TRANSACTION SNAPSHOT snapshot_id SET SESSION CHARACTERISTICS AS TRANSACTION transaction_mode [, ...] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE The SET TRANSACTION command sets the characteristics of the current transaction. It has no effect on any subsequent transactions. SET SESSION CHARACTERISTICS sets the default transaction characteristics for subsequent transactions of a session. These defaults can be overridden by SET TRANSACTION for an individual transaction. The available transaction characteristics are the transaction isolation level, the transaction access mode (read/write or read-only), and the deferrable mode. In addition, a snapshot can be selected, though only for the current transaction, not as a session default. The isolation level of a transaction determines what data the transaction can see when other transactions are running concurrently: READ COMMITTED A statement can only see rows committed before it began. This is the default. REPEATABLE READ All statements of the current transaction can only see rows committed before the first query or data-modification statement was executed in this transaction. SERIALIZABLE All statements of the current transaction can only see rows committed before the first query or data-modification statement was executed in this transaction. If a pattern of reads and writes among concurrent serializable transactions would create a situation which could not have occurred for any serial (one-at-a-time) execution of those transactions, one of them will be rolled back with a serialization_failure error. The SQL standard defines one additional level, READ UNCOMMITTED. In IvorySQL READ UNCOMMITTED is treated as READ COMMITTED. The transaction isolation level cannot be changed after the first query or data-modification statement (SELECT, INSERT, DELETE, UPDATE, FETCH, or COPY) of a transaction has been executed. See Chapter 13 for more information about transaction isolation and concurrency control. The transaction access mode determines whether the transaction is read/write or read-only. Read/write is the default. When a transaction is read-only, the following SQL commands are disallowed: INSERT, UPDATE, DELETE, and COPY FROM if the table they would write to is not a temporary table; all CREATE, ALTER, and DROP commands; COMMENT, GRANT, REVOKE, TRUNCATE; and EXPLAIN ANALYZE and EXECUTE if the command they would execute is among those listed. This is a high-level notion of read-only that does not prevent all writes to disk. The DEFERRABLE transaction property has no effect unless the transaction is also SERIALIZABLE and READ ONLY. When all three of these properties are selected for a transaction, the transaction may block when first acquiring its snapshot, after which it is able to run without the normal overhead of a SERIALIZABLE transaction and without any risk of contributing to or being canceled by a serialization failure. This mode is well suited for long-running reports or backups. The SET TRANSACTION SNAPSHOT command allows a new transaction to run with the same snapshot as an existing transaction. The pre-existing transaction must have exported its snapshot with the pg_export_snapshot function. That function returns a snapshot identifier, which must be given to SET TRANSACTION SNAPSHOT to specify which snapshot is to be imported. The identifier must be written as a string literal in this command, for example '00000003-0000001B-1'. SET TRANSACTION SNAPSHOT can only be executed at the start of a transaction, before the first query or data-modification statement (SELECT, INSERT, DELETE, UPDATE, FETCH, or COPY) of the transaction. Furthermore, the transaction must already be set to SERIALIZABLE or REPEATABLE READ isolation level (otherwise, the snapshot would be discarded immediately, since READ COMMITTED mode takes a new snapshot for each command). If the importing transaction uses SERIALIZABLE isolation level, then the transaction that exported the snapshot must also use that isolation level. Also, a non-read-only serializable transaction cannot import a snapshot from a read-only transaction. If SET TRANSACTION is executed without a prior START TRANSACTION or BEGIN, it emits a warning and otherwise has no effect. It is possible to dispense with SET TRANSACTION by instead specifying the desired transaction_modes in BEGIN or START TRANSACTION. But that option is not available for SET TRANSACTION SNAPSHOT. The session default transaction modes can also be set or examined via the configuration parameters default_transaction_isolation, default_transaction_read_only, and default_transaction_deferrable. (In fact SET SESSION CHARACTERISTICS is just a verbose equivalent for setting these variables with SET.) This means the defaults can be set in the configuration file, via ALTER DATABASE, etc. Consult Chapter 20 for more information. The current transaction’s modes can similarly be set or examined via the configuration parameters transaction_isolation, transaction_read_only, and transaction_deferrable. Setting one of these parameters acts the same as the corresponding SET TRANSACTION option, with the same restrictions on when it can be done. However, these parameters cannot be set in the configuration file, or from any source other than live SQL. To begin a new transaction with the same snapshot as an already existing transaction, first export the snapshot from the existing transaction. That will return the snapshot identifier, for example: BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SELECT pg_export_snapshot(); pg_export_snapshot --------------------- 00000003-0000001B-1 (1 row) Then give the snapshot identifier in a SET TRANSACTION SNAPSHOT command at the beginning of the newly opened transaction: BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SET TRANSACTION SNAPSHOT '00000003-0000001B-1'; These commands are defined in the SQL standard, except for the DEFERRABLE transaction mode and the SET TRANSACTION SNAPSHOT form, which are IvorySQL extensions. SERIALIZABLE is the default transaction isolation level in the standard. In IvorySQL the default is ordinarily READ COMMITTED, but you can change it as mentioned above. In the SQL standard, there is one other transaction characteristic that can be set with these commands: the size of the diagnostics area. This concept is specific to embedded SQL, and therefore is not implemented in the IvorySQL server. The SQL standard requires commas between successive transaction_modes, but for historical reasons IvorySQL allows the commas to be omitted. START TRANSACTION [ transaction_mode [, ...] ] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE This command begins a new transaction block. If the isolation level, read/write mode, or deferrable mode is specified, the new transaction has those characteristics, as if SET TRANSACTION was executed. This is the same as the BEGIN command. Refer to SET TRANSACTION for information on the meaning of the parameters to this statement. In the standard, it is not necessary to issue START TRANSACTION to start a transaction block: any SQL command implicitly begins a block. IvorySQL’s behavior can be seen as implicitly issuing a COMMIT after each command that does not follow START TRANSACTION (or BEGIN), and it is therefore often called “autocommit”. Other relational database systems might offer an autocommit feature as a convenience. The DEFERRABLE transaction_mode is a IvorySQL language extension. The SQL standard requires commas between successive transaction_modes, but for historical reasons IvorySQL allows the commas to be omitted. See also the compatibility section of SET TRANSACTION. SQL input consists of a sequence of commands. A command is composed of a sequence of tokens, terminated by a semicolon (“;”). The end of the input stream also terminates a command. Which tokens are valid depends on the syntax of the particular command. A token can be a key word, an identifier, a quoted identifier, a literal (or constant), or a special character symbol. Tokens are normally separated by whitespace (space, tab, newline), but need not be if there is no ambiguity (which is generally only the case if a special character is adjacent to some other token type). For example, the following is (syntactically) valid SQL input: SELECT * FROM MY_TABLE; UPDATE MY_TABLE SET A = 5; INSERT INTO MY_TABLE VALUES (3, 'hi there'); This is a sequence of three commands, one per line (although this is not required; more than one command can be on a line, and commands can usefully be split across lines). Additionally, comments can occur in SQL input. They are not tokens, they are effectively equivalent to whitespace. The SQL syntax is not very consistent regarding what tokens identify commands and which are operands or parameters. The first few tokens are generally the command name, so in the above example we would usually speak of a “SELECT”, an “UPDATE”, and an “INSERT” command. But for instance the UPDATE command always requires a SET token to appear in a certain position, and this particular variation of INSERT also requires a VALUES in order to be complete. Tokens such as SELECT, UPDATE, or VALUES in the example above are examples of key words, that is, words that have a fixed meaning in the SQL language. The tokens MY_TABLE and A are examples of identifiers. They identify names of tables, columns, or other database objects, depending on the command they are used in. Therefore they are sometimes simply called “names”. Key words and identifiers have the same lexical structure, meaning that one cannot know whether a token is an identifier or a key word without knowing the language. A complete list of key words can be found in Appendix C. SQL identifiers and key words must begin with a letter (a-z, but also letters with diacritical marks and non-Latin letters) or an underscore (_). Subsequent characters in an identifier or key word can be letters, underscores, digits (0-9), or dollar signs ($). Note that dollar signs are not allowed in identifiers according to the letter of the SQL standard, so their use might render applications less portable. The SQL standard will not define a key word that contains digits or starts or ends with an underscore, so identifiers of this form are safe against possible conflict with future extensions of the standard. The system uses no more than NAMEDATALEN-1 bytes of an identifier; longer names can be written in commands, but they will be truncated. By default, NAMEDATALEN is 64 so the maximum identifier length is 63 bytes. If this limit is problematic, it can be raised by changing the NAMEDATALEN constant in src/include/pg_config_manual.h. Key words and unquoted identifiers are case insensitive. Therefore: UPDATE MY_TABLE SET A = 5; can equivalently be written as: uPDaTE my_TabLE SeT a = 5; A convention often used is to write key words in upper case and names in lower case, e.g.: UPDATE my_table SET a = 5; There is a second kind of identifier: the delimited identifier or quoted identifier. It is formed by enclosing an arbitrary sequence of characters in double-quotes (\"). A delimited identifier is always an identifier, never a key word. So \"select\" could be used to refer to a column or table named “select”, whereas an unquoted select would be taken as a key word and would therefore provoke a parse error when used where a table or column name is expected. The example can be written with quoted identifiers like this: UPDATE \"my_table\" SET \"a\" = 5; Quoted identifiers can contain any character, except the character with code zero. (To include a double quote, write two double quotes.) This allows constructing table or column names that would otherwise not be possible, such as ones containing spaces or ampersands. The length limitation still applies. Quoting an identifier also makes it case-sensitive, whereas unquoted names are always folded to lower case. For example, the identifiers FOO, foo, and \"foo\" are considered the same by IvorySQL, but \"Foo\" and \"FOO\" are different from these three and each other. (The folding of unquoted names to lower case in IvorySQL is incompatible with the SQL standard, which says that unquoted names should be folded to upper case. Thus, foo should be equivalent to \"FOO\" not \"foo\" according to the standard. If you want to write portable applications you are advised to always quote a particular name or never quote it.) A variant of quoted identifiers allows including escaped Unicode characters identified by their code points. This variant starts with U& (upper or lower case U followed by ampersand) immediately before the opening double quote, without any spaces in between, for example U&\"foo\". (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the identifier \"data\" could be written as U&\"d\\0061t\\+000061\" The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters: U&\"\\0441\\043B\\043E\\043D\" If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example: U&\"d!0061t!+000061\" UESCAPE '!' The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character. Note that the escape character is written in single quotes, not double quotes, after UESCAPE. To include the escape character in the identifier literally, write it twice. Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.) If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that’s not possible. There are three kinds of implicitly-typed constants in IvorySQL: strings, bit strings, and numbers. Constants can also be specified with explicit types, which can enable more accurate representation and more efficient handling by the system. These alternatives are discussed in the following subsections. A string constant in SQL is an arbitrary sequence of characters bounded by single quotes ('), for example 'This is a string'. To include a single-quote character within a string constant, write two adjacent single quotes, e.g., 'Dianne''s horse'. Note that this is not the same as a double-quote character (\"). Two string constants that are only separated by whitespace with at least one newline are concatenated and effectively treated as if the string had been written as one constant. For example: SELECT 'foo' 'bar'; is equivalent to: SELECT 'foobar'; but: SELECT 'foo' 'bar'; is not valid syntax. (This slightly bizarre behavior is specified by SQL; IvorySQL is following the standard.) IvorySQL also accepts “escape” string constants, which are an extension to the SQL standard. An escape string constant is specified by writing the letter E (upper or lower case) just before the opening single quote, e.g., E’foo'. (When continuing an escape string constant across lines, write E only before the first opening quote.) Within an escape string, a backslash character (\\) begins a C-like backslash escape sequence, in which the combination of backslash and following character(s) represent a special byte value. Table 5.1. Backslash Escape Sequences Backslash Escape Sequence Interpretation \\b backspace \\f form feed \\n newline \\r carriage return \\t tab *`o*, `*`oo*, `*`ooo*` (o = 0–7) octal byte value \\x*`h, `\\x`hh` (h* = 0–9, A–F) hexadecimal byte value \\u*`xxxx, `\\U`xxxxxxxx` (x* = 0–9, A–F) 16 or 32-bit hexadecimal Unicode character value Any other character following a backslash is taken literally. Thus, to include a backslash character, write two backslashes (\\\\). Also, a single quote can be included in an escape string by writing \\', in addition to the normal way of ''. It is your responsibility that the byte sequences you create, especially when using the octal or hexadecimal escapes, compose valid characters in the server character set encoding. A useful alternative is to use Unicode escapes or the alternative Unicode escape syntax, ; then the server will check that the character conversion is possible. Caution If the configuration parameter standard_conforming_strings is off, then IvorySQL recognizes backslash escapes in both regular and escape string constants. However, as of IvorySQL, the default is on, meaning that backslash escapes are recognized only in escape string constants. This behavior is more standards-compliant, but might break applications which rely on the historical behavior, where backslash escapes were always recognized. As a workaround, you can set this parameter to off, but it is better to migrate away from using backslash escapes. If you need to use a backslash escape to represent a special character, write the string constant with an E.In addition to standard_conforming_strings, the configuration parameters escape_string_warning and backslash_quote govern treatment of backslashes in string constants.The character with the code zero cannot be in a string constant. IvorySQL also supports another type of escape syntax for strings that allows specifying arbitrary Unicode characters by code point. A Unicode escape string constant starts with U& (upper or lower case letter U followed by ampersand) immediately before the opening quote, without any spaces in between, for example U&'foo'. (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the string 'data' could be written as U&'d\\0061t\\+000061' The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters: U&'\\0441\\043B\\043E\\043D' If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example: U&'d!0061t!+000061' UESCAPE '!' The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character. To include the escape character in the string literally, write it twice. Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.) If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that’s not possible. Also, the Unicode escape syntax for string constants only works when the configuration parameter standard_conforming_strings is turned on. This is because otherwise this syntax could confuse clients that parse the SQL statements to the point that it could lead to SQL injections and similar security issues. If the parameter is set to off, this syntax will be rejected with an error message. While the standard syntax for specifying string constants is usually convenient, it can be difficult to understand when the desired string contains many single quotes or backslashes, since each of those must be doubled. To allow more readable queries in such situations, IvorySQL provides another way, called “dollar quoting”, to write string constants. A dollar-quoted string constant consists of a dollar sign ($), an optional “tag” of zero or more characters, another dollar sign, an arbitrary sequence of characters that makes up the string content, a dollar sign, the same tag that began this dollar quote, and a dollar sign. For example, here are two different ways to specify the string “Dianne’s horse” using dollar quoting: $$Dianne's horse$$ $SomeTag$Dianne's horse$SomeTag$ Notice that inside the dollar-quoted string, single quotes can be used without needing to be escaped. Indeed, no characters inside a dollar-quoted string are ever escaped: the string content is always written literally. Backslashes are not special, and neither are dollar signs, unless they are part of a sequence matching the opening tag. It is possible to nest dollar-quoted string constants by choosing different tags at each nesting level. This is most commonly used in writing function definitions. For example: $function$ BEGIN RETURN ($1 ~ $q$[\\t\\r\\n\\v\\\\]$q$); END; $function$ Here, the sequence $q$[\\t\\r\\n\\v\\\\]$q$ represents a dollar-quoted literal string [\\t\\r\\n\\v\\\\], which will be recognized when the function body is executed by IvorySQL. But since the sequence does not match the outer dollar quoting delimiter $function$, it is just some more characters within the constant so far as the outer string is concerned. The tag, if any, of a dollar-quoted string follows the same rules as an unquoted identifier, except that it cannot contain a dollar sign. Tags are case sensitive, so $tag$String content$tag$ is correct, but $TAG$String content$tag$ is not. A dollar-quoted string that follows a keyword or identifier must be separated from it by whitespace; otherwise the dollar quoting delimiter would be taken as part of the preceding identifier. Dollar quoting is not part of the SQL standard, but it is often a more convenient way to write complicated string literals than the standard-compliant single quote syntax. It is particularly useful when representing string constants inside other constants, as is often needed in procedural function definitions. With single-quote syntax, each backslash in the above example would have to be written as four backslashes, which would be reduced to two backslashes in parsing the original string constant, and then to one when the inner string constant is re-parsed during function execution. Bit-string constants look like regular string constants with a B (upper or lower case) immediately before the opening quote (no intervening whitespace), e.g., B'1001'. The only characters allowed within bit-string constants are 0 and 1. Alternatively, bit-string constants can be specified in hexadecimal notation, using a leading X (upper or lower case), e.g., X'1FF'. This notation is equivalent to a bit-string constant with four binary digits for each hexadecimal digit. Both forms of bit-string constant can be continued across lines in the same way as regular string constants. Dollar quoting cannot be used in a bit-string constant. Numeric constants are accepted in these general forms: digits digits.[digits][e[+-]digits] [digits].digits[e[+-]digits] digitse[+-]digits where digits is one or more decimal digits (0 through 9). At least one digit must be before or after the decimal point, if one is used. At least one digit must follow the exponent marker (e), if one is present. There cannot be any spaces or other characters embedded in the constant. Note that any leading plus or minus sign is not actually considered part of the constant; it is an operator applied to the constant. These are some examples of valid numeric constants: 42 3.5 4. .001 5e2 1.925e-3 A numeric constant that contains neither a decimal point nor an exponent is initially presumed to be type integer if its value fits in type integer (32 bits); otherwise it is presumed to be type bigint if its value fits in type bigint (64 bits); otherwise it is taken to be type numeric. Constants that contain decimal points and/or exponents are always initially presumed to be type numeric. The initially assigned data type of a numeric constant is just a starting point for the type resolution algorithms. In most cases the constant will be automatically coerced to the most appropriate type depending on context. When necessary, you can force a numeric value to be interpreted as a specific data type by casting it. For example, you can force a numeric value to be treated as type real (float4) by writing: REAL '1.23' -- string style 1.23::REAL -- IvorySQL (historical) style These are actually just special cases of the general casting notations discussed next. A constant of an arbitrary type can be entered using any one of the following notations: type 'string' 'string'::type CAST ( 'string' AS type ) The string constant’s text is passed to the input conversion routine for the type called type. The result is a constant of the indicated type. The explicit type cast can be omitted if there is no ambiguity as to the type the constant must be (for example, when it is assigned directly to a table column), in which case it is automatically coerced. The string constant can be written using either regular SQL notation or dollar-quoting. It is also possible to specify a type coercion using a function-like syntax: typename ( 'string' ) but not all type names can be used in this way. The ::, CAST(), and function-call syntaxes can also be used to specify run-time type conversions of arbitrary expressions. To avoid syntactic ambiguity, the `type 'string'` syntax can only be used to specify the type of a simple literal constant. Another restriction on the `type 'string'` syntax is that it does not work for array types; use :: or CAST() to specify the type of an array constant. The CAST() syntax conforms to SQL. The `type 'string'` syntax is a generalization of the standard: SQL specifies this syntax only for a few data types, but IvorySQL allows it for all types. The syntax with :: is historical IvorySQL usage, as is the function-call syntax. An operator name is a sequence of up to NAMEDATALEN-1 (63 by default) characters from the following list: \\+ - * / < > = ~ ! @ # % ^ & | ` ? There are a few restrictions on operator names, however: -- and /* cannot appear anywhere in an operator name, since they will be taken as the start of a comment. A multiple-character operator name cannot end in + or -, unless the name also contains at least one of these characters: ~ ! @ # % ^ & | ` ? For example, @- is an allowed operator name, but *- is not. This restriction allows IvorySQL to parse SQL-compliant queries without requiring spaces between tokens. When working with non-SQL-standard operator names, you will usually need to separate adjacent operators with spaces to avoid ambiguity. For example, if you have defined a prefix operator named @, you cannot write X*@Y; you must write X* @Y to ensure that IvorySQL reads it as two operator names not one. Some characters that are not alphanumeric have a special meaning that is different from being an operator. Details on the usage can be found at the location where the respective syntax element is described. This section only exists to advise the existence and summarize the purposes of these characters. A dollar sign ($) followed by digits is used to represent a positional parameter in the body of a function definition or a prepared statement. In other contexts the dollar sign can be part of an identifier or a dollar-quoted string constant. Parentheses (()) have their usual meaning to group expressions and enforce precedence. In some cases parentheses are required as part of the fixed syntax of a particular SQL command. Brackets ([]) are used to select the elements of an array. Commas (,) are used in some syntactical constructs to separate the elements of a list. The semicolon (;) terminates an SQL command. It cannot appear anywhere within a command, except within a string constant or quoted identifier. The colon (:) is used to select “slices” from arrays. In certain SQL dialects (such as Embedded SQL), the colon is used to prefix variable names. The asterisk (*) is used in some contexts to denote all the fields of a table row or composite value. It also has a special meaning when used as the argument of an aggregate function, namely that the aggregate does not require any explicit parameter. The period (.) is used in numeric constants, and to separate schema, table, and column names. A comment is a sequence of characters beginning with double dashes and extending to the end of the line, e.g.: -- This is a standard SQL comment Alternatively, C-style block comments can be used: /* multiline comment * with nesting: /* nested block comment */ */ where the comment begins with / and extends to the matching occurrence of /. These block comments nest, as specified in the SQL standard but unlike C, so that one can comment out larger blocks of code that might contain existing block comments. A comment is removed from the input stream before further syntax analysis and is effectively replaced by whitespace. Table 5.2 shows the precedence and associativity of the operators in IvorySQL. Most operators have the same precedence and are left-associative. The precedence and associativity of the operators is hard-wired into the parser. Add parentheses if you want an expression with multiple operators to be parsed in some other way than what the precedence rules imply. Table 5.2. Operator Precedence (highest to lowest) Operator/Element Associativity Description . left table/column name separator :: left IvorySQL-style typecast [ ] left array element selection + - right unary plus, unary minus ^ left exponentiation * / % left multiplication, division, modulo + - left addition, subtraction (any other operator) left all other native and user-defined operators BETWEEN IN LIKE ILIKE SIMILAR range containment, set membership, string matching < > = ⇐ >= <> comparison operators IS ISNULL NOTNULL IS TRUE, IS FALSE, IS NULL, IS DISTINCT FROM, etc. NOT right logical negation AND left logical conjunction OR left logical disjunction Note that the operator precedence rules also apply to user-defined operators that have the same names as the built-in operators mentioned above. For example, if you define a “” operator for some custom data type it will have the same precedence as the built-in “” operator, no matter what yours does. When a schema-qualified operator name is used in the OPERATOR syntax, as for example in: SELECT 3 OPERATOR(pg_catalog.+) 4; the OPERATOR construct is taken to have the default precedence shown in Table 5.2 for “any other operator”. This is true no matter which specific operator appears inside OPERATOR(). Note In particular, ⇐ >= and <> used to be treated as generic operators; IS tests used to have higher priority; and NOT BETWEEN and related constructs acted inconsistently, being taken in some cases as having the precedence of NOT rather than BETWEEN. These rules were changed for better compliance with the SQL standard and to reduce confusion from inconsistent treatment of logically equivalent constructs. In most cases, these changes will result in no behavioral change, or perhaps in “no such operator” failures which can be resolved by adding parentheses. However there are corner cases in which a query might change behavior without any parsing error being reported. Value expressions are used in a variety of contexts, such as in the target list of the SELECT command, as new column values in INSERT or UPDATE, or in search conditions in a number of commands. The result of a value expression is sometimes called a scalar, to distinguish it from the result of a table expression (which is a table). Value expressions are therefore also called scalar expressions (or even simply expressions). The expression syntax allows the calculation of values from primitive parts using arithmetic, logical, set, and other operations. A value expression is one of the following: A constant or literal value A column reference A positional parameter reference, in the body of a function definition or prepared statement A subscripted expression A field selection expression An operator invocation A function call An aggregate expression A window function call A type cast A collation expression A scalar subquery An array constructor A row constructor Another value expression in parentheses (used to group subexpressions and override precedence) In addition to this list, there are a number of constructs that can be classified as an expression but do not follow any general syntax rules. These generally have the semantics of a function or operator . An example is the IS NULL clause. A column can be referenced in the form: correlation.columnname correlation is the name of a table (possibly qualified with a schema name), or an alias for a table defined by means of a FROM clause. The correlation name and separating dot can be omitted if the column name is unique across all the tables being used in the current query. A positional parameter reference is used to indicate a value that is supplied externally to an SQL statement. Parameters are used in SQL function definitions and in prepared queries. Some client libraries also support specifying data values separately from the SQL command string, in which case parameters are used to refer to the out-of-line data values. The form of a parameter reference is: $number For example, consider the definition of a function, dept, as: CREATE FUNCTION dept(text) RETURNS dept AS $$ SELECT * FROM dept WHERE name = $1 $$ LANGUAGE SQL; Here the $1 references the value of the first function argument whenever the function is invoked. If an expression yields a value of an array type, then a specific element of the array value can be extracted by writing expression[subscript] or multiple adjacent elements (an “array slice”) can be extracted by writing expression[lower_subscript:upper_subscript] (Here, the brackets [ ] are meant to appear literally.) Each subscript is itself an expression, which will be rounded to the nearest integer value. In general the array expression must be parenthesized, but the parentheses can be omitted when the expression to be subscripted is just a column reference or positional parameter. Also, multiple subscripts can be concatenated when the original array is multidimensional. For example: mytable.arraycolumn[4] mytable.two_d_column[17][34] $1[10:42] (arrayfunction(a,b))[42] The parentheses in the last example are required. If an expression yields a value of a composite type (row type), then a specific field of the row can be extracted by writing expression.fieldname In general the row expression must be parenthesized, but the parentheses can be omitted when the expression to be selected from is just a table reference or positional parameter. For example: mytable.mycolumn $1.somecolumn (rowfunction(a,b)).col3 (Thus, a qualified column reference is actually just a special case of the field selection syntax.) An important special case is extracting a field from a table column that is of a composite type: (compositecol).somefield (mytable.compositecol).somefield The parentheses are required here to show that compositecol is a column name not a table name, or that mytable is a table name not a schema name in the second case. You can ask for all fields of a composite value by writing .*: (compositecol).* This notation behaves differently depending on context. There are two possible syntaxes for an operator invocation: expression operator expression (binary infix operator) operator expression (unary prefix operator) where the operator token follows the syntax rules , or is one of the key words AND, OR, and NOT, or is a qualified operator name in the form: OPERATOR(schema.operatorname) Which particular operators exist and whether they are unary or binary depends on what operators have been defined by the system or the user. The syntax for a function call is the name of a function (possibly qualified with a schema name), followed by its argument list enclosed in parentheses: function_name ([expression [, expression ... ]] ) For example, the following computes the square root of 2: sqrt(2) Other functions can be added by the user. When issuing queries in a database where some users mistrust other users, The arguments can optionally have names attached. Note A function that takes a single argument of composite type can optionally be called using field-selection syntax, and conversely field selection can be written in functional style. That is, the notations col(table) and table.col are interchangeable. This behavior is not SQL-standard but is provided in IvorySQL because it allows use of functions to emulate “computed fields”. An aggregate expression represents the application of an aggregate function across the rows selected by a query. An aggregate function reduces multiple inputs to a single output value, such as the sum or average of the inputs. The syntax of an aggregate expression is one of the following: aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ FILTER ( WHERE filter_clause ) ] where aggregate_name is a previously defined aggregate (possibly qualified with a schema name) and expression is any value expression that does not itself contain an aggregate expression or a window function call. The optional order_by_clause and filter_clause are described below. The first form of aggregate expression invokes the aggregate once for each input row. The second form is the same as the first, since ALL is the default. The third form invokes the aggregate once for each distinct value of the expression (or distinct set of values, for multiple expressions) found in the input rows. The fourth form invokes the aggregate once for each input row; since no particular input value is specified, it is generally only useful for the count() aggregate function. The last form is used with *ordered-set aggregate functions, which are described below. Most aggregate functions ignore null inputs, so that rows in which one or more of the expression(s) yield null are discarded. This can be assumed to be true, unless otherwise specified, for all built-in aggregates. For example, count(*) yields the total number of input rows; count(f1) yields the number of input rows in which f1 is non-null, since count ignores nulls; and count(distinct f1) yields the number of distinct non-null values of f1. Ordinarily, the input rows are fed to the aggregate function in an unspecified order. In many cases this does not matter; for example, min produces the same result no matter what order it receives the inputs in. However, some aggregate functions (such as array_agg and string_agg) produce results that depend on the ordering of the input rows. When using such an aggregate, the optional order_by_clause can be used to specify the desired ordering. The order_by_clause has the same syntax as for a query-level ORDER BY clause, except that its expressions are always just expressions and cannot be output-column names or numbers. For example: SELECT array_agg(a ORDER BY b DESC) FROM table; When dealing with multiple-argument aggregate functions, note that the ORDER BY clause goes after all the aggregate arguments. For example, write this: SELECT string_agg(a, ',' ORDER BY a) FROM table; not this: SELECT string_agg(a ORDER BY a, ',') FROM table; -- incorrect The latter is syntactically valid, but it represents a call of a single-argument aggregate function with two ORDER BY keys (the second one being rather useless since it’s a constant). If DISTINCT is specified in addition to an order_by_clause, then all the ORDER BY expressions must match regular arguments of the aggregate; that is, you cannot sort on an expression that is not included in the DISTINCT list. Note The ability to specify both DISTINCT and ORDER BY in an aggregate function is a IvorySQL extension. Placing ORDER BY within the aggregate’s regular argument list, as described so far, is used when ordering the input rows for general-purpose and statistical aggregates, for which ordering is optional. There is a subclass of aggregate functions called ordered-set aggregates for which an order_by_clause is required, usually because the aggregate’s computation is only sensible in terms of a specific ordering of its input rows. Typical examples of ordered-set aggregates include rank and percentile calculations. For an ordered-set aggregate, the order_by_clause is written inside WITHIN GROUP (…​), as shown in the final syntax alternative above. The expressions in the order_by_clause are evaluated once per input row just like regular aggregate arguments, sorted as per the order_by_clause's requirements, and fed to the aggregate function as input arguments. (This is unlike the case for a non-WITHIN GROUP order_by_clause, which is not treated as argument(s) to the aggregate function.) The argument expressions preceding WITHIN GROUP, if any, are called direct arguments to distinguish them from the aggregated arguments listed in the order_by_clause. Unlike regular aggregate arguments, direct arguments are evaluated only once per aggregate call, not once per input row. This means that they can contain variables only if those variables are grouped by GROUP BY; this restriction is the same as if the direct arguments were not inside an aggregate expression at all. Direct arguments are typically used for things like percentile fractions, which only make sense as a single value per aggregation calculation. The direct argument list can be empty; in this case, write just () not (*). (IvorySQL will actually accept either spelling, but only the first way conforms to the SQL standard.) An example of an ordered-set aggregate call is: SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_cont ----------------- 50489 which obtains the 50th percentile, or median, value of the income column from table households. Here, 0.5 is a direct argument; it would make no sense for the percentile fraction to be a value varying across rows. If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the aggregate function; other rows are discarded. For example: SELECT count(*) AS unfiltered, count(*) FILTER (WHERE i < 5) AS filtered FROM generate_series(1,10) AS s(i); unfiltered | filtered ------------+---------- 10 | 4 (1 row) Other aggregate functions can be added by the user. An aggregate expression can only appear in the result list or HAVING clause of a SELECT command. It is forbidden in other clauses, such as WHERE, because those clauses are logically evaluated before the results of aggregates are formed. When an aggregate expression appears in a subquery,the aggregate is normally evaluated over the rows of the subquery. But an exception occurs if the aggregate’s arguments (and filter_clause if any) contain only outer-level variables: the aggregate then belongs to the nearest such outer level, and is evaluated over the rows of that query. The aggregate expression as a whole is then an outer reference for the subquery it appears in, and acts as a constant over any one evaluation of that subquery. The restriction about appearing only in the result list or HAVING clause applies with respect to the query level that the aggregate belongs to. A window function call represents the application of an aggregate-like function over some portion of the rows selected by a query. Unlike non-window aggregate calls, this is not tied to grouping of the selected rows into a single output row — each row remains separate in the query output. However the window function has access to all the rows that would be part of the current row’s group according to the grouping specification (PARTITION BY list) of the window function call. The syntax of a window function call is one of the following: function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) where window_definition has the syntax [ existing_window_name ] [ PARTITION BY expression [, ...] ] [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ] [ frame_clause ] The optional frame_clause can be one of { RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ] { RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end [ frame_exclusion ] where frame_start and frame_end can be one of UNBOUNDED PRECEDING offset PRECEDING CURRENT ROW offset FOLLOWING UNBOUNDED FOLLOWING and frame_exclusion can be one of EXCLUDE CURRENT ROW EXCLUDE GROUP EXCLUDE TIES EXCLUDE NO OTHERS Here, expression represents any value expression that does not itself contain window function calls. window_name is a reference to a named window specification defined in the query’s WINDOW clause. Alternatively, a full window_definition can be given within parentheses, using the same syntax as for defining a named window in the WINDOW clause; see the SELECT reference page for details. It’s worth pointing out that OVER wname is not exactly equivalent to OVER (wname …​); the latter implies copying and modifying the window definition, and will be rejected if the referenced window specification includes a frame clause. The PARTITION BY clause groups the rows of the query into partitions, which are processed separately by the window function. PARTITION BY works similarly to a query-level GROUP BY clause, except that its expressions are always just expressions and cannot be output-column names or numbers. Without PARTITION BY, all rows produced by the query are treated as a single partition. The ORDER BY clause determines the order in which the rows of a partition are processed by the window function. It works similarly to a query-level ORDER BY clause, but likewise cannot use output-column names or numbers. Without ORDER BY, rows are processed in an unspecified order. The frame_clause specifies the set of rows constituting the window frame, which is a subset of the current partition, for those window functions that act on the frame instead of the whole partition. The set of rows in the frame can vary depending on which row is the current row. The frame can be specified in RANGE, ROWS or GROUPS mode; in each case, it runs from the frame_start to the frame_end. If frame_end is omitted, the end defaults to CURRENT ROW. A frame_start of UNBOUNDED PRECEDING means that the frame starts with the first row of the partition, and similarly a frame_end of UNBOUNDED FOLLOWING means that the frame ends with the last row of the partition. In RANGE or GROUPS mode, a frame_start of CURRENT ROW means the frame starts with the current row’s first peer row (a row that the window’s ORDER BY clause sorts as equivalent to the current row), while a frame_end of CURRENT ROW means the frame ends with the current row’s last peer row. In ROWS mode, CURRENT ROW simply means the current row. In the offset PRECEDING and offset FOLLOWING frame options, the offset must be an expression not containing any variables, aggregate functions, or window functions. The meaning of the offset depends on the frame mode: In ROWS mode, the offset must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of rows before or after the current row. In GROUPS mode, the offset again must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of peer groups before or after the current row’s peer group, where a peer group is a set of rows that are equivalent in the ORDER BY ordering. (There must be an ORDER BY clause in the window definition to use GROUPS mode.) In RANGE mode, these options require that the ORDER BY clause specify exactly one column. The offset specifies the maximum difference between the value of that column in the current row and its value in preceding or following rows of the frame. The data type of the offset expression varies depending on the data type of the ordering column. For numeric ordering columns it is typically of the same type as the ordering column, but for datetime ordering columns it is an interval. For example, if the ordering column is of type date or timestamp, one could write RANGE BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING. The offset is still required to be non-null and non-negative, though the meaning of “non-negative” depends on its data type. In any case, the distance to the end of the frame is limited by the distance to the end of the partition, so that for rows near the partition ends the frame might contain fewer rows than elsewhere. Notice that in both ROWS and GROUPS mode, 0 PRECEDING and 0 FOLLOWING are equivalent to CURRENT ROW. This normally holds in RANGE mode as well, for an appropriate data-type-specific meaning of “zero”. The frame_exclusion option allows rows around the current row to be excluded from the frame, even if they would be included according to the frame start and frame end options. EXCLUDE CURRENT ROW excludes the current row from the frame. EXCLUDE GROUP excludes the current row and its ordering peers from the frame. EXCLUDE TIES excludes any peers of the current row from the frame, but not the current row itself. EXCLUDE NO OTHERS simply specifies explicitly the default behavior of not excluding the current row or its peers. The default framing option is RANGE UNBOUNDED PRECEDING, which is the same as RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. With ORDER BY, this sets the frame to be all rows from the partition start up through the current row’s last ORDER BY peer. Without ORDER BY, this means all rows of the partition are included in the window frame, since all rows become peers of the current row. Restrictions are that frame_start cannot be UNBOUNDED FOLLOWING, frame_end cannot be UNBOUNDED PRECEDING, and the frame_end choice cannot appear earlier in the above list of frame_start and frame_end options than the frame_start choice does — for example RANGE BETWEEN CURRENT ROW AND `offset PRECEDING` is not allowed. But, for example, ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING is allowed, even though it would never select any rows. If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the window function; other rows are discarded. Only window functions that are aggregates accept a FILTER clause. Other window functions can be added by the user. Also, any built-in or user-defined general-purpose or statistical aggregate can be used as a window function. (Ordered-set and hypothetical-set aggregates cannot presently be used as window functions.) The syntaxes using are used for calling parameter-less aggregate functions as window functions, for example count() OVER (PARTITION BY x ORDER BY y). The asterisk (*) is customarily not used for window-specific functions. Window-specific functions do not allow DISTINCT or ORDER BY to be used within the function argument list. Window function calls are permitted only in the SELECT list and the ORDER BY clause of the query. A type cast specifies a conversion from one data type to another. IvorySQL accepts two equivalent syntaxes for type casts: CAST ( expression AS type ) expression::type The CAST syntax conforms to SQL; the syntax with :: is historical IvorySQL usage. When a cast is applied to a value expression of a known type, it represents a run-time type conversion. The cast will succeed only if a suitable type conversion operation has been defined. Notice that this is subtly different from the use of casts with constants. A cast applied to an unadorned string literal represents the initial assignment of a type to a literal constant value, and so it will succeed for any type (if the contents of the string literal are acceptable input syntax for the data type). An explicit type cast can usually be omitted if there is no ambiguity as to the type that a value expression must produce (for example, when it is assigned to a table column); the system will automatically apply a type cast in such cases. However, automatic casting is only done for casts that are marked “OK to apply implicitly” in the system catalogs. Other casts must be invoked with explicit casting syntax. This restriction is intended to prevent surprising conversions from being applied silently. It is also possible to specify a type cast using a function-like syntax: typename ( expression ) However, this only works for types whose names are also valid as function names. For example, double precision cannot be used this way, but the equivalent float8 can. Also, the names interval, time, and timestamp can only be used in this fashion if they are double-quoted, because of syntactic conflicts. Therefore, the use of the function-like cast syntax leads to inconsistencies and should probably be avoided. Note The function-like syntax is in fact just a function call. When one of the two standard cast syntaxes is used to do a run-time conversion, it will internally invoke a registered function to perform the conversion. By convention, these conversion functions have the same name as their output type, and thus the “function-like syntax” is nothing more than a direct invocation of the underlying conversion function. Obviously, this is not something that a portable application should rely on. For further details see CREATE CAST. The COLLATE clause overrides the collation of an expression. It is appended to the expression it applies to: expr COLLATE collation where collation is a possibly schema-qualified identifier. The COLLATE clause binds tighter than operators; parentheses can be used when necessary. If no collation is explicitly specified, the database system either derives a collation from the columns involved in the expression, or it defaults to the default collation of the database if no column is involved in the expression. The two common uses of the COLLATE clause are overriding the sort order in an ORDER BY clause, for example: SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE \"C\"; and overriding the collation of a function or operator call that has locale-sensitive results, for example: SELECT * FROM tbl WHERE a > 'foo' COLLATE \"C\"; Note that in the latter case the COLLATE clause is attached to an input argument of the operator we wish to affect. It doesn’t matter which argument of the operator or function call the COLLATE clause is attached to, because the collation that is applied by the operator or function is derived by considering all arguments, and an explicit COLLATE clause will override the collations of all other arguments. (Attaching non-matching COLLATE clauses to more than one argument, however, is an error.) Thus, this gives the same result as the previous example: SELECT * FROM tbl WHERE a COLLATE \"C\" > 'foo'; But this is an error: SELECT * FROM tbl WHERE (a > 'foo') COLLATE \"C\"; because it attempts to apply a collation to the result of the > operator, which is of the non-collatable data type boolean. A scalar subquery is an ordinary SELECT query in parentheses that returns exactly one row with one column. The SELECT query is executed and the single returned value is used in the surrounding value expression. It is an error to use a query that returns more than one row or more than one column as a scalar subquery. (But if, during a particular execution, the subquery returns no rows, there is no error; the scalar result is taken to be null.) The subquery can refer to variables from the surrounding query, which will act as constants during any one evaluation of the subquery. For example, the following finds the largest city population in each state: SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name) FROM states; An array constructor is an expression that builds an array value using values for its member elements. A simple array constructor consists of the key word ARRAY, a left square bracket [, a list of expressions (separated by commas) for the array element values, and finally a right square bracket ]. For example: SELECT ARRAY[1,2,3+4]; array --------- {1,2,7} (1 row) By default, the array element type is the common type of the member expressions, determined using the same rules as for UNION or CASE constructs. You can override this by explicitly casting the array constructor to the desired type, for example: SELECT ARRAY[1,2,22.7]::integer[]; array ---------- {1,2,23} (1 row) This has the same effect as casting each expression to the array element type individually. Multidimensional array values can be built by nesting array constructors. In the inner constructors, the key word ARRAY can be omitted. For example, these produce the same result: SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]]; array --------------- {{1,2},{3,4}} (1 row) SELECT ARRAY[[1,2],[3,4]]; array --------------- {{1,2},{3,4}} (1 row) Since multidimensional arrays must be rectangular, inner constructors at the same level must produce sub-arrays of identical dimensions. Any cast applied to the outer ARRAY constructor propagates automatically to all the inner constructors. Multidimensional array constructor elements can be anything yielding an array of the proper kind, not only a sub-ARRAY construct. For example: CREATE TABLE arr(f1 int[], f2 int[]); INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]); SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr; array ------------------------------------------------ {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}} (1 row) You can construct an empty array, but since it’s impossible to have an array with no type, you must explicitly cast your empty array to the desired type. For example: SELECT ARRAY[]::integer[]; array ------- {} (1 row) It is also possible to construct an array from the results of a subquery. In this form, the array constructor is written with the key word ARRAY followed by a parenthesized (not bracketed) subquery. For example: SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%'); array ------------------------------------------------------------------ {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412} (1 row) SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i)); array ---------------------------------- {{1,2},{2,4},{3,6},{4,8},{5,10}} (1 row) The subquery must return a single column. If the subquery’s output column is of a non-array type, the resulting one-dimensional array will have an element for each row in the subquery result, with an element type matching that of the subquery’s output column. If the subquery’s output column is of an array type, the result will be an array of the same type but one higher dimension; in this case all the subquery rows must yield arrays of identical dimensionality, else the result would not be rectangular. The subscripts of an array value built with ARRAY always begin with one. A row constructor is an expression that builds a row value (also called a composite value) using values for its member fields. A row constructor consists of the key word ROW, a left parenthesis, zero or more expressions (separated by commas) for the row field values, and finally a right parenthesis. For example: SELECT ROW(1,2.5,'this is a test'); The key word ROW is optional when there is more than one expression in the list. A row constructor can include the syntax rowvalue., which will be expanded to a list of the elements of the row value, just as occurs when the . syntax is used at the top level of a SELECT list .For example, if table t has columns f1 and f2, these are the same: SELECT ROW(t.*, 42) FROM t; SELECT ROW(t.f1, t.f2, 42) FROM t; Note Before PostgreSQL 8.2, the . syntax was not expanded in row constructors, so that writing ROW(t., 42) created a two-field row whose first field was another row value. The new behavior is usually more useful. If you need the old behavior of nested row values, write the inner row value without .*, for instance ROW(t, 42). By default, the value created by a ROW expression is of an anonymous record type. If necessary, it can be cast to a named composite type — either the row type of a table, or a composite type created with CREATE TYPE AS. An explicit cast might be needed to avoid ambiguity. For example: CREATE TABLE mytable(f1 int, f2 float, f3 text); CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- No cast needed since only one getf1() exists SELECT getf1(ROW(1,2.5,'this is a test')); getf1 ------- 1 (1 row) CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric); CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- Now we need a cast to indicate which function to call: SELECT getf1(ROW(1,2.5,'this is a test')); ERROR: function getf1(record) is not unique SELECT getf1(ROW(1,2.5,'this is a test')::mytable); getf1 ------- 1 (1 row) SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype)); getf1 ------- 11 (1 row) Row constructors can be used to build composite values to be stored in a composite-type table column, or to be passed to a function that accepts a composite parameter. Also, it is possible to compare two row values or test a row with IS NULL or IS NOT NULL, for example: SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows The order of evaluation of subexpressions is not defined. In particular, the inputs of an operator or function are not necessarily evaluated left-to-right or in any other fixed order. Furthermore, if the result of an expression can be determined by evaluating only some parts of it, then other subexpressions might not be evaluated at all. For instance, if one wrote: SELECT true OR somefunc(); then somefunc() would (probably) not be called at all. The same would be the case if one wrote: SELECT somefunc() OR true; Note that this is not the same as the left-to-right “short-circuiting” of Boolean operators that is found in some programming languages. As a consequence, it is unwise to use functions with side effects as part of complex expressions. It is particularly dangerous to rely on side effects or evaluation order in WHERE and HAVING clauses, since those clauses are extensively reprocessed as part of developing an execution plan. Boolean expressions (AND/OR/NOT combinations) in those clauses can be reorganized in any manner allowed by the laws of Boolean algebra. When it is essential to force evaluation order, a CASE construct can be used. For example, this is an untrustworthy way of trying to avoid division by zero in a WHERE clause: SELECT ... WHERE x > 0 AND y/x > 1.5; But this is safe: SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END; A CASE construct used in this fashion will defeat optimization attempts, so it should only be done when necessary. (In this particular example, it would be better to sidestep the problem by writing y > 1.5*x instead.) CASE is not a cure-all for such issues, however. One limitation of the technique illustrated above is that it does not prevent early evaluation of constant subexpressions. As described in Section 38.7, functions and operators marked IMMUTABLE can be evaluated when the query is planned rather than when it is executed. Thus for example SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab; is likely to result in a division-by-zero failure due to the planner trying to simplify the constant subexpression, even if every row in the table has x > 0 so that the ELSE arm would never be entered at run time. While that particular example might seem silly, related cases that don’t obviously involve constants can occur in queries executed within functions, since the values of function arguments and local variables can be inserted into queries as constants for planning purposes. Within PL/pgSQL functions, for example, using an IF-THEN-ELSE statement to protect a risky computation is much safer than just nesting it in a CASE expression. Another limitation of the same kind is that a CASE cannot prevent evaluation of an aggregate expression contained within it, because aggregate expressions are computed before other expressions in a SELECT list or HAVING clause are considered. For example, the following query can cause a division-by-zero error despite seemingly having protected against it: SELECT CASE WHEN min(employees) > 0 THEN avg(expenses / employees) END FROM departments; The min() and avg() aggregates are computed concurrently over all the input rows, so if any row has employees equal to zero, the division-by-zero error will occur before there is any opportunity to test the result of min(). Instead, use a WHERE or FILTER clause to prevent problematic input rows from reaching an aggregate function in the first place. IvorySQL allows functions that have named parameters to be called using either positional or named notation. Named notation is especially useful for functions that have a large number of parameters, since it makes the associations between parameters and actual arguments more explicit and reliable. In positional notation, a function call is written with its argument values in the same order as they are defined in the function declaration. In named notation, the arguments are matched to the function parameters by name and can be written in any order. In either notation, parameters that have default values given in the function declaration need not be written in the call at all. But this is particularly useful in named notation, since any combination of parameters can be omitted; while in positional notation parameters can only be omitted from right to left. IvorySQL also supports mixed notation, which combines positional and named notation. In this case, positional parameters are written first and named parameters appear after them. The following examples will illustrate the usage of all three notations, using the following function definition: CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false) RETURNS text AS $$ SELECT CASE WHEN $3 THEN UPPER($1 || ' ' || $2) ELSE LOWER($1 || ' ' || $2) END; $$ LANGUAGE SQL IMMUTABLE STRICT; Function concat_lower_or_upper has two mandatory parameters, a and b. Additionally there is one optional parameter uppercase which defaults to false. The a and b inputs will be concatenated, and forced to either upper or lower case depending on the uppercase parameter. The remaining details of this function definition are not important here . Positional notation is the traditional mechanism for passing arguments to functions in IvorySQL. An example is: SELECT concat_lower_or_upper('Hello', 'World', true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) All arguments are specified in order. The result is upper case since uppercase is specified as true. Another example is: SELECT concat_lower_or_upper('Hello', 'World'); concat_lower_or_upper ----------------------- hello world (1 row) Here, the uppercase parameter is omitted, so it receives its default value of false, resulting in lower case output. In positional notation, arguments can be omitted from right to left so long as they have defaults. In named notation, each argument’s name is specified using ⇒ to separate it from the argument expression. For example: SELECT concat_lower_or_upper(a => 'Hello', b => 'World'); concat_lower_or_upper ----------------------- hello world (1 row) Again, the argument uppercase was omitted so it is set to false implicitly. One advantage of using named notation is that the arguments may be specified in any order, for example: SELECT concat_lower_or_upper(a => 'Hello', b => 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) SELECT concat_lower_or_upper(a => 'Hello', uppercase => true, b => 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) An older syntax based on \":=\" is supported for backward compatibility: SELECT concat_lower_or_upper(a := 'Hello', uppercase := true, b := 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) The mixed notation combines positional and named notation. However, as already mentioned, named arguments cannot precede positional arguments. For example: SELECT concat_lower_or_upper('Hello', 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) In the above query, the arguments a and b are specified positionally, while uppercase is specified by name. In this example, that adds little except documentation. With a more complex function having numerous parameters that have default values, named or mixed notation can save a great deal of writing and reduce chances for error. Note Named and mixed call notations currently cannot be used when calling an aggregate function (but they do work when an aggregate function is used as a window function). ALTER TABLE [ IF EXISTS ] [ ONLY ] name [ * ] action; action: ADD ( add_coldef [, ... ] ) | MODIFY ( modify_coldef [, ... ] ) | DROP [ COLUMN ] ( column_name [, ... ] ) add_coldef: column_name data_type modify_coldef: column_name data_type alter_using alter_using: USING expression name Table name. column_name Column name. data_type Column type. expression The value expression. ADD keyword Adds a column to the table, either one or more columns. MODIFY keyword Modify a column of the table, you can modify one or more columns. DROP keyword Deletes a column of a table, you can delete one or more columns. USING keyword Modifies the value of a column. ADD: create table tb_test1(id int, flg char(10)); alter table tb_test1 add (name varchar); ALTER TABLE tb_test1 ADD adress varchar, ADD num int, ADD flg1 char; \\d tb_test1 Table \"public.tb_test1\" Column | Type | Collation | Nullable | Default --------+-----------------+-----------+----------+--------- id | pg_catalog.int4 | | | flg | char(10) | | | name | varchar2(4000) | | | adress | varchar2(4000) | | | num | pg_catalog.int4 | | | flg1 | char(1) | | | MODIFY: create table tb_test2(id int, flg char(10), num varchar); insert into tb_test2 values('1', 2, '3'); ALTER TABLE tb_test2 ALTER COLUMN id TYPE char; \\d tb_test2 Table \"public.tb_test2\" Column | Type | Collation | Nullable | Default --------+----------------+-----------+----------+--------- id | char(1) | | | flg | char(10) | | | num | varchar2(4000) | | | DROP: create table tb_test3(id int, flg1 char(10), flg2 char(11), flg3 char(12), flg4 char(13), flg5 char(14), flg6 char(15)); ALTER TABLE tb_test3 DROP id; \\d tb_test3 Table \"public.tb_test3\" Column | Type | Collation | Nullable | Default --------+----------+-----------+----------+--------- flg1 | char(10) | | | flg2 | char(11) | | | flg3 | char(12) | | | flg4 | char(13) | | | flg5 | char(14) | | | flg6 | char(15) | | | [ WITH [ RECURSIVE ] with_query [, ...] ] DELETE [ FROM ] [ ONLY ] table_name [ * ] [ [ AS ] alias ] [ USING using_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name The name of the table. alias The table alias. using_list A list of table expressions that allow columns from other tables to appear in the WHERE condition. condition An expression that returns a boolean type value. cursor_name The name of the cursor to be used in the WHERE CURRENT OF case. output_expression An expression that is calculated by DELETE and returned after each row is deleted. output_name The name of the returned column. create table tb_test4(id int, flg char(10)); insert into tb_test4 values(1, '2'), (5, '6'); delete from tb_test4 where id = 1; table tb_test4; id | flg ----+------------ 5 | 6 (1 row) [ WITH [ RECURSIVE ] with_query [, ...] ] UPDATE [ ONLY ] table_name [ * ] [ [ AS ] alias ] SET { [ table_name | alias ] column_name = { expression | DEFAULT } | ( [ table_name | alias ] column_name [, ...] ) = [ ROW ] ( { expression | DEFAULT } [, ...] ) | ( [ table_name | alias ] column_name [, ...] ) = ( sub-SELECT ) } [, ...] [ FROM from_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name Table name. alias Table alias. column_name Column name. expression Value expression. sub-SELECT select clause. from_list Table expression. condition An expression that returns a value of type boolean. cursor_name The name of the cursor to be used in the WHERE CURRENT OF case. output_expression An expression that is computed by DELETE and returned after each row is deleted. output_name The name of the column being returned. create table tb_test5(id int, flg char(10)); insert into tb_test5 values(1, '2'), (3, '4'), (5, '6'); update tb_test5 a set a.id = 33 where a.id = 3; table tb_test5; Id | flg ----+------------ 1 | 2 5 | 6 33 | 4 (3 rows) set ivorysql.compatible_mode to oracle; create table students(student_id varchar(20) primary key , student_name varchar(40), student_pid int); select student_id,student_name from students group by student_id; ERROR: column \"students.student_name\" must appear in the GROUP BY clause or be used in an aggregate function SELECT 100 AS value FROM DUAL UNION SELECT 200 AS value FROM DUAL UNION SELECT 100 AS value FROM DUAL; value ------- 100 200 (2 rows) select_statement MINUS [ ALL | DISTINCT ] select_statement; select_statement Any SELECT statement without the ORDER BY, LIMIT, FOR NO KEY UPDATE, FOR UPDATE, FOR SHARE, and FOR KEY SHARE clauses. ALL keyword contains duplicate row results. DISTINCT keyword shows the elimination of duplicate rows. select * from generate_series(1, 3) g(i) MINUS select * from generate_series(1, 3) g(i) where i = 1; i --- 2 3 (2 rows) Use q\\' to escape special characters. q\\' escaped characters are usually used after ! [] {} () \\<> and other escaping characters, you can also use \\, letters, numbers, \\=, +, -, *, \\&, \\$, \\%, #, etc., no spaces are allowed. select q''' is goog ''; ?column? ------------ ' is goog (1 row) SELECT [ database {schema} | schema ] sequence {nextval | currval}; `sequence`Sequence Name. create sequence sq; select sq.nextval; nextval ----------- 1 (1 row) select sq.currval; nextval ----------- 1 (1 row) Convert the given timestamp without time zone to the specified timestamp with time zone, or return NULL if the specified time zone or timestamp is NULL. Parameters Description day Timestamp without time zone tz Specified time zone select from_tz('2021-11-08 09:12:39','Asia/Shanghai') from dual; from_tz ----------------------------------- 2021-11-08 09:12:39 Asia/Shanghai (1 row) select from_tz('2021-11-08 09:12:39','SAST') from dual; from_tz -------------------------- 2021-11-08 09:12:39 SAST select from_tz(NULL,'SAST') from dual; from_tz --------- (1 row) select from_tz('2021-11-08 09:12:31',NULL) from dual; from_tz --------- (1 row) Get the timestamp of the current database system. select oracle.systimestamp(); systimestamp ------------------------------- 2021-12-02 14:38:59.879642+08 (1 row) select systimestamp; statement_timestamp ------------------------------- 2021-12-02 14:39:33.262828+08 Converts the given timestamp with time zone to UTC time without time zone. Parameters Description day Need to convert time stamp with time zone select sys_extract_utc('2018-03-28 11:30:00.00 +09:00'::timestamptz) from dual; sys_extract_utc --------------------- 2018-03-28 02:30:00 (1 row) select oracle.sys_extract_utc(NULL) from dual; sys_extract_utc ----------------- (1 row) Gets the time zone of the current session. select sessiontimezone() from dual; sessiontimezone ----------------- PRC (1 row) set timezone to UTC; select oracle.sessiontimezone(); sessiontimezone ----------------- UTC (1 row) next_day returns the date of the first weekday with the same format name, which is later than the current date. The return type is always DATE, regardless of the date's data type. The return value has the same hour, minute, and second parts as the Parameters date. Parameters Description value Start Timestamp weekday The day of the week, can be \"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" or 0,1,2,3,4,5,6,0 for Sunday select next_day(to_timestamp('2020-02-29 14:40:50', 'YYYY-MM-DD HH24:MI:SS'), 'Tuesday') from dual; next_day --------------------- 2020-03-03 14:40:50 (1 row) select next_day('2020-07-01 19:43:51 +8'::timestamptz, 1) from dual; next_day --------------------- 2020-07-05 19:43:51 (1 row) last_day returns the last day of the month in which the slot date falls. Parameters Description value Specified timestamp select last_day(timestamp '2020-05-17 13:27:19') from dual; last_day --------------------- 2020-05-31 13:27:19 (1 row) select last_day('2020-11-29 19:20:40 +08'::timestamptz) from dual; last_day --------------------- 2020-11-30 19:20:40 (1 row) add_months returns the date plus an integer month. date Parameters can be date-time values or any value that can be implicitly converted to DATE. integer Parameters can be an integer or any value that can be implicitly converted to an integer. Parameters Description day oracle.date type, the timestamp that needs to be changed value A shaping data, the number of months to be added select add_months(date '2020-02-15',7) from dual; add_months --------------------- 2020-09-15 00:00:00 (1 row) select add_months(timestamp '2018-12-15 19:12:09',12) from dual; add_months --------------------- 2019-12-15 19:12:09 (1 row) sysdate returns the operating system time of the database server. select sysdate; statement_sysdate --------------------- 2021-12-09 16:20:34 (1 row) select sysdate(); sysdate --------------------- 2021-12-09 16:21:39 (1 row) Convert the time of the first time zone to the time of the second time zone. The time zones include \"ast\", \"adt\", \"bst\", \"bdt\", \"cst\", \"cdt\", \"est\", \"edt\", \"gmt\", \"hst\", \"hdt\", \"mst\", \"mdt\", \"nst\", \"pst\", \"pdt\", \"yst\", \"ydt\". Parameters Description day Timestamp to be converted tz1 Timestamped time zones tz2 Target time zone select new_time(timestamp '2020-12-12 17:45:18', 'AST', 'ADT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'BST', 'BDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'CST', 'CDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) The trunc function returns a date, truncated in the specified format. fmt includes \"Y\", \"YY\", \"YYYY\", \"YYYY\", \"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \" MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". Parameters Description value The specified date (oracle.date, timestamp, timestamptz) fmt The specified format, if omitted, defaults to \"DDD\" select trunc(timestamp '2020-07-28 19:16:12', 'Q'); trunc --------------------- 2020-07-01 00:00:00 (1 row) select trunc(timestamptz '2020-09-27 18:30:21 + 08', 'MONTH'); trunc ------------------------ 2020-09-01 00:00:00+08 (1 row) The round function returns a date, rounded to the specified format. fmt includes \"Y\", \"YY\", \"YYYY\", \"YYYY\", \"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D \", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". Parameters Description value The date being converted (oracle.date, timestamp, timestamptz) fmt The specified format, if omitted, defaults to \"DDD\" select round(timestamp '2050-06-12 16:40:55', 'IYYY'); round --------------------- 2050-01-03 00:00:00 (1 row) TO_CHAR (str,[fmt]) Converts the input Parameters to a TEXT data type value according to the given format. If fmt is omitted, the data will be converted to a TEXT value in the system default format. If str is null, the function returns null. str Input Parameters (any type). fmt Input format Parameters, see format fmt for details. select to_char('3 2:20:05' ); to_char ----------------- 3 days 02:20:05 (1 row) select to_char('4.00'::numeric); to_char --------- 4 (1 row) select to_char(NULL); to_char --------- (1 row) select to_char(123,'xx'); to_char --------- 7b (1 row) TO_NUMBER(str,[fmt1]) Converts the input Parameters str to a value of the NUMREIC data type according to the given format. If fmt1 is omitted, the data will be converted to a NUMERIC value in the system default format. If str is NUMERIC, the function returns str. If str calculates to null, the function returns null. If it cannot be converted to the NUMERIC data type, the function returns an error. str input Parameters include the following data types (double precision, numeric, text, integer, etc., but must be implicitly converted to numeric). fmt1 Input format Parameters, see format fmt1 for details. select to_number(1210.73::numeric, 9999.99::numeric); to_number ----------- 1210.73 (1 row) select to_number(NULL); to_number ----------- (1 row) select to_number('123'::text); to_number ----------- 123 (1 row) TO_DATE(str,[fmt]) Converts the input Parameters str to a date data type value according to the given format. If fmt is omitted, the data will be converted to a date value in the system default format. If str is null, the function returns null. If fmt is J, for Julian, then char must be an integer. The function returns an error if it cannot be converted to DATE. str input Parameters (integer, text, can be implicitly converted to the above type, string that matches the date format). fmt input format Parameters, see format fmt for details. select to_date('50-11-28 ','RR-MM-dd '); to_date --------------------- 1950-11-28 00:00:00 (1 row) select to_date(2454336, 'J'); to_date --------------------- 2007-08-23 00:00:00 (1 row) select to_date('2019/11/22', 'yyyy-mm-dd'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('20-11-28 10:14:22','YY-MM-dd hh24:mi:ss'); to_date --------------------- 2020-11-28 10:14:22 (1 row) select to_date('2019/11/22'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('2019/11/27 10:14:22'); to_date --------------------- 2019-11-27 10:14:22 (1 row) select to_date('2020','RR'); to_date --------------------- 2020-01-01 00:00:00 (1 row) select to_date(NULL); to_date --------- (1 row) select to_date('-4712-07-23 14:31:23', 'syyyy-mm-dd hh24:mi:ss'); to_date ---------------------- -4712-07-23 14:31:23 (1 row) TO_TIMESTAMP(str,[fmt]) Converts the input Parameters str to a timestamp without a time zone according to the given format. If fmt is omitted, the data is converted to a timestamp with no time zone value in the system default format. If str is null, the function returns null. If it cannot be converted to a timestamp without a time zone, the function returns an error. str input Parameters (double precision,text, which can be implicitly converted to the above type). fmt Input format Parameters, see format fmt for details. select to_timestamp(1212121212.55::numeric); to_timestamp --------------------------- 2008-05-30 12:20:12.55 (1 row) select to_timestamp('2020/03/03 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp ------------------------ 2020-03-03 13:13:18 (1 row) select to_timestamp(NULL,NULL); to_timestamp -------------- (1 row) TO_YMINTERVAL(str) Converts the input Parameters str time interval to a time interval in the year-to-month range. Only the year and month are processed, other parts are omitted. If the input Parameters is NULL, the function returns NULL, and if the input Parameters is in the wrong format, the function returns an error. str Input Parameters (text, can be implicitly converted to text type, must be in time interval format. (SQL interval format compatible with SQL standard, ISO duration format compatible with ISO 8601:2004 standard). select to_yminterval('P1Y-2M2D'); to_yminterval --------------- +000000000-10 (1 row) select to_yminterval('P1Y2M2D'); to_yminterval --------------- +000000001-02 (1 row) select to_yminterval('-01-02'); to_yminterval --------------- -000000001-02 (1 row) TO_DSINTERVAL(str) converts the time interval of the input Parameters str to a time interval in the range of days to seconds. Input Parameters include: day, hour, minute, second and microsecond. If the input Parameters is NULL, the function returns NULL, and if the input Parameters contains the year and month or is in the wrong format, the function returns an error. str Input Parameters (text, can be implicitly converted to text type, must be in time interval format. (SQL interval format compatible with SQL standard, ISO duration format compatible with ISO 8601:2004 standard). select to_dsinterval('100 00 :02 :00'); to_dsinterval ------------------------------- +000000100 00:02:00.000000000 (1 row) select to_dsinterval('-100 00:02:00'); to_dsinterval ------------------------------- -000000100 00:02:00.000000000 (1 row) select to_dsinterval(NULL); to_dsinterval --------------- (1 row) TO_TIMESTAMP_TZ(str,[fmt]) Converts the input Parameters str to a timestamp with a time zone according to the given format. If fmt is omitted, the data will be converted to a timestamp with a time zone value in the system default format. If str is null, the function returns null. If it cannot be converted to a timestamp with a time zone, the function returns an error. str input Parameters (text, which can be implicitly converted to a text type). fmt Enter format Parameters, see format fmt for details. select to_timestamp_tz('2019','yyyy'); to_timestamp_tz ------------------------ 2019-01-01 00:00:00+08 (1 row) select to_timestamp_tz('2019-11','yyyy-mm'); to_timestamp_tz ------------------------ 2019-11-01 00:00:00+08 (1 row) select to_timestamp_tz('2003/12/13 10:13:18 +7:00'); to_timestamp_tz ------------------------ 2003-12-13 11:13:18+08 (1 row) select to_timestamp_tz('2019/12/13 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp_tz ------------------------ 2019-12-13 13:13:18+08 (1 row) select to_timestamp_tz(NULL); to_timestamp_tz ----------------- (1 row) GREATEST(expr1,expr2,…​) Gets the maximum value in the input list of one or more expressions. If the result of any expr calculation is NULL, the function returns NULL. expr1` Enter Parameters (of any type). `expr2` Enter Parameters (of any type). `... select greatest('a','b','A','B'); greatest ---------- b (1 row) select greatest(',','.','/',';','!','@','?'); greatest ---------- @ (1 row) select greatest('瀚','高','数','据','库'); greatest ---------- 高 (1 row) SELECT greatest('HARRY', 'HARRIOT', 'HARRA'); greatest ---------- HARRY (1 row) SELECT greatest('HARRY', 'HARRIOT', NULL); greatest ---------- (1 row) SELECT greatest(1.1, 2.22, 3.33); greatest ---------- 3.33 (1 row) SELECT greatest('A', 6, 7, 5000, 'E', 'F','G') A; a --- G (1 row) LEAST(expr1,expr2,…​) Gets the smallest value in the input list of one or more expressions. If the result of any expr calculation is NULL, the function returns NULL. expr1` Enter Parameters (of any type). `expr2` Enter Parameters (of any type). `... SELECT least(1,' 2', '3' ); least ------- 1 (1 row) SELECT least(NULL, NULL, NULL); least ------- (1 row) SELECT least('A', 6, 7, 5000, 'E', 'F','G') A; a ------ 5000 (1 row) select least(1,3,5,10); least ------- 1 (1 row) select least('a','A','b','B'); least ------- A (1 row) select least(',','.','/',';','!','@'); least ------- ! (1 row) select least('瀚','高','据','库'); least ------- 库 (1 row) SELECT least('HARRY', 'HARRIOT', NULL); least ------- (1 row) NLS_LENGTH_SEMANTICS enables you to create CHAR and VARCHAR2 columns using byte or character length semantics. Existing columns are not affected. In this case, the default semantics is BYTE. SET NLS_LENGTH_SEMANTICS TO [NONE | BYTE | CHAR]; BYTE: The data is stored in byte length. CHAR:Data is stored in character length. NONE:Data is stored using native IvorySQL storage. create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 SET NLS_LENGTH_SEMANTICS TO BYTE; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- byte (1 row) insert into test values ('Hello,Mr.li'); 2021-12-14 15:28:11.906 HKT [6774] ERROR: value too long for type varchar2(5 byte) 2021-12-14 15:28:11.906 HKT [6774] STATEMENT: insert into test values ('Hello,Mr.li'); ERROR: value too long for type varchar2(5 byte) Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character. VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 PL/iSQL is IvorySQL’s procedural language for writing custom functions, procedures and packages for IvorySQL. PL/iSQL is derived from IvorySQL’s PL/pgsql with some added features, but syntactically PL/iSQL is closer to Oracle’s PL/SQL. This document Describes the basic structure and construction of PL/iSQL programs. iSQL is a procedural block structure language that supports four different program types, PACKAGES, PROCEDURES, FUNCTIONS, and TRIGGERS. iSQL supports four different program types, PACKAGES, PROCEDURES, FUNCTIONS, and TRIGGERS. iSQL uses the same block structure for each type of supported program. A block consists of up to three parts: a declaration part, an executable, and an exception part. The declaration and exception sections are optional. [DECLARE declarations] BEGIN statements [ EXCEPTION WHEN <exception_condition> THEN statements] END; A block can consist of at least one executable section Contains one or more iSQL statements in the BEGIN and END keywords. CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS BEGIN NULL; END; / All keywords are case-insensitive. Identifiers are implicitly converted to lowercase unless double-quoted, just as they are in normal SQL commands. The declaration section can be used to declare variables and cursors, and depending on the context in which the block is used, the declaration section can begin with the keyword DECLARE. CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS DECLARE quantity integer := 30; c_row pg_class%ROWTYPE; r_cursor refcursor; CURSOR c1 RETURN pg_proc%ROWTYPE; BEGIN NULL; end; / An optional exception section can also be included in a BEGIN - END block. The exception section begins with the keyword EXCEPTION and continues until the end of the block in which it appears. If a statement within the block throws an exception, program control goes to the exception section, which may or may not handle the thrown exception, depending on the contents of the exception and exception sections. CREATE OR REPLACE FUNCTION reraise_test() RETURN void AS BEGIN BEGIN RAISE syntax_error; EXCEPTION WHEN syntax_error THEN BEGIN raise notice 'exception % thrown in inner block, reraising', sqlerrm; RAISE; EXCEPTION WHEN OTHERS THEN raise notice 'RIGHT - exception % caught in inner block', sqlerrm; END; END; EXCEPTION WHEN OTHERS THEN raise notice 'WRONG - exception % caught in outer block', sqlerrm; END; / Note Like PL/pgSQL, PL/iSQL uses BEGIN/END to group statements, and do not confuse them with the SQL commands of the same name used for transaction control. PL/iSQL’s BEGIN/END are used only for grouping; they do not start or end transactions To create a PL/iSQL program from a psql client, you can use a syntax similar to PL/pgSQL’s $$ CREATE FUNCTION func() RETURNS void as $$ .. end$$ language plisql; Alternatively, you can use the Oracle-compliant syntax of references and language specifications without $$ and end the program definition with / (forward slash). The */ (forward slash) must be on the newline character CREATE FUNCTION func() RETURN void AS … END; / CREATE [OR REPLACE] PROCEDURE procedure_name [(parameter_list)] is [DECLARE] -- variable declaration BEGIN -- stored procedure body END; / CREATE [OR REPLACE] FUNCTION function_name ([parameter_list]) RETURN return_type AS [DECLARE] -- variable declaration BEGIN -- function body return statement END; / CREATE [ OR REPLACE ] PACKAGE [schema.] *package_name* [invoker_rights_clause] [IS | AS] item_list[, item_list ...] END [*package_name*]; invoker_rights_clause: AUTHID [CURRENT_USER | DEFINER] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] function_declaration: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype; procedure_declaration: PROCEDURE procedure_name [(parameter_declaration[, ...])] type_definition: record_type_definition | ref_cursor_type_definition cursor_declaration: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype; item_declaration: cursor_declaration | cursor_variable_declaration | record_variable_declaration | variable_declaration | record_type_definition: TYPE record_type IS RECORD ( variable_declaration [, variable_declaration]... ) ; ref_cursor_type_definition: TYPE type IS REF CURSOR [ RETURN type%ROWTYPE ]; cursor_variable_declaration: curvar curtype; record_variable_declaration: recvar { record_type | rowtype_attribute | record_type%TYPE }; variable_declaration: varname datatype [ [ NOT NULL ] := expr ] parameter_declaration: parameter_name [IN] datatype [[:= | DEFAULT] expr] CREATE [ OR REPLACE ] PACKAGE BODY [schema.] package_name [IS | AS] [item_list[, item_list ...]] | item_list_2 [, item_list_2 ...] [initialize_section] END [package_name]; initialize_section: BEGIN statement[, ...] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] item_list_2: [ function_declaration function_definition procedure_declaration procedure_definition cursor_definition ] function_definition: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype [IS | AS] [declare_section] body; procedure_definition: PROCEDURE procedure_name [(parameter_declaration[, ...])] [IS | AS] [declare_section] body; cursor_definition: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype IS select_statement; body: BEGIN statement[, ...] END [name]; statement: [<<LABEL>>] pl_statments[, ...]; { CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... [ START WITH condition ] | START WITH condition CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... } The CONNECT BY query syntax begins with the CONNECT BY keywords, which define hierarchical interdependencies between parent and child rows. The result must be further qualified by specifying the PRIOR keyword in the conditional part of the CONNECT BY clause. The PRIOR PRIOR keyword is a unary operator that relates the previous row to the current row. This keyword can be used to the left or right of the equality condition. START WITH This clause specifies the line from which the hierarchy begins. NOCYCLE No operation statement. Currently only supported by the syntax. This clause indicates that data is returned even if a loop exists. LEVEL Returns the level of the current row in the hierarchy, starting at 1 at the root node and incrementing by 1 at each level thereafter. CONNECT_BY_ROOT expr Returns the parent column of the current row in the hierarchy. SYS_CONNECT_BY_PATH(col, chr) It is a function that returns the value of the column from the root to the current node, separated by the character \"chr\". This function currently has the following limitations. Additional columns can be used for most expressions, such as function calls, CASE statements and general expressions, but there are some unsupported columns, such as ROW, TYPECAST, COLLATE, GROUPING clauses, etc. In case two or more columns are the same, you may need to output the column name, Example such as SELECT CONNECT_BY_ROOT col AS \"col1\", CONNECT_BY_ROOT col AS \"col2\" …​. Indirect operators or \"*\" are not supported Loop detection is not supported CREATE UNIQUE INDEX [IF NOT EXISTS] name ON table_name [USING method] (columns) GLOBAL CREATE UNIQUE INDEX myglobalindex on mytable(bid) GLOBAL; During the creation of a globally unique index, the system performs an index scan on all existing partitions and raises an error if it finds duplicate entries from other partitions than the current one. Example. Command create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); create table gidxpart2 partition of gidxpart for values from (100000) to (199999); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); insert into gidxpart (a, b, c) values (150000, 572814, 'inserted second on gidxpart2'); create unique index on gidxpart (b) global; Output ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated. During global unique index creation, the system performs an index scan on all existing partitions and raises an error if duplicate items are found in other partitions than the current one. Command create table gidx_part (a int, b int, c text) partition by range (a); create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (10); create table gidxpart2 partition of gidxpart for values from (10) to (100); create unique index gidx_u on gidxpart using btree(b) global; insert into gidxpart values (1, 1, 'first'); insert into gidxpart values (11, 11, 'eleventh'); insert into gidxpart values (2, 11, 'duplicated (b)=(11) on other partition'); Output ERROR: duplicate key value violates unique constraint \"gidxpart2_b_idx\" DETAIL: Key (b)=(11) already exists. When appending a new table to a partitioned table with a globally unique index, the system performs a duplicate check on all existing partitions. If a duplicate item is found in an existing partition that matches a tuple in the appended table, an error is raised and the append fails. Appending requires a SHARE LOCK on all existing partitions. If one of the partitions is doing a concurrent INSERT, the append will wait for it to complete first. This can be improved in a future release Command create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); create unique index on gidxpart (b) global; create table gidxpart2 (a int, b int, c text); insert into gidxpart2 (a, b, c) values (150000, 572814, 'dup inserted on gidxpart2'); alter table gidxpart attach partition gidxpart2 for values from (100000) to (199999); Output ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated.","title":"Developer Guide","component":"ivorysql-doc","version":"master","name":"developer_guide","url":"/ivorysql-doc/master/developer_guide.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"1.1. Architecture Overview","hash":"architecture-overview","id":2},{"text":"1.2. 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Append and detach","hash":"append-and-detach","id":273},{"text":"8.3.1. Global uniqueness guarantee for append statements","hash":"global-uniqueness-guarantee-for-append-statements","id":274},{"text":"8.3.2. Example","hash":"example-31","id":275}]},"4":{"id":4,"text":"PostgreSQL provides comprehensive server-side support for the GB18030 character set. GB18030 is a Chinese national standard designed to include all Chinese characters and various minority scripts, aiming for alignment with Unicode. Proper configuration and use of the GB18030 character set within PostgreSQL are essential for processing and storing Chinese data that must comply with this standard. Server-side GB18030 support should provide the following features: 1. Support for GB18030 as a server encoding: initdb -E GB18030 is available, and SHOW server_encoding displays GB18030. 2. Provide bidirectional conversion between GB18030 and UTF8. 3. Support for multibyte character boundary determination. PostgreSQL has historically supported the GB18030-2000 standard as a client-side encoding. Support for conversion between the GB18030_2022 character set and UTF-8 is provided via an extension. To enable GB18030 as a server encoding, modifications are made to pg_enc, and new low-level functions are added to the PostgreSQL encoding framework for invocation by the core system. A global variable, is_load_gb18030_2022, is introduced with a default value of true. When the -E option is used during initdb, the get_encoding_id function checks the specified encoding name. If the name is gb18030_2022, it is internally mapped to the gb18030 encoding ID, and the is_load_gb18030_2022 flag is set to true. If the -E option is GB18030, the flag is set to false. At the appropriate stage in the startup process, the system checks this flag to determine if the extension should be loaded. If required, the load_gb18030_2022 function is executed, and the gb18030_2022 extension is added to the shared_preload_libraries parameter in ivorysql.conf. if (encoding_name && *encoding_name) { encoding_name_modify = pg_strdup(encoding_name); if(pg_strcasecmp(encoding_name,\"gb18030_2022\") == 0) { encoding_name_modify = pg_strdup(\"gb18030\"); is_load_gb18030_2022 = true; } else if(pg_strcasecmp(encoding_name,\"gb18030\") == 0) is_load_gb18030_2022 = false; if ((enc = pg_valid_server_encoding((const char *)encoding_name_modify)) >= 0) return enc; } Function pointers for GB18030 are added in wchar.c: pg_gb180302wchar_with_len(const unsigned char *from, pg_wchar *to, int len) gb18030 → wchar pg_wchar2gb18030_with_len(const pg_wchar *from, unsigned char *to, int len) wchar → gb18030 pg_gb18030_mblen(const unsigned char *s)：Returns 1/2/4. pg_gb18030_dsplen(const unsigned char *s)：Calculates the display width of a character. ASCII characters have a width of 1, while others are also treated as having a width of 1 . pg_gb18030_verifier(const unsigned char *s, int len)：Verifies that a byte sequence is a valid GB18030 character, rejecting illegal sequences. Receiving Data When a client using UTF-8 encoding connects to the server, the server, upon receiving data, invokes its internal utf8_to_gb18030 function. This converts the data to the GB18030 format, which is then validated and stored. Sending Data When the same client executes a SELECT query, the server reads the native GB18030 data from disk or memory. It then calls the gb18030_to_utf8 function to convert the data to UTF-8 format before sending it to the client via the network protocol. A new data file, GB18030-2022.xml, is introduced. This file is parsed by a Perl script to generate mapping files that provide the logic for the gb18030_to_utf8() and utf8_to_gb18030() conversion functions. The implementation prioritizes a table-driven approach, falling back to algorithmic mapping for ranges not covered by the tables. static inline uint32 unicode_to_utf8word(uint32 c) { uint32 word; if (c <= 0x7F) { word = c; } else if (c <= 0x7FF) { word = (0xC0 | ((c >> 6) & 0x1F)) << 8; word |= 0x80 | (c & 0x3F); } else if (c <= 0xFFFF) { word = (0xE0 | ((c >> 12) & 0x0F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } else { word = (0xF0 | ((c >> 18) & 0x07)) << 24; word |= (0x80 | ((c >> 12) & 0x3F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } return word; } static uint32 conv_18030_2022_to_utf8(uint32 code) { #define conv18030(minunicode, mincode, maxcode) \\ if (code >= mincode && code <= maxcode) \\ return unicode_to_utf8word(gb_linear(code) - gb_linear(mincode) + minunicode) conv18030(0x0452, 0x8130D330, 0x8136A531); conv18030(0x2643, 0x8137A839, 0x8138FD38); conv18030(0x361B, 0x8230A633, 0x8230F237); conv18030(0x3CE1, 0x8231D438, 0x8232AF32); conv18030(0x4160, 0x8232C937, 0x8232F837); conv18030(0x44D7, 0x8233A339, 0x8233C931); conv18030(0x478E, 0x8233E838, 0x82349638); conv18030(0x49B8, 0x8234A131, 0x8234E733); conv18030(0x9FA6, 0x82358F33, 0x8336C738); conv18030(0xE865, 0x8336D030, 0x84308534); conv18030(0xFA2A, 0x84309C38, 0x84318537); conv18030(0xFFE6, 0x8431A234, 0x8431A439); conv18030(0x10000, 0x90308130, 0xE3329A35); /* No mapping exists */ return 0; }","title":"Server-Side Support for the GB18030 Character Set","component":"ivorysql-doc","version":"master","name":"gb18030","url":"/ivorysql-doc/master/gb18030.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Implementation Details","hash":"implementation-details","id":2},{"text":"2.1. Specifying GB18030 or GB18030_2022 with the -E Option during initdb","hash":"specifying-gb18030-or-gb18030_2022-with-the-e-option-during-initdb","id":3},{"text":"2.2. Multibyte Character Handling","hash":"multibyte-character-handling","id":4},{"text":"2.3. Client-Server Interaction","hash":"client-server-interaction","id":5}]},"5":{"id":5,"text":"The installation methods for IvorySQL include the following five: Yum installation Docker installation rpm installation Source code installation deb installation This chapter will provide detailed instructions on the installation, execution, and uninstallation processes for each method. For a quicker access to IvorySQL, please refer to Quick installation. Before getting started, please create an user and grant it root privileges. All the installation steps will be performed by this user. Here we just name it 'ivorysql'. Create or edit IvorySQL yum repository configuration /etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 After saving and exiting, you can install IvorySQL 4 with the following steps $ sudo dnf install -y ivorysql5-5.0 Get IvorySQL image from Docker Hub $ docker pull ivorysql/ivorysql:5.0-ubi8 Run IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 -e Parameter Explanation Parameter Name Required Description IVORYSQL_USER No Database user, default is ivorysql IVORYSQL_PASSWORD yes Database password IVORYSQL_DB no Database name,default is ivorysql POSTGRES_HOST_AUTH_METHOD no Modify host authentication method,reference value:md5 POSTGRES_INITDB_ARGS no Add additional parameters to initdb,reference value:\"--data-checksums\" PGDATA no Define the data directory to be located in another path or folder (e.g., subdirectory), defaulting to /var/lib/ivorysql/data POSTGRES_INITDB_WALDIR no Define the IvorySQL transaction folder path, which defaults to a subdirectory within the data directory (PGDATA) It is not recommended to set the POSTGRES_HOST_AUTH_METHOD parameter to trust, as this will make the IVORYSQL_PASSWORD setting ineffective. If the POSTGRES_HOST_AUTH_METHOD parameter is set to scram-sha-256, it is also necessary to set POSTGRES_INITDB_ARGS to --auth-host=scram-sha-256 to ensure proper initialization of the database. Installing dependencies $ sudo dnf install -y lz4 libicu libxslt python3 Getting rpms $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-9d890e9-20251120.x86_64.rpm Installing rpms Use the following command to install all the rpms: $ sudo yum --disablerepo=* localinstall *.rpm IvorySQL then will be installed in the /usr/ivory-5/ directory. Installing dependencies $ sudo dnf install -y bison readline-devel zlib-devel openssl-devel uuid-devel $ sudo dnf groupinstall -y 'Development Tools' Getting source code $ git clone https://github.com/IvorySQL/IvorySQL.git $ cd IvorySQL $ git checkout -b IVORY_REL_5_STABLE origin/IVORY_REL_5_STABLE Configuring In the IvorySQL directory run the following command with --prefix to specify the directory where you want the database to be installed: $ ./configure --prefix=/usr/local/ivorysql/ivorysql-5 Compiling Run the following command to compile the source code: $ make When the compilation is completed, you can test the result with 'make check' or 'make all-check-world' before your installation. Installing Run the following command to install the database system, IvorySQL then will be installed in the directory specified by --prefix: $ sudo make install Installing dependencies $ sudo apt -y install pkg-config libreadline-dev libicu-dev libldap2-dev uuid-dev tcl-dev libperl-dev python3-dev bison flex openssl libssl-dev libpam-dev libxml2-dev libxslt-dev libossp-uuid-dev libselinux-dev gettext Getting deb $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-a50789d-20250304.amd64.deb Installing deb $ sudo dpkg -i IvorySQL-5.0-a50789d-20250304.amd64.deb IvorySQL will then be installed in the /usr/ivory-5/ directory. Users following the instructions in Yum installation, rpm installation, Source code installation and deb installation need to manually start the database. Granting permissions Execute the following command to grant permissions to the installation user. The example user is ivorysql, and the installation directory is /usr/ivory-5/: $ sudo chown -R ivorysql:ivorysql /usr/ivory-5/ Setting environment variables Add below contents in ~/.bash_profile file and source to make it effective: PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile Initializing database $ mkdir /usr/ivory-5/data $ initdb -D /usr/ivory-5/data The -D option specifies the directory where the database cluster should be stored. This is the only information required by initdb, but you can avoid writing it by setting the PGDATA environment variable, which can be convenient since the database server can find the database directory later by the same variable. For more options, refer to initdb --help. Starting IvorySQL service $ pg_ctl -D /usr/ivory-5/data -l ivory.log start The -D option specifies the file system location of the database configuration files. If this option is omitted, the environment variable PGDATA in [setting-environment-variables] is used. -l option appends the server log output to filename. If the file does not exist, it is created. For more options, refer to pg_ctl --help. Confirm it’s successfully started: $ ps -ef | grep postgres ivorysql 130427 1 0 02:45 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 130428 130427 0 02:45 ? 00:00:00 postgres: checkpointer ivorysql 130429 130427 0 02:45 ? 00:00:00 postgres: background writer ivorysql 130431 130427 0 02:45 ? 00:00:00 postgres: walwriter ivorysql 130432 130427 0 02:45 ? 00:00:00 postgres: autovacuum launcher ivorysql 130433 130427 0 02:45 ? 00:00:00 postgres: logical replication launcher ivorysql 130445 130274 0 02:45 pts/1 00:00:00 grep --color=auto postgres Connect to IovrySQL via psql: $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# The -d option specifies the name of the database to connect to. ivorysql is the default database of IvorySQL. However,IvorySQL of lower versions need the users themselves to connect to postgres database at the first connection and then create the ivorysql database.The latest IvorySQL can do all these for users. For more options, refer to psql --help. When running IvorySQL in Docker, additional parameters need to be added, like: psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 No matter which method is used for the uninstallation, make sure the service has been stopped cleanly and your data has been backed up safely. Run the following commands in turn and clean the residual folders: $ sudo dnf remove -y ivorysql5-5.0 Stop IvorySQL container and remove IvorySQL image: $ docker stop ivorysql $ docker rm ivorysql $ docker rmi ivorysql/ivorysql:5.0-ubi8 Uninstall the rpms and clear the residual folders: $ sudo yum remove --disablerepo=* ivorysql5\\* $ sudo rm -rf IvorySQL-5.0 Uninstall the database system, then clear the residual folders: $ sudo make uninstall $ make clean $ sudo rm -rf IvorySQL-5.0 Uninstall the database system, then clear the residual folders: $ sudo dpkg -P IvorySQL-5.0 $ sudo rm -rf IvorySQL-5.0","title":"Installation","component":"ivorysql-doc","version":"master","name":"installation_guide","url":"/ivorysql-doc/master/installation_guide.html","titles":[{"text":"1. Introduction","hash":"introduction","id":1},{"text":"2. Yum installation","hash":"Yum-installation","id":2},{"text":"3. Docker installation","hash":"Docker-installation","id":3},{"text":"4. rpm installation","hash":"Rpm-installation","id":4},{"text":"5. Source code installation","hash":"Source-code-installation","id":5},{"text":"6. deb installation","hash":"deb-installation","id":6},{"text":"7. Start Database","hash":"start-database","id":7},{"text":"8. Connecting to IvorySQL","hash":"connecting-to-ivorysql","id":8},{"text":"9. Uninstallation","hash":"uninstallation","id":9},{"text":"9.1. Uninstallation for yum installation","hash":"uninstallation-for-yum-installation","id":10},{"text":"9.2. Uninstallation for docker installation","hash":"uninstallation-for-docker-installation","id":11},{"text":"9.3. Uninstallation for rpm installation","hash":"uninstallation-for-rpm-installation","id":12},{"text":"9.4. Uninstallation for source code installation","hash":"uninstallation-for-source-code-installation","id":13},{"text":"9.5. Uninstallation for deb installation","hash":"uninstallation-for-deb-installation","id":14}]},"6":{"id":6,"text":"Database migration refers to the process of transferring data from one database to another, and the databases at both ends may be PostgreSql, IvoySQL, MySQL, Oracle, SQL Server, etc. The migration process is a challenging, complex process that requires a thorough understanding of how databases work and their characteristics. If the application has been deployed to the production environment and is in a normal operating state, a smooth application migration is required after database migration to maintain uninterrupted business operation and no data loss. After migration, databases and systems should meet the following requirements: The migrated database system should fully host the data of the original database system. Avoid data loss during migration that causes incomplete data to the new database system. The migrated database system should fully adapt to the functions of the original database. Avoid the inability to run or throw errors of the entire business system due to data types, syntax, and functions that are not supported after migration, and there is no alternative. The migrated database should be adapted to the upstream and downstream of the entire business system to ensure the stable and reliable operation of the entire business system. The comprehensive performance of the migrated database cannot be weaker than that of the original database, providing performance guarantee for the entire business system. Ora2Pg is a free tool for migrating Oracle databases to an IvorySQL-compatible schema. It connects to your Oracle database, automatically scans and extracts its structure or data, and then generates SQL scripts that can be loaded into an IvorySQL database. Ora2Pg can migrate from a reverse-engineered Oracle database to a large enterprise database, or simply copy some Oracle data into an IvorySQL database. It is very easy to use and does not require any Oracle database knowledge without providing the parameters required to connect to Oracle Database. Ora2Pg consists of a Perl script (ora2pg) and a Perl module(Ora2Pg.pm), the only thing that needs to be done is to modify its configuration file, ora2pg.conf, set the DSN to connect to the Oracle database, and an optional SCHEMA name. ONCE THAT’S DONE, YOU ONLY NEED TO SET THE EXPORTED TYPE: TABLE (INCLUDING CONSTRAINTS AND INDEXES), VIEW, MVIEW, TABLESPACE, SEQUENCE, INDEXES, TRIGGER, GRANT, FUNCTION, PROCEDURE, PACKAGE, PARTITION, TYPE, INSERT OR COPY, FDW, QUERY, KETTLE, AND SYNONYM. By default, Ora2Pg exports an SQL file, which can be executed through the IvorySQL client tool psql. When performing data migration, you can set the DSN of the target database in the configuration file to import data directly from Oracle into the IvorySQL database. Object Whether ora2pg is supported view yes trigger yes，In some cases, you need to modify the script manually sequence yes function yes procedure yes，In some cases, you need to modify the script manually type yes，In some cases, you need to modify the script manually materialized view yes，In some cases, you need to modify the script manually linux Oracle Version IvorySQL Version Centos Stream 9 19.0.0.0 4.2 # dnf install -y perl perl-ExtUtils-CBuilder perl-ExtUtils-MakeMaker # perl -v This is perl 5, version 16, subversion 3 (v5.16.3) built for x86_64-linux-thread-multi (with 44 registered patches, see perl -V for more detail) Copyright 1987-2012, Larry Wall Perl may be copied only under the terms of either the Artistic License or the GNU General Public License, which may be found in the Perl 5 source kit. Complete documentation for Perl, including FAQ lists, should be found on this system using \"man perl\" or \"perldoc perl\". If you have access to the Internet, point your browser at http://www.perl.org/, the Perl Home Page. DBI，Database Independent Interface，is the interface of the Perl language to connect to the database Download address: https://cpan.metacpan.org/authors/id/T/TI/TIMB/DBI-1.643.tar.gz # tar zxvf DBI-1.643.tar.gz # cd DBI-1.643/ # perl Makefile.PL # make # make install Download address：https://sourceforge.net/projects/ora2pg/ Set environment variables; Load environment variables; Because ORACLE must be defined_ HOME environment variable; This example configures environment variables under the ivorysql user export ORACLE_HOME=/usr/lib/oracle/18.3/client64 # tar -zxvf DBD-Oracle-1.76.tar.gz # source /home/postgres/.bashrc # cd DBD-Oracle-1.76 # perl Makefile.PL # make # make install Download address：https://metacpan.org/release/DBD-Pg/ Set environment variables： export POSTGRES_HOME=/opt/ivorysql/3.2 # tar -zxvf DBD-Pg-3.80.tar.gz # source /home/ivorysql/.bashrc # cd DBD-Pg-3.8.0 # perl Makefile.PL # make # make install Download address：https://sourceforge.net/projects/ora2pg/ # tar -xjf ora2pg-24.0.tar.bz2 # cd ora2pg-xx/ # perl Makefile.PL PREFIX=<your_install_dir> # make && make install Installed in/usr/local/bin/directory by default Check the software environment: # vi check.pl #!/usr/bin/perl use strict; use ExtUtils::Installed; my $inst= ExtUtils::Installed->new(); my @modules = $inst->modules(); foreach(@modules) { my $ver = $inst->version($_) || \"???\"; printf(\"%-12s -- %s\\n\", $_, $ver); } exit; # perl check.pl DBD::Oracle -- 1.76 DBD::Pg -- 3.8.0 DBI -- 1.643 Ora2Pg -- 24.0 Perl -- 5.16.3 Set environment variables export PERL5LIB=<your_install_dir> #export PERL5LIB=/usr/local/bin/ Update oracle statistics to improve performance BEGIN DBMS_STATS.GATHER_SCHEMA_STATS('SH'); DBMS_STATS.GATHER_SCHEMA_STATS('SCOTT'); DBMS_STATS.GATHER_SCHEMA_STATS('HR'); DBMS_STATS.GATHER_DATABASE_STATS ; DBMS_STATS.GATHER_DICTIONARY_STATS; END;/ Query the source end object pair type SYS@PROD1>set pagesize 200 SYS@PROD1>select distinct OBJECT_TYPE from dba_objects where OWNER in ('SH','SCOTT','HR'); OBJECT_TYPE ------------------- INDEX PARTITION TABLE PARTITION SEQUENCE PROCEDURE LOB X TRIGGER DIMENSION X MATERIALIZED VIEW TABLE INDEX VIEW 11 rows selected. Configure ora2pg.conf By default, Ora2Pg will find the/etc/ora2pg/ora2pg.conf configuration file. If the file exists, you only need to execute:/usr/local/bin/ora2pg cat /etc/ora2pg/ora2pg.conf.dist | grep -v ^# |grep -v ^$ >ora2pg.conf vi ora2pg.conf # cat ora2pg.conf ORACLE_HOME /opt/oracle/product/19c/dbhome_1 ORACLE_DSN dbi:Oracle:host=localhost;sid=ORCLCDB;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 SKIP fkeys ukeys checks TYPE TABLE,VIEW,GRANT,SEQUENCE,TABLESPACE,PROCEDURE,TRIGGER,FUNCTION,PACKAGE,PARTITION,TYPE,MVIEW,QUERY,DBLINK,SYNONYM,DIRECTORY,TEST,TEST_VIEW NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh.sql Only one type of export can be executed at the same time, so the TYPE instruction must be unique. If you have more than one, only the last one will be found in the file. But I can export multiple types at the same time. Please note that you can link multiple exports by providing a comma-separated list of export types to the TYPE directive, but in this case, you cannot use COPY or INSERT with other export types. Some export types cannot or should not be directly loaded into the IvorySQL database, and still require little manual editing. This is the case for GRANT, TABLESPACE, TRIGGER, FUNCTION, PROCEDURE, TYPE, QUERY and PACKAGE export types, especially if you have PLSQL code or Oracle specific SQL. For TABLESPACE, you must ensure that the file path exists on the system. For SYNONYM, you can ensure that the owner and schema of the object correspond to the new PostgreSQL database design. It is recommended to export the table structure one type at a time to avoid other errors affecting each other. After setting the Oracle database DSN, you can execute ora2pg to check whether it is valid： # ora2pg -t SHOW_VERSION -c config/ora2pg.conf Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 It is not easy to estimate the cost of the migration process from Oracle to PostgreSQL. In order to obtain a good evaluation of the migration cost, Ora2Pg will check all database objects, all functions and stored procedures to detect whether there are still some objects and PL/SQL code that cannot be automatically converted by Ora2Pg. Ora2Pg has a content analysis mode, which checks the Oracle database to generate a text report about the content contained in the Oracle database and the content that cannot be exported. # ora2pg -t SHOW_REPORT --estimate_cost -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 11/11 objects types (100.0%) end of objects auditing. ------------------------------------------------------------------------------- Ora2Pg v24.0 - Database Migration Report ------------------------------------------------------------------------------- Version Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 Schema SH Size 287.25 MB ------------------------------------------------------------------------------- Object Number Invalid Estimated cost Comments Details ------------------------------------------------------------------------------- DATABASE LINK 0 0 0 Database links will be exported as SQL/MED IvorySQL's Foreign Data Wrapper (FDW) extensions using oracle_fdw. DIMENSION 5 0 0 GLOBAL TEMPORARY TABLE 0 0 0 Global temporary table are not supported by PostgreSQL and will not be exported. You will have to rewrite some application code to match the PostgreSQL temporary table behavior. INDEX 20 0 3.4 14 index(es) are concerned by the export, others are automatically generated and will do so on PostgreSQL. Bitmap will be exported as btree_gin index(es) and hash index(es) will be exported as b-tree index(es) if any. Domain index are exported as b-tree but commented to be edited to mainly use FTS. Cluster, bitmap join and IOT indexes will not be exported at all. Reverse indexes are not exported too, you may use a trigram-based index (see pg_trgm) or a reverse() function based index and search. Use 'varchar_pattern_ops', 'text_pattern_ops' or 'bpchar_pattern_ops' operators in your indexes to improve search with the LIKE operator respectively into varchar, text or char columns. 11 bitmap index(es). 1 domain index(es). 2 b-tree index(es). INDEX PARTITION 196 0 0 Only local indexes partition are exported, they are build on the column used for the partitioning. JOB 0 0 0 Job are not exported. You may set external cron job with them. MATERIALIZED VIEW 2 0 6 All materialized view will be exported as snapshot materialized views, they are only updated when fully refreshed. SYNONYM 0 0 0 SYNONYMs will be exported as views. SYNONYMs do not exists with PostgreSQL but a common workaround is to use views or set the PostgreSQL search_path in your session to access object outside the current schema. TABLE 11 0 1.1 1 external table(s) will be exported as standard table. See EXTERNAL_TO_FDW configuration directive to export as file_fdw foreign tables or use COPY in your code if you just want to load data from external files. Total number of rows: 1063384. Top 10 of tables sorted by number of rows:. sales has 918843 rows. costs has 82112 rows. customers has 55500 rows. supplementary_demographics has 4500 rows. times has 1826 rows. promotions has 503 rows. products has 72 rows. countries has 23 rows. channels has 5 rows. sales_transactions_ext has 0 rows. Top 10 of largest tables:. TABLE PARTITION 56 0 5.6 Partitions are exported using table inheritance and check constraint. Hash and Key partitions are not supported by PostgreSQL and will not be exported. 56 RANGE partitions.. VIEW 1 0 1 Views are fully supported but can use specific functions. ------------------------------------------------------------------------------- Total 291 0 17.10 17.10 cost migration units means approximatively 1 man-day(s). The migration unit was set to 5 minute(s) ------------------------------------------------------------------------------ Migration level : A-1 ------------------------------------------------------------------------------- Migration levels: A - Migration that might be run automatically B - Migration with code rewrite and a human-days cost up to 5 days C - Migration with code rewrite and a human-days cost above 5 days Technical levels: 1 = trivial: no stored functions and no triggers 2 = easy: no stored functions but with triggers, no manual rewriting 3 = simple: stored functions and/or triggers, no manual rewriting 4 = manual: no stored functions but with triggers or views with code rewriting 5 = difficult: stored functions and/or triggers with code rewriting ------------------------------------------------------------------------------- # ora2pg -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 12/12 tables (100.0%) end of table export. [========================>] 1/1 views (100.0%) end of output. [========================>] 0/0 sequences (100.0%) end of output. [========================>] 0/0 procedures (100.0%) end of procedures export. [========================>] 0/0 triggers (100.0%) end of output. [========================>] 0/0 functions (100.0%) end of functions export. [========================>] 0/0 packages (100.0%) end of output. [========================>] 56/56 partitions (100.0%) end of output. [========================>] 0/0 types (100.0%) end of output. [========================>] 2/2 materialized views (100.0%) end of output. [========================>] 0/0 dblink (100.0%) end of output. [========================>] 0/0 synonyms (100.0%) end of output. [========================>] 2/2 directory (100.0%) end of output. Fixing function calls in output files.... Configure the type of ora2pg.conf as COPY or INSERT # cp ora2pg.conf sh_data.conf # vi sh_data.conf ORACLE_HOME /usr/lib/oracle/18.3/client64 ORACLE_DSN dbi:Oracle:host=10.85.10.6 ;sid=PROD1;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 DISABLE_UNLOGGED 1 SKIP fkeys ukeys checks TYPE COPY NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh_data.sql Export Data # ora2pg -c sh_data.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 5/5 rows (100.0%) Table CHANNELS (5 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1995 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_H1_1997 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1996 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). …………………………………………………………… [========================>] 4500/4500 rows (100.0%) Table SUPPLEMENTARY_DEMOGRAPHICS (4500 recs/sec) [=======================> ] 1061558/1063384 total rows (99.8%) - (45 sec., avg: 23590 recs/sec). [========================>] 1826/1826 rows (100.0%) Table TIMES (1826 recs/sec) [========================>] 1063384/1063384 total rows (100.0%) - (45 sec., avg: 23630 recs/sec). [========================>] 1063384/1063384 rows (100.0%) on total estimated data (45 sec., avg: 23630 recs/sec) Fixing function calls in output files... To view the exported file: # ls -lrt *.sql -rw-r--r-- 1 root root 15716 Jul 2 21:21 TABLE_sh.sql -rw-r--r-- 1 root root 858 Jul 2 21:21 VIEW_sh.sql -rw-r--r-- 1 root root 2026 Jul 2 21:21 TABLESPACE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 SEQUENCE_sh.sql -rw-r--r-- 1 root root 2382 Jul 2 21:21 GRANT_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 TRIGGER_sh.sql -rw-r--r-- 1 root root 346 Jul 2 21:21 PROCEDURE_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 PACKAGE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 FUNCTION_sh.sql -rw-r--r-- 1 root root 6771 Jul 2 21:21 PARTITION_sh.sql -rw-r--r-- 1 root root 341 Jul 2 21:21 TYPE_sh.sql -rw-r--r-- 1 root root 342 Jul 2 21:21 QUERY_sh.sql -rw-r--r-- 1 root root 950 Jul 2 21:21 MVIEW_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 SYNONYM_sh.sql -rw-r--r-- 1 root root 926 Jul 2 21:21 DIRECTORY_sh.sql -rw-r--r-- 1 root root 343 Jul 2 21:21 DBLINK_sh.sql -rw-r--r-- 1 root root 55281235 Jul 2 17:11 sh_data.sql Export HR and SCOTT user data in the same way. Create ORCL database # su - ivorysql Last login: Tue Jul 2 20:04:30 CST 2019 on pts/3 $ createdb orcl $ psql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Collate | Ctype | ICU Locale | Locale Provider | Access privileges -----------+----------+-----------+---------+-------+------------+-----------------+----------------------- ivorysql | ivorysql | SQL_ASCII | C | C | | libc | orcl | ivorysql | SQL_ASCII | C | C | | libc | postgres | ivorysql | SQL_ASCII | C | C | | libc | template0 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql (5 rows) ivorysql=# Create SH, HR, SCOTT users: $ psql orcl psql (18.0) Type \"help\" for help. orcl=# orcl=# create user sh with password 'sh'; CREATE ROLE Because of the materialized view, in TABLE_ The sh.sql contains the index of the materialized view, which will fail to create. You need to first create a table, then create a materialized view, and finally create an index. Cancel the materialized view index and create it separately later: CREATE INDEX fw_psc_s_mv_chan_bix ON fweek_pscat_sales_mv (channel_id); CREATE INDEX fw_psc_s_mv_promo_bix ON fweek_pscat_sales_mv (promo_id); CREATE INDEX fw_psc_s_mv_subcat_bix ON fweek_pscat_sales_mv (prod_subcategory); CREATE INDEX fw_psc_s_mv_wd_bix ON fweek_pscat_sales_mv (week_ending_day); CREATE TEXT SEARCH CONFIGURATION en (COPY = pg_catalog.english); ALTER TEXT SEARCH CONFIGURATION en ALTER MAPPING FOR hword, hword_part, word WITH unaccent, english_stem; psql orcl -f tab.sql.sql ALTER TABLE PARTITION sh.sales OWNER TO sh; COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT ALTER TABLE ALTER TABLE ALTER TABLE ……………………………… cat psql orcl -f GRANT_sh.sql CREATE USER SH WITH PASSWORD 'change_my_secret' LOGIN; ALTER TABLE sh.fweek_pscat_sales_mv OWNER TO sh; GRANT ALL ON sh.fweek_pscat_sales_mv TO sh; Materialized views require relevant query permissions, so import permissions. Please keep up with users here $ psql orcl sh -f MVIEW_sh.sql SELECT 0 SELECT 0 CREATE INDEX CREATE INDEX CREATE INDEX CREATE INDEX $ psql orcl -f VIEW_sh.sql SET SET SET CREATE VIEW $ psql orcl -f PARTITION_sh.sql SET SET SET CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE ………………………… $ psql orcl -f sh_data.sql SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 4500 SET COPY 1826 COMMIT Source database and target side extract part of objects for comparison： SYS@PROD1>select count(*) from sh.products; COUNT(*) ---------- 72 orcl=# select count(*) from sh.products; count ------- 72 (1 row) --------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.channels; COUNT(*) ---------- 5 orcl=# select count(*) from sh.channels; count ------- 5 (1 row) -------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.customers ; COUNT(*) ---------- 55500 orcl=# select count(*) from sh.customers ; count ------- 55500 (1 row) When using, there are two options — project_ Base and — init_ Project indicates to ora2pg that he must create a project template, which contains the work tree, configuration files and scripts for exporting all objects from the Oracle database. Generate a generic configuration file. 1. Create script export_ Schema.sh to automatically perform all exports. 2. Create script import_ All.sh to automatically perform all imports. example： mkdir -p /ora2pg/migration # ora2pg --project_base /ora2pg/migration/ --init_project test_project Creating project test_project. /ora2pg/migration//test_project/ schema/ dblinks/ directories/ functions/ grants/ mviews/ packages/ partitions/ procedures/ sequences/ synonyms/ tables/ tablespaces/ triggers/ types/ views/ sources/ functions/ mviews/ packages/ partitions/ procedures/ triggers/ types/ views/ data/ config/ reports/ Generating generic configuration file Creating script export_schema.sh to automate all exports. Creating script import_all.sh to automate all imports.","title":"Migration guide","component":"ivorysql-doc","version":"master","name":"migration_guide","url":"/ivorysql-doc/master/migration_guide.html","titles":[{"text":"1. Migration overview","hash":"migration-overview","id":1},{"text":"2. Migration tool——Ora2Pg","hash":"migration-toolora2pg","id":2},{"text":"3. Migrate Oracle Database to IvorySQL","hash":"migrate-oracle-database-to-ivorysql","id":3},{"text":"3.1. Environment preparation","hash":"environment-preparation","id":4},{"text":"3.2. Environment-dependent installation","hash":"environment-dependent-installation","id":5},{"text":"3.2.1. Install Perl","hash":"install-perl","id":6},{"text":"3.2.2. Install the DBI module","hash":"install-the-dbi-module","id":7},{"text":"3.2.3. Install DBD-Oracle","hash":"install-dbd-oracle","id":8},{"text":"3.2.4. Install DBD-PG (optional)","hash":"install-dbd-pg-optional","id":9},{"text":"3.3. Install Ora2pg","hash":"install-ora2pg","id":10},{"text":"3.4. Source side preparation","hash":"source-side-preparation","id":11},{"text":"3.5. Ora2pg export table structure","hash":"ora2pg-export-table-structure","id":12},{"text":"3.5.1. Test connection","hash":"test-connection","id":13},{"text":"3.5.2. Migration cost assessment","hash":"migration-cost-assessment","id":14},{"text":"3.5.3. Export SH table structure","hash":"export-sh-table-structure","id":15},{"text":"3.5.4. Export SH user data","hash":"export-sh-user-data","id":16},{"text":"3.6. Create orcl library in IvorySQL environment","hash":"create-orcl-library-in-ivorysql-environment","id":17},{"text":"4. Migration Portal","hash":"migration-portal","id":18},{"text":"4.1. Import table structure","hash":"import-table-structure","id":19},{"text":"4.2. Authorize objects","hash":"authorize-objects","id":20},{"text":"4.3. Import materialized view structure","hash":"import-materialized-view-structure","id":21},{"text":"4.4. Import View","hash":"import-view","id":22},{"text":"4.5. Import partition table","hash":"import-partition-table","id":23},{"text":"4.6. Import data","hash":"import-data","id":24},{"text":"5. Data validation","hash":"data-validation","id":25},{"text":"6. Generate migration template","hash":"generate-migration-template","id":26}]},"7":{"id":7,"text":"Since IvorySQL is based on PostgreSQL, it is recommended that when reading and understanding this section, O&M staff also refer to doc。 The IvorySQL version number consists of a major version and a minor version. For example, 3 in IvorySQL 3.2 is the major version and 2 is the minor version. Releasing a minor version is not going to change the internal storage format, so it is always compatible with the same major version. For example, IvorySQL 3.4 is compatible with Ivory SQL 3.0 and the subsequent Ivory SQL 3.x. Upgrading for these compatible versions is as simple as shutting down the database service, installing a replacement binary executable, and restarting the service. ​ Next, we focus on cross-version upgrades of IvorySQL, for example, from IvorySQL 2.3 to IvorySQL 3.2. Major version upgrades may modify the internal data storage format and therefore require additional operations to be performed. The common cross-version upgrade methods and applicable scenarios are as follows. Upgrade method Applicable scenarios Shutdown time pg_dumpall Small to medium sized databases, e.g. less than 100GB to support cross-platform data migration Depends on the size of the database pg_upgrade Large and medium-sized databases, e.g., >100GB local upgrade A few minutes Logical Replication Large and medium-sized databases, e.g. >100GB cross-platform support A few seconds Attention： New major releases usually introduce some user-visible incompatibilities and may therefore require application programming changes. All user-visible changes are listed in description，Please pay special attention to the section labeled \"Migration\". Although you may upgrade from one major version to another without upgrading an intermediate version, you should read the major release notes for all intermediate versions. The traditional cross-version upgrade method uses pg_dump/pg_dumpall to logically backup the database and then restore it in the new version via pg_restore. It is recommended to use the new version of pg_dump/pg_dumpall tool when exporting the old version of the database. You can take advantage of its latest parallel export and restore features, while reducing database bloat problems. Logical backup and restore is very simple but slow. Downtime depends on the size of the database, so it is suitable for small to medium sized database upgrades. ​The following describes how this upgrade method works. If the current IvorySQL software installation directory is located in /usr/local/pgsql and the data directory is located in /usr/local/pgsql/data, we do the upgrade on the same server. 1.Stop the application before performing a logical backup and make sure that no data is updated, as updates after the backup has started will not be exported. If necessary, modify the /usr/local/pgsql/data/pg_hba.conf file to disable others from accessing the database. Then backup the database. pg_dumpall > outputfile If you have installed a new version of IvorySQL, you can use the new version of the pg_dumpall command to back up the old version of the database. 2.Stop the backend services of older versions. pg_ctl stop Or stop the background service by other means. 3.If the installation directory does not contain a specific version identifier, the directory can be renamed and modified back if necessary. Directories can be renamed using a command similar to the following. mv /usr/local/pgsql /usr/local/pgsql.old 4.Install the new version of IvorySQL software, if the installation directory is still /usr/local/pgsql. 5.Initializing a new database cluster requires the use of a database specific user (usually postgres; if upgrading the version, this user should already exist) to perform the operation. /usr/local/pgsql/bin/initdb -D /usr/local/pgsql/data 6.Change the old configuration file pg_hba.conf, postgresql.conf, etc. in the corresponding new configuration file. 7.To start a new version of the backend service using a dedicated database user. /usr/local/pgsql/bin/postgres -D /usr/local/pgsql/data 8.Finally, using the new version of the psql command to restore the data. /usr/local/pgsql/bin/psql -d postgres -f outputfile To reduce downtime, you can install the new version of IvorySQL to another directory, while starting the service using a different port. Then perform both the export and import of the database. pg_dumpall -p 5432 | psql -d postgres -p 5433 When the above operation is executed, the old and new versions of the backend service run simultaneously, with the new version using port 5433 and the old version using port 5432. The pg_upgrade tool is a built-in cross-version upgrade utility in PostgreSQL that enables in-place database upgrades without requiring export and import operations. Since IvorySQL is derived from PostgreSQL, it can also use the pg_upgrade tool for major version upgrades. Below is a brief introduction on how to use pg_upgrade to upgrade IvorySQL to the latest 5.0 version on the CentOS8 platform. pg_upgrade provides a pre-upgrade compatibility check (using the -c or --check option), which can identify issues such as plugin or data type incompatibilities. If the --link option is specified, the new version service can directly use the existing database files without copying, typically completing the upgrade in just a few minutes. Commonly used parameters include: -b bindir, --old-bindir=bindir: The directory of the old IvorySQL executable files; -B bindir, --new-bindir=bindir: The directory of the new IvorySQL executable files; -d configdir, --old-datadir=configdir: The data directory of the old version; -D configdir, --new-datadir=configdir: The data directory of the new version; -c, --check: Only check upgrade compatibility without modifying any data; -k, --link: Upgrade using hard link method; Upgrade preparation: First, stop the old version of the IvorySQL 4.6 database: /usr/ivory-4/bin/pg_ctl -D ./data stop Then install the new version of the IvorySQL 5.0 database: dnf install -y ivorysql5-5.0 Initialize the new IvorySQL 5.0 data directory: /usr/ivory-5/bin/initdb -D ./data Check version compatibility: /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ --check The appearance of \"Clusters are compatible\" at the end indicates that there are no compatibility issues between the two versions of data, and the upgrade can proceed. Official upgrade: /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ Seeing \"Upgrade Complete\" indicates that the upgrade has been successfully completed. Update statistics: pg_upgrade creates new system tables and reuses old data for the upgrade. However, statistics are not migrated during the upgrade process. Therefore, before enabling the new version, you should first recollect statistics to avoid incorrect query plans caused by missing statistics. Start the new version of the database. /usr/ivory-5/bin/pg_ctl -D ./data -l logfile start Manually run the vacuum command vacuum --all --analyze-in-stage -h 127.0.0.1 -p 1521 Cleanup after upgrade rm -rf /home/ivorysql/test/4.6/data pg_upgrade Documentoutlines the steps required above. We can also create a fallback server using logical replication of an updated version of IvorySQL, which supports replication between different major versions of IvorySQL. The fallback server can be on the same computer or on a different computer. Once it is synchronized with the primary server (running an older version of IvorySQL), you can switch hosts and use the backup server as the host, and then shut down the older database instance. Such a switchover allows an upgrade with only a few seconds of downtime. This upgrade method can be used with built-in logical replication tools and external logical replication systems such as pglogical, Slony, Londiste, and Bucardo. IvorySQL is based on PostgreSQL and is updated at the same frequency as PostgreSQL, with one major release per year and one minor release per quarter. IvorySQL 5.0 is based on PostgreSQL 18.0, and all versions of IvorySQL are backward compatible.The relevant version features can be viewed by looking at Official Website。 IvorySQL uses the concept of Roles to manage database access rights. A role can be thought of as a database user or a group of database users, depending on how the role is set. Roles can own database objects (for example, tables and functions) and can delegate permissions on those objects to other roles to control who can access which objects. In addition, membership in one role can be granted to another role, allowing member roles to use the permissions granted to another role. The concept of roles encompasses the concepts of \"user\" and \"group\". The database roles are conceptually completely separate from the operating system users. It may in fact be easier to maintain a correspondence, but this is not necessary. Database roles are global within a database cluster installation (and not within a separate database). To create a role, use the CREATE ROLE SQL command. CREATE ROLE name; Name follows the rules of SQL identifiers: either unadorned with no special characters or surrounded by double quotes (in fact, you will always have to add additional options to the command, such as LOGIN. see below for more details). To remove an existing role, use the similar DROP ROLE command. DROP ROLE name; For convenience, the createuser and dropuser programs are provided as wrappers for these SQL commands, which can be invoked from the shell command line at createuser name dropuser name To determine the set of existing roles, check the pg_roles system directory, e.g. SELECT rolname FROM pg_roles; The \\du meta command of the psql program can also be used to list existing roles. To bootstrap a database system, a system that has just been initialized always contains a predefined role. This role is always a \"superuser\" and by default (unless changed when running initdb) it is named the same as the OS user who initialized the database cluster. By convention, this role will be named postgres. In order to create more roles, you must first connect as the initial role. Each connection to the database server is established using a particular role name, and this role determines the initial access rights to the command that initiates the connection. The role name to use for a particular database connection is indicated by the client, which initiates the connection request in an application-related style. For example, the psql program uses the -U command line option to specify which role to connect under. Many applications assume that this name is the current operating system user by default (including createuser and psql). Therefore it is often convenient to maintain a name correspondence between the role and the OS user. The set of database roles that a given client connection can use to connect to is determined by the authentication settings of that client, so a client is not limited to connecting with a role that matches its OS user, just as a person’s login name does not need to match her real name. Because role identity determines the set of permissions available to a connected client, be careful when setting up a multi-user environment to configure permissions. A database role can have a number of attributes that define the permissions of the role and interact with the client authentication system. It is often convenient to group users together to facilitate the management of permissions: in this way, permissions can be granted to or reclaimed from an entire group. This is done in IvorySQL by creating a role that represents a group, and then granting membership in that group role to individual user roles. Since roles can own database objects and hold privileges to access other objects, deleting a role is often not a one-time DROP ROLE solution. Any objects owned by that user must first be deleted or transferred to another owner, and any privileges that have been granted to that role must be withdrawn. For more details on database access management, refers to doc. IvorySQL is based on PostgreSQL and has a full SQL with defined data objects that can be referred to doc.On top of that, IvorySQL has done some Oracle proprietary data object compatibility for Oracle compatibility. Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character. VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 IvorySQL is developed based on PostgreSQL, with full SQL, query data specific operations can be referred to doc. IvorySQL implements portions of the SQL/MED specification, allowing you to access data that resides outside IvorySQL using regular SQL queries. Such data is referred to as foreign data. (Note that this usage is not to be confused with foreign keys, which are a type of constraint within the database.) Foreign data is accessed with help from a foreign data wrapper. A foreign data wrapper is a library that can communicate with an external data source, hiding the details of connecting to the data source and obtaining data from it. There are some foreign data wrappers available as contrib modules;see Appendix F. Other kinds of foreign data wrappers might be found as third party products. If none of the existing foreign data wrappers suit your needs, you can write your own; see doc. To access foreign data, you need to create a foreign server object, which defines how to connect to a particular external data source according to the set of options used by its supporting foreign data wrapper. Then you need to create one or more foreign tables, which define the structure of the remote data. A foreign table can be used in queries just like a normal table, but a foreign table has no storage in the IvorySQL server. Whenever it is used, IvorySQL asks the foreign data wrapper to fetch data from the external source, or transmit data to the external source in the case of update commands. Accessing remote data may require authenticating to the external data source. This information can be provided by a user mapping, which can provide additional data such as user names and passwords based on the current IvorySQL role. As with everything that contains valuable data, IvorySQL databases should be backed up regularly. While the procedure is essentially simple, it is important to have a clear understanding of the underlying techniques and assumptions. There are three fundamentally different approaches to backing up IvorySQL data: SQL dump File system level backup Continuous archiving The idea behind this dump method is to generate a file with SQL commands that, when fed back to the server, will recreate the database in the same state as it was at the time of the dump. IvorySQL provides the utility program pg_dumpfor this purpose. The basic usage of this command is: pg_dump dbname > dumpfile As you see, pg_dump writes its result to the standard output. We will see below how this can be useful. While the above command creates a text file, pg_dump can create files in other formats that allow for parallelism and more fine-grained control of object restoration. pg_dump is a regular IvorySQL client application (albeit a particularly clever one). This means that you can perform this backup procedure from any remote host that has access to the database. pg_dump must have read access to all tables that you want to back up, so in order to back up the entire database you almost always have to run it as a database superuser. (If you do not have sufficient privileges to back up the entire database, you can still back up portions of the database to which you do have access using options such as -n `schema` or -t `table`.) ​To specify which database server pg_dump should contact, use the command line options -h `host` and -p `port. The default host is the local host or whatever your `HOST environment variable specifies. Similarly, the default port is indicated by the PORT environment variable or, failing that, by the compiled-in default. (Conveniently, the server will normally have the same compiled-in default.) pg_dump will by default connect with the database user name that is equal to the current operating system user name. To override this, either specify the -U option or set the environment variable PGUSER. Remember that pg_dump connections are subject to the normal client authentication mechanisms 。 ​ An important advantage of pg_dump over the other backup methods described later is that pg_dump’s output can generally be re-loaded into newer versions of IvorySQL, whereas file-level backups and continuous archiving are both extremely server-version-specific. pg_dump is also the only method that will work when transferring a database to a different machine architecture, such as going from a 32-bit to a 64-bit server. ​ Dumps created by pg_dump are internally consistent, meaning, the dump represents a snapshot of the database at the time pg_dump began running. pg_dump does not block other operations on the database while it is working. (Exceptions are those operations that need to operate with an exclusive lock, such as most forms of ALTER TABLE.) Text files created by pg_dump are intended to be read in by the psql program. The general command form to restore a dump is psql dbname < dumpfile where dumpfile is the file output by the pg_dump command. The database dbname will not be created by this command, so you must create it yourself from template0 before executing psql (e.g., with createdb -T template0 `dbname`). psql supports options similar to pg_dump for specifying the database server to connect to and the user name to use. See the psql reference page for more information. Non-text file dumps are restored using the pg_restore utility. Before restoring an SQL dump, all the users who own objects or were granted permissions on objects in the dumped database must already exist. If they do not, the restore will fail to recreate the objects with the original ownership and/or permissions. (Sometimes this is what you want, but usually it is not.) By default, the psql script will continue to execute after an SQL error is encountered. You might wish to run psql with the ON_ERROR_STOP variable set to alter that behavior and have psql exit with an exit status of 3 if an SQL error occurs: psql --set ON_ERROR_STOP=on dbname < infile Either way, you will only have a partially restored database. Alternatively, you can specify that the whole dump should be restored as a single transaction, so the restore is either fully completed or fully rolled back. This mode can be specified by passing the -1 or --single-transaction command-line options to psql. When using this mode, be aware that even a minor error can rollback a restore that has already run for many hours. However, that might still be preferable to manually cleaning up a complex database after a partially restored dump. The ability of pg_dump and psql to write to or read from pipes makes it possible to dump a database directly from one server to another, for example: pg_dump -h host1 dbname | psql -h host2 dbname Important：The dumps produced by pg_dump are relative to template0. This means that any languages, procedures, etc. added via template1 will also be dumped by pg_dump. As a result, when restoring, if you are using a customized template1, you must create the empty database from template0, as in the example above. After restoring a backup, it is wise to run ANALYZE on each database so the query optimizer has useful statistics. pg_dump dumps only a single database at a time, and it does not dump information about roles or tablespaces (because those are cluster-wide rather than per-database). To support convenient dumping of the entire contents of a database cluster, the pg_dumpall program is provided. pg_dumpall backs up each database in a given cluster, and also preserves cluster-wide data such as role and tablespace definitions. The basic usage of this command is: pg_dumpall > dumpfile The resulting dump can be restored with psql: psql -f dumpfile ivorysql (Actually, you can specify any existing database name to start from, but if you are loading into an empty cluster then ivorysql should usually be used.) It is always necessary to have database superuser access when restoring a pg_dumpall dump, as that is required to restore the role and tablespace information. If you use tablespaces, make sure that the tablespace paths in the dump are appropriate for the new installation. pg_dumpall works by emitting commands to re-create roles, tablespaces, and empty databases, then invoking pg_dump for each database. This means that while each database will be internally consistent, the snapshots of different databases are not synchronized. Cluster-wide data can be dumped alone using the pg_dumpall --globals-only option. This is necessary to fully backup the cluster if running the pg_dump command on individual databases. Some operating systems have maximum file size limits that cause problems when creating large pg_dump output files. Fortunately, pg_dump can write to the standard output, so you can use standard Unix tools to work around this potential problem. There are several possible methods: Use compressed dumps. You can use your favorite compression program, for example gzip: pg_dump dbname | gzip > filename.gz Reload with： gunzip -c filename.gz | psql dbname or: cat filename.gz | gunzip | psql dbname Use split. The split command allows you to split the output into smaller files that are acceptable in size to the underlying file system. For example, to make 2 gigabyte chunks: pg_dump dbname | split -b 2G - filename Reload with: cat filename* | psql dbname If using GNU split, it is possible to use it and gzip together: pg_dump dbname | split -b 2G -−filter='gzip > $FILE.gz' It can be restored using zcat. Use pg_dump’s custom dump format. If IvorySQL was built on a system with the zlib compression library installed, the custom dump format will compress data as it writes it to the output file. This will produce dump file sizes similar to using gzip, but it has the added advantage that tables can be restored selectively. The following command dumps a database using the custom dump format: pg_dump -Fc dbname > filename A custom-format dump is not a script for psql, but instead must be restored with pg_restore, for example: pg_restore -d dbname filename See the pg_dump and pg_restore reference pages for details. For very large databases, you might need to combine split with one of the other two approaches. Use pg_dump’s parallel dump feature. To speed up the dump of a large database, you can use pg_dump’s parallel mode. This will dump multiple tables at the same time. You can control the degree of parallelism with the -j parameter. Parallel dumps are only supported for the \"directory\" archive format. pg_dump -j num -F d -f out.dir dbname You can use pg_restore -j to restore a dump in parallel. This will work for any archive of either the \"custom\" or the \"directory\" archive mode, whether or not it has been created with pg_dump -j. An alternative backup strategy is to directly copy the files that IvorySQL uses to store the data in the database，You can use whatever method you prefer for doing file system backups; for example: tar -cf backup.tar /usr/local/pgsql/data There are two restrictions, however, which make this method impractical, or at least inferior to the pg_dump method: The database server must be shut down in order to get a usable backup. Half-way measures such as disallowing all connections will not work (in part because tar and similar tools do not take an atomic snapshot of the state of the file system, but also because of internal buffering within the server).Needless to say, you also need to shut down the server before restoring the data. If you have dug into the details of the file system layout of the database, you might be tempted to try to back up or restore only certain individual tables or databases from their respective files or directories. This will not work because the information contained in these files is not usable without the commit log files, pg_xact/*, which contain the commit status of all transactions. A table file is only usable with this information. Of course it is also impossible to restore only a table and the associated pg_xact data because that would render all other tables in the database cluster useless. So file system backups only work for complete backup and restoration of an entire database cluster. ​ An alternative file-system backup approach is to make a “consistent snapshot” of the data directory, if the file system supports that functionality (and you are willing to trust that it is implemented correctly). The typical procedure is to make a “frozen snapshot” of the volume containing the database, then copy the whole data directory (not just parts, see above) from the snapshot to a backup device, then release the frozen snapshot. This will work even while the database server is running. However, a backup created in this way saves the database files in a state as if the database server was not properly shut down; therefore, when you start the database server on the backed-up data, it will think the previous server instance crashed and will replay the WAL log. This is not a problem; just be aware of it (and be sure to include the WAL files in your backup). You can perform a CHECKPOINT before taking the snapshot to reduce recovery time. ​ If your database is spread across multiple file systems, there might not be any way to obtain exactly-simultaneous frozen snapshots of all the volumes. For example, if your data files and WAL log are on different disks, or if tablespaces are on different file systems, it might not be possible to use snapshot backup because the snapshots must be simultaneous. Read your file system documentation very carefully before trusting the consistent-snapshot technique in such situations. ​ If simultaneous snapshots are not possible, one option is to shut down the database server long enough to establish all the frozen snapshots. Another option is to perform a continuous archiving base backup,because such backups are immune to file system changes during the backup. This requires enabling continuous archiving just during the backup process; restore is done using continuous archive recovery ​ Another option is to use rsync to perform a file system backup. This is done by first running rsync while the database server is running, then shutting down the database server long enough to do an rsync --checksum. (--checksum is necessary because rsync only has file modification-time granularity of one second.) The second rsync will be quicker than the first, because it has relatively little data to transfer, and the end result will be consistent because the server was down. This method allows a file system backup to be performed with minimal downtime. Note that a file system backup will typically be larger than an SQL dump. (pg_dump does not need to dump the contents of indexes for example, just the commands to recreate them.) However, taking a file system backup might be faster. At all times,IvorySQLmaintains a write ahead log (WAL) in the pg_wal/ subdirectory of the cluster’s data directory. The log records every change made to the database’s data files. This log exists primarily for crash-safety purposes: if the system crashes, the database can be restored to consistency by “replaying” the log entries made since the last checkpoint. However, the existence of the log makes it possible to use a third strategy for backing up databases: we can combine a file-system-level backup with backup of the WAL files. If recovery is needed, we restore the file system backup and then replay from the backed-up WAL files to bring the system to a current state. This approach is more complex to administer than either of the previous approaches, but it has some significant benefits: We do not need a perfectly consistent file system backup as the starting point. Any internal inconsistency in the backup will be corrected by log replay (this is not significantly different from what happens during crash recovery). So we do not need a file system snapshot capability, just tar or a similar archiving tool. Since we can combine an indefinitely long sequence of WAL files for replay, continuous backup can be achieved simply by continuing to archive the WAL files. This is particularly valuable for large databases, where it might not be convenient to take a full backup frequently. It is not necessary to replay the WAL entries all the way to the end. We could stop the replay at any point and have a consistent snapshot of the database as it was at that time. Thus, this technique supports point-in-time recovery: it is possible to restore the database to its state at any time since your base backup was taken. If we continuously feed the series of WAL files to another machine that has been loaded with the same base backup file, we have a warm standby system: at any point we can bring up the second machine and it will have a nearly-current copy of the database. Note: pg_dump and pg_dumpall do not produce file-system-level backups and cannot be used as part of a continuous-archiving solution. Such dumps are logical and do not contain enough information to be used by WAL replay. ​ As with the plain file-system-backup technique, this method can only support restoration of an entire database cluster, not a subset. Also, it requires a lot of archival storage: the base backup might be bulky, and a busy system will generate many megabytes of WAL traffic that have to be archived. Still, it is the preferred backup technique in many situations where high reliability is needed. ​ To recover successfully using continuous archiving (also called “online backup” by many database vendors), you need a continuous sequence of archived WAL files that extends back at least as far as the start time of your backup. So to get started, you should set up and test your procedure for archiving WAL files before you take your first base backup. Accordingly, we first discuss the mechanics of archiving WAL files.For more information on how to create archives and backups and the key points during operation, please refer to doc。 COPY moves data between IvorySQL tables and standard file-system files. COPY TO copies the contents of a table to a file, while COPY FROM copies data from a file to a table (appending the data to whatever is in the table already). COPY TO can also copy the results of a SELECT query. If a column list is specified, COPY TO copies only the data in the specified columns to the file. For COPY FROM, each field in the file is inserted, in order, into the specified column. Table columns not specified in the COPY FROM column list will receive their default values. COPY with a file name instructs the IvorySQL server to directly read from or write to a file. The file must be accessible by the IvorySQL user (the user ID the server runs as) and the name must be specified from the viewpoint of the server. When PROGRAM is specified, the server executes the given command and reads from the standard output of the program, or writes to the standard input of the program. The command must be specified from the viewpoint of the server, and be executable by the IvorySQL user. When STDIN or STDOUT is specified, data is transmitted via the connection between the client and the server. ​ Each backend running COPY will report its progress in the pg_stat_progress_copy view. COPY table_name [ ( column_name [, ...] ) ] FROM { 'filename' | PROGRAM 'command' | STDIN } [ [ WITH ] ( option [, ...] ) ] [ WHERE condition ] COPY { table_name [ ( column_name [, ...] ) ] | ( query ) } TO { 'filename' | PROGRAM 'command' | STDOUT } [ [ WITH ] ( option [, ...] ) ] where option can be one of: FORMAT format_name FREEZE [ boolean ] DELIMITER 'delimiter_character' NULL 'null_string' HEADER [ boolean ] QUOTE 'quote_character' ESCAPE 'escape_character' FORCE_QUOTE { ( column_name [, ...] ) | * } FORCE_NOT_NULL ( column_name [, ...] ) FORCE_NULL ( column_name [, ...] ) ENCODING 'encoding_name' For detailed parameter settings, please refer to doc. On successful completion, a COPY command returns a command tag of the form COPY count The count is the number of rows copied. Note： psql will print this command tag only if the command was not COPY …​ TO STDOUT, or the equivalent psql meta-command \\copy …​ to stdout. This is to prevent confusing the command tag with the data that was just printed. COPY TO can be used only with plain tables, not views, and does not copy rows from child tables or child partitions. For example, COPY `table TO` copies the same rows as SELECT * FROM ONLY `table. The syntax `COPY (SELECT * FROM `table) TO …​` can be used to dump all of the rows in an inheritance hierarchy, partitioned table, or view. COPY FROM can be used with plain, foreign, or partitioned tables or with views that have INSTEAD OF INSERT triggers. You must have select privilege on the table whose values are read by COPY TO, and insert privilege on the table into which values are inserted by COPY FROM. It is sufficient to have column privileges on the column(s) listed in the command. If row-level security is enabled for the table, the relevant SELECT policies will apply to COPY `table TO` statements. Currently, COPY FROM is not supported for tables with row-level security. Use equivalent INSERT statements instead. ​ Files named in a COPY command are read or written directly by the server, not by the client application. Therefore, they must reside on or be accessible to the database server machine, not the client. They must be accessible to and readable or writable by the IvorySQL user (the user ID the server runs as), not the client. Similarly, the command specified with PROGRAM is executed directly by the server, not by the client application, must be executable by the IvorySQL user. COPY naming a file or command is only allowed to database superusers or users who are granted one of the roles pg_read_server_files, pg_write_server_files, or pg_execute_server_program, since it allows reading or writing any file or running a program that the server has privileges to access. ​ Do not confuse COPY with the psql instruction \\copy. \\copy invokes COPY FROM STDIN or COPY TO STDOUT, and then fetches/stores the data in a file accessible to the psql client. Thus, file accessibility and access rights depend on the client rather than the server when \\copy is used. It is recommended that the file name used in COPY always be specified as an absolute path. This is enforced by the server in the case of COPY TO, but for COPY FROM you do have the option of reading from a file specified by a relative path. The path will be interpreted relative to the working directory of the server process (normally the cluster’s data directory), not the client’s working directory. Executing a command with PROGRAM might be restricted by the operating system’s access control mechanisms, such as SELinux. COPY FROM will invoke any triggers and check constraints on the destination table. However, it will not invoke rules. For identity columns, the COPY FROM command will always write the column values provided in the input data, like the INSERT option OVERRIDING SYSTEM VALUE. COPY input and output is affected by DateStyle. To ensure portability to other IvorySQL installations that might use non-default DateStyle settings, DateStyle should be set to ISO before using COPY TO. It is also a good idea to avoid dumping data with IntervalStyle set to sql_standard, because negative interval values might be misinterpreted by a server that has a different setting for IntervalStyle. Input data is interpreted according to ENCODING option or the current client encoding, and output data is encoded in ENCODING or the current client encoding, even if the data does not pass through the client but is read from or written to a file directly by the server. COPY stops operation at the first error. This should not lead to problems in the event of a COPY TO, but the target table will already have received earlier rows in a COPY FROM. These rows will not be visible or accessible, but they still occupy disk space. This might amount to a considerable amount of wasted disk space if the failure happened well into a large copy operation. You might wish to invoke VACUUM to recover the wasted space. FORCE_NULL and FORCE_NOT_NULL can be used simultaneously on the same column. This results in converting quoted null strings to null values and unquoted null strings to empty strings. When the text format is used, the data read or written is a text file with one line per table row. Columns in a row are separated by the delimiter character. The column values themselves are strings generated by the output function, or acceptable to the input function, of each attribute’s data type. The specified null string is used in place of columns that are null. COPY FROM will raise an error if any line of the input file contains more or fewer columns than are expected. End of data can be represented by a single line containing just backslash-period (\\.). An end-of-data marker is not necessary when reading from a file, since the end of file serves perfectly well; it is needed only when copying data to or from client applications using pre-3.0 client protocol. Backslash characters (\\) can be used in the COPY data to quote data characters that might otherwise be taken as row or column delimiters. In particular, the following characters must be preceded by a backslash if they appear as part of a column value: backslash itself, newline, carriage return, and the current delimiter character. The specified null string is sent by COPY TO without adding any backslashes; conversely, COPY FROM matches the input against the null string before removing backslashes. Therefore, a null string such as \\N cannot be confused with the actual data value \\N (which would be represented as \\\\N). The following special backslash sequences are recognized by COPY FROM: Sequence Represents \\b Backspace (ASCII 8) \\f Form feed (ASCII 12) \\n Newline (ASCII 10) \\r Carriage return (ASCII 13) \\t Tab (ASCII 9) \\v Vertical tab (ASCII 11) \\digits Backslash followed by one to three octal digits specifies the byte with that numeric code \\xdigits Backslash x followed by one or two hex digits specifies the byte with that numeric code Presently, COPY TO will never emit an octal or hex-digits backslash sequence, but it does use the other sequences listed above for those control characters. Any other backslashed character that is not mentioned in the above table will be taken to represent itself. However, beware of adding backslashes unnecessarily, since that might accidentally produce a string matching the end-of-data marker (\\.) or the null string (\\N by default). These strings will be recognized before any other backslash processing is done. It is strongly recommended that applications generating COPY data convert data newlines and carriage returns to the \\n and \\r sequences respectively. At present it is possible to represent a data carriage return by a backslash and carriage return, and to represent a data newline by a backslash and newline. However, these representations might not be accepted in future releases. They are also highly vulnerable to corruption if the COPY file is transferred across different machines (for example, from Unix to Windows or vice versa). All backslash sequences are interpreted after encoding conversion. The bytes specified with the octal and hex-digit backslash sequences must form valid characters in the database encoding. COPY TO will terminate each row with a Unix-style newline (“\\n”). Servers running on Microsoft Windows instead output carriage return/newline (“\\r\\n”), but only for COPY to a server file; for consistency across platforms, COPY TO STDOUT always sends “\\n” regardless of server platform. COPY FROM can handle lines ending with newlines, carriage returns, or carriage return/newlines. To reduce the risk of error due to un-backslashed newlines or carriage returns that were meant as data, COPY FROM will complain if the line endings in the input are not all alike. This format option is used for importing and exporting the Comma Separated Value (CSV) file format used by many other programs, such as spreadsheets. Instead of the escaping rules used by IvorySQL’s standard text format, it produces and recognizes the common CSV escaping mechanism. The values in each record are separated by the DELIMITER character. If the value contains the delimiter character, the QUOTE character, the NULL string, a carriage return, or line feed character, then the whole value is prefixed and suffixed by the QUOTE character, and any occurrence within the value of a QUOTE character or the ESCAPE character is preceded by the escape character. You can also use FORCE_QUOTE to force quotes when outputting non-NULL values in specific columns. The CSV format has no standard way to distinguish a NULL value from an empty string. IvorySQL’s COPY handles this by quoting. A NULL is output as the NULL parameter string and is not quoted, while a non-NULL value matching the NULL parameter string is quoted. For example, with the default settings, a NULL is written as an unquoted empty string, while an empty string data value is written with double quotes (\"\"). Reading values follows similar rules. You can use FORCE_NOT_NULL to prevent NULL input comparisons for specific columns. You can also use FORCE_NULL to convert quoted null string data values to NULL. Because backslash is not a special character in the CSV format, \\., the end-of-data marker, could also appear as a data value. To avoid any misinterpretation, a \\. data value appearing as a lone entry on a line is automatically quoted on output, and on input, if quoted, is not interpreted as the end-of-data marker. If you are loading a file created by another application that has a single unquoted column and might have a value of \\., you might need to quote that value in the input file. Note CSV format, all characters are significant. A quoted value surrounded by white space, or any characters other than DELIMITER, will include those characters. This can cause errors if you import data from a system that pads CSV lines with white space out to some fixed width. If such a situation arises you might need to preprocess the CSV file to remove the trailing white space, before importing the data into IvorySQL. Note CSV format will both recognize and produce CSV files with quoted values containing embedded carriage returns and line feeds. Thus the files are not strictly one line per table row like text-format files. Note Many programs produce strange and occasionally perverse CSV files, so the file format is more a convention than a standard. Thus you might encounter some files that cannot be imported using this mechanism, and COPY might produce files that other programs cannot process. The binary format option causes all data to be stored/read as binary format rather than as text. It is somewhat faster than the text and CSV formats, but a binary-format file is less portable across machine architectures and IvorySQL versions. Also, the binary format is very data type specific; for example it will not work to output binary data from a smallint column and read it into an integer column, even though that would work fine in text format. The binary file format consists of a file header, zero or more tuples containing the row data, and a file trailer. Headers and data are in network byte order. File Header The file header consists of 15 bytes of fixed fields, followed by a variable-length header extension area. The fixed fields are: Signature 11-byte sequence PGCOPY\\n\\377\\r\\n\\0 — note that the zero byte is a required part of the signature. (The signature is designed to allow easy identification of files that have been munged by a non-8-bit-clean transfer. This signature will be changed by end-of-line-translation filters, dropped zero bytes, dropped high bits, or parity changes.) Flags field 32-bit integer bit mask to denote important aspects of the file format. Bits are numbered from 0 (LSB) to 31 (MSB). Note that this field is stored in network byte order (most significant byte first), as are all the integer fields used in the file format. Bits 16–31 are reserved to denote critical file format issues; a reader should abort if it finds an unexpected bit set in this range. Bits 0–15 are reserved to signal backwards-compatible format issues; a reader should simply ignore any unexpected bits set in this range. Currently only one flag bit is defined, and the rest must be zero: Bit 16 If 1, OIDs are included in the data; if 0, not. Oid system columns are not supported in IvorySQL anymore, but the format still contains the indicator. Header extension area length 32-bit integer, length in bytes of remainder of header, not including self. Currently, this is zero, and the first tuple follows immediately. Future changes to the format might allow additional data to be present in the header. A reader should silently skip over any header extension data it does not know what to do with. ​The header extension area is envisioned to contain a sequence of self-identifying chunks. The flags field is not intended to tell readers what is in the extension area. Specific design of header extension contents is left for a later release. This design allows for both backwards-compatible header additions (add header extension chunks, or set low-order flag bits) and non-backwards-compatible changes (set high-order flag bits to signal such changes, and add supporting data to the extension area if needed). Tuples Each tuple begins with a 16-bit integer count of the number of fields in the tuple. (Presently, all tuples in a table will have the same count, but that might not always be true.) Then, repeated for each field in the tuple, there is a 32-bit length word followed by that many bytes of field data. (The length word does not include itself, and can be zero.) As a special case, -1 indicates a NULL field value. No value bytes follow in the NULL case. ​ There is no alignment padding or any other extra data between fields. Presently, all data values in a binary-format file are assumed to be in binary format (format code one). It is anticipated that a future extension might add a header field that allows per-column format codes to be specified. To determine the appropriate binary format for the actual tuple data you should consult the PostgreSQL source, in particular the *send and *recv functions for each column’s data type (typically these functions are found in the src/backend/utils/adt/ directory of the source distribution). If OIDs are included in the file, the OID field immediately follows the field-count word. It is a normal field except that it’s not included in the field-count. Note that oid system columns are not supported in current versions of IvorySQL. The file trailer consists of a 16-bit integer word containing -1. This is easily distinguished from a tuple’s field-count word. ​A reader should report an error if a field-count word is neither -1 nor the expected number of columns. This provides an extra check against somehow getting out of sync with the data. The following example copies a table to the client using the vertical bar (|) as the field delimiter: COPY country TO STDOUT (DELIMITER '|'); ​ To copy data from a file into the country table: COPY country TO STDOUT (DELIMITER '|'); To copy into a file just the countries whose names start with 'A': COPY (SELECT * FROM country WHERE country_name LIKE 'A%') TO '/usr1/proj/bray/sql/a_list_countries.copy'; ​ To copy into a compressed file, you can pipe the output through an external compression program: COPY country TO PROGRAM 'gzip > /usr1/proj/bray/sql/country_data.gz'; ​ Here is a sample of data suitable for copying into a table from STDIN: AF AFGHANISTAN AL ALBANIA DZ ALGERIA ZM ZAMBIA ZW ZIMBABWE Note that the white space on each line is actually a tab character. ​ The following is the same data, output in binary format. The data is shown after filtering through the Unix utility od -c. The table has three columns; the first has type char(2), the second has type text, and the third has type integer. All the rows have a null value in the third column. 0000000 P G C O P Y \\n 377 \\r \\n \\0 \\0 \\0 \\0 \\0 \\0 0000020 \\0 \\0 \\0 \\0 003 \\0 \\0 \\0 002 A F \\0 \\0 \\0 013 A 0000040 F G H A N I S T A N 377 377 377 377 \\0 003 0000060 \\0 \\0 \\0 002 A L \\0 \\0 \\0 007 A L B A N I 0000100 A 377 377 377 377 \\0 003 \\0 \\0 \\0 002 D Z \\0 \\0 \\0 0000120 007 A L G E R I A 377 377 377 377 \\0 003 \\0 \\0 0000140 \\0 002 Z M \\0 \\0 \\0 006 Z A M B I A 377 377 0000160 377 377 \\0 003 \\0 \\0 \\0 002 Z W \\0 \\0 \\0 \\b Z I 0000200 M B A B W E 377 377 377 377 377 377 The remaining details can see doc. Query performance can be affected by a variety of factors. Some of these factors can be controlled by the user, while others are fundamentals of the system’s lower-level design. IvorySQL devises a query plan for each query it receives. Choosing the right plan to match the query structure and the properties of the data is absolutely critical for good performance, so the system includes a complex planner that tries to choose good plans. You can use the EXPLAIN command to see what query plan the planner creates for any query. Plan-reading is an art that requires some experience to master, but this section attempts to cover the basics. ​ The examples use `EXPLAIN’s default “text” output format, which is compact and convenient for humans to read. If you want to feed `EXPLAIN’s output to a program for further analysis, you should use one of its machine-readable output formats (XML, JSON, or YAML) instead. The structure of a query plan is a tree of plan nodes. Nodes at the bottom level of the tree are scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. There are also non-table row sources, such as VALUES clauses and set-returning functions in FROM, which have their own scan node types. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes above the scan nodes to perform these operations. Again, there is usually more than one possible way to do these operations, so different node types can appear here too. The output of EXPLAIN has one line for each node in the plan tree, showing the basic node type plus the cost estimates that the planner made for the execution of that plan node. Additional lines might appear, indented from the node’s summary line, to show additional properties of the node. The very first line (the summary line for the topmost node) has the estimated total execution cost for the plan; it is this number that the planner seeks to minimize. Here is a trivial example, just to show what the output looks like: EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) Since this query has no WHERE clause, it must scan all the rows of the table, so the planner has chosen to use a simple sequential scan plan. The numbers that are quoted in parentheses are (left to right): Estimated start-up cost. This is the time expended before the output phase can begin, e.g., time to do the sorting in a sort node. Estimated total cost. This is stated on the assumption that the plan node is run to completion, i.e., all available rows are retrieved. In practice a node’s parent node might stop short of reading all available rows (see the LIMIT example below). Estimated number of rows output by this plan node. Again, the node is assumed to be run to completion. Estimated average width of rows output by this plan node (in bytes). ​ The costs are measured in arbitrary units determined by the planner’s cost parameters .Traditional practice is to measure the costs in units of disk page fetches; that is, seq_page_cost is conventionally set to 1.0 and the other cost parameters are set relative to that. The examples in this section are run with the default cost parameters. ​ It’s important to understand that the cost of an upper-level node includes the cost of all its child nodes. It’s also important to realize that the cost only reflects things that the planner cares about. In particular, the cost does not consider the time spent transmitting result rows to the client, which could be an important factor in the real elapsed time; but the planner ignores it because it cannot change it by altering the plan. (Every correct plan will output the same row set, we trust.) The rows value is a little tricky because it is not the number of rows processed or scanned by the plan node, but rather the number emitted by the node. This is often less than the number scanned, as a result of filtering by any WHERE-clause conditions that are being applied at the node. Ideally the top-level rows estimate will approximate the number of rows actually returned, updated, or deleted by the query. Returning to our example: EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) ​ These numbers are derived very straightforwardly. If you do: SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1'; ​ you will find that tenk1 has 358 disk pages and 10000 rows. The estimated cost is computed as (disk pages read * seq_page_cost) + (rows scanned * cpu_tuple_cost). By default, seq_page_cost is 1.0 and cpu_tuple_cost is 0.01, so the estimated cost is (358 * 1.0) + (10000 * 0.01) = 458. Now let’s modify the query to add a WHERE condition: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000; QUERY PLAN ------------------------------------------------------------ Seq Scan on tenk1 (cost=0.00..483.00 rows=7001 width=244) Filter: (unique1 < 7000) ​ Notice that the EXPLAIN output shows the WHERE clause being applied as a “filter” condition attached to the Seq Scan plan node. This means that the plan node checks the condition for each row it scans, and outputs only the ones that pass the condition. The estimate of output rows has been reduced because of the WHERE clause. However, the scan will still have to visit all 10000 rows, so the cost hasn’t decreased; in fact it has gone up a bit (by 10000 * cpu_operator_cost, to be exact) to reflect the extra CPU time spent checking the WHERE condition. The actual number of rows this query would select is 7000, but the rows estimate is only approximate. If you try to duplicate this experiment, you will probably get a slightly different estimate; moreover, it can change after each ANALYZE command, because the statistics produced by ANALYZE are taken from a randomized sample of the table. Now, let’s make the condition more restrictive: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) ​ Here the planner has decided to use a two-step plan: the child plan node visits an index to find the locations of rows matching the index condition, and then the upper plan node actually fetches those rows from the table itself. Fetching rows separately is much more expensive than reading them sequentially, but because not all the pages of the table have to be visited, this is still cheaper than a sequential scan. (The reason for using two plan levels is that the upper plan node sorts the row locations identified by the index into physical order before reading them, to minimize the cost of separate fetches. The “bitmap” mentioned in the node names is the mechanism that does the sorting.) Now let’s add another condition to the WHERE clause: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND stringu1 = 'xxx'; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.04..229.43 rows=1 width=244) Recheck Cond: (unique1 < 100) Filter: (stringu1 = 'xxx'::name) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) ​ The added condition stringu1 = 'xxx' reduces the output row count estimate, but not the cost because we still have to visit the same set of rows. Notice that the stringu1 clause cannot be applied as an index condition, since this index is only on the unique1 column. Instead it is applied as a filter on the rows retrieved by the index. Thus the cost has actually gone up slightly to reflect this extra checking. In some cases the planner will prefer a “simple” index scan plan: EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42; QUERY PLAN -------------------------------------------------------------------​----------- Index Scan using tenk1_unique1 on tenk1 (cost=0.29..8.30 rows=1 width=244) Index Cond: (unique1 = 42) ​ In this type of plan the table rows are fetched in index order, which makes them even more expensive to read, but there are so few that the extra cost of sorting the row locations is not worth it. You’ll most often see this plan type for queries that fetch just a single row. It’s also often used for queries that have an ORDER BY condition that matches the index order, because then no extra sorting step is needed to satisfy the ORDER BY. In this example, adding ORDER BY unique1 would use the same plan because the index already implicitly provides the requested ordering. The planner may implement an ORDER BY clause in several ways. The above example shows that such an ordering clause may be implemented implicitly. The planner may also add an explicit sort step: EXPLAIN SELECT * FROM tenk1 ORDER BY unique1; QUERY PLAN ------------------------------------------------------------------- Sort (cost=1109.39..1134.39 rows=10000 width=244) Sort Key: unique1 -> Seq Scan on tenk1 (cost=0.00..445.00 rows=10000 width=244) If a part of the plan guarantees an ordering on a prefix of the required sort keys, then the planner may instead decide to use an `incremental sort` step: EXPLAIN SELECT * FROM tenk1 ORDER BY four, ten LIMIT 100; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Limit (cost=521.06..538.05 rows=100 width=244) -> Incremental Sort (cost=521.06..2220.95 rows=10000 width=244) Sort Key: four, ten Presorted Key: four -> Index Scan using index_tenk1_on_four on tenk1 (cost=0.29..1510.08 rows=10000 width=244) ​ Compared to regular sorts, sorting incrementally allows returning tuples before the entire result set has been sorted, which particularly enables optimizations with LIMIT queries. It may also reduce memory usage and the likelihood of spilling sorts to disk, but it comes at the cost of the increased overhead of splitting the result set into multiple sorting batches. If there are separate indexes on several of the columns referenced in WHERE, the planner might choose to use an AND or OR combination of the indexes: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​------------------ Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) Index Cond: (unique2 > 9000) ​ But this requires visiting both indexes, so it’s not necessarily a win compared to using just one index and treating the other condition as a filter. If you vary the ranges involved you’ll see the plan change accordingly. Here is an example showing the effects of LIMIT: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------ Limit (cost=0.29..14.48 rows=2 width=244) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..71.27 rows=10 width=244) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) ​ This is the same query as above, but we added a LIMIT so that not all the rows need be retrieved, and the planner changed its mind about what to do. Notice that the total cost and row count of the Index Scan node are shown as if it were run to completion. However, the Limit node is expected to stop after retrieving only a fifth of those rows, so its total cost is only a fifth as much, and that’s the actual estimated cost of the query. This plan is preferred over adding a Limit node to the previous plan because the Limit could not avoid paying the startup cost of the bitmap scan, so the total cost would be something over 25 units with that approach. Let’s try joining two tables, using the columns we have been discussing: EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..118.62 rows=10 width=488) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) Index Cond: (unique2 = t1.unique2) ​ In this plan, we have a nested-loop join node with two table scans as inputs, or children. The indentation of the node summary lines reflects the plan tree structure. The join’s first, or “outer”, child is a bitmap scan similar to those we saw before. Its cost and row count are the same as we’d get from SELECT …​ WHERE unique1 < 10 because we are applying the WHERE clause unique1 < 10 at that node. The t1.unique2 = t2.unique2 clause is not relevant yet, so it doesn’t affect the row count of the outer scan. The nested-loop join node will run its second, or “inner” child once for each row obtained from the outer child. Column values from the current outer row can be plugged into the inner scan; here, the t1.unique2 value from the outer row is available, so we get a plan and costs similar to what we saw above for a simple SELECT …​ WHERE t2.unique2 = `constant` case. (The estimated cost is actually a bit lower than what was seen above, as a result of caching that’s expected to occur during the repeated index scans on t2.) The costs of the loop node are then set on the basis of the cost of the outer scan, plus one repetition of the inner scan for each outer row (10 * 7.91, here), plus a little CPU time for join processing. In this example the join’s output row count is the same as the product of the two scans' row counts, but that’s not true in all cases because there can be additional WHERE clauses that mention both tables and so can only be applied at the join point, not to either input scan. Here’s an example: EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t2.unique2 < 10 AND t1.hundred < t2.hundred; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..49.46 rows=33 width=488) Join Filter: (t1.hundred < t2.hundred) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Materialize (cost=0.29..8.51 rows=10 width=244) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..8.46 rows=10 width=244) Index Cond: (unique2 < 10) ​ The condition t1.hundred < t2.hundred can’t be tested in the tenk2_unique2 index, so it’s applied at the join node. This reduces the estimated output row count of the join node, but does not change either input scan. Notice that here the planner has chosen to “materialize” the inner relation of the join, by putting a Materialize plan node atop it. This means that the t2 index scan will be done just once, even though the nested-loop join node needs to read that data ten times, once for each row from the outer relation. The Materialize node saves the data in memory as it’s read, and then returns the data from memory on each subsequent pass. When dealing with outer joins, you might see join plan nodes with both “Join Filter” and plain “Filter” conditions attached. Join Filter conditions come from the outer join’s ON clause, so a row that fails the Join Filter condition could still get emitted as a null-extended row. But a plain Filter condition is applied after the outer-join rules and so acts to remove rows unconditionally. In an inner join there is no semantic difference between these types of filters. If we change the query’s selectivity a bit, we might get a very different join plan: EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Hash Join (cost=230.47..713.98 rows=101 width=488) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) -> Hash (cost=229.20..229.20 rows=101 width=244) -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) Here, the planner has chosen to use a hash join, in which rows of one table are entered into an in-memory hash table, after which the other table is scanned and the hash table is probed for matches to each row. Again note how the indentation reflects the plan structure: the bitmap scan on tenk1 is the input to the Hash node, which constructs the hash table. That’s then returned to the Hash Join node, which reads rows from its outer child plan and searches the hash table for each one. Another possible type of join is a merge join, illustrated here: EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Merge Join (cost=198.11..268.19 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Sort (cost=197.83..200.33 rows=1000 width=244) Sort Key: t2.unique2 -> Seq Scan on onek t2 (cost=0.00..148.00 rows=1000 width=244) ​ Merge join requires its input data to be sorted on the join keys. In this plan the tenk1 data is sorted by using an index scan to visit the rows in the correct order, but a sequential scan and sort is preferred for onek, because there are many more rows to be visited in that table. (Sequential-scan-and-sort frequently beats an index scan for sorting many rows, because of the nonsequential disk access required by the index scan.) One way to look at variant plans is to force the planner to disregard whatever strategy it thought was the cheapest, using the enable/disable flags .For example, if we’re unconvinced that sequential-scan-and-sort is the best way to deal with table onek in the previous example, we could try SET enable_sort = off; EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​----------------------- Merge Join (cost=0.56..292.65 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Index Scan using onek_unique2 on onek t2 (cost=0.28..224.79 rows=1000 width=244) ​ which shows that the planner thinks that sorting onek by index-scanning is about 12% more expensive than sequential-scan-and-sort. Of course, the next question is whether it’s right about that. We can investigate that using EXPLAIN ANALYZE, as discussed below. It is possible to check the accuracy of the planner’s estimates by using EXPLAIN’s `ANALYZE option. With this option, EXPLAIN actually executes the query, and then displays the true row counts and true run time accumulated within each plan node, along with the same estimates that a plain EXPLAIN shows. For example, we might get a result like this: EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Nested Loop (cost=4.65..118.62 rows=10 width=488) (actual time=0.128..0.377 rows=10 loops=1) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) (actual time=0.057..0.121 rows=10 loops=1) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) (actual time=0.024..0.024 rows=10 loops=1) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) (actual time=0.021..0.022 rows=1 loops=10) Index Cond: (unique2 = t1.unique2) Planning time: 0.181 ms Execution time: 0.501 ms Note that the “actual time” values are in milliseconds of real time, whereas the cost estimates are expressed in arbitrary units; so they are unlikely to match up. The thing that’s usually most important to look for is whether the estimated row counts are reasonably close to reality. In this example the estimates were all dead-on, but that’s quite unusual in practice. ​ In some query plans, it is possible for a subplan node to be executed more than once. For example, the inner index scan will be executed once per outer row in the above nested-loop plan. In such cases, the loops value reports the total number of executions of the node, and the actual time and rows values shown are averages per-execution. This is done to make the numbers comparable with the way that the cost estimates are shown. Multiply by the loops value to get the total time actually spent in the node. In the above example, we spent a total of 0.220 milliseconds executing the index scans on tenk2. In some cases EXPLAIN ANALYZE shows additional execution statistics beyond the plan node execution times and row counts. For example, Sort and Hash nodes provide extra information: EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2 ORDER BY t1.fivethous; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------------​------ Sort (cost=717.34..718.09 rows=101 width=488) (actual time=7.761..7.774 rows=100 loops=1) Sort Key: t1.fivethous Sort Method: quicksort Memory: 77kB -> Hash Join (cost=230.47..713.98 rows=101 width=488) (actual time=0.711..7.427 rows=100 loops=1) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) (actual time=0.007..2.583 rows=10000 loops=1) -> Hash (cost=229.20..229.20 rows=101 width=244) (actual time=0.659..0.659 rows=100 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 28kB -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) (actual time=0.080..0.526 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.049..0.049 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.194 ms Execution time: 8.008 ms ​ The Sort node shows the sort method used (in particular, whether the sort was in-memory or on-disk) and the amount of memory or disk space needed. The Hash node shows the number of hash buckets and batches as well as the peak amount of memory used for the hash table. (If the number of batches exceeds one, there will also be disk space usage involved, but that is not shown.) Another type of extra information is the number of rows removed by a filter condition: EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE ten < 7; QUERY PLAN -------------------------------------------------------------------​-------------------------------------- Seq Scan on tenk1 (cost=0.00..483.00 rows=7000 width=244) (actual time=0.016..5.107 rows=7000 loops=1) Filter: (ten < 7) Rows Removed by Filter: 3000 Planning time: 0.083 ms Execution time: 5.905 ms These counts can be particularly valuable for filter conditions applied at join nodes. The “Rows Removed” line only appears when at least one scanned row, or potential join pair in the case of a join node, is rejected by the filter condition. A case similar to filter conditions occurs with “lossy” index scans. For example, consider this search for polygons containing a specific point: EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Seq Scan on polygon_tbl (cost=0.00..1.05 rows=1 width=32) (actual time=0.044..0.044 rows=0 loops=1) Filter: (f1 @> '((0.5,2))'::polygon) Rows Removed by Filter: 4 Planning time: 0.040 ms Execution time: 0.083 ms The planner thinks (quite correctly) that this sample table is too small to bother with an index scan, so we have a plain sequential scan in which all the rows got rejected by the filter condition. But if we force an index scan to be used, we see: SET enable_seqscan TO off; EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------- Index Scan using gpolygonind on polygon_tbl (cost=0.13..8.15 rows=1 width=32) (actual time=0.062..0.062 rows=0 loops=1) Index Cond: (f1 @> '((0.5,2))'::polygon) Rows Removed by Index Recheck: 1 Planning time: 0.034 ms Execution time: 0.144 ms ​ Here we can see that the index returned one candidate row, which was then rejected by a recheck of the index condition. This happens because a GiST index is “lossy” for polygon containment tests: it actually returns the rows with polygons that overlap the target, and then we have to do the exact containment test on those rows. EXPLAIN has a BUFFERS option that can be used with ANALYZE to get even more run time statistics: EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) (actual time=0.323..0.342 rows=10 loops=1) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) Buffers: shared hit=15 -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) (actual time=0.309..0.309 rows=0 loops=1) Buffers: shared hit=7 -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Buffers: shared hit=2 -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) (actual time=0.227..0.227 rows=999 loops=1) Index Cond: (unique2 > 9000) Buffers: shared hit=5 Planning time: 0.088 ms Execution time: 0.423 ms ​ The numbers provided by BUFFERS help to identify which parts of the query are the most I/O-intensive. Keep in mind that because EXPLAIN ANALYZE actually runs the query, any side-effects will happen as usual, even though whatever results the query might output are discarded in favor of printing the EXPLAIN data. If you want to analyze a data-modifying query without changing your tables, you can roll the command back afterwards, for example: BEGIN; EXPLAIN ANALYZE UPDATE tenk1 SET hundred = hundred + 1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------- Update on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=14.628..14.628 rows=0 loops=1) -> Bitmap Heap Scan on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=0.101..0.439 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.079 ms Execution time: 14.727 ms ROLLBACK; ​ As seen in this example, when the query is an INSERT, UPDATE, or DELETE command, the actual work of applying the table changes is done by a top-level Insert, Update, or Delete plan node. The plan nodes underneath this node perform the work of locating the old rows and/or computing the new data. So above, we see the same sort of bitmap table scan we’ve seen already, and its output is fed to an Update node that stores the updated rows. It’s worth noting that although the data-modifying node can take a considerable amount of run time (here, it’s consuming the lion’s share of the time), the planner does not currently add anything to the cost estimates to account for that work. That’s because the work to be done is the same for every correct query plan, so it doesn’t affect planning decisions. When an UPDATE or DELETE command affects an inheritance hierarchy, the output might look like this: EXPLAIN UPDATE parent SET f2 = f2 + 1 WHERE f1 = 101; QUERY PLAN -------------------------------------------------------------------​---------------- Update on parent (cost=0.00..24.53 rows=4 width=14) Update on parent Update on child1 Update on child2 Update on child3 -> Seq Scan on parent (cost=0.00..0.00 rows=1 width=14) Filter: (f1 = 101) -> Index Scan using child1_f1_key on child1 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child2_f1_key on child2 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child3_f1_key on child3 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) ​ In this example the Update node needs to consider three child tables as well as the originally-mentioned parent table. So there are four input scanning subplans, one per table. For clarity, the Update node is annotated to show the specific target tables that will be updated, in the same order as the corresponding subplans. The Planning time shown by EXPLAIN ANALYZE is the time it took to generate the query plan from the parsed query and optimize it. It does not include parsing or rewriting. The Execution time shown by EXPLAIN ANALYZE includes executor start-up and shut-down time, as well as the time to run any triggers that are fired, but it does not include parsing, rewriting, or planning time. Time spent executing BEFORE triggers, if any, is included in the time for the related Insert, Update, or Delete node; but time spent executing AFTER triggers is not counted there because AFTER triggers are fired after completion of the whole plan. The total time spent in each trigger (either BEFORE or AFTER) is also shown separately. Note that deferred constraint triggers will not be executed until end of transaction and are thus not considered at all by EXPLAIN ANALYZE. There are two significant ways in which run times measured by EXPLAIN ANALYZE can deviate from normal execution of the same query. First, since no output rows are delivered to the client, network transmission costs and I/O conversion costs are not included. Second, the measurement overhead added by EXPLAIN ANALYZE can be significant, especially on machines with slow gettimeofday() operating-system calls. You can use the pg_test_timing tool to measure the overhead of timing on your system. EXPLAIN results should not be extrapolated to situations much different from the one you are actually testing; for example, results on a toy-sized table cannot be assumed to apply to large tables. The planner’s cost estimates are not linear and so it might choose a different plan for a larger or smaller table. An extreme example is that on a table that only occupies one disk page, you’ll nearly always get a sequential scan plan whether indexes are available or not. The planner realizes that it’s going to take one disk page read to process the table in any case, so there’s no value in expending additional page reads to look at an index. (We saw this happening in the polygon_tbl example above.) There are cases in which the actual and estimated values won’t match up well, but nothing is really wrong. One such case occurs when plan node execution is stopped short by a LIMIT or similar effect. For example, in the LIMIT query we used before, EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------ Limit (cost=0.29..14.71 rows=2 width=244) (actual time=0.177..0.249 rows=2 loops=1) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..72.42 rows=10 width=244) (actual time=0.174..0.244 rows=2 loops=1) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) Rows Removed by Filter: 287 Planning time: 0.096 ms Execution time: 0.336 ms ​ the estimated cost and row count for the Index Scan node are shown as though it were run to completion. But in reality the Limit node stopped requesting rows after it got two, so the actual row count is only 2 and the run time is less than the cost estimate would suggest. This is not an estimation error, only a discrepancy in the way the estimates and true values are displayed. Merge joins also have measurement artifacts that can confuse the unwary. A merge join will stop reading one input if it’s exhausted the other input and the next key value in the one input is greater than the last key value of the other input; in such a case there can be no more matches and so no need to scan the rest of the first input. This results in not reading all of one child, with results like those mentioned for LIMIT. Also, if the outer (first) child contains rows with duplicate key values, the inner (second) child is backed up and rescanned for the portion of its rows matching that key value. EXPLAIN ANALYZE counts these repeated emissions of the same inner rows as if they were real additional rows. When there are many outer duplicates, the reported actual row count for the inner child plan node can be significantly larger than the number of rows that are actually in the inner relation. BitmapAnd and BitmapOr nodes always report their actual row counts as zero, due to implementation limitations. Normally, EXPLAIN will display every plan node created by the planner. However, there are cases where the executor can determine that certain nodes need not be executed because they cannot produce any rows, based on parameter values that were not available at planning time. (Currently this can only happen for child nodes of an Append or MergeAppend node that is scanning a partitioned table.) When this happens, those plan nodes are omitted from the EXPLAIN output and a Subplans Removed: `N` annotation appears instead. As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates. One component of the statistics is the total number of entries in each table and index, as well as the number of disk blocks occupied by each table and index. This information is kept in the table pg_class, in the columns reltuples and relpages. We can look at it with queries similar to this one: SELECT relname, relkind, reltuples, relpages FROM pg_class WHERE relname LIKE 'tenk1%'; relname | relkind | reltuples | relpages ----------------------+---------+-----------+---------- tenk1 | r | 10000 | 358 tenk1_hundred | i | 10000 | 30 tenk1_thous_tenthous | i | 10000 | 30 tenk1_unique1 | i | 10000 | 30 tenk1_unique2 | i | 10000 | 30 (5 rows) ​ Here we can see that tenk1 contains 10000 rows, as do its indexes, but the indexes are (unsurprisingly) much smaller than the table. For efficiency reasons, reltuples and relpages are not updated on-the-fly, and so they usually contain somewhat out-of-date values. They are updated by VACUUM, ANALYZE, and a few DDL commands such as CREATE INDEX. A VACUUM or ANALYZE operation that does not scan the entire table (which is commonly the case) will incrementally update the reltuples count on the basis of the part of the table it did scan, resulting in an approximate value. In any case, the planner will scale the values it finds in pg_class to match the current physical table size, thus obtaining a closer approximation. Most queries retrieve only a fraction of the rows in a table, due to WHERE clauses that restrict the rows to be examined. The planner thus needs to make an estimate of the selectivity of WHERE clauses, that is, the fraction of rows that match each condition in the WHERE clause. The information used for this task is stored in the pg_statistic system catalog. Entries in pg_statistic are updated by the ANALYZE and VACUUM ANALYZE commands, and are always approximate even when freshly updated. Rather than look at pg_statistic directly, it’s better to look at its view pg_stats when examining the statistics manually. pg_stats is designed to be more easily readable. Furthermore, pg_stats is readable by all, whereas pg_statistic is only readable by a superuser. (This prevents unprivileged users from learning something about the contents of other people’s tables from the statistics. The pg_stats view is restricted to show only rows about tables that the current user can read.) For example, we might do: SELECT attname, inherited, n_distinct, array_to_string(most_common_vals, E'\\n') as most_common_vals FROM pg_stats WHERE tablename = 'road'; attname | inherited | n_distinct | most_common_vals ---------+-----------+------------+------------------------------------ name | f | -0.363388 | I- 580 Ramp+ | | | I- 880 Ramp+ | | | Sp Railroad + | | | I- 580 + | | | I- 680 Ramp name | t | -0.284859 | I- 880 Ramp+ | | | I- 580 Ramp+ | | | I- 680 Ramp+ | | | I- 580 + | | | State Hwy 13 Ramp (2 rows) Note that two rows are displayed for the same column, one corresponding to the complete inheritance hierarchy starting at the road table (inherited=t), and another one including only the road table itself (inherited=f). The amount of information stored in pg_statistic by ANALYZE, in particular the maximum number of entries in the most_common_vals and histogram_bounds arrays for each column, can be set on a column-by-column basis using the ALTER TABLE SET STATISTICS command, or globally by setting the default_statistics_target configuration variable. The default limit is presently 100 entries. Raising the limit might allow more accurate planner estimates to be made, particularly for columns with irregular data distributions, at the price of consuming more space in pg_statistic and slightly more time to compute the estimates. Conversely, a lower limit might be sufficient for columns with simple data distributions. Further details about the planner’s use of statistics can be found in doc. It is common to see slow queries running bad execution plans because multiple columns used in the query clauses are correlated. The planner normally assumes that multiple conditions are independent of each other, an assumption that does not hold when column values are correlated. Regular statistics, because of their per-individual-column nature, cannot capture any knowledge about cross-column correlation. However, IvorySQL has the ability to compute multivariate statistics, which can capture such information. Because the number of possible column combinations is very large, it’s impractical to compute multivariate statistics automatically. Instead, extended statistics objects, more often called just statistics objects, can be created to instruct the server to obtain statistics across interesting sets of columns. Statistics objects are created using the CREATE STATISTICS command. Creation of such an object merely creates a catalog entry expressing interest in the statistics. Actual data collection is performed by ANALYZE (either a manual command, or background auto-analyze). The collected values can be examined in the pg_statistic_ext_data catalog. ANALYZE computes extended statistics based on the same sample of table rows that it takes for computing regular single-column statistics. Since the sample size is increased by increasing the statistics target for the table or any of its columns (as described in the previous section), a larger statistics target will normally result in more accurate extended statistics, as well as more time spent calculating them. The following subsections describe the kinds of extended statistics that are currently supported. The simplest kind of extended statistics tracks functional dependencies, a concept used in definitions of database normal forms. We say that column b is functionally dependent on column a if knowledge of the value of a is sufficient to determine the value of b, that is there are no two rows having the same value of a but different values of b. In a fully normalized database, functional dependencies should exist only on primary keys and superkeys. However, in practice many data sets are not fully normalized for various reasons; intentional denormalization for performance reasons is a common example. Even in a fully normalized database, there may be partial correlation between some columns, which can be expressed as partial functional dependency. The existence of functional dependencies directly affects the accuracy of estimates in certain queries. If a query contains conditions on both the independent and the dependent column(s), the conditions on the dependent columns do not further reduce the result size; but without knowledge of the functional dependency, the query planner will assume that the conditions are independent, resulting in underestimating the result size. To inform the planner about functional dependencies, ANALYZE can collect measurements of cross-column dependency. Assessing the degree of dependency between all sets of columns would be prohibitively expensive, so data collection is limited to those groups of columns appearing together in a statistics object defined with the dependencies option. It is advisable to create dependencies statistics only for column groups that are strongly correlated, to avoid unnecessary overhead in both ANALYZE and later query planning. Here is an example of collecting functional-dependency statistics: CREATE STATISTICS stts (dependencies) ON city, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxname, stxkeys, stxddependencies FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts'; stxname | stxkeys | stxddependencies ---------+---------+------------------------------------------ stts | 1 5 | {\"1 => 5\": 1.000000, \"5 => 1\": 0.423130} (1 row) ​ Here it can be seen that column 1 (zip code) fully determines column 5 (city) so the coefficient is 1.0, while city only determines zip code about 42% of the time, meaning that there are many cities (58%) that are represented by more than a single ZIP code. When computing the selectivity for a query involving functionally dependent columns, the planner adjusts the per-condition selectivity estimates using the dependency coefficients so as not to produce an underestimate. Functional dependencies are currently only applied when considering simple equality conditions that compare columns to constant values, and IN clauses with constant values. They are not used to improve estimates for equality conditions comparing two columns or comparing a column to an expression, nor for range clauses, LIKE or any other type of condition. When estimating with functional dependencies, the planner assumes that conditions on the involved columns are compatible and hence redundant. If they are incompatible, the correct estimate would be zero rows, but that possibility is not considered. For example, given a query like SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '94105'; ​ the planner will disregard the city clause as not changing the selectivity, which is correct. However, it will make the same assumption about SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '90210'; ​ even though there will really be zero rows satisfying this query. Functional dependency statistics do not provide enough information to conclude that, however. In many practical situations, this assumption is usually satisfied; for example, there might be a GUI in the application that only allows selecting compatible city and ZIP code values to use in a query. But if that’s not the case, functional dependencies may not be a viable option. Single-column statistics store the number of distinct values in each column. Estimates of the number of distinct values when combining more than one column (for example, for GROUP BY a, b) are frequently wrong when the planner only has single-column statistical data, causing it to select bad plans. To improve such estimates, ANALYZE can collect n-distinct statistics for groups of columns. As before, it’s impractical to do this for every possible column grouping, so data is collected only for those groups of columns appearing together in a statistics object defined with the ndistinct option. Data will be collected for each possible combination of two or more columns from the set of listed columns. Continuing the previous example, the n-distinct counts in a table of ZIP codes might look like the following: CREATE STATISTICS stts2 (ndistinct) ON city, state, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxkeys AS k, stxdndistinct AS nd FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts2'; -[ RECORD 1 ]------------------------------------------------------​-- k | 1 2 5 nd | {\"1, 2\": 33178, \"1, 5\": 33178, \"2, 5\": 27435, \"1, 2, 5\": 33178} (1 row) ​ This indicates that there are three combinations of columns that have 33178 distinct values: ZIP code and state; ZIP code and city; and ZIP code, city and state (the fact that they are all equal is expected given that ZIP code alone is unique in this table). On the other hand, the combination of city and state has only 27435 distinct values. It’s advisable to create ndistinct statistics objects only on combinations of columns that are actually used for grouping, and for which misestimation of the number of groups is resulting in bad plans. Otherwise, the ANALYZE cycles are just wasted. Another type of statistic stored for each column are most-common value lists. This allows very accurate estimates for individual columns, but may result in significant misestimates for queries with conditions on multiple columns. To improve such estimates, ANALYZE can collect MCV lists on combinations of columns. Similarly to functional dependencies and n-distinct coefficients, it’s impractical to do this for every possible column grouping. Even more so in this case, as the MCV list (unlike functional dependencies and n-distinct coefficients) does store the common column values. So data is collected only for those groups of columns appearing together in a statistics object defined with the mcv option. Continuing the previous example, the MCV list for a table of ZIP codes might look like the following (unlike for simpler types of statistics, a function is required for inspection of MCV contents): CREATE STATISTICS stts3 (mcv) ON city, state FROM zipcodes; ANALYZE zipcodes; SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid), pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts3'; index | values | nulls | frequency | base_frequency -------+------------------------+-------+-----------+---------------- 0 | {Washington, DC} | {f,f} | 0.003467 | 2.7e-05 1 | {Apo, AE} | {f,f} | 0.003067 | 1.9e-05 2 | {Houston, TX} | {f,f} | 0.002167 | 0.000133 3 | {El Paso, TX} | {f,f} | 0.002 | 0.000113 4 | {New York, NY} | {f,f} | 0.001967 | 0.000114 5 | {Atlanta, GA} | {f,f} | 0.001633 | 3.3e-05 6 | {Sacramento, CA} | {f,f} | 0.001433 | 7.8e-05 7 | {Miami, FL} | {f,f} | 0.0014 | 6e-05 8 | {Dallas, TX} | {f,f} | 0.001367 | 8.8e-05 9 | {Chicago, IL} | {f,f} | 0.001333 | 5.1e-05 ... (99 rows) ​ This indicates that the most common combination of city and state is Washington in DC, with actual frequency (in the sample) about 0.35%. The base frequency of the combination (as computed from the simple per-column frequencies) is only 0.0027%, resulting in two orders of magnitude under-estimates. It’s advisable to create MCV statistics objects only on combinations of columns that are actually used in conditions together, and for which misestimation of the number of groups is resulting in bad plans. Otherwise, the ANALYZE and planning cycles are just wasted. It is possible to control the query planner to some extent by using the explicit JOIN syntax. To see why this matters, we first need some background. In a simple join query, such as: SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; ​ the planner is free to join the given tables in any order. For example, it could generate a query plan that joins A to B, using the WHERE condition a.id = b.id, and then joins C to this joined table, using the other WHERE condition. Or it could join B to C and then join A to that result. Or it could join A to C and then join them with B — but that would be inefficient, since the full Cartesian product of A and C would have to be formed, there being no applicable condition in the WHERE clause to allow optimization of the join. (All joins in the IvorySQL executor happen between two input tables, so it’s necessary to build up the result in one or another of these fashions.) The important point is that these different join possibilities give semantically equivalent results but might have hugely different execution costs. Therefore, the planner will explore all of them to try to find the most efficient query plan. When a query only involves two or three tables, there aren’t many join orders to worry about. But the number of possible join orders grows exponentially as the number of tables expands. Beyond ten or so input tables it’s no longer practical to do an exhaustive search of all the possibilities, and even for six or seven tables planning might take an annoyingly long time. When there are too many input tables, the IvorySQL planner will switch from exhaustive search to a genetic probabilistic search through a limited number of possibilities. (The switch-over threshold is set by the geqo_threshold run-time parameter.) The genetic search takes less time, but it won’t necessarily find the best possible plan. When the query involves outer joins, the planner has less freedom than it does for plain (inner) joins. For example, consider: SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); ​ Although this query’s restrictions are superficially similar to the previous example, the semantics are different because a row must be emitted for each row of A that has no matching row in the join of B and C. Therefore the planner has no choice of join order here: it must join B to C and then join A to that result. Accordingly, this query takes less time to plan than the previous query. In other cases, the planner might be able to determine that more than one join order is safe. For example, given: SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id); it is valid to join A to either B or C first. Currently, only FULL JOIN completely constrains the join order. Most practical cases involving LEFT JOIN or RIGHT JOIN can be rearranged to some extent. Explicit inner join syntax (INNER JOIN, CROSS JOIN, or unadorned JOIN) is semantically the same as listing the input relations in FROM, so it does not constrain the join order. Even though most kinds of JOIN don’t completely constrain the join order, it is possible to instruct the IvorySQL query planner to treat all JOIN clauses as constraining the join order anyway. For example, these three queries are logically equivalent: SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); ​ But if we tell the planner to honor the JOIN order, the second and third take less time to plan than the first. This effect is not worth worrying about for only three tables, but it can be a lifesaver with many tables. To force the planner to follow the join order laid out by explicit `JOIN`s, set the join_collapse_limit run-time parameter to 1. (Other possible values are discussed below.) You do not need to constrain the join order completely in order to cut search time, because it’s OK to use JOIN operators within items of a plain FROM list. For example, consider: SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...; ​ With join_collapse_limit = 1, this forces the planner to join A to B before joining them to other tables, but doesn’t constrain its choices otherwise. In this example, the number of possible join orders is reduced by a factor of 5. Constraining the planner’s search in this way is a useful technique both for reducing planning time and for directing the planner to a good query plan. If the planner chooses a bad join order by default, you can force it to choose a better order via JOIN syntax — assuming that you know of a better order, that is. Experimentation is recommended. A closely related issue that affects planning time is collapsing of subqueries into their parent query. For example, consider: SELECT * FROM x, y, (SELECT * FROM a, b, c WHERE something) AS ss WHERE somethingelse; This situation might arise from use of a view that contains a join; the view’s SELECT rule will be inserted in place of the view reference, yielding a query much like the above. Normally, the planner will try to collapse the subquery into the parent, yielding: SELECT * FROM x, y, a, b, c WHERE something AND somethingelse; ​ This usually results in a better plan than planning the subquery separately. (For example, the outer WHERE conditions might be such that joining X to A first eliminates many rows of A, thus avoiding the need to form the full logical output of the subquery.) But at the same time, we have increased the planning time; here, we have a five-way join problem replacing two separate three-way join problems. Because of the exponential growth of the number of possibilities, this makes a big difference. The planner tries to avoid getting stuck in huge join search problems by not collapsing a subquery if more than from_collapse_limit FROM items would result in the parent query. You can trade off planning time against quality of plan by adjusting this run-time parameter up or down. from_collapse_limit and join_collapse_limit are similarly named because they do almost the same thing: one controls when the planner will “flatten out” subqueries, and the other controls when it will flatten out explicit joins. Typically you would either set join_collapse_limit equal to from_collapse_limit (so that explicit joins and subqueries act similarly) or set join_collapse_limit to 1 (if you want to control join order with explicit joins). But you might set them differently if you are trying to fine-tune the trade-off between planning time and run time. One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible. When using multiple INSERT`s, turn off autocommit and just do one commit at the end. (In plain SQL, this means issuing `BEGIN at the start and COMMIT at the end. Some client libraries might do this behind your back, in which case you need to make sure the library does it when you want it done.) If you allow each insertion to be committed separately, IvorySQL is doing a lot of work for each row that is added. An additional benefit of doing all insertions in one transaction is that if the insertion of one row were to fail then the insertion of all rows inserted up to that point would be rolled back, so you won’t be stuck with partially loaded data. Use COPY to load all the rows in one command, instead of using a series of INSERT commands. The COPY command is optimized for loading large numbers of rows; it is less flexible than INSERT, but incurs significantly less overhead for large data loads. Since COPY is a single command, there is no need to disable autocommit if you use this method to populate a table. If you cannot use COPY, it might help to use PREPARE to create a prepared INSERT statement, and then use EXECUTE as many times as required. This avoids some of the overhead of repeatedly parsing and planning INSERT. Different interfaces provide this facility in different ways; look for “prepared statements” in the interface documentation. Note that loading a large number of rows using COPY is almost always faster than using INSERT, even if PREPARE is used and multiple insertions are batched into a single transaction. COPY is fastest when used within the same transaction as an earlier CREATE TABLE or TRUNCATE command. In such cases no WAL needs to be written, because in case of an error, the files containing the newly loaded data will be removed anyway. However, this consideration only applies when wal_level is minimal as all commands must write WAL otherwise. If you are loading a freshly created table, the fastest method is to create the table, bulk load the table’s data using COPY, then create any indexes needed for the table. Creating an index on pre-existing data is quicker than updating it incrementally as each row is loaded. If you are adding large amounts of data to an existing table, it might be a win to drop the indexes, load the table, and then recreate the indexes. Of course, the database performance for other users might suffer during the time the indexes are missing. One should also think twice before dropping a unique index, since the error checking afforded by the unique constraint will be lost while the index is missing. Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing. What’s more, when you load data into a table with existing foreign key constraints, each new row requires an entry in the server’s list of pending trigger events (since it is the firing of a trigger that checks the row’s foreign key constraint). Loading many millions of rows can cause the trigger event queue to overflow available memory, leading to intolerable swapping or even outright failure of the command. Therefore it may be necessary, not just desirable, to drop and re-apply foreign keys when loading large amounts of data. If temporarily removing the constraint isn’t accept. Temporarily increasing the maintenance_work_mem configuration variable when loading large amounts of data can lead to improved performance. This will help to speed up CREATE INDEX commands and ALTER TABLE ADD FOREIGN KEY commands. It won’t do much for COPY itself, so this advice is only useful when you are using one or both of the above techniques. Temporarily increasing the max_wal_size configuration variable can also make large data loads faster. This is because loading a large amount of data into IvorySQL will cause checkpoints to occur more often than the normal checkpoint frequency (specified by the checkpoint_timeout configuration variable). Whenever a checkpoint occurs, all dirty pages must be flushed to disk. By increasing max_wal_size temporarily during bulk data loads, the number of checkpoints that are required can be reduced. When loading large amounts of data into an installation that uses WAL archiving or streaming replication, it might be faster to take a new base backup after the load has completed than to process a large amount of incremental WAL data. To prevent incremental WAL logging while loading, disable archiving and streaming replication, by setting wal_level to minimal, archive_mode to off, and max_wal_senders to zero. But note that changing these settings requires a server restart, and makes any base backups taken before unavailable for archive recovery and standby server, which may lead to data loss. Aside from avoiding the time for the archiver or WAL sender to process the WAL data, doing this will actually make certain commands faster, because they do not to write WAL at all if wal_level is minimal and the current subtransaction (or top-level transaction) created or truncated the table or index they change. (They can guarantee crash safety more cheaply by doing an fsync at the end than by writing WAL.) Whenever you have significantly altered the distribution of data within a table, running ANALYZE is strongly recommended. This includes bulk loading large amounts of data into the table. Running ANALYZE (or VACUUM ANALYZE) ensures that the planner has up-to-date statistics about the table. With no statistics or obsolete statistics, the planner might make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics. Note that if the autovacuum daemon is enabled, it might run ANALYZE automatically. Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To restore a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while restoring a dump, not while creating it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.) By default, pg_dump uses COPY, and when it is generating a complete schema-and-data dump, it is careful to load data before creating indexes and foreign keys. So in this case several guidelines are handled automatically. What is left for you to do is to: Set appropriate (i.e., larger than normal) values for maintenance_work_mem and max_wal_size. If using WAL archiving or streaming replication, consider disabling them during the restore. To do that, set archive_mode to off, wal_level to minimal, and max_wal_senders to zero before loading the dump. Afterwards, set them back to the right values and take a fresh base backup. Experiment with the parallel dump and restore modes of both pg_dump and pg_restore and find the optimal number of concurrent jobs to use. Dumping and restoring in parallel by means of the -j option should give you a significantly higher performance over the serial mode. Consider whether the whole dump should be restored as a single transaction. To do that, pass the -1 or --single-transaction command-line option to psql or pg_restore. When using this mode, even the smallest of errors will rollback the entire restore, possibly discarding many hours of processing. Depending on how interrelated the data is, that might seem preferable to manual cleanup, or not. COPY commands will run fastest if you use a single transaction and have WAL archiving turned off. If multiple CPUs are available in the database server, consider using pg_restore’s --jobs option. This allows concurrent data loading and index creation. Run ANALYZE afterwards. Durability is a database feature that guarantees the recording of committed transactions even if the server crashes or loses power. However, durability adds significant database overhead, so if your site does not require such a guarantee, IvorySQL can be configured to run much faster. The following are configuration changes you can make to improve performance in such cases. Except as noted below, durability is still guaranteed in case of a crash of the database software; only an abrupt operating system crash creates a risk of data loss or corruption when these settings are used. Place the database cluster’s data directory in a memory-backed file system (i.e., RAM disk). This eliminates all database disk I/O, but limits data storage to the amount of available memory (and perhaps swap). Turn off fsync; there is no need to flush data to disk. Turn off synchronous_commit; there might be no need to force WAL writes to disk on every commit. This setting does risk transaction loss (though not data corruption) in case of a crash of the database. Turn off full_page_writes; there is no need to guard against partial page writes. Increase max_wal_size and checkpoint_timeout; this reduces the frequency of checkpoints, but increases the storage requirements of /pg_wal. Create unlogged tables to avoid WAL writes, though it makes the tables non-crash-safe.","title":"Operation and maintenance management guide","component":"ivorysql-doc","version":"master","name":"operation_guide","url":"/ivorysql-doc/master/operation_guide.html","titles":[{"text":"1. Upgrade IvorySQL version","hash":"upgrade-ivorysql-version","id":1},{"text":"1.1. Overview of upgrade scheme","hash":"overview-of-upgrade-scheme","id":2},{"text":"1.2. Upgrade data via pg_dumpall","hash":"upgrade-data-via-pg_dumpall","id":3},{"text":"1.3. Upgrade with the pg_upgrade utility","hash":"upgrade-with-the-pg_upgrade-utility","id":4},{"text":"1.4. Upgrade data by copying","hash":"upgrade-data-by-copying","id":5},{"text":"2. Managing IvorySQL Versions","hash":"managing-ivorysql-versions","id":6},{"text":"3. Managing IvorySQL database access","hash":"managing-ivorysql-database-access","id":7},{"text":"4. Defining Data Objects","hash":"defining-data-objects","id":8},{"text":"4.1. VARCHAR2","hash":"varchar2","id":9},{"text":"4.1.1. Overview","hash":"overview","id":10},{"text":"4.1.2. Grammar","hash":"grammar","id":11},{"text":"4.1.3. Use Cases","hash":"use-cases","id":12},{"text":"5. Search Data","hash":"search-data","id":13},{"text":"6. 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Multivariate N-Distinct Counts","hash":"multivariate-n-distinct-counts","id":42},{"text":"9.2.2.3. Multivariate MCV Lists","hash":"multivariate-mcv-lists","id":43},{"text":"9.3. Controlling the Planner with Explicit JOIN Clauses","hash":"controlling-the-planner-with-explicit-join-clauses","id":44},{"text":"9.4. Populating a Database","hash":"populating-a-database","id":45},{"text":"9.4.1. Disable Autocommit","hash":"disable-autocommit","id":46},{"text":"9.4.2. Use COPY","hash":"use-copy","id":47},{"text":"9.4.3. Remove Indexes","hash":"remove-indexes","id":48},{"text":"9.4.4. Remove Foreign Key Constraints","hash":"remove-foreign-key-constraints","id":49},{"text":"9.4.5. Increase maintenance_work_mem","hash":"increase-maintenance_work_mem","id":50},{"text":"9.4.6. Increase max_wal_size","hash":"increase-max_wal_size","id":51},{"text":"9.4.7. Disable WAL Archival and Streaming Replication","hash":"disable-wal-archival-and-streaming-replication","id":52},{"text":"9.4.8. Run ANALYZE Afterwards","hash":"run-analyze-afterwards","id":53},{"text":"9.4.9. Some Notes about pg_dump","hash":"some-notes-about-pg_dump","id":54},{"text":"9.5. Non-Durable Settings","hash":"non-durable-settings","id":55}]},"8":{"id":8,"text":"[Release Date: Nov 25, 2025] IvorySQL 5.0, based on PostgreSQL 18.0, introduces significant Oracle compatibility improvements, PL/iSQL enhancements, and new globalization capabilities while refreshing packaging, automation, and tooling. For a comprehensive list of updates, please visit our documentation site. PostgreSQL 18.0 An asynchronous I/O (AIO) subsystem that can improve performance of sequential scans, bitmap heap scans, vacuums, and other operations. pg_upgrade now retains optimizer statistics. Support for \"skip scan\" lookups that allow using multicolumn B-tree indexes in more cases. uuidv7() function for generating timestamp-ordered UUIDs. Virtual generated columns that compute their values during read operations. This is now the default for generated columns. OAuth authentication support. OLD and NEW support for RETURNING clauses in INSERT, UPDATE, DELETE, and MERGE commands. Temporal constraints, or constraints over ranges, for PRIMARY KEY, UNIQUE, and FOREIGN KEY constraints. For further details, visit PostgreSQL’s release notes. Oracle-compatible ROWID support: Feature #126 Ensures IvorySQL row identifiers align with Oracle semantics for seamless cross-database tooling. PL/iSQL CALL invocation syntax: Feature #764 Adds the Oracle-style CALL entry point so stored procedures can be invoked consistently. PL/iSQL %ROWTYPE support: Feature #765 Allows variables to mirror entire table or cursor rows for concise PL/iSQL coding. PL/iSQL %TYPE support: Feature #766 Enables variables to adopt the data type of existing columns or variables to reduce drift. Case-sensitive compatibility switch: Feature #767 Preserves identifier case to match Oracle behavior when required. NLS parameter compatibility: Feature #768 Honors Oracle-style NLS settings such as NLS_DATE_FORMAT and NLS_TIMESTAMP_FORMAT. Empty string to NULL translation: Feature #769 Converts zero-length strings to NULL to match Oracle compatibility rules. Parser switching capability: Feature #770 Adds toggles between Oracle and PostgreSQL parsers for per-session flexibility. GB18030 database encoding: Feature #771 Provides GB18030 initialization and create-database options for full Chinese market coverage. Oracle-compatible SYS_GUID: Feature #773 Implements the Oracle SYS_GUID function to generate RAW-based GUIDs. Oracle-compatible SYS_CONTEXT: Feature #774 Delivers the Oracle SYS_CONTEXT API for querying session and environment metadata. Oracle-compatible USERENV: Feature #775 Adds the USERENV function so sessions can inspect Oracle-style contextual details. Oracle-compatible function syntax: Feature #776 Supports Oracle constructs such as EDITIONABLE/NONEDITIONABLE, RETURN, IS, and OUT …​ NOCOPY options. Oracle-compatible procedure syntax: Feature #777 Enables procedure DDL with Oracle options, EXEC invocation, and ALTER PROCEDURE support. libpq OUT parameter plumbing: Feature #778 Extends client protocol handling so OUT parameters can be consumed like OCI. Procedure OUT parameter support: Feature #779 Allows stored procedures to declare IN, OUT, and IN OUT modes per Oracle conventions. Function OUT parameter support: Feature #780 Permits Oracle-style OUT parameters within functions, including IN OUT combinations. Nested subprograms: Feature #781 Introduces support for declaring functions or procedures within other subprograms, including overloading. Oracle-compatible INSTR: Feature #782 Matches Oracle INSTR behavior for substring searches and position checks. Oracle-compatible FORCE VIEW: Feature #783 Lets views be created even when referenced objects do not yet exist, mirroring Oracle’s FORCE option. Oracle-compatible LIKE operator: Feature #784 Aligns pattern-matching semantics with Oracle for predictable wildcard behavior. Online Experience: IvorySQL v5.0: Featuare #887 An interactive, browser-based environment will be launched to allow users to explore and evaluate IvorySQL v5.0 in real time — no installation required. Packaging for All Platforms: Featuare #949 Provides multi-platform media packages for X86、ARM、MIPS、LoongArch architecture. Containerized Deployment Support (Docker Compose & Docker Swarm): Supports deployment of standalone IvorySQL databases and high-availability clusters in Docker Swarm and Docker Compose environments. Containerized Deployment Support (Kubernetes): Supports deployment of standalone IvorySQL databases and high-availability clusters on Kubernetes (K8S) using Helm. IvorySQL Operator v5 released (Kubernetes): The IvorySQL Operator v5 has been adapted to support IvorySQL v5.0, with upgrades to system component versions and database extension versions. IvorySQL Cloud v5 released (Unified Lifecycle & Visual Control Plane): Offers a fully managed, graphical control plane that handles IvorySQL v5 database subscriptions, orchestrates end-to-end lifecycle operations, and integrates surrounding ecosystem services. pg_cron: Feature #882 Scheduled job execution within the database layer will be available through pg_cron integration. pgAudit: Feature #929 Provides detailed session and/or object audit logging via the standard PostgreSQL logging facility. PostGIS: Feature #880 Spatial data processing and geospatial analytics will be enabled through PostGIS compatibility. pgRouting: Feature #881 Network and routing analysis features will be introduced with pgRouting support. PGroonga: Feature #879 Full-text search capabilities will be enhanced via planned PGroonga support. ddlx: Feature #877 Support for ddlx to enable advanced schema introspection and automated DDL generation. pgsql-http: Feature #883 Allow the database to initiate HTTP/HTTPS requests internally, enabling seamless communication between the database and external web services. system_stats： Feature #946 System level statistics will be provided by system_stats support. plpgsql_check: Feature #915 Static code analysis on PL/pgSQL functions to identify errors, warnings, and potential issues before runtime execution pgvector: Feature #878 Integration with pgvector to empower AI/ML workloads through native vector similarity search. Repaired unused_oids and duplicate_oids catalog tooling so header scans correctly detect conflicts without false positives: Issue #841 Added .gitignore coverage for libpq/ivytest artifacts to prevent generated binaries and logs from polluting developer trees: Issue #843 Extended GitHub workflow regression runs to cover builds configured with --with-libnuma, preventing future breakages on NUMA-enabled hosts: Issue #869 Enabled psql users to access CREATE PACKAGE syntax help via \\h create package, closing the CLI documentation gap for PL/iSQL packages: Issue #936 Eliminated the MainLoop dangling-pointer scenario that triggered intermittent segmentation faults under concurrency stress: Issue #898 Re-enabled oracle_test/modules/*/sql cases by fixing harness assumptions so Oracle-compatibility suites execute end-to-end again: Issue #897 Updated README.md and README_CN.md to reflect IvorySQL v5 feature set, packaging, and onboarding instructions: Issue #896 Corrected globally unique index enforcement so related regression tests now pass reliably across supported platforms: Issue #894 IvorySQL’s development is maintained across four main repositories: IvorySQL database source code: https://github.com/IvorySQL/IvorySQL IvorySQL official website: https://github.com/IvorySQL/Ivory-www IvorySQL documentation: https://github.com/IvorySQL/IvorySQL-docs IvorySQL Docker: https://github.com/IvorySQL/docker_library The following individuals (in alphabetical order) have contributed to this release as patch authors, committers, reviewers, testers, or reporters of issues. Carlos Chong ccwxl Cédric Villemain elodiefb Fawei Zhao Ge Sui Grant Zhou Imran Zaheer jerome-peng Jiaoshun Tian luss Martin Gerhardy msdnchina omstack otegami rophy Shaolin Chu Shawn Yan Shihua Yang Shiji Niu Shuisen Tong shlei6067 sjw1933 Xiangyu Liang Xiaohui Liu Xinjie Lv xuexiaoganghs Xueyu Gao yangchunwanwusheng Yanliang Lei Yasir Hussain Shah Yuan Li Zheng Tao Zhenhao Pan Zhuoyan Shi","title":"","component":"ivorysql-doc","version":"master","name":"release_notes","url":"/ivorysql-doc/master/release_notes.html","titles":[{"text":"1. Version Overview","hash":"version-overview","id":1},{"text":"2. Enhancements","hash":"enhancements","id":2},{"text":"3. New Features","hash":"new-features","id":3},{"text":"3.1. 21 New Oracle Compatibility Features","hash":"21-new-oracle-compatibility-features","id":4},{"text":"3.2. Online Trial and Multi-Platform Distribution Packages","hash":"online-trial-and-multi-platform-distribution-packages","id":5},{"text":"3.3. Cloud-Native & Containerized","hash":"cloud-native-containerized","id":6},{"text":"3.4. Support for 10 Additional PostgreSQL Extensions","hash":"support-for-10-additional-postgresql-extensions","id":7},{"text":"4. Fixed Issues","hash":"fixed-issues","id":8},{"text":"5. Source Code","hash":"source-code","id":9},{"text":"6. Contributors","hash":"contributors","id":10}]},"9":{"id":9,"text":"This part contains reference information for IvorySQL client applications and utilities. Not all of these commands are of general utility; some might require special privileges. The common feature of Client Applications is that they can be run on any host, independent of where the database server resides. When specified on the command line, user and database names have their case preserved — the presence of spaces or special characters might require quoting. Table names and other identifiers do not have their case preserved, except where documented, and might require quoting. category Tool name Description Client Applications clusterdb clusterdb is a utility for reclustering tables in a IvorySQL database. It finds tables that have previously been clustered, and clusters them again on the same index that was last used. Tables that have never been clustered are not affected.clusterdb is a wrapper around the SQL command CLUSTER. There is no effective difference between clustering databases via this utility and via other methods for accessing the server. createdb createdb creates a new IvorySQL database.Normally, the database user who executes this command becomes the owner of the new database. However, a different owner can be specified via the -O option, if the executing user has appropriate privileges.createdb is a wrapper around the SQL command CREATE DATABASE. There is no effective difference between creating databases via this utility and via other methods for accessing the server. createuser createuser creates a new IvorySQL user (or more precisely, a role). Only superusers and users with CREATEROLE privilege can create new users, so createuser must be invoked by someone who can connect as a superuser or a user with CREATEROLE privilege. createuser is a wrapper around the SQL command CREATE ROLE. There is no effective difference between creating users via this utility and via other methods for accessing the server. dropdb dropdb destroys an existing IvorySQL database. The user who executes this command must be a database superuser or the owner of the database.dropdb is a wrapper around the SQL command DROP DATABASE. There is no effective difference between dropping databases via this utility and via other methods for accessing the server. dropuser dropuser removes an existing IvorySQL user. Only superusers and users with the CREATEROLE privilege can remove IvorySQL users. (To remove a superuser, you must yourself be a superuser.)dropuser is a wrapper around the SQL command DROP ROLE. There is no effective difference between dropping users via this utility and via other methods for accessing the server. ecpg ecpg is the embedded SQL preprocessor for C programs. It converts C programs with embedded SQL statements to normal C code by replacing the SQL invocations with special function calls. The output files can then be processed with any C compiler tool chain.ecpg will convert each input file given on the command line to the corresponding C output file. If an input file name does not have any extension, .pgc is assumed. The file’s extension will be replaced by .c to construct the output file name. But the output file name can be overridden using the -o option.If an input file name is just -, ecpg reads the program from standard input (and writes to standard output, unless that is overridden with -o).This reference page does not describe the embedded SQL language. pg_amcheck pg_amcheck supports running amcheck's corruption checking functions against one or more databases, with options to select which schemas, tables and indexes to check, which kinds of checking to perform, and whether to perform the checks in parallel, and if so, the number of parallel connections to establish and use. pg_basebackup pg_basebackup is used to take a base backup of a running IvorySQL database cluster. The backup is taken without affecting other clients of the database, and can be used both for point-in-time recovery and as the starting point for a log-shipping or streaming-replication standby server.pg_basebackup makes an exact copy of the database cluster’s files, while making sure the server is put into and out of backup mode automatically. Backups are always taken of the entire database cluster; it is not possible to back up individual databases or database objects. For selective backups, another tool such as pg_dump must be used.The backup is made over a regular IvorySQL connection that uses the replication protocol. The connection must be made with a user ID that has REPLICATION permissions or is a superuser, and pg_hba.conf must permit the replication connection. The server must also be configured with max_wal_senders set high enough to provide at least one walsender for the backup plus one for WAL streaming (if used).There can be multiple pg_basebackup`s running at the same time, but it is usually better from a performance point of view to take only one backup, and copy the result.pg_basebackup can make a base backup from not only a primary server but also a standby. To take a backup from a standby, set up the standby so that it can accept replication connections (that is, set `max_wal_senders and hot_standby, and configure its pg_hba.conf appropriately). You will also need to enable full_page_writes on the primary. pgbench pgbench is a simple program for running benchmark tests on IvorySQL. It runs the same sequence of SQL commands over and over, possibly in multiple concurrent database sessions, and then calculates the average transaction rate (transactions per second). By default, pgbench tests a scenario that is loosely based on TPC-B, involving five SELECT, UPDATE, and INSERT commands per transaction. However, it is easy to test other cases by writing your own transaction script files. pg_config The pg_config utility prints configuration parameters of the currently installed version of IvorySQL. It is intended, for example, to be used by software packages that want to interface to IvorySQL to facilitate finding the required header files and libraries. pg_dump pg_dump is a utility for backing up a IvorySQL database. It makes consistent backups even if the database is being used concurrently. pg_dump does not block other users accessing the database (readers or writers).pg_dump only dumps a single database. To back up an entire cluster, or to back up global objects that are common to all databases in a cluster (such as roles and tablespaces), use pg_dumpall. Dumps can be output in script or archive file formats. Script dumps are plain-text files containing the SQL commands required to reconstruct the database to the state it was in at the time it was saved. To restore from such a script, feed it to psql. Script files can be used to reconstruct the database even on other machines and other architectures; with some modifications, even on other SQL database products.The alternative archive file formats must be used with pg_restore to rebuild the database. They allow pg_restore to be selective about what is restored, or even to reorder the items prior to being restored. The archive file formats are designed to be portable across architectures.When used with one of the archive file formats and combined with pg_restore, pg_dump provides a flexible archival and transfer mechanism. pg_dump can be used to backup an entire database, then pg_restore can be used to examine the archive and/or select which parts of the database are to be restored. The most flexible output file formats are the “custom” format ( -Fc ) and the “directory” format ( -Fd ). They allow for selection and reordering of all archived items, support parallel restoration, and are compressed by default. The “directory” format is the only format that supports parallel dumps.While running pg_dump, one should examine the output for any warnings (printed on standard error), especially in light of the limitations listed below. pg_dumpall pg_dumpall is a utility for writing out (“dumping”) all IvorySQL databases of a cluster into one script file. The script file contains SQL commands that can be used as input to psql to restore the databases. It does this by calling pg_dump for each database in the cluster. pg_dumpall also dumps global objects that are common to all databases, namely database roles, tablespaces, and privilege grants for configuration parameters. (pg_dump does not save these objects.)Since pg_dumpall reads tables from all databases you will most likely have to connect as a database superuser in order to produce a complete dump. Also you will need superuser privileges to execute the saved script in order to be allowed to add roles and create databases.The SQL script will be written to the standard output. Use the -f/--file option or shell operators to redirect it into a file.pg_dumpall needs to connect several times to the IvorySQL server (once per database). If you use password authentication it will ask for a password each time. It is convenient to have a ~/.pgpass file in such cases. pg_isready pg_isready is a utility for checking the connection status of a IvorySQL database server. The exit status specifies the result of the connection check. pg_receivewal pg_receivewal is used to stream the write-ahead log from a running IvorySQL cluster. The write-ahead log is streamed using the streaming replication protocol, and is written to a local directory of files. This directory can be used as the archive location for doing a restore using point-in-time recovery.pg_receivewal streams the write-ahead log in real time as it’s being generated on the server, and does not wait for segments to complete like archive_command does. For this reason, it is not necessary to set archive_timeout when using pg_receivewal.Unlike the WAL receiver of a IvorySQL standby server, pg_receivewal by default flushes WAL data only when a WAL file is closed. The option --synchronous must be specified to flush WAL data in real time. Since pg_receivewal does not apply WAL, you should not allow it to become a synchronous standby when synchronous_commit equals remote_apply. If it does, it will appear to be a standby that never catches up, and will cause transaction commits to block. To avoid this, you should either configure an appropriate value for synchronous_standby_names, or specify application_name for pg_receivewal that does not match it, or change the value of synchronous_commit to something other than remote_apply.The write-ahead log is streamed over a regular IvorySQL connection and uses the replication protocol. The connection must be made with a user having REPLICATION permissions or a superuser, and pg_hba.conf must permit the replication connection. The server must also be configured with max_wal_senders set high enough to leave at least one session available for the stream. pg_recvlogical pg_recvlogical controls logical decoding replication slots and streams data from such replication slots.It creates a replication-mode connection, so it is subject to the same constraints as pg_receivewal, plus those for logical replication .pg_recvlogical has no equivalent to the logical decoding SQL interface’s peek and get modes. It sends replay confirmations for data lazily as it receives it and on clean exit. To examine pending data on a slot without consuming it, use pg_logical_slot_peek_changes. pg_restore pg_restore is a utility for restoring a IvorySQL database from an archive created by pg_dump in one of the non-plain-text formats. It will issue the commands necessary to reconstruct the database to the state it was in at the time it was saved. The archive files also allow pg_restore to be selective about what is restored, or even to reorder the items prior to being restored. The archive files are designed to be portable across architectures.pg_restore can operate in two modes. If a database name is specified, pg_restore connects to that database and restores archive contents directly into the database. Otherwise, a script containing the SQL commands necessary to rebuild the database is created and written to a file or standard output. This script output is equivalent to the plain text output format of pg_dump. Some of the options controlling the output are therefore analogous to pg_dump options.Obviously, pg_restore cannot restore information that is not present in the archive file. For instance, if the archive was made using the “dump data as INSERT commands” option, pg_restore will not be able to load the data using COPY statements. pg_verifybackup pg_verifybackup is used to check the integrity of a database cluster backup taken using pg_basebackup against a backup_manifest generated by the server at the time of the backup. The backup must be stored in the \"plain\" format; a \"tar\" format backup can be checked after extracting it.It is important to note that the validation which is performed by pg_verifybackup does not and cannot include every check which will be performed by a running server when attempting to make use of the backup. Even if you use this tool, you should still perform test restores and verify that the resulting databases work as expected and that they appear to contain the correct data. However, pg_verifybackup can detect many problems that commonly occur due to storage problems or user error.Backup verification proceeds in four stages. First, pg_verifybackup reads the backup_manifest file. If that file does not exist, cannot be read, is malformed, or fails verification against its own internal checksum, pg_verifybackup will terminate with a fatal error. psql psql is a terminal-based front-end to IvorySQL. It enables you to type in queries interactively, issue them to IvorySQL, and see the query results. Alternatively, input can be from a file or from command line arguments. In addition, psql provides a number of meta-commands and various shell-like features to facilitate writing scripts and automating a wide variety of tasks. reindexdb reindexdb is a utility for rebuilding indexes in a IvorySQL database.reindexdb is a wrapper around the SQL command REINDEX. There is no effective difference between reindexing databases via this utility and via other methods for accessing the server. vacuumdb vacuumdb is a utility for cleaning a IvorySQL database. vacuumdb will also generate internal statistics used by the IvorySQL query optimizer. Server Applications initdb(1) initdb creates a new IvorySQL database cluster. A database cluster is a collection of databases that are managed by a single server instance.Creating a database cluster consists of creating the directories in which the database data will live, generating the shared catalog tables (tables that belong to the whole cluster rather than to any particular database), and creating the postgres, template1, and template0 databases. The postgres database is a default database meant for use by users, utilities and third party applications. template1 and template0 are meant as source databases to be copied by later CREATE DATABASE commands. template0 should never be modified, but you can add objects to template1, which by default will be copied into databases created later.Although initdb will attempt to create the specified data directory, it might not have permission if the parent directory of the desired data directory is root-owned. To initialize in such a setup, create an empty data directory as root, then use chown to assign ownership of that directory to the database user account, then su to become the database user to run initdb. Server Applications initdb(2) initdb must be run as the user that will own the server process, because the server needs to have access to the files and directories that initdb creates. Since the server cannot be run as root, you must not run initdb as root either. (It will in fact refuse to do so.)For security reasons the new cluster created by initdb will only be accessible by the cluster owner by default. The --allow-group-access option allows any user in the same group as the cluster owner to read files in the cluster. This is useful for performing backups as a non-privileged user.initdb initializes the database cluster’s default locale and character set encoding. These can also be set separately for each database when it is created. initdb determines those settings for the template databases, which will serve as the default for all other databases. By default, initdb uses the locale provider libc, takes the locale settings from the environment, and determines the encoding from the locale settings. This is almost always sufficient, unless there are special requirements.To choose a different locale for the cluster, use the option --locale. There are also individual options --lc-* (see below) to set values for the individual locale categories. Note that inconsistent settings for different locale categories can give nonsensical results, so this should be used with care.Alternatively, the ICU library can be used to provide locale services. (Again, this only sets the default for subsequently created databases.) To select this option, specify --locale-provider=icu. To choose the specific ICU locale ID to apply, use the option --icu-locale. Note that for implementation reasons and to support legacy code, initdb will still select and initialize libc locale settings when the ICU locale provider is used.When initdb runs, it will print out the locale settings it has chosen. If you have complex requirements or specified multiple options, it is advisable to check that the result matches what was intended. pg_archivecleanup pg_archivecleanup is designed to be used as an archive_cleanup_command to clean up WAL file archives when running as a standby server .pg_archivecleanup can also be used as a standalone program to clean WAL file archives. pg_checksums pg_checksums checks, enables or disables data checksums in a IvorySQL cluster. The server must be shut down cleanly before running pg_checksums. When verifying checksums, the exit status is zero if there are no checksum errors, and nonzero if at least one checksum failure is detected. When enabling or disabling checksums, the exit status is nonzero if the operation failed.When verifying checksums, every file in the cluster is scanned. When enabling checksums, each relation file block with a changed checksum is rewritten in-place. Disabling checksums only updates the file pg_control. pg_controldata pg_controldata prints information initialized during initdb, such as the catalog version. It also shows information about write-ahead logging and checkpoint processing. This information is cluster-wide, and not specific to any one database.This utility can only be run by the user who initialized the cluster because it requires read access to the data directory. You can specify the data directory on the command line, or use the environment variable PGDATA. This utility supports the options -V and --version, which print the pg_controldata version and exit. It also supports options -? and --help, which output the supported arguments. pg_ctl pg_ctl is a utility for initializing a IvorySQL database cluster, starting, stopping, or restarting the IvorySQL database server (postgres), or displaying the status of a running server. Although the server can be started manually, pg_ctl encapsulates tasks such as redirecting log output and properly detaching from the terminal and process group. It also provides convenient options for controlled shutdown. pg_resetwal pg_resetwal clears the write-ahead log (WAL) and optionally resets some other control information stored in the pg_control file. This function is sometimes needed if these files have become corrupted. It should be used only as a last resort, when the server will not start due to such corruption.After running this command, it should be possible to start the server, but bear in mind that the database might contain inconsistent data due to partially-committed transactions. You should immediately dump your data, run initdb, and restore. After restore, check for inconsistencies and repair as needed.This utility can only be run by the user who installed the server, because it requires read/write access to the data directory. For safety reasons, you must specify the data directory on the command line. pg_resetwal does not use the environment variable PGDATA.If pg_resetwal complains that it cannot determine valid data for pg_control, you can force it to proceed anyway by specifying the -f (force) option. In this case plausible values will be substituted for the missing data. Most of the fields can be expected to match, but manual assistance might be needed for the next OID, next transaction ID and epoch, next multitransaction ID and offset, and WAL starting location fields. These fields can be set using the options discussed below. If you are not able to determine correct values for all these fields, -f can still be used, but the recovered database must be treated with even more suspicion than usual: an immediate dump and restore is imperative. Do not execute any data-modifying operations in the database before you dump, as any such action is likely to make the corruption worse. pg_rewind(1) pg_rewind is a tool for synchronizing a IvorySQL cluster with another copy of the same cluster, after the clusters' timelines have diverged. A typical scenario is to bring an old primary server back online after failover as a standby that follows the new primary.After a successful rewind, the state of the target data directory is analogous to a base backup of the source data directory. Unlike taking a new base backup or using a tool like rsync, pg_rewind does not require comparing or copying unchanged relation blocks in the cluster. Only changed blocks from existing relation files are copied; all other files, including new relation files, configuration files, and WAL segments, are copied in full. As such the rewind operation is significantly faster than other approaches when the database is large and only a small fraction of blocks differ between the clusters.pg_rewind examines the timeline histories of the source and target clusters to determine the point where they diverged, and expects to find WAL in the target cluster’s pg_wal directory reaching all the way back to the point of divergence. The point of divergence can be found either on the target timeline, the source timeline, or their common ancestor. In the typical failover scenario where the target cluster was shut down soon after the divergence, this is not a problem, but if the target cluster ran for a long time after the divergence, its old WAL files might no longer be present. In this case, you can manually copy them from the WAL archive to the pg_wal directory, or run pg_rewind with the -c option to automatically retrieve them from the WAL archive. The use of pg_rewind is not limited to failover, e.g., a standby server can be promoted, run some write transactions, and then rewound to become a standby again.After running pg_rewind, WAL replay needs to complete for the data directory to be in a consistent state. pg_rewind(2) When the target server is started again it will enter archive recovery and replay all WAL generated in the source server from the last checkpoint before the point of divergence. If some of the WAL was no longer available in the source server when pg_rewind was run, and therefore could not be copied by the pg_rewind session, it must be made available when the target server is started. This can be done by creating a recovery.signal file in the target data directory and by configuring a suitable restore_command in IvorySQL.conf.pg_rewind requires that the target server either has the wal_log_hints option enabled in IvorySQL.conf or data checksums enabled when the cluster was initialized with initdb. Neither of these are currently on by default. full_page_writes must also be set to on, but is enabled by default. pg_test_fsync pg_test_fsync is intended to give you a reasonable idea of what the fastest wal_sync_method is on your specific system, as well as supplying diagnostic information in the event of an identified I/O problem. However, differences shown by pg_test_fsync might not make any significant difference in real database throughput, especially since many database servers are not speed-limited by their write-ahead logs. pg_test_fsync reports average file sync operation time in microseconds for each wal_sync_method, which can also be used to inform efforts to optimize the value of commit_delay. pg_test_timing pg_test_timing is a tool to measure the timing overhead on your system and confirm that the system time never moves backwards. Systems that are slow to collect timing data can give less accurate EXPLAIN ANALYZE results. pg_upgrade Major IvorySQL releases regularly add new features that often change the layout of the system tables, but the internal data storage format rarely changes. pg_upgrade uses this fact to perform rapid upgrades by creating new system tables and simply reusing the old user data files. If a future major release ever changes the data storage format in a way that makes the old data format unreadable, pg_upgrade will not be usable for such upgrades. (The community will attempt to avoid such situations.)pg_upgrade does its best to make sure the old and new clusters are binary-compatible, e.g., by checking for compatible compile-time settings, including 32/64-bit binaries. It is important that any external modules are also binary compatible, though this cannot be checked by pg_upgrade. pg_waldump pg_waldump displays the write-ahead log (WAL) and is mainly useful for debugging or educational purposes.This utility can only be run by the user who installed the server, because it requires read-only access to the data directory. postgres postgres is the IvorySQL database server. In order for a client application to access a database it connects (over a network or locally) to a running postgres instance. The postgres instance then starts a separate server process to handle the connection. clusterdb [connection-option…​] [ --verbose | -v ] [ --table | -t table ] …​ [dbname] clusterdb` [*`connection-option`*...] [ `--verbose` | `-v` ] `--all` | `-a clusterdb accepts the following command-line arguments: -a --all Cluster all databases. [-d] dbname dbname Specifies the name of the database to be clustered, when -a / --all is not used. If this is not specified, the database name is read from the environment variable PGDATABASE. If that is not set, the user name specified for the connection is used. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --echo Echo the commands that clusterdb generates and sends to the server. -q --quiet Do not display progress messages. -t table --table=table Cluster table only. Multiple tables can be clustered by writing multiple -t switches. -v --verbose Print detailed information during processing. -V --version Print the clusterdb version and exit. -? --help Show help about clusterdb command line arguments, and exit.clusterdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force clusterdb to prompt for a password before connecting to a database.This option is never essential, since clusterdb will automatically prompt for a password if the server demands password authentication. However, clusterdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to to discover which databases should be clustered, when -a / --all is used. If not specified, the postgres database will be used, or if that does not exist, template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. Also, connection string parameters other than the database name itself will be re-used when connecting to other databases. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see CLUSTER and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To cluster the database test: $ clusterdb test To cluster a single table foo in a database named xyzzy: $ clusterdb --table=foo xyzzy createdb — create a new IvorySQL database createdb [connection-option…​] [option…​] [dbname [description]] createdb accepts the following command-line arguments: dbname Specifies the name of the database to be created. The name must be unique among all IvorySQL databases in this cluster. The default is to create a database with the same name as the current system user. description Specifies a comment to be associated with the newly created database. -D tablespace --tablespace=tablespace Specifies the default tablespace for the database. (This name is processed as a double-quoted identifier.) -e --echo Echo the commands that createdb generates and sends to the server. -E encoding --encoding=encoding Specifies the character encoding scheme to be used in this database. -l locale --locale=locale Specifies the locale to be used in this database. This is equivalent to specifying both --lc-collate and --lc-ctype. --lc-collate=`locale` Specifies the LC_COLLATE setting to be used in this database. --lc-ctype=locale Specifies the LC_CTYPE setting to be used in this database. --icu-locale=`locale` Specifies the ICU locale ID to be used in this database, if the ICU locale provider is selected. --locale-provider={libc|icu} Specifies the locale provider for the database’s default collation. -O `owner` --owner=`owner` Specifies the database user who will own the new database. (This name is processed as a double-quoted identifier.) -T template --template=template Specifies the template database from which to build this database. (This name is processed as a double-quoted identifier.) -V --version Print the createdb version and exit. -? --help Show help about createdb command line arguments, and exit. The options -D, -l, -E, -O, and -T correspond to options of the underlying SQL command CREATE DATABASE; see there for more information about them. createdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or the local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force createdb to prompt for a password before connecting to a database.This option is never essential, since createdb will automatically prompt for a password if the server demands password authentication. However, createdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=`dbname` Specifies the name of the database to connect to when creating the new database. If not specified, the postgres database will be used; if that does not exist (or if it is the name of the new database being created), template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. PGDATABASE If set, the name of the database to create, unless overridden on the command line. PGHOST PGPORT PGUSER Default connection parameters. PGUSER also determines the name of the database to create, if it is not specified on the command line or by PGDATABASE. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see CREATE DATABASE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To create the database demo using the default database server: $ createdb demo To create the database demo using the server on host eden, port 5000, using the template0 template database, here is the command-line command and the underlying SQL command: $ createdb -p 5000 -h eden -T template0 -e demo CREATE DATABASE demo TEMPLATE template0; createuser — define a new IvorySQL user account createuser [connection-option…​] [option…​] [username] createuser accepts the following command-line arguments: username Specifies the name of the IvorySQL user to be created. -c number --connection-limit=number Set a maximum number of connections for the new user. The default is to set no limit. -d --createdb The new user will be allowed to create databases. -D --no-createdb The new user will not be allowed to create databases. This is the default. -e --echo Echo the commands that createuser generates and sends to the server. -E --encrypted This option is obsolete but still accepted for backward compatibility. -g role --role=role Indicates role to which this role will be added immediately as a new member. Multiple roles to which this role will be added as a member can be specified by writing multiple -g switches. -i --inherit The new role will automatically inherit privileges of roles it is a member of. This is the default. -I --no-inherit The new role will not automatically inherit privileges of roles it is a member of. --interactive Prompt for the user name if none is specified on the command line, and also prompt for whichever of the options -d / -D, -r / -R, -s / -S is not specified on the command line. -l --login The new user will be allowed to log in (that is, the user name can be used as the initial session user identifier). This is the default. -L --no-login The new user will not be allowed to log in. (A role without login privilege is still useful as a means of managing database permissions.) -P --pwprompt If given, createuser will issue a prompt for the password of the new user. This is not necessary if you do not plan on using password authentication. -r --createrole The new user will be allowed to create new roles. That is, this user will have CREATEROLE privilege. -R --no-createrole The new user will not be allowed to create new roles. This is the default. -s --superuser The new user will be a superuser. -S --no-superuser The new user will not be a superuser. This is the default. -V --version Print the createuser version and exit. --replication The new user will have the REPLICATION privilege, which is described more fully in the documentation for CREATE ROLE. --no-replication The new user will not have the REPLICATION privilege, which is described more fully in the documentation for CREATE ROLE. -? --help Show help about createuser command line arguments, and exit. createuser also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as (not the user name to create). -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force createuser to prompt for a password (for connecting to the server, not for the password of the new user).This option is never essential, since createuser will automatically prompt for a password if the server demands password authentication. However, createuser will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see CREATE ROLE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To create a user joe on the default database server: $ createuser joe To create a user joe on the default database server with prompting for some additional attributes: $ createuser --interactive joe Shall the new role be a superuser? (y/n) n Shall the new role be allowed to create databases? (y/n) n Shall the new role be allowed to create more new roles? (y/n) n To create the same user joe using the server on host eden, port 5000, with attributes explicitly specified, taking a look at the underlying command: $ createuser -h eden -p 5000 -S -D -R -e joe CREATE ROLE joe NOSUPERUSER NOCREATEDB NOCREATEROLE INHERIT LOGIN; To create the user joe as a superuser, and assign a password immediately: $ createuser -P -s -e joe Enter password for new role: xyzzy Enter it again: xyzzy CREATE ROLE joe PASSWORD 'md5b5f5ba1a423792b526f799ae4eb3d59e' SUPERUSER CREATEDB CREATEROLE INHERIT LOGIN; In the above example, the new password isn’t actually echoed when typed, but we show what was typed for clarity. As you see, the password is encrypted before it is sent to the client. dropdb — remove a IvorySQL database dropdb [connection-option…​] [option…​] dbname dropdb accepts the following command-line arguments: dbname Specifies the name of the database to be removed. -e --echo Echo the commands that dropdb generates and sends to the server. -f --force Attempt to terminate all existing connections to the target database before dropping it. See DROP DATABASE for more information on this option. -i --interactive Issues a verification prompt before doing anything destructive. -V --version Print the dropdb version and exit. --if-exists Do not throw an error if the database does not exist. A notice is issued in this case. -? --help Show help about dropdb command line arguments, and exit. dropdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force dropdb to prompt for a password before connecting to a database.This option is never essential, since dropdb will automatically prompt for a password if the server demands password authentication. However, dropdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to in order to drop the target database. If not specified, the postgres database will be used; if that does not exist (or is the database being dropped), template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq . In case of difficulty, see DROP DATABASE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To destroy the database demo on the default database server: $ dropdb demo To destroy the database demo using the server on host eden, port 5000, with verification and a peek at the underlying command: $ dropdb -p 5000 -h eden -i -e demo Database \"demo\" will be permanently deleted. Are you sure? (y/n) y DROP DATABASE demo; dropuser — remove a IvorySQL user account dropuser [connection-option…​] [option…​] [username] dropuser accepts the following command-line arguments: username Specifies the name of the IvorySQL user to be removed. You will be prompted for a name if none is specified on the command line and the -i / --interactive option is used. -e --echo Echo the commands that dropuser generates and sends to the server. -i --interactive Prompt for confirmation before actually removing the user, and prompt for the user name if none is specified on the command line. -V --version Print the dropuser version and exit. --if-exists Do not throw an error if the user does not exist. A notice is issued in this case. -? --help Show help about dropuser command line arguments, and exit. dropuser also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as (not the user name to drop). -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force dropuser to prompt for a password before connecting to a database.This option is never essential, since dropuser will automatically prompt for a password if the server demands password authentication. However, dropuser will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see DROP ROLE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To remove user joe from the default database server: $ dropuser joe To remove user joe using the server on host eden, port 5000, with verification and a peek at the underlying command: $ dropuser -p 5000 -h eden -i -e joe Role \"joe\" will be permanently removed. Are you sure? (y/n) y DROP ROLE joe; ecpg — embedded SQL C preprocessor ecpg [option…​] file…​ ecpg accepts the following command-line arguments: -c Automatically generate certain C code from SQL code. Currently, this works for EXEC SQL TYPE. -C mode Set a compatibility mode. mode can be INFORMIX, INFORMIX_SE, or ORACLE. -D `symbol` Define a C preprocessor symbol. -h Process header files. When this option is specified, the output file extension becomes .h not .c, and the default input file extension is .pgh not .pgc. Also, the -c option is forced on. -i Parse system include files as well. -I `directory` Specify an additional include path, used to find files included via EXEC SQL INCLUDE. Defaults are . (current directory), /usr/local/include, the IvorySQL include directory which is defined at compile time (default: /usr/local/pgsql/include), and /usr/include, in that order. -o `filename` Specifies that ecpg should write all its output to the given filename. Write -o - to send all output to standard output. -r `option` Selects run-time behavior. Option can be one of the following:`no_indicator`Do not use indicators but instead use special values to represent null values. Historically there have been databases using this approach.prepare`Prepare all statements before using them. Libecpg will keep a cache of prepared statements and reuse a statement if it gets executed again. If the cache runs full, libecpg will free the least used statement.`questionmarks Allow question mark as placeholder for compatibility reasons. This used to be the default long ago. -t Turn on autocommit of transactions. In this mode, each SQL command is automatically committed unless it is inside an explicit transaction block. In the default mode, commands are committed only when EXEC SQL COMMIT is issued. -v Print additional information including the version and the \"include\" path. --version Print the ecpg version and exit. -? --help Show help about ecpg command line arguments, and exit. When compiling the preprocessed C code files, the compiler needs to be able to find the ECPG header files in the IvorySQL include directory. Therefore, you might have to use the -I option when invoking the compiler (e.g., -I/usr/local/pgsql/include). Programs using C code with embedded SQL have to be linked against the libecpg library, for example using the linker options -L/usr/local/pgsql/lib -lecpg. The value of either of these directories that is appropriate for the installation can be found out using pg_config. If you have an embedded SQL C source file named prog1.pgc, you can create an executable program using the following sequence of commands: ecpg prog1.pgc cc -I/usr/local/pgsql/include -c prog1.c cc -o prog1 prog1.o -L/usr/local/pgsql/lib -lecpg pg_amcheck — checks for corruption in one or more IvorySQL databases pg_amcheck [option…​] [dbname] The following command-line options control what is checked: -a --all Check all databases, except for any excluded via --exclude-database. -d pattern --database=pattern Check databases matching the specified pattern, except for any excluded by --exclude-database. This option can be specified more than once. -D pattern --exclude-database=pattern Exclude databases matching the given pattern. This option can be specified more than once. -i pattern --index=pattern Check indexes matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.This is similar to the --relation option, except that it applies only to indexes, not to other relation types. -I pattern --exclude-index=pattern Exclude indexes matching the specified pattern. This option can be specified more than once.This is similar to the --exclude-relation option, except that it applies only to indexes, not other relation types. -r `pattern` --relation=`pattern` Check relations matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.Patterns may be unqualified, e.g. myrel*, or they may be schema-qualified, e.g. myschema*.myrel* or database-qualified and schema-qualified, e.g. mydb*.myscheam*.myrel*. A database-qualified pattern will add matching databases to the list of databases to be checked. -R pattern --exclude-relation=pattern Exclude relations matching the specified pattern. This option can be specified more than once.As with --relation, the pattern may be unqualified, schema-qualified, or database- and schema-qualified. -s pattern --schema=pattern Check tables and indexes in schemas matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.To select only tables in schemas matching a particular pattern, consider using something like --table=SCHEMAPAT.* --no-dependent-indexes. To select only indexes, consider using something like --index=SCHEMAPAT..A schema pattern may be database-qualified. For example, you may write --schema=mydb.myschema* to select schemas matching myschema* in databases matching mydb*. -S pattern --exclude-schema=pattern Exclude tables and indexes in schemas matching the specified pattern. This option can be specified more than once.As with --schema, the pattern may be database-qualified. -t pattern --table=pattern Check tables matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.This is similar to the --relation option, except that it applies only to tables, materialized views, and sequences, not to indexes. -T pattern --exclude-table=pattern Exclude tables matching the specified pattern. This option can be specified more than once.This is similar to the --exclude-relation option, except that it applies only to tables, materialized views, and sequences, not to indexes. --no-dependent-indexes By default, if a table is checked, any btree indexes of that table will also be checked, even if they are not explicitly selected by an option such as --index or --relation. This option suppresses that behavior. --no-dependent-toast By default, if a table is checked, its toast table, if any, will also be checked, even if it is not explicitly selected by an option such as --table or --relation. This option suppresses that behavior. --no-strict-names By default, if an argument to --database, --table, --index, or --relation matches no objects, it is a fatal error. This option downgrades that error to a warning. The following command-line options control checking of tables: --exclude-toast-pointers By default, whenever a toast pointer is encountered in a table, a lookup is performed to ensure that it references apparently-valid entries in the toast table. These checks can be quite slow, and this option can be used to skip them. --on-error-stop After reporting all corruptions on the first page of a table where corruption is found, stop processing that table relation and move on to the next table or index.Note that index checking always stops after the first corrupt page. This option only has meaning relative to table relations. --skip=`option` If all-frozen is given, table corruption checks will skip over pages in all tables that are marked as all frozen.If all-visible is given, table corruption checks will skip over pages in all tables that are marked as all visible.By default, no pages are skipped. This can be specified as none, but since this is the default, it need not be mentioned. --startblock=`block` Start checking at the specified block number. An error will occur if the table relation being checked has fewer than this number of blocks. This option does not apply to indexes, and is probably only useful when checking a single table relation. See --endblock for further caveats. --endblock=`block` End checking at the specified block number. An error will occur if the table relation being checked has fewer than this number of blocks. This option does not apply to indexes, and is probably only useful when checking a single table relation. If both a regular table and a toast table are checked, this option will apply to both, but higher-numbered toast blocks may still be accessed while validating toast pointers, unless that is suppressed using --exclude-toast-pointers. The following command-line options control checking of B-tree indexes: --heapallindexed For each index checked, verify the presence of all heap tuples as index tuples in the index using amcheck's heapallindexed option. --parent-check For each btree index checked, use amcheck's bt_index_parent_check function, which performs additional checks of parent/child relationships during index checking.The default is to use amcheck’s bt_index_check function, but note that use of the --rootdescend option implicitly selects bt_index_parent_check. --rootdescend For each index checked, re-find tuples on the leaf level by performing a new search from the root page for each tuple using amcheck's rootdescend option.Use of this option implicitly also selects the --parent-check option.This form of verification was originally written to help in the development of btree index features. It may be of limited use or even of no use in helping detect the kinds of corruption that occur in practice. It may also cause corruption checking to take considerably longer and consume considerably more resources on the server. The extra checks performed against B-tree indexes when the --parent-check option or the --rootdescend option is specified require relatively strong relation-level locks. These checks are the only checks that will block concurrent data modification from INSERT, UPDATE, and DELETE commands. The following command-line options control the connection to the server: -h hostname --host=hostname Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_amcheck to prompt for a password before connecting to a database.This option is never essential, since pg_amcheck will automatically prompt for a password if the server demands password authentication. However, pg_amcheck will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies a database or connection string to be used to discover the list of databases to be checked. If neither --all nor any option including a database pattern is used, no such connection is required and this option does nothing. Otherwise, any connection string parameters other than the database name which are included in the value for this option will also be used when connecting to the databases being checked. If this option is omitted, the default is postgres or, if that fails, template1. Other options are also available: -e --echo Echo to stdout all SQL sent to the server. -j `num` --jobs=`num` Use num concurrent connections to the server, or one per object to be checked, whichever is less.The default is to use a single connection. -P --progress Show progress information. Progress information includes the number of relations for which checking has been completed, and the total size of those relations. It also includes the total number of relations that will eventually be checked, and the estimated size of those relations. -v --verbose Print more messages. In particular, this will print a message for each relation being checked, and will increase the level of detail shown for server errors. -V --version Print the pg_amcheck version and exit. --install-missing --install-missing=`schema` Install any missing extensions that are required to check the database(s). If not yet installed, each extension’s objects will be installed into the given schema, or if not specified into schema pg_catalog.At present, the only required extension is amcheck. -? --help Show help about pg_amcheck command line arguments, and exit. pg_basebackup — take a base backup of a IvorySQL cluster pg_basebackup [option…​] The following command-line options control the location and format of the output: -D directory --pgdata=directory Sets the target directory to write the output to. pg_basebackup will create this directory (and any missing parent directories) if it does not exist. If it already exists, it must be empty.When the backup is in tar format, the target directory may be specified as - (dash), causing the tar file to be written to stdout.This option is required. -F format --format=format Selects the format for the output. format can be one of the following: p plain Write the output as plain files, with the same layout as the source server’s data directory and tablespaces. When the cluster has no additional tablespaces, the whole database will be placed in the target directory. If the cluster contains additional tablespaces, the main data directory will be placed in the target directory, but all other tablespaces will be placed in the same absolute path as they have on the source server. (See --tablespace-mapping to change that.)This is the default format. t tar Write the output as tar files in the target directory. The main data directory’s contents will be written to a file named base.tar, and each other tablespace will be written to a separate tar file named after that tablespace’s OID.If the target directory is specified as - (dash), the tar contents will be written to standard output, suitable for piping to (for example) gzip. This is only allowed if the cluster has no additional tablespaces and WAL streaming is not used. -R --write-recovery-conf Creates a standby.signal file and appends connection settings to the IvorySQL.auto.conf file in the target directory (or within the base archive file when using tar format). This eases setting up a standby server using the results of the backup.The IvorySQL.auto.conf file will record the connection settings and, if specified, the replication slot that pg_basebackup is using, so that streaming replication will use the same settings later on. -t target --target=target Instructs the server where to place the base backup. The default target is client, which specifies that the backup should be sent to the machine where pg_basebackup is running. If the target is instead set to server:/some/path, the backup will be stored on the machine where the server is running in the /some/path directory. Storing a backup on the server requires superuser privileges or having privileges of the pg_write_server_files role. If the target is set to blackhole, the contents are discarded and not stored anywhere. This should only be used for testing purposes, as you will not end up with an actual backup.Since WAL streaming is implemented by pg_basebackup rather than by the server, this option cannot be used together with -Xstream. Since that is the default, when this option is specified, you must also specify either -Xfetch or -Xnone. -T olddir=newdir --tablespace-mapping=olddir=newdir Relocates the tablespace in directory olddir to newdir during the backup. To be effective, olddir must exactly match the path specification of the tablespace as it is defined on the source server. (But it is not an error if there is no tablespace in olddir on the source server.) Meanwhile newdir is a directory in the receiving host’s filesystem. As with the main target directory, newdir need not exist already, but if it does exist it must be empty. Both olddir and newdir must be absolute paths. If either path needs to contain an equal sign (=), precede that with a backslash. This option can be specified multiple times for multiple tablespaces.If a tablespace is relocated in this way, the symbolic links inside the main data directory are updated to point to the new location. So the new data directory is ready to be used for a new server instance with all tablespaces in the updated locations.Currently, this option only works with plain output format; it is ignored if tar format is selected. --waldir=waldir Sets the directory to write WAL (write-ahead log) files to. By default WAL files will be placed in the pg_wal subdirectory of the target directory, but this option can be used to place them elsewhere. waldir must be an absolute path. As with the main target directory, waldir need not exist already, but if it does exist it must be empty. This option can only be specified when the backup is in plain format. -X method --wal-method=method Includes the required WAL (write-ahead log) files in the backup. This will include all write-ahead logs generated during the backup. Unless the method none is specified, it is possible to start a postmaster in the target directory without the need to consult the log archive, thus making the output a completely standalone backup.The following methods for collecting the write-ahead logs are supported: n none Don’t include write-ahead logs in the backup. f fetch`The write-ahead log files are collected at the end of the backup. Therefore, it is necessary for the source server’s wal_keep_size parameter to be set high enough that the required log data is not removed before the end of the backup. If the required log data has been recycled before it’s time to transfer it, the backup will fail and be unusable.When tar format is used, the write-ahead log files will be included in the `base.tar file.s stream`Stream write-ahead log data while the backup is being taken. This method will open a second connection to the server and start streaming the write-ahead log in parallel while running the backup. Therefore, it will require two replication connections not just one. As long as the client can keep up with the write-ahead log data, using this method requires no extra write-ahead logs to be saved on the source server.When tar format is used, the write-ahead log files will be written to a separate file named `pg_wal.tar (if the server is a version earlier than 10, the file will be named pg_xlog.tar).This value is the default. -z --gzip Enables gzip compression of tar file output, with the default compression level. Compression is only available when using the tar format, and the suffix .gz will automatically be added to all tar filenames. -Z level -Z method[:*detail*] --compress=level --compress=method[:*detail*] Requests compression of the backup. If client or server is included, it specifies where the compression is to be performed. Compressing on the server will reduce transfer bandwidth but will increase server CPU consumption. The default is client except when --target is used. In that case, the backup is not being sent to the client, so only server compression is sensible. When -Xstream, which is the default, is used, server-side compression will not be applied to the WAL. To compress the WAL, use client-side compression, or specify -Xfetch.The compression method can be set to gzip, lz4, zstd, or none for no compression. A compression detail string can optionally be specified. If the detail string is an integer, it specifies the compression level. Otherwise, it should be a comma-separated list of items, each of the form keyword or keyword=value. Currently, the supported keywords are level and workers.If no compression level is specified, the default compression level will be used. If only a level is specified without mentioning an algorithm, gzip compression will be used if the level is greater than 0, and no compression will be used if the level is 0.When the tar format is used with gzip, lz4, or zstd, the suffix .gz, .lz4, or .zst, respectively, will be automatically added to all tar filenames. When the plain format is used, client-side compression may not be specified, but it is still possible to request server-side compression. If this is done, the server will compress the backup for transmission, and the client will decompress and extract it.When this option is used in combination with -Xstream, pg_wal.tar will be compressed using gzip if client-side gzip compression is selected, but will not be compressed if any other compression algorithm is selected, or if server-side compression is selected. The following command-line options control the generation of the backup and the invocation of the program: -c {fast|spread} --checkpoint={fast|spread} Sets checkpoint mode to fast (immediate) or spread (the default) . -C --create-slot Specifies that the replication slot named by the --slot option should be created before starting the backup. An error is raised if the slot already exists. -l label --label=label Sets the label for the backup. If none is specified, a default value of “pg_basebackup base backup” will be used. -n --no-clean By default, when pg_basebackup aborts with an error, it removes any directories it might have created before discovering that it cannot finish the job (for example, the target directory and write-ahead log directory). This option inhibits tidying-up and is thus useful for debugging.Note that tablespace directories are not cleaned up either way. -N --no-sync By default, pg_basebackup will wait for all files to be written safely to disk. This option causes pg_basebackup to return without waiting, which is faster, but means that a subsequent operating system crash can leave the base backup corrupt. Generally, this option is useful for testing but should not be used when creating a production installation. -P --progress Enables progress reporting. Turning this on will deliver an approximate progress report during the backup. Since the database may change during the backup, this is only an approximation and may not end at exactly 100%. In particular, when WAL log is included in the backup, the total amount of data cannot be estimated in advance, and in this case the estimated target size will increase once it passes the total estimate without WAL. -r rate --max-rate=rate Sets the maximum transfer rate at which data is collected from the source server. This can be useful to limit the impact of pg_basebackup on the server. Values are in kilobytes per second. Use a suffix of M to indicate megabytes per second. A suffix of k is also accepted, and has no effect. Valid values are between 32 kilobytes per second and 1024 megabytes per second.This option always affects transfer of the data directory. Transfer of WAL files is only affected if the collection method is fetch. -S slotname --slot=slotname This option can only be used together with -X stream. It causes WAL streaming to use the specified replication slot. If the base backup is intended to be used as a streaming-replication standby using a replication slot, the standby should then use the same replication slot name as primary_slot_name. This ensures that the primary server does not remove any necessary WAL data in the time between the end of the base backup and the start of streaming replication on the new standby.The specified replication slot has to exist unless the option -C is also used.If this option is not specified and the server supports temporary replication slots (version 10 and later), then a temporary replication slot is automatically used for WAL streaming. -v --verbose Enables verbose mode. Will output some extra steps during startup and shutdown, as well as show the exact file name that is currently being processed if progress reporting is also enabled. --manifest-checksums=algorithm Specifies the checksum algorithm that should be applied to each file included in the backup manifest. Currently, the available algorithms are NONE, CRC32C, SHA224, SHA256, SHA384, and SHA512. The default is CRC32C.If NONE is selected, the backup manifest will not contain any checksums. Otherwise, it will contain a checksum of each file in the backup using the specified algorithm. In addition, the manifest will always contain a SHA256 checksum of its own contents. The SHA algorithms are significantly more CPU-intensive than CRC32C, so selecting one of them may increase the time required to complete the backup.Using a SHA hash function provides a cryptographically secure digest of each file for users who wish to verify that the backup has not been tampered with, while the CRC32C algorithm provides a checksum that is much faster to calculate; it is good at catching errors due to accidental changes but is not resistant to malicious modifications. Note that, to be useful against an adversary who has access to the backup, the backup manifest would need to be stored securely elsewhere or otherwise verified not to have been modified since the backup was taken. pg_verifybackup can be used to check the integrity of a backup against the backup manifest. --manifest-force-encode Forces all filenames in the backup manifest to be hex-encoded. If this option is not specified, only non-UTF8 filenames are hex-encoded. This option is mostly intended to test that tools which read a backup manifest file properly handle this case. --no-estimate-size Prevents the server from estimating the total amount of backup data that will be streamed, resulting in the backup_total column in the pg_stat_progress_basebackup view always being NULL.Without this option, the backup will start by enumerating the size of the entire database, and then go back and send the actual contents. This may make the backup take slightly longer, and in particular it will take longer before the first data is sent. This option is useful to avoid such estimation time if it’s too long.This option is not allowed when using --progress. --no-manifest Disables generation of a backup manifest. If this option is not specified, the server will generate and send a backup manifest which can be verified using pg_verifybackup. The manifest is a list of every file present in the backup with the exception of any WAL files that may be included. It also stores the size, last modification time, and an optional checksum for each file. --no-slot Prevents the creation of a temporary replication slot for the backup.By default, if log streaming is selected but no slot name is given with the -S option, then a temporary replication slot is created (if supported by the source server).The main purpose of this option is to allow taking a base backup when the server has no free replication slots. Using a replication slot is almost always preferred, because it prevents needed WAL from being removed by the server during the backup. --no-verify-checksums Disables verification of checksums, if they are enabled on the server the base backup is taken from.By default, checksums are verified and checksum failures will result in a non-zero exit status. However, the base backup will not be removed in such a case, as if the --no-clean option had been used. Checksum verification failures will also be reported in the pg_stat_database view. The following command-line options control the connection to the source server: -d connstr --dbname=connstr Specifies parameters used to connect to the server, as a connection string; these will override any conflicting command line options.The option is called --dbname for consistency with other client applications, but because pg_basebackup doesn’t connect to any particular database in the cluster, any database name in the connection string will be ignored. -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for a Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -s interval --status-interval=interval Specifies the number of seconds between status packets sent back to the source server. Smaller values allow more accurate monitoring of backup progress from the server. A value of zero disables periodic status updates completely, although an update will still be sent when requested by the server, to avoid timeout-based disconnects. The default value is 10 seconds. -U username --username=username Specifies the user name to connect as. -w --no-password Prevents issuing a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Forces pg_basebackup to prompt for a password before connecting to the source server.This option is never essential, since pg_basebackup will automatically prompt for a password if the server demands password authentication. However, pg_basebackup will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. Other options are also available: -V --version Prints the pg_basebackup version and exits. -? --help Shows help about pg_basebackup command line arguments, and exits. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. At the beginning of the backup, a checkpoint needs to be performed on the source server. This can take some time (especially if the option --checkpoint=fast is not used), during which pg_basebackup will appear to be idle. The backup will include all files in the data directory and tablespaces, including the configuration files and any additional files placed in the directory by third parties, except certain temporary files managed by IvorySQL. But only regular files and directories are copied, except that symbolic links used for tablespaces are preserved. Symbolic links pointing to certain directories known to IvorySQL are copied as empty directories. Other symbolic links and special device files are skipped. In plain format, tablespaces will be backed up to the same path they have on the source server, unless the option --tablespace-mapping is used. Without this option, running a plain format base backup on the same host as the server will not work if tablespaces are in use, because the backup would have to be written to the same directory locations as the original tablespaces. When tar format is used, it is the user’s responsibility to unpack each tar file before starting a IvorySQL server that uses the data. If there are additional tablespaces, the tar files for them need to be unpacked in the correct locations. In this case the symbolic links for those tablespaces will be created by the server according to the contents of the tablespace_map file that is included in the base.tar file. pg_basebackup works with servers of the same or an older major version. pg_basebackup will preserve group permissions for data files if group permissions are enabled on the source cluster. To create a base backup of the server at mydbserver and store it in the local directory /usr/local/pgsql/data: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data To create a backup of the local server with one compressed tar file for each tablespace, and store it in the directory backup, showing a progress report while running: $ pg_basebackup -D backup -Ft -z -P To create a backup of a single-tablespace local database and compress this with bzip2: $ pg_basebackup -D - -Ft -X fetch | bzip2 > backup.tar.bz2 (This command will fail if there are multiple tablespaces in the database.) To create a backup of a local database where the tablespace in /opt/ts is relocated to ./backup/ts: $ pg_basebackup -D backup/data -T /opt/ts=$(pwd)/backup/ts To create a backup of a local server with one tar file for each tablespace compressed with gzip at level 9, stored in the directory backup: $ pg_basebackup -D backup -Ft --compress=gzip:9 pgbench — run a benchmark test on IvorySQL pgbench -i [option…​] [dbname] pgbench [option…​] [dbname] pgbench -i creates four tables pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers, destroying any existing tables of these names. Be very careful to use another database if you have tables having these names! At the default “scale factor” of 1, the tables initially contain this many rows: table # of rows --------------------------------- pgbench_branches 1 pgbench_tellers 10 pgbench_accounts 100000 pgbench_history 0 You can (and, for most purposes, probably should) increase the number of rows by using the -s (scale factor) option. The -F (fillfactor) option might also be used at this point. Once you have done the necessary setup, you can run your benchmark with a command that doesn’t include -i, that is pgbench [ options ] dbname In nearly all cases, you’ll need some options to make a useful test. The most important options are -c (number of clients), -t (number of transactions), -T (time limit), and -f (specify a custom script file). See below for a full list. The following is divided into three subsections. Different options are used during database initialization and while running benchmarks, but some options are useful in both cases. pgbench accepts the following command-line initialization arguments: dbname Specifies the name of the database to test in. If this is not specified, the environment variable PGDATABASE is used. If that is not set, the user name specified for the connection is used. -i --initialize Required to invoke initialization mode. -I init_steps --init-steps=init_steps Perform just a selected set of the normal initialization steps. init_steps specifies the initialization steps to be performed, using one character per step. Each step is invoked in the specified order. The default is dtgvp. The available steps are:`d` (Drop)Drop any existing pgbench tables.t (create Tables)Create the tables used by the standard pgbench scenario, namely pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers.g or G (Generate data, client-side or server-side)Generate data and load it into the standard tables, replacing any data already present.With g (client-side data generation), data is generated in pgbench client and then sent to the server. This uses the client/server bandwidth extensively through a COPY. pgbench uses the FREEZE option with version 14 or later of IvorySQL to speed up subsequent VACUUM, unless partitions are enabled. Using g causes logging to print one message every 100,000 rows while generating data for the pgbench_accounts table.With G (server-side data generation), only small queries are sent from the pgbench client and then data is actually generated in the server. No significant bandwidth is required for this variant, but the server will do more work. Using G causes logging not to print any progress message while generating data.The default initialization behavior uses client-side data generation (equivalent to g).v (Vacuum)Invoke VACUUM on the standard tables.p (create Primary keys)Create primary key indexes on the standard tables.f (create Foreign keys)Create foreign key constraints between the standard tables. (Note that this step is not performed by default.) -F fillfactor --fillfactor= fillfactor Create the pgbench_accounts, pgbench_tellers and pgbench_branches tables with the given fillfactor. Default is 100. -n --no-vacuum Perform no vacuuming during initialization. (This option suppresses the v initialization step, even if it was specified in -I.) -q --quiet Switch logging to quiet mode, producing only one progress message per 5 seconds. The default logging prints one message each 100,000 rows, which often outputs many lines per second (especially on good hardware).This setting has no effect if G is specified in -I. -s scale_factor --scale=scale_factor Multiply the number of rows generated by the scale factor. For example, -s 100 will create 10,000,000 rows in the pgbench_accounts table. Default is 1. When the scale is 20,000 or larger, the columns used to hold account identifiers (aid columns) will switch to using larger integers (bigint), in order to be big enough to hold the range of account identifiers. --foreign-keys Create foreign key constraints between the standard tables. (This option adds the f step to the initialization step sequence, if it is not already present.) --index-tablespace=index_tablespace Create indexes in the specified tablespace, rather than the default tablespace. --partition-method=`NAME` Create a partitioned pgbench_accounts table with NAME method. Expected values are range or hash. This option requires that --partitions is set to non-zero. If unspecified, default is range. --partitions=NUM Create a partitioned pgbench_accounts table with NUM partitions of nearly equal size for the scaled number of accounts. Default is 0, meaning no partitioning. --tablespace=`tablespace` Create tables in the specified tablespace, rather than the default tablespace. --unlogged-tables Create all tables as unlogged tables, rather than permanent tables. pgbench accepts the following command-line benchmarking arguments: -b scriptname[@weight] --builtin=scriptname[@weight] Add the specified built-in script to the list of scripts to be executed. Available built-in scripts are: tpcb-like, simple-update and select-only. Unambiguous prefixes of built-in names are accepted. With the special name list, show the list of built-in scripts and exit immediately.Optionally, write an integer weight after @ to adjust the probability of selecting this script versus other ones. The default weight is 1. See below for details. -c clients --client=clients Number of clients simulated, that is, number of concurrent database sessions. Default is 1. -C --connect Establish a new connection for each transaction, rather than doing it just once per client session. This is useful to measure the connection overhead. -d --debug Print debugging output. -D varname=value --define=varname=value Define a variable for use by a custom script (see below). Multiple -D options are allowed. -f filename[@weight] --file=filename[@weight] Add a transaction script read from filename to the list of scripts to be executed.Optionally, write an integer weight after @ to adjust the probability of selecting this script versus other ones. The default weight is 1. (To use a script file name that includes an @ character, append a weight so that there is no ambiguity, for example filen@me@1.) See below for details. -j threads --jobs=threads Number of worker threads within pgbench. Using more than one thread can be helpful on multi-CPU machines. Clients are distributed as evenly as possible among available threads. Default is 1. -l --log Write information about each transaction to a log file. See below for details. -L limit --latency-limit=limit Transactions that last more than limit milliseconds are counted and reported separately, as late.When throttling is used (--rate=…​), transactions that lag behind schedule by more than limit ms, and thus have no hope of meeting the latency limit, are not sent to the server at all. They are counted and reported separately as skipped.When the --max-tries option is used, a transaction which fails due to a serialization anomaly or from a deadlock will not be retried if the total time of all its tries is greater than limit ms. To limit only the time of tries and not their number, use --max-tries=0. By default, the option --max-tries is set to 1 and transactions with serialization/deadlock errors are not retried. -M querymode --protocol=querymode Protocol to use for submitting queries to the server:`simple`: use simple query protocol. extended : use extended query protocol. prepared : use extended query protocol with prepared statements.In the prepared mode, pgbench reuses the parse analysis result starting from the second query iteration, so pgbench runs faster than in other modes.The default is simple query protocol. -n --no-vacuum Perform no vacuuming before running the test. This option is necessary if you are running a custom test scenario that does not include the standard tables pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers. -N --skip-some-updates Run built-in simple-update script. Shorthand for -b simple-update. -P sec --progress=sec Show progress report every sec seconds. The report includes the time since the beginning of the run, the TPS since the last report, and the transaction latency average, standard deviation, and the number of failed transactions since the last report. Under throttling (-R), the latency is computed with respect to the transaction scheduled start time, not the actual transaction beginning time, thus it also includes the average schedule lag time. When --max-tries is used to enable transaction retries after serialization/deadlock errors, the report includes the number of retried transactions and the sum of all retries. -r --report-per-command Report the following statistics for each command after the benchmark finishes: the average per-statement latency (execution time from the perspective of the client), the number of failures, and the number of retries after serialization or deadlock errors in this command. The report displays retry statistics only if the --max-tries option is not equal to 1. -R rate --rate=rate Execute transactions targeting the specified rate instead of running as fast as possible (the default). The rate is given in transactions per second. If the targeted rate is above the maximum possible rate, the rate limit won’t impact the results.The rate is targeted by starting transactions along a Poisson-distributed schedule time line. The expected start time schedule moves forward based on when the client first started, not when the previous transaction ended. That approach means that when transactions go past their original scheduled end time, it is possible for later ones to catch up again.When throttling is active, the transaction latency reported at the end of the run is calculated from the scheduled start times, so it includes the time each transaction had to wait for the previous transaction to finish. The wait time is called the schedule lag time, and its average and maximum are also reported separately. The transaction latency with respect to the actual transaction start time, i.e., the time spent executing the transaction in the database, can be computed by subtracting the schedule lag time from the reported latency.If --latency-limit is used together with --rate, a transaction can lag behind so much that it is already over the latency limit when the previous transaction ends, because the latency is calculated from the scheduled start time. Such transactions are not sent to the server, but are skipped altogether and counted separately.A high schedule lag time is an indication that the system cannot process transactions at the specified rate, with the chosen number of clients and threads. When the average transaction execution time is longer than the scheduled interval between each transaction, each successive transaction will fall further behind, and the schedule lag time will keep increasing the longer the test run is. When that happens, you will have to reduce the specified transaction rate. -s scale_factor --scale=scale_factor Report the specified scale factor in pgbench’s output. With the built-in tests, this is not necessary; the correct scale factor will be detected by counting the number of rows in the pgbench_branches table. However, when testing only custom benchmarks (-f option), the scale factor will be reported as 1 unless this option is used. -S --select-only Run built-in select-only script. Shorthand for -b select-only. -t transactions --transactions=transactions Number of transactions each client runs. Default is 10. -T seconds --time=seconds Run the test for this many seconds, rather than a fixed number of transactions per client. -t and -T are mutually exclusive. -v --vacuum-all Vacuum all four standard tables before running the test. With neither -n nor -v, pgbench will vacuum the pgbench_tellers and pgbench_branches tables, and will truncate pgbench_history. --aggregate-interval=`seconds` Length of aggregation interval (in seconds). May be used only with -l option. With this option, the log contains per-interval summary data, as described below. --failures-detailed Report failures in per-transaction and aggregation logs, as well as in the main and per-script reports, grouped by the following types:serialization failures;deadlock failures. --log-prefix=`prefix` Set the filename prefix for the log files created by --log. The default is pgbench_log. --max-tries=`number_of_tries` Enable retries for transactions with serialization/deadlock errors and set the maximum number of these tries. This option can be combined with the --latency-limit option which limits the total time of all transaction tries; moreover, you cannot use an unlimited number of tries (--max-tries=0) without --latency-limit or --time. The default value is 1 and transactions with serialization/deadlock errors are not retried. --progress-timestamp When showing progress (option -P), use a timestamp (Unix epoch) instead of the number of seconds since the beginning of the run. The unit is in seconds, with millisecond precision after the dot. This helps compare logs generated by various tools. --random-seed=seed Set random generator seed. Seeds the system random number generator, which then produces a sequence of initial generator states, one for each thread. Values for seed may be: time (the default, the seed is based on the current time), rand (use a strong random source, failing if none is available), or an unsigned decimal integer value. The random generator is invoked explicitly from a pgbench script (random…​ functions) or implicitly (for instance option --rate uses it to schedule transactions). When explicitly set, the value used for seeding is shown on the terminal. Any value allowed for seed may also be provided through the environment variable PGBENCH_RANDOM_SEED. To ensure that the provided seed impacts all possible uses, put this option first or use the environment variable.Setting the seed explicitly allows to reproduce a pgbench run exactly, as far as random numbers are concerned. As the random state is managed per thread, this means the exact same pgbench run for an identical invocation if there is one client per thread and there are no external or data dependencies. From a statistical viewpoint reproducing runs exactly is a bad idea because it can hide the performance variability or improve performance unduly, e.g., by hitting the same pages as a previous run. However, it may also be of great help for debugging, for instance re-running a tricky case which leads to an error. Use wisely. --sampling-rate=`rate` Sampling rate, used when writing data into the log, to reduce the amount of log generated. If this option is given, only the specified fraction of transactions are logged. 1.0 means all transactions will be logged, 0.05 means only 5% of the transactions will be logged.Remember to take the sampling rate into account when processing the log file. For example, when computing TPS values, you need to multiply the numbers accordingly (e.g., with 0.01 sample rate, you’ll only get 1/100 of the actual TPS). --show-script=scriptname Show the actual code of builtin script scriptname on stderr, and exit immediately. --verbose-errors Print messages about all errors and failures (errors without retrying) including which limit for retries was exceeded and how far it was exceeded for the serialization/deadlock failures. (Note that in this case the output can be significantly increased.). pgbench also accepts the following common command-line arguments for connection parameters: -h hostname --host=hostname The database server’s host name -p port --port=port The database server’s port number -U login --username=login The user name to connect as -V --version Print the pgbench version and exit. -? --help Show help about pgbench command line arguments, and exit. A successful run will exit with status 0. Exit status 1 indicates static problems such as invalid command-line options or internal errors which are supposed to never occur. Early errors that occur when starting benchmark such as initial connection failures also exit with status 1. Errors during the run such as database errors or problems in the script will result in exit status 2. In the latter case, pgbench will print partial results. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_config — retrieve information about the installed version of IvorySQL pg_config [option…​] To use pg_config, supply one or more of the following options: --bindir Print the location of user executables. Use this, for example, to find the psql program. This is normally also the location where the pg_config program resides. --docdir Print the location of documentation files. --htmldir Print the location of HTML documentation files. --includedir Print the location of C header files of the client interfaces. --pkgincludedir Print the location of other C header files. --includedir-server Print the location of C header files for server programming. --libdir Print the location of object code libraries. --pkglibdir Print the location of dynamically loadable modules, or where the server would search for them. (Other architecture-dependent data files might also be installed in this directory.) --localedir Print the location of locale support files. (This will be an empty string if locale support was not configured when IvorySQL was built.) --mandir Print the location of manual pages. --sharedir Print the location of architecture-independent support files. --sysconfdir Print the location of system-wide configuration files. --pgxs Print the location of extension makefiles. --configure Print the options that were given to the configure script when IvorySQL was configured for building. This can be used to reproduce the identical configuration, or to find out with what options a binary package was built. (Note however that binary packages often contain vendor-specific custom patches.) See also the examples below. --cc Print the value of the CC variable that was used for building IvorySQL. This shows the C compiler used. --cppflags Print the value of the CPPFLAGS variable that was used for building IvorySQL. This shows C compiler switches needed at preprocessing time (typically, -I switches). --cflags Print the value of the CFLAGS variable that was used for building IvorySQL. This shows C compiler switches. --cflags_sl Print the value of the CFLAGS_SL variable that was used for building IvorySQL. This shows extra C compiler switches used for building shared libraries. --ldflags Print the value of the LDFLAGS variable that was used for building IvorySQL. This shows linker switches. --ldflags_ex Print the value of the LDFLAGS_EX variable that was used for building IvorySQL. This shows linker switches used for building executables only. --ldflags_sl Print the value of the LDFLAGS_SL variable that was used for building IvorySQL. This shows linker switches used for building shared libraries only. --libs Print the value of the LIBS variable that was used for building IvorySQL. This normally contains -l switches for external libraries linked into IvorySQL. --version Print the version of IvorySQL. -? --help Show help about pg_config command line arguments, and exit. If more than one option is given, the information is printed in that order, one item per line. If no options are given, all available information is printed, with labels. To reproduce the build configuration of the current IvorySQL installation, run the following command: eval ./configure `pg_config --configure` The output of pg_config --configure contains shell quotation marks so arguments with spaces are represented correctly. Therefore, using eval is required for proper results. pg_dump — extract a IvorySQL database into a script file or other archive file pg_dump [connection-option…​] [option…​] [dbname] The following command-line options control the content and format of the output. dbname Specifies the name of the database to be dumped. If this is not specified, the environment variable `PGDATABASE` is used. If that is not set, the user name specified for the connection is used. -a --data-only Dump only the data, not the schema (data definitions). Table data, large objects, and sequence values are dumped.This option is similar to, but for historical reasons not identical to, specifying `--section=data`. -b --blobs Include large objects in the dump. This is the default behavior except when `--schema`, `--table`, or `--schema-only` is specified. The `-b` switch is therefore only useful to add large objects to dumps where a specific schema or table has been requested. Note that blobs are considered data and therefore will be included when `--data-only` is used, but not when `--schema-only` is. -B --no-blobs Exclude large objects in the dump.When both `-b` and `-B` are given, the behavior is to output large objects, when data is being dumped, see the `-b` documentation. -c --clean Output commands to clean (drop) database objects prior to outputting the commands for creating them. (Unless `--if-exists` is also specified, restore might generate some harmless error messages, if any objects were not present in the destination database.)This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. -C --create Begin the output with a command to create the database itself and reconnect to the created database. (With a script of this form, it doesn't matter which database in the destination installation you connect to before running the script.) If `--clean` is also specified, the script drops and recreates the target database before reconnecting to it.With `--create`, the output also includes the database's comment if any, and any configuration variable settings that are specific to this database, that is, any `ALTER DATABASE ... SET ...` and `ALTER ROLE ... IN DATABASE ... SET ...` commands that mention this database. Access privileges for the database itself are also dumped, unless `--no-acl` is specified.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. -e `pattern` --extension=`pattern` Dump only extensions matching *`pattern`*. When this option is not specified, all non-system extensions in the target database will be dumped. Multiple extensions can be selected by writing multiple `-e` switches. The *`pattern`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands , so multiple extensions can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards.Any configuration relation registered by `pg_extension_config_dump` is included in the dump if its extension is specified by `--extension`.NoteWhen `-e` is specified, pg_dump makes no attempt to dump any other database objects that the selected extension(s) might depend upon. Therefore, there is no guarantee that the results of a specific-extension dump can be successfully restored by themselves into a clean database. -E `encoding` --encoding=`encoding` Create the dump in the specified character set encoding. By default, the dump is created in the database encoding. (Another way to get the same result is to set the `PGCLIENTENCODING` environment variable to the desired dump encoding.) -f `file` --file=`file` Send output to the specified file. This parameter can be omitted for file based output formats, in which case the standard output is used. It must be given for the directory output format however, where it specifies the target directory instead of a file. In this case the directory is created by `pg_dump` and must not exist before. -F `format` --format=`format` Selects the format of the output. *`format`* can be one of the following:`p` `plain`Output a plain-text SQL script file (the default).`c` `custom`Output a custom-format archive suitable for input into pg_restore. Together with the directory output format, this is the most flexible output format in that it allows manual selection and reordering of archived items during restore. This format is also compressed by default.`d` `directory`Output a directory-format archive suitable for input into pg_restore. This will create a directory with one file for each table and blob being dumped, plus a so-called Table of Contents file describing the dumped objects in a machine-readable format that pg_restore can read. A directory format archive can be manipulated with standard Unix tools; for example, files in an uncompressed archive can be compressed with the gzip tool. This format is compressed by default and also supports parallel dumps.`t` `tar`Output a `tar`-format archive suitable for input into pg_restore. The tar format is compatible with the directory format: extracting a tar-format archive produces a valid directory-format archive. However, the tar format does not support compression. Also, when using tar format the relative order of table data items cannot be changed during restore. -j `njobs` --jobs=`njobs` Run the dump in parallel by dumping *`njobs`* tables simultaneously. This option may reduce the time needed to perform the dump but it also increases the load on the database server. You can only use this option with the directory output format because this is the only output format where multiple processes can write their data at the same time.pg_dump will open *`njobs`* + 1 connections to the database, so make sure your http://www.postgresql.org/docs/17/runtime-config-connection.html#GUC-MAX-CONNECTIONS[max_connections] setting is high enough to accommodate all connections.Requesting exclusive locks on database objects while running a parallel dump could cause the dump to fail. The reason is that the pg_dump leader process requests shared locks on the objects that the worker processes are going to dump later in order to make sure that nobody deletes them and makes them go away while the dump is running. If another client then requests an exclusive lock on a table, that lock will not be granted but will be queued waiting for the shared lock of the leader process to be released. Consequently any other access to the table will not be granted either and will queue after the exclusive lock request. This includes the worker process trying to dump the table. Without any precautions this would be a classic deadlock situation. To detect this conflict, the pg_dump worker process requests another shared lock using the `NOWAIT` option. If the worker process is not granted this shared lock, somebody else must have requested an exclusive lock in the meantime and there is no way to continue with the dump, so pg_dump has no choice but to abort the dump.To perform a parallel dump, the database server needs to support synchronized snapshots, a feature that was introduced in IvorySQL for primary servers and 10 for standbys. With this feature, database clients can ensure they see the same data set even though they use different connections. `pg_dump -j` uses multiple database connections; it connects to the database once with the leader process and once again for each worker job. Without the synchronized snapshot feature, the different worker jobs wouldn't be guaranteed to see the same data in each connection, which could lead to an inconsistent backup. -n `pattern` --schema=`pattern` Dump only schemas matching *`pattern`*; this selects both the schema itself, and all its contained objects. When this option is not specified, all non-system schemas in the target database will be dumped. Multiple schemas can be selected by writing multiple `-n` switches. The *`pattern`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands, so multiple schemas can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards. NoteWhen `-n` is specified, pg_dump makes no attempt to dump any other database objects that the selected schema(s) might depend upon. Therefore, there is no guarantee that the results of a specific-schema dump can be successfully restored by themselves into a clean database.NoteNon-schema objects such as blobs are not dumped when `-n` is specified. You can add blobs back to the dump with the `--blobs` switch. -N `pattern` --exclude-schema=`pattern` Do not dump any schemas matching *`pattern`*. The pattern is interpreted according to the same rules as for `-n`. `-N` can be given more than once to exclude schemas matching any of several patterns.When both `-n` and `-N` are given, the behavior is to dump just the schemas that match at least one `-n` switch but no `-N` switches. If `-N` appears without `-n`, then schemas matching `-N` are excluded from what is otherwise a normal dump. -O --no-owner Do not output commands to set ownership of objects to match the original database. By default, pg_dump issues `ALTER OWNER` or `SET SESSION AUTHORIZATION` statements to set ownership of created database objects. These statements will fail when the script is run unless it is started by a superuser (or the same user that owns all of the objects in the script). To make a script that can be restored by any user, but will give that user ownership of all the objects, specify `-O`.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. -R --no-reconnect This option is obsolete but still accepted for backwards compatibility. -s --schema-only Dump only the object definitions (schema), not data.This option is the inverse of `--data-only`. It is similar to, but for historical reasons not identical to, specifying `--section=pre-data --section=post-data`.(Do not confuse this with the `--schema` option, which uses the word “schema” in a different meaning.)To exclude table data for only a subset of tables in the database, see `--exclude-table-data`. -S `username` --superuser=`username` Specify the superuser user name to use when disabling triggers. This is relevant only if `--disable-triggers` is used. (Usually, it's better to leave this out, and instead start the resulting script as superuser.) -t `pattern` --table=`pattern` Dump only tables with names matching *`pattern`*. Multiple tables can be selected by writing multiple `-t` switches. The *`pattern`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands , so multiple tables can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards; As well as tables, this option can be used to dump the definition of matching views, materialized views, foreign tables, and sequences. It will not dump the contents of views or materialized views, and the contents of foreign tables will only be dumped if the corresponding foreign server is specified with `--include-foreign-data`.The `-n` and `-N` switches have no effect when `-t` is used, because tables selected by `-t` will be dumped regardless of those switches, and non-table objects will not be dumped.NoteWhen `-t` is specified, pg_dump makes no attempt to dump any other database objects that the selected table(s) might depend upon. Therefore, there is no guarantee that the results of a specific-table dump can be successfully restored by themselves into a clean database. -T `pattern` --exclude-table=`pattern` Do not dump any tables matching *`pattern`*. The pattern is interpreted according to the same rules as for `-t`. `-T` can be given more than once to exclude tables matching any of several patterns.When both `-t` and `-T` are given, the behavior is to dump just the tables that match at least one `-t` switch but no `-T` switches. If `-T` appears without `-t`, then tables matching `-T` are excluded from what is otherwise a normal dump. -v --verbose Specifies verbose mode. This will cause pg_dump to output detailed object comments and start/stop times to the dump file, and progress messages to standard error. Repeating the option causes additional debug-level messages to appear on standard error. -V --version Print the pg_dump version and exit. -x --no-privileges --no-acl Prevent dumping of access privileges (grant/revoke commands). -Z `0..9` --compress=`0..9` Specify the compression level to use. Zero means no compression. For the custom and directory archive formats, this specifies compression of individual table-data segments, and the default is to compress at a moderate level. For plain text output, setting a nonzero compression level causes the entire output file to be compressed, as though it had been fed through gzip; but the default is not to compress. The tar archive format currently does not support compression at all. --binary-upgrade This option is for use by in-place upgrade utilities. Its use for other purposes is not recommended or supported. The behavior of the option may change in future releases without notice. --column-inserts --attribute-inserts Dump data as `INSERT` commands with explicit column names (`INSERT INTO *`table`* (*`column`*, ...) VALUES ...`). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. Any error during restoring will cause only rows that are part of the problematic `INSERT` to be lost, rather than the entire table contents. --disable-dollar-quoting This option disables the use of dollar quoting for function bodies, and forces them to be quoted using SQL standard string syntax. --disable-triggers This option is relevant only when creating a data-only dump. It instructs pg_dump to include commands to temporarily disable triggers on the target tables while the data is restored. Use this if you have referential integrity checks or other triggers on the tables that you do not want to invoke during data restore.Presently, the commands emitted for `--disable-triggers` must be done as superuser. So, you should also specify a superuser name with `-S`, or preferably be careful to start the resulting script as a superuser.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. --enable-row-security This option is relevant only when dumping the contents of a table which has row security. By default, pg_dump will set http://www.postgresql.org/docs/17/runtime-config-client.html#GUC-ROW-SECURITY[row_security] to off, to ensure that all data is dumped from the table. If the user does not have sufficient privileges to bypass row security, then an error is thrown. This parameter instructs pg_dump to set http://www.postgresql.org/docs/17/runtime-config-client.html#GUC-ROW-SECURITY[row_security] to on instead, allowing the user to dump the parts of the contents of the table that they have access to.Note that if you use this option currently, you probably also want the dump be in `INSERT` format, as the `COPY FROM` during restore does not support row security. --exclude-table-data=`pattern` Do not dump data for any tables matching *`pattern`*. The pattern is interpreted according to the same rules as for `-t`. `--exclude-table-data` can be given more than once to exclude tables matching any of several patterns. This option is useful when you need the definition of a particular table even though you do not need the data in it.To exclude data for all tables in the database, see `--schema-only`. --extra-float-digits=`ndigits` Use the specified value of `extra_float_digits` when dumping floating-point data, instead of the maximum available precision. Routine dumps made for backup purposes should not use this option. --if-exists Use conditional commands (i.e., add an `IF EXISTS` clause) when cleaning database objects. This option is not valid unless `--clean` is also specified. --include-foreign-data=`foreignserver` Dump the data for any foreign table with a foreign server matching *`foreignserver`* pattern. Multiple foreign servers can be selected by writing multiple `--include-foreign-data` switches. Also, the *`foreignserver`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands, so multiple foreign servers can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards; The only exception is that an empty pattern is disallowed.NoteWhen `--include-foreign-data` is specified, pg_dump does not check that the foreign table is writable. Therefore, there is no guarantee that the results of a foreign table dump can be successfully restored. --inserts Dump data as `INSERT` commands (rather than `COPY`). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. Any error during restoring will cause only rows that are part of the problematic `INSERT` to be lost, rather than the entire table contents. Note that the restore might fail altogether if you have rearranged column order. The `--column-inserts` option is safe against column order changes, though even slower. --load-via-partition-root When dumping data for a table partition, make the `COPY` or `INSERT` statements target the root of the partitioning hierarchy that contains it, rather than the partition itself. This causes the appropriate partition to be re-determined for each row when the data is loaded. This may be useful when restoring data on a server where rows do not always fall into the same partitions as they did on the original server. That could happen, for example, if the partitioning column is of type text and the two systems have different definitions of the collation used to sort the partitioning column.It is best not to use parallelism when restoring from an archive made with this option, because pg_restore will not know exactly which partition(s) a given archive data item will load data into. This could result in inefficiency due to lock conflicts between parallel jobs, or perhaps even restore failures due to foreign key constraints being set up before all the relevant data is loaded. --lock-wait-timeout=`timeout` Do not wait forever to acquire shared table locks at the beginning of the dump. Instead fail if unable to lock a table within the specified *`timeout`*. The timeout may be specified in any of the formats accepted by `SET statement_timeout` --no-comments Do not dump comments. --no-publications Do not dump publications. --no-security-labels Do not dump security labels. --no-subscriptions Do not dump subscriptions. --no-sync By default, `pg_dump` will wait for all files to be written safely to disk. This option causes `pg_dump` to return without waiting, which is faster, but means that a subsequent operating system crash can leave the dump corrupt. Generally, this option is useful for testing but should not be used when dumping data from production installation. --no-tablespaces Do not output commands to select tablespaces. With this option, all objects will be created in whichever tablespace is the default during restore.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. --no-toast-compression Do not output commands to set TOAST compression methods. With this option, all columns will be restored with the default compression setting. --no-unlogged-table-data Do not dump the contents of unlogged tables and sequences. This option has no effect on whether or not the table and sequence definitions (schema) are dumped; it only suppresses dumping the table and sequence data. Data in unlogged tables and sequences is always excluded when dumping from a standby server. --on-conflict-do-nothing Add `ON CONFLICT DO NOTHING` to `INSERT` commands. This option is not valid unless `--inserts`, `--column-inserts` or `--rows-per-insert` is also specified. --quote-all-identifiers Force quoting of all identifiers. This option is recommended when dumping a database from a server whose IvorySQL major version is different from pg_dump's, or when the output is intended to be loaded into a server of a different major version. By default, pg_dump quotes only identifiers that are reserved words in its own major version. This sometimes results in compatibility issues when dealing with servers of other versions that may have slightly different sets of reserved words. Using `--quote-all-identifiers` prevents such issues, at the price of a harder-to-read dump script. --rows-per-insert=`nrows` Dump data as `INSERT` commands (rather than `COPY`). Controls the maximum number of rows per `INSERT` command. The value specified must be a number greater than zero. Any error during restoring will cause only rows that are part of the problematic `INSERT` to be lost, rather than the entire table contents. --section=`sectionname` Only dump the named section. The section name can be `pre-data`, `data`, or `post-data`. This option can be specified more than once to select multiple sections. The default is to dump all sections.The data section contains actual table data, large-object contents, and sequence values. Post-data items include definitions of indexes, triggers, rules, and constraints other than validated check constraints. Pre-data items include all other data definition items. --serializable-deferrable Use a `serializable` transaction for the dump, to ensure that the snapshot used is consistent with later database states; but do this by waiting for a point in the transaction stream at which no anomalies can be present, so that there isn't a risk of the dump failing or causing other transactions to roll back with a `serialization_failure`.This option is not beneficial for a dump which is intended only for disaster recovery. It could be useful for a dump used to load a copy of the database for reporting or other read-only load sharing while the original database continues to be updated. Without it the dump may reflect a state which is not consistent with any serial execution of the transactions eventually committed. For example, if batch processing techniques are used, a batch may show as closed in the dump without all of the items which are in the batch appearing.This option will make no difference if there are no read-write transactions active when pg_dump is started. If read-write transactions are active, the start of the dump may be delayed for an indeterminate length of time. Once running, performance with or without the switch is the same. --snapshot=`snapshotname` Use the specified synchronized snapshot when making a dump of the database.This option is useful when needing to synchronize the dump with a logical replication slot or with a concurrent session.In the case of a parallel dump, the snapshot name defined by this option is used rather than taking a new snapshot. --strict-names Require that each extension (`-e`/`--extension`), schema (`-n`/`--schema`) and table (`-t`/`--table`) qualifier match at least one extension/schema/table in the database to be dumped. Note that if none of the extension/schema/table qualifiers find matches, pg_dump will generate an error even without `--strict-names`.This option has no effect on `-N`/`--exclude-schema`, `-T`/`--exclude-table`, or `--exclude-table-data`. An exclude pattern failing to match any objects is not considered an error. --use-set-session-authorization Output SQL-standard `SET SESSION AUTHORIZATION` commands instead of `ALTER OWNER` commands to determine object ownership. This makes the dump more standards-compatible, but depending on the history of the objects in the dump, might not restore properly. Also, a dump using `SET SESSION AUTHORIZATION` will certainly require superuser privileges to restore correctly, whereas `ALTER OWNER` requires lesser privileges. -? --help Show help about pg_dump command line arguments, and exit. The following command-line options control the database connection parameters. -d `dbname` --dbname=`dbname` Specifies the name of the database to connect to. This is equivalent to specifying *`dbname`* as the first non-option argument on the command line. The *`dbname`* can be a http://www.postgresql.org/docs/17/libpq-connect.html#LIBPQ-CONNSTRING[connection string]. If so, connection string parameters will override any conflicting command line options. -h `host` --host=`host` Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the `PGHOST` environment variable, if set, else a Unix domain socket connection is attempted. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the `PGPORT` environment variable, if set, or a compiled-in default. -U `username` --username=`username` User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a `.pgpass` file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_dump to prompt for a password before connecting to a database.This option is never essential, since pg_dump will automatically prompt for a password if the server demands password authentication. However, pg_dump will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing `-W` to avoid the extra connection attempt. --role=`rolename` Specifies a role name to be used to create the dump. This option causes pg_dump to issue a `SET ROLE` *`rolename`* command after connecting to the database. It is useful when the authenticated user (specified by `-U`) lacks privileges needed by pg_dump, but can switch to a role with the required rights. Some installations have a policy against logging in directly as a superuser, and use of this option allows dumps to be made without violating the policy. PGDATABASE PGHOST PGOPTIONS PGPORT PGUSER Default connection parameters. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are `always`, `auto` and `never`. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq. pg_dump internally executes SELECT statements. If you have problems running pg_dump, make sure you are able to select information from the database using, for example, psql. Also, any default connection settings and environment variables used by the libpq front-end library will apply. The database activity of pg_dump is normally collected by the cumulative statistics system. If this is undesirable, you can set parameter track_counts to false via PGOPTIONS or the ALTER USER command. If your database cluster has any local additions to the template1 database, be careful to restore the output of pg_dump into a truly empty database; otherwise you are likely to get errors due to duplicate definitions of the added objects. To make an empty database without any local additions, copy from template0 not template1, for example: CREATE DATABASE foo WITH TEMPLATE template0; When a data-only dump is chosen and the option --disable-triggers is used, pg_dump emits commands to disable triggers on user tables before inserting the data, and then commands to re-enable them after the data has been inserted. If the restore is stopped in the middle, the system catalogs might be left in the wrong state. The dump file produced by pg_dump does not contain the statistics used by the optimizer to make query planning decisions. Therefore, it is wise to run ANALYZE after restoring from a dump file to ensure optimal performance. When dumping logical replication subscriptions, pg_dump will generate CREATE SUBSCRIPTION commands that use the connect = false option, so that restoring the subscription does not make remote connections for creating a replication slot or for initial table copy. That way, the dump can be restored without requiring network access to the remote servers. It is then up to the user to reactivate the subscriptions in a suitable way. If the involved hosts have changed, the connection information might have to be changed. It might also be appropriate to truncate the target tables before initiating a new full table copy. If users intend to copy initial data during refresh they must create the slot with two_phase = false. After the initial sync, the two_phase option will be automatically enabled by the subscriber if the subscription had been originally created with two_phase = true option. To dump a database called mydb into an SQL-script file: $ pg_dump mydb > db.sql To reload such a script into a (freshly created) database named newdb: $ psql -d newdb -f db.sql To dump a database into a custom-format archive file: $ pg_dump -Fc mydb > db.dump To dump a database into a directory-format archive: $ pg_dump -Fd mydb -f dumpdir To dump a database into a directory-format archive in parallel with 5 worker jobs: $ pg_dump -Fd mydb -j 5 -f dumpdir To reload an archive file into a (freshly created) database named newdb: $ pg_restore -d newdb db.dump To reload an archive file into the same database it was dumped from, discarding the current contents of that database: $ pg_restore -d postgres --clean --create db.dump To dump a single table named mytab: $ pg_dump -t mytab mydb > db.sql To dump all tables whose names start with emp in the detroit schema, except for the table named employee_log: $ pg_dump -t 'detroit.emp*' -T detroit.employee_log mydb > db.sql To dump all schemas whose names start with east or west and end in gsm, excluding any schemas whose names contain the word test: $ pg_dump -n 'east*gsm' -n 'west*gsm' -N '*test*' mydb > db.sql The same, using regular expression notation to consolidate the switches: $ pg_dump -n '(east|west)*gsm' -N '*test*' mydb > db.sql To dump all database objects except for tables whose names begin with ts_: $ pg_dump -T 'ts_*' mydb > db.sql To specify an upper-case or mixed-case name in -t and related switches, you need to double-quote the name; else it will be folded to lower case.But double quotes are special to the shell, so in turn they must be quoted. Thus, to dump a single table with a mixed-case name, you need something like $ pg_dump -t \"\\\"MixedCaseName\\\"\" mydb > mytab.sql pg_dumpall — extract a IvorySQL database cluster into a script file pg_dumpall [connection-option…​] [option…​] The following command-line options control the content and format of the output. -a --data-only Dump only the data, not the schema (data definitions). -c --clean Include SQL commands to clean (drop) databases before recreating them. DROP commands for roles and tablespaces are added as well. -E encoding --encoding=encoding Create the dump in the specified character set encoding. By default, the dump is created in the database encoding. (Another way to get the same result is to set the PGCLIENTENCODING environment variable to the desired dump encoding.) -f filename --file=filename Send output to the specified file. If this is omitted, the standard output is used. -g --globals-only Dump only global objects (roles and tablespaces), no databases. -O --no-owner Do not output commands to set ownership of objects to match the original database. By default, pg_dumpall issues ALTER OWNER or SET SESSION AUTHORIZATION statements to set ownership of created schema elements. These statements will fail when the script is run unless it is started by a superuser (or the same user that owns all of the objects in the script). To make a script that can be restored by any user, but will give that user ownership of all the objects, specify -O. -r --roles-only Dump only roles, no databases or tablespaces. -s --schema-only Dump only the object definitions (schema), not data. -S username --superuser=username Specify the superuser user name to use when disabling triggers. This is relevant only if --disable-triggers is used. (Usually, it’s better to leave this out, and instead start the resulting script as superuser.) -t --tablespaces-only Dump only tablespaces, no databases or roles. -v --verbose Specifies verbose mode. This will cause pg_dumpall to output start/stop times to the dump file, and progress messages to standard error. Repeating the option causes additional debug-level messages to appear on standard error. The option is also passed down to pg_dump. -V --version Print the pg_dumpall version and exit. -x --no-privileges --no-acl Prevent dumping of access privileges (grant/revoke commands). --binary-upgrade This option is for use by in-place upgrade utilities. Its use for other purposes is not recommended or supported. The behavior of the option may change in future releases without notice. --column-inserts --attribute-inserts Dump data as INSERT commands with explicit column names (INSERT INTO table (column, …​) VALUES …​). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. --disable-dollar-quoting This option disables the use of dollar quoting for function bodies, and forces them to be quoted using SQL standard string syntax. --disable-triggers This option is relevant only when creating a data-only dump. It instructs pg_dumpall to include commands to temporarily disable triggers on the target tables while the data is restored. Use this if you have referential integrity checks or other triggers on the tables that you do not want to invoke during data restore.Presently, the commands emitted for --disable-triggers must be done as superuser. So, you should also specify a superuser name with -S, or preferably be careful to start the resulting script as a superuser. --exclude-database=`pattern` Do not dump databases whose name matches pattern. Multiple patterns can be excluded by writing multiple --exclude-database switches. The pattern parameter is interpreted as a pattern according to the same rules used by psql’s \\d commands, so multiple databases can also be excluded by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent shell wildcard expansion. --extra-float-digits=`ndigits` Use the specified value of extra_float_digits when dumping floating-point data, instead of the maximum available precision. Routine dumps made for backup purposes should not use this option. --if-exists Use conditional commands (i.e., add an IF EXISTS clause) to drop databases and other objects. This option is not valid unless --clean is also specified. --inserts Dump data as INSERT commands (rather than COPY). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. Note that the restore might fail altogether if you have rearranged column order. The --column-inserts option is safer, though even slower. --load-via-partition-root When dumping data for a table partition, make the COPY or INSERT statements target the root of the partitioning hierarchy that contains it, rather than the partition itself. This causes the appropriate partition to be re-determined for each row when the data is loaded. This may be useful when restoring data on a server where rows do not always fall into the same partitions as they did on the original server. That could happen, for example, if the partitioning column is of type text and the two systems have different definitions of the collation used to sort the partitioning column. --lock-wait-timeout=`timeout` Do not wait forever to acquire shared table locks at the beginning of the dump. Instead, fail if unable to lock a table within the specified timeout. The timeout may be specified in any of the formats accepted by SET statement_timeout. --no-comments Do not dump comments. --no-publications Do not dump publications. --no-role-passwords Do not dump passwords for roles. When restored, roles will have a null password, and password authentication will always fail until the password is set. Since password values aren’t needed when this option is specified, the role information is read from the catalog view pg_roles instead of pg_authid. Therefore, this option also helps if access to pg_authid is restricted by some security policy. --no-security-labels Do not dump security labels. --no-subscriptions Do not dump subscriptions. --no-sync By default, pg_dumpall will wait for all files to be written safely to disk. This option causes pg_dumpall to return without waiting, which is faster, but means that a subsequent operating system crash can leave the dump corrupt. Generally, this option is useful for testing but should not be used when dumping data from production installation. --no-table-access-method Do not output commands to select table access methods. With this option, all objects will be created with whichever table access method is the default during restore. --no-tablespaces Do not output commands to create tablespaces nor select tablespaces for objects. With this option, all objects will be created in whichever tablespace is the default during restore. --no-toast-compression Do not output commands to set TOAST compression methods. With this option, all columns will be restored with the default compression setting. --no-unlogged-table-data Do not dump the contents of unlogged tables. This option has no effect on whether or not the table definitions (schema) are dumped; it only suppresses dumping the table data. --on-conflict-do-nothing Add ON CONFLICT DO NOTHING to INSERT commands. This option is not valid unless --inserts or --column-inserts is also specified. --quote-all-identifiers Force quoting of all identifiers. This option is recommended when dumping a database from a server whose IvorySQL major version is different from pg_dumpall’s, or when the output is intended to be loaded into a server of a different major version. By default, pg_dumpall quotes only identifiers that are reserved words in its own major version. This sometimes results in compatibility issues when dealing with servers of other versions that may have slightly different sets of reserved words. Using --quote-all-identifiers prevents such issues, at the price of a harder-to-read dump script. --rows-per-insert=`nrows` Dump data as INSERT commands (rather than COPY). Controls the maximum number of rows per INSERT command. The value specified must be a number greater than zero. Any error during restoring will cause only rows that are part of the problematic INSERT to be lost, rather than the entire table contents. --use-set-session-authorization Output SQL-standard SET SESSION AUTHORIZATION commands instead of ALTER OWNER commands to determine object ownership. This makes the dump more standards compatible, but depending on the history of the objects in the dump, might not restore properly. -? --help Show help about pg_dumpall command line arguments, and exit. The following command-line options control the database connection parameters. -d connstr --dbname=connstr Specifies parameters used to connect to the server, as a connection string; these will override any conflicting command line options.The option is called --dbname for consistency with other client applications, but because pg_dumpall needs to connect to many databases, the database name in the connection string will be ignored. Use the -l option to specify the name of the database used for the initial connection, which will dump global objects and discover what other databases should be dumped. -h host --host=host Specifies the host name of the machine on which the database server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -l dbname --database=dbname Specifies the name of the database to connect to for dumping global objects and discovering what other databases should be dumped. If not specified, the postgres database will be used, and if that does not exist, template1 will be used. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_dumpall to prompt for a password before connecting to a database.This option is never essential, since pg_dumpall will automatically prompt for a password if the server demands password authentication. However, pg_dumpall will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt.Note that the password prompt will occur again for each database to be dumped. Usually, it’s better to set up a ~/.pgpass file than to rely on manual password entry. --role=`rolename` Specifies a role name to be used to create the dump. This option causes pg_dumpall to issue a SET ROLE rolename command after connecting to the database. It is useful when the authenticated user (specified by -U) lacks privileges needed by pg_dumpall, but can switch to a role with the required rights. Some installations have a policy against logging in directly as a superuser, and use of this option allows dumps to be made without violating the policy. PGHOST PGOPTIONS PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq Since pg_dumpall calls pg_dump internally, some diagnostic messages will refer to pg_dump. The --clean option can be useful even when your intention is to restore the dump script into a fresh cluster. Use of --clean authorizes the script to drop and re-create the built-in postgres and template1 databases, ensuring that those databases will retain the same properties (for instance, locale and encoding) that they had in the source cluster. Without the option, those databases will retain their existing database-level properties, as well as any pre-existing contents. Once restored, it is wise to run ANALYZE on each database so the optimizer has useful statistics. You can also run vacuumdb -a -z to analyze all databases. The dump script should not be expected to run completely without errors. In particular, because the script will issue CREATE ROLE for every role existing in the source cluster, it is certain to get a “role already exists” error for the bootstrap superuser, unless the destination cluster was initialized with a different bootstrap superuser name. This error is harmless and should be ignored. Use of the --clean option is likely to produce additional harmless error messages about non-existent objects, although you can minimize those by adding --if-exists. pg_dumpall requires all needed tablespace directories to exist before the restore; otherwise, database creation will fail for databases in non-default locations. To dump all databases: $ pg_dumpall > db.out To restore database(s) from this file, you can use: $ psql -f db.out postgres It is not important to which database you connect here since the script file created by pg_dumpall will contain the appropriate commands to create and connect to the saved databases. An exception is that if you specified --clean, you must connect to the postgres database initially; the script will attempt to drop other databases immediately, and that will fail for the database you are connected to. pg_isready — check the connection status of a IvorySQL server pg_isready [connection-option…​] [option…​] -d dbname --dbname=dbname Specifies the name of the database to connect to. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -h hostname --host=hostname Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix-domain socket. -p port --port=port Specifies the TCP port or the local Unix-domain socket file extension on which the server is listening for connections. Defaults to the value of the PGPORT environment variable or, if not set, to the port specified at compile time, usually 5432. -q --quiet Do not display status message. This is useful when scripting. -t seconds --timeout=seconds The maximum number of seconds to wait when attempting connection before returning that the server is not responding. Setting to 0 disables. The default is 3 seconds. -U username --username=username Connect to the database as the user username instead of the default. -V --version Print the pg_isready version and exit. -? --help Show help about pg_isready command line arguments, and exit. pg_isready returns 0 to the shell if the server is accepting connections normally, 1 if the server is rejecting connections (for example during startup), 2 if there was no response to the connection attempt, and 3 if no attempt was made (for example due to invalid parameters). pg_isready, like most other IvorySQL utilities, also uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. It is not necessary to supply correct user name, password, or database name values to obtain the server status; however, if incorrect values are provided, the server will log a failed connection attempt. Standard Usage: $ pg_isready /tmp:5432 - accepting connections $ echo $? 0 Running with connection parameters to a IvorySQL cluster in startup: $ pg_isready -h localhost -p 5433 localhost:5433 - rejecting connections $ echo $? 1 Running with connection parameters to a non-responsive IvorySQL cluster: $ pg_isready -h someremotehost someremotehost:5432 - no response $ echo $? 2 pg_receivewal — stream write-ahead logs from a IvorySQL server pg_receivewal [option…​] -D directory --directory=directory Directory to write the output to.This parameter is required. -E lsn --endpos=lsn Automatically stop replication and exit with normal exit status 0 when receiving reaches the specified LSN.If there is a record with LSN exactly equal to lsn, the record will be processed. --if-not-exists Do not error out when --create-slot is specified and a slot with the specified name already exists. -n --no-loop Don’t loop on connection errors. Instead, exit right away with an error. --no-sync This option causes pg_receivewal to not force WAL data to be flushed to disk. This is faster, but means that a subsequent operating system crash can leave the WAL segments corrupt. Generally, this option is useful for testing but should not be used when doing WAL archiving on a production deployment.This option is incompatible with --synchronous. -s interval --status-interval=interval Specifies the number of seconds between status packets sent back to the server. This allows for easier monitoring of the progress from server. A value of zero disables the periodic status updates completely, although an update will still be sent when requested by the server, to avoid timeout disconnect. The default value is 10 seconds. -S slotname --slot=slotname Require pg_receivewal to use an existing replication slot, When this option is used, pg_receivewal will report a flush position to the server, indicating when each segment has been synchronized to disk so that the server can remove that segment if it is not otherwise needed.When the replication client of pg_receivewal is configured on the server as a synchronous standby, then using a replication slot will report the flush position to the server, but only when a WAL file is closed. Therefore, that configuration will cause transactions on the primary to wait for a long time and effectively not work satisfactorily. The option --synchronous (see below) must be specified in addition to make this work correctly. --synchronous Flush the WAL data to disk immediately after it has been received. Also send a status packet back to the server immediately after flushing, regardless of --status-interval.This option should be specified if the replication client of pg_receivewal is configured on the server as a synchronous standby, to ensure that timely feedback is sent to the server. -v --verbose Enables verbose mode. -Z level -Z method[:*detail*] --compress=level --compress=method[:*detail*] Enables compression of write-ahead logs.The compression method can be set to gzip, lz4 (if IvorySQL was compiled with --with-lz4) or none for no compression. A compression detail string can optionally be specified. If the detail string is an integer, it specifies the compression level. Otherwise, it should be a comma-separated list of items, each of the form keyword or keyword=value. Currently, the only supported keyword is level.If no compression level is specified, the default compression level will be used. If only a level is specified without mentioning an algorithm, gzip compression will be used if the level is greater than 0, and no compression will be used if the level is 0.The suffix .gz will automatically be added to all filenames when using gzip, and the suffix .lz4 is added when using lz4. The following command-line options control the database connection parameters. -d connstr --dbname=connstr Specifies parameters used to connect to the server, as a connection string; these will override any conflicting command line options.The option is called --dbname for consistency with other client applications, but because pg_receivewal doesn’t connect to any particular database in the cluster, database name in the connection string will be ignored. -h `host` --host=`host` Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U `username` --username=`username` User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_receivewal to prompt for a password before connecting to a database.This option is never essential, since pg_receivewal will automatically prompt for a password if the server demands password authentication. However, pg_receivewal will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. pg_receivewal can perform one of the two following actions in order to control physical replication slots: --create-slot Create a new physical replication slot with the name specified in --slot, then exit. --drop-slot Drop the replication slot with the name specified in --slot, then exit. Other options are also available: -V --version Print the pg_receivewal version and exit. -? --help Show help about pg_receivewal command line arguments, and exit. pg_receivewal will exit with status 0 when terminated by the SIGINT signal. (That is the normal way to end it. Hence it is not an error.) For fatal errors or other signals, the exit status will be nonzero. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. When using pg_receivewal instead of archive_command as the main WAL backup method, it is strongly recommended to use replication slots. Otherwise, the server is free to recycle or remove write-ahead log files before they are backed up, because it does not have any information, either from archive_command or the replication slots, about how far the WAL stream has been archived. Note, however, that a replication slot will fill up the server’s disk space if the receiver does not keep up with fetching the WAL data. pg_receivewal will preserve group permissions on the received WAL files if group permissions are enabled on the source cluster. To stream the write-ahead log from the server at mydbserver and store it in the local directory /usr/local/pgsql/archive: $ pg_receivewal -h mydbserver -D /usr/local/pgsql/archive pg_recvlogical — control IvorySQL logical decoding streams pg_recvlogical [option…​] At least one of the following options must be specified to select an action: --create-slot Create a new logical replication slot with the name specified by --slot, using the output plugin specified by --plugin, for the database specified by --dbname.The --two-phase can be specified with --create-slot to enable decoding of prepared transactions. --drop-slot Drop the replication slot with the name specified by --slot, then exit. --start Begin streaming changes from the logical replication slot specified by --slot, continuing until terminated by a signal. If the server side change stream ends with a server shutdown or disconnect, retry in a loop unless --no-loop is specified.The stream format is determined by the output plugin specified when the slot was created.The connection must be to the same database used to create the slot. --create-slot and --start can be specified together. --drop-slot cannot be combined with another action. The following command-line options control the location and format of the output and other replication behavior: -E lsn --endpos=lsn In --start mode, automatically stop replication and exit with normal exit status 0 when receiving reaches the specified LSN. If specified when not in --start mode, an error is raised.If there’s a record with LSN exactly equal to lsn, the record will be output.The --endpos option is not aware of transaction boundaries and may truncate output partway through a transaction. Any partially output transaction will not be consumed and will be replayed again when the slot is next read from. Individual messages are never truncated. -f filename --file=filename Write received and decoded transaction data into this file. Use - for stdout. -F interval_seconds --fsync-interval=interval_seconds Specifies how often pg_recvlogical should issue fsync() calls to ensure the output file is safely flushed to disk.The server will occasionally request the client to perform a flush and report the flush position to the server. This setting is in addition to that, to perform flushes more frequently.Specifying an interval of 0 disables issuing fsync() calls altogether, while still reporting progress to the server. In this case, data could be lost in the event of a crash. -I lsn --startpos=lsn In --start mode, start replication from the given LSN. For details on the effect of this. --if-not-exists Do not error out when --create-slot is specified and a slot with the specified name already exists. -n --no-loop When the connection to the server is lost, do not retry in a loop, just exit. -o name[=value] --option=name[=value] Pass the option name to the output plugin with, if specified, the option value value. Which options exist and their effects depends on the used output plugin. -P plugin --plugin=plugin When creating a slot, use the specified logical decoding output plugin. This option has no effect if the slot already exists. -s interval_seconds --status-interval=interval_seconds This option has the same effect as the option of the same name in pg_receivewal. See the description there. -S slot_name --slot=slot_name In --start mode, use the existing logical replication slot named slot_name. In --create-slot mode, create the slot with this name. In --drop-slot mode, delete the slot with this name. -t --two-phase Enables decoding of prepared transactions. This option may only be specified with --create-slot -v --verbose Enables verbose mode. The following command-line options control the database connection parameters. -d dbname --dbname=dbname The database to connect to. See the description of the actions for what this means in detail. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. Defaults to the user name. -h hostname-or-ip --host=hostname-or-ip Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U user --username=user User name to connect as. Defaults to current operating system user name. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_recvlogical to prompt for a password before connecting to a database.This option is never essential, since pg_recvlogical will automatically prompt for a password if the server demands password authentication. However, pg_recvlogical will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. The following additional options are available: -V --version Print the pg_recvlogical version and exit. -? --help Show help about pg_recvlogical command line arguments, and exit. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_recvlogical will preserve group permissions on the received WAL files if group permissions are enabled on the source cluster. pg_restore — restore a IvorySQL database from an archive file created by pg_dump pg_restore [connection-option…​] [option…​] [filename] pg_restore accepts the following command line arguments. filename Specifies the location of the archive file (or directory, for a directory-format archive) to be restored. If not specified, the standard input is used. -a --data-only Restore only the data, not the schema (data definitions). Table data, large objects, and sequence values are restored, if present in the archive.This option is similar to, but for historical reasons not identical to, specifying --section=data. -c --clean Clean (drop) database objects before recreating them. (Unless --if-exists is used, this might generate some harmless error messages, if any objects were not present in the destination database.) -C --create Create the database before restoring into it. If --clean is also specified, drop and recreate the target database before connecting to it.With --create, pg_restore also restores the database’s comment if any, and any configuration variable settings that are specific to this database, that is, any ALTER DATABASE …​ SET …​ and ALTER ROLE …​ IN DATABASE …​ SET …​ commands that mention this database. Access privileges for the database itself are also restored, unless --no-acl is specified.When this option is used, the database named with -d is used only to issue the initial DROP DATABASE and CREATE DATABASE commands. All data is restored into the database name that appears in the archive. -d dbname --dbname=dbname Connect to database dbname and restore directly into the database. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --exit-on-error Exit if an error is encountered while sending SQL commands to the database. The default is to continue and to display a count of errors at the end of the restoration. -f `filename` --file=`filename` Specify output file for generated script, or for the listing when used with -l. Use - for stdout. -F format --format=format Specify format of the archive. It is not necessary to specify the format, since pg_restore will determine the format automatically. If specified, it can be one of the following:`c` custom`The archive is in the custom format of pg_dump.`d directory`The archive is a directory archive.`t tar`The archive is a `tar archive. -I index --index=index Restore definition of named index only. Multiple indexes may be specified with multiple -I switches. -j number-of-jobs --jobs=number-of-jobs Run the most time-consuming steps of pg_restore — those that load data, create indexes, or create constraints — concurrently, using up to number-of-jobs concurrent sessions. This option can dramatically reduce the time to restore a large database to a server running on a multiprocessor machine. This option is ignored when emitting a script rather than connecting directly to a database server.Each job is one process or one thread, depending on the operating system, and uses a separate connection to the server.The optimal value for this option depends on the hardware setup of the server, of the client, and of the network. Factors include the number of CPU cores and the disk setup. A good place to start is the number of CPU cores on the server, but values larger than that can also lead to faster restore times in many cases. Of course, values that are too high will lead to decreased performance because of thrashing.Only the custom and directory archive formats are supported with this option. The input must be a regular file or directory (not, for example, a pipe or standard input). Also, multiple jobs cannot be used together with the option --single-transaction. -l --list List the table of contents of the archive. The output of this operation can be used as input to the -L option. Note that if filtering switches such as -n or -t are used with -l, they will restrict the items listed. -L list-file --use-list=list-file Restore only those archive elements that are listed in list-file, and restore them in the order they appear in the file. Note that if filtering switches such as -n or -t are used with -L, they will further restrict the items restored.list-file is normally created by editing the output of a previous -l operation. Lines can be moved or removed, and can also be commented out by placing a semicolon (;) at the start of the line. See below for examples. -n schema --schema=schema Restore only objects that are in the named schema. Multiple schemas may be specified with multiple -n switches. This can be combined with the -t option to restore just a specific table. -N schema --exclude-schema=schema Do not restore objects that are in the named schema. Multiple schemas to be excluded may be specified with multiple -N switches.When both -n and -N are given for the same schema name, the -N switch wins and the schema is excluded. -O --no-owner Do not output commands to set ownership of objects to match the original database. By default, pg_restore issues ALTER OWNER or SET SESSION AUTHORIZATION statements to set ownership of created schema elements. These statements will fail unless the initial connection to the database is made by a superuser (or the same user that owns all of the objects in the script). With -O, any user name can be used for the initial connection, and this user will own all the created objects. -P function-name(argtype [, …​]) --function=function-name(argtype [, …​]) Restore the named function only. Be careful to spell the function name and arguments exactly as they appear in the dump file’s table of contents. Multiple functions may be specified with multiple -P switches. -R --no-reconnect This option is obsolete but still accepted for backwards compatibility. -s --schema-only Restore only the schema (data definitions), not data, to the extent that schema entries are present in the archive.This option is the inverse of --data-only. It is similar to, but for historical reasons not identical to, specifying --section=pre-data --section=post-data.(Do not confuse this with the --schema option, which uses the word “schema” in a different meaning.) -S username --superuser=username Specify the superuser user name to use when disabling triggers. This is relevant only if --disable-triggers is used. -t table --table=table Restore definition and/or data of only the named table. For this purpose, “table” includes views, materialized views, sequences, and foreign tables. Multiple tables can be selected by writing multiple -t switches. This option can be combined with the -n option to specify table(s) in a particular schema.NoteWhen -t is specified, pg_restore makes no attempt to restore any other database objects that the selected table(s) might depend upon. Therefore, there is no guarantee that a specific-table restore into a clean database will succeed.NoteThis flag does not behave identically to the -t flag of pg_dump. There is not currently any provision for wild-card matching in pg_restore, nor can you include a schema name within its -t. And, while pg_dump’s -t flag will also dump subsidiary objects (such as indexes) of the selected table(s), pg_restore’s -t flag does not include such subsidiary objects. -T trigger --trigger=trigger Restore named trigger only. Multiple triggers may be specified with multiple -T switches. -v --verbose Specifies verbose mode. This will cause pg_restore to output detailed object comments and start/stop times to the output file, and progress messages to standard error. Repeating the option causes additional debug-level messages to appear on standard error. -V --version Print the pg_restore version and exit. -x --no-privileges --no-acl Prevent restoration of access privileges (grant/revoke commands). -1 --single-transaction Execute the restore as a single transaction (that is, wrap the emitted commands in BEGIN / COMMIT). This ensures that either all the commands complete successfully, or no changes are applied. This option implies --exit-on-error. --disable-triggers This option is relevant only when performing a data-only restore. It instructs pg_restore to execute commands to temporarily disable triggers on the target tables while the data is restored. Use this if you have referential integrity checks or other triggers on the tables that you do not want to invoke during data restore.Presently, the commands emitted for --disable-triggers must be done as superuser. So you should also specify a superuser name with -S or, preferably, run pg_restore as a IvorySQL superuser. --enable-row-security This option is relevant only when restoring the contents of a table which has row security. By default, pg_restore will set row_security to off, to ensure that all data is restored in to the table. If the user does not have sufficient privileges to bypass row security, then an error is thrown. This parameter instructs pg_restore to set row_security to on instead, allowing the user to attempt to restore the contents of the table with row security enabled. This might still fail if the user does not have the right to insert the rows from the dump into the table.Note that this option currently also requires the dump be in INSERT format, as COPY FROM does not support row security. --if-exists Use conditional commands (i.e., add an IF EXISTS clause) to drop database objects. This option is not valid unless --clean is also specified. --no-comments Do not output commands to restore comments, even if the archive contains them. --no-data-for-failed-tables By default, table data is restored even if the creation command for the table failed (e.g., because it already exists). With this option, data for such a table is skipped. This behavior is useful if the target database already contains the desired table contents. For example, auxiliary tables for IvorySQL extensions such as PostGIS might already be loaded in the target database; specifying this option prevents duplicate or obsolete data from being loaded into them.This option is effective only when restoring directly into a database, not when producing SQL script output. --no-publications Do not output commands to restore publications, even if the archive contains them. --no-security-labels Do not output commands to restore security labels, even if the archive contains them. --no-subscriptions Do not output commands to restore subscriptions, even if the archive contains them. --no-table-access-method Do not output commands to select table access methods. With this option, all objects will be created with whichever access method is the default during restore. --no-tablespaces Do not output commands to select tablespaces. With this option, all objects will be created in whichever tablespace is the default during restore. --section=`sectionname` Only restore the named section. The section name can be pre-data, data, or post-data. This option can be specified more than once to select multiple sections. The default is to restore all sections.The data section contains actual table data as well as large-object definitions. Post-data items consist of definitions of indexes, triggers, rules and constraints other than validated check constraints. Pre-data items consist of all other data definition items. --strict-names Require that each schema (-n/--schema) and table (-t/--table) qualifier match at least one schema/table in the backup file. --use-set-session-authorization Output SQL-standard SET SESSION AUTHORIZATION commands instead of ALTER OWNER commands to determine object ownership. This makes the dump more standards-compatible, but depending on the history of the objects in the dump, might not restore properly. -? --help Show help about pg_restore command line arguments, and exit. pg_restore also accepts the following command line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_restore to prompt for a password before connecting to a database.This option is never essential, since pg_restore will automatically prompt for a password if the server demands password authentication. However, pg_restore will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --role=`rolename` Specifies a role name to be used to perform the restore. This option causes pg_restore to issue a SET ROLE rolename command after connecting to the database. It is useful when the authenticated user (specified by -U) lacks privileges needed by pg_restore, but can switch to a role with the required rights. Some installations have a policy against logging in directly as a superuser, and use of this option allows restores to be performed without violating the policy. PGHOST PGOPTIONS PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq. However, it does not read PGDATABASE when a database name is not supplied. When a direct database connection is specified using the -d option, pg_restore internally executes SQL statements. If you have problems running pg_restore, make sure you are able to select information from the database using, for example, psql. Also, any default connection settings and environment variables used by the libpq front-end library will apply. If your installation has any local additions to the template1 database, be careful to load the output of pg_restore into a truly empty database; otherwise you are likely to get errors due to duplicate definitions of the added objects. To make an empty database without any local additions, copy from template0 not template1, for example: CREATE DATABASE foo WITH TEMPLATE template0; The limitations of pg_restore are detailed below. When restoring data to a pre-existing table and the option --disable-triggers is used, pg_restore emits commands to disable triggers on user tables before inserting the data, then emits commands to re-enable them after the data has been inserted. If the restore is stopped in the middle, the system catalogs might be left in the wrong state. pg_restore cannot restore large objects selectively; for instance, only those for a specific table. If an archive contains large objects, then all large objects will be restored, or none of them if they are excluded via -L, -t, or other options. Assume we have dumped a database called mydb into a custom-format dump file: $ pg_dump -Fc mydb > db.dump To drop the database and recreate it from the dump: $ dropdb mydb $ pg_restore -C -d postgres db.dump The database named in the -d switch can be any database existing in the cluster; pg_restore only uses it to issue the CREATE DATABASE command for mydb. With -C, data is always restored into the database name that appears in the dump file. To restore the dump into a new database called newdb: $ createdb -T template0 newdb $ pg_restore -d newdb db.dump Notice we don’t use -C, and instead connect directly to the database to be restored into. Also note that we clone the new database from template0 not template1, to ensure it is initially empty. To reorder database items, it is first necessary to dump the table of contents of the archive: $ pg_restore -l db.dump > db.list The listing file consists of a header and one line for each item, e.g.: ; ; Archive created at Mon Sep 14 13:55:39 2009 ; dbname: DBDEMOS ; TOC Entries: 81 ; Compression: 9 ; Dump Version: 1.10-0 ; Format: CUSTOM ; Integer: 4 bytes ; Offset: 8 bytes ; Dumped from database version: 8.3.5 ; Dumped by pg_dump version: 8.3.8 ; ; ; Selected TOC Entries: ; 3; 2615 2200 SCHEMA - public pasha 1861; 0 0 COMMENT - SCHEMA public pasha 1862; 0 0 ACL - public pasha 317; 1247 17715 TYPE public composite pasha 319; 1247 25899 DOMAIN public domain0 pasha Semicolons start a comment, and the numbers at the start of lines refer to the internal archive ID assigned to each item. Lines in the file can be commented out, deleted, and reordered. For example: 10; 145433 TABLE map_resolutions postgres ;2; 145344 TABLE species postgres ;4; 145359 TABLE nt_header postgres 6; 145402 TABLE species_records postgres ;8; 145416 TABLE ss_old postgres could be used as input to pg_restore and would only restore items 10 and 6, in that order: $ pg_restore -L db.list db.dump pg_verifybackup — verify the integrity of a base backup of a IvorySQL cluster pg_verifybackup [option…​] pg_verifybackup accepts the following command-line arguments: -e --exit-on-error Exit as soon as a problem with the backup is detected. If this option is not specified, pg_verifybackup will continue checking the backup even after a problem has been detected, and will report all problems detected as errors. -i path --ignore=path Ignore the specified file or directory, which should be expressed as a relative path name, when comparing the list of data files actually present in the backup to those listed in the backup_manifest file. If a directory is specified, this option affects the entire subtree rooted at that location. Complaints about extra files, missing files, file size differences, or checksum mismatches will be suppressed if the relative path name matches the specified path name. This option can be specified multiple times. -m path --manifest-path=path Use the manifest file at the specified path, rather than one located in the root of the backup directory. -n --no-parse-wal Don’t attempt to parse write-ahead log data that will be needed to recover from this backup. -q --quiet Don’t print anything when a backup is successfully verified. -s --skip-checksums Do not verify data file checksums. The presence or absence of files and the sizes of those files will still be checked. This is much faster, because the files themselves do not need to be read. -w path --wal-directory=path Try to parse WAL files stored in the specified directory, rather than in pg_wal. This may be useful if the backup is stored in a separate location from the WAL archive. Other options are also available: -V --version Print the pg_verifybackup version and exit. -? --help Show help about pg_verifybackup command line arguments, and exit. To create a base backup of the server at mydbserver and verify the integrity of the backup: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ pg_verifybackup /usr/local/pgsql/data To create a base backup of the server at mydbserver, move the manifest somewhere outside the backup directory, and verify the backup: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/backup1234 $ mv /usr/local/pgsql/backup1234/backup_manifest /my/secure/location/backup_manifest.1234 $ pg_verifybackup -m /my/secure/location/backup_manifest.1234 /usr/local/pgsql/backup1234 To verify a backup while ignoring a file that was added manually to the backup directory, and also skipping checksum verification: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ edit /usr/local/pgsql/data/note.to.self $ pg_verifybackup --ignore=note.to.self --skip-checksums /usr/local/pgsql/data psql — IvorySQL interactive terminal psql [option…​] [dbname [username]] -a --echo-all Print all nonempty input lines to standard output as they are read. (This does not apply to lines read interactively.) This is equivalent to setting the variable ECHO to all. -A --no-align Switches to unaligned output mode. (The default output mode is aligned.) This is equivalent to \\pset format unaligned. -b --echo-errors Print failed SQL commands to standard error output. This is equivalent to setting the variable ECHO to errors. -c command --command=command Specifies that psql is to execute the given command string, command. This option can be repeated and combined in any order with the -f option. When either -c or -f is specified, psql does not read commands from standard input; instead it terminates after processing all the -c and -f options in sequence.command must be either a command string that is completely parsable by the server (i.e., it contains no psql-specific features), or a single backslash command. Thus you cannot mix SQL and psql meta-commands within a -c option. To achieve that, you could use repeated -c options or pipe the string into psql, for example:`psql -c '\\x' -c 'SELECT * FROM foo;' or`echo '\\x \\\\ SELECT * FROM foo;' | psql `(\\\\` is the separator meta-command.)Each SQL command string passed to -c is sent to the server as a single request. Because of this, the server executes it as a single transaction even if the string contains multiple SQL commands, unless there are explicit BEGIN/COMMIT commands included in the string to divide it into multiple transactions.If having several commands executed in one transaction is not desired, use repeated -c commands or feed multiple commands to psql’s standard input, either using echo as illustrated above, or via a shell here-document, for example:`psql <<EOF \\x SELECT * FROM foo; EOF ` --csv Switches to CSV (Comma-Separated Values) output mode. This is equivalent to \\pset format csv. -d dbname --dbname=dbname Specifies the name of the database to connect to. This is equivalent to specifying dbname as the first non-option argument on the command line. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --echo-queries Copy all SQL commands sent to the server to standard output as well. This is equivalent to setting the variable ECHO to queries. -E --echo-hidden Echo the actual queries generated by \\d and other backslash commands. You can use this to study psql’s internal operations. This is equivalent to setting the variable ECHO_HIDDEN to on. -f filename --file=filename Read commands from the file filename, rather than standard input. This option can be repeated and combined in any order with the -c option. When either -c or -f is specified, psql does not read commands from standard input; instead it terminates after processing all the -c and -f options in sequence. Except for that, this option is largely equivalent to the meta-command \\i.If filename is - (hyphen), then standard input is read until an EOF indication or \\q meta-command. This can be used to intersperse interactive input with input from files. Note however that Readline is not used in this case (much as if -n had been specified).Using this option is subtly different from writing psql < `filename. In general, both will do what you expect, but using `-f enables some nice features such as error messages with line numbers. There is also a slight chance that using this option will reduce the start-up overhead. On the other hand, the variant using the shell’s input redirection is (in theory) guaranteed to yield exactly the same output you would have received had you entered everything by hand. -F separator --field-separator=separator Use separator as the field separator for unaligned output. This is equivalent to \\pset fieldsep or \\f. -h hostname --host=hostname Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix-domain socket. -H --html Switches to HTML output mode. This is equivalent to \\pset format html or the \\H command. -l --list List all available databases, then exit. Other non-connection options are ignored. This is similar to the meta-command \\list.When this option is used, psql will connect to the database postgres, unless a different database is named on the command line (option -d or non-option argument, possibly via a service entry, but not via an environment variable). -L filename --log-file=filename Write all query output into file filename, in addition to the normal output destination. -n --no-readline Do not use Readline for line editing and do not use the command history -o filename --output=filename Put all query output into file filename. This is equivalent to the command \\o. -p port --port=port Specifies the TCP port or the local Unix-domain socket file extension on which the server is listening for connections. Defaults to the value of the PGPORT environment variable or, if not set, to the port specified at compile time, usually 5432. -P assignment --pset=assignment Specifies printing options, in the style of \\pset. Note that here you have to separate name and value with an equal sign instead of a space. For example, to set the output format to LaTeX, you could write -P format=latex. -q --quiet Specifies that psql should do its work quietly. By default, it prints welcome messages and various informational output. If this option is used, none of this happens. This is useful with the -c option. This is equivalent to setting the variable QUIET to on. -R separator --record-separator=separator Use separator as the record separator for unaligned output. This is equivalent to \\pset recordsep. -s --single-step Run in single-step mode. That means the user is prompted before each command is sent to the server, with the option to cancel execution as well. Use this to debug scripts. -S --single-line Runs in single-line mode where a newline terminates an SQL command, as a semicolon does.NoteThis mode is provided for those who insist on it, but you are not necessarily encouraged to use it. In particular, if you mix SQL and meta-commands on a line the order of execution might not always be clear to the inexperienced user. -t --tuples-only Turn off printing of column names and result row count footers, etc. This is equivalent to \\t or \\pset tuples_only. -T table_options --table-attr=table_options Specifies options to be placed within the HTML table tag. See \\pset tableattr for details. -U `username` --username=`username` Connect to the database as the user username instead of the default. (You must have permission to do so, of course.) -v assignment --set=assignment --variable=assignment Perform a variable assignment, like the \\set meta-command. Note that you must separate name and value, if any, by an equal sign on the command line. To unset a variable, leave off the equal sign. To set a variable with an empty value, use the equal sign but leave off the value. These assignments are done during command line processing, so variables that reflect connection state will get overwritten later. -V --version Print the psql version and exit. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available from other sources such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password.Note that this option will remain set for the entire session, and so it affects uses of the meta-command \\connect as well as the initial connection attempt. -W --password Force psql to prompt for a password before connecting to a database, even if the password will not be used.If the server requires password authentication and a password is not available from other sources such as a .pgpass file, psql will prompt for a password in any case. However, psql will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt.Note that this option will remain set for the entire session, and so it affects uses of the meta-command \\connect as well as the initial connection attempt. -x --expanded Turn on the expanded table formatting mode. This is equivalent to \\x or \\pset expanded. -X, --no-psqlrc Do not read the start-up file (neither the system-wide psqlrc file nor the user’s ~/.psqlrc file). -z --field-separator-zero Set the field separator for unaligned output to a zero byte. This is equivalent to \\pset fieldsep_zero. -0 --record-separator-zero Set the record separator for unaligned output to a zero byte. This is useful for interfacing, for example, with xargs -0. This is equivalent to \\pset recordsep_zero. -1 --single-transaction This option can only be used in combination with one or more -c and/or -f options. It causes psql to issue a BEGIN command before the first such option and a COMMIT command after the last one, thereby wrapping all the commands into a single transaction. If any of the commands fails and the variable ON_ERROR_STOP was set, a ROLLBACK command is sent instead. This ensures that either all the commands complete successfully, or no changes are applied.If the commands themselves contain BEGIN, COMMIT, or ROLLBACK, this option will not have the desired effects. Also, if an individual command cannot be executed inside a transaction block, specifying this option will cause the whole transaction to fail. -? --help[=`topic]` Show help about psql and exit. The optional topic parameter (defaulting to options) selects which part of psql is explained: commands describes psql’s backslash commands; options describes the command-line options that can be passed to psql; and variables shows help about psql configuration variables. psql returns 0 to the shell if it finished normally, 1 if a fatal error of its own occurs (e.g., out of memory, file not found), 2 if the connection to the server went bad and the session was not interactive, and 3 if an error occurred in a script and the variable ON_ERROR_STOP was set. reindexdb — reindex a IvorySQL database reindexdb [connection-option…​] [option…​] [ -S | --schema schema ] …​ [ -t | --table table ] …​ [ -i | --index index ] …​ [dbname] reindexdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all reindexdb [connection-option…​] [option…​] -s | --system [dbname] reindexdb accepts the following command-line arguments: -a --all Reindex all databases. --concurrently Use the CONCURRENTLY option. See REINDEX, where all the caveats of this option are explained in detail. [-d] dbname dbname Specifies the name of the database to be reindexed, when -a/--all is not used. If this is not specified, the database name is read from the environment variable PGDATABASE. If that is not set, the user name specified for the connection is used. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --echo Echo the commands that reindexdb generates and sends to the server. -i index --index=index Recreate index only. Multiple indexes can be recreated by writing multiple -i switches. -j njobs --jobs=njobs Execute the reindex commands in parallel by running njobs commands simultaneously. This option may reduce the processing time but it also increases the load on the database server.reindexdb will open njobs connections to the database, so make sure your max_connections setting is high enough to accommodate all connections.Note that this option is incompatible with the --index and --system options. -q --quiet Do not display progress messages. -s --system Reindex database’s system catalogs only. -S schema --schema=schema Reindex schema only. Multiple schemas can be reindexed by writing multiple -S switches. -t table --table=table Reindex table only. Multiple tables can be reindexed by writing multiple -t switches. --tablespace=tablespace Specifies the tablespace where indexes are rebuilt. (This name is processed as a double-quoted identifier.) -v --verbose Print detailed information during processing. -V --version Print the reindexdb version and exit. -? --help Show help about reindexdb command line arguments, and exit. reindexdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force reindexdb to prompt for a password before connecting to a database.This option is never essential, since reindexdb will automatically prompt for a password if the server demands password authentication. However, reindexdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to to discover which databases should be reindexed, when -a / --all is used. If not specified, the postgres database will be used, or if that does not exist, template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. Also, connection string parameters other than the database name itself will be re-used when connecting to other databases. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq . In case of difficulty, see REINDEX and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. reindexdb might need to connect several times to the IvorySQL server, asking for a password each time. It is convenient to have a ~/.pgpass file in such cases. To reindex the database test: $ reindexdb test To reindex the table foo and the index bar in a database named abcd: $ reindexdb --table=foo --index=bar abcd vacuumdb — garbage-collect and analyze a IvorySQL database vacuumdb [connection-option…​] [option…​] [ -t | --table table [( column [,…​] )] ] …​ [dbname] vacuumdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all vacuumdb accepts the following command-line arguments: -a --all Vacuum all databases. [-d] `dbname` `dbname` Specifies the name of the database to be cleaned or analyzed, when -a/--all is not used. If this is not specified, the database name is read from the environment variable PGDATABASE. If that is not set, the user name specified for the connection is used. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. --disable-page-skipping Disable skipping pages based on the contents of the visibility map. -e --echo Echo the commands that vacuumdb generates and sends to the server. -f --full Perform “full” vacuuming. -F --freeze Aggressively “freeze” tuples. --force-index-cleanup Always remove index entries pointing to dead tuples. -j njobs --jobs=njobs Execute the vacuum or analyze commands in parallel by running njobs commands simultaneously. This option may reduce the processing time but it also increases the load on the database server.vacuumdb will open njobs connections to the database, so make sure your max_connections setting is high enough to accommodate all connections.Note that using this mode together with the -f (FULL) option might cause deadlock failures if certain system catalogs are processed in parallel. --min-mxid-age mxid_age Only execute the vacuum or analyze commands on tables with a multixact ID age of at least mxid_age. This setting is useful for prioritizing tables to process to prevent multixact ID wraparound .For the purposes of this option, the multixact ID age of a relation is the greatest of the ages of the main relation and its associated TOAST table, if one exists. Since the commands issued by vacuumdb will also process the TOAST table for the relation if necessary, it does not need to be considered separately. --min-xid-age xid_age Only execute the vacuum or analyze commands on tables with a transaction ID age of at least xid_age. This setting is useful for prioritizing tables to process to prevent transaction ID wraparound.For the purposes of this option, the transaction ID age of a relation is the greatest of the ages of the main relation and its associated TOAST table, if one exists. Since the commands issued by vacuumdb will also process the TOAST table for the relation if necessary, it does not need to be considered separately. --no-index-cleanup Do not remove index entries pointing to dead tuples. --no-process-toast Skip the TOAST table associated with the table to vacuum, if any. --no-truncate Do not truncate empty pages at the end of the table. -P parallel_workers --parallel=parallel_workers Specify the number of parallel workers for parallel vacuum. This allows the vacuum to leverage multiple CPUs to process indexes. See VACUUM. -q --quiet Do not display progress messages. --skip-locked Skip relations that cannot be immediately locked for processing -t `table [ (column [,…​]) ]` --table=`table [ (column [,…​]) ]` Clean or analyze table only. Column names can be specified only in conjunction with the --analyze or --analyze-only options. Multiple tables can be vacuumed by writing multiple -t switches.TipIf you specify columns, you probably have to escape the parentheses from the shell. (See examples below.) -v --verbose Print detailed information during processing. -V --version Print the vacuumdb version and exit. -z --analyze Also calculate statistics for use by the optimizer. -Z --analyze-only Only calculate statistics for use by the optimizer (no vacuum). --analyze-in-stages Only calculate statistics for use by the optimizer (no vacuum), like --analyze-only. Run three stages of analyze; the first stage uses the lowest possible statistics target and subsequent stages build the full statistics.This option is only useful to analyze a database that currently has no statistics or has wholly incorrect ones, such as if it is newly populated from a restored dump or by pg_upgrade. Be aware that running with this option in a database with existing statistics may cause the query optimizer choices to become transiently worse due to the low statistics targets of the early stages. -? --help Show help about vacuumdb command line arguments, and exit. vacuumdb also accepts the following command-line arguments for connection parameters: -h `host` --host=`host` Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U `username` --username=`username` User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force vacuumdb to prompt for a password before connecting to a database.This option is never essential, since vacuumdb will automatically prompt for a password if the server demands password authentication. However, vacuumdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to to discover which databases should be vacuumed, when -a / --all is used. If not specified, the postgres database will be used, or if that does not exist, template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. Also, connection string parameters other than the database name itself will be re-used when connecting to other databases. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq. In case of difficulty, see VACUUM and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. vacuumdb might need to connect several times to the IvorySQL server, asking for a password each time. It is convenient to have a ~/.pgpass file in such cases. To clean the database test: $ vacuumdb test To clean and analyze for the optimizer a database named bigdb: $ vacuumdb --analyze bigdb To clean a single table foo in a database named xyzzy, and analyze a single column bar of the table for the optimizer: $ vacuumdb --analyze --verbose --table='foo(bar)' xyzzy initdb — create a new IvorySQL database cluster initdb [option…​] [ --pgdata | -D ] directory -A authmethod --auth=authmethod This option specifies the default authentication method for local users used in pg_hba.conf (host and local lines). Do not use trust unless you trust all local users on your system. trust is the default for ease of installation. --auth-host=`authmethod` This option specifies the authentication method for local users via TCP/IP connections used in pg_hba.conf (host lines). --auth-local=`authmethod` This option specifies the authentication method for local users via Unix-domain socket connections used in pg_hba.conf (local lines). -D directory --pgdata=directory This option specifies the directory where the database cluster should be stored. This is the only information required by initdb, but you can avoid writing it by setting the PGDATA environment variable, which can be convenient since the database server (postgres) can find the database directory later by the same variable. -E `encoding` --encoding=`encoding` Selects the encoding of the template databases. This will also be the default encoding of any database you create later, unless you override it then. The default is derived from the locale, if the libc locale provider is used, or UTF8 if the ICU locale provider is used. -g --allow-group-access Allows users in the same group as the cluster owner to read all cluster files created by initdb. This option is ignored on Windows as it does not support POSIX-style group permissions. --icu-locale=locale Specifies the ICU locale ID, if the ICU locale provider is used. -k --data-checksums Use checksums on data pages to help detect corruption by the I/O system that would otherwise be silent. Enabling checksums may incur a noticeable performance penalty. If set, checksums are calculated for all objects, in all databases. All checksum failures will be reported in the pg_stat_database view. --locale=locale Sets the default locale for the database cluster. If this option is not specified, the locale is inherited from the environment that initdb runs in. --lc-collate=locale --lc-ctype=locale --lc-messages=locale --lc-monetary=locale --lc-numeric=locale --lc-time=locale Like --locale, but only sets the locale in the specified category. --no-locale Equivalent to --locale=C. --locale-provider={libc|icu} This option sets the locale provider for databases created in the new cluster. It can be overridden in the CREATE DATABASE command when new databases are subsequently created. The default is libc. -N --no-sync By default, initdb will wait for all files to be written safely to disk. This option causes initdb to return without waiting, which is faster, but means that a subsequent operating system crash can leave the data directory corrupt. Generally, this option is useful for testing, but should not be used when creating a production installation. --no-instructions By default, initdb will write instructions for how to start the cluster at the end of its output. This option causes those instructions to be left out. This is primarily intended for use by tools that wrap initdb in platform-specific behavior, where those instructions are likely to be incorrect. --pwfile=filename Makes initdb read the database superuser’s password from a file. The first line of the file is taken as the password. -S --sync-only Safely write all database files to disk and exit. This does not perform any of the normal initdb operations. -T config --text-search-config=config Sets the default text search configuration. -U username --username=username Selects the user name of the database superuser. This defaults to the name of the effective user running initdb. It is really not important what the superuser’s name is, but one might choose to keep the customary name postgres, even if the operating system user’s name is different. -W --pwprompt Makes initdb prompt for a password to give the database superuser. If you don’t plan on using password authentication, this is not important. Otherwise you won’t be able to use password authentication until you have a password set up. -X directory --waldir=directory This option specifies the directory where the write-ahead log should be stored. --wal-segsize=size Set the WAL segment size, in megabytes. This is the size of each individual file in the WAL log. The default size is 16 megabytes. The value must be a power of 2 between 1 and 1024 (megabytes). This option can only be set during initialization, and cannot be changed later.It may be useful to adjust this size to control the granularity of WAL log shipping or archiving. Also, in databases with a high volume of WAL, the sheer number of WAL files per directory can become a performance and management problem. Increasing the WAL file size will reduce the number of WAL files. Other, less commonly used, options are also available: -d --debug Print debugging output from the bootstrap backend and a few other messages of lesser interest for the general public. The bootstrap backend is the program initdb uses to create the catalog tables. This option generates a tremendous amount of extremely boring output. --discard-caches Run the bootstrap backend with the debug_discard_caches=1 option. This takes a very long time and is only of use for deep debugging. -L directory Specifies where initdb should find its input files to initialize the database cluster. This is normally not necessary. You will be told if you need to specify their location explicitly. -n --no-clean By default, when initdb determines that an error prevented it from completely creating the database cluster, it removes any files it might have created before discovering that it cannot finish the job. This option inhibits tidying-up and is thus useful for debugging. Other options: -V --version Print the initdb version and exit. -? --help Show help about initdb command line arguments, and exit. PGDATA Specifies the directory where the database cluster is to be stored; can be overridden using the -D option. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. TZ Specifies the default time zone of the created database cluster. The value should be a full time zone name This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq initdb can also be invoked via pg_ctl initdb. pg_archivecleanup — clean up IvorySQL WAL archive files pg_archivecleanup [option…​] archivelocation oldestkeptwalfile pg_archivecleanup accepts the following command-line arguments: -d Print lots of debug logging output on stderr. -n Print the names of the files that would have been removed on stdout (performs a dry run). -V --version Print the pg_archivecleanup version and exit. -x extension Provide an extension that will be stripped from all file names before deciding if they should be deleted. This is typically useful for cleaning up archives that have been compressed during storage, and therefore have had an extension added by the compression program. For example: -x .gz. -? --help Show help about pg_archivecleanup command line arguments, and exit. The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. On Linux or Unix systems, you might use: archive_cleanup_command = 'pg_archivecleanup -d /mnt/standby/archive %r 2>>cleanup.log' where the archive directory is physically located on the standby server, so that the archive_command is accessing it across NFS, but the files are local to the standby. This will: produce debugging output in cleanup.log remove no-longer-needed files from the archive directory pg_checksums — enable, disable or check data checksums in a IvorySQL database cluster pg_checksums [option…​] [[ -D | --pgdata ] datadir] The following command-line options are available: -D directory --pgdata=directory Specifies the directory where the database cluster is stored. -c --check Checks checksums. This is the default mode if nothing else is specified. -d --disable Disables checksums. -e --enable Enables checksums. -f filenode --filenode=filenode Only validate checksums in the relation with filenode filenode. -N --no-sync By default, pg_checksums will wait for all files to be written safely to disk. This option causes pg_checksums to return without waiting, which is faster, but means that a subsequent operating system crash can leave the updated data directory corrupt. Generally, this option is useful for testing but should not be used on a production installation. This option has no effect when using --check. -P --progress Enable progress reporting. Turning this on will deliver a progress report while checking or enabling checksums. -v --verbose Enable verbose output. Lists all checked files. -V --version Print the pg_checksums version and exit. -? --help Show help about pg_checksums command line arguments, and exit. PGDATA Specifies the directory where the database cluster is stored; can be overridden using the -D option. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. Enabling checksums in a large cluster can potentially take a long time. During this operation, the cluster or other programs that write to the data directory must not be started or else data loss may occur. When using a replication setup with tools which perform direct copies of relation file blocks (for example pg_rewind), enabling or disabling checksums can lead to page corruptions in the shape of incorrect checksums if the operation is not done consistently across all nodes. When enabling or disabling checksums in a replication setup, it is thus recommended to stop all the clusters before switching them all consistently. Destroying all standbys, performing the operation on the primary and finally recreating the standbys from scratch is also safe. If pg_checksums is aborted or killed while enabling or disabling checksums, the cluster’s data checksum configuration remains unchanged, and pg_checksums can be re-run to perform the same operation. pg_controldata — display control information of a IvorySQL database cluster pg_controldata [option] [[ -D | --pgdata ] datadir] PGDATA Default data directory location PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_ctl — initialize, start, stop, or control a IvorySQL server pg_ctl init[db] [-D datadir] [-s] [-o initdb-options] pg_ctl start [-D datadir] [-l filename] [-W] [-t seconds] [-s] [-o options] [-p path] [-c] pg_ctl stop [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] pg_ctl restart [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] [-o options] [-c] pg_ctl reload [-D datadir] [-s] pg_ctl status [-D datadir] pg_ctl promote [-D datadir] [-W] [-t seconds] [-s] pg_ctl logrotate [-D datadir] [-s] pg_ctl kill signal_name process_id On Microsoft Windows, also: pg_ctl register [-D datadir] [-N servicename] [-U username] [-P password] [-S a[uto] | d[emand] ] [-e source] [-W] [-t seconds] [-s] [-o options] pg_ctl unregister [-N servicename] -c --core-files Attempt to allow server crashes to produce core files, on platforms where this is possible, by lifting any soft resource limit placed on core files. This is useful in debugging or diagnosing problems by allowing a stack trace to be obtained from a failed server process. -D datadir --pgdata=datadir Specifies the file system location of the database configuration files. If this option is omitted, the environment variable PGDATA is used. -l filename --log=filename Append the server log output to filename. If the file does not exist, it is created. The umask is set to 077, so access to the log file is disallowed to other users by default. -m mode --mode=mode Specifies the shutdown mode. mode can be smart, fast, or immediate, or the first letter of one of these three. If this option is omitted, fast is the default. -o options --options=options Specifies options to be passed directly to the postgres command. -o can be specified multiple times, with all the given options being passed through.The options should usually be surrounded by single or double quotes to ensure that they are passed through as a group. -o initdb-options --options=initdb-options Specifies options to be passed directly to the initdb command. -o can be specified multiple times, with all the given options being passed through.The initdb-options should usually be surrounded by single or double quotes to ensure that they are passed through as a group. -p path Specifies the location of the postgres executable. By default the postgres executable is taken from the same directory as pg_ctl, or failing that, the hard-wired installation directory. It is not necessary to use this option unless you are doing something unusual and get errors that the postgres executable was not found.In init mode, this option analogously specifies the location of the initdb executable. -s --silent Print only errors, no informational messages. -t seconds --timeout=seconds Specifies the maximum number of seconds to wait when waiting for an operation to complete (see option -w). Defaults to the value of the PGCTLTIMEOUT environment variable or, if not set, to 60 seconds. -V --version Print the pg_ctl version and exit. -w --wait Wait for the operation to complete. This is supported for the modes start, stop, restart, promote, and register, and is the default for those modes.When waiting, pg_ctl repeatedly checks the server’s PID file, sleeping for a short amount of time between checks. Startup is considered complete when the PID file indicates that the server is ready to accept connections. Shutdown is considered complete when the server removes the PID file. pg_ctl returns an exit code based on the success of the startup or shutdown.If the operation does not complete within the timeout (see option -t), then pg_ctl exits with a nonzero exit status. But note that the operation might continue in the background and eventually succeed. -W --no-wait Do not wait for the operation to complete. This is the opposite of the option -w.If waiting is disabled, the requested action is triggered, but there is no feedback about its success. In that case, the server log file or an external monitoring system would have to be used to check the progress and success of the operation.In prior releases of IvorySQL, this was the default except for the stop mode. -? --help Show help about pg_ctl command line arguments, and exit. If an option is specified that is valid, but not relevant to the selected operating mode, pg_ctl ignores it. -e source Name of the event source for pg_ctl to use for logging to the event log when running as a Windows service. The default is IvorySQL. Note that this only controls messages sent from pg_ctl itself; once started, the server will use the event source specified by its event_source parameter. Should the server fail very early in startup, before that parameter has been set, it might also log using the default event source name IvorySQL. -N `servicename` Name of the system service to register. This name will be used as both the service name and the display name. The default is IvorySQL. -P `password` Password for the user to run the service as. -S start-type Start type of the system service. start-type can be auto, or demand, or the first letter of one of these two. If this option is omitted, auto is the default. -U username User name for the user to run the service as. For domain users, use the format DOMAIN\\username. PGCTLTIMEOUT Default limit on the number of seconds to wait when waiting for startup or shutdown to complete. If not set, the default is 60 seconds. PGDATA Default data directory location. Most pg_ctl modes require knowing the data directory location; therefore, the -D option is required unless PGDATA is set. pg_ctl, like most other IvorySQL utilities, also uses the environment variables supported by libpq. postmaster.pid pg_ctl examines this file in the data directory to determine whether the server is currently running. postmaster.opts If this file exists in the data directory, pg_ctl (in restart mode) will pass the contents of the file as options to postgres, unless overridden by the -o option. The contents of this file are also displayed in status mode. To start the server, waiting until the server is accepting connections: $ pg_ctl start To start the server using port 5433, and running without fsync, use: $ pg_ctl -o \"-F -p 5433\" start To stop the server, use: $ pg_ctl stop The -m option allows control over how the server shuts down: $ pg_ctl stop -m smart Restarting the server is almost equivalent to stopping the server and starting it again, except that by default, pg_ctl saves and reuses the command line options that were passed to the previously-running instance. To restart the server using the same options as before, use: $ pg_ctl restart But if -o is specified, that replaces any previous options. To restart using port 5433, disabling fsync upon restart: $ pg_ctl -o \"-F -p 5433\" restart Here is sample status output from pg_ctl: $ pg_ctl status pg_ctl: server is running (PID: 13718) /usr/local/pgsql/bin/postgres \"-D\" \"/usr/local/pgsql/data\" \"-p\" \"5433\" \"-B\" \"128\" The second line is the command that would be invoked in restart mode. pg_resetwal — reset the write-ahead log and other control information of a IvorySQL database cluster pg_resetwal [ -f | --force ] [ -n | --dry-run ] [option…​] [ -D | --pgdata ] datadir -f --force Force pg_resetwal to proceed even if it cannot determine valid data for pg_control, as explained above. -n --dry-run The -n / --dry-run option instructs pg_resetwal to print the values reconstructed from pg_control and values about to be changed, and then exit without modifying anything. This is mainly a debugging tool, but can be useful as a sanity check before allowing pg_resetwal to proceed for real. -V --version Display version information, then exit. -? --help Show help, then exit. The following options are only needed when pg_resetwal is unable to determine appropriate values by reading pg_control. Safe values can be determined as described below. For values that take numeric arguments, hexadecimal values can be specified by using the prefix 0x. -c xid,xid --commit-timestamp-ids=xid,xid Manually set the oldest and newest transaction IDs for which the commit time can be retrieved.A safe value for the oldest transaction ID for which the commit time can be retrieved (first part) can be determined by looking for the numerically smallest file name in the directory pg_commit_ts under the data directory. Conversely, a safe value for the newest transaction ID for which the commit time can be retrieved (second part) can be determined by looking for the numerically greatest file name in the same directory. The file names are in hexadecimal. -e xid_epoch --epoch=xid_epoch Manually set the next transaction ID’s epoch.The transaction ID epoch is not actually stored anywhere in the database except in the field that is set by pg_resetwal, so any value will work so far as the database itself is concerned. You might need to adjust this value to ensure that replication systems such as Slony-I and Skytools work correctly — if so, an appropriate value should be obtainable from the state of the downstream replicated database. -l walfile --next-wal-file=walfile Manually set the WAL starting location by specifying the name of the next WAL segment file.The name of next WAL segment file should be larger than any WAL segment file name currently existing in the directory pg_wal under the data directory. These names are also in hexadecimal and have three parts. The first part is the “timeline ID” and should usually be kept the same. For example, if 00000001000000320000004A is the largest entry in pg_wal, use -l 00000001000000320000004B or higher.Note that when using nondefault WAL segment sizes, the numbers in the WAL file names are different from the LSNs that are reported by system functions and system views. This option takes a WAL file name, not an LSN.Note`pg_resetwal` itself looks at the files in pg_wal and chooses a default -l setting beyond the last existing file name. Therefore, manual adjustment of -l should only be needed if you are aware of WAL segment files that are not currently present in pg_wal, such as entries in an offline archive; or if the contents of pg_wal have been lost entirely. -m mxid,mxid --multixact-ids=mxid,mxid Manually set the next and oldest multitransaction ID.A safe value for the next multitransaction ID (first part) can be determined by looking for the numerically largest file name in the directory pg_multixact/offsets under the data directory, adding one, and then multiplying by 65536 (0x10000). Conversely, a safe value for the oldest multitransaction ID (second part of -m) can be determined by looking for the numerically smallest file name in the same directory and multiplying by 65536. The file names are in hexadecimal, so the easiest way to do this is to specify the option value in hexadecimal and append four zeroes. -o oid --next-oid=oid Manually set the next OID.There is no comparably easy way to determine a next OID that’s beyond the largest one in the database, but fortunately it is not critical to get the next-OID setting right. -O mxoff --multixact-offset=mxoff Manually set the next multitransaction offset.A safe value can be determined by looking for the numerically largest file name in the directory pg_multixact/members under the data directory, adding one, and then multiplying by 52352 (0xCC80). The file names are in hexadecimal. There is no simple recipe such as the ones for other options of appending zeroes. --wal-segsize=wal_segment_size Set the new WAL segment size, in megabytes. The value must be set to a power of 2 between 1 and 1024 (megabytes). See the same option of initdb for more information.NoteWhile pg_resetwal will set the WAL starting address beyond the latest existing WAL segment file, some segment size changes can cause previous WAL file names to be reused. It is recommended to use -l together with this option to manually set the WAL starting address if WAL file name overlap will cause problems with your archiving strategy. -u xid --oldest-transaction-id=xid Manually set the oldest unfrozen transaction ID.A safe value can be determined by looking for the numerically smallest file name in the directory pg_xact under the data directory and then multiplying by 1048576 (0x100000). Note that the file names are in hexadecimal. It is usually easiest to specify the option value in hexadecimal too. For example, if 0007 is the smallest entry in pg_xact, -u 0x700000 will work (five trailing zeroes provide the proper multiplier). -x xid --next-transaction-id=xid Manually set the next transaction ID.A safe value can be determined by looking for the numerically largest file name in the directory pg_xact under the data directory, adding one, and then multiplying by 1048576 (0x100000). Note that the file names are in hexadecimal. It is usually easiest to specify the option value in hexadecimal too. For example, if 0011 is the largest entry in pg_xact, -x 0x1200000 will work (five trailing zeroes provide the proper multiplier). PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This command must not be used when the server is running. pg_resetwal will refuse to start up if it finds a server lock file in the data directory. If the server crashed then a lock file might have been left behind; in that case you can remove the lock file to allow pg_resetwal to run. But before you do so, make doubly certain that there is no server process still alive. pg_resetwal works only with servers of the same major version. pg_rewind — synchronize a IvorySQL data directory with another data directory that was forked from it pg_rewind [option…​] { -D | --target-pgdata } directory { --source-pgdata=`directory` | --source-server=`connstr` } If pg_rewind fails while processing, then the data folder of the target is likely not in a state that can be recovered. In such a case, taking a new fresh backup is recommended. As pg_rewind copies configuration files entirely from the source, it may be required to correct the configuration used for recovery before restarting the target server, especially if the target is reintroduced as a standby of the source. If you restart the server after the rewind operation has finished but without configuring recovery, the target may again diverge from the primary. pg_rewind will fail immediately if it finds files it cannot write directly to. This can happen for example when the source and the target server use the same file mapping for read-only SSL keys and certificates. If such files are present on the target server it is recommended to remove them before running pg_rewind. After doing the rewind, some of those files may have been copied from the source, in which case it may be necessary to remove the data copied and restore back the set of links used before the rewind. pg_rewind accepts the following command-line arguments: -D directory --target-pgdata=directory This option specifies the target data directory that is synchronized with the source. The target server must be shut down cleanly before running pg_rewind --source-pgdata=directory Specifies the file system path to the data directory of the source server to synchronize the target with. This option requires the source server to be cleanly shut down. --source-server=connstr Specifies a libpq connection string to connect to the source IvorySQL server to synchronize the target with. The connection must be a normal (non-replication) connection with a role having sufficient permissions to execute the functions used by pg_rewind on the source server (see Notes section for details) or a superuser role. This option requires the source server to be running and accepting connections. -R --write-recovery-conf Create standby.signal and append connection settings to IvorySQL.auto.conf in the output directory. --source-server is mandatory with this option. -n --dry-run Do everything except actually modifying the target directory. -N --no-sync By default, pg_rewind will wait for all files to be written safely to disk. This option causes pg_rewind to return without waiting, which is faster, but means that a subsequent operating system crash can leave the data directory corrupt. Generally, this option is useful for testing but should not be used on a production installation. -P --progress Enables progress reporting. Turning this on will deliver an approximate progress report while copying data from the source cluster. -c --restore-target-wal Use restore_command defined in the target cluster configuration to retrieve WAL files from the WAL archive if these files are no longer available in the pg_wal directory. --config-file=filename Use the specified main server configuration file for the target cluster. This affects pg_rewind when it uses internally the postgres command for the rewind operation on this cluster (when retrieving restore_command with the option -c/--restore-target-wal and when forcing a completion of crash recovery). --debug Print verbose debugging output that is mostly useful for developers debugging pg_rewind. --no-ensure-shutdown pg_rewind requires that the target server is cleanly shut down before rewinding. By default, if the target server is not shut down cleanly, pg_rewind starts the target server in single-user mode to complete crash recovery first, and stops it. By passing this option, pg_rewind skips this and errors out immediately if the server is not cleanly shut down. Users are expected to handle the situation themselves in that case. -V --version Display version information, then exit. -? --help Show help, then exit. When --source-server option is used, pg_rewind also uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. When executing pg_rewind using an online cluster as source, a role having sufficient permissions to execute the functions used by pg_rewind on the source cluster can be used instead of a superuser. Here is how to create such a role, named rewind_user here: CREATE USER rewind_user LOGIN; GRANT EXECUTE ON function pg_catalog.pg_ls_dir(text, boolean, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_stat_file(text, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text, bigint, bigint, boolean) TO rewind_user; When executing pg_rewind using an online cluster as source which has been recently promoted, it is necessary to execute a CHECKPOINT after promotion such that its control file reflects up-to-date timeline information, which is used by pg_rewind to check if the target cluster can be rewound using the designated source cluster. The basic idea is to copy all file system-level changes from the source cluster to the target cluster: 1.Scan the WAL log of the target cluster, starting from the last checkpoint before the point where the source cluster’s timeline history forked off from the target cluster. For each WAL record, record each data block that was touched. This yields a list of all the data blocks that were changed in the target cluster, after the source cluster forked off. If some of the WAL files are no longer available, try re-running pg_rewind with the -c option to search for the missing files in the WAL archive. 2.Copy all those changed blocks from the source cluster to the target cluster, either using direct file system access (--source-pgdata) or SQL (--source-server). Relation files are now in a state equivalent to the moment of the last completed checkpoint prior to the point at which the WAL timelines of the source and target diverged plus the current state on the source of any blocks changed on the target after that divergence. 3.Copy all other files, including new relation files, WAL segments, pg_xact, and configuration files from the source cluster to the target cluster. Similarly to base backups, the contents of the directories pg_dynshmem/, pg_notify/, pg_replslot/, pg_serial/, pg_snapshots/, pg_stat_tmp/, and pg_subtrans/ are omitted from the data copied from the source cluster. The files backup_label, tablespace_map, pg_internal.init, postmaster.opts, and postmaster.pid, as well as any file or directory beginning with pgsql_tmp, are omitted. 4.Create a backup_label file to begin WAL replay at the checkpoint created at failover and configure the pg_control file with a minimum consistency LSN defined as the result of pg_current_wal_insert_lsn() when rewinding from a live source or the last checkpoint LSN when rewinding from a stopped source. 5.When starting the target, IvorySQL replays all the required WAL, resulting in a data directory in a consistent state. pg_test_fsync — determine fastest wal_sync_method for IvorySQL pg_test_fsync [option…​] pg_test_fsync accepts the following command-line options: -f --filename Specifies the file name to write test data in. This file should be in the same file system that the pg_wal directory is or will be placed in. (pg_wal contains the WAL files.) The default is pg_test_fsync.out in the current directory. -s --secs-per-test Specifies the number of seconds for each test. The more time per test, the greater the test’s accuracy, but the longer it takes to run. The default is 5 seconds, which allows the program to complete in under 2 minutes. -V --version Print the pg_test_fsync version and exit. -? --help Show help about pg_test_fsync command line arguments, and exit. The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_test_timing — measure timing overhead pg_test_timing [option…​] pg_test_timing accepts the following command-line options: -d `duration` --duration=`duration` Specifies the test duration, in seconds. Longer durations give slightly better accuracy, and are more likely to discover problems with the system clock moving backwards. The default test duration is 3 seconds. -V --version Print the pg_test_timing version and exit. -? --help Show help about pg_test_timing command line arguments, and exit. Good results will show most (>90%) individual timing calls take less than one microsecond. Average per loop overhead will be even lower, below 100 nanoseconds. This example from an Intel i7-860 system using a TSC clock source shows excellent performance: Testing timing overhead for 3 seconds. Per loop time including overhead: 35.96 ns Histogram of timing durations: < us % of total count 1 96.40465 80435604 2 3.59518 2999652 4 0.00015 126 8 0.00002 13 16 0.00000 2 Note that different units are used for the per loop time than the histogram. The loop can have resolution within a few nanoseconds (ns), while the individual timing calls can only resolve down to one microsecond (us). When the query executor is running a statement using EXPLAIN ANALYZE, individual operations are timed as well as showing a summary. The overhead of your system can be checked by counting rows with the psql program: CREATE TABLE t AS SELECT * FROM generate_series(1,100000); \\timing SELECT COUNT(*) FROM t; EXPLAIN ANALYZE SELECT COUNT(*) FROM t; The i7-860 system measured runs the count query in 9.8 ms while the EXPLAIN ANALYZE version takes 16.6 ms, each processing just over 100,000 rows. That 6.8 ms difference means the timing overhead per row is 68 ns, about twice what pg_test_timing estimated it would be. Even that relatively small amount of overhead is making the fully timed count statement take almost 70% longer. On more substantial queries, the timing overhead would be less problematic. On some newer Linux systems, it’s possible to change the clock source used to collect timing data at any time. A second example shows the slowdown possible from switching to the slower acpi_pm time source, on the same system used for the fast results above: # cat /sys/devices/system/clocksource/clocksource0/available_clocksource tsc hpet acpi_pm # echo acpi_pm > /sys/devices/system/clocksource/clocksource0/current_clocksource # pg_test_timing Per loop time including overhead: 722.92 ns Histogram of timing durations: < us % of total count 1 27.84870 1155682 2 72.05956 2990371 4 0.07810 3241 8 0.01357 563 16 0.00007 3 In this configuration, the sample EXPLAIN ANALYZE above takes 115.9 ms. That’s 1061 ns of timing overhead, again a small multiple of what’s measured directly by this utility. That much timing overhead means the actual query itself is only taking a tiny fraction of the accounted for time, most of it is being consumed in overhead instead. In this configuration, any EXPLAIN ANALYZE totals involving many timed operations would be inflated significantly by timing overhead. FreeBSD also allows changing the time source on the fly, and it logs information about the timer selected during boot: # dmesg | grep \"Timecounter\" Timecounter \"ACPI-fast\" frequency 3579545 Hz quality 900 Timecounter \"i8254\" frequency 1193182 Hz quality 0 Timecounters tick every 10.000 msec Timecounter \"TSC\" frequency 2531787134 Hz quality 800 # sysctl kern.timecounter.hardware=TSC kern.timecounter.hardware: ACPI-fast -> TSC Other systems may only allow setting the time source on boot. On older Linux systems the \"clock\" kernel setting is the only way to make this sort of change. And even on some more recent ones, the only option you’ll see for a clock source is \"jiffies\". Jiffies are the older Linux software clock implementation, which can have good resolution when it’s backed by fast enough timing hardware, as in this example: $ cat /sys/devices/system/clocksource/clocksource0/available_clocksource jiffies $ dmesg | grep time.c time.c: Using 3.579545 MHz WALL PM GTOD PIT/TSC timer. time.c: Detected 2400.153 MHz processor. $ pg_test_timing Testing timing overhead for 3 seconds. Per timing duration including loop overhead: 97.75 ns Histogram of timing durations: < us % of total count 1 90.23734 27694571 2 9.75277 2993204 4 0.00981 3010 8 0.00007 22 16 0.00000 1 32 0.00000 1 Collecting accurate timing information is normally done on computers using hardware clocks with various levels of accuracy. With some hardware the operating systems can pass the system clock time almost directly to programs. A system clock can also be derived from a chip that simply provides timing interrupts, periodic ticks at some known time interval. In either case, operating system kernels provide a clock source that hides these details. But the accuracy of that clock source and how quickly it can return results varies based on the underlying hardware. Inaccurate time keeping can result in system instability. Test any change to the clock source very carefully. Operating system defaults are sometimes made to favor reliability over best accuracy. And if you are using a virtual machine, look into the recommended time sources compatible with it. Virtual hardware faces additional difficulties when emulating timers, and there are often per operating system settings suggested by vendors. The Time Stamp Counter (TSC) clock source is the most accurate one available on current generation CPUs. It’s the preferred way to track the system time when it’s supported by the operating system and the TSC clock is reliable. There are several ways that TSC can fail to provide an accurate timing source, making it unreliable. Older systems can have a TSC clock that varies based on the CPU temperature, making it unusable for timing. Trying to use TSC on some older multicore CPUs can give a reported time that’s inconsistent among multiple cores. This can result in the time going backwards, a problem this program checks for. And even the newest systems can fail to provide accurate TSC timing with very aggressive power saving configurations. Newer operating systems may check for the known TSC problems and switch to a slower, more stable clock source when they are seen. If your system supports TSC time but doesn’t default to that, it may be disabled for a good reason. And some operating systems may not detect all the possible problems correctly, or will allow using TSC even in situations where it’s known to be inaccurate. The High Precision Event Timer (HPET) is the preferred timer on systems where it’s available and TSC is not accurate. The timer chip itself is programmable to allow up to 100 nanosecond resolution, but you may not see that much accuracy in your system clock. Advanced Configuration and Power Interface (ACPI) provides a Power Management (PM) Timer, which Linux refers to as the acpi_pm. The clock derived from acpi_pm will at best provide 300 nanosecond resolution. Timers used on older PC hardware include the 8254 Programmable Interval Timer (PIT), the real-time clock (RTC), the Advanced Programmable Interrupt Controller (APIC) timer, and the Cyclone timer. These timers aim for millisecond resolution. pg_upgrade — upgrade a IvorySQL server instance pg_upgrade -b oldbindir [-B newbindir] -d oldconfigdir -D newconfigdir [option…​] pg_upgrade accepts the following command-line arguments: -b bindir --old-bindir=bindir the old IvorySQL executable directory; environment variable PGBINOLD -B bindir --new-bindir=bindir the new IvorySQL executable directory; default is the directory where pg_upgrade resides; environment variable PGBINNEW -c --check check clusters only, don’t change any data -d configdir --old-datadir=configdir the old database cluster configuration directory; environment variable PGDATAOLD -D configdir --new-datadir=configdir the new database cluster configuration directory; environment variable PGDATANEW -j `njobs` --jobs=`njobs` number of simultaneous processes or threads to use -k --link use hard links instead of copying files to the new cluster -N --no-sync By default, pg_upgrade will wait for all files of the upgraded cluster to be written safely to disk. This option causes pg_upgrade to return without waiting, which is faster, but means that a subsequent operating system crash can leave the data directory corrupt. Generally, this option is useful for testing but should not be used on a production installation. -o options --old-options options options to be passed directly to the old postgres command; multiple option invocations are appended -O options --new-options options options to be passed directly to the new postgres command; multiple option invocations are appended -p port --old-port=port the old cluster port number; environment variable PGPORTOLD -P port --new-port=port the new cluster port number; environment variable PGPORTNEW -r --retain retain SQL and log files even after successful completion -s dir --socketdir=dir directory to use for postmaster sockets during upgrade; default is current working directory; environment variable PGSOCKETDIR -U username --username=username cluster’s install user name; environment variable PGUSER -v --verbose enable verbose internal logging -V --version display version information, then exit --clone Use efficient file cloning (also known as “reflinks” on some systems) instead of copying files to the new cluster. This can result in near-instantaneous copying of the data files, giving the speed advantages of -k/--link while leaving the old cluster untouched.File cloning is only supported on some operating systems and file systems. If it is selected but not supported, the pg_upgrade run will error. At present, it is supported on Linux (kernel 4.5 or later) with Btrfs and XFS (on file systems created with reflink support), and on macOS with APFS. -? --help show help, then exit These are the steps to perform an upgrade with pg_upgrade: 1.Optionally move the old cluster If your installation directory is not version-specific, e.g., /usr/local/pgsql, it is necessary to move the current IvorySQL install directory so it does not interfere with the new IvorySQL installation. Once the current IvorySQL server is shut down, it is safe to rename the IvorySQL installation directory; assuming the old directory is /usr/local/pgsql, you can do: mv /usr/local/pgsql /usr/local/pgsql.old to rename the directory. 2.For source installs, build the new version Build the new IvorySQL source with configure flags that are compatible with the old cluster. pg_upgrade will check pg_controldata to make sure all settings are compatible before starting the upgrade. 3.Install the new IvorySQL binaries Install the new server’s binaries and support files. pg_upgrade is included in a default installation. For source installs, if you wish to install the new server in a custom location, use the prefix variable: make prefix=/usr/local/pgsql.new install 4.Initialize the new IvorySQL cluster Initialize the new cluster using initdb. Again, use compatible initdb flags that match the old cluster. Many prebuilt installers do this step automatically. There is no need to start the new cluster. 5.Install extension shared object files Many extensions and custom modules, whether from contrib or another source, use shared object files (or DLLs), e.g., pgcrypto.so. If the old cluster used these, shared object files matching the new server binary must be installed in the new cluster, usually via operating system commands. Do not load the schema definitions, e.g., CREATE EXTENSION pgcrypto, because these will be duplicated from the old cluster. If extension updates are available, pg_upgrade will report this and create a script that can be run later to update them. 6.Copy custom full-text search files Copy any custom full text search files (dictionary, synonym, thesaurus, stop words) from the old to the new cluster. 7.Adjust authentication pg_upgrade will connect to the old and new servers several times, so you might want to set authentication to peer in pg_hba.conf or use a ~/.pgpass file . 8.Stop both servers Make sure both database servers are stopped using, on Unix, e.g.: pg_ctl -D /opt/IvorySQL/1.5 stop pg_ctl -D /opt/IvorySQL/2.1 stop or on Windows, using the proper service names: NET STOP IvorySQL-1.5 NET STOP IvorySQL-2.1 Streaming replication and log-shipping standby servers can remain running until a later step. 9.Prepare for standby server upgrades Verify that the old standby servers are caught up by running pg_controldata against the old primary and standby clusters. Verify that the “Latest checkpoint location” values match in all clusters. (There will be a mismatch if old standby servers were shut down before the old primary or if the old standby servers are still running.) Also, make sure wal_level is not set to minimal in the IvorySQL.conf file on the new primary cluster. 10.Run pg_upgrade Always run the pg_upgrade binary of the new server, not the old one. pg_upgrade requires the specification of the old and new cluster’s data and executable (bin) directories. You can also specify user and port values, and whether you want the data files linked or cloned instead of the default copy behavior. If you use link mode, the upgrade will be much faster (no file copying) and use less disk space, but you will not be able to access your old cluster once you start the new cluster after the upgrade. Link mode also requires that the old and new cluster data directories be in the same file system. (Tablespaces and pg_wal can be on different file systems.) Clone mode provides the same speed and disk space advantages but does not cause the old cluster to be unusable once the new cluster is started. Clone mode also requires that the old and new data directories be in the same file system. This mode is only available on certain operating systems and file systems. The --jobs option allows multiple CPU cores to be used for copying/linking of files and to dump and restore database schemas in parallel; a good place to start is the maximum of the number of CPU cores and tablespaces. This option can dramatically reduce the time to upgrade a multi-database server running on a multiprocessor machine. For Windows users, you must be logged into an administrative account, and then start a shell as the postgres user and set the proper path: RUNAS /USER:postgres \"CMD.EXE\" SET PATH=%PATH%;C:\\Program Files\\IvorySQL\\15\\bin; and then run pg_upgrade with quoted directories, e.g.: pg_upgrade.exe --old-datadir \"C:/Program Files/IvorySQL/1.5/data\" --new-datadir \"C:/Program Files/IvorySQL/2.1/data\" --old-bindir \"C:/Program Files/IvorySQL/1.5/bin\" --new-bindir \"C:/Program Files/IvorySQL/2.1/bin\" Once started, pg_upgrade will verify the two clusters are compatible and then do the upgrade. You can use pg_upgrade --check to perform only the checks, even if the old server is still running. pg_upgrade --check will also outline any manual adjustments you will need to make after the upgrade. If you are going to be using link or clone mode, you should use the option --link or --clone with --check to enable mode-specific checks. pg_upgrade requires write permission in the current directory. Obviously, no one should be accessing the clusters during the upgrade. pg_upgrade defaults to running servers on port 50432 to avoid unintended client connections. You can use the same port number for both clusters when doing an upgrade because the old and new clusters will not be running at the same time. However, when checking an old running server, the old and new port numbers must be different. If an error occurs while restoring the database schema, pg_upgrade will exit and you will have to revert to the old cluster .To try pg_upgrade again, you will need to modify the old cluster so the pg_upgrade schema restore succeeds. If the problem is a contrib module, you might need to uninstall the contrib module from the old cluster and install it in the new cluster after the upgrade, assuming the module is not being used to store user data. 11.Upgrade streaming replication and log-shipping standby servers If you used link mode and have Streaming Replication or Log-Shipping standby servers, you can follow these steps to quickly upgrade them. You will not be running pg_upgrade on the standby servers, but rather rsync on the primary. Do not start any servers yet. If you did not use link mode, do not have or do not want to use rsync, or want an easier solution, skip the instructions in this section and simply recreate the standby servers once pg_upgrade completes and the new primary is running. Install the new IvorySQL binaries on standby servers Make sure the new binaries and support files are installed on all standby servers. Make sure the new standby data directories do *not\\* exist Make sure the new standby data directories do *not* exist or are empty. If initdb was run, delete the standby servers' new data directories. Install extension shared object files Install the same extension shared object files on the new standbys that you installed in the new primary cluster. Stop standby servers If the standby servers are still running, stop them now using the above instructions. Save configuration files Save any configuration files from the old standbys' configuration directories you need to keep, e.g., `IvorySQL.conf` (and any files included by it), `IvorySQL.auto.conf`, `pg_hba.conf`, because these will be overwritten or removed in the next step. Run rsync When using link mode, standby servers can be quickly upgraded using rsync. To accomplish this, from a directory on the primary server that is above the old and new database cluster directories, run this on the *primary* for each standby server: ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive old_cluster new_cluster remote_dir ``` where `old_cluster` and `new_cluster` are relative to the current directory on the primary, and `remote_dir` is *above* the old and new cluster directories on the standby. The directory structure under the specified directories on the primary and standbys must match. Consult the rsync manual page for details on specifying the remote directory, e.g., ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive /opt/IvorySQL/1.5 \\ /opt/IvorySQL/2.1 standby.example.com:/opt/IvorySQL ``` You can verify what the command will do using rsync's `--dry-run` option. While rsync must be run on the primary for at least one standby, it is possible to run rsync on an upgraded standby to upgrade other standbys, as long as the upgraded standby has not been started. What this does is to record the links created by pg_upgrade's link mode that connect files in the old and new clusters on the primary server. It then finds matching files in the standby's old cluster and creates links for them in the standby's new cluster. Files that were not linked on the primary are copied from the primary to the standby. (They are usually small.) This provides rapid standby upgrades. Unfortunately, rsync needlessly copies files associated with temporary and unlogged tables because these files don't normally exist on standby servers. If you have relocated `pg_wal` outside the data directories, rsync must be run on those directories too. Configure streaming replication and log-shipping standby servers Configure the servers for log shipping. (You do not need to run `pg_backup_start()` and `pg_backup_stop()` or take a file system backup as the standbys are still synchronized with the primary.) Replication slots are not copied and must be recreated. 12.Restore pg_hba.conf If you modified pg_hba.conf, restore its original settings. It might also be necessary to adjust other configuration files in the new cluster to match the old cluster, e.g., IvorySQL.conf (and any files included by it), IvorySQL.auto.conf. 13.Start the new server The new server can now be safely started, and then any rsync’ed standby servers. 14.Post-upgrade processing If any post-upgrade processing is required, pg_upgrade will issue warnings as it completes. It will also generate script files that must be run by the administrator. The script files will connect to each database that needs post-upgrade processing. Each script should be run using: psql --username=postgres --file=script.sql postgres The scripts can be run in any order and can be deleted once they have been run. Caution In general it is unsafe to access tables referenced in rebuild scripts until the rebuild scripts have run to completion; doing so could yield incorrect results or poor performance. Tables not referenced in rebuild scripts can be accessed immediately. 15.Statistics Because optimizer statistics are not transferred by pg_upgrade, you will be instructed to run a command to regenerate that information at the end of the upgrade. You might need to set connection parameters to match your new cluster. 16.Delete old cluster Once you are satisfied with the upgrade, you can delete the old cluster’s data directories by running the script mentioned when pg_upgrade completes. (Automatic deletion is not possible if you have user-defined tablespaces inside the old data directory.) You can also delete the old installation directories (e.g., bin, share). 17.Reverting to old cluster If, after running pg_upgrade, you wish to revert to the old cluster, there are several options: If the --check option was used, the old cluster was unmodified; it can be restarted. If the --link option was not used, the old cluster was unmodified; it can be restarted. If the --link option was used, the data files might be shared between the old and new cluster: If pg_upgrade aborted before linking started, the old cluster was unmodified; it can be restarted. If you did not start the new cluster, the old cluster was unmodified except that, when linking started, a .old suffix was appended to $PGDATA/global/pg_control. To reuse the old cluster, remove the .old suffix from $PGDATA/global/pg_control; you can then restart the old cluster. If you did start the new cluster, it has written to shared files and it is unsafe to use the old cluster. The old cluster will need to be restored from backup in this case. pg_upgrade creates various working files, such as schema dumps, stored within pg_upgrade_output.d in the directory of the new cluster. Each run creates a new subdirectory named with a timestamp formatted as per ISO 8601 (%Y%m%dT%H%M%S), where all its generated files are stored. pg_upgrade_output.d and its contained files will be removed automatically if pg_upgrade completes successfully; but in the event of trouble, the files there may provide useful debugging information. pg_upgrade launches short-lived postmasters in the old and new data directories. Temporary Unix socket files for communication with these postmasters are, by default, made in the current working directory. In some situations the path name for the current directory might be too long to be a valid socket name. In that case you can use the -s option to put the socket files in some directory with a shorter path name. For security, be sure that that directory is not readable or writable by any other users. (This is not supported on Windows.) All failure, rebuild, and reindex cases will be reported by pg_upgrade if they affect your installation; post-upgrade scripts to rebuild tables and indexes will be generated automatically. If you are trying to automate the upgrade of many clusters, you should find that clusters with identical database schemas require the same post-upgrade steps for all cluster upgrades; this is because the post-upgrade steps are based on the database schemas, and not user data. For deployment testing, create a schema-only copy of the old cluster, insert dummy data, and upgrade that. pg_upgrade does not support upgrading of databases containing table columns using these reg* OID-referencing system data types: regcollation regconfig regdictionary regnamespace regoper regoperator regproc regprocedure (regclass, regrole, and regtype can be upgraded.) If you want to use link mode and you do not want your old cluster to be modified when the new cluster is started, consider using the clone mode. If that is not available, make a copy of the old cluster and upgrade that in link mode. To make a valid copy of the old cluster, use rsync to create a dirty copy of the old cluster while the server is running, then shut down the old server and run rsync --checksum again to update the copy with any changes to make it consistent. (--checksum is necessary because rsync only has file modification-time granularity of one second.) . If your file system supports file system snapshots or copy-on-write file copies, you can use that to make a backup of the old cluster and tablespaces, though the snapshot and copies must be created simultaneously or while the database server is down. pg_waldump — display a human-readable rendering of the write-ahead log of a IvorySQL database cluster pg_waldump [option…​] [startseg [endseg]] The following command-line options control the location and format of the output: startseg Start reading at the specified log segment file. This implicitly determines the path in which files will be searched for, and the timeline to use. endseg Stop after reading the specified log segment file. -b --bkp-details Output detailed information about backup blocks. -B block --block=block Only display records that modify the given block. The relation must also be provided with --relation or -R. -e end --end=end Stop reading at the specified WAL location, instead of reading to the end of the log stream. -f --follow After reaching the end of valid WAL, keep polling once per second for new WAL to appear. -F fork --fork=fork If provided, only display records that modify blocks in the given fork. The valid values are main for the main fork, fsm for the free space map, vm for the visibility map, and init for the init fork. -n limit --limit=limit Display the specified number of records, then stop. -p path --path=path Specifies a directory to search for log segment files or a directory with a pg_wal subdirectory that contains such files. The default is to search in the current directory, the pg_wal subdirectory of the current directory, and the pg_wal subdirectory of PGDATA. -q --quiet Do not print any output, except for errors. This option can be useful when you want to know whether a range of WAL records can be successfully parsed but don’t care about the record contents. -r rmgr --rmgr=rmgr Only display records generated by the specified resource manager. You can specify the option multiple times to select multiple resource managers. If list is passed as name, print a list of valid resource manager names, and exit.Extensions may define custom resource managers, but pg_waldump does not load the extension module and therefore does not recognize custom resource managers by name. Instead, you can specify the custom resource managers as custom where “” is the three-digit resource manager ID. Names of this form will always be considered valid. -R tblspc / db / rel --relation=tblspc / db / rel Only display records that modify blocks in the given relation. The relation is specified with tablespace OID, database OID, and relfilenode separated by slashes, for example 1234/12345/12345. This is the same format used for relations in the program’s output. -s start --start=start WAL location at which to start reading. The default is to start reading the first valid log record found in the earliest file found. -t timeline --timeline=timeline Timeline from which to read log records. The default is to use the value in startseg, if that is specified; otherwise, the default is 1. -V --version Print the pg_waldump version and exit. -w --fullpage Only display records that include full page images. -x xid --xid=xid Only display records marked with the given transaction ID. -z --stats[=record] Display summary statistics (number and size of records and full-page images) instead of individual records. Optionally generate statistics per-record instead of per-rmgr.If pg_waldump is terminated by signal SIGINT (Control + C), the summary of the statistics computed is displayed up to the termination point. This operation is not supported on Windows. -? --help Show help about pg_waldump command line arguments, and exit. PGDATA Data directory; see also the -p option. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. Can give wrong results when the server is running. Only the specified timeline is displayed (or the default, if none is specified). Records in other timelines are ignored. pg_waldump cannot read WAL files with suffix .partial. If those files need to be read, .partial suffix needs to be removed from the file name. postgres — IvorySQL database server postgres [option…​] postgres accepts the following command-line arguments. You can save typing most of these options by setting up a configuration file. Some (safe) options can also be set from the connecting client in an application-dependent way to apply only for that session. For example, if the environment variable PGOPTIONS is set, then libpq-based clients will pass that string to the server, which will interpret it as postgres command-line options. -B nbuffers Sets the number of shared buffers for use by the server processes. The default value of this parameter is chosen automatically by initdb. Specifying this option is equivalent to setting the shared_buffers configuration parameter. -c name=value Sets a named run-time parameter. Most of the other command line options are in fact short forms of such a parameter assignment. -c can appear multiple times to set multiple parameters. -C name Prints the value of the named run-time parameter, and exits. (See the -c option above for details.) This returns values from postgresql.conf, modified by any parameters supplied in this invocation. It does not reflect parameters supplied when the cluster was started.This can be used on a running server for most parameters. This option is meant for other programs that interact with a server instance, such as pg_ctl, to query configuration parameter values. User-facing applications should instead use SHOW or the pg_settings view. -d debug-level Sets the debug level. The higher this value is set, the more debugging output is written to the server log. Values are from 1 to 5. It is also possible to pass -d 0 for a specific session, which will prevent the server log level of the parent postgres process from being propagated to this session. -D datadir Specifies the file system location of the database configuration files. -e Sets the default date style to “European”, that is DMY ordering of input date fields. This also causes the day to be printed before the month in certain date output formats. -F Disables fsync calls for improved performance, at the risk of data corruption in the event of a system crash. Specifying this option is equivalent to disabling the fsync configuration parameter. Read the detailed documentation before using this! -h hostname Specifies the IP host name or address on which postgres is to listen for TCP/IP connections from client applications. The value can also be a comma-separated list of addresses, or * to specify listening on all available interfaces. An empty value specifies not listening on any IP addresses, in which case only Unix-domain sockets can be used to connect to the server. Defaults to listening only on localhost. Specifying this option is equivalent to setting the listen_addresses configuration parameter. -i Allows remote clients to connect via TCP/IP (Internet domain) connections. Without this option, only local connections are accepted. This option is equivalent to setting listen_addresses to * in IvorySQL.conf or via -h.This option is deprecated since it does not allow access to the full functionality of listen_addresses. It’s usually better to set listen_addresses directly. -k directory Specifies the directory of the Unix-domain socket on which postgres is to listen for connections from client applications. The value can also be a comma-separated list of directories. An empty value specifies not listening on any Unix-domain sockets, in which case only TCP/IP sockets can be used to connect to the server. The default value is normally /tmp, but that can be changed at build time. Specifying this option is equivalent to setting the unix_socket_directories configuration parameter. -l Enables secure connections using SSL. IvorySQL must have been compiled with support for SSL for this option to be available. -N max-connections Sets the maximum number of client connections that this server will accept. The default value of this parameter is chosen automatically by initdb. Specifying this option is equivalent to setting the max_connections configuration parameter. -p port Specifies the TCP/IP port or local Unix domain socket file extension on which postgres is to listen for connections from client applications. Defaults to the value of the PGPORT environment variable, or if PGPORT is not set, then defaults to the value established during compilation (normally 5432). If you specify a port other than the default port, then all client applications must specify the same port using either command-line options or PGPORT. -s Print time information and other statistics at the end of each command. This is useful for benchmarking or for use in tuning the number of buffers. -S work-mem Specifies the base amount of memory to be used by sorts and hash tables before resorting to temporary disk files. -V --version Print the postgres version and exit. --name=value Sets a named run-time parameter; a shorter form of -c. --describe-config This option dumps out the server’s internal configuration variables, descriptions, and defaults in tab-delimited COPY format. It is designed primarily for use by administration tools. -? --help Show help about postgres command line arguments, and exit. The options described here are used mainly for debugging purposes, and in some cases to assist with recovery of severely damaged databases. There should be no reason to use them in a production database setup. They are listed here only for use by IvorySQL system developers. Furthermore, these options might change or be removed in a future release without notice. -f { s | i | o | b | t | n | m | h } Forbids the use of particular scan and join methods: s and i disable sequential and index scans respectively, o, b and t disable index-only scans, bitmap index scans, and TID scans respectively, while n, m, and h disable nested-loop, merge and hash joins respectively.Neither sequential scans nor nested-loop joins can be disabled completely; the -fs and -fn options simply discourage the optimizer from using those plan types if it has any other alternative. -n This option is for debugging problems that cause a server process to die abnormally. The ordinary strategy in this situation is to notify all other server processes that they must terminate and then reinitialize the shared memory and semaphores. This is because an errant server process could have corrupted some shared state before terminating. This option specifies that postgres will not reinitialize shared data structures. A knowledgeable system programmer can then use a debugger to examine shared memory and semaphore state. -O Allows the structure of system tables to be modified. This is used by initdb. -P Ignore system indexes when reading system tables, but still update the indexes when modifying the tables. This is useful when recovering from damaged system indexes. -t pa[rser] | pl[anner] | e[xecutor] Print timing statistics for each query relating to each of the major system modules. This option cannot be used together with the -s option. -T This option is for debugging problems that cause a server process to die abnormally. The ordinary strategy in this situation is to notify all other server processes that they must terminate and then reinitialize the shared memory and semaphores. This is because an errant server process could have corrupted some shared state before terminating. This option specifies that postgres will stop all other server processes by sending the signal SIGSTOP, but will not cause them to terminate. This permits system programmers to collect core dumps from all server processes by hand. -v protocol Specifies the version number of the frontend/backend protocol to be used for a particular session. This option is for internal use only. -W seconds A delay of this many seconds occurs when a new server process is started, after it conducts the authentication procedure. This is intended to give an opportunity to attach to the server process with a debugger. The following options only apply to the single-user mode (see Single-User Mode below). --single Selects the single-user mode. This must be the first argument on the command line. database Specifies the name of the database to be accessed. This must be the last argument on the command line. If it is omitted it defaults to the user name. -E Echo all commands to standard output before executing them. -j Use semicolon followed by two newlines, rather than just newline, as the command entry terminator. -r filename Send all server log output to filename. This option is only honored when supplied as a command-line option. PGCLIENTENCODING Default character encoding used by clients. (The clients can override this individually.) This value can also be set in the configuration file. PGDATA Default data directory location PGDATESTYLE Default value of the DateStyle run-time parameter. (The use of this environment variable is deprecated.) PGPORT Default port number (preferably set in the configuration file) A failure message mentioning semget or shmget probably indicates you need to configure your kernel to provide adequate shared memory and semaphores. You might be able to postpone reconfiguring your kernel by decreasing shared_buffers to reduce the shared memory consumption of IvorySQL, and/or by reducing max_connections to reduce the semaphore consumption. A failure message suggesting that another server is already running should be checked carefully, for example by using the command $ ps ax | grep postgres or $ ps -ef | grep postgres depending on your system. If you are certain that no conflicting server is running, you can remove the lock file mentioned in the message and try again. A failure message indicating inability to bind to a port might indicate that that port is already in use by some non-IvorySQL process. You might also get this error if you terminate postgres and immediately restart it using the same port; in this case, you must simply wait a few seconds until the operating system closes the port before trying again. Finally, you might get this error if you specify a port number that your operating system considers to be reserved. For example, many versions of Unix consider port numbers under 1024 to be “trusted” and only permit the Unix superuser to access them. The utility command pg_ctl can be used to start and shut down the postgres server safely and comfortably. If at all possible, do not use SIGKILL to kill the main postgres server. Doing so will prevent postgres from freeing the system resources (e.g., shared memory and semaphores) that it holds before terminating. This might cause problems for starting a fresh postgres run. To terminate the postgres server normally, the signals SIGTERM, SIGINT, or SIGQUIT can be used. The first will wait for all clients to terminate before quitting, the second will forcefully disconnect all clients, and the third will quit immediately without proper shutdown, resulting in a recovery run during restart. The SIGHUP signal will reload the server configuration files. It is also possible to send SIGHUP to an individual server process, but that is usually not sensible. To cancel a running query, send the SIGINT signal to the process running that command. To terminate a backend process cleanly, send SIGTERM to that process. See also pg_cancel_backend. The postgres server uses SIGQUIT to tell subordinate server processes to terminate without normal cleanup. This signal should not be used by users. It is also unwise to send SIGKILL to a server process — the main postgres process will interpret this as a crash and will force all the sibling processes to quit as part of its standard crash-recovery procedure. The -- options will not work on FreeBSD or OpenBSD. Use -c instead. This is a bug in the affected operating systems; a future release of IvorySQL will provide a workaround if this is not fixed. To start a single-user mode server, use a command like postgres --single -D /usr/local/pgsql/data other-options my_database Provide the correct path to the database directory with -D, or make sure that the environment variable PGDATA is set. Also specify the name of the particular database you want to work in. Normally, the single-user mode server treats newline as the command entry terminator; there is no intelligence about semicolons, as there is in psql. To continue a command across multiple lines, you must type backslash just before each newline except the last one. The backslash and adjacent newline are both dropped from the input command. Note that this will happen even when within a string literal or comment. But if you use the -j command line switch, a single newline does not terminate command entry; instead, the sequence semicolon-newline-newline does. That is, type a semicolon immediately followed by a completely empty line. Backslash-newline is not treated specially in this mode. Again, there is no intelligence about such a sequence appearing within a string literal or comment. In either input mode, if you type a semicolon that is not just before or part of a command entry terminator, it is considered a command separator. When you do type a command entry terminator, the multiple statements you’ve entered will be executed as a single transaction. To quit the session, type EOF (Control+D, usually). If you’ve entered any text since the last command entry terminator, then EOF will be taken as a command entry terminator, and another EOF will be needed to exit. Note that the single-user mode server does not provide sophisticated line-editing features (no command history, for example). Single-user mode also does not do any background processing, such as automatic checkpoints or replication. To start postgres in the background using default values, type: $ nohup postgres >logfile 2>&1 </dev/null & To start postgres with a specific port, e.g., 1234: $ postgres -p 1234 To connect to this server using psql, specify this port with the -p option: $ psql -p 1234 or set the environment variable PGPORT: $ export PGPORT=1234 $ psql Named run-time parameters can be set in either of these styles: $ postgres -c work_mem=1234 $ postgres --work-mem=1234 Either form overrides whatever setting might exist for work_mem in IvorySQL.conf. Notice that underscores in parameter names can be written as either underscore or dash on the command line. Except for short-term experiments, it’s probably better practice to edit the setting in IvorySQL.conf than to rely on a command-line switch to set a parameter.","title":"Tool Reference","component":"ivorysql-doc","version":"master","name":"tools_reference","url":"/ivorysql-doc/master/tools_reference.html","titles":[{"text":"1. List of tools","hash":"list-of-tools","id":1},{"text":"2. Client Applications","hash":"client-applications","id":2},{"text":"2.1. clusterdb","hash":"clusterdb","id":3},{"text":"2.1.1. Synopsis","hash":"synopsis","id":4},{"text":"2.1.2. Options","hash":"options","id":5},{"text":"2.1.3. Environment","hash":"environment","id":6},{"text":"2.1.4. Diagnostics","hash":"diagnostics","id":7},{"text":"2.1.5. Examples","hash":"examples","id":8},{"text":"2.2. createdb","hash":"createdb","id":9},{"text":"2.2.1. Synopsis","hash":"synopsis-2","id":10},{"text":"2.2.2. Options","hash":"options-2","id":11},{"text":"2.2.3. Environment","hash":"environment-2","id":12},{"text":"2.2.4. Diagnostics","hash":"diagnostics-2","id":13},{"text":"2.2.5. Examples","hash":"examples-2","id":14},{"text":"2.3. createuser","hash":"createuser","id":15},{"text":"2.3.1. Synopsis","hash":"synopsis-3","id":16},{"text":"2.3.2. Options","hash":"options-3","id":17},{"text":"2.3.3. 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Notes","hash":"notes","id":36},{"text":"2.6.4. Examples","hash":"examples-6","id":37},{"text":"2.7. pg_amcheck","hash":"pg_amcheck","id":38},{"text":"2.7.1. Synopsis","hash":"synopsis-7","id":39},{"text":"2.7.2. Options","hash":"options-7","id":40},{"text":"2.7.3. Warning","hash":"warning","id":41},{"text":"2.8. pg_basebackup","hash":"pg_basebackup","id":42},{"text":"2.8.1. Synopsis","hash":"synopsis-8","id":43},{"text":"2.8.2. Options","hash":"options-8","id":44},{"text":"2.8.3. Environment","hash":"environment-6","id":45},{"text":"2.8.4. Notes","hash":"notes-2","id":46},{"text":"2.8.5. Examples","hash":"examples-7","id":47},{"text":"2.9. pgbench","hash":"pgbench","id":48},{"text":"2.9.1. Synopsis","hash":"synopsis-9","id":49},{"text":"2.9.2. Caution","hash":"caution","id":50},{"text":"2.9.3. Options","hash":"options-9","id":51},{"text":"2.9.4. Initialization Options","hash":"initialization-options","id":52},{"text":"2.9.5. 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Options","hash":"options-12","id":70},{"text":"2.12.3. Environment","hash":"environment-9","id":71},{"text":"2.12.4. Notes","hash":"notes-4","id":72},{"text":"2.12.5. Examples","hash":"examples-9","id":73},{"text":"2.13. pg_isready","hash":"pg_isready","id":74},{"text":"2.13.1. Synopsis","hash":"synopsis-13","id":75},{"text":"2.13.2. Options","hash":"options-13","id":76},{"text":"2.13.3. Exit Status","hash":"exit-status-2","id":77},{"text":"2.13.4. Environment","hash":"environment-10","id":78},{"text":"2.13.5. Notes","hash":"notes-5","id":79},{"text":"2.13.6. Examples","hash":"examples-10","id":80},{"text":"2.14. pg_receivewal","hash":"pg_receivewal","id":81},{"text":"2.14.1. Synopsis","hash":"synopsis-14","id":82},{"text":"2.14.2. Options","hash":"options-14","id":83},{"text":"2.14.3. Exit Status","hash":"exit-status-3","id":84},{"text":"2.14.4. Environment","hash":"environment-11","id":85},{"text":"2.14.5. Notes","hash":"notes-6","id":86},{"text":"2.14.6. Examples","hash":"examples-11","id":87},{"text":"2.15. pg_recvlogical","hash":"pg_recvlogical","id":88},{"text":"2.15.1. Synopsis","hash":"synopsis-15","id":89},{"text":"2.15.2. Options","hash":"options-15","id":90},{"text":"2.15.3. Environment","hash":"environment-12","id":91},{"text":"2.15.4. Notes","hash":"notes-7","id":92},{"text":"2.16. pg_restore","hash":"pg_restore","id":93},{"text":"2.16.1. Synopsis","hash":"synopsis-16","id":94},{"text":"2.16.2. Options","hash":"options-16","id":95},{"text":"2.16.3. Environment","hash":"environment-13","id":96},{"text":"2.16.4. Diagnostics","hash":"diagnostics-7","id":97},{"text":"2.16.5. Notes","hash":"notes-8","id":98},{"text":"2.16.6. Examples","hash":"examples-12","id":99},{"text":"2.17. pg_verifybackup","hash":"pg_verifybackup","id":100},{"text":"2.17.1. Synopsis","hash":"synopsis-17","id":101},{"text":"2.17.2. Options","hash":"options-17","id":102},{"text":"2.17.3. Examples","hash":"examples-13","id":103},{"text":"2.18. psql","hash":"psql","id":104},{"text":"2.18.1. Synopsis","hash":"synopsis-18","id":105},{"text":"2.18.2. Options","hash":"options-18","id":106},{"text":"2.18.3. Exit Status","hash":"exit-status-4","id":107},{"text":"2.19. reindexdb","hash":"reindexdb","id":108},{"text":"2.19.1. Synopsis","hash":"synopsis-19","id":109},{"text":"2.19.2. Options","hash":"options-19","id":110},{"text":"2.19.3. Environment","hash":"environment-14","id":111},{"text":"2.19.4. Diagnostics","hash":"diagnostics-8","id":112},{"text":"2.19.5. Notes","hash":"notes-9","id":113},{"text":"2.19.6. Examples","hash":"examples-14","id":114},{"text":"2.20. vacuumdb","hash":"vacuumdb","id":115},{"text":"2.20.1. Synopsis","hash":"synopsis-20","id":116},{"text":"2.20.2. Options","hash":"options-20","id":117},{"text":"2.20.3. Environment","hash":"environment-15","id":118},{"text":"2.20.4. Diagnostics","hash":"diagnostics-9","id":119},{"text":"2.20.5. 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Notes","hash":"notes-12","id":137},{"text":"3.4. pg_controldata","hash":"pg_controldata","id":138},{"text":"3.4.1. Synopsis","hash":"synopsis-24","id":139},{"text":"3.4.2. Environment","hash":"environment-19","id":140},{"text":"3.5. pg_ctl","hash":"pg_ctl","id":141},{"text":"3.5.1. Synopsis","hash":"synopsis-25","id":142},{"text":"3.5.2. Options","hash":"options-24","id":143},{"text":"3.5.3. Options for Windows","hash":"options-for-windows","id":144},{"text":"3.5.4. Environment","hash":"environment-20","id":145},{"text":"3.5.5. Files","hash":"files","id":146},{"text":"3.5.6. Examples","hash":"examples-17","id":147},{"text":"3.5.6.1. Starting the Server","hash":"starting-the-server","id":148},{"text":"3.5.6.2. Stopping the Server","hash":"stopping-the-server","id":149},{"text":"3.5.6.3. Restarting the Server","hash":"restarting-the-server","id":150},{"text":"3.5.6.4. Showing the Server Status","hash":"showing-the-server-status","id":151},{"text":"3.6. pg_resetwal","hash":"pg_resetwal","id":152},{"text":"3.6.1. Synopsis","hash":"synopsis-26","id":153},{"text":"3.6.2. Options","hash":"options-25","id":154},{"text":"3.6.3. Environment","hash":"environment-21","id":155},{"text":"3.6.4. Notes","hash":"notes-13","id":156},{"text":"3.7. pg_rewind","hash":"pg_rewind","id":157},{"text":"3.7.1. Synopsis","hash":"synopsis-27","id":158},{"text":"3.7.2. Warning","hash":"warning-2","id":159},{"text":"3.7.3. Options","hash":"options-26","id":160},{"text":"3.7.4. Environment","hash":"environment-22","id":161},{"text":"3.7.5. Notes","hash":"notes-14","id":162},{"text":"3.7.6. How It Works","hash":"how-it-works","id":163},{"text":"3.8. pg_test_fsync","hash":"pg_test_fsync","id":164},{"text":"3.8.1. Synopsis","hash":"synopsis-28","id":165},{"text":"3.8.2. Options","hash":"options-27","id":166},{"text":"3.8.3. Environment","hash":"environment-23","id":167},{"text":"3.9. pg_test_timing","hash":"pg_test_timing","id":168},{"text":"3.9.1. Synopsis","hash":"synopsis-29","id":169},{"text":"3.9.2. Options","hash":"options-28","id":170},{"text":"3.9.3. Usage","hash":"usage","id":171},{"text":"3.9.3.1. Interpreting Results","hash":"interpreting-results","id":172},{"text":"3.9.3.2. Measuring Executor Timing Overhead","hash":"measuring-executor-timing-overhead","id":173},{"text":"3.9.3.3. Changing Time Sources","hash":"changing-time-sources","id":174},{"text":"3.9.3.4. Clock Hardware and Timing Accuracy","hash":"clock-hardware-and-timing-accuracy","id":175},{"text":"3.10. pg_upgrade","hash":"pg_upgrade","id":176},{"text":"3.10.1. Synopsis","hash":"synopsis-30","id":177},{"text":"3.10.2. Options","hash":"options-29","id":178},{"text":"3.10.3. Usage","hash":"usage-2","id":179},{"text":"3.10.4. Notes","hash":"notes-15","id":180},{"text":"3.11. pg_waldump","hash":"pg_waldump","id":181},{"text":"3.11.1. Synopsis","hash":"synopsis-31","id":182},{"text":"3.11.2. Options","hash":"options-30","id":183},{"text":"3.11.3. Environment","hash":"environment-24","id":184},{"text":"3.11.4. Notes","hash":"notes-16","id":185},{"text":"3.12. postgres","hash":"postgres","id":186},{"text":"3.12.1. Synopsis","hash":"synopsis-32","id":187},{"text":"3.12.2. Options","hash":"options-31","id":188},{"text":"3.12.3. General Purpose","hash":"general-purpose","id":189},{"text":"3.12.4. Semi-Internal Options","hash":"semi-internal-options","id":190},{"text":"3.12.5. Options for Single-User Mode","hash":"options-for-single-user-mode","id":191},{"text":"3.12.6. Environment","hash":"environment-25","id":192},{"text":"3.12.7. Diagnostics","hash":"diagnostics-10","id":193},{"text":"3.12.8. Notes","hash":"notes-17","id":194},{"text":"3.12.9. Bugs","hash":"bugs","id":195},{"text":"3.12.10. Single-User Mode","hash":"single-user-mode","id":196},{"text":"3.12.11. Examples","hash":"examples-18","id":197}]},"10":{"id":10,"text":"IvorySQL is the only Oracle compatible open source PostgreSQL. Get started by downloading the code from Github. Go to IvorySQL Release Page. IvorySQL project is an open source project proposed by Highgo Software to add the Oracle compatibility features into the popular PostgreSQL database. It is Apache licensed Open Source and always free to use. Any comments please contact support@ivorysql.org IvorySQL v5.0 pdf documentation","title":"Welcome to IvorySQL","component":"ivorysql-doc","version":"master","name":"welcome","url":"/ivorysql-doc/master/welcome.html","titles":[{"text":"Getting Started","hash":"getting-started","id":1},{"text":"Releases","hash":"releases","id":2},{"text":"About IvorySQL","hash":"about-ivorysql","id":3},{"text":"Docs Download","hash":"docs-download","id":4}]},"11":{"id":11,"text":"In order to support both the PG mode and Oracle-compatible mode, IvorySQL allows specifying the -m parameter during initdb to obtain either a PG mode database or an Oracle-compatible mode database. If the -m parameter is not specified, it defaults to Oracle-compatible mode. If the -m parameter is specified as pg, the database will no longer be compatible with Oracle syntax. initdb -m initialization requires judgment of different modes, among which oracle mode requires the execution of SQL statements postgres_oracle.bki. The default is Oracle compatibility mode, and the process is as follows: Startup: When starting, it determines whether it is an Oracle compatibility mode based on the initialization mode; Description: database_mode: Used to indicate initialization mode; database_mode=DB_PG，PG mode, and cannot be switched; database_mode=DB_ORACLE， oracle compatibility mode; Initialize the PG mode: ./initdb -D ../data -m pg Initialize the Oracle compatibility mode: ./initdb -D ../data -m oracle or ./initdb -D ../data","title":"Dual-mode design","component":"ivorysql-doc","version":"master","name":"dual_mode_design","url":"/ivorysql-doc/master/architecture/dual_mode_design.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function","hash":"function","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"12":{"id":12,"text":"The dual parser framework is divided into two main parts: the SQL side and the server-side programming language. The basic approach is to introduce a new set of Oracle-compatible syntax and lexical rules. When Oracle compatibility is enabled, the parser follows Oracle-style syntax analysis to generate the corresponding syntax tree. Implementation Details: Under src/backend/, create a new directory named oracle_parser. Copy scan.l and gram.y from src/backend/parser/ to the oracle_parser directory, renaming them to ora_gram.y and ora_scan.l. Add Oracle-style syntax and lexical analysis code to these files. Copy keywords.c to the oracle_parser directory to store Oracle-specific keywords. Compile the oracle_parser directory into a dynamic library named libparser_oracle.so. When Oracle compatibility is enabled, the ivorysql.conf file is appended to the end of the postgresql.conf file. Add liboracle_parser to the shared_preload_libraries parameter in ivorysql.conf to ensure the dynamic library is loaded automatically when the database starts. A new function pointer ora_raw_parser is introduced. When the libparser_oracle.so library is loaded, the _PG_init() function assigns the address of oracle_raw_parser() to ora_raw_parser. The _PG_fini() function resets ora_raw_parser to NULL when the compatibility mode is switched. Each backend process calls the BackendInitialize() function, which sets port→connmode based on the port number the backend process is connected to. If the port is an Oracle-compatible port, connmode is set to 'o'; otherwise, it is set to 'p'. After this, PostgresMain() calls InitIvorysql(). If port→connmode is 'o', the function SetConfigOption(\"ivorysql.compatible_mode\", \"oracle\", PGC_USERSET, PGC_S_OVERRIDE) is called. Since this parameter has an assign_hook, executing assign_hook() within SetConfigOption() is equivalent to calling assign_compatible_mode(), which sets sql_raw_parser = ora_raw_parser. During SQL statement analysis, the function pg_parse_query()→raw_parser() calls either standard_parser() or ora_raw_parser() through the function pointer sql_raw_parser. The diagram below illustrates what happens during SQL statement analysis. A new language, pliSQL, is added to the system table pg_language. Implementation Details: Copy the plpgsql directory from the PostgreSQL source code and rename it to plisql. Rename all files in the plisql directory to start with plisql. Since plpgsql is a language, the registration functions for plpgsql (e.g., plpgsql_validator, plpgsql_call_handler, plpgsql_inline_handler) are renamed to start with plisql. All other functions in the directory are also renamed to start with plisql. The plisql directory is built as a plugin. If the database mode is Oracle during initdb, this plugin is created. The plugin registers the pliSQL language in the database system tables. The pliSQL side does not have its own lexical rules; it relies on the lexical rules of the SQL side. Therefore, for pliSQL, Oracle-compatible lexical rules are enforced. The main modification is in the plisql_scanner_init function, which calls ora_scanner_init(). The internal_yylex() function in the plisql directory calls ora_core_yylex. The syntax rules for pliSQL are defined in plisql/src/pl_gram.y, where the syntax rules for Oracle-compatible PL/SQL blocks are implemented. When creating a function on the SQL side, if no language is specified: In Oracle compatibility mode, the default language is plisql. In PostgreSQL compatibility mode, the default language is plpgsql. In oracle_parser, ora_gram.y defaults to plisql, while in the PostgreSQL parser, gram.y defaults to plpgsql. For anonymous blocks, if no language is specified: In Oracle compatibility mode, the default language is plisql. In PostgreSQL compatibility mode, the default language is plpgsql. The ExecuteDoStmt function also determines the default language based on the compatibility mode.","title":"Dual Parser Framework for Oracle Compatibility","component":"ivorysql-doc","version":"master","name":"dual_parser","url":"/ivorysql-doc/master/architecture/dual_parser.html","titles":[{"text":"1. Lexical and Syntax Separation on the SQL Side","hash":"lexical-and-syntax-separation-on-the-sql-side","id":1},{"text":"2. Lexical and Syntax Separation for Server-Side Programming Language","hash":"lexical-and-syntax-separation-for-server-side-programming-language","id":2}]},"13":{"id":13,"text":"In order to minimize changes to the original PostgreSQL, it is compatible with Oracle. We need to implement a framework for dual-parser, dual-port, modal PL/PGSQL to implement PL/iSQL. The implementation flowchart is as follows: The Ivorysql port 5432 is kept compatible with the original PostgreSQL situation, so Ivorysql uses another separate port to log in, which defaults to 1521. Log in from this port, and the Oracle compatibility mode is used by default. If you need to log in from port 5432 and also enter compatibility mode, you must configure it using SET ivorysql.compatible_mode=oracle. In order to minimize the interference between Oracle syntax and PostgreSQL syntax, a parser module has been added to handle oracle-related syntax. Added PL/iSQL procedural language compatible with Oracle’s PL/SQL procedural language.","title":"IVORYSQL framework design","component":"ivorysql-doc","version":"master","name":"framework_design","url":"/ivorysql-doc/master/architecture/framework_design.html","titles":[{"text":"1. Function","hash":"function","id":1},{"text":"1.1. Dual-port design","hash":"dual-port-design","id":2},{"text":"1.2. Parser module design","hash":"parser-module-design","id":3},{"text":"1.3. Added PL/iSQL procedural language","hash":"added-plisql-procedural-language","id":4}]},"14":{"id":14,"text":"IvorySQL supports two database modes during the initialization process: PG Mode: Maintains compatibility with native PostgreSQL. Oracle Mode (default): Provides Oracle syntax compatibility and enhanced features. Users can specify the initialization mode using the initdb command parameters to meet compatibility requirements for different scenarios. During the initial phase, initdb parses the input command-line parameters. Parameter Description Options Default Value -m Specifies the database mode oracle/pg oracle -C Sets the case conversion mode for Oracle compatibility interchange/normal/lowercase interchange Parameter Parsing Workflow: 1.Inherits PostgreSQL’s existing parameter processing mechanism. 2.Adds parsing logic for the mode selection parameter -m. 3.Introduces a processing module for the case conversion parameter -C. The setup_data_file_paths() function is executed to configure critical file paths: if (DB_PG == database_mode) set_input(&bki_file, \"postgres.bki\"); else set_input(&bki_file, \"postgres_oracle.bki\"); Path Verification Mechanism: 1.Verifies the existence of the BKI files (postgres_oracle.bki / postgres.bki). 2.Confirms the availability of the configuration file templates. 3.Establishes the system directory structure corresponding to the database mode. The core initialization operations are performed by the initialize_data_directory() function: Calls create_data_directory() to create the main data directory (PGDATA). Uses create_xlog_or_symlink() to establish the WAL log directory. Iteratively creates standard subdirectories such as base and global. Calls set_null_conf() to create an empty postgresql.conf file. In Oracle mode, additionally creates the ivorysql.conf configuration file. Calls setup_config() to write configuration information to postgresql.conf. In Oracle mode, additional configuration information is written to ivorysql.conf. Executes bootstrap_template1() to load the corresponding BKI file and initialize the template1 template database. IvorySQL additionally sets the database mode (oracle/pg) and case conversion mode for the template1 template database. load_plisql(): Installs the PL/iSQL procedural language for Oracle PL/SQL compatibility. load_ivorysql_ora(): Loads the core Oracle compatibility layer extension. make_ivorysql(): Creates the default ivorysql database. When the database mode is Oracle, usernames are forcibly converted to lowercase. Note In PostgreSQL, \"bki\" stands for \"Bootstrap Kit.\" The Bootstrap Kit is a collection of tools and files used internally by PostgreSQL to bootstrap the database system. The Bootstrap Kit is primarily used to initialize the foundational structure of the PostgreSQL database system, including the system catalog, system tables, and other necessary system objects. These system catalogs and tables store metadata information about the PostgreSQL database system, such as database objects, table structures, and index information. The genbki.pl script processes system table files like pg_class.h, pg_namespace.h, and pg_proc.h, and generates postgres.bki for initdb to load. This primarily involves loading initialization data, such as creating system tables, system functions, and predefined types. During the compilation phase before initdb, genbki.pl generates both postgres.bki and postgres_ora.bki. While postgres.bki remains unchanged from PostgreSQL, postgres_ora.bki includes additional system objects for Oracle compatibility.","title":"initdb Process","component":"ivorysql-doc","version":"master","name":"initdb_process","url":"/ivorysql-doc/master/architecture/initdb_process.html","titles":[{"text":"1. Parameter Parsing and Processing","hash":"parameter-parsing-and-processing","id":1},{"text":"2. File Path Initialization","hash":"file-path-initialization","id":2},{"text":"3. Data Directory Initialization Process","hash":"data-directory-initialization-process","id":3},{"text":"3.1. Directory Structure Creation","hash":"directory-structure-creation","id":4},{"text":"3.2. Configuration File Initialization","hash":"configuration-file-initialization","id":5},{"text":"3.3. Template Database Bootstrapping","hash":"template-database-bootstrapping","id":6},{"text":"3.4. Oracle-Compatible Username Handling","hash":"oracle-compatible-username-handling","id":7}]},"15":{"id":15,"text":"In order to be compatible with Oracle, it is necessary to add some variables for controlling the database execution results on the basis of the original GUC variables, so as to achieve the same behavior as Oracle. In order to better add compatible GUC parameters and to minimize changes to the PG kernel source code, we need to design a framework to add GUC to a unified location. When adding guc parameters, we need to add them uniformly in the ivy_guc.c. Where Ivy_ConfigureNamesBool, Ivy_ConfigureNamesInt, Ivy_ConfigureNamesString, Ivy_ConfigureNamesReal and Ivy_ConfigureNamesEnum represent 5 different types of guc parameters. When adding guc parameters, simply add the value of guc to the corresponding array. Variable Description ivorysql.compatible_mode Indicates which database (pg/oracle) is currently compatible with, which can be viewed through the show command. The set command changes this variable, and the reset command resets it to the database mode at the time of connection. Resetting all will affect this variable ivorysql.database_mode Indicates the current database schema (pg/oracle), which can be viewed through the show command. The set/reset/reset all command does not affect this variable ivorysql.datetime_ignore_nls_mask Indicates whether the date format will be affected by the NLS parameter. The default value is 0, which can be set using the set command. The reset command resets the date format, and the reset all command resets the variable ivorysql.enable_emptystring_to_NULL The value is (on/off), and when this variable is on, it will convert the inserted empty string into a NULL value for storage ivorysql.identifier_case_switch Set character case conversion mode ivorysql.listen_address Indicates the address for compatibility mode listening. When initializing the database, read the configuration from the ivorysql.conf file, modify the value in the configuration file, and restart the database to take effect. This can be viewed through the show command ivorysql.port Indicates the port number for connecting in compatibility mode. When initializing the database, read the configuration from the ivorysql.conf file and modify the value in the configuration file. To take effect, restart the database and view it through the show command nls_date_format Represents the default date format, which can be viewed through the show command and defaults to 'YYYY-MM-DD'. It can be set through the set command and reset back to the default value through the reset command. The reset all command will reset this variable nls_length_semantics Compatible with oracle parameters of the same name, controlling the size of memory occupied by a character nls_timestamp_format Compatible with oracle parameters of the same name, controlling date format with time nls_timestamp_tz_format Compatible with oracle parameters of the same name, controlling the date format with time zone shared_preload_libraries When initializing the database, read from the ivorysql.conf file and view it through the show command. Modify the value in the configuration file and restart the database to take effect. The optional values ​​of this GUC variable are from 0 to 15. optional values Types not formatted through NLS 0 Does not block any types, all time formats are formatted by NLS. 1 date 2 timestamp 3 date、timestamp 4 timestamptz 5 date、timestamptz 6 timestamp、timestamptz 7 date、timestamp、timestamptz 8 timestampltz 9 date、timestampltz 10 timestamp、timestampltz 11 date、timestamp、timestampltz 12 timestamptz、timestampltz 13 date、timestamptz、timestampltz 14 timestamp、timestamptz、timestampltz 15 date、timestamp、timestamptz、timestampltz Usage Example(date) check value of nls_date_format and datetime_ignore_nls_mask ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# show nls_date_format; nls_date_format ----------------- YYYY-MM-DD (1 row) ivorysql=# show ivorysql.datetime_ignore_nls_mask; ivorysql.datetime_ignore_nls_mask ----------------------------------- 0 (1 row) create a table for testing ivorysql=# create table test_nls_date(a int, created_at date); CREATE TABLE insert data ivorysql=# insert into test_nls_date values(1, '2024/04/05'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 2024-04-05 (1 row) modify nls_date_format ivorysql=# set nls_date_format to 'yy-mm-dd'; SET Insert NLS formatted data and view, insert successfully. ivorysql=# insert into test_nls_date values(2, '24/04/15'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows) Changing the date type to not undergo NLS processing, and inserting the same data, such as changing it to 1 (3, 5, 7, etc.), will result in an error upon data insertion. NLS formatting will not affect the query results for dates. ivorysql=# set ivorysql.datetime_ignore_nls_mask to 1; SET ivorysql=# insert into test_nls_date values(3, '24/05/15'); ERROR: date/time field value out of range: \"24/05/15\" LINE 1: insert into test_nls_date values(3, '24/05/15'); ^ HINT: Perhaps you need a different \"datestyle\" setting. ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows)","title":"GUC Framework","component":"ivorysql-doc","version":"master","name":"guc_framework","url":"/ivorysql-doc/master/architecture/guc_framework.html","titles":[{"text":"1. New GUC variables","hash":"new-guc-variables","id":1},{"text":"1.1. Achieve","hash":"achieve","id":2},{"text":"1.2. New variables(currently)","hash":"new-variablescurrently","id":3},{"text":"1.3. Example","hash":"example","id":4},{"text":"1.3.1. ivorysql.datetime_ignore_nls_mask","hash":"ivorysql-datetime_ignore_nls_mask","id":5}]},"16":{"id":16,"text":"The IvorySQL Cloud platform is a comprehensive solution that integrates the IvorySQL database and its surrounding ecosystem to deliver end-to-end database and resource management capabilities. Before starting the installation, compile and install the following projects from GitHub: Frontend: https://github.com/IvorySQL/ivory-cloud-web Backend: https://github.com/IvorySQL/ivory-cloud Prepare a Kubernetes cluster (version 1.23) and install ivory-operator on the master node: https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE The IvorySQL Cloud platform currently supports installation on Linux systems. The required packages are listed below. Component Package Frontend dist Backend cloudnative-1.0-SNAPSHOT.jar K8S cluster docker.io/ivorysql/ivory-operator:v5.0 docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 In addition, install the following supporting components: Backend database: Stores and manages all data related to cloud resources, user information, access control, billing, and more. Use a PostgreSQL-compatible database such as PostgreSQL, HighGo DB, or IvorySQL. NGINX: Hosts the web user interface of the cloud platform. Complete the preparation steps on every server before installation. Deploy IvorySQL Cloud on a Kubernetes (1.23) cluster that already has a default storage class. Disable the firewall on every server to ensure full connectivity. systemctl stop firewalld.service systemctl disable firewalld.service Install the backend database yourself by following the instructions on the IvorySQL official website. # Clone the code git clone https://github.com/IvorySQL/ivory-cloud.git # Go to the project root cd ivory-cloud Ensure that every .sh file in ivory-cloud\\cloudnative\\src\\main\\resources\\monitor and its subdirectories uses the Unix format. If not, run dos2unix to convert them. dos2unix cloudnative\\src\\main\\resources\\monitor\\* # Build mvn clean mvn package -D maven.test.skip=true The packaged artifact `cloudnative-1.0-SNAPSHOT.jar` can be found under `ivory-cloud/cloudnative/target`. Execute the following steps on the Kubernetes server: # Create a working directory mkdir -p /home/ivory # Upload `ivory-cloud/cloudnative/target/cloudnative-1.0-SNAPSHOT.jar` to the directory created above # Configuration files ## Create a configuration directory mkdir -p /home/ivory/config ## Upload configuration files Copy the following files from `ivory-cloud/cloudnative/src/main/resources` to `/home/ivory/config`: application.yaml application-native.yaml spring_pro_logback.xml ## Update the configuration Replace `url`, `username`, and `password` with the database information configured in <<Backend database>>. ## /home/ivory/config/application-native.yaml datasource: druid: db-type: com.alibaba.druid.pool.DruidDataSource driver-class-name: org.postgresql.Driver url: jdbc:postgresql://127.0.0.1:5432/ivorysql username: ivorysql password: \"ivory@123\" # Install JDK 1.8 yum install -y java-1.8.0-openjdk.x86_64 [root@cloud ivory]# pwd /home/ivory/ [root@cloud ivory]# nohup java -jar cloudnative-1.0-SNAPSHOT.jar > log_native 2>&1 & [root@cloud ivory]# ps -ef | grep java root 77494 1 0 Oct09 ? 00:03:07 java -jar cloudnative-1.0-SNAPSHOT.jar ## Fetch the code git clone https://github.com/IvorySQL/ivory-cloud-web.git ## Go to the project root cd ivorysql-cloud-web ## Install dependencies npm install ## Build for production npm run build:prod # Create a deployment directory [root@cloud opt]# mkdir -p /opt/cloud/web # Copy the generated `dist` folder to /opt/cloud/web # Grant permissions [root@cloud web]# chmod 755 /opt/cloud/web/dist [root@cloud web]# chmod -R 777 /opt/cloud/web/dist Edit the configuration file: [root@cloud dist]# pwd /home/cloud/web/dist [root@cloud dist]# vi config.js var PLATFROM_CONFIG = {}; // Replace with the IP address of the current server PLATFROM_CONFIG.baseUrl = \"http://192.168.31.43:8081/cloudapi/api/v1\" // true: show the “Register” button on the login page // false: hide the “Register” button on the login page globalShowRegister = true // Hide the cloud-native database? true: hide; false: show disableNative = false // Database type dbtype = \"IvorySQL\" dbversion = \"5.0\" The IvorySQL Cloud host must have NGINX installed to serve the web interface. Users can select any installation method; the following steps are provided for reference. [root@cloud web]# wget https://nginx.org/download/nginx-1.20.1.tar.gz [root@cloud web]# ls -lrt total 3924 -rwxrwxr-x. 1 root root 1061461 May 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 Oct 9 16:43 dist.tar.gz drwxrwxrwx. 4 root root 103 Oct 21 13:20 dist [root@host30 cloud]# yum -y install pcre-devel [root@host30 cloud]# yum -y install openssl openssl-devel NGINX is installed under the directory specified by --prefix during configure. The example below installs it to /opt/cloud/nginx. ## Extract nginx-1.20.1.tar.gz [root@cloud web]# tar -zxvf nginx-1.20.1.tar.gz ## Verify that nginx-1.20.1 was created [root@cloud web]# ls -lrt total 3924 -rwxrwxr-x. 1 root root 1061461 May 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 Oct 9 16:43 dist.tar.gz drwxrwxr-x. 9 1001 1001 186 Oct 9 16:53 nginx-1.20.1 drwxrwxrwx. 4 root root 103 Oct 21 13:20 dist ## Configure [root@cloud web]# cd nginx-1.20.1 [root@cloud nginx-1.20.1]# ./configure --prefix=/opt/cloud/nginx --with-http_ssl_module ## Compile and install [root@cloud nginx-1.20.1]# make [root@cloud nginx-1.20.1]# make install The configuration file is stored under /opt/cloud/nginx. Adjust it according to the README on GitHub, and replace the IP with the address of the current server. server { listen 9104; server_name 192.168.31.43; location / { root /opt/cloud/web/dist; index index.html index.htm; } error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } } [root@cloud sbin]# pwd /opt/cloud/nginx/sbin [root@cloud sbin]# ./nginx -c /opt/cloud/nginx/conf/nginx.conf [root@cloud sbin]# ps -ef | grep nginx root 2179 131037 0 09:46 pts/1 00:00:00 grep --color=auto nginx root 55047 1 0 Oct21 ? 00:00:00 nginx: master process ./nginx -c /opt/cloud/nginx/conf/nginx.conf nobody 55048 55047 0 Oct21 ? 00:00:00 nginx: worker process Set up the Kubernetes cluster yourself. This section describes how to install ivory-operator on the cluster and preload container images. Refer to the README on GitHub: https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE If your servers have direct access to Docker Hub, you can skip this step. Otherwise, preload the following images on every node in the Kubernetes cluster. docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/pgbouncer:ubi8-1.23.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 docker.io/prom/prometheus:v2.33.5 docker.io/prom/alertmanager:v0.22.2 docker.io/grafana/grafana:8.5.10","title":"Installation Guide","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_installation","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html","titles":[{"text":"1. IvorySQL Cloud Platform Installation","hash":"ivorysql-cloud-platform-installation","id":1},{"text":"2. Pre-installation Checklist","hash":"pre-installation-checklist","id":2},{"text":"2.1. Disable the firewall","hash":"disable-the-firewall","id":3},{"text":"2.2. Backend deployment","hash":"backend-deployment","id":4},{"text":"2.2.1. Backend database","hash":"backend-db","id":5},{"text":"2.2.2. Backend services","hash":"backend-services","id":6},{"text":"2.2.2.1. Compile the backend service","hash":"compile-the-backend-service","id":7},{"text":"2.2.2.2. Deploy the service","hash":"deploy-the-service","id":8},{"text":"2.2.3. Start the backend service","hash":"start-the-backend-service","id":9},{"text":"2.3. Frontend deployment","hash":"frontend-deployment","id":10},{"text":"2.3.1. Compile the frontend","hash":"compile-the-frontend","id":11},{"text":"2.3.2. Update directory and file permissions","hash":"update-directory-and-file-permissions","id":12},{"text":"2.3.3. Update config.js","hash":"update-config-js","id":13},{"text":"2.4. Install and configure NGINX","hash":"install-and-configure-nginx","id":14},{"text":"2.4.1. Download the NGINX source package","hash":"download-the-nginx-source-package","id":15},{"text":"2.4.2. Install dependencies","hash":"install-dependencies","id":16},{"text":"2.4.3. Build and install NGINX","hash":"build-and-install-nginx","id":17},{"text":"2.4.4. Update nginx.conf","hash":"update-nginx-conf","id":18},{"text":"2.4.5. Start NGINX","hash":"start-nginx","id":19},{"text":"2.5. Operator deployment","hash":"operator-deployment","id":20},{"text":"2.5.1. Install ivory-operator","hash":"install-ivory-operator","id":21},{"text":"2.5.2. Load container images","hash":"load-container-images","id":22}]},"17":{"id":17,"text":"IvorySQL Cloud is a web-based service platform that can be accessed from any computer through a browser. After installing the cloud service platform on the server with IP 192.168.31.43, open a browser and enter http://192.168.31.43:9104/ (9104 is the port configured in nginx.conf.default) to reach the login page: On the login page, enter the prompted information to access the IvorySQL Cloud service platform: Click the avatar in the upper-right corner to display the current username and the Log Out action. Click Log Out to exit; click the username to stay on the current page: After signing in as the admin user, click K8S Cluster Management in the left navigation bar to open the cluster list. Click Add Kubernetes Cluster in the upper-left corner, fill in the cluster information, and submit. The cluster management page lists details for each cluster and allows you to edit or delete them. Sign in with the demo user. Click Database Subscription in the left navigation, fill in the database parameters, and click Next: Confirm. Review the information and click Confirm. After confirming, the page automatically redirects to Database Management to show the subscription task. Displays all databases managed by the cloud service platform. Sign in with the demo user. Go to Database Management, select a database, click More in the Actions column, and choose Restart. Review the information and click Confirm. Sign in with the demo user. Go to Database Management, select a database, and click its Instance ID. On the database details page, click the password icon. Enter a new password and click Confirm. Sign in with the demo user. Go to Database Management, select a database, click More in the Actions column, and choose Delete Instance. Review the confirmation window and click Confirm. This feature requires additional plug-ins such as TopoLVM. Sign in with the demo user. Click Storage Expansion, select a database, then click Edit in the Actions column; alternatively, go to Database Management, click More, and choose Storage Expansion. Enter the expanded storage size and click Confirm. Sign in with the demo user. Click Specification Change, select a database, then click Edit in the Actions column; or go to Database Management, click More, and choose Specification Change. Enter the new specification and click Confirm. Sign in with the demo user. Go to Database Backup, select a database, and click Backup in the Actions column; or go to Database Management, click More, and choose Backup. Enter a backup name and click Confirm. Sign in with the demo user. Go to Database Restore, select a database, and click View in the Actions column; or go to Database Management, click More, and choose Restore. Select the backup file and click Restore in the Actions column. Enter the target database information. Use the database password from before the backup. Continue following the workflow described in “4.1 Database Subscription”. Sign in with the demo user. Click Monitoring Tools in the left navigation and select the cluster where the database resides; or go to Database Management, select a database, click More, and choose Monitoring. After the monitoring stack is created, repeat step (2) to open the monitoring page, then sign in with admin/admin and click Login. Click the magnifying glass icon to view monitoring metrics. Sign in with the demo user. Go to Database Management, select a database, click More in the Actions column, and choose Login. On the new page, enter the database account sysdba@ivyo.com and the database password, then click Login. Once the connection is established, you can operate on the database.","title":"User Guide","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_usage","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html","titles":[{"text":"1. Sign In and Sign Out","hash":"sign-in-and-sign-out","id":1},{"text":"1.1. Sign In","hash":"sign-in","id":2},{"text":"1.2. Sign Out","hash":"sign-out","id":3},{"text":"2. Administrator Features","hash":"administrator-features","id":4},{"text":"2.1. Add a Cluster","hash":"add-a-cluster","id":5},{"text":"2.2. Manage Clusters","hash":"manage-clusters","id":6},{"text":"3. demo User Features","hash":"demo-user-features","id":7},{"text":"3.1. Database Subscription","hash":"database-subscription","id":8},{"text":"3.2. Database Management","hash":"database-management","id":9},{"text":"3.3. Restart a Database","hash":"restart-a-database","id":10},{"text":"3.4. Change the Password","hash":"change-the-password","id":11},{"text":"3.5. Delete an Instance","hash":"delete-an-instance","id":12},{"text":"3.6. Storage Expansion","hash":"storage-expansion","id":13},{"text":"3.7. Specification Change","hash":"specification-change","id":14},{"text":"3.8. Database Backup","hash":"database-backup","id":15},{"text":"3.9. Database Restore","hash":"database-restore","id":16},{"text":"3.10. Database Monitoring","hash":"database-monitoring","id":17},{"text":"3.11. Visual Login Tool","hash":"visual-login-tool","id":18}]},"18":{"id":18,"text":"Currently, PostgreSQL’s CALL statement has the following limitations: Does not support the INTO clause; Cannot call functions that return values; Cannot assign results to client-side variables (i.e., Oracle’s binding variables / host variables). To enhance compatibility with Oracle, IvorySQL has implemented support for the CALL func(…​) INTO :var; syntax, allowing users to receive function return values through binding variables (e.g., :x). This behavior (including precision checks and error handling) aligns with Oracle’s standards. Since PostgreSQL/IvorySQL inherently does not support direct assignment to client-side variables at the SQL level, this solution adopts a \"client-side rewriting + server-side collaboration\" approach: When the CALL statement includes binding variables (e.g., :x): The client rewrites it into a special anonymous PL block (DO $$ ... $$); Sends it using the extended query protocol to transmit parameter type and precision information; The server executes this anonymous block and returns the result to the client; The client then writes the result to the corresponding binding variable. When the CALL statement does not contain binding variables: The behavior remains fully consistent with native PostgreSQL, using the simple query protocol without any rewriting. To ensure compatibility with the CALL [INTO] statement in the interface, it must be converted into an anonymous PL/iSQL block, leveraging the support for OUT parameters in anonymous blocks to achieve functional equivalence. This requires the get_parameter_description function to correctly identify CALL statements and, when encountering CALL INTO, return the rewritten PL statement. Correspondingly, the get_hostvariables routine needs to store this information (such as whether it is a CALL statement, whether it includes INTO, the rewritten statement, etc.) in the HostVariable structure. The definition of HostVariable is as follows: typedef struct HostVariable { HostVariableEntry *hostvars; int length; bool isdostmt; bool iscallstmt; // Whether it is from a CALL statement char *convertcall; // Rewritten statement } HostVariable; On the server side, modifications are required in the syntax parser section. Add CALL INTO grammar rules in ora_gram.y, and generate rewritten PL statements in the action section, such as \"x := add(1,2);\". CallStmt: CALL func_application { CallStmt *n = makeNode(CallStmt); n->funccall = castNode(FuncCall, $2); $$ = (Node *)n; } | CALL func_application INTO ORAPARAM { CallStmt *n = makeNode(CallStmt); OraParamRef *hostvar = makeNode(OraParamRef); char *callstr = NULL; n->funccall = castNode(FuncCall, $2); hostvar->number = 0; hostvar->location = @4; hostvar->name = $4; n->hostvariable = hostvar; callstr = pnstrdup(pg_yyget_extra(yyscanner)->core_yy_extra.scanbuf + @2, @3 - @2); n->callinto = psprintf(\"%s := %s;\", $4, callstr); pfree(callstr); $$ = (Node *)n; } ; The CallStmt structure needs to store the INTO clause and the converted PL statement. typedef struct CallStmt { NodeTag type; FuncCall *funccall; /* from the parser */ FuncExpr *funcexpr; /* transformed call, with only input args */ List *outargs; /* transformed output-argument expressions */ OraParamRef *hostvariable; /* only used for get_parameter_description() */ char *callinto; /* rewrite CALL INTO to a PL assign stmt */ } CallStmt; To distinguish between regular DO statements and anonymous blocks converted from CALL statements, a new keyword GENERATED FROM CALL is added to the syntax. opt_do_from_where: GENERATED FROM CALL { $$ = true; } | /*EMPTY*/ { $$ = false; } ; The generated DoStmt node will set do_from_call = true for executor identification. typedef struct DoStmt { NodeTag type; List *args; /* List of DefElem nodes */ List *paramsmode; /* List of parameters mode */ List *paramslen; /* List of length for parameter datatypes */ bool do_from_call; /* True if DoStmt is come from CallStmt */ } DoStmt; In the IVY interface, placeholder information is obtained through a Set-Returning Function (SRF) called get_parameter_description. This function needs to identify the type of input statement and return the rewritten PL/iSQL assignment statement when encountering CALL INTO statements. To achieve this, IvorySQL has extended the return structure (TupleDesc) of this function: a new hint field has been added specifically to return the rewritten PL code for CALL INTO statements; for other types of statements, this field remains NULL. Additionally, the first field of the first tuple in the original function result set previously used only true/false to distinguish whether the statement was an anonymous block. To more accurately identify statement types (especially CALL statements), it has now been modified to return the corresponding parse tree’s CommandTag. All these metadata details are ultimately encapsulated into a user context structure to enable efficient passing and reuse across multiple invocations of the SRF function. { OraParamExtralData *extral; const char *cmdtag; char *callintoexpr; } outparam_fctx; The IVY-prefixed interfaces involved in CALL include: IvyStmtExecute IvyStmtExecute2 IvyexecPreparedStatement IvyexecPreparedStatement2 In the aforementioned interfaces, user-provided CALL [INTO] statements are rewritten into a \"special\" anonymous block statement. To clearly identify such anonymous blocks converted from CALL statements, a dedicated type has been added to the statement type definitions of the interface. The purpose of this type is to correctly recognize such statements in IvyHandleDostmt and generate execution statements in the form of:DO ... USING … — GENERATED FROM CALL typedef enum IvyStmtType { IVY_STMT_UNKNOW, IVY_STMT_DO, IVY_STMT_DOFROMCALL, /* new statement type */ IVY_STMT_DOHANDLED, IVY_STMT_OTHERS } IvyStmtType; When rewriting CALL statements, if encountering a CALL INTO statement that invokes a function, the interface needs to internally adjust the order of bound variables. This adjustment is completely transparent to users: when binding parameters, users only need to follow the order in which the variables appear in the CALL statement—that is, the variable in the INTO clause is positioned last in the original statement. However, in the rewritten special anonymous block, this INTO variable will appear as the left-hand side (i.e., the first parameter) of an assignment expression. Therefore, the interface must internally adjust the binding order correctly to ensure the execution logic matches the user’s expectations. All interface routines involved in this logic must implement this handling. The relevant routines are as follows: Ivyreplacenamebindtoposition Ivyreplacenamebindtoposition2 Ivyreplacenamebindtoposition3 In interfaces such as IvyexecPreparedStatement and IvyexecPreparedStatement2, users must explicitly provide paramvalues, paramlengths, paramformats, and parammode for each parameter. For CALL statements, the order of elements in these parameter arrays must be adjusted according to the rewritten anonymous block structure to ensure binding consistency with the execution logic. Among them, IvyexecPreparedStatement2 is more specialized: it requires users to additionally provide an output binding list of type IvyBindOutInfo*. This list is not only used to bind OUT parameters but is also utilized by IvyAssignPLISQLOutParameter to identify the data type of each OUT parameter when retrieving PL/iSQL procedure results. Therefore, when processing CALL statements, the interface first reorders the user-provided IvyBindOutInfo* list (moving the INTO-bound output variable to the first position) and then writes it into the IvyPreparedStatement statement handle for subsequent assignment. Regarding precision handling for output parameters: When there is a mismatch between the precision of an output binding variable in a CALL statement and the actual returned value, the system may either raise an error or automatically truncate—the specific behavior depends on whether the data type of the binding variable exactly matches the parameter type declared in the procedure/function. In the PL/iSQL inline handler, the precise data type of each OUT parameter can be obtained through ParamListInfo during the binding phase. If the currently executed anonymous block is a special DoStmt converted from a CALL statement, the system performs the following checks during assignment: If the type recorded in ParamListInfo exactly matches the formal parameter type of the function/stored procedure, a forced type conversion is applied for assignment. Otherwise, an implicit type conversion is used for assignment. This mechanism is designed to be compatible with Oracle’s behavior, ensuring safe and reasonable assignment even when types do not fully match. -- Original CALL statement: CALL my_func(:in1, :in2) INTO :out; -- Rewritten as: do $$BEGIN :out := my_func(:in1, :in2); END$$ using out INOUT, in1 INOUT, in2 INOUT paramslength -1,-1,-1 GENERATED FROM CALL;","title":"CALL INTO","component":"ivorysql-doc","version":"master","name":"call_into","url":"/ivorysql-doc/master/compatibility_features_design/call_into.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Overall Design Approach","hash":"overall-design-approach","id":2},{"text":"3. Implementation Details","hash":"implementation-details","id":3},{"text":"3.1. psql","hash":"psql","id":4},{"text":"3.2. Server-side","hash":"server-side","id":5},{"text":"3.3. Interface layer","hash":"interface-layer","id":6}]},"19":{"id":19,"text":"To meet Oracle’s quoted identifier case compatibility requirements, IvorySQL has designed three case conversion modes for quoted identifiers. If the parameter -C is appended during database initialization, with values of normal/interchange/lowercase, the Initdb.c→main() function in the code will process this parameter and set the global variable caseswitchmode according to the parameter value. Then the initdb command will start a postgres process in -boot mode to set up the template1 template database, while passing the parameter -C ivorysql.identifier_case_switch=caseswitchmode to the new process. This newly started backend process will write the identifier_case_switch information to the pg_control file through the following code: BootstrapModeMain() -> BootStrapXLOG(); /* save database compatible level value */ ControlFile->dbmode = bootstrap_database_mode; ControlFile->casemode = identifier_case_switch; /* some additional ControlFile fields are set in WriteControlFile() */ WriteControlFile(); When a user starts the database using the pg_ctl command, the postmaster process will read the contents of the pg_control file. The code call path is: PostmasterMain()-->SetCaseGucOption()-->GetCaseSwitchModeFromControl() After reading the parameter value, the SetConfigOption() function is called to assign the value. When each new backend process starts, since it is forked from the postmaster process, it automatically has the same ivorysql.identifier_case_switch parameter value. First, it processes the startup packet, and if it contains database or user parameters, the parameter values are processed accordingly based on identifier_case_switch. The source code is: BackendMain()->BackendInitialize()-->ProcessStartupPacket() Additionally, when processing user SQL statements, if they contain identifiers, the same processing is performed. The code is distributed in: SplitIdentifierString(), quoteOneName() and SplitGUCList() functions.","title":"Quoted Identifier Case Conversion","component":"ivorysql-doc","version":"master","name":"case_conversion","url":"/ivorysql-doc/master/compatibility_features_design/case_conversion.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation Details","hash":"implementation-details","id":2}]},"20":{"id":20,"text":"In Oracle databases, empty strings ('') are treated as NULL values, which is an important Oracle feature. To maintain compatibility with this Oracle behavior, IvorySQL provides the empty string to NULL conversion functionality. When this feature is enabled, empty strings in SQL statements are automatically converted to NULL values, ensuring behavioral consistency for Oracle applications running on IvorySQL. The parameter ivorysql.enable_emptystring_to_NULL corresponds to the GUC variable enable_emptystring_to_NULL. In the file ora_scan.l, you can see how this variable is used: case xe: yylval->str = litbufdup(yyscanner); if (strcmp(yylval->str, \"\") == 0 && ORA_PARSER == compatible_db && enable_emptystring_to_NULL) { return NULL_P; } return SCONST; Where xe represents strings enclosed in quotes: <xe> extended quoted strings (support backslash escape sequences) The logic of the above code is that during lexical analysis, if an empty string is encountered and the empty-to-NULL conversion feature is enabled, it returns NULL_P; otherwise, it returns SCONST. In the grammar analysis file ora_gram.y, the statement insert into abc values(''); is parsed as follows: values_clause: VALUES '(' expr_list ')' { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1($3); $$ = (Node *) n; } expr_list: a_expr { $$ = list_make1($1); } a_expr: c_expr { $$ = $1; } c_expr: AexprConst { $$ = $1; } AexprConst: Iconst | Sconst { $$ = makeStringConst($1, @1); } | NULL_P { $$ = makeNullAConst(@1); } The above code constructs corresponding nodes to handle NULL_P or SCONST returned from the lexical analysis.","title":"Empty String to NULL Conversion","component":"ivorysql-doc","version":"master","name":"empty_string_to_null","url":"/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation","hash":"implementation","id":2}]},"21":{"id":21,"text":"Force View offers Oracle-compatible CREATE [OR REPLACE] FORCE VIEW and ALTER VIEW …​ COMPILE behavior, allowing developers to persist a placeholder view even when dependencies are missing and then switch it back to a normal view once dependencies are available. The system records the original SQL text and identifier case mode in a dedicated catalog entry so that automatic or manual recompilation can restore the view with Oracle-style warnings and error reporting. In PostgreSQL, a base table referenced by a view cannot be dropped unless CASCADE is used. Oracle, however, allows the base table to be dropped while retaining the Force View and marking it as invalid. To align with Oracle semantics, IvorySQL introduces new grammar rules and a catalog that stores Force View metadata so that the Force state can be shared across sessions. Register the Force View keywords in src/backend/oracle_parser/ora_gram.y. Set ViewStmt→force during the grammar phase, and generate the AT_ForceViewCompile option when parsing AlterTableStmt. Preserve ViewStmt→stmt_literal for later reuse when the placeholder is materialized. /* Insert or update the pg_force_view catalog as needed */ if (need_store) { ...... StoreForceViewQuery(address.objectId, stmt->replace, ident_case, stmt->stmt_literal ? stmt->stmt_literal : queryString); } Add bool force and char *stmt_literal to ViewStmt in src/include/nodes/parsenodes.h. Define AT_ForceViewCompile consistently between parsenodes.h and tablecmds.c so that ALTER VIEW …​ COMPILE flows into the regular alter-table machinery. parse_analyze still follows the native path; Force mode intervenes only after a parsing error occurs. DefineView() in src/backend/commands/view.c now handles both normal and Force views. The function first attempts normal parsing inside a PG_TRY/PG_CATCH; if the semantic analysis fails, it checks stmt→force to decide whether to enter the Force View branch. On success it continues to StoreViewQuery(); on failure it falls back to the Force View logic so that the view object still exists. src/include/catalog/pg_force_view.h defines the catalog table, whose fields include the view OID (fvoid), identifier case (ident_case), and the original SQL text (source). The unique index pg_force_view_fvoid_index plus the syscache FORCEVIEWOID (declared in src/include/catalog/syscache_info.h) enable lookups by view OID. The catalog enables TOAST so lengthy SQL definitions are preserved in full, covering complex migration scripts. CATALOG(pg_force_view,9120,ForceViewRelationId) { /* oid of force view */ Oid fvoid; /* see IDENT_CASE__xxx constants below */ char ident_case; #ifdef CATALOG_VARLEN /* variable-length fields start here */ /* sql definition */ text source; #endif } FormData_pg_force_view; bool rel_is_force_view(Oid relid) in src/backend/commands/view.c determines whether a view is in Force state by checking whether the _RETURN rule exists. Force views still register as relkind = RELKIND_VIEW, avoiding extra compatibility branches. pg_class.relhasrules is set to false while in placeholder mode, which becomes part of the detection logic. After successful parsing, DefineView() calls DefineVirtualRelation() to populate pg_class, pg_attribute, and related catalogs. StoreViewQuery() generates the _RETURN rule and records dependencies. This path never touches pg_force_view; the view is immediately ready for use. CreateForceVirtualPlaceholder() in src/backend/commands/view.c creates or reuses a placeholder view: If the view does not exist, it calls DefineVirtualRelation() to create the base object without a _RETURN rule. If a Force View already exists, it reuses the current record and updates column definitions or cleans up legacy metadata. If a normal view exists and OR REPLACE is specified, it invokes make_view_invalid() to invalidate the old definition before installing the placeholder. StoreForceViewQuery() persists stmt_literal and the current ivorysql.identifier_case_switch to pg_force_view so identifier case semantics can be restored later. After the placeholder is created, the client receives WARNING: View created with compilation errors, indicating the view cannot yet be used. make_view_invalid() is triggered when dependencies are dropped, altered, or otherwise compromised. The routine removes the _RETURN rule, clears pg_depend entries, resets pg_class.relhasrules, and truncates pg_attribute column metadata. It also captures CREATE FORCE VIEW …​ AS <pg_get_viewdef> and saves it in pg_force_view, while setting ident_case to IDENT_CASE_UNDEFINE to indicate the SQL is system-generated. The view remains visible in metadata catalogs but runtime access detects the Force flag. Because _RETURN is missing, the executor calls compile_force_view() when opening the view; if compilation fails, it raises view \"<schema>.<name>\" has errors. Users can recover by issuing ALTER VIEW …​ COMPILE or CREATE OR REPLACE FORCE VIEW once dependencies are ready. addRangeTableEntry() in parse_relation.c and the target-relation open logic in parse_clause.c call compile_force_view() after detecting a Force view. The function reruns raw_parser and parse_analyze; on success it reinstalls the _RETURN rule, and on failure it aborts the statement with the encountered error. AT_ForceViewCompile executes during phase 2 in tablecmds.c, acquiring AccessExclusiveLock before invoking compile_force_view(). Successful compilation behaves like a normal ALTER VIEW; failures emit WARNING: View altered with compilation errors, and the view stays in placeholder mode. compile_force_view() reads pg_force_view.source, rebuilds a ViewStmt, and calls compile_force_view_internal(). The routine uses checkViewColumns() to compare legacy columns, allowing additions but rejecting incompatible type changes; new columns are applied through AT_AddColumnToView(). _RETURN is regenerated via StoreViewQuery(), and DeleteForceView() removes the catalog record so the view becomes a standard one again.","title":"Force View","component":"ivorysql-doc","version":"master","name":"force_view","url":"/ivorysql-doc/master/compatibility_features_design/force_view.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation Description","hash":"implementation-description","id":2},{"text":"2.1. Grammar and Parsing Entry Points","hash":"grammar-and-parsing-entry-points","id":3},{"text":"2.1.1. Force View Syntax Support","hash":"force-view-syntax-support","id":4},{"text":"2.1.2. AST Field Extensions","hash":"ast-field-extensions","id":5},{"text":"2.1.3. Parsing Entry","hash":"parsing-entry","id":6},{"text":"2.2. Force View Metadata","hash":"force-view-metadata","id":7},{"text":"2.2.1. pg_force_view Catalog","hash":"pg_force_view-catalog","id":8},{"text":"2.2.2. View State","hash":"view-state","id":9},{"text":"2.3. Creation and Replacement Flow","hash":"creation-and-replacement-flow","id":10},{"text":"2.3.1. Normal View","hash":"normal-view","id":11},{"text":"2.3.2. Force View","hash":"force-view","id":12},{"text":"2.4. Dependency Invalidations and Rollback","hash":"dependency-invalidations-and-rollback","id":13},{"text":"2.4.1. Active Invalidation Logic","hash":"active-invalidation-logic","id":14},{"text":"2.4.2. Observable Behavior After Invalidation","hash":"observable-behavior-after-invalidation","id":15},{"text":"2.5. Automatic and Manual Compilation","hash":"automatic-and-manual-compilation","id":16},{"text":"2.5.1. Automatic Compilation Triggers","hash":"automatic-compilation-triggers","id":17},{"text":"2.5.2. Manual Compilation","hash":"manual-compilation","id":18},{"text":"2.5.3. Column Checks and Metadata Updates","hash":"column-checks-and-metadata-updates","id":19}]},"22":{"id":22,"text":"PostgreSQL supports functions and stored procedures, but there are syntax differences between PostgreSQL and Oracle. To enable Oracle’s PL/SQL statements to run on IvorySQL—i.e., to achieve \"syntax compatibility\"—IvorySQL adopts the following solution: If an Oracle clause has a counterpart clause with the same function in IvorySQL, it is directly mapped to the corresponding IvorySQL clause; otherwise, only the syntax of the Oracle clause is implemented, while its function is not. PL/iSQL is the name of the procedural language in IvorySQL, specifically designed to be compatible with Oracle’s PL/SQL statements. To achieve compatibility with Oracle-style syntax for functions and stored procedures, corresponding adjustments need to be made to the psql client tool, the SQL layer, and the PL/iSQL layer. Oracle’s sqlplus tool uses a slash (/) to terminate functions and stored procedures. IvorySQL’s client tool, psql, needs to be compatible with the same syntax. This means that the conventional mechanism—where statements are sent to the server upon encountering a semicolon—becomes ineffective when dealing with Oracle-style function and stored procedure commands; instead, a slash (/) is used to send the commands. To this end the following fields are added to the PsqlScanStateData structure: bool cancel_semicolon_terminator; /* not send command when semicolon found */ /* * State to track boundaries of Oracle ANONYMOUS BLOCK. * Case 1: Statements starting with << ident >> is Oracle anonymous block. */ int token_count; /* # of tokens, not blank or newline since start of statement */ bool anonymous_label_start; /* T if the first token is \"<<\" */ bool anonymous_label_ident; /* T if the second token is an identifier */ bool anonymous_label_end; /* T if the third token is \">>\" */ /* * Case 2: DECLARE BEGIN ... END is Oracle anonymous block syntax. * DECLARE can also be a PostgreSQL cursor declaration statement, we need to tell this. */ bool maybe_anonymous_declare_start; /* T if the first token is DECLARE */ int token_cursor_idx; /* the position of keyword CURSOR in SQL statement */ /* * Case 3: DECLARE BEGIN ... END is Oracle anonymous block syntax. * BEGIN can also be a PostgreSQL transaction statement. */ bool maybe_anonymous_begin_start; /* T if the first token is BEGIN */ Meanwhile, modify ora_psqlscan.l and add or update the corresponding lexical rules. Below is a code snippet example: { if (is_oracle_slash(cur_state, cur_state->scanline)) { /* Terminate lexing temporarily */ cur_state->cancel_semicolon_terminator = false; cur_state->maybe_anonymous_declare_start = false; cur_state->maybe_anonymous_begin_start = false; cur_state->anonymous_label_start = false; cur_state->anonymous_label_ident = false; cur_state->anonymous_label_end = false; cur_state->start_state = YY_START; cur_state->token_count = 0; cur_state->token_cursor_idx = 0; cur_state->identifier_count = 0; cur_state->begin_depth = 0; cur_state->ora_plsql_expect_end_symbol = END_SYMBOL_INVALID; return LEXRES_SEMI; } ECHO; The psql tool needs to detect the meaning of the slash (/), to avoid identifying slashes in comments and other parts as terminators. To this end, a separate interface is_oracle_slash is added in the oracle_fe_utils/ora_psqlscan.l file for detection. bool is_oracle_slash(PsqlScanState state, const char *line) { bool result = false; switch (state->start_state) { case INITIAL: case xqs: /* treat these like INITIAL */ { int len, i; bool has_slash = false; len = strlen(line); for (i = 0; i < len; i++) { /* allow special char */ if (line[i] == '\\t' || line[i] == '\\n' || line[i] == '\\r' || line[i] == ' ') continue; if (line[i] == '/') { if (has_slash) break; has_slash = true; continue; } /* others */ break; } if (i == len && has_slash) result = true; } break; default: break; } return result; } The SQL layer needs to be able to recognize the creation syntax for functions and stored procedures, and this is achieved by modifying ora_base_yylex. This function prefetches and caches tokens: if the token follows Oracle syntax, it organizes an SCONST and sends it to the PL/SQL layer; otherwise, it retrieves the previously preread tokens from the stack and processes them according to the native PostgreSQL logic. The following fields are added to the ora_base_yy_extra_type structure: /* * The native PG only cache one-token info include yylloc, yylval and token * number in yyextra, IvorySQL cache multiple tokens info using two arrays. */ int max_pushbacks; /* the max size of cache array */ int loc_pushback; /* # of used tokens */ int num_pushbacks; /* # of cached tokens */ int *pushback_token; /* token number array */ TokenAuxData *pushback_auxdata; /* auxdata array */ OraBodyStyle body_style; int body_start; int body_level; Add operation interfaces for the token stack: push_back_token forward_token ora_internal_yylex internal_yylex  In the ora_base_yylex function, when creating functions, procedures, or anonymous blocks, some tokens are preread. These tokens are cached into the stack using the aforementioned structure, and this is done to construct an SCONST that conforms to Oracle PL/SQL syntax and send it to the PL/iSQL layer for processing. For details, please refer to the source code. This part mainly modifies the pl_gram.y file to achieve compatibility with the syntax of PL/SQL functions and stored procedures. It enables compatibility with Oracle PL/SQL syntax forms without affecting PostgreSQL’s native PL/pgSQL. Below is a code example for the compatibility of the DECLARE section; for more details, please refer to the IvorySQL source code. /* * The declaration section of the outermost block in Oracle does not have the DECLARE keyword. */ ora_outermost_pl_block: ora_decl_sect K_BEGIN proc_sect exception_sect K_END opt_label { PLiSQL_stmt_block *new; new = palloc0(sizeof(PLiSQL_stmt_block)); new->cmd_type = PLISQL_STMT_BLOCK; new->lineno = plisql_location_to_lineno(@2); new->stmtid = ++plisql_curr_compile->nstatements; new->label = $1.label; new->n_initvars = $1.n_initvars; new->initvarnos = $1.initvarnos; new->body = $3; new->exceptions = $4; check_labels($1.label, $6, @6); plisql_ns_pop(); $$ = (PLiSQL_stmt *)new; } ; ora_decl_sect: opt_block_label opt_ora_decl_start opt_ora_decl_stmts { if ($2) { if ($1 == NULL) { plisql_ns_push(NULL, PLISQL_LABEL_BLOCK); } } } opt_ora_decl_stmts { if ($4) { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; /* Remember variables declared in decl_stmts */ $$.n_initvars = plisql_add_initdatums(&($$.initvarnos)); } else { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); $$.n_initvars = 0; if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; $$.initvarnos = NULL; } } ; opt_ora_decl_start: K_DECLARE { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = true; } | /*EMPTY*/ { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = false; } ; opt_ora_decl_stmts: ora_decl_stmts { $$ = true; } | /*EMPTY*/ { $$ = false; } ora_decl_stmts: ora_decl_stmts ora_decl_stmt | ora_decl_stmt ; ora_decl_stmt: decl_statement ;","title":"PL/iSQL function and stored procedure","component":"ivorysql-doc","version":"master","name":"function_procedure","url":"/ivorysql-doc/master/compatibility_features_design/function_procedure.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation description","hash":"implementation-description","id":2},{"text":"2.1. Client tool psql","hash":"client-tool-psql","id":3},{"text":"2.2. SQL layer","hash":"sql-layer","id":4},{"text":"2.3. PL/iSQL layer","hash":"plisql-layer","id":5}]},"23":{"id":23,"text":"The LIKE syntax in Oracle and IvorySQL is identical. Their difference lies in the expression types: Oracle supports using the LIKE keyword with wildcards for fuzzy queries on columns of numeric, date, and string types. Native PostgreSQL only supports string types, not date or numeric types. IvorySQL achieves Oracle-compatible LIKE operator functionality by extending data type support and operator overloading. In PostgreSQL, the fundamental string type is text, so LIKE is text-based. Other PostgreSQL types implicitly convert to text, enabling automatic conversion without creating operators. In IvorySQL, the Oracle-compatible string type is varchar2. Therefore, a LIKE operator for varchar2 is created, and other Oracle types also utilize the LIKE operator by implicitly converting to varchar2 without requiring additional operator creation. In the previous implementation of Oracle-compatible data types, IvorySQL established implicit conversions from certain data types (such as integer, float8, float4) to varchar2, but not directly to text. To achieve LIKE operator compatibility for these types, there are two approaches: 1、Add a separate LIKE operator for each individual type. 2、Implement a base LIKE operator for varchar2. In the second approach, since IvorySQL already supports implicit conversions from float8, integer, number, etc., to varchar2, these data types can share the same operator. Thus, only a single LIKE operator for varchar2 needs to be created. You can use the following SQL statement to check existing implicit type conversions. -- Check existing implicit conversion paths (where 9503 is the OID of varchar2) SELECT t1.typname AS source_type, t2.typname AS target_type FROM pg_cast C JOIN pg_type t1 ON C.castsource = t1.OID JOIN pg_type t2 ON C.casttarget = t2.OID WHERE C.casttarget = 9503; After reviewing, it can be observed that all the types requiring compatibility have implicit conversions defined in this table. Therefore, these types can be directly converted to the text type for fuzzy query operations. CREATE OR REPLACE FUNCTION sys.varchar2like(varchar2, varchar2) RETURNS bool AS $$ SELECT $1::text LIKE $2::text; $$ LANGUAGE SQL IMMUTABLE STRICT; CREATE OPERATOR ~~ ( PROCEDURE = sys.varchar2like, LEFTARG = varchar2, RIGHTARG = varchar2 ); The first code segment defines a function named sys.varchar2like, which accepts two parameters of type varchar2. By converting them to PostgreSQL’s native text type, it performs standard LIKE pattern matching and ultimately returns a boolean value indicating whether the match is successful. The second code segment creates an operator named ~~, which uses the previously defined varchar2like function as its implementation. It specifies that both the left and right operands of this operator must be of type varchar2. This establishes an Oracle-compatible LIKE operator in IvorySQL. When users employ the ~~ operator, it essentially invokes PostgreSQL’s native LIKE functionality after type conversion, thereby achieving semantic compatibility with Oracle’s LIKE behavior.","title":"Functional Overview","component":"ivorysql-doc","version":"master","name":"like_operator","url":"/ivorysql-doc/master/compatibility_features_design/like_operator.html","titles":[{"text":"1. Implementation Principle","hash":"implementation-principle","id":1},{"text":"1.1. Type Conversion","hash":"type-conversion","id":2},{"text":"1.2. Core Function Implementation","hash":"core-function-implementation","id":3}]},"24":{"id":24,"text":"Nested subfunctions refer to functions or procedures defined inside another function, stored procedure, or anonymous block; they are also called subprocs or inner functions. Parent functions are the outer functions, stored procedures, or anonymous blocks that host nested subfunctions and are responsible for invoking them during execution. When a DECLARE block contains a function …​ is/as begin …​ end construct, pl_gram.y calls plisql_build_subproc_function() (similar to creating a regular function and updating the entry in pg_proc): Create a PLiSQL_subproc_function entry in the parent PLiSQL_function’s `subprocfuncs[] array to store the name, arguments, return type, and other attributes, and record the index fno as the identifier of this subfunction. Call plisql_check_subprocfunc_properties() to validate the combination of declaration and definition attributes. Nested subfunctions share the parent’s datum table. During compilation, PLiSQL_function→datums describes variables and record fields inside the subfunction, while PLiSQL_execstate→datums keeps the runtime values. If the subfunction uses polymorphic parameters, the parser stores its source code in subprocfunc→src and sets has_poly_argument to true so that the executor can recompile it for each distinct argument type. The parent PLiSQL_function gains a subprocfuncs array, each element being the PLiSQL_subproc_function created earlier. Each PLiSQL_subproc_function has a HTAB *poly_tab pointer that is initialized on the first compilation when has_poly_argument is true. The hash key is PLiSQL_func_hashkey, which records the subfunction’s fno and input argument types; the value is the compiled PLiSQL_function * execution context. PostgreSQL builds a ParseState structure during compilation. plisql_subprocfunc_ref() locates the parent PLiSQL_function through ParseState→p_subprocfunc_hook() and calls plisql_ns_lookup() to gather all fno values for subfunctions sharing the same name, then selects the best match based on argument count and types. When FuncExpr nodes are created, the subfunction call is tagged for later execution: function_from = FUNC_FROM_SUBPROCFUNC, parent_func points to the parent PLiSQL_function, and funcid = fno. In plisql_call_handler(), when function_from == FUNC_FROM_SUBPROCFUNC, the runtime fetches the appropriate PLiSQL_subproc_function via the pair (parent_func, fno): For non-polymorphic subfunctions, reuse the precompiled action tree stored in subprocfunc→function. For polymorphic subfunctions, probe poly_tab; if there is no cached plan, call plisql_dynamic_compile_subproc() to compile one and store it in the cache. Before execution, plisql_init_subprocfunc_globalvar() forks relevant entries from the parent’s datum table so the subfunction can access the latest parent variables without polluting the parent scope. After execution, plisql_assign_out_subprocfunc_globalvar() writes back the necessary variables. pl_gram.y adds productions for subprocedure declarations and nested definitions, and records metadata such as lastoutvardno and subprocedure descriptors. Nested subfunctions can reference variables from the parent scope, other subprocedures, and user-defined types. Whenever a function …​ is/as begin …​ end construct is seen inside a DECLARE block, pl_gram.y invokes plisql_build_subproc_function(): Insert a PLiSQL_subproc_function entry into the parent PLiSQL_function→subprocfuncs[], storing the name, arguments, return type, and other attributes, and assign an index fno. Call plisql_check_subprocfunc_properties() to verify that declarations and definitions are consistent and to prevent duplicate or missing declarations from introducing semantic errors. The parent program’s datum tables hold the variables accessible to nested subfunctions during compilation and execution: PLiSQL_function→datums preserves the variable and record metadata visible during compilation. PLiSQL_execstate→datums carries the live values at runtime. When a subfunction contains polymorphic arguments, the parser will: Copy the subfunction source text into subprocfunc→src. Set has_poly_argument = true to prepare for dynamic recompilation based on actual argument types. The parent PLiSQL_function includes a subprocfuncs array, with each element corresponding to a PLiSQL_subproc_function. Each PLiSQL_subproc_function maintains an optional HTAB *poly_tab; when has_poly_argument is true, the cache is initialized on the first compile. Keys are PLiSQL_func_hashkey (subfunction fno plus argument types), and values are the compiled PLiSQL_function plans. During compilation, PostgreSQL creates a ParseState. plisql_subprocfunc_ref() plugs into ParseState→p_subprocfunc_hook, reusing the namespace lookup logic to gather candidates. plisql_get_subprocfunc_detail() then chooses the best match based on argument count, types, and named parameters, enabling overloaded dispatch. When constructing FuncExpr nodes, the compiler attaches metadata so the executor can recognize nested calls: function_from = FUNC_FROM_SUBPROCFUNC. parent_func references the owning PLiSQL_function. funcid = fno, enabling direct lookup of the subfunction definition. plisql_subprocfunc_ref() implements ParseState→p_subprocfunc_hook and reuses the namespace search to find nested subfunctions. plisql_get_subprocfunc_detail() applies matching rules for argument count, type, and naming to pick the optimal overload. plisql_call_handler() checks function_from; if it is a nested subfunction, the handler locates PLiSQL_subproc_function via (parent_func, fno). For regular subfunctions, reuse the cached plan stored in subprocfunc→function. For polymorphic subfunctions, consult poly_tab; on a miss, call plisql_dynamic_compile_subproc() to build and cache a specialized plan. plisql_init_subprocfunc_globalvar() copies the relevant entries from the parent datum table before the subfunction runs to expose the latest state. plisql_assign_out_subprocfunc_globalvar() writes back OUT/INOUT variables after execution to keep parent and child scopes consistent without mutual pollution. psqlscan.l adjusts the push/pop logic of proc_func_define_level and begin_depth so the nested subfunction body is transmitted to the SQL engine as a whole. Statements are sent only when the nesting depth returns to zero and a semicolon is reached, avoiding partial dispatch of subfunction blocks. Regular functions obtain metadata via funcid from pg_proc; nested subfunctions rely on FuncExpr.parent_func, which holds the parent PLiSQL_function. A set of callback pointers (registered through plisql_register_internal_func()) allows the SQL layer to fetch nested subfunction names, return types, and OUT parameter information on demand.","title":"Nested Subfunctions","component":"ivorysql-doc","version":"master","name":"nested_function","url":"/ivorysql-doc/master/compatibility_features_design/nested_function.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Implementation Notes","hash":"implementation-notes","id":2},{"text":"2.1. Syntax Recognition for Nested Subfunctions","hash":"syntax-recognition-for-nested-subfunctions","id":3},{"text":"2.1.1. Detecting Nested Definitions","hash":"detecting-nested-definitions","id":4},{"text":"2.1.2. Storing Datum Entries","hash":"storing-datum-entries","id":5},{"text":"2.1.3. Preserving Polymorphic Templates","hash":"preserving-polymorphic-templates","id":6},{"text":"2.2. Recompiling the Parent Program","hash":"recompiling-the-parent-program","id":7},{"text":"2.3. Name Resolution During Invocation","hash":"name-resolution-during-invocation","id":8},{"text":"3. Module Design","hash":"module-design","id":9},{"text":"3.1. PL/iSQL Grammar Extensions","hash":"plisql-grammar-extensions","id":10},{"text":"3.2. Datum Storage","hash":"datum-storage","id":11},{"text":"3.3. Polymorphic Templates","hash":"polymorphic-templates","id":12},{"text":"3.4. Parent Recompilation","hash":"parent-recompilation","id":13},{"text":"3.5. Parser Hooks","hash":"parser-hooks","id":14},{"text":"3.6. FuncExpr Annotation","hash":"funcexpr-annotation","id":15},{"text":"3.7. Nested Subfunction Lookup","hash":"nested-subfunction-lookup","id":16},{"text":"3.8. Execution Path","hash":"execution-path","id":17},{"text":"3.9. Variable Synchronization","hash":"variable-synchronization","id":18},{"text":"3.10. Statement Dispatch in psql","hash":"statement-dispatch-in-psql","id":19},{"text":"3.11. Retrieving Return Information on the SQL Side","hash":"retrieving-return-information-on-the-sql-side","id":20}]},"25":{"id":25,"text":"IvorySQL provides Oracle-compatible NLS (National Language Support) parameter functionality, including the following parameters: Parameter Name Description ivorysql.datetime_ignore_nls_mask Indicates whether the date format ignores the influence of NLS parameters. Default is 0. nls_length_semantics Oracle-compatible parameter that specifies whether the size unit for CHAR, VARCHAR, and VARCHAR2 type modifiers is bytes or characters. nls_date_format Specifies the default date format, which can be viewed using the SHOW command. Default is 'YYYY-MM-DD'. nls_timestamp_format Oracle-compatible parameter that controls the format of timestamps. nls_timestamp_tz_format Oracle-compatible parameter that controls the format of timestamps with time zones. nls_territory Oracle-compatible parameter that specifies the default region for the database. nls_iso_currency Oracle-compatible parameter that assigns a unique currency symbol to a specified country or region. nls_currency Oracle-compatible parameter that specifies the symbol for the local currency, corresponding to the placeholder L in numeric string formats. In IvorySQL, data types have an attribute modifier called typmod, which provides additional information about the type. For example, in VARCHAR(n), n is the type modifier. When creating or modifying table columns, you can specify the length type, such as: ivorysql=# create table t1(name varchar2(2 byte)); For columns of type CHAR, VARCHAR, or VARCHAR2, if the length type is not explicitly specified, IvorySQL uses the value of the nls_length_semantics parameter to determine the length type. The possible values are BYTE and CHAR, with BYTE being the default. Note that the nls_length_semantics parameter only affects newly created columns and has no impact on existing columns. In the syntax parsing file ora_gram.y, the following code converts the original CHAR, VARCHAR, or VARCHAR2 types to oracharchar or oracharbyte based on nls_length_semantics. CharacterWithLength: character '(' Iconst ')' { if (ORA_PARSER == compatible_db) { if (strcmp($1, \"bpchar\")) { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oravarcharchar\"; else $1 = \"oravarcharbyte\"; } else { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oracharchar\"; else $1 = \"oracharbyte\"; } $$ = OracleSystemTypeName($1); $$->typmods = list_make1(makeIntConst($3, @3)); $$->location = @1; } else { ... } } ; The input/output functions for the oracharchar and oracharbyte data types in IvorySQL include: oravarcharchartypmodout() oravarcharbytetypmodout() oracharbytetypmodout() oracharchartypmodout() These functions call the C-language function anychar_typmodout(), which adjusts the output to include BYTE or CHAR based on the value of nls_length_semantics. Another role of nls_length_semantics is to limit column lengths in tables. If the original VARCHAR type is converted to oracharchar, the function oravarcharchar() is called, and pg_mbcharcliplen() calculates the character length instead of the byte length. Datum oravarcharchar(PG_FUNCTION_ARGS) { VarChar *source = PG_GETARG_VARCHAR_PP(0); int32 typmod = PG_GETARG_INT32(1); bool isExplicit = PG_GETARG_BOOL(2); int32 len, maxlen; size_t maxmblen; char *s_data; len = VARSIZE_ANY_EXHDR(source); s_data = VARDATA_ANY(source); maxlen = typmod - VARHDRSZ; /* No work if typmod is invalid or supplied data fits it already */ if (maxlen < 0 || len <= maxlen) PG_RETURN_VARCHAR_P(source); maxmblen = pg_mbcharcliplen(s_data, len, maxlen); ... } This parameter is defined as an int value, where the lower four bits indicate whether to ignore NLS parameter influence on the corresponding date-time formats. The mask definitions are: #define ORADATE_MASK 0x01 #define ORATIMESTAMP_MASK 0x02 #define ORATIMESTAMPTZ_MASK 0x04 #define ORATIMESTAMPLTZ_MASK 0x08 In the source code, this GUC parameter is used in the following functions: oradate_in() oratimestamp_in() oratimestampltz_in() oratimestamptz_in() If the corresponding mask is set, the native PostgreSQL processing function is called; otherwise, the compatibility code is invoked, and NLS formats are ignored. These three GUC parameters serve as format strings in the function ora_do_to_timestamp() for checking and parsing input strings. Their default values are: char *nls_date_format = \"YYYY-MM-DD\"; char *nls_timestamp_format = \"YYYY-MM-DD HH24:MI:SS.FF6\"; char *nls_timestamp_tz_format = \"YYYY-MM-DD HH24:MI:SS.FF6 TZH:TZM\"; Setting these values to \"pg\" disables NLS-specific behavior, reverting to PostgreSQL’s default behavior. Currently, nls_territory and nls_iso_currency support the values CHINA and AMERICA. The default values are: char *nls_territory = \"AMERICA\"; char *nls_currency = \"$\"; char *nls_iso_currency = \"AMERICA\"; These parameters will be used in the Oracle-compatible function to_number(). The to_number() function has not yet been implemented.","title":"Functional Overview","component":"ivorysql-doc","version":"master","name":"nls_parameter","url":"/ivorysql-doc/master/compatibility_features_design/nls_parameter.html","titles":[{"text":"1. Implementation Principles","hash":"implementation-principles","id":1},{"text":"1.1. Parameter nls_length_semantics","hash":"parameter-nls_length_semantics","id":2},{"text":"1.2. GUC Parameter datetime_ignore_nls_mask","hash":"guc-parameter-datetime_ignore_nls_mask","id":3},{"text":"1.3. GUC Parameters nls_date_format/nls_timestamp_format/nls_timestamp_tz_format","hash":"guc-parameters-nls_date_formatnls_timestamp_formatnls_timestamp_tz_format","id":4},{"text":"1.4. GUC Parameters nls_currency/nls_iso_currency/nls_territory","hash":"guc-parameters-nls_currencynls_iso_currencynls_territory","id":5}]},"26":{"id":26,"text":"IvorySQL provides Oracle-compatible OUT parameter functionality, including functions and procedures with OUT parameters, support for OUT parameters in anonymous blocks, and libpq support for OUT parameters. For PL/pgSQL functions, when creating a function, the system table pg_proc stores the total number of parameters (including OUT parameters) and their corresponding data types. In the interpret_function_parameter_list() function, which processes function parameters, it checks the parameter mode. If the mode is IN OUT, the parameter cannot have a default value. In the make_return_stmt function, the error handling for detecting OUT parameters has been removed. By modifying the FuncnameGetCandidates function, function lookup now matches all parameters, including OUT parameters. During function compilation, a row variable is constructed to hold OUT parameter variables and the return value variable. The compiled function’s return type (function→fn_rettype) is modified to RECORDOID. During function execution, the ExecInitFunc function calls the new ExecInitFuncOutParams function to construct OUT parameter computation nodes. The plisql_out_param function separates the function return value and OUT parameter values from the tuple and assigns values to the OUT parameters externally. To support binding variables in the form of colon placeholders, the ora_scan.l file was modified to add syntax that returns ORAPARAM when a colon placeholder is encountered. In the ora_gram.y file, handling for ORAPARAM was added to the c_expr and plassign_target assignment syntax, constructing an OraParamRef node. New DO + USING syntax was added: DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, ...] The ora_gram.y file was modified to support the DO+USING syntax. The DoStmt structure now includes a paramsmode field to store a list of binding variable modes and other information for anonymous blocks. Modifications to the PBE (Parse, Bind, Execute) process include: In the exec_parse_message function, the parameter mode is identified based on the parameter type OID passed from the application interface. In the exec_bind_message function, for anonymous blocks with DO+USING, the parameter modes following USING are identified, and parameter information is passed to the executor. Execution of anonymous blocks with OUT parameters: 1. In the PortalStart function, for anonymous block statements, the CreateTupleDescFromParams function is called to construct parameter description information. A new value, PROKIND_ANONYMOUS_BLOCK, was added to the PLiSQL_function member fn_prokind to indicate an anonymous block. In the plisql_exec_function function, anonymous blocks with OUT parameters are evaluated. The plisql_anonymous_return_out_parameter function is called to construct a PLiSQL_row type variable for OUT parameters, and the evaluated row type variable is used as the return value of the anonymous block function. The libpq interface was modified to support binding by position and by parameter name, involving changes to the SQL layer, PL/pgSQL layer, and libpq interface layer. SQL Layer: A system function get_parameter_description was implemented on the server side. This function returns the relationship between variable names and their positions based on the SQL statement. It is used in libpq interface functions. The first row of the result shows the SQL type in the name column, followed by rows displaying placeholder names and position information. ivorysql=# select * from get_parameter_description('insert into t values(:x, :y);'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows) Support for anonymous block statements is not yet implemented. PL/pgSQL Layer: The PL/pgSQL block adjusts the internal identification of parameters based on their position or name. The execution function retrieves parameter values and type information from the binding handle. For OUT parameters, a special handling of return column names is applied. If a parameter is an OUT parameter, its column name is formatted as _column_xxx, where xxx is the position of the OUT parameter. This allows assigning values to OUT parameters from the result set based on the binding position and return position. At the PLiSQL execution layer, parameter names are converted to internal identifiers (e.g., $number) based on their position. When returning to the client, description information is sent to libpq, ensuring that the returned column names are constructed from parameter names. The libpq side assigns values to OUT parameters based on these column names. libpq Interface Layer: Provides functions for preparing, binding, and executing statements, similar to the corresponding OCI functions. The general calling process is as follows: Use IvyHandleAlloc to allocate statement and error handles. Call IvyStmtPrepare to prepare the statement. Call IvyBindByPos or IvyBindByName to bind parameters. Call IvyStmtExecute to execute, which can be repeated. Call IvyFreeHandle to release the statement and error handles. Additionally, a series of interface functions have been implemented, including Ivyconnectdb, Ivystatus, Ivyexec, IvyresultStatus, IvyCreatePreparedStatement, IvybindOutParameterByPos, IvyexecPreparedStatement, IvyexecPreparedStatement2, Ivynfields, Ivyntuples, and Ivyclear.","title":"Functional Overview","component":"ivorysql-doc","version":"master","name":"out_parameter","url":"/ivorysql-doc/master/compatibility_features_design/out_parameter.html","titles":[{"text":"1. Implementation Principles","hash":"implementation-principles","id":1},{"text":"1.1. Functions with OUT Parameters","hash":"functions-with-out-parameters","id":2},{"text":"1.2. Support for OUT Parameters in Anonymous Blocks","hash":"support-for-out-parameters-in-anonymous-blocks","id":3},{"text":"1.3. Calling Functions with OUT Parameters in libpq","hash":"calling-functions-with-out-parameters-in-libpq","id":4}]},"27":{"id":27,"text":"IvorySQL provides Oracle-compatible RowID functionality. RowID is a pseudo-column automatically generated by the database when a table is created, returning the address of each row in the database. RowID should have the following characteristics: 1. Logically identifies each row with a unique value 2. Allows quick querying and modification of other columns in the table via ROWID, but cannot be inserted or modified itself 3. Users can control whether this feature is enabled In IvorySQL, the system column ctid represents the physical location of a data row in a table, also known as the tuple identifier, which consists of a pair of values (block number and row index). The ctid allows for quick lookup of data rows in a table, behaving similarly to Oracle’s RowID. However, the ctid value may change (e.g., during UPDATE or VACUUM FULL), making it unsuitable as a long-term row identifier. We chose a composite type consisting of the table’s OID and a sequence value as the RowID value, where the sequence is a system column. If the RowID functionality is enabled, a sequence named table-id_rowid_seq is created simultaneously with the table. Additionally, in the heap_form_tuple constructor, the length of HeapTupleHeaderData is increased by 8 bytes, and the td→t_infomask = HEAP_HASROWID bit is set to indicate the presence of the RowID. When the RowID functionality is enabled via the GUC parameter, or by the WITH ROWID option in table creation, or by executing ALTER TABLE … SET WITH ROWID on an existing table, a sequence is created by adding a sequence creation command. /* * Build a CREATE SEQUENCE command to create the sequence object, * and add it to the list of things to be done before this CREATE/ALTER TABLE */ seqstmt = makeNode(CreateSeqStmt); seqstmt->with_rowid = true; seqstmt->sequence = makeRangeVar(snamespace, sname, -1); seqstmt->options = lcons(makeDefElem(\"as\", (Node *) makeTypeNameFromOid(INT8OID, -1), -1), seqstmt->options); seqstmt->options = lcons(makeDefElem(\"nocache\", NULL, -1), seqstmt->options); To enable fast querying of a row using the RowID pseudo-column, a UNIQUE index is automatically created on the RowID column of the table by default to provide efficient query performance. The implementation of the RowID column as a system attribute column is achieved by adding a new system column in heap.c. /* * Compatible Oracle ROWID pseudo column. */ static const FormData_pg_attribute a7 = { .attname = {\"rowid\"}, .atttypid = ROWIDOID, .attlen = -1, .attnum = RowIdAttributeNumber, .attcacheoff = -1, .atttypmod = -1, .attbyval = false, .attalign = TYPALIGN_SHORT, .attstorage = TYPSTORAGE_PLAIN, .attnotnull = true, .attislocal = true, }; A boolean field relhasrowid has been added to the pg_class system table to indicate whether the WITH ROWID option was specified during table creation. If the WITH ROWID option is included when creating a table, relhasrowid is set to t; otherwise, it is set to f. This value is also updated when a user executes the ALTER TABLE … SET WITH ROWID or ALTER TABLE … SET WITHOUT ROWID command. /* T if we generate ROWIDs for rows of rel */ bool relhasrowid BKI_DEFAULT(f); Regarding RowID storage, if the RowID pseudo-column functionality is enabled, the heap_form_tuple function will add 8 bytes to the HeapTupleHeaderData to store the sequence value, depending on whether tdhasrowid in the TupleDesc parameter is true. In the heap_prepare_insert function, the nextval of the sequence is obtained and stored in the corresponding position in the HeapTupleHeader. if (relation->rd_rel->relhasrowid) { // Get the sequence next value seqnum = nextval_internal(relation->rd_rowdSeqid, true); // Set the HeapTupleHeader HeapTupleSetRowId(tup, seqnum); }","title":"RowID","component":"ivorysql-doc","version":"master","name":"rowid","url":"/ivorysql-doc/master/compatibility_features_design/rowid.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Implementation Principles","hash":"implementation-principles","id":2}]},"28":{"id":28,"text":"IvorySQL’s sys_guid() is a powerful random number generation function that generates and returns a 16-byte database-level unique identifier (raw value). The sys_guid() function of IvorySQL is implemented by modifying the code of the uuid-ossp plugin. To make full use of various underlying libraries of uuid, the following logic is adopted: If the uuid-ossp exists in the system, uuid_make() will be used; If the uuid-e2fs exists in the system, uuid_generate_random() will be used； Otherwise use arc4random()； Meanwhile modify the code so that IvorySQL can load uuid-ossp extension automatically.","title":"sys_guid() function","component":"ivorysql-doc","version":"master","name":"sys_guid_function","url":"/ivorysql-doc/master/compatibility_features_design/sys_guid_function.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation Description","hash":"implementation-description","id":2}]},"29":{"id":29,"text":"IvorySQL provides Oracle-compatible PL/SQL data type functionality, including %TYPE and %ROWTYPE. This is a passive process. The current implementation records the dependency between functions (stored procedures) and tablename.columnname. When the referenced type changes, the function (stored procedure) cache is invalidated based on this dependency. As a result, when the function is called, it undergoes forced compilation, ensuring that the function retrieves the latest variable type. A field named prostatus is added to the system table pg_proc to indicate the status of a function (stored procedure), with three possible states: validate (v), invalidate (i), and N/A (n). After a function is successfully compiled, this status is set to valid. When parsing function content, the functions plisql_parse_cwordtype, plisql_parse_wordrowtype, and plisql_parse_cwordrowtype identify objects referenced by %TYPE and %ROWTYPE, record them in the plisql_referenced_object linked list, and finally add them to the pg_depend system table. Add a new dependency type, DEPENDENCY_TYPE = 't', to represent %TYPE or %ROWTYPE dependencies. When adding object reference relationships to the pg_depend system table, set the dependency type to 't'. When performing operations on a table (such as modifying the table type or deleting the table), check the pg_depend system table. If there exists a dependency type deptype='t' and the dependent object is a function, call the plisql_free_function function to clear the function cache and update the function status prostatus in the pg_proc system table to N/A (n). Add bool notnull member in structure PLiSQL_type; /* * Postgres data type */ typedef struct PLiSQL_type { bool notnull; /* the type is built by variable%type, * isnull or notnull of the variable */ In the plisql_parse_wordtype or plisql_parse_cwordtype functions responsible for parsing %TYPE type functions, determine if the referenced variable type specifies a NOT NULL constraint, and set the bool notnull attribute of the returned datatype member to true. In the decl_statement grammar of the pl_gram.y file, assign the notnull attribute of the PLiSQL_variable *var variable based on the bool notnull member of PLiSQL_type. This way, the constraints of the referenced variable are inherited. Add support of %ROWTYPE for func_type in ora_gram.y | type_function_name attrs '%' ROWTYPE { $$ = makeTypeNameFromNameList(lcons(makeString($1), $2)); $$->row_type = true; $$->location = @1; } Add a member bool row_type to the TypeName struct to indicate whether %ROWTYPE is specified. typedef struct TypeName { bool pct_type; /* %TYPE specified? */ bool row_type; /* %ROWTYPE specified? */ In the LookupTypeName function, if the row_type member of TypeName is TRUE, obtain the schema name and table name from the names member of TypeName, and then retrieve the table’s typeoid. In ora_gram.y, add new syntax to support VALUES without requiring parentheses '(' afterward. values_clause_no_parens: VALUES columnref { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1(list_make1($2)); n->valuesIsrow = true; $$ = (Node *) n; } Add a field bool valuesIsrow to the SelectStmt struct to indicate that values is a row. When the transformInsertStmt function processes the INSERT …​ VALUES statement, if valuesIsrow is true, calls the new function transformRowExpression to convert row_variable into the equivalent row_variable.field1, …​, row_variable.fieldN. When transforming an UPDATE statement, i.e., when calling the transformUpdateStmt function, if in Oracle compatibility mode, invoke the newly added transformIvyUpdateTargetList function. In this new function, for cases where the origTlist (i.e., targetList) does not contain a name of row, execute the transformUpdateTargetList function following the original UPDATE transform process. For cases where the origTlist parameter contains a name row, since row can be used as a column name in PostgreSQL but is a reserved keyword in Oracle and cannot be used as a column name, it is necessary to determine whether row is a column in the table being updated. If row is not a column in the table to be updated, call the new function transformUpdateRowTargetList to convert the sql statement UPDATE table_name SET ROW = row_variable [WHERE …]; into equivalent UPDATE table_name SET table_name.column1 = row_variable.column1, table_name.column2 = row_variable.filed2,… table_name.columnN = row_variable.columnN [WHERE …]; If the variable row_variable in the statement UPDATE table_name SET ROW = row_variable is not a composite type, execute the transformUpdateTargetList function following the original UPDATE transform process. If the variable row_variable in the statement is a composite type, the column named row in the table is also a composite type, and their type OIDs match, execute the transformUpdateTargetList function following the original UPDATE transform process. In all other cases, call the new function transformUpdateRowTargetList for processing.","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"type_rowtype","url":"/ivorysql-doc/master/compatibility_features_design/type_rowtype.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation description","hash":"implementation-description","id":2},{"text":"2.1. If the reference changes, variables declared with %TYPE or %ROWTYPE will change accordingly","hash":"if-the-reference-changes-variables-declared-with-type-or-rowtype-will-change-accordingly","id":3},{"text":"2.2. Variables declared with %TYPE inherit the constraints of the referenced variable.","hash":"variables-declared-with-type-inherit-the-constraints-of-the-referenced-variable","id":4},{"text":"2.3. Use table_name%ROWTYPE or view_name%ROWTYPE as the parameter type of a function / stored procedure or the return type of a function","hash":"use-table_namerowtype-or-view_namerowtype-as-the-parameter-type-of-a-function-stored-procedure-or-the-return-type-of-a-function","id":5},{"text":"2.4. Enhancement to INSERT statement","hash":"enhancement-to-insert-statement","id":6},{"text":"2.5. Enhancement to UPDATE statement","hash":"enhancement-to-update-statement","id":7}]},"30":{"id":30,"text":"Get IvorySQL image from Docker Hub $ docker pull ivorysql/ivorysql:5.0-ubi8 Running IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 Check if the IvorySQL container is running successfully $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql Pull IvorySQL Image from Docker Hub [highgo@manager-node1 ~]$ podman pull ivorysql/ivorysql:5.0-ubi8 ✔ docker.io/ivorysql/ivorysql:5.0-ubi8 Trying to pull docker.io/ivorysql/ivorysql:5.0-ubi8... Getting image source signatures Copying blob 5885448c5c88 done | Copying blob 6c502b378234 done | Copying blob 8b4f2b90d6b6 done | Copying blob 9b000f2935f6 done | Copying blob 806f782da874 done | Copying blob e4c51845a9eb done | Copying blob dcb1e9a04275 done | Copying blob 285a279173f8 done | Copying blob 1f6f247b9ae0 done | Copying blob 3cc81bed8614 done | Copying blob 863c87bf25eb done | Copying blob 4f4fb700ef54 done | Copying config 88e1bbeda8 done | Writing manifest to image destination 88e1bbeda81c51d88e12cbd2b19730498f1343d1c64bb3dddc8ffcb08a1f965f Run IvorySQL Container $ podman run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=123456 -d ivorysql/ivorysql:5.0-ubi8 Check if IvorySQL Container is Running Successfully [highgo@manager-node1 ~]$ podman ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 368dee58d5ef docker.io/ivorysql/ivorysql:5.0-ubi8 postgres 20 seconds ago Up 20 seconds 0.0.0.0:5434->5432/tcp, 1521/tcp, 5866/tcp ivorysql [highgo@manager-node1 ~]$ podman exec -it ivorysql /bin/bash [root@8cc631eb413d /]# ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=#","title":"Docker & Podman deployment IvorySQL","component":"ivorysql-doc","version":"master","name":"docker_podman_deployment","url":"/ivorysql-doc/master/containerization/docker_podman_deployment.html","titles":[{"text":"1. Running IvorySQL in docker","hash":"running-ivorysql-in-docker","id":1},{"text":"2. Running with Podman","hash":"running-with-podman","id":2}]},"31":{"id":31,"text":"Prepare three servers with network connectivity and set up a Swarm cluster. The test cluster names and corresponding IP addresses are as follows: manager-node1: 192.168.21.205 manager-node2: 192.168.21.164 manager-node3: 192.168.21.51 [root@manager-node1 docker-swarm]# docker node ls ID HOSTNAME STATUS AVAILABILITY MANAGER STATUS ENGINE VERSION y9d9wd9t2ncy4t9bvw6bg9sjs * manager-node1 Ready Active Reachable 26.1.4 iv17o6m9t9e06vd9iu1o6damd manager-node2 Ready Active Leader 25.0.4 vjnax76qj812mlvut6cv4qotl manager-node3 Ready Active Reachable 24.0.6 Download the source code [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-swarm Deploy a three-node etcd cluster [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-etcd.yml ivoryhac-etcd Creating network ivoryhac-etcd_etcd-net Creating service ivoryhac-etcd_etcd3 Creating service ivoryhac-etcd_etcd1 Creating service ivoryhac-etcd_etcd2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 You can customize the external database directory by modifying the volumes section in docker-swarm-ivypatroni.yml. After making changes, adjust the directory permissions and ownership accordingly. Example as follows: mkdir -p /home/ivorysql/{data,patroni} chown -R 1000:1000 /home/ivorysql/{data,patroni} chmod 700 /home/ivorysql/{data,patroni} Deploy an IvorySQL High Availability Cluster [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-ivypatroni.yml ivoryhac-patroni Since --detach=false was not specified, tasks will be created in the background. In a future release, --detach=false will become the default. Creating service ivoryhac-patroni_ivypatroni1 Creating service ivoryhac-patroni_ivypatroni2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 uzdvjq5j2gwt ivorysql-hac_ivypatroni1 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1521->1521/tcp, *:5866->5866/tcp fr0m9chu3ce8 ivorysql-hac_ivypatroni2 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1522->1521/tcp, *:5867->5866/tcp Connect to the database using psql via Oracle port and PostgreSQL port [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) Uninstall the IvorySQL cluster [root@manager-node1 ~] docker stack rm ivoryhac-patroni [root@manager-node1 ~] docker stack rm ivoryhac-etcd Download the source code [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-compose Copy files to another server Copy the etcd and ivypatroni Docker Compose files to other servers respectively. For example, to the test server: 192.168.21.205 will host etcd1+ivorypatroni1， 192.168.21.164 will host etcd2+ivorypatroni2， 192.168.21.51 will host etcd3+ivorypatroni3 Deploy a three-node etcd cluster, taking node1 as an example [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-etcd1.yml up -d [+] Running 1/1 ✔ Container etcd Started 0.1s Deploy an IvorySQL high-availability cluster. Deploy the ivyhac service on each node, using node1 as an example. [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-ivypatroni_1.yml up -d [+] Running 1/1 ✔ Container ivyhac1 Started 0.1s [root@manager-node1 docker-compose]# docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 736c0d188bdd ivorysql/docker-compose-ha-cluster:5.0-4.0.6-ubi8 \"/bin/sh /docker-ent…\" 18 seconds ago Up 17 seconds ivyhac1 9d8e04e4f819 quay.io/coreos/etcd:v3.5.8 \"/usr/local/bin/etcd\" 24 minutes ago Up 24 minutes etcd At this point, the one-primary-two-standby cluster setup is complete. Connect to the database using psql via Oracle-compatible ports and PostgreSQL ports. [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) Uninstall the IvorySQL cluster, using node1 as an example. [root@manager-node1 ~] docker-compose -f ./docker-compose-ivypatroni_1.yml down [root@manager-node1 ~] docker-compose -f ./docker-compose-etcd1.yml down","title":"Docker Swarm & Docker Compose Deploying IvorySQL High Availability Cluster","component":"ivorysql-doc","version":"master","name":"docker_swarm_compose_deployment","url":"/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html","titles":[{"text":"1. Building IvorySQL HA Cluster using Docker Swarm","hash":"building-ivorysql-ha-cluster-using-docker-swarm","id":1},{"text":"2. Set up an IvorySQL HA Cluster using Docker Compose","hash":"set-up-an-ivorysql-ha-cluster-using-docker-compose","id":2}]},"32":{"id":32,"text":"On the master node of the k8s cluster, create a namespace named ivorysql. [root@k8s-master ~]# kubectl create ns ivorysql Download the latest docker_library code. [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git Enter the single-node directory [root@k8s-master ~]# cd docker_library/k8s-cluster/single [root@k8s-master single]# vim statefulset.yaml #Update the PVC information and database password in the StatefulSet to match your actual environment. Use statefulset.yaml to create a single-node pod. [root@k8s-master single]# kubectl apply -f statefulset.yaml service/ivorysql-svc created statefulset.apps/ivorysql created Wait for the single-node pod to be successfully created. [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 0/1 ContainerCreating 0 47s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 47s NAME READY AGE statefulset.apps/ivorysql 0/1 47s [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 1/1 Running 0 2m39s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 2m39s NAME READY AGE statefulset.apps/ivorysql 1/1 2m39s Connect to IvorySQL via its PostgreSQL port using the psql [root@k8s-master single]# psql -U ivorysql -p 32106 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# exit Connect to IvorySQL’s Oracle-compatible port using psql. [root@k8s-master single]# psql -U ivorysql -p 31887 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) Uninstall Single-node container [root@k8s-master single]# kubectl delete -f statefulset.yaml Access the master node of the k8s cluster and create a namespace named ivorysql. [root@k8s-master ~]# kubectl create ns ivorysql Download the latest docker_library code. [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git Enter the high-availability cluster directory. [root@k8s-master ~]# cd docker_library/k8s-cluster/ha-cluster/helm_charts [root@k8s-master single]# vim values.yaml #Adjust the PVC settings, cluster size, and other configurations in values.yaml according to your environment. For the database password, check templates/secret.yaml and modify it as needed. Deploy the high-availability cluster using Helm commands. [root@k8s-master helm_charts]# helm install ivorysql-ha-cluster -n ivorysql . NAME: ivorysql-ha-cluster LAST DEPLOYED: Wed Sep 10 09:45:36 2025 NAMESPACE: ivorysql STATUS: deployed REVISION: 1 TEST SUITE: None [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 42s pod/ivorysql-patroni-hac-1 0/1 Running 0 18s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 42s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 42s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 1/3 42s Wait until all pods are running successfully, indicating the cluster deployment is complete. [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 88s pod/ivorysql-patroni-hac-1 1/1 Running 0 64s pod/ivorysql-patroni-hac-2 1/1 Running 0 41s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 88s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 88s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 3/3 88s Connect to the PostgreSQL and Oracle ports of the cluster’s primary node using psql. [root@k8s-master helm_charts]# psql -U ivorysql -p 32391 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32477 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# Use psql to connect to the PostgreSQL and Oracle ports of the cluster’s standby node. [root@k8s-master helm_charts]# psql -U ivorysql -p 30111 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32654 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# Uninstall high-availability cluster [root@k8s-master helm_charts]# helm uninstall ivorysql-ha-cluster -n ivorysql Remove PVC [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-2 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-2 -n ivorysql","title":"Deploying single-node containers and high-availability clusters on k8s","component":"ivorysql-doc","version":"master","name":"k8s_deployment","url":"/ivorysql-doc/master/containerization/k8s_deployment.html","titles":[{"text":"1. Single-node container","hash":"single-node-container","id":1},{"text":"2. High Availability Cluster","hash":"high-availability-cluster","id":2}]},"33":{"id":33,"text":"Fork ivory-operator repository and clone it to your host machine： YOUR_GITHUB_UN=\"<your GitHub username>\" git clone --depth 1 \"git@github.com:${YOUR_GITHUB_UN}/ivory-operator.git\" cd ivory-operator Run the following commands： kubectl apply -k examples/kustomize/install/namespace kubectl apply --server-side -k examples/kustomize/install/default Throughout this tutorial, we will be building on the example provided in the examples/kustomize/ivory. When referring to a nested object within a YAML manifest, we will be using the . format similar to kubectl explain. For example, if we want to refer to the deepest element in this yaml file: spec: hippos: appetite: huge we would say spec.hippos.appetite. kubectl explain is your friend. You can use kubectl explain ivorycluster to introspect the ivorycluster.ivory-operator.ivorysql.org custom resource definition. Creating an Ivory cluster is pretty simple. Using the example in the examples/kustomize/ivory directory, all we have to do is run: kubectl apply -k examples/kustomize/ivory and IVYO will create a simple Ivory cluster named hippo in the ivory-operator namespace. You can track the status of your Ivory cluster using kubectl describe on the ivoryclusters.ivory-operator.ivorysql.org custom resource: kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo and you can track the state of the Ivory Pod using the following command: kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance IVYO created an Ivory cluster based on the information provided to it in the Kustomize manifests located in the examples/kustomize/ivory directory. Let’s better understand what happened by inspecting the examples/kustomize/ivory/ivory.yaml file: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi When we ran the kubectl apply command earlier, what we did was create a ivorycluster custom resource in Kubernetes. IVYO detected that we added a new ivorycluster resource and started to create all the objects needed to run Ivory in Kubernetes! What else happened? IVYO read the value from metadata.name to provide the Ivory cluster with the name hippo. Additionally, IVYO knew which containers to use for Ivory and pgBackRest by looking at the values in spec.image and spec.backups.pgbackrest.image respectively. The value in spec.postgresVersion is important as it will help IVYO track which major version of Ivory you are using. IVYO knows how many Ivory instances to create through the spec.instances section of the manifest. While name is optional, we opted to give it the name instance1. We could have also created multiple replicas and instances during cluster initialization, but we will cover that more when we discuss how to scale and create a HA Ivory cluster. A very important piece of your ivorycluster custom resource is the dataVolumeClaimSpec section. This describes the storage that your Ivory instance will use. It is modeled after the Persistent Volume Claim. If you do not provide a spec.instances.dataVolumeClaimSpec.storageClassName, then the default storage class in your Kubernetes environment is used. As part of creating an Ivory cluster, we also specify information about our backup archive. IVYO uses pgBackRest, an open source backup and restore tool designed to handle terabyte-scale backups. As part of initializing our cluster, we can specify where we want our backups and archives (write-ahead logs or WAL) stored. We will talk about this portion of the ivorycluster spec in greater depth in the disaster recovery section of this tutorial, and also see how we can store backups in Amazon S3, Google GCS, and Azure Blob Storage. The most common occurrence of this is due to PVCs not being bound. Ensure that you have set up your storage options correctly in any volumeClaimSpec. You can always update your settings and reapply your changes with kubectl apply. Also ensure that you have enough persistent volumes available: your Kubernetes administrator may need to provision more. If you are on OpenShift, you may need to set spec.openshift to true. We’re up and running — now let’s connect to our Ivory cluster! It’s one thing to create an Ivory cluster; it’s another thing to connect to it. Let’s explore how IVYO makes it possible to connect to an Ivory cluster! IVYO creates a series of Kubernetes Services to provide stable endpoints for connecting to your Ivory databases. These endpoints make it easy to provide a consistent way for your application to maintain connectivity to your data. To inspect what services are available, you can run the following command: kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo will yield something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 3h14m hippo-ha-config ClusterIP None <none> <none> 3h14m hippo-pods ClusterIP None <none> <none> 3h14m hippo-primary ClusterIP None <none> 5432/TCP 3h14m hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 3h14m You do not need to worry about most of these Services, as they are used to help manage the overall health of your Ivory cluster. For the purposes of connecting to your database, the Service of interest is called hippo-primary. Thanks to IVYO, you do not need to even worry about that, as that information is captured within a Secret! When your Ivory cluster is initialized, IVYO will bootstrap a database and Ivory user that your application can access. This information is stored in a Secret named with the pattern <clusterName>-pguser-<userName>. For our hippo cluster, this Secret is called hippo-pguser-hippo. This Secret contains the information you need to connect your application to your Ivory database: user: The name of the user account. password: The password for the user account. dbname: The name of the database that the user has access to by default. host: The name of the host of the database. This references the Service of the primary Ivory instance. port: The port that the database is listening on. uri: A PostgresSQL connection URI that provides all the information for logging into the Ivory database. jdbc-uri: A PostgresSQL JDBC connection URI that provides all the information for logging into the Ivory database via the JDBC driver. All connections are over TLS. IVYO provides its own certificate authority (CA) to allow you to securely connect your applications to your Ivory clusters. This allows you to use the verify-full \"SSL mode\" of Ivory, which provides eavesdropping protection and prevents MITM attacks. You can also choose to bring your own CA, which is described later in this tutorial in the Customize Cluster section. By default, IVYO deploys Services with the ClusterIP Service type. Based on how you want to expose your database, you may want to modify the Services to use a different Service type and NodePort value. You can modify the Services that IVYO manages from the following attributes: spec.service - this manages the Service for connecting to an Ivory primary. spec.userInterface.pgAdmin.service - this manages the Service for connecting to the pgAdmin management tool. For example, say you want to set the Ivory primary to use a NodePort service, a specific nodePort value, and set a specific annotation and label, you would add the following to your manifest: spec: service: metadata: annotations: my-annotation: value1 labels: my-label: value2 type: NodePort nodePort: 32000 For our hippo cluster, you would see the Service type and nodePort modification as well as the annotation and label. For example: kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo will yield something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha NodePort 10.105.57.191 <none> 5432:32000/TCP 48s hippo-ha-config ClusterIP None <none> <none> 48s hippo-pods ClusterIP None <none> <none> 48s hippo-primary ClusterIP None <none> 5432/TCP 48s hippo-replicas ClusterIP 10.106.18.99 <none> 5432/TCP 48s and the top of the output from running kubectl -n ivory-operator describe svc hippo-ha will show our custom annotation and label have been added: Name: hippo-ha Namespace: ivory-operator Labels: my-label=value2 ivory-operator.ivorysql.org/cluster=hippo ivory-operator.ivorysql.org/patroni=hippo-ha Annotations: my-annotation: value1 Note that setting the nodePort value is not allowed when using the (default) ClusterIP type, and it must be in-range and not otherwise in use or the operation will fail. Additionally, be aware that any annotations or labels provided here will win in case of conflicts with any annotations or labels a user configures elsewhere. Finally, if you are exposing your Services externally and are relying on TLS verification, you will need to use the custom TLS features of IVYO). For this tutorial, we are going to connect Keycloak, an open source identity management application. Keycloak can be deployed on Kubernetes and is backed by an Ivory database. We provide an example of deploying Keycloak andan ivorycluster, the manifest below deploys it using our hippo cluster that is already running: kubectl apply --filename=- <<EOF apiVersion: apps/v1 kind: Deployment metadata: name: keycloak namespace: ivory-operator labels: app.kubernetes.io/name: keycloak spec: selector: matchLabels: app.kubernetes.io/name: keycloak template: metadata: labels: app.kubernetes.io/name: keycloak spec: containers: - image: quay.io/keycloak/keycloak:latest args: [\"start-dev\"] name: keycloak env: - name: DB_VENDOR value: \"ivory\" - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } - name: KEYCLOAK_ADMIN value: \"admin\" - name: KEYCLOAK_ADMIN_PASSWORD value: \"admin\" - name: KC_PROXY value: \"edge\" ports: - name: http containerPort: 8080 - name: https containerPort: 8443 readinessProbe: httpGet: path: /realms/master port: 8080 restartPolicy: Always EOF Notice this part of the manifest: - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } The above manifest shows how all of these values are derived from the hippo-pguser-hippo Secret. This means that we do not need to know any of the connection credentials or have to insecurely pass them around — they are made directly available to the application! Using this method, you can tie application directly into your GitOps pipeline that connect to Ivory without any prior knowledge of how IVYO will deploy Ivory: all of the information your application needs is propagated into the Secret! Now that we have seen how to connect an application to a cluster, let’s learn how to create a high availability Ivory cluster! Ivory is known for its reliability: it is very stable and typically \"just works.\" However, there are many things that can happen in a distributed environment like Kubernetes that can affect Ivory uptime, including: The database storage disk fails or some other hardware failure occurs The network on which the database resides becomes unreachable The host operating system becomes unstable and crashes A key database file becomes corrupted A data center is lost A Kubernetes component (e.g. a Service) is accidentally deleted There may also be downtime events that are due to the normal case of operations, such as performing a minor upgrade, security patching of operating system, hardware upgrade, or other maintenance. The good news: IVYO is prepared for this, and your Ivory cluster is protected from many of these scenarios. However, to maximize your high availability (HA), let’s first scale up your Ivory cluster. IVYO provides several ways to add replicas to make a HA cluster: Increase the spec.instances.replicas value Add an additional entry in spec.instances For the purposes of this tutorial, we will go with the first method and set spec.instances.replicas to 2. Your manifest should look similar to: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory Within moment, you should see a new Ivory instance initializing! You can see all of your Ivory Pods for the hippo cluster by running the following command: kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance-set Let’s test our high availability set up. An important part of building a resilient Ivory environment is testing its resiliency, so let’s run a few tests to see how IVYO performs under pressure! Let’s try removing the primary Service that our application is connected to. This test does not actually require a HA Ivory cluster, but it will demonstrate IVYO’s ability to react to environmental changes and heal things to ensure your applications can stay up. Recall in the connecting a Ivory cluster that we observed the Services that IVYO creates, e.g.: kubectl -n ivory-operator get svc \\ --selector=ivory-operator.ivorysql.org/cluster=hippo yields something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 4h8m hippo-ha-config ClusterIP None <none> <none> 4h8m hippo-pods ClusterIP None <none> <none> 4h8m hippo-primary ClusterIP None <none> 5432/TCP 4h8m hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 4h8m We also mentioned that the application is connected to the hippo-primary Service. What happens if we were to delete this Service? kubectl -n ivory-operator delete svc hippo-primary This would seem like it could create a downtime scenario, but run the above selector again: kubectl -n ivory-operator get svc \\ --selector=ivory-operator.ivorysql.org/cluster=hippo You should see something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 4h8m hippo-ha-config ClusterIP None <none> <none> 4h8m hippo-pods ClusterIP None <none> <none> 4h8m hippo-primary ClusterIP None <none> 5432/TCP 3s hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 4h8m Wow — IVYO detected that the primary Service was deleted and it recreated it! Based on how your application connects to Ivory, it may not have even noticed that this event took place! Now let’s try a more extreme downtime event. StatefulSets are a Kubernetes object that provide helpful mechanisms for managing Pods that interface with stateful applications, such as databases. They provide a stable mechanism for managing Pods to help ensure data is retrievable in a predictable way. What happens if we remove the StatefulSet that is pointed to the Pod that represents the Ivory primary? First, let’s determine which Pod is the primary. We’ll store it in an environmental variable for convenience. PRIMARY_POD=$(kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[*].metadata.labels.ivory-operator\\.ivorysql\\.org/instance}') Inspect the environmental variable to see which Pod is the current primary: echo $PRIMARY_POD should yield something similar to: hippo-instance1-zj5s We can use the value above to delete the StatefulSet associated with the current Ivory primary instance: kubectl delete sts -n ivory-operator \"${PRIMARY_POD}\" Let’s see what happens. Try getting all of the StatefulSets for the Ivory instances in the hippo cluster: kubectl get sts -n ivory-operator \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance You should see something similar to: NAME READY AGE hippo-instance1-6kbw 1/1 15m hippo-instance1-zj5s 0/1 1s IVYO recreated the StatefulSet that was deleted! After this \"catastrophic\" event, IVYO proceeds to heal the Ivory instance so it can rejoin the cluster. We cover the high availability process in greater depth later in the documentation. What about the other instance? We can see that it became the new primary though the following command: kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[*].metadata.labels.ivory-operator\\.ivorysql\\.org/instance}' which should yield something similar to: hippo-instance1-6kbw You can test that the failover successfully occurred in a few ways. You can connect to the example Keycloak application that we deployed in the previous section. Based on Keycloak’s connection retry logic, you may need to wait a moment for it to reconnect, but you will see it connected and resume being able to read and write data. You can also connect to the Ivory instance directly and execute the following command: SELECT NOT pg_catalog.pg_is_in_recovery() is_primary; If it returns true (or t), then the Ivory instance is a primary! What if IVYO was down during the downtime event? Failover would still occur: the Ivory HA system works independently of IVYO and can maintain its own uptime. IVYO will still need to assist with some of the healing aspects, but your application will still maintain read/write connectivity to your Ivory cluster! IvorySQL supports synchronous replication, which is a replication mode designed to limit the risk of transaction loss. Synchronous replication waits for a transaction to be written to at least one additional server before it considers the transaction to be committed. For more information on synchronous replication, please read about IVYO’s high availability architecture To add synchronous replication to your Ivory cluster, you can add the following to your spec: spec: patroni: dynamicConfiguration: synchronous_mode: true While PostgreSQL defaults synchronous_commit to on, you may also want to explicitly set it, in which case the above block becomes: spec: patroni: dynamicConfiguration: synchronous_mode: true postgresql: parameters: synchronous_commit: \"on\" Note that Patroni, which manages many aspects of the cluster’s availability, will favor availability over synchronicity. This means that if a synchronous replica goes down, Patroni will allow for asynchronous replication to continue as well as writes to the primary. However, if you want to disable all writing if there are no synchronous replicas available, you would have to enable synchronous_mode_strict, i.e.: spec: patroni: dynamicConfiguration: synchronous_mode: true synchronous_mode_strict: true Kubernetes affinity rules, which include Pod anti-affinity and Node affinity, can help you to define where you want your workloads to reside. Pod anti-affinity is important for high availability: when used correctly, it ensures that your Ivory instances are distributed amongst different Nodes. Node affinity can be used to assign instances to specific Nodes, e.g. to utilize hardware that’s optimized for databases. IVYO sets up several labels for Ivory cluster management that can be used for Pod anti-affinity or affinity rules in general. These include: ivory-operator.ivorysql.org/cluster: This is assigned to all managed Pods in a Ivory cluster. The value of this label is the name of your Ivory cluster, in this case: hippo. ivory-operator.ivorysql.org/instance-set: This is assigned to all Ivory instances within a group of spec.instances. In the example above, the value of this label is instance1. If you do not assign a label, the value is automatically set by IVYO using a NN format, e.g. 00. ivory-operator.ivorysql.org/instance: This is a unique label assigned to each Ivory instance containing the name of the Ivory instance. Let’s look at how we can set up affinity rules for our Ivory cluster to help improve high availability. Kubernetes has two types of Pod anti-affinity: Preferred: With preferred (preferredDuringSchedulingIgnoredDuringExecution) Pod anti-affinity, Kubernetes will make a best effort to schedule Pods matching the anti-affinity rules to different Nodes. However, if it is not possible to do so, then Kubernetes may schedule one or more Pods to the same Node. Required: With required (requiredDuringSchedulingIgnoredDuringExecution) Pod anti-affinity, Kubernetes mandates that each Pod matching the anti-affinity rules must be scheduled to different Nodes. However, a Pod may not be scheduled if Kubernetes cannot find a Node that does not contain a Pod matching the rules. There is a trade-off with these two types of pod anti-affinity: while \"required\" anti-affinity will ensure that all the matching Pods are scheduled on different Nodes, if Kubernetes cannot find an available Node, your Ivory instance may not be scheduled. Likewise, while \"preferred\" anti-affinity will make a best effort to scheduled your Pods on different Nodes, Kubernetes may compromise and schedule more than one Ivory instance of the same cluster on the same Node. By understanding these trade-offs, the makeup of your Kubernetes cluster, and your requirements, you can choose the method that makes the most sense for your Ivory deployment. We’ll show examples of both methods below! First, let’s deploy our Ivory cluster with preferred Pod anti-affinity. Note that if you have a single-node Kubernetes cluster, you will not see your Ivory instances deployed to different nodes. However, your Ivory instances will be deployed. We can set up our HA Ivory cluster with preferred Pod anti-affinity like so: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Apply those changes in your Kubernetes cluster. Let’s take a closer look at this section: affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 spec.instances.affinity.podAntiAffinity follows the standard Kubernetes Pod anti-affinity spec. The values for the matchLabels are derived from what we described in the previous section: ivory-operator.ivorysql.org/cluster is set to our cluster name of hippo, and ivory-operator.ivorysql.org/instance-set is set to the instance set name of instance1. We choose a topologyKey of kubernetes.io/hostname, which is standard in Kubernetes clusters. Preferred Pod anti-affinity will perform a best effort to schedule your Ivory Pods to different nodes. Let’s see how you can require your Ivory Pods to be scheduled to different nodes. Required Pod anti-affinity forces Kubernetes to scheduled your Ivory Pods to different Nodes. Note that if Kubernetes is unable to schedule all Pods to different Nodes, some of your Ivory instances may become unavailable. Using the previous example, let’s indicate to Kubernetes that we want to use required Pod anti-affinity for our Ivory clusters: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Apply those changes in your Kubernetes cluster. If you are in a single Node Kubernetes clusters, you will notice that not all of your Ivory instance Pods will be scheduled. This is due to the requiredDuringSchedulingIgnoredDuringExecution preference. However, if you have enough Nodes available, you will see the Ivory instance Pods scheduled to different Nodes: kubectl get pods -n ivory-operator -o wide \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance Node affinity can be used to assign your Ivory instances to Nodes with specific hardware or to guarantee a Ivory instance resides in a specific zone. Node affinity can be set within the spec.instances.affinity.nodeAffinity attribute, following the standard Kubernetes node affinity spec. Let’s see an example with required Node affinity. Let’s say we have a set of Nodes that are reserved for database usage that have a label workload-role=db. We can create a Ivory cluster with a required Node affinity rule to scheduled all of the databases to those Nodes using the following configuration: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: workload-role operator: In values: - db backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi In addition to affinity and anti-affinity settings, Kubernetes Pod Topology Spread Constraints can also help you to define where you want your workloads to reside. However, while PodAffinity allows any number of Pods to be added to a qualifying topology domain, and PodAntiAffinity allows only one Pod to be scheduled into a single topology domain, topology spread constraints allow you to distribute Pods across different topology domains with a finer level of control. The spread constraint API fields can be configured for instance, PgBouncer and pgBackRest repo host pods. The basic configuration is as follows: topologySpreadConstraints: - maxSkew: <integer> topologyKey: <string> whenUnsatisfiable: <string> labelSelector: <object> where \"maxSkew\" describes the maximum degree to which Pods can be unevenly distributed, \"topologyKey\" is the key that defines a topology in the Nodes' Labels, \"whenUnsatisfiable\" specifies what action should be taken when \"maxSkew\" can’t be satisfied, and \"labelSelector\" is used to find matching Pods. To help illustrate how you might use this with your cluster, we can review examples for configuring spread constraints on our Instance and pgBackRest repo host Pods. For this example, assume we have a three node Kubernetes cluster where the first node is labeled with my-node-label=one, the second node is labeled with my-node-label=two and the final node is labeled my-node-label=three. The label key my-node-label will function as our topologyKey. Note all three nodes in our examples will be schedulable, so a Pod could live on any of the three Nodes. To begin, we can set our topology spread constraints on our cluster Instance Pods. Given this configuration instances: - name: instance1 replicas: 5 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: ivory-operator.ivorysql.org/instance-set: instance1 we will expect 5 Instance pods to be created. Each of these Pods will have the standard ivory-operator.ivorysql.org/instance-set: instance1 Label set, so each Pod will be properly counted when determining the maxSkew. Since we have 3 nodes with a maxSkew of 1 and we’ve set whenUnsatisfiable to DoNotSchedule, we should see 2 Pods on 2 of the nodes and 1 Pod on the remaining Node, thus ensuring our Pods are distributed as evenly as possible. We can also set topology spread constraints on our cluster’s pgBackRest repo host pod. While we normally will only have a single pod per cluster, we could use a more generic label to add a preference that repo host Pods from different clusters are distributed among our Nodes. For example, by setting our matchLabel value to ivory-operator.ivorysql.org/pgbackrest: \"\" and our whenUnsatisfiable value to ScheduleAnyway, we will allow our repo host Pods to be scheduled no matter what Nodes may be available, but attempt to minimize skew as much as possible. repoHost: topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/pgbackrest: \"\" Now that each of our Pods has our desired Topology Spread Constraints defined, let’s put together a complete cluster definition: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 5 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: ivory-operator.ivorysql.org/instance-set: instance1 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1G backups: pgbackrest: image: {{< param imagePGBackrest >}} repoHost: topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/pgbackrest: \"\" repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1G You can then apply those changes in your Kubernetes cluster. Once your cluster finishes deploying, you can check that your Pods are assigned to the correct Nodes: kubectl get pods -n ivory-operator -o wide --selector=ivory-operator.ivorysql.org/cluster=hippo We’ve now seen how IVYO helps your application stay \"always on\" with your Ivory database. Now let’s explore how IVYO can minimize or eliminate downtime for operations that would normally cause that, such as resizing your Ivory cluster. You did it — the application is a success! Traffic is booming, so much so that you need to add more resources to your Ivory cluster. However, you’re worried that any resize operation may cause downtime and create a poor experience for your end users. This is where IVYO comes in: IVYO will help orchestrate rolling out any potentially disruptive changes to your cluster to minimize or eliminate downtime for your application. To do so, we will assume that you have deployed a high availability Ivory cluster as described in the previous section. Let’s dive in. Memory and CPU resources are an important component for vertically scaling your Ivory cluster. Coupled with tweaks to your Ivory configuration file, allocating more memory and CPU to your cluster can help it to perform better under load. It’s important for instances in the same high availability set to have the same resources. IVYO lets you adjust CPU and memory within the resources sections of the ivoryclusters.ivory-operator.ivorysql.org custom resource. These include: spec.instances.resources section, which sets the resource values for the IvorySQL container, as well as any init containers in the associated pod and containers created by the pgDataVolume and pgWALVolume data migration jobs. spec.instances.sidecars.replicaCertCopy.resources section, which sets the resources for the replica-cert-copy sidecar container. spec.backups.pgbackrest.repoHost.resources section, which sets the resources for the pgBackRest repo host container, as well as any init containers in the associated pod and containers created by the pgBackRestVolume data migration job. spec.backups.pgbackrest.sidecars.pgbackrest.resources section, which sets the resources for the pgbackrest sidecar container. spec.backups.pgbackrest.sidecars.pgbackrestConfig.resources section, which sets the resources for the pgbackrest-config sidecar container. spec.backups.pgbackrest.jobs.resources section, which sets the resources for any pgBackRest backup job. spec.backups.pgbackrest.restore.resources section, which sets the resources for manual pgBackRest restore jobs. spec.dataSource.ivorycluster.resources section, which sets the resources for pgBackRest restore jobs created during the cloning process. The layout of these resources sections should be familiar: they follow the same pattern as the standard Kubernetes structure for setting container resources. Note that these settings also allow for the configuration of QoS classes. For example, using the spec.instances.resources section, let’s say we want to update our hippo Ivory cluster so that each instance has a limit of 2.0 CPUs and 4Gi of memory. We can make the following changes to the manifest: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi In particular, we added the following to spec.instances: resources: limits: cpu: 2.0 memory: 4Gi Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory Now, let’s watch how the rollout happens: watch \"kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance \\ -o=jsonpath='{range .items[*]}{.metadata.name}{\\\"\\t\\\"}{.metadata.labels.ivory-operator\\.ivorysql\\.org/role}{\\\"\\t\\\"}{.status.phase}{\\\"\\t\\\"}{.spec.containers[].resources.limits}{\\\"\\n\\\"}{end}'\" Observe how each Pod is terminated one-at-a-time. This is part of a \"rolling update\". Because updating the resources of a Pod is a destructive action, IVYO first applies the CPU and memory changes to the replicas. IVYO ensures that the changes are successfully applied to a replica instance before moving on to the next replica. Once all of the changes are applied, IVYO will perform a \"controlled switchover\": it will promote a replica to become a primary, and apply the changes to the final Ivory instance. By rolling out the changes in this way, IVYO ensures there is minimal to zero disruption to your application: you are able to successfully roll out updates and your users may not even notice! Your application is a success! Your data continues to grow, and it’s becoming apparently that you need more disk. That’s great: you can resize your PVC directly on your ivoryclusters.ivory-operator.ivorysql.org custom resource with minimal to zero downtime. PVC resizing, also known as volume expansion, is a function of your storage class: it must support volume resizing. Additionally, PVCs can only be sized up: you cannot shrink the size of a PVC. You can adjust PVC sizes on all of the managed storage instances in a Ivory instance that are using Kubernetes storage. These include: spec.instances.dataVolumeClaimSpec.resources.requests.storage: The Ivory data directory (aka your database). spec.backups.pgbackrest.repos.volume.volumeClaimSpec.resources.requests.storage: The pgBackRest repository when using \"volume\" storage The above should be familiar: it follows the same pattern as the standard Kubernetes PVC structure. For example, let’s say we want to update our hippo Ivory cluster so that each instance now uses a 10Gi PVC and our backup repository uses a 20Gi PVC. We can do so with the following markup: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi In particular, we added the following to spec.instances: dataVolumeClaimSpec: resources: requests: storage: 10Gi and added the following to spec.backups.pgbackrest.repos.volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory Not all Kubernetes Storage Classes allow for volume expansion. However, with IVYO, you can still resize your Ivory cluster data volumes even if your storage class does not allow it! Let’s go back to the previous example: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi First, create a new instance that has the larger volume size. Call this instance instance2. The manifest would look like this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi - name: instance2 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi Take note of the block that contains instance2: - name: instance2 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi This creates a second set of two Ivory instances, both of which come up as replicas, that have a larger PVC. Once this new instance set is available and they are caught to the primary, you can then apply the following manifest: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance2 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi This will promote one of the instances with the larger PVC to be the new primary and remove the instances with the smaller PVCs! This method can also be used to shrink PVCs to use a smaller amount. There are many reasons why a IvorySQL Pod may not be scheduled: Resources are unavailable. Ensure that you have a Kubernetes Node with enough resources to satisfy your memory or CPU Request. PVC cannot be provisioned. Ensure that you request a PVC size that is available, or that your PVC storage class is set up correctly. Ensure that your storage class supports PVC resizing. You can check that by inspecting the allowVolumeExpansion attribute: kubectl get sc If the storage class does not support PVC resizing, you can use the technique described above to resize PVCs using a second instance set. You’ve now resized your Ivory cluster, but how can you configure Ivory to take advantage of the new resources? Let’s look at how we can customize the Ivory cluster configuration. Part of the trick of managing multiple instances in an Ivory cluster is ensuring all of the configuration changes are propagated to each of them. This is where IVYO helps: when you make an Ivory configuration change for a cluster, IVYO will apply it to all of the Ivory instances. For example, in our previous step we added CPU and memory limits of 2.0 and 4Gi respectively. Let’s tweak some of the Ivory settings to better use our new resources. We can do this in the spec.patroni.dynamicConfiguration section. Here is an example updated manifest that tweaks several settings: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB In particular, we added the following to spec: patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory IVYO will go and apply these settings, restarting each Ivory instance when necessary. You can verify that the changes are present using the Ivory SHOW command, e.g. SHOW work_mem; should yield something similar to: work_mem ---------- 2MB All connections in IVYO use TLS to encrypt communication between components. IVYO sets up a PKI and certificate authority (CA) that allow you create verifiable endpoints. However, you may want to bring a different TLS infrastructure based upon your organizational requirements. The good news: IVYO lets you do this! There are a few different TLS endpoints that can be customized for IVYO, including those of the Ivory cluster and controlling how Ivory instances authenticate with each other. Let’s look at how we can customize TLS by defining a spec.customTLSSecret, used to both identify the cluster and encrypt communications; and a spec.customReplicationTLSSecret, used for replication authentication. To customize the TLS for an Ivory cluster, you will need to create two Secrets in the Namespace of your Ivory cluster. One of these Secrets will be the customTLSSecret and the other will be the customReplicationTLSSecret. Both secrets contain a TLS key (tls.key), TLS certificate (tls.crt) and CA certificate (ca.crt) to use. If spec.customTLSSecret is provided you must also provide spec.customReplicationTLSSecret and both must contain the same ca.crt. The custom TLS and custom replication TLS Secrets should contain the following fields (though see below for a workaround if you cannot control the field names of the Secret’s data): data: ca.crt: <value> tls.crt: <value> tls.key: <value> For example, if you have files named ca.crt, hippo.key, and hippo.crt stored on your local machine, you could run the following command to create a Secret from those files: kubectl create secret generic -n ivory-operator hippo-cluster.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=hippo.key \\ --from-file=tls.crt=hippo.crt After you create the Secrets, you can specify the custom TLS Secret in your ivorycluster.ivory-operator.ivorysql.org custom resource. For example, if you created a hippo-cluster.tls Secret and a hippo-replication.tls Secret, you would add them to your Ivory cluster: spec: customTLSSecret: name: hippo-cluster.tls customReplicationTLSSecret: name: hippo-replication.tls If you’re unable to control the key-value pairs in the Secret, you can create a mapping to tell the Ivory Operator what key holds the expected value. That would look similar to this: spec: customTLSSecret: name: hippo.tls items: - key: <tls.crt key in the referenced hippo.tls Secret> path: tls.crt - key: <tls.key key in the referenced hippo.tls Secret> path: tls.key - key: <ca.crt key in the referenced hippo.tls Secret> path: ca.crt For instance, if the hippo.tls Secret had the tls.crt in a key named hippo-tls.crt, the tls.key in a key named hippo-tls.key, and the ca.crt in a key named hippo-ca.crt, then your mapping would look like: spec: customTLSSecret: name: hippo.tls items: - key: hippo-tls.crt path: tls.crt - key: hippo-tls.key path: tls.key - key: hippo-ca.crt path: ca.crt Although the custom TLS and custom replication TLS Secrets share the same ca.crt, they do not share the same tls.crt: Your spec.customTLSSecret TLS certificate should have a Common Name (CN) setting that matches the primary Service name. This is the name of the cluster suffixed with -primary. For example, for our hippo cluster this would be hippo-primary. Your spec.customReplicationTLSSecret TLS certificate should have a Common Name (CN) setting that matches _ivoryrepl, which is the preset replication user. As with the other changes, you can roll out the TLS customizations with kubectl apply. There are several ways to add your own custom Kubernetes Labels to your Ivory cluster. Cluster: You can apply labels to any IVYO managed object in a cluster by editing the spec.metadata.labels section of the custom resource. Ivory: You can apply labels to an Ivory instance set and its objects by editing spec.instances.metadata.labels. pgBackRest: You can apply labels to pgBackRest and its objects by editing ivoryclusters.spec.backups.pgbackrest.metadata.labels. There are several ways to add your own custom Kubernetes Annotations to your Ivory cluster. Cluster: You can apply annotations to any IVYO managed object in a cluster by editing the spec.metadata.annotations section of the custom resource. Ivory: You can apply annotations to an Ivory instance set and its objects by editing spec.instances.metadata.annotations. pgBackRest: You can apply annotations to pgBackRest and its objects by editing spec.backups.pgbackrest.metadata.annotations. IVYO allows you to use pod priority classes to indicate the relative importance of a pod by setting a priorityClassName field on your Ivory cluster. This can be done as follows: Instances: Priority is defined per instance set and is applied to all Pods in that instance set by editing the spec.instances.priorityClassName section of the custom resource. Dedicated Repo Host: Priority defined under the repoHost section of the spec is applied to the dedicated repo host by editing the spec.backups.pgbackrest.repoHost.priorityClassName section of the custom resource. Backup (manual and scheduled): Priority is defined under the spec.backups.pgbackrest.jobs.priorityClassName section and applies that priority to all pgBackRest backup Jobs (manual and scheduled). Restore (data source or in-place): Priority is defined for either a \"data source\" restore or an in-place restore by editing the spec.dataSource.ivorycluster.priorityClassName section of the custom resource. Data Migration: The priority defined for the first instance set in the spec (array position 0) is used for the PGDATA and WAL migration Jobs. The pgBackRest repo migration Job will use the priority class applied to the repoHost. IvorySQL commits transactions by storing changes in its Write-Ahead Log (WAL). Because the way WAL files are accessed and utilized often differs from that of data files, and in high-performance situations, it can desirable to put WAL files on separate storage volume. With IVYO, this can be done by adding the walVolumeClaimSpec block to your desired instance in your ivorycluster spec, either when your cluster is created or anytime thereafter: spec: instances: - name: instance walVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi This volume can be removed later by removing the walVolumeClaimSpec section from the instance. Note that when changing the WAL directory, care is taken so as not to lose any WAL files. IVYO only deletes the PVC once there are no longer any WAL files on the previously configured volume. IVYO can run SQL for you as part of the cluster creation and initialization process. IVYO runs the SQL using the psql client so you can use meta-commands to connect to different databases, change error handling, or set and use variables. Its capabilities are described in the psql documentation. The Ivory cluster spec accepts a reference to a ConfigMap containing your init SQL file. Update your cluster spec to include the ConfigMap name, spec.databaseInitSQL.name, and the data key, spec.databaseInitSQL.key, for your SQL file. For example, if you create your ConfigMap with the following command: kubectl -n ivory-operator create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql You would add the following section to your ivorycluster spec: spec: databaseInitSQL: key: init.sql name: hippo-init-sql The ConfigMap must exist in the same namespace as your Ivory cluster. After you add the ConfigMap reference to your spec, apply the change with kubectl apply -k examples/kustomize/ivory. IVYO will create your hippo cluster and run your initialization SQL once the cluster has started. You can verify that your SQL has been run by checking the databaseInitSQL status on your Ivory cluster. While the status is set, your init SQL will not be run again. You can check cluster status with the kubectl describe command: kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo In some cases, due to how Kubernetes treats ivorycluster status, IVYO may run your SQL commands more than once. Please ensure that the commands defined in your init SQL are idempotent. Now that databaseInitSQL is defined in your cluster status, verify database objects have been created as expected. After verifying, we recommend removing the spec.databaseInitSQL field from your spec. Removing the field from the spec will also remove databaseInitSQL from the cluster status. IVYO uses the psql interactive terminal to execute SQL statements in your database. Statements are passed in using standard input and the filename flag (e.g. psql -f -). SQL statements are executed as superuser in the default maintenance database. This means you have full control to create database objects, extensions, or run any SQL statements that you might need. If you are creating users or databases, please see the User/Database Management documentation. Databases created through the user management section of the spec can be referenced in your initialization sql. For example, if a database zoo is defined: spec: users: - name: hippo databases: - \"zoo\" You can connect to zoo by adding the following psql meta-command to your SQL: \\c zoo create table t_zoo as select s, md5(random()::text) from generate_Series(1,5) s; By default, psql commits each SQL command as it completes. To combine multiple commands into a single transaction, use the BEGIN and COMMIT commands. BEGIN; create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; COMMIT; The exit code from psql will determine when the databaseInitSQL status is set. When psql returns 0 the status will be set and SQL will not be run again. When psql returns with an error exit code the status will not be set. IVYO will continue attempting to execute the SQL as part of its reconcile loop until psql returns normally. If psql exits with a failure, you will need to edit the file in your ConfigMap to ensure your SQL statements will lead to a successful psql return. The easiest way to make live changes to your ConfigMap is to use the following kubectl edit command: kubectl -n <cluster-namespace> edit configmap hippo-init-sql Be sure to transfer any changes back over to your local file. Another option is to make changes in your local file and use kubectl --dry-run to create a template and pipe the output into kubectl apply: kubectl create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql --dry-run=client -o yaml | kubectl apply -f - If you edit your ConfigMap and your changes aren’t showing up, you may be waiting for IVYO to reconcile your cluster. After some time, IVYO will automatically reconcile the cluster or you can trigger reconciliation by applying any change to your cluster (e.g. with kubectl apply -k examples/kustomize/ivory). To ensure that psql returns a failure exit code when your SQL commands fail, set the ON_ERROR_STOP variable as part of your SQL file: \\set ON_ERROR_STOP \\echo Any error will lead to exit code 3 create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; If your Ivory configuration settings are not present, ensure that you are using the syntax that Ivory expects. You can see this in the Ivory configuration documentation. You’ve now seen how you can further customize your Ivory cluster, but what about managing users and databases? That’s a great question that is answered in the next section. IVYO comes with some out-of-the-box conveniences for managing users and databases in your Ivory cluster. However, you may have requirements where you need to create additional users, adjust user privileges or add additional databases to your cluster. For detailed information for how user and database management works in IVYO, please see the User Management section of the architecture guide. You can create a new user with the following snippet in the ivorycluster custom resource. Let’s add this to our hippo database: spec: users: - name: rhino You can now apply the changes and see that the new user is created. Note the following: The user would only be able to connect to the default ivory database. The user will not have any connection credentials populated into the hippo-pguser-rhino Secret. The user is unprivileged. Let’s create a new database named zoo that we will let the rhino user access: spec: users: - name: rhino databases: - zoo Inspect the hippo-pguser-rhino Secret. You should now see that the dbname and uri fields are now populated! We can set role privileges by using the standard role attributes that Ivory provides and adding them to the spec.users.options. Let’s say we want the rhino to become a superuser (be careful about doling out Ivory superuser privileges!). You can add the following to the spec: spec: users: - name: rhino databases: - zoo options: \"SUPERUSER\" There you have it: we have created a Ivory user named rhino with superuser privileges that has access to the rhino database (though a superuser has access to all databases!). Let’s say you want to revoke the superuser privilege from rhino. You can do so with the following: spec: users: - name: rhino databases: - zoo options: \"NOSUPERUSER\" If you want to add multiple privileges, you can add each privilege with a space between them in options, e.g.: spec: users: - name: rhino databases: - zoo options: \"CREATEDB CREATEROLE\" By default, IVYO does not give you access to the ivory user. However, you can get access to this account by doing the following: spec: users: - name: ivory This will create a Secret of the pattern <clusterName>-pguser-ivory that contains the credentials of the ivory account. For our hippo cluster, this would be hippo-pguser-ivory. IVYO does not delete users automatically: after you remove the user from the spec, it will still exist in your cluster. To remove a user and all of its objects, as a superuser you will need to run DROP OWNED in each database the user has objects in, and DROP ROLE in your Ivory cluster. For example, with the above rhino user, you would run the following: DROP OWNED BY rhino; DROP ROLE rhino; Note that you may need to run DROP OWNED BY rhino CASCADE; based upon your object ownership structure — be very careful with this command! IVYO does not delete databases automatically: after you remove all instances of the database from the spec, it will still exist in your cluster. To completely remove the database, you must run the DROP DATABASE command as a Ivory superuser. For example, to remove the zoo database, you would execute the following: DROP DATABASE zoo; Let’s look at how IVYO handles disaster recovery! Perhaps someone accidentally dropped the users table. Perhaps you want to clone your production database to a step-down environment. Perhaps you want to exercise your disaster recovery system (and it is important that you do!). Regardless of scenario, it’s important to know how you can perform a \"restore\" operation with IVYO to be able to recovery your data from a particular point in time, or clone a database for other purposes. Let’s look at how we can perform different types of restore operations. First, let’s understand the core restore properties on the custom resource. IVYO offers the ability to restore from an existing ivorycluster or a remote cloud-based data source, such as S3, GCS, etc. For more on that, see the Clone From Backups Stored in S3 / GCS / Azure Blob Storage section. Note that you cannot use both a local ivorycluster data source and a remote cloud-based data source at one time; if both the dataSource.ivorycluster and dataSource.pgbackrest fields are filled in, the local ivorycluster data source will take precedence. There are several attributes on the custom resource that are important to understand as part of the restore process. All of these attributes are grouped together in the spec.dataSource.ivorycluster section of the custom resource. Please review the table below to understand how each of these attributes work in the context of setting up a restore operation. spec.dataSource.ivorycluster.clusterName: The name of the cluster that you are restoring from. This corresponds to the metadata.name attribute on a different ivorycluster custom resource. spec.dataSource.ivorycluster.clusterNamespace: The namespace of the cluster that you are restoring from. Used when the cluster exists in a different namespace. spec.dataSource.ivorycluster.repoName: The name of the pgBackRest repository from the spec.dataSource.ivorycluster.clusterName to use for the restore. Can be one of repo1, repo2, repo3, or repo4. The repository must exist in the other cluster. spec.dataSource.ivorycluster.options: Any additional pgBackRest restore options or general options that IVYO allows. For example, you may want to set --process-max to help improve performance on larger databases; but you will not be able to set`--target-action`, since that option is currently disallowed. (IVYO always sets it to promote if a --target is present, and otherwise leaves it blank.) spec.dataSource.ivorycluster.resources: Setting resource limits and requests of the restore job can ensure that it runs efficiently. spec.dataSource.ivorycluster.affinity: Custom Kubernetes affinity rules constrain the restore job so that it only runs on certain nodes. spec.dataSource.ivorycluster.tolerations: Custom Kubernetes tolerations allow the restore job to run on tainted nodes. Let’s walk through some examples for how we can clone and restore our databases. Let’s create a clone of our hippo cluster that we created previously. We know that our cluster is named hippo (based on its metadata.name) and that we only have a single backup repository called repo1. Let’s call our new cluster elephant. We can create a clone of the hippo cluster using a manifest like this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Note this section of the spec: spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 This is the part that tells IVYO to create the elephant cluster as an independent copy of the hippo cluster. The above is all you need to do to clone a Ivory cluster! IVYO will work on creating a copy of your data on a new persistent volume claim (PVC) and work on initializing your cluster to spec. Easy! Did someone drop the user table? You may want to perform a point-in-time-recovery (PITR) to revert your database back to a state before a change occurred. Fortunately, IVYO can help you do that. You can set up a PITR using the restore command of pgBackRest, the backup management tool that powers the disaster recovery capabilities of IVYO. You will need to set a few options on spec.dataSource.ivorycluster.options to perform a PITR. These options include: --type=time: This tells pgBackRest to perform a PITR. --target: Where to perform the PITR to. An example recovery target is 2021-06-09 14:15:11-04. The timezone specified here as -04 for EDT. Please see the pgBackRest documentation for other timezone options. --set (optional): Choose which backup to start the PITR from. A few quick notes before we begin: To perform a PITR, you must have a backup that finished before your PITR time. In other words, you can’t perform a PITR back to a time where you do not have a backup! All relevant WAL files must be successfully pushed for the restore to complete correctly. Be sure to select the correct repository name containing the desired backup! With that in mind, let’s use the elephant example above. Let’s say we want to perform a point-in-time-recovery (PITR) to 2021-06-09 14:15:11-04, we can use the following manifest: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi The section to pay attention to is this: spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" Notice how we put in the options to specify where to make the PITR. Using the above manifest, IVYO will go ahead and create a new Ivory cluster that recovers its data up until 2021-06-09 14:15:11-04. At that point, the cluster is promoted and you can start accessing your database from that specific point in time! Similar to the PITR restore described above, you may want to perform a similar reversion back to a state before a change occurred, but without creating another IvorySQL cluster. Fortunately, IVYO can help you do this as well. You can set up a PITR using the restore command of pgBackRest, the backup management tool that powers the disaster recovery capabilities of IVYO. You will need to set a few options on spec.backups.pgbackrest.restore.options to perform a PITR. These options include: --type=time: This tells pgBackRest to perform a PITR. --target: Where to perform the PITR to. An example recovery target is 2021-06-09 14:15:11-04. --set (optional): Choose which backup to start the PITR from. A few quick notes before we begin: To perform a PITR, you must have a backup that finished before your PITR time. In other words, you can’t perform a PITR back to a time where you do not have a backup! All relevant WAL files must be successfully pushed for the restore to complete correctly. Be sure to select the correct repository name containing the desired backup! To perform an in-place restore, users will first fill out the restore section of the spec as follows: spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" And to trigger the restore, you will then annotate the ivorycluster as follows: kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-restore=id1 And once the restore is complete, in-place restores can be disabled: spec: backups: pgbackrest: restore: enabled: false Notice how we put in the options to specify where to make the PITR. Using the above manifest, IVYO will go ahead and re-create your Ivory cluster to recover its data up until 2021-06-09 14:15:11-04. At that point, the cluster is promoted and you can start accessing your database from that specific point in time! You might need to restore specific databases from a cluster backup, for performance reasons or to move selected databases to a machine that does not have enough space to restore the entire cluster backup. pgBackRest supports this case, but it is important to make sure this is what you want. Restoring in this manner will restore the requested database from backup and make it accessible, but all of the other databases in the backup will NOT be accessible after restore. For example, if your backup includes databases test1, test2, and test3, and you request that test2 be restored, the test1 and test3 databases will NOT be accessible after restore is completed. Please review the pgBackRest documentation on the limitations on restoring individual databases. You can restore individual databases from a backup using a spec similar to the following: spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --db-include=hippo where --db-include=hippo would restore only the contents of the hippo database. Advanced high-availability and disaster recovery strategies involve spreading your database clusters across data centers to help maximize uptime. IVYO provides ways to deploy ivoryclusters that can span multiple Kubernetes clusters using an external storage system or IvorySQL streaming replication. The disaster recovery architecture documentation provides a high-level overview of standby clusters with IVYO can be found in the disaster recovery architecture documentation. This tutorial section will describe how to create three different types of standby clusters, one using an external storage system, one that is streaming data directly from the primary, and one that takes advantage of both external storage and streaming. These example clusters can be created in the same Kubernetes cluster, using a single IVYO instance, or spread across different Kubernetes clusters and IVYO instances with the correct storage and networking configurations. A repo-based standby will recover from WAL files a pgBackRest repo stored in external storage. The primary cluster should be created with a cloud-based backup configuration. The following manifest defines a ivorycluster with standby.enabled set to true and repoName configured to point to the s3 repo configured in the primary: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" standby: enabled: true repoName: repo1 A streaming standby relies on an authenticated connection to the primary over the network. The primary cluster should be accessible via the network and allow TLS authentication (TLS is enabled by default). In the following manifest, we have standby.enabled set to true and have provided both the host and port that point to the primary cluster. We have also defined customTLSSecret and customReplicationTLSSecret to provide certs that allow the standby to authenticate to the primary. For this type of standby, you must use custom TLS: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: repos: - name: repo1 volume: volumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true host: \"192.0.2.2\" port: 5432 Another option is to create a standby cluster using an external pgBackRest repo that streams from the primary. With this setup, the standby cluster will continue recovering from the pgBackRest repo if streaming replication falls behind. In this manifest, we have enabled the settings from both previous examples: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true repoName: repo1 host: \"192.0.2.2\" port: 5432 At some point, you will want to promote the standby to start accepting both reads and writes. This has the net effect of pushing WAL (transaction archives) to the pgBackRest repository, so we need to ensure we don’t accidentally create a split-brain scenario. Split-brain can happen if two primary instances attempt to write to the same repository. If the primary cluster is still active, make sure you shutdown the primary before trying to promote the standby cluster. Once the primary is inactive, we can promote the standby cluster by removing or disabling its spec.standby section: spec: standby: enabled: false This change triggers the promotion of the standby leader to a primary IvorySQL instance and the cluster begins accepting writes. You can clone a Ivory cluster from backups that are stored in AWS S3 (or a storage system that uses the S3 protocol), GCS, or Azure Blob Storage without needing an active Ivory cluster! The method to do so is similar to how you clone from an existing ivorycluster. This is useful if you want to have a data set for people to use but keep it compressed on cheaper storage. For the purposes of this example, let’s say that you created a Ivory cluster named hippo that has its backups stored in S3 that looks similar to this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 manual: repoName: repo1 options: - --type=full repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" Ensure that the credentials in ivyo-s3-creds match your S3 credentials. For more details on deploying a Ivory cluster using S3 for backups, please see the Backups section of the tutorial. For optimal performance when creating a new cluster from an active cluster, ensure that you take a recent full backup of the previous cluster. The above manifest is set up to take a full backup. Assuming hippo is created in the ivory-operator namespace, you can trigger a full backup with the following command: kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-backup=\"$( date '+%F_%H:%M:%S' )\" Wait for the backup to complete. Once this is done, you can delete the Ivory cluster. Now, let’s clone the data from the hippo backup into a new cluster called elephant. You can use a manifest similar to this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/elephant/repo1 repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" There are a few things to note in this manifest. First, note that the spec.dataSource.pgbackrest object in our new ivorycluster is very similar but slightly different from the old ivorycluster’s spec.backups.pgbackrest object. The key differences are: No image is necessary when restoring from a cloud-based data source stanza is a required field when restoring from a cloud-based data source backups.pgbackrest has a repos field, which is an array dataSource.pgbackrest has a repo field, which is a single object Note also the similarities: We are reusing the secret for both (because the new restore pod needs to have the same credentials as the original backup pod) The repo object is the same The global object is the same This is because the new restore pod for the elephant ivorycluster will need to reuse the configuration and credentials that were originally used in setting up the hippo ivorycluster. In this example, we are creating a new cluster which is also backing up to the same S3 bucket; only the spec.backups.pgbackrest.global field has changed to point to a different path. This will ensure that the new elephant cluster will be pre-populated with the data from `hippo’s backups, but will backup to its own folders, ensuring that the original backup repository is appropriately preserved. Deploy this manifest to create the elephant Ivory cluster. Observe that it comes up and running: kubectl -n ivory-operator describe ivorycluster elephant When it is ready, you will see that the number of expected instances matches the number of ready instances, e.g.: Instances: Name: 00 Ready Replicas: 1 Replicas: 1 Updated Replicas: 1 The previous example shows how to use an existing S3 repository to pre-populate a ivorycluster while using a new S3 repository for backing up. But ivoryclusters that use cloud-based data sources can also use local repositories. For example, assuming a ivorycluster called rhino that was meant to pre-populate from the original hippo ivorycluster, the manifest would look like this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: rhino spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Now we’ve seen how to clone a cluster and perform a point-in-time-recovery, let’s see how we can monitor our Ivory cluster to detect and prevent issues from occurring. While having high availability and disaster recovery systems in place helps in the event of something going wrong with your IvorySQL cluster, monitoring helps you anticipate problems before they happen. Additionally, monitoring can help you diagnose and resolve issues that may cause degraded performance rather than downtime. Let’s look at how IVYO allows you to enable monitoring in your cluster. Let’s look at how we can add the IvorySQL Exporter sidecar to your cluster using the kustomize/ivory example in the Postgres Operator examples repository. Monitoring tools are added using the spec.monitoring section of the custom resource. Currently, the only monitoring tool supported is the IvorySQL Exporter configured with pgMonitor. In the kustomize/ivory/ivory.yaml file, add the following YAML to the spec: monitoring: pgmonitor: exporter: image: {{< param imagePostgresExporter >}} Save your changes and run: kubectl apply -k kustomize/ivory IVYO will detect the change and add the Exporter sidecar to all Ivory Pods that exist in your cluster. IVYO will also do the work to allow the Exporter to connect to the database and gather metrics that can be accessed using the IVYO Monitoring stack. IVYO allows you to configure the exporter sidecar to use TLS encryption. If you provide a custom TLS Secret via the exporter spec: monitoring: pgmonitor: exporter: customTLSSecret: name: hippo.tls Like other custom TLS Secrets that can be configured with IVYO, the Secret will need to be created in the same Namespace as your PostgresCluster. It should also contain the TLS key (tls.key) and TLS certificate (tls.crt) needed to enable encryption. data: tls.crt: <value> tls.key: <value> After you configure TLS for the exporter, you will need to update your Prometheus deployment to use TLS, and your connection to the exporter will be encrypted. Check out the Prometheus documentation for more information on configuring TLS for Prometheus. Once the IvorySQL Exporter has been enabled in your cluster, follow the steps outlined in IVYO Monitoring to install the monitoring stack. This will allow you to deploy a pgMonitor configuration of Prometheus, Grafana, and Alertmanager monitoring tools in Kubernetes. These tools will be set up by default to connect to the Exporter containers on your Ivory Pods. While the default Kustomize install should work in most Kubernetes environments, it may be necessary to further customize the project according to your specific needs. For instance, by default fsGroup is set to 26 for the securityContext defined for the various Deployments comprising the IVYO Monitoring stack: securityContext: fsGroup: 26 In most Kubernetes environments this setting is needed to ensure processes within the container have the permissions needed to write to any volumes mounted to each of the Pods comprising the IVYO Monitoring stack. However, when installing in an OpenShift environment (and more specifically when using the restricted Security Context Constraint), the fsGroup setting should be removed since OpenShift will automatically handle setting the proper fsGroup within the Pod’s securityContext. Additionally, within this same section it may also be necessary to modify the supplmentalGroups setting according to your specific storage configuration: securityContext: supplementalGroups : 65534 Therefore, the following files (located under kustomize/monitoring) should be modified and/or patched (e.g. using additional overlays) as needed to ensure the securityContext is properly defined for your Kubernetes environment: deploy-alertmanager.yaml deploy-grafana.yaml deploy-prometheus.yaml And to modify the configuration for the various storage resources (i.e. PersistentVolumeClaims) created by the IVYO Monitoring installer, the kustomize/monitoring/pvcs.yaml file can also be modified. Additionally, it is also possible to further customize the configuration for the various components comprising the IVYO Monitoring stack (Grafana, Prometheus and/or AlertManager) by modifying the following configuration resources: alertmanager-config.yaml alertmanager-rules-config.yaml grafana-datasources.yaml prometheus-config.yaml Finally, please note that the default username and password for Grafana can be updated by modifying the Grafana Secret in file kustomize/monitoring/grafana-secret.yaml. Once the Kustomize project has been modified according to your specific needs, IVYO Monitoring can then be installed using kubectl and Kustomize: kubectl apply -k kustomize/monitoring And similarly, once IVYO Monitoring has been installed, it can uninstalled using kubectl and Kustomize: kubectl delete -k kustomize/monitoring Now that we can monitor our cluster, let’s explore how connection pooling can be enabled using IVYO and how it is helpful. Connection pooling can be helpful for scaling and maintaining overall availability between your application and the database. IVYO helps facilitate this by supporting the PgBouncer connection pooler and state manager. Let’s look at how we can a connection pooler and connect it to our application! Let’s look at how we can add a connection pooler using the kustomize/keycloak example in the Ivory Operator repository examples folder. Connection poolers are added using the spec.proxy section of the custom resource. Currently, the only connection pooler supported is PgBouncer. The only required attribute for adding a PgBouncer connection pooler is to set the spec.proxy.pgBouncer.image attribute. In the kustomize/keycloak/ivory.yaml file, add the following YAML to the spec: proxy: pgBouncer: image: {{< param imageIvoryPGBouncer >}} (You can also find an example of this in the kustomize/examples/high-availability example). Save your changes and run: kubectl apply -k kustomize/keycloak IVYO will detect the change and create a new PgBouncer Deployment! That was fairly easy to set up, so now let’s look at how we can connect our application to the connection pooler. When a connection pooler is deployed to the cluster, IVYO adds additional information to the user Secrets to allow for applications to connect directly to the connection pooler. Recall that in this example, our user Secret is called keycloakdb-pguser-keycloakdb. Describe the user Secret: kubectl -n ivory-operator describe secrets keycloakdb-pguser-keycloakdb You should see that there are several new attributes included in this Secret that allow for you to connect to your Ivory instance via the connection pooler: pgbouncer-host: The name of the host of the PgBouncer connection pooler. This references the Service of the PgBouncer connection pooler. pgbouncer-port: The port that the PgBouncer connection pooler is listening on. pgbouncer-uri: A PostgreSQL connection URI that provides all the information for logging into the Ivory database via the PgBouncer connection pooler. pgbouncer-jdbc-uri: A PostgreSQL JDBC connection URI that provides all the information for logging into the Ivory database via the PgBouncer connection pooler using the JDBC driver. Note that by default, the connection string disable JDBC managing prepared transactions for optimal use with PgBouncer. Open up the file in kustomize/keycloak/keycloak.yaml. Update the DB_ADDR and DB_PORT values to be the following: - name: DB_ADDR valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-port } } This changes Keycloak’s configuration so that it will now connect through the connection pooler. Apply the changes: kubectl apply -k kustomize/keycloak Kubernetes will detect the changes and begin to deploy a new Keycloak Pod. When it is completed, Keycloak will now be connected to Ivory via the PgBouncer connection pooler! IVYO deploys every cluster and component over TLS. This includes the PgBouncer connection pooler. If you are using your own custom TLS setup, you will need to provide a Secret reference for a TLS key / certificate pair for PgBouncer in spec.proxy.pgBouncer.customTLSSecret. Your TLS certificate for PgBouncer should have a Common Name (CN) setting that matches the PgBouncer Service name. This is the name of the cluster suffixed with -pgbouncer. For example, for our hippo cluster this would be hippo-pgbouncer. For the keycloakdb example, it would be keycloakdb-pgbouncer. To customize the TLS for PgBouncer, you will need to create a Secret in the Namespace of your Ivory cluster that contains the TLS key (tls.key), TLS certificate (tls.crt) and the CA certificate (ca.crt) to use. The Secret should contain the following values: data: ca.crt: <value> tls.crt: <value> tls.key: <value> For example, if you have files named ca.crt, keycloakdb-pgbouncer.key, and keycloakdb-pgbouncer.crt stored on your local machine, you could run the following command: kubectl create secret generic -n ivory-operator keycloakdb-pgbouncer.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=keycloakdb-pgbouncer.key \\ --from-file=tls.crt=keycloakdb-pgbouncer.crt You can specify the custom TLS Secret in the spec.proxy.pgBouncer.customTLSSecret.name field in your ivorycluster.ivory-operator.ivorysql.org custom resource, e.g.: spec: proxy: pgBouncer: customTLSSecret: name: keycloakdb-pgbouncer.tls The PgBouncer connection pooler is highly customizable, both from a configuration and Kubernetes deployment standpoint. Let’s explore some of the customizations that you can do! PgBouncer configuration can be customized through spec.proxy.pgBouncer.config. After making configuration changes, IVYO will roll them out to any PgBouncer instance and automatically issue a \"reload\". There are several ways you can customize the configuration: spec.proxy.pgBouncer.config.global: Accepts key-value pairs that apply changes globally to PgBouncer. spec.proxy.pgBouncer.config.databases: Accepts key-value pairs that represent PgBouncer database definitions. spec.proxy.pgBouncer.config.users: Accepts key-value pairs that represent connection settings applied to specific users. spec.proxy.pgBouncer.config.files: Accepts a list of files that are mounted in the /etc/pgbouncer directory and loaded before any other options are considered using PgBouncer’s include directive. For example, to set the connection pool mode to transaction, you would set the following configuration: spec: proxy: pgBouncer: config: global: pool_mode: transaction For a reference on PgBouncer configuration please see: https://www.pgbouncer.org/config.html IVYO deploys one PgBouncer instance by default. You may want to run multiple PgBouncer instances to have some level of redundancy, though you still want to be mindful of how many connections are going to your Ivory database! You can manage the number of PgBouncer instances that are deployed through the spec.proxy.pgBouncer.replicas attribute. You can manage the CPU and memory resources given to a PgBouncer instance through the spec.proxy.pgBouncer.resources attribute. The layout of spec.proxy.pgBouncer.resources should be familiar: it follows the same pattern as the standard Kubernetes structure for setting container resources. For example, let’s say we want to set some CPU and memory limits on our PgBouncer instances. We could add the following configuration: spec: proxy: pgBouncer: resources: limits: cpu: 200m memory: 128Mi As IVYO deploys the PgBouncer instances using a Deployment these changes are rolled out using a rolling update to minimize disruption between your application and Ivory instances! You can apply custom annotations and labels to your PgBouncer instances through the spec.proxy.pgBouncer.metadata.annotations and spec.proxy.pgBouncer.metadata.labels attributes respectively. Note that any changes to either of these two attributes take precedence over any other custom labels you have added. You can control the pod anti-affinity, pod affinity, and node affinity through the spec.proxy.pgBouncer.affinity attribute, specifically: spec.proxy.pgBouncer.affinity.nodeAffinity: controls node affinity for the PgBouncer instances. spec.proxy.pgBouncer.affinity.podAffinity: controls Pod affinity for the PgBouncer instances. spec.proxy.pgBouncer.affinity.podAntiAffinity: controls Pod anti-affinity for the PgBouncer instances. Each of the above follows the standard Kubernetes specification for setting affinity. For example, to set a preferred Pod anti-affinity rule for the kustomize/keycloak example, you would want to add the following to your configuration: spec: proxy: pgBouncer: affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: keycloakdb ivory-operator.ivorysql.org/role: pgbouncer topologyKey: kubernetes.io/hostname You can deploy PgBouncer instances to Nodes with Taints by setting Tolerations through spec.proxy.pgBouncer.tolerations. This attribute follows the Kubernetes standard tolerations layout. For example, if there were a set of Nodes with a Taint of role=connection-poolers:NoSchedule that you want to schedule your PgBouncer instances to, you could apply the following configuration: spec: proxy: pgBouncer: tolerations: - effect: NoSchedule key: role operator: Equal value: connection-poolers Note that setting a toleration does not necessarily mean that the PgBouncer instances will be assigned to Nodes with those taints. Tolerations act as a key: they allow for you to access Nodes. If you want to ensure that your PgBouncer instances are deployed to specific nodes, you need to combine setting tolerations with node affinity. Besides using affinity, anti-affinity and tolerations, you can also set Topology Spread Constraints through spec.proxy.pgBouncer.topologySpreadConstraints. This attribute follows the Kubernetes standard topology spread contraint layout. For example, since each of of our pgBouncer Pods will have the standard ivory-operator.ivorysql.org/role: pgbouncer Label set, we can use this Label when determining the maxSkew. In the example below, since we have 3 nodes with a maxSkew of 1 and we’ve set whenUnsatisfiable to ScheduleAnyway, we should ideally see 1 Pod on each of the nodes, but our Pods can be distributed less evenly if other constraints keep this from happening. proxy: pgBouncer: replicas: 3 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/role: pgbouncer If you want to ensure that your PgBouncer instances are deployed more evenly (or not deployed at all), you need to update whenUnsatisfiable to DoNotSchedule. Now that we can enable connection pooling in a cluster, Let’s explore some administrative tasks such as manually restarting IvorySQL using IVYO. How do we do that? There are times when you might need to manually restart IvorySQL. This can be done by adding or updating a custom annotation to the cluster’s spec.metadata.annotations section. IVYO will notice the change and perform a rolling restart. For example, if you have a cluster named hippo in the namespace ivory-operator, all you need to do is patch the hippo ivorycluster with the following: kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"metadata\":{\"annotations\":{\"restarted\":\"'\"$(date)\"'\"}}}}' Watch your hippo cluster: you will see the rolling update has been triggered and the restart has begun. You can shut down an Ivory cluster by setting the spec.shutdown attribute to true. You can do this by editing the manifest, or, in the case of the hippo cluster, executing a command like the below: kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"shutdown\": true}}' The effect of this is that all the Kubernetes workloads for this cluster are scaled to 0. You can verify this with the following command: kubectl get deploy,sts,cronjob --selector=ivory-operator.ivorysql.org/cluster=hippo -n ivory-operator NAME READY AGE statefulset.apps/hippo-00-lwgx 0/0 1h NAME SCHEDULE SUSPEND ACTIVE cronjob.batch/hippo-repo1-full @daily True 0 To turn an Ivory cluster that is shut down back on, you can set spec.shutdown to false. You can pause the Ivory cluster reconciliation process by setting the spec.paused attribute to true. You can do this by editing the manifest, or, in the case of the hippo cluster, executing a command like the below: kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"paused\": true}}' Pausing a cluster will suspend any changes to the cluster’s current state until reconciliation is resumed. This allows you to fully control when changes to the ivorycluster spec are rolled out to the Ivory cluster. While paused, no statuses are updated other than the \"Progressing\" condition. To resume reconciliation of an Ivory cluster, you can either set spec.paused to false or remove the setting from your manifest. Credentials should be invalidated and replaced (rotated) as often as possible to minimize the risk of their misuse. Unlike passwords, every TLS certificate has an expiration, so replacing them is inevitable. In fact, IVYO automatically rotates the client certificates that it manages before the expiration date on the certificate. A new client certificate will be generated after 2/3rds of its working duration; so, for instance, a IVYO-created certificate with an expiration date 12 months in the future will be replaced by IVYO around the eight month mark. This is done so that you do not have to worry about running into problems or interruptions of service with an expired certificate. If you want to rotate a single client certificate, you can regenerate the certificate of an existing cluster by deleting the tls.key field from its certificate Secret. Is it time to rotate your IVYO root certificate? All you need to do is delete the ivyo-root-cacert secret. IVYO will regenerate it and roll it out seamlessly, ensuring your apps continue communicating with the Ivory cluster without having to update any configuration or deal with any downtime. kubectl delete secret ivyo-root-cacert IVYO only updates secrets containing the generated root certificate. It does not touch custom certificates. When you use your own TLS certificates with IVYO, you are responsible for replacing them appropriately. Here’s how. IVYO automatically detects and loads changes to the contents of IvorySQL server and replication Secrets without downtime. You or your certificate manager need only replace the values in the Secret referenced by spec.customTLSSecret. If instead you change spec.customTLSSecret to refer to a new Secret or new fields, IVYO will perform a rolling restart. When changing the IvorySQL certificate authority, make sure to update customReplicationTLSSecret as well. There may be times when you want to change the primary in your HA cluster. This can be done using the patroni.switchover section of the ivorycluster spec. It allows you to enable switchovers in your ivoryclusters, target a specific instance as the new primary, and run a failover if your ivorycluster has entered a bad state. Let’s go through the process of performing a switchover! First you need to update your spec to prepare your cluster to change the primary. Edit your spec to have the following fields: spec: patroni: switchover: enabled: true After you apply this change, IVYO will be looking for the trigger to perform a switchover in your cluster. You will trigger the switchover by adding the ivory-operator.ivorysql.org/trigger-switchover annotation to your custom resource. The best way to set this annotation is with a timestamp, so you know when you initiated the change. For example, for our hippo cluster, we can run the following command to trigger the switchover: kubectl annotate -n ivory-operator ivorycluster hippo \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" If you want to perform another switchover you can re-run the annotation command and add the --overwrite flag: kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" IVYO will detect this annotation and use the Patroni API to request a change to the current primary! The roles on your database instance Pods will start changing as Patroni works. The new primary will have the master role label, and the old primary will be updated to replica. The status of the switch will be tracked using the status.patroni.switchover field. This will be set to the value defined in your trigger annotation. If you use a timestamp as the annotation this is another way to determine when the switchover was requested. After the instance Pod labels have been updated and status.patroni.switchover has been set, the primary has been changed on your cluster! After changing the primary, we recommend that you disable switchovers by setting spec.patroni.switchover.enabled to false or remove the field from your spec entirely. If the field is removed the corresponding status will also be removed from the ivorycluster. Another option you have when switching the primary is providing a target instance as the new primary. This target instance will be used as the candidate when performing the switchover. The spec.patroni.switchover.targetInstance field takes the name of the instance that you are switching to. This name can be found in a couple different places; one is as the name of the StatefulSet and another is on the database Pod as the ivory-operator.ivorysql.org/instance label. The following commands can help you determine who is the current primary and what name to use as the targetInstance: $ kubectl get pods -l ivory-operator.ivorysql.org/cluster=hippo \\ -L ivory-operator.ivorysql.org/instance \\ -L ivory-operator.ivorysql.org/role -n ivory-operator NAME READY STATUS RESTARTS AGE INSTANCE ROLE hippo-instance1-jdb5-0 3/3 Running 0 2m47s hippo-instance1-jdb5 master hippo-instance1-wm5p-0 3/3 Running 0 2m47s hippo-instance1-wm5p replica In our example cluster hippo-instance1-jdb5 is currently the primary meaning we want to target hippo-instance1-wm5p in the switchover. Now that you know which instance is currently the primary and how to find your targetInstance, let’s update your cluster spec: spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p After applying this change you will once again need to trigger the switchover by annotating the ivorycluster (see above commands). You can verify the switchover has completed by checking the Pod role labels and status.patroni.switchover. Finally, we have the option to failover when your cluster has entered an unhealthy state. The only spec change necessary to accomplish this is updating the spec.patroni.switchover.type field to the Failover type. One note with this is that a targetInstance is required when performing a failover. Based on the example cluster above, assuming hippo-instance1-wm5p is still a replica, we can update the spec: spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p type: Failover Apply this spec change and your ivorycluster will be prepared to perform the failover. Again you will need to trigger the switchover by annotating the ivorycluster (see above commands) and verify that the Pod role labels and status.patroni.switchover are updated accordingly. Errors encountered in the switchover process can leave your cluster in a bad state. If you encounter issues, found in the operator logs, you can update the spec to fix the issues and apply the change. Once the change has been applied, IVYO will attempt to perform the switchover again. We’ve covered a lot in terms of building, maintaining, scaling, customizing, restarting, and expanding our Ivory cluster. However, there may come a time where we need to delete our Ivory cluster. How do we do that? There comes a time when it is necessary to delete your cluster. If you have been following along with the example, you can delete your Ivory cluster by simply running: kubectl delete -k examples/kustomize/ivory IVYO will remove all of the objects associated with your cluster. With data retention, this is subject to the retention policy of your PVC. 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We are eager to get contributions from members of the wider IvorySQL community. If you want to see your changes to code or documents added to IvorySQL and appear in future versions, you need to understand the content of this section. ​IvorySQL community welcomes and appreciates all types of contributions and we are looking forward to your participation! Every member, contributor and leader should read our principles of conduct. We promise that everyone can participate in community and pay equal attention to everyone, no matter who. We are committed to acting and interacting in a way that contributes to the establishment of an open, enthusiastic, diverse, inclusive and healthy community. Our team is a continuously open team, focusing on parts of IvorySQL. In our team, there are reviewers, submitters and maintainers, and we have one or more repositories. The decision for the team is made by the maintainer. The typical promotion path for IvorySQL developers is from user to reviewer, then submitter and maintainer. But getting more roles doesn’t mean you have any privileges to other community members. Everyone in the IvorySQL community is equal and has the responsibility to cooperate constructively with other contributors to build a friendly community. These roles are natural rewards for your significant contributions to the development of IvorySQL, and provide you with more rights in the development workflow to improve your efficiency. At the same time，this requires you to undertake some additional duty: ​Team honor: now you are already one of the team reviewers/submitters/maintainers, it means that you represent the project and your team members. So, please be Mr.Nice Guy to defend the reputation of the team. ​Responsibility: submitters/maintainers have the right to merge pull requests, therefore, they take additional responsibility to deal with the consequences of accepting changes to the code base or documents. When a bug occurs, they should fix it. If they can not solve it, they should roll back the project. Also, they need to help the release manager solve any problems found in the test cycle. Before contributing, we need to know the current version of IvorySQL and the version of the document.At present, we maintain versions after version 5.0. Our version follows the update pace of PG. Please update to the latest version before contributing. After that, we need to read the format requirements carefully and be familiar with code format, code comment format, issue format, pull PR title format, document contribution format, and article contribution format. These can help you become a contributor of IvorySQL as soon as possible. IvorySQL is developed on GitHub. Anyone who wishes to contribute to it must have a Github account and be familiar with Git tools and workflow. It is also recommended that you follow the developer’s mailing list since some of the contributions may generate more detailed discussions there. Once you have your GitHub account, fork this repository so that you can have your private copy to start hacking on and to use as a source of pull requests. If the contribution you’re submitting is original work, you can assume that IvorySQL will release it as part of an overall IvorySQL release available to the downstream consumers under the Apache License, Version 2.0. If the contribution you’re submitting is NOT original work you have to indicate the name of the license and also make sure that it is similar in terms to the Apache License 2.0. Apache Software Foundation maintains a list of these licenses under Category A. In addition to that, you may be required to make proper attributions. Finally, keep in mind that it is NEVER a good idea to remove licensing headers from the work that is not your original one. Even if you are using parts of the file that originally had a licensing header at the top you should err on the side of preserving it. As always, if you are not quite sure about the licensing implications of your contributions, feel free to reach out to us on the developer mailing list. You can upload your modified bugs, new functions and other codes to your personal warehouse, and finally submit PR requests to merge them on the official website: https://github.com/IvorySQL/IvorySQL. The IvorySQL community provides Chinese and English documents. English documents are saved in …​ document repository, Chinese documents are saved in i18n document repository. You can contribute to one of them or both. GitHub: https://github.com/IvorySQL/IvorySQL Gitee:https://gitee.com/IvorySQL/ IvorySQL official website:https://github.com/IvorySQL/Ivory-www Mailing List website:https://lists.ivorysql.org/ You can submit your article to the blog in the IvorySQL-WWW code warehouse, or send it to the mailbox renjiao@highgo.com. Your chances of getting feedback and seeing your code merged into the project greatly depend on how granular your changes are. If you happen to have a bigger change in mind, we highly recommend engaging on the developer’s mailing list first and sharing your proposal with us before you spend a lot of time writing code. Even when your proposal gets validated by the community, we still recommend doing the actual work as a series of small, self-contained commits. This makes the reviewer’s job much easier and increases the timeliness of feedback. When it comes to C and C++ parts of IvorySQL, we follow PostgreSQL Coding Conventions. In addition to that: For C and Perl code, please run pgindent if necessary. We recommend using git diff --color when reviewing your changes so that you don’t have any spurious whitespace issues in the code that you submit. All new functionality that is contributed to IvorySQL should be covered by regression tests that are contributed alongside it. If you are uncertain about how to test or document your work, please raise the question on the ivorysql-hackers mailing list and the developer community will do its best to help you. At the very minimum, you should always be running make installcheck-world to make sure that you’re not breaking anything. If the change you’re working on touches functionality that is common between PostgreSQL and IvorySQL, you may be asked to forward-port it to PostgreSQL. This is not only so that we keep reducing the delta between the two projects, but also so that any change that is relevant to PostgreSQL can benefit from a much broader review of the upstream PostgreSQL community. In general, it is a good idea to keep both codebases handy so you can be sure whether your changes may need to be forward-ported. Once you are ready to share your work with the IvorySQL core team and the rest of the IvorySQL community, you should push all the commits to a branch in your own repository forked from the official IvorySQL and send us a pull request. A submitted pull request with passing validation checks is assumed to be available for peer review. Peer review is the process that ensures that contributions to IvorySQL are of high quality and align well with the road map and community expectations. Every member of the IvorySQL community is encouraged to review pull requests and provide feedback. Since you don’t have to be a core team member to be able to do that, we recommend following a stream of pull reviews to anybody who’s interested in becoming a long-term contributor to IvorySQL. One outcome of the peer review could be a consensus that you need to modify your pull request in certain ways. GitHub allows you to push additional commits into a branch from which a pull request was sent. Those additional commits will be then visible to all of the reviewers. A peer review converges when it receives at least one +1 and no -1s votes from the participants. At that point, you should expect one of the core team members to pull your changes into the project. At any time during the patch review, you may experience delays based on the availability of reviewers and core team members. Please be patient. That being said, don’t get discouraged either. If you’re not getting expected feedback for a few days add a comment asking for updates on the pull request itself or send an email to the mailing list. On occasion, you will see core team members committing directly to the repository without going through the pull request workflow. This is reserved for small changes only and the rule of thumb we use is this: if the change touches any functionality that may result in a test failure, then it has to go through a pull request workflow. If, on the other hand, the change is in the non-functional part of the codebase (such as fixing a typo inside of a comment block) core team members can decide to just commit to the repository directly. 1 Enter IvorySQL official website:https://github.com/IvorySQL/IvorySQL 2 Click New issue 1 bug report Title: ## Bug Report Describe the bug \\### IvorySQL Version The version of IvorySQL you are using \\### OS Version (uname -a) Operating system version(uname -a) \\### Configuration options ( config.status --config ) \\### Current Behavior \\### Expected behavior/code \\### Step to reproduce \\### Additional context that can be helpful for identifying the problem 2 Enhancement Title: ## Enhancement Describe the functions that you expect to be strengthened 3 Feature Request Title: ## Feature Request Describe the feature that you expect to be real Click submit new issue button. WELL DONE! 1 Open the ivorysql warehouse: https://github.com/IvorySQL/IvorySQL 2 Click the fork button in the upper right corner, Wait for the fork to finish cd $working_dir # $working_dir can be replaced by the directory where you want to place repo. For example, `cd ~/Documents/GitHub` git clone git@github.com:$user/IvorySQL.git # `$user` can be replaced by your GitHub ID. cd $working_dir/IvorySQL git checkout -b new-branch-name You can modify the code in new-branch-name. Git add <file> Git commit -m “commit-message” Git push -u origin new-branch-name 1 Open your warehouse: https://github.com/$user/IvorySQL ($user is your GitHub ID) . 2 Click Compare & pull request button and create a PR. A PR submission should contain only one function or one bug. Prohibit submitting multiple functions at one time. 1 Open your warehouse: https://github.com/$user/IvorySQL ($user is your GitHub ID) 。 2 Click Compare & pull request button. Fix test Describe the function leave a comment Give a detailed description of the submission function Click Create pull request button. WELL DONE! (1) Download Markdown or Typora document editor. (2) Check whether the source warehouse has updates. If there are updates, please update and synchronize to your own warehouse first. Refer to the following steps to update to the latest version: git remote git fetch upstream git merge upstream/main git push (3) Familiar with format [_pecification]. The IvorySQL community provides Chinese and English documents. English documents are saved in IvorySQL document repository, Chinese documents are saved in i18n document repository. You can contribute to one of them or both. You can start from any of following to help improve the IvorySQL documents on the IvorySQL website: ​ (1) Prepare complete documents. ​ (2) Fix incorrect spelling and formatting (Punctuation, space, indentation, code block, etc) . ​ (3) Improper or outdated instructions corrected or updated. ​ (4) Add missing content (sentences, paragraphs, or new documents) . ​ (5) Translate document from English to Chinese, or from Chinese to English. ​ (6) Submit, reply and resolve document issues or document-i18n issues. ​ (7) (Advanced) View pull requests created by others. The IvorySQL document is written in 'markdown'. To ensure the quality and consistency of the format, certain Markdown rules should be followed when modifying and updating the document. Markdown Specification ​ 1 Titles are used incrementally from the first level, and skipping is prohibited. For example: The third level title cannot be used directly under the first level title; The fourth level title cannot be used directly under the second level title. ​ 2 The title must use the ATX style uniformly. Indicate the title level by adding # before the title. ​ 3 The leading symbol # of the title must be followed by a blank space. ​ 4 The leading symbol \"#\" of the title can only be followed by one blank space and then the title content. There can be no more than one space. ​ 5 The title must appear at the beginning of a line, there must be no space before the # sign of the title. ​ 6 Only Chinese and English question marks, back quotes, Chinese and English single and double quotes and other symbols can appear at the end of the title. Other symbols such as colon, comma, period and exclamation point cannot be used at the end of the title. ​ 7 One line must be empty above the title. ​ 8 The same title cannot appear continuously in the document. If the first level title is # TiDB architecture, the next level title cannot be # # TiDB architecture. If it is not a continuous title, the title content can be repeated. ​ 9 Only one first level title in document. ​ 10 In general, except for TOC.md files, which can be indented by two spaces, other .md files must be indented by four spaces by default foe each level of indentation. ​ 11 Tab is not allowed in documents(including code blocks) . If indentation is required, spaces must be uniformly used instead. ​ 12 Continuous blank lines are prohibited. ​ 13 Multiple spaces are not allowed after the block reference symbol > . Only one space can be used, followed by the reference content. ​ 14 When using a ordered list, it must start from 1 and increase in order. ​ 15 When using a list, the identifier (+, -, * or number) of each list item can only be left blank, followed by the list content. ​ 16 The list (includeing ordered and unordered lists) must be empty before and after each line. ​ 17 There must be one blank line before and after the code block. ​ 18 Exposed URLs are prohibited in documents. If you want users to click and open the URL directly, wrap the URL with a pair of angle brackets (<URL>) . If the exposed URL must be used due to special circumstances, and the user does not need to open it by clicking, a pair of back quatation marks (URL) will be used to wrap the URL. ​ 19 When using bold, italic and other emphasis effects, redundant spaces are prohibited in the emphasis identifier, such as text . ​ 20 No extra space is allowed in the code block wrapped by a single backquote, such as ` text `. ​ 21 No extra spaces are allowed on both sides of the link text, such as [Link](https://www.example.com/) ​ 22 The link must have a link path. [Empty link]() and [empty link](#) are not allowed. 1 Titles are used incrementally from the first level, and skipping is prohibited. # Heading 1 ### Heading 3 We skipped out a 2nd level heading in this document 2 The title must use the ATX style uniformly. Indicate the title level by adding # before the title. # Heading 1 ## Heading 2 ### Heading 3 #### Heading 4 ## Another Heading 2 ### Another Heading 3 3 The leading symbol # of the title must be followed by a blank space. Multiple spaces after # are prohibited, and spaces before # are prohibited. Incorrect Example: # Heading 1 ## Heading 2 Correct Example: # Heading 1 ## Heading 2 4 Only Chinese and English question marks, back quotes, Chinese and English single and double quotes and other symbols can appear at the end of the title. Incorrect Example: # This is a heading. Correct Example: # This is a heading 5 One line must be empty above the title. Incorrect Example: # Heading 1 Some text Some more text## Heading 2 Correct Example: # Heading 1 Some text Some more text ## Heading 2 6 he same title cannot appear continuously in the document. If the first level title is # TiDB architecture, the next level title cannot be ## TiDB architecture. If it is not a continuous title, the title content can be repeated. Incorrect Example: # Some text ## Some text Correct Example: # Some text ## Some more text 7 Only one first level title in document. Incorrect Example: # Top level heading # Another top-level heading Correct Example: # Title ## Heading ## Another heading 8 In general, except for TOC.md files, which can be indented by two spaces, other .md files must be indented by four spaces by default foe each level of indentation. Incorrect Example: * List item * Nested list item indented by 3 spaces Correct Example: * List item * Nested list item indented by 4 spaces 9 Tab is not allowed in documents(including code blocks) . If indentation is required, spaces must be uniformly used instead. Incorrect Example: Some text * hard tab character used to indent the list item Correct Example: Some text * Spaces used to indent the list item instead 10 Continuous blank lines are prohibited. Incorrect Example: Some text here Some more text here Correct Example: Some text here Some more text here 11 Multiple spaces are not allowed after the block reference symbol > . Only one space can be used, followed by the reference content. Incorrect Example: > This is a blockquote with bad indentation> there should only be one. Correct Example: > This is a blockquote with correct> indentation. 12 When using a ordered list, it must start from 1 and increase in order. Incorrect Example: 1. Do this. 1. Do that. 1. Done. 0. Do this. 1. Do that. 2. Done. Correct Example: 1. Do this. 2. Do that. 3. Done. 13 When using a list, the identifier (+, -, * or number) of each list item can only be left blank, followed by the list content. Correct Example: * Foo * Bar * Baz 1. Foo * Bar 1. Baz 14 The list (includeing ordered and unordered lists) must be empty before and after each line. Incorrect Example: Some text* Some* List 1. Some2. List Some text Correct Example: Some text * Some * List 1. Some 2. List Some text 15 There must be one blank line before and after the code block. Incorrect Example: Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text Correct Example: Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 16 Exposed URLs are prohibited in documents. If you want users to click and open the URL directly, wrap the URL with a pair of angle brackets (<URL>) . If the exposed URL must be used due to special circumstances, and the user does not need to open it by clicking, a pair of back quatation marks (URL) will be used to wrap the URL. Incorrect Example: For more information, see https://www.example.com/. Correct Example: For more information, see <https://www.example.com/>. 17 When using bold, italic and other emphasis effects, redundant spaces are prohibited in the emphasis identifier, such as text . Incorrect Example: Here is some ** bold ** text. Here is some * italic * text. Here is some more __ bold __ text. Here is some more _ italic _ text. Correct Example: Here is some **bold** text. Here is some *italic* text. Here is some more __bold__ text. Here is some more _italic_ text. 18 No extra space is allowed in the code block wrapped by a single backquote, such as ` text `. Incorrect Example: some text some text Correct Example: some text 19 No extra spaces are allowed on both sides of the link text, such as [ Link ](https://www.example.com/) . Incorrect Example: [a link](https://www.example.com/) Correct Example: [a link](https://www.example.com/) 20 The link must have a link path. [Empty link]() and [empty link](#) are not allowed. Incorrect Example: [an empty link]() [an empty fragment](#) Correct Example: [a valid link](https://example.com/) [a valid fragment](#fragment) 21 Code blocks in the document are wrapped with three backquote, and the use of indented four-space code blocks is prohibited. Incorrect Example: Some text. # Indented code More text. Correct Example: ​```ruby # Fenced code ​``` More text. In order to test your modifications, you need to prepare the following environment. Node.js install Antora install Please refer to Antora docs。 After installation, the following display on the terminal indicates successful installation. Firstly, you need to know the location of the corresponding UI for the webpage, as shown in the following figure: The UI templates for both Chinese and English web pages are basically the same, so when making modifications, it is best to ensure that both templates are modified at the same time. After uploading the modified UI to your personal Github, you can consider generating your modified web page locally. IvorySQL Document Site is built by Antora. Before running Antora, remember to modify the corresponding playbook.yml file. After completing the above process, please run the command antora antora-playbook.yml --stacktrace on the terminal, and then patiently wait. After the successful operation is completed, you can view the webpage you have generated. You can start uploading to our ivorysql_web, the process of submitting PR is the same as before. Thank you for your contribution to the community _. We will consider whether to update the website after the review. 1 Download Markdown or Typora . 2 Check whether the source warehouse (https://github.com/IvorySQL/Ivory-www) has updates. If there are updates, please update and synchronize to your own warehouse first. Refer to the following steps to update to the latest version: # Download source code git clone https://github.com/IvorySQL/Ivory-www.git # Synchronize updates warehouse git pull 3 Familiar with format ([_specification_2]) The IvorySQL community provides Chinese and English documents. English documents are saved in IvorySQL document repository, Chinese documents are saved in i18n document repository. You can contribute to one of them or both. Let’s take a quick look at the information about the maintenance of the IvorySQL blog before contributing. It is helpful for you to submit blog and to be a contributor. (1) Clone code to local warehouse git clone https://github.com/IvorySQL/Ivory-www.git (2) Create a branch git checkout -b <branch-name> (3) Create a directory of your own articles in the blog directory, and please name your own directory according to the ([Specification](#7.4 Specification) ) . # Make English blog directory and files cd Ivory-www/blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md # Make Chinese blog directory and files cd Ivory-www/i18n/zh-CN/docusaurus-plugin-content-blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md (4) Write the blog to publish in index.md, put the required pictures in the blog in the same directory as index.md. (5) Submit Blog git add <file-path> git commit -m \"<message>\" git push origin <branch-name>:<branch-name> (1) Format of folder naming: year-month-day-foldername Example: 2022-1-28-ivorysql-arrived (2) File property is index.md (3) Picture property is .png, and put the pictures to be uploaded into the folder to be submitted in advance. Notice:The name of every picture is unique and cannot be repeated. Example: po-one.png Blogs are written in markdown or Typora, you can understand the design of blog by reading Blog | Docusaurus. (1) The header of blog includes the following information: --- slug: IvorySQL title: Welcome to IvorySQL community authors: [official] authorTwitter: IvorySql tags: [IvorySQL, Welcome, Database, Join Us] --- Prompt:You can copy the above template to your file and edit it. Notice:1) Add one space after slug, title, authors, tags. 2) The name of every slug is unique, the Chinese and English versions of the same blog can be the same. (2) Text format The text paragraph title is h2/\"Second level title\"; The body uses the default font size. (3) Naming format of inserted pictures [Hello](Hello-banner.png) (4) Naming format of inserted hyperlink [name](link) Github page Download source code and published packages. IvorySQL Document Site uses Antora to build. Also, IvorySQL Document Site is open source. It consists of three parts, such as ivorysql_docs, ivorysql_web and ivory-doc-builder. Welcome everyone who is willing to participate in open source work to join us, and remember to follow our code of conduct _. Due to the fact that IvorySQL Document Site is all hosted on Github, this allows any users to fork our document repository into their personal repository, make modifications to it, and then submit a PR. After being reviewed by our open source team, the modifications can be updated to our Document Site. In order to achieve the goat of correcting document errors more conveniently, you first need to establish a personal warehouse according to the size you want to update. As follows: If you want to modify the existing content or add a new page, you only need to fork ivorysql_docs to your personal repository. If you want to participate more deeply in the construction of IvorySQL Document Site, in addition to the ivorysql_docs, you also need to fork ivorysql_web and ivory-doc-builder to your personal repository. This section will introduce the process of modifying webpage content after discovering that it is inappropriate. In the upper right corner of a webpage with incorrect content, there is a button called edit this page, click on the button. As shown in the figure: After clicking, it will redirect to the editing page where we store the current page source .adoc file. Please modify the content in the Asciidoc format. As shown in the figure: After editing is completed. As shown in the figure: After confirming the update. As shown in the figure: Next, the relevant person of the open source team will be responsible for reviewing the content you submitted. After the review is completed, the updates you submitted will appear on the corresponding page. This section will introduce how to add new page components to a website, and the modifications involved in adding new pages mainly include the following: Add the .adoc file in the CN/modules/ROOT/pages/vX.X. Modify the CN/modules/ROOT/nav.adoc. If the modification involves modifying or adding images, please modify the images in images. Add the .adoc file in the EN/modules/ROOT/pages/vX.X. Modify the EN/modules/ROOT/nav.adoc. If the modification involves modifying or adding images, please modify the images in images. Firstly, you need to download the warehouse that you fork from IvorySQL to your personal computer. git clone https://github.com/$username$/ivorysql_docs.git Then, place the .adoc file to be added in the correct directory, remembering that both Chinese and English files should be prepared (Chinese and English files should have the same name), and each file should be placed in the correct directory. At the same time, modify the corresponding nav.adoc file (the modification method can refer to the existing content of the file). After the above modifications are completed, submit them to the personal warehouse first. git add . git commit -m \"$describe your change$\" git push Afterwards, submit the PR as follows If you are not satisfied with simply submitting on the webpage or only modifying the webpage content, or if you want to modify the webpage UI, this section will help you. Before reading this section, you need to confirm whether your Github personal repository has forked ivorysql_docs, ivorysql_web and ivory-doc-builder. The deployment of web pages is currently the responsibility of the open-source team. We value every submission and issue, so please do not worry about your contribution to the community being buried. If you don’t have much time, you can send an email to ivorysql-docs@ivorysql.org. We will have dedicated staff to handle every your letter, and we looking forward to hearing from you.","title":"IvorySQL Community Contribution Guide","component":"ivorysql-doc","version":"master","name":"community_contribution_guide","url":"/ivorysql-doc/master/contribution/community_contribution_guide.html","titles":[{"text":"1. Summary","hash":"summary","id":1},{"text":"1.1. Illustration","hash":"illustration","id":2},{"text":"1.2. Principles of Conduct","hash":"principles-of-conduct","id":3},{"text":"1.3. Description of Community Governance","hash":"description-of-community-governance","id":4},{"text":"2. Contributor’s Guide","hash":"contributors-guide","id":5},{"text":"2.1. Preparation before Contribution","hash":"preparation-before-contribution","id":6},{"text":"2.1.1. Getting started","hash":"getting-started","id":7},{"text":"2.1.2. Licensing of IvorySQL contributions","hash":"licensing-of-ivorysql-contributions","id":8},{"text":"2.2. What Contribution Can You Make","hash":"what-contribution-can-you-make","id":9},{"text":"2.2.1. Code Contribution","hash":"code-contribution","id":10},{"text":"2.2.2. Document Contribution(https://www.ivorysql.org/zh-CN/docs/intro)","hash":"document-contributionhttpswww-ivorysql-orgzh-cndocsintro","id":11},{"text":"2.2.3. Test IvorySQL and Report Bugs","hash":"test-ivorysql-and-report-bugs","id":12},{"text":"2.2.4. Participate in the Construction of IvorySQL Website","hash":"participate-in-the-construction-of-ivorysql-website","id":13},{"text":"2.2.5. Answer Questions on the Mailing List","hash":"answer-questions-on-the-mailing-list","id":14},{"text":"2.2.6. Contribute Article","hash":"contribute-article","id":15},{"text":"2.3. How to Contribute","hash":"how-to-contribute","id":16},{"text":"2.3.1. Coding Guidelines","hash":"coding-guidelines","id":17},{"text":"2.3.2. Changes applicable to upstream PostgreSQL","hash":"changes-applicable-to-upstream-postgresql","id":18},{"text":"2.3.3. Patch submission","hash":"patch-submission","id":19},{"text":"2.3.4. Patch review","hash":"patch-review","id":20},{"text":"2.3.5. Direct commits to the repository","hash":"direct-commits-to-the-repository","id":21},{"text":"3. Submit Issue","hash":"submit-issue","id":22},{"text":"3.1. First: Get into New issue page:","hash":"first-get-into-new-issue-page","id":23},{"text":"3.1.1. Second:Select the issue type","hash":"secondselect-the-issue-type","id":24},{"text":"3.1.2. Third: Submit","hash":"third-submit","id":25},{"text":"4. Contribute Code","hash":"contribute-code","id":26},{"text":"4.1. First: Fork ivorysql.org  warehouse","hash":"first-fork-ivorysql-org-warehouse","id":27},{"text":"4.2. Second: Clone the warehouse to local","hash":"second-clone-the-warehouse-to-local","id":28},{"text":"4.3. Third: Create a new Branch","hash":"third-create-a-new-branch","id":29},{"text":"4.4. Fourth: Edit Document or Modify Code","hash":"fourth-edit-document-or-modify-code","id":30},{"text":"4.5. Fifth: Generate commit","hash":"fifth-generate-commit","id":31},{"text":"4.6. Sixth: Push the modification to the remote end","hash":"sixth-push-the-modification-to-the-remote-end","id":32},{"text":"4.7. Seventh: Create a Pull Request","hash":"seventh-create-a-pull-request","id":33},{"text":"5. Submit PR","hash":"submit-pr","id":34},{"text":"5.1. First:Create a Pull Request","hash":"firstcreate-a-pull-request","id":35},{"text":"5.2. Second:Fill in PR information","hash":"secondfill-in-pr-information","id":36},{"text":"5.3. Third:Submit PR","hash":"thirdsubmit-pr","id":37},{"text":"6. Edit Documents","hash":"edit-documents","id":38},{"text":"6.1. Preparation","hash":"preparation","id":39},{"text":"6.2. Where to Contribute","hash":"where-to-contribute","id":40},{"text":"6.3. Specification","hash":"specification","id":41},{"text":"6.4. Example","hash":"example","id":42},{"text":"6.5. Environmental preparation","hash":"environmental-preparation","id":43},{"text":"6.6. Generate web pages","hash":"generate-web-pages","id":44},{"text":"7. Submit Blog","hash":"submit-blog","id":45},{"text":"7.1. Preparation","hash":"preparation-2","id":46},{"text":"7.2. Where to Congtribute","hash":"where-to-congtribute","id":47},{"text":"7.3. How to Contribute","hash":"how-to-contribute-2","id":48},{"text":"7.4. Specification","hash":"specification-2","id":49},{"text":"7.4.1. Submit specifications","hash":"submit-specifications","id":50},{"text":"7.4.2. Write blog","hash":"write-blog","id":51},{"text":"8. Website Contribution Guide","hash":"website-contribution-guide","id":52},{"text":"8.1. How to Contribute","hash":"how-to-contribute-3","id":53},{"text":"8.2. Modify Content","hash":"modify-content","id":54},{"text":"8.3. Add Page","hash":"add-page","id":55},{"text":"8.4. Test","hash":"test","id":56},{"text":"8.5. Deploy web pages","hash":"deploy-web-pages","id":57},{"text":"8.6. Tip","hash":"tip","id":58}]},"35":{"id":35,"text":"IvorySQL is maintained by a core team of developers with commit rights to the main IvorySQL repository on GitHub. At the same time, we are very eager to receive contributions from anybody in the wider IvorySQL community. This section covers all you need to know if you want to see your code or documentation changes be added to IvorySQL and appear in future releases. IvorySQL is developed on GitHub, and anybody wishing to contribute to it will have to have a GitHub account and be familiar with Git tools and workflow. It is also recommended that you follow the developer’s mailing list since some of the contributions may generate more detailed discussions there. Once you have your GitHub account, fork this repository so that you can have your private copy to start hacking on and to use as a source of pull requests. If the contribution you’re submitting is original work, you can assume that IvorySQL will release it as part of an overall IvorySQL release available to the downstream consumers under the Apache License, Version 2.0. If the contribution you’re submitting is NOT original work you have to indicate the name of the license and also make sure that it is similar in terms to the Apache License 2.0. Apache Software Foundation maintains a list of these licenses under Category A. In addition to that, you may be required to make proper attributions. Finally, keep in mind that it is NEVER a good idea to remove licensing headers from the work that is not your original one. Even if you are using parts of the file that originally had a licensing header at the top you should err on the side of preserving it. As always, if you are not quite sure about the licensing implications of your contributions, feel free to reach out to us on the developer mailing list. Your chances of getting feedback and seeing your code merged into the project greatly depend on how granular your changes are. If you happen to have a bigger change in mind, we highly recommend engaging on the developer’s mailing list first and sharing your proposal with us before you spend a lot of time writing code. Even when your proposal gets validated by the community, we still recommend doing the actual work as a series of small, self-contained commits. This makes the reviewer’s job much easier and increases the timeliness of feedback. When it comes to C and C++ parts of IvorySQL, we try to follow PostgreSQL Coding Conventions. In addition to that: For C and Perl code, please run pgindent if necessary. We recommend using git diff --color when reviewing your changes so that you don’t have any spurious whitespace issues in the code that you submit. All new functionality that is contributed to IvorySQL should be covered by regression tests that are contributed alongside it. If you are uncertain about how to test or document your work, please raise the question on the ivorysql-hackers mailing list and the developer community will do its best to help you. At the very minimum, you should always be running make installcheck-world to make sure that you’re not breaking anything. If the change you’re working on touches functionality that is common between PostgreSQL and IvorySQL, you may be asked to forward-port it to PostgreSQL. This is not only so that we keep reducing the delta between the two projects, but also so that any change that is relevant to PostgreSQL can benefit from a much broader review of the upstream PostgreSQL community. In general, it is a good idea to keep both codebases handy so you can be sure whether your changes may need to be forward-ported. Once you are ready to share your work with the IvorySQL core team and the rest of the IvorySQL community, you should push all the commits to a branch in your own repository forked from the official IvorySQL and send us a pull request. A submitted pull request with passing validation checks is assumed to be available for peer review. Peer review is the process that ensures that contributions to IvorySQL are of high quality and align well with the road map and community expectations. Every member of the IvorySQL community is encouraged to review pull requests and provide feedback. Since you don’t have to be a core team member to be able to do that, we recommend following a stream of pull reviews to anybody who’s interested in becoming a long-term contributor to IvorySQL. One outcome of the peer review could be a consensus that you need to modify your pull request in certain ways. GitHub allows you to push additional commits into a branch from which a pull request was sent. Those additional commits will be then visible to all of the reviewers. A peer review converges when it receives at least one +1 and no -1s votes from the participants. At that point, you should expect one of the core team members to pull your changes into the project. At any time during the patch review, you may experience delays based on the availability of reviewers and core team members. Please be patient. That being said, don’t get discouraged either. If you’re not getting expected feedback for a few days add a comment asking for updates on the pull request itself or send an email to the mailing list. On occasion, you will see core team members committing directly to the repository without going through the pull request workflow. This is reserved for small changes only and the rule of thumb we use is this: if the change touches any functionality that may result in a test failure, then it has to go through a pull request workflow. If, on the other hand, the change is in the non-functional part of the codebase (such as fixing a typo inside of a comment block) core team members can decide to just commit to the repository directly.","title":"FAQ","component":"ivorysql-doc","version":"master","name":"faq","url":"/ivorysql-doc/master/contribution/faq.html","titles":[{"text":"1. Contributing","hash":"contributing","id":1},{"text":"2. Getting started","hash":"getting-started","id":2},{"text":"3. Licensing of IvorySQL contributions","hash":"licensing-of-ivorysql-contributions","id":3},{"text":"4. Coding guidelines","hash":"coding-guidelines","id":4},{"text":"5. Changes applicable to upstream PostgreSQL","hash":"changes-applicable-to-upstream-postgresql","id":5},{"text":"6. Patch submission","hash":"patch-submission","id":6},{"text":"7. Patch review","hash":"patch-review","id":7},{"text":"8. Direct commits to the repository","hash":"direct-commits-to-the-repository","id":8}]},"36":{"id":36,"text":"IvorySQL adapts the following CPU architectures: Index Architecture Name Adapt to brands Multi-platform Media Packages 1 x86_64 Intel, AMD, ZHAOXIN, HYGON deb, rpm 2 aarch64 Phytium, Kunpeng deb, rpm 3 mips64el Loongson3000，Loongson4000 deb, rpm 4 loongarch64 Loongson5000 deb, rpm","title":"CPU Architecture Adaption","component":"ivorysql-doc","version":"master","name":"cpu_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html","titles":[]},"37":{"id":37,"text":"IvorySQL adapts following operating systems: Index OS name OS introduction Certification 1 Kylin Advanced Server Operating System V11 Kylin Advanced Server Operating System V11 is a new-generation server operating system tailor-made for enterprise-level critical businesses by Kylin Software, leveraging its years of accumulated technological R&D experience and rich application practice. With high reliability, high availability, high security, high performance, and high scalability as its core advantages, the product, on the basis of in-depth integration of AI technology, has further built a key technology system through independent innovation breakthroughs, including the high-speed network protocol MPTCP, hot patch management, intelligent fault diagnosis, scenario-based optimization, and secure container image repository. It serves as a solid, secure, and reliable supporting foundation for the informatization construction of Party and government organs, the digital transformation of key industries, and the implementation of major national projects. 2 openKylin 2.0 SP1 OpenAtom openKylin is an open-source project incubated and operated by the OpenAtom Foundation. It was co-founded by basic software and hardware enterprises, non-profit organizations, associations, institutions of higher education, scientific research institutions, and individual developers. With the community vision of \"providing the world with an open-source operating system deeply integrated with artificial intelligence technology\", the project aims to jointly build a world-leading root community for intelligent desktop open-source operating systems on the basis of openness, voluntariness, equality, and collaboration, and promote the prosperity and development of Linux open-source technology and its software and hardware ecosystem. 3 OpenAnolis OS 23 Anolis OS 23, the Longxin Operating System, is an operating system developed by the OpenAnolis community based on the Open Source Ecosystem Development and Cooperation Initiative. It independently selects components from upstream native communities, undergoes continuous evolution, and ensures compatibility and stability. As an enterprise-level operating system built on Linux Kernel 6.6 LTS, Anolis OS 23 relies on in-depth optimization of the ANCK 6.6 kernel and fully supports domestic chips such as Haiguang, Phytium, Loongson (LoongArch), and Zhaoxin, as well as general-purpose x86_64/ARM64 architectures. It features specialized enhancements for virtualization, security features, and performance optimization. Through hierarchical architecture design and intelligent tuning tools, it maximizes the performance of hardware-software collaboration. Meanwhile, it natively supports AI ecosystem components and provides secure AI container images, accelerating model development and inference deployment. In terms of development toolchains, it integrates GCC 12.3+/LLVM 17, Python 3.11, Rust, and more, enabling efficient multi-platform development. For the desktop ecosystem, it is compatible with GNOME and DDE desktop environments, and expands the ecosystem to meet diverse scenario needs by integrating the Linglong package manager. Anolis OS 23 supports various common applications and domestic applications, helping enterprises achieve efficient, secure, and reliable digital transformation. 4 deppin 20 Deepin OS is a Linux distribution dedicated to providing global users with an elegant, user-friendly, secure, and reliable operating system. The official version of Deepin OS 20 (Build 1002) adopts a unified design style, with a redesigned desktop environment and applications, bringing a fresh visual experience. Its underlying repository has been upgraded to Debian 10.5, and the system installation adopts a dual-kernel mechanism (Kernel 5.4, Kernel 5.7), which comprehensively enhances system stability and compatibility. Additionally, it features a newly designed launcher menu, fingerprint recognition, and enhanced system security; some pre-installed applications in the system have been upgraded to the latest versions—all designed to deliver a better experience for you.","title":"Operating System Adaption","component":"ivorysql-doc","version":"master","name":"os_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html","titles":[]},"38":{"id":38,"text":"IvorySQL, as an advanced open-source database compatible with Oracle and based on PostgreSQL, has powerful extension capabilities and supports a rich ecosystem of plugins. These plugins can help users enhance database functionality in different scenarios, including geospatial information processing, vector retrieval, full-text search, data definition extraction, and path planning. The following is a list of major plugins currently officially compatible with and supported by IvorySQL: + Index Plugin Name Version Function Description Use Cases 1 postgis 3.5.4 Provides geospatial data support for IvorySQL, including spatial indexes, spatial functions, and geographic object storage Geographic Information Systems (GIS), map services, location data analysis 2 pgvector 0.8.1 Supports vector similarity search, can be used to store and retrieve high-dimensional vector data AI applications, image retrieval, recommendation systems, semantic search 3 pgddl (DDL Extractor) 0.31 Extracts DDL (Data Definition Language) statements from databases, facilitating version management and migration Database version control, CI/CD integration, structure comparison and synchronization 4 pg_cron​ 1.6.0 Provides database-internal scheduled task scheduling functionality, supports regular SQL statement execution Data cleanup, regular statistics, automated maintenance tasks 5 pgsql-http​ 1.7.0 Allows HTTP requests to be initiated in SQL, interacting with external web services Data collection, API integration, microservice calls 6 plpgsql_check 2.8 Provides static analysis functionality for PL/pgSQL code, can detect potential errors during development Stored procedure development, code quality checking, debugging and optimization 7 pgroonga 4.0.4 Provides full-text search functionality for non-English languages, meeting the needs of high-performance applications Full-text search capabilities for languages like Chinese, Japanese, and Korean 8 pgaudit 18.0 Provides fine-grained auditing, recording database operation logs to support security auditing and compliance checks Database security auditing, compliance checks, audit report generation 9 pgrouting 3.8.0 Provides routing computation for geospatial data, supporting multiple algorithms and data formats Geospatial analysis, route planning, logistics optimization 10 system_stats 3.2 Provide functions for accessing system-level statistics. system monitor 11 pg_ai_query 0.1.1 AI-driven natural language to SQL extension supporting multiple LLMs AI-assisted querying, natural language database interaction These plugins have all been tested and adapted by the IvorySQL team to ensure stable operation in the IvorySQL environment. Users can select appropriate plugins based on business needs to further enhance the capabilities and flexibility of the database system. We will continue to expand and enrich the IvorySQL plugin ecosystem. Community developers are welcome to submit new plugin adaptation suggestions or code contributions. For more detailed usage methods and the latest compatible versions of each plugin, please refer to the corresponding documentation chapters for each plugin.","title":"","component":"ivorysql-doc","version":"master","name":"ecosystem_overview","url":"/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html","titles":[{"text":"IvorySQL Ecosystem Plugin Compatibility List","hash":"ivorysql-ecosystem-plugin-compatibility-list","id":1}]},"39":{"id":39,"text":"pg_ai_query is an AI-powered natural language to SQL extension for IvorySQL/PostgreSQL. It leverages Large Language Models (LLMs) to convert user’s natural language descriptions directly into executable SQL query statements. It supports multiple AI models including OpenAI, Anthropic Claude, and Google Gemini. Project URL: https://github.com/benodiwal/pg_ai_query/tree/main License: Apache-2.0 Key features: Natural Language to SQL: Convert plain text descriptions into valid SQL queries Multi-model Support: Supports gpt-4o-mini, gpt-4o, gpt-5, claude-3-haiku-20240307, claude-sonnet-4-5-20250929, claude-4.5-opus and other large models System Table Protection: Blocks access to information_schema and pg_catalog Limited Scope: Only operates on user tables Configurable Limits: Built-in row limit enforcement API Key Security: Secure handling of API credentials The following demonstration uses v0.1.1 as an example. Prerequisites PostgreSQL 14+ with development headers CMake 3.16+ C++20 compatible compiler API key from OpenAI, Anthropic, or Google (Gemini) Install dependencies sudo apt-get install libcurl4-openssl-dev Compile and install IvorySQL If you need to compile IvorySQL from source, you can refer to the following configuration: ./configure \\ --prefix=$PWD/inst \\ --enable-cassert \\ --enable-debug \\ --enable-tap-tests \\ --enable-rpath \\ --enable-nls \\ --enable-injection-points \\ --with-tcl \\ --with-python \\ --with-gssapi \\ --with-pam \\ --with-ldap \\ --with-openssl \\ --with-libedit-preferred \\ --with-uuid=e2fs \\ --with-ossp-uuid \\ --with-libxml \\ --with-libxslt \\ --with-perl \\ --with-icu \\ --with-libnuma Compile and install pg_ai_query git clone --recurse-submodules https://github.com/benodiwal/pg_ai_query.git cd pg_ai_query mkdir build && cd build export PATH=\"$HOME/works/repo/ivorysql/IvorySQL/inst/bin:$PATH\" cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/works/repo/ivorysql/IvorySQL/inst make && sudo make install Create extension CREATE EXTENSION pg_ai_query; Create a ~/.pg_ai.config configuration file in your home directory: [general] log_level = \"INFO\" enable_logging = false [query] enforce_limit = true default_limit = 1000 [response] show_explanation = true show_warnings = true show_suggested_visualization = false use_formatted_response = false [anthropic] # Your Anthropic API key (if using Claude) api_key = \"******\" # Default model to use (options: claude-sonnet-4-5-20250929) default_model = \"claude-sonnet-4-5-20250929\" # Custom API endpoint (optional) - for Anthropic-compatible APIs api_endpoint = \"https://open.bigmodel.cn/api/anthropic\" [prompts] # Use file paths to read custom prompts system_prompt = /home/highgo/.pg_ai.prompts explain_system_prompt = /home/highgo/.pg_ai.explain.prompts For more examples, please refer to: https://github.com/benodiwal/pg_ai_query/blob/main/docs/src/examples.md SELECT generate_query('Find all users'); Example output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135507cc16362d5d324ccd generate_query -------------------------------------------------------- SELECT * FROM public.users LIMIT 1000; + -- Explanation: -- Retrieves all columns and rows from the users table. + -- Warning: INFO: Applied LIMIT 1000 to prevent large result sets. Remove LIMIT if you need all data. + -- Note: Row limit was automatically applied to this query for safety (1 row) Execute the query: SELECT * FROM public.users LIMIT 1000; Output: id | name | email | age | created_at | city ----+---------------+-------------------+-----+----------------------------+--------------- 1 | Alice Johnson | alice@example.com | 28 | 2026-02-04 15:47:55.208111 | New York 2 | Bob Smith | bob@example.com | 35 | 2026-02-04 15:47:55.208111 | San Francisco 3 | Carol Davis | carol@example.com | 31 | 2026-02-04 15:47:55.208111 | Chicago 4 | David Wilson | david@example.com | 27 | 2026-02-04 15:47:55.208111 | Seattle 5 | Eva Brown | eva@example.com | 33 | 2026-02-04 15:47:55.208111 | Boston (5 rows) Generate test data SELECT generate_query('Generate 100 user records and insert into users table'); Output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_2026021114092101601c5650864a2d generate_query -------------------------------------------------------------------------------------------------------- INSERT INTO public.users (name, email, age, city, status) SELECT 'User_' || generate_series AS name, 'user' || generate_series || '@example.com' AS email, (18 + (generate_series % 50)) AS age, (ARRAY['Beijing','Shanghai','Guangzhou','Shenzhen','Hangzhou'])[1 + (generate_series % 5)] AS city, 'active' AS status FROM generate_series(1, 100); + -- Explanation: -- Generates 100 simulated user records and inserts them into the users table. Data includes auto-generated names, unique emails, random ages (18-67), random cities, and default status. + -- Warnings: -- 1. INFO: Relies on DEFAULT auto-increment setting for id column in users table, id is not manually inserted. -- 2. INFO: Uses generate_series function to generate sequence data, which is a PostgreSQL/IvorySQL feature. -- 3. WARN: Ensure users table is empty or id sequence does not conflict before running, otherwise duplicate inserts may occur. -- 4. WARN: Email format is simple simulation, actual environment may require more complex logic or duplicate checking. (1 row) Case-insensitive query SELECT generate_query('Show users from beijing, beijing is non-Case insensitive'); Output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211142845878f5f1a5a2f44a7 generate_query ----------------------------------------- SELECT id, name, email, age, created_at, city, status FROM public.users WHERE LOWER(city) = LOWER('beijing') LIMIT 100; + -- Explanation: -- Selects all user details for users located in Beijing, performing a case-insensitive match on the city column. + -- Warnings: -- 1. INFO: Using LOWER() on both sides ensures case-insensitive matching but may prevent the database from using a standard index on the city column if one exists. -- 2. INFO: Row limit of 100 applied to prevent large result sets. + -- Note: Row limit was automatically applied to this query for safety (1 row) SELECT explain_query('SELECT * FROM orders WHERE user_id = 12'); Output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211175909d47a6871bcca4897 explain_query -------------------------------------------------------------------------------------------------------------- 1. Query Overview + - This query aims to retrieve all records from the orders table where user_id equals 12 (SELECT *). - This is a typical query that filters data based on a specific field (user_id). + 2. Performance Summary + - Total execution time: 0.021 ms - Planning time: 0.430 ms - Total cost: 18.12 - Rows returned: 0 rows (Actual Rows: 0) - Rows scanned: 0 rows (Rows Removed by Filter: 0) + 3. Execution Plan Analysis + - Key step: Sequential Scan - The database performed a full table scan on the orders table. - The planner estimated finding 3 rows, but actual execution returned 0 rows. - Filter condition: orders.user_id = 12, which means the database must read every row in the table to check this condition. + 4. Performance Issues + - Full table scan risk: Although the current table data volume is small (execution time is only 0.021ms), using Seq Scan (sequential scan) means the database is not using an index. If the orders table grows to millions of rows over time, this query approach will become extremely slow (high I/O consumption). - Missing index: The plan shows no index was used to locate rows where user_id = 12, indicating that a necessary B-Tree index may be missing on the user_id column. + 5. Optimization Recommendations + - Primary recommendation: Create an index on the user_id column to avoid full table scans. This will transform the query from O(N) (scanning all rows) to O(log N) (index lookup). - SQL optimization example: + CREATE INDEX idx_orders_user_id ON orders(user_id); + 6. Index Recommendations + - Recommended index: Create a B-Tree index on the user_id column of the orders table. - Reason: The query condition is based on equality comparison (=) of user_id. After creating the index, IvorySQL (PostgreSQL) will be able to use the index to quickly locate data, significantly reducing query time and resource consumption, especially when data volume is large. (1 row) Use English: While AI supports multiple languages, English works best Understand your database structure: The more you understand your database structure, the more accurate the generated queries will be Refine iteratively: Start broad, then add details step by step to improve results Be explicit: If you know specific tables or columns, mention them in your prompts - this helps AI generate more precise queries When tables referenced in the query do not exist, the system returns an error message: SELECT generate_query('List all products and prices'); Error output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135642777cbc5c82ca4a85 ERROR: Query generation failed: Cannot generate query. Referenced table(s) for 'products' or 'goods' do not exist in the database. Available tables: public.orders, public.student_scores, public.users, sys.dual In this case, the AI informs you of the available table list to help adjust your query.","title":"pg_ai_query","component":"ivorysql-doc","version":"master","name":"pg_ai_query","url":"/ivorysql-doc/master/ecosystem_components/pg_ai_query.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Quick Start","hash":"quick-start","id":2},{"text":"2.1. Installation","hash":"installation","id":3},{"text":"2.2. Configuration","hash":"configuration","id":4},{"text":"3. Usage Examples","hash":"usage-examples","id":5},{"text":"3.1. Basic Usage","hash":"basic-usage","id":6},{"text":"3.2. generate_query Examples","hash":"generate_query-examples","id":7},{"text":"3.3. explain_query Examples","hash":"explain_query-examples","id":8},{"text":"4. Best Practices","hash":"best-practices","id":9},{"text":"4.1. Prompt Writing Guidelines","hash":"prompt-writing-guidelines","id":10},{"text":"4.2. Error Handling Examples","hash":"error-handling-examples","id":11}]},"40":{"id":40,"text":"Running periodic tasks in PostgreSQL, such as executing VACUUM or deleting old data, is a common requirement. A simple way to achieve this is to configure cron or other external daemons to periodically connect to the database and run commands. However, as databases increasingly run as managed services or standalone containers, configuring and running a separate daemon often becomes impractical. Additionally, it’s difficult to make your cron jobs aware of failover or schedule tasks across cluster nodes. pg_cron is an open-source scheduled task extension for PostgreSQL that allows setting up cron-style task scheduling directly within the database for automating data maintenance tasks (cleanup, aggregation), database health checks, executing stored procedures and custom functions, and other operations. It stores cron jobs in tables, and periodic tasks automatically fail over with the PostgreSQL server. For more details, see pg_cron documentation. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 # Clone pg_cron source code git clone https://github.com/citusdata/pg_cron.git cd pg_cron # Set pg_config path to PATH environment variable, e.g.: export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make make install # Shared preload extensions shared_preload_libraries = 'pg_cron' # Specify task metadata storage database (default current database) cron.database_name = 'ivorysql' # Maximum number of concurrent tasks allowed cron.max_running_jobs = 5 pg_ctl restart -D ./data -l logfile Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension pg_cron; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'pg_cron'; name | default_version | installed_version | comment ---------+-----------------+-------------------+--------------------------- pg_cron | 1.6 | |Job scheduler for PostgreSQL (1 row) SELECT cron.schedule( 'nightly-data-cleanup', -- Task name (unique identifier) '0 3 * * *', -- Cron expression (daily at UTC 3:00) $$DELETE FROM logs WHERE created_at < now() - interval '30 days'$$ -- SQL to execute ); Cron expression quick reference: Example Meaning '0 * * * *' Execute every hour on the hour '*/15 * * * *' Execute every 15 minutes '0 9 * * 1-5' Execute at 9 AM on weekdays '0 1 1 * *' Execute at 1 AM on the 1st of every month pg_cron also allows using '$' to represent the last day of the month. # View all tasks SELECT * FROM cron.job; # View task execution history SELECT * FROM cron.job_run_details ORDER BY start_time DESC LIMIT 10; # Delete task SELECT cron.unschedule('nightly-data-cleanup'); # Pause task (update status) UPDATE cron.job SET active = false WHERE jobname = 'delete-job-run-details';","title":"pg_cron","component":"ivorysql-doc","version":"master","name":"pg_cron","url":"/ivorysql-doc/master/ecosystem_components/pg_cron.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation and Configuration","hash":"installation-and-configuration","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"2.2. Configuration File (ivorysql.conf)","hash":"configuration-file-ivorysql-conf","id":4},{"text":"2.3. Restart Service","hash":"restart-service","id":5},{"text":"2.4. Create Extension and Confirm pg_cron Version","hash":"create-extension-and-confirm-pg_cron-version","id":6},{"text":"3. Core Functionality Usage","hash":"core-functionality-usage","id":7},{"text":"3.1. Creating Scheduled Tasks","hash":"creating-scheduled-tasks","id":8},{"text":"3.2. Task Management","hash":"task-management","id":9}]},"41":{"id":41,"text":"PgAudit is an auditing extension for IvorySQL that produces traceable log records for critical operations such as DDL, DML, and DCL. With the audit trail, database administrators can meet compliance requirements, quickly detect abnormal behavior, and identify accountability and impact scope when incidents occur. Comprehensive auditing: Captures SELECT, INSERT, UPDATE, DELETE, DDL commands, privilege changes, and more to build a complete activity timeline. Flexible scope control: Supports global, role-based, and object-level auditing, allowing fine-grained configuration by user, role, schema, or operation type. Seamless integration: Reuses PostgreSQL’s standard logging subsystem and works with tools like syslog and logrotate, aligning with existing log ingestion and analysis pipelines. Compliance ready: Generates structured audit logs suitable for meeting regulatory requirements in finance, government, and other regulated industries. Security enhancement: Records and inspects database activity to surface unauthorized access, anomalous DML, or potential data leakage risks in time. Operations insight: Helps replay operational actions, locate performance bottlenecks, and support SQL tuning and incident troubleshooting. A IvorySQL installation (recommended version aligned with the targeted PgAudit release). Build toolchain: gcc, make, tar, etc. Database superuser privileges to modify ivorysql.conf and restart the instance. Taking PgAudit 18.0 as an example: wget https://github.com/pgaudit/pgaudit/archive/refs/tags/18.0.tar.gz tar -xf 18.0.tar.gz cd pgaudit-18.0 make install USE_PGXS=1 PG_CONFIG=$PGHOME/bin/pg_config The commands above expect the environment variable PGHOME to point to the installed IvorySQL home directory. After installation, pgaudit.so will be placed in IvorySQL’s extension directory. Modify ivorysql.conf to load the plugin and configure common parameters: ---- shared_preload_libraries = 'pgaudit' # Requires an instance restart pgaudit.log = 'read, write, ddl' # Sample audit scope; adjust as needed ---- Restart or reload the database instance so the shared library configuration takes effect. CREATE EXTENSION IF NOT EXISTS pgaudit; SELECT name, default_version, installed_version, comment FROM pg_available_extensions WHERE name = 'pgaudit'; If the returned installed_version matches the expected release, the extension has been installed successfully. Execute the following SQL sample: [source,sql] ---- CREATE TABLE audit_demo(id serial PRIMARY KEY, info text); INSERT INTO audit_demo(info) VALUES ('pgaudit test'); SELECT * FROM audit_demo; UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1; DELETE FROM audit_demo WHERE id = 1; ---- Check the audit logs on the database server: tail -f $PGDATA/log/*.log | grep 'AUDIT:' 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE TABLE,TABLE,public.audit_demo,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE INDEX,INDEX,public.audit_demo_pkey,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,ALTER SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.117 CST [11451] LOG: AUDIT: SESSION,2,1,WRITE,INSERT,,,INSERT INTO audit_demo(info) VALUES ('pgaudit test'),<not logged> 2025-10-31 15:56:32.121 CST [11451] LOG: AUDIT: SESSION,3,1,READ,SELECT,,,SELECT * FROM audit_demo,<not logged> 2025-10-31 15:56:32.122 CST [11451] LOG: AUDIT: SESSION,4,1,WRITE,UPDATE,,,UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1,<not logged> 2025-10-31 15:56:32.127 CST [11451] LOG: AUDIT: SESSION,5,1,WRITE,DELETE,,,DELETE FROM audit_demo WHERE id = 1,<not logged> To record parameter values as well, enable pgaudit.log_parameter = 'on': ivorysql=# SHOW pgaudit.log_parameter; pgaudit.log_parameter ----------------------- on (1 row)","title":"PgAudit","component":"ivorysql-doc","version":"master","name":"pgaudit","url":"/ivorysql-doc/master/ecosystem_components/pgaudit.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Key Features","hash":"key-features","id":2},{"text":"3. Installation and Deployment","hash":"installation-and-deployment","id":3},{"text":"3.1. Prerequisites","hash":"prerequisites","id":4},{"text":"3.2. Compile and Install PgAudit","hash":"compile-and-install-pgaudit","id":5},{"text":"3.3. Baseline Configuration Before Registering the Extension","hash":"baseline-configuration-before-registering-the-extension","id":6},{"text":"3.4. Create the Extension and Verify","hash":"create-the-extension-and-verify","id":7},{"text":"4. Usage","hash":"usage","id":8}]},"42":{"id":42,"text":"pgddl is a SQL function extension specifically designed for PostgreSQL databases. It can generate clear, formatted SQL DDL (Data Definition Language) scripts directly from the database system catalog, such as CREATE TABLE or ALTER FUNCTION. It solves the problem that PostgreSQL natively lacks commands like SHOW CREATE TABLE, allowing users to easily obtain object creation statements in a pure SQL environment without relying on external tools (such as pg_dump). This extension provides a complete solution through a set of simple SQL functions, with advantages including: requiring only SQL queries to operate, supporting flexible object filtering through WHERE clauses, and intelligently handling dependencies between objects to generate complete scripts including Drop and Create steps. This makes it particularly suitable for scenarios such as database change management, upgrade script writing, and structural auditing. It should be noted that ddlx is still under development and may not yet cover all PostgreSQL object types and advanced options. Generated scripts should always be checked and tested in non-production environments first to ensure their correctness and safety. The IvorySQL installation package already integrates the pgddl plugin. If IvorySQL is installed using the installation package, pgddl can usually be used without manual installation. Other installation methods can refer to the source code installation steps below. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 Download pgddl v0.31 code from https://github.com/lacanoid/pgddl. cd pgddl # Set the PG_CONFIG environment variable to the pg_config path, e.g.: /usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/path/to/pg_config make PG_CONFIG=/path/to/pg_config install Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension ddlx; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'ddlx'; name | default_version | installed_version | comment ------+-----------------+-------------------+------------------------- ddlx | 0.31 | 0.31 | DDL eXtractor functions (1 row) For pgddl usage, please refer to the ddlx Official Documentation","title":"pgddl (DDL Extractor)","component":"ivorysql-doc","version":"master","name":"pgddl","url":"/ivorysql-doc/master/ecosystem_components/pgddl.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm ddlx Version","hash":"create-extension-and-confirm-ddlx-version","id":4},{"text":"4. Usage","hash":"usage","id":5}]},"43":{"id":43,"text":"PostgreSQL has built-in full-text search functionality, but when dealing with large-scale data, complex queries, and non-English languages (especially CJK languages like Chinese, Japanese, and Korean), its functionality and performance may not meet the requirements of high-performance applications. PGroonga was created to address this need. It is a PostgreSQL extension that deeply integrates Groonga, a high-performance full-featured full-text search engine, with the PostgreSQL database. Groonga itself is an excellent open-source search engine, renowned for its extreme speed and rich functionality, particularly excelling at handling multilingual text. PGroonga’s mission is to seamlessly bring Groonga’s powerful capabilities into the PostgreSQL world, providing users with an experience that far exceeds native full-text search. The PGroonga plugin is already included in the IvorySQL installation package. If you installed IvorySQL using the official released package, you typically do not need to manually install PGroonga and can skip the installation steps. You can choose their preferred installation method for PGroonga from the PGroonga package installation page. The IvorySQL community provides source code installation steps, demonstrated below using PGroonga v4.0.4 as an example. Setup Environment Operating System: Ubuntu 24.04 CPU Architecture: x86_64 IvorySQL: v5.0 When compile PGroonga, there is an option: HAVE_MSGPACK=1, which is used to support WAL. Enabling this option requires installing msgpack-c 1.4.1 or newer version. sudo apt install libmsgpack-dev Ensure Groonga >= 14.0.0 is installed. sudo apt install groonga libgroonga-dev Verify Groonga installation: highgo@ubuntu:~/work/IvorySQL/inst$ groonga --version Groonga 15.1.7 [Linux,x86_64,utf8,match-escalation-threshold=0,nfkc,mecab,message-pack,mruby,onigmo,zlib,lz4,zstandard,epoll,apache-arrow,xxhash,blosc,h3,simdjson,llama.cpp] wget https://packages.groonga.org/source/pgroonga/pgroonga-4.0.4.tar.gz tar xvf pgroonga-4.0.4.tar.gz cd pgroonga-4.0.4 Before running make , ensure that the pg_config command is in the PATH environment variable. For example, if IvorySQL is installed at ~/work/IvorySQL/inst , set the environment variables as follows: export PGHOME=~/work/IvorySQL/inst export PGDATA=$PGHOME/data export PATH=$PGHOME/bin:$PATH Then execute the following commands to compile and install: make HAVE_MSGPACK=1 make install Connect to the database with psql in pg mode and execute the following commands: postgres=# CREATE extension pgroonga; CREATE EXTENSION postgres=# SELECT * FROM pg_available_extensions WHERE name = 'pgroonga'; name | default_version | installed_version | comment ----------+-----------------+-------------------+-------------------------------------------------------------------------------- pgroonga | 4.0.4 | 4.0.4 | Super fast and all languages supported full text search index based on Groonga (1 row) postgres=# select version(); version -------------------------------------------------------------------------------- PostgreSQL (IvorySQL 5.0) 18.0 on x86_64-linux, compiled by gcc-13.3.0, 64-bit (1 row) For PGroonga usage, please refer to the PGroonga Official Documentation","title":"PGroonga","component":"ivorysql-doc","version":"master","name":"pgroonga","url":"/ivorysql-doc/master/ecosystem_components/pgroonga.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Dependencies","hash":"dependencies","id":3},{"text":"2.1.1. Install msgpack-c","hash":"install-msgpack-c","id":4},{"text":"2.1.2. Install Groonga","hash":"install-groonga","id":5},{"text":"2.2. Compile and Install PGroonga","hash":"compile-and-install-pgroonga","id":6},{"text":"2.2.1. Download and Extract the PGroonga Source Code","hash":"download-and-extract-the-pgroonga-source-code","id":7},{"text":"2.2.2. Compile","hash":"compile","id":8},{"text":"3. Create Extension PGroonga and Confirm the version","hash":"create-extension-pgroonga-and-confirm-the-version","id":9},{"text":"4. Usage","hash":"usage","id":10}]},"44":{"id":44,"text":"pgRouting is an open-source geospatial routing extension library built on PostgreSQL/PostGIS databases. It endows databases with powerful network analysis capabilities, enabling them to handle complex path planning and graph theory computation problems, such as calculating the shortest path between two points, performing Traveling Salesman Problem (TSP) analysis, or computing service area coverage. It embeds routing algorithms directly into the database, thereby avoiding complex data transfer and computation at the application layer. The core advantage of this extension lies in its ability to leverage PostgreSQL’s powerful data management capabilities and PostGIS’s rich spatial functions to perform efficient computation on spatial network data directly within the database. This not only simplifies application development processes but also significantly improves the performance of large-scale network analysis by reducing data movement. pgRouting is widely used in logistics and distribution, traffic navigation, network analysis, urban planning, and supply chain management, among other fields. Its open-source nature has attracted continuous contributions and improvements from developers worldwide, making it one of the preferred tools for path analysis and network solving in the spatial database domain. IvorySQL 5.0 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 Install dependencies It depends on perl, which is generally already installed when installing IvorySQL, so no need to install it here. CMake version requirement >= 3.12, Boost version >= 1.56 # Install dependencies sudo apt install cmake libboost-all-dev Compile and install wget https://github.com/pgRouting/pgrouting/releases/download/v3.8.0/pgrouting-3.8.0.tar.gz tar xvf pgrouting-3.8.0.tar.gz cd pgrouting-3.8.0 mkdir build cd build cmake .. -DPOSTGRESQL_PG_CONFIG=/path/to/pg_config # eg: /usr/local/ivorysql/ivorysql-5/bin/pg_config make sudo make install Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension pgrouting; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'pgrouting'; name | default_version | installed_version | comment -----------+-----------------+-------------------+--------------------- pgrouting | 3.8.0 | | pgRouting Extension (1 row) For pgRouting usage, please refer to the pgRouting Official Documentation","title":"pgRouting","component":"ivorysql-doc","version":"master","name":"pgrouting","url":"/ivorysql-doc/master/ecosystem_components/pgrouting.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm pgRouting Version","hash":"create-extension-and-confirm-pgrouting-version","id":4},{"text":"4. Usage","hash":"usage","id":5}]},"45":{"id":45,"text":"pgsql-http is an open-source extension designed for PostgreSQL databases that allows users to initiate HTTP requests directly within the database, acting as a built-in web client. The core purpose of this extension is to bridge the gap between databases and external web services, enabling interaction with external web services and API endpoints through simple SQL function calls without relying on external applications or middleware. With this extension, developers can directly retrieve network data (GET), submit data (POST/PUT), update (PATCH), or delete (DELETE) remote resources in SQL queries, triggers, or stored procedures. It provides rich functionality, including setting request headers, automatic URL encoding, sending JSON data, and parsing response status, headers, and content, greatly simplifying the process of integrating external data into database operations. Typical application scenarios include: real-time retrieval of external data (such as exchange rates, weather information) and storing it in tables; automatic notification of microservices through triggers when data changes; cleaning data in the database and directly submitting it to external APIs. It provides a powerful and flexible solution for building database-centric integrated applications. The pgsql-http plugin has been integrated into the IvorySQL installation package. If IvorySQL is installed using the installation package, pgsql-http can usually be used without manual installation. Other installation methods can refer to the source code installation steps below. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 Install Dependencies It depends on libcurl, and libcurl development files (such as libcurl4-openssl-dev) need to be installed in advance # Install dependencies sudo apt install libcurl4-openssl-dev Compile and Install Download the 1.7.0 source package pgsql-http-1.7.0.tar.gz from https://github.com/pramsey/pgsql-http/releases/tag/v1.7.0 tar xvf pgsql-http-1.7.0.tar.gz cd pgsql-http-1.7.0 # Ensure pg_config is accessible in PATH, e.g.: /usr/local/ivorysql/ivorysql-5/bin/pg_config make sudo make install Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension http; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'http'; name | default_version | installed_version | comment -----------+-----------------+-------------------+------------------------------------------------------------------------- http | 1.7 | 1.7 | HTTP client for PostgreSQL, allows web page retrieval inside the database. (1 row) For pgsql-http usage, please refer to pgsql-http official documentation","title":"pgsql-http","component":"ivorysql-doc","version":"master","name":"pgsql_http","url":"/ivorysql-doc/master/ecosystem_components/pgsql_http.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm http Version","hash":"create-extension-and-confirm-http-version","id":4},{"text":"4. Usage","hash":"usage","id":5}]},"46":{"id":46,"text":"The vector database is an important component of Generative Artificial Intelligence (GenAI). As a significant extension of PostgreSQL, pgvector not only supports vector calculations of up to 16000 dimensions but also provides powerful vector operations and indexing capabilities, enabling PostgreSQL to directly transform into an efficient vector database. IvorySQL, being developed based on PostgreSQL, inherits the seamless integration capability with pgvector extension, thereby offering users a wider range of data processing and analysis options. Additionally, in Oracle compatibility mode, the pgvector extension is also available, providing great convenience for Oracle users to use vector databases, allowing for easy migration and management of data and achieving more efficient business operations. PGVector has two indexing algorithms, IVFFLAT and HNSW. The working principle of IVFFLAT is to cluster similar vectors into regions and build an inverted index mapping each region to its vectors. This allows queries to focus on a subset of the data, enabling fast searches. By adjusting the parameters of lists and probes, IVFFLAT can balance the speed and accuracy of the dataset, enabling PostgreSQL to perform rapid semantic similarity searches on complex data. Through simple queries, applications can find the nearest neighbors to a query vector among millions of high-dimensional vectors. For tasks such as natural language processing and information retrieval, IVFFLAT provides an effective solution. When building an IVFFLAT index, you need to decide how many lists to include in the index. Each list represents a \"center\" which are computed using the k-means algorithm. Once all centers are determined, IVFFLAT determines which center each vector is closest to and adds it to the index. When querying vector data, you can decide how many centers to check, which is determined by the ivfflat.probes parameter. This results in a trade-off between ANN performance/recall: the more centers accessed, the more accurate the results, but at the expense of performance. HNSW (Hierarchical Navigating Small World) is a graph-based indexing algorithm consisting of multiple layers of neighborhood graphs, hence the name \"hierarchical\" NSW method. It constructs multiple layers of navigation graphs for a given graph according to certain rules, with the upper layers of the graph being sparser and the distances between nodes farther apart; and the lower layers of the graph being denser and the distances between nodes closer together. HNSW algorithm is a classic trade-off between space and time, as it achieves high search quality and speed, but at the cost of significant memory overhead. This is because it not only requires storing all vectors in memory but also maintaining the structure of the graph, which also needs to be stored. The IvorySQL 5(above version) has been installed in the environment, and the installation path is /usr/local/ivorysql/ivorysql-5 Setting PG_CONFIG export PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config Pull pg_vector source code git clone --branch v0.8.1 https://github.com/pgvector/pgvector.git Install pgvector cd pgvector sudo --preserve-env=PG_CONFIG make sudo --preserve-env=PG_CONFIG make install Create pgvector extension [ivorysql@localhost ivorysql-5]$ psql psql (18.0) Type \"help\" for help. ivorysql=# create extension vector; CREATE EXTENSION Now, pgvector is installed completely. For more usage cases, please refer to pgvector document In IvorySQL’s Oracle compatibility mode, the pgvector extension can also work correctly. We suggest users to test using port 1521, using the command: psql -p 1521. ivorysql=# CREATE TABLE items5 (id bigserial PRIMARY KEY, name varchar2(20), num number(20), embedding bit(3)); CREATE TABLE ivorysql=# INSERT INTO items5 (name, num, embedding) VALUES ('1st oracle data',0, '000'), ('2nd oracle data', 111, '111'); INSERT 0 2 ivorysql=# SELECT * FROM items5 ORDER BY bit_count(embedding # '101') LIMIT 5; id | name | num | embedding ----+-----------------+-----+----------- 2 | 2nd oracle data | 111 | 111 1 | 1st oracle data | 0 | 000 ivorysql=# declare i vector(3) := '[1,2,3]'; begin raise notice '%', i; end; ivorysql-# / NOTICE: [1,2,3] DO ivorysql=# CREATE OR REPLACE PROCEDURE ora_procedure() AS p vector(3) := '[4,5,6]'; begin raise notice '%', p; end; / CREATE PROCEDURE ivorysql=# call ora_procedure(); NOTICE: [4,5,6] CALL ivorysql=# CREATE OR REPLACE FUNCTION AddVector(a vector(3), b vector(3)) RETURN vector(3) IS BEGIN RETURN a + b; END; / CREATE FUNCTION ivorysql=# SELECT AddVector('[1,2,3]','[4,5,6]') FROM DUAL; addvector ---------------- [5,7,9] (1 row)","title":"pgvector","component":"ivorysql-doc","version":"master","name":"pgvector","url":"/ivorysql-doc/master/ecosystem_components/pgvector.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Principles","hash":"principles","id":2},{"text":"2.1. IVFFLAT","hash":"ivfflat","id":3},{"text":"2.2. HNSW","hash":"hnsw","id":4},{"text":"3. Installation","hash":"installation","id":5},{"text":"3.1. Source Code Installation","hash":"source-code-installation","id":6},{"text":"4. Oracle Compatible","hash":"oracle-compatible","id":7},{"text":"4.1. Data Type","hash":"data-type","id":8},{"text":"4.2. Anonymous Block","hash":"anonymous-block","id":9},{"text":"4.3. PROCEDURE","hash":"procedure","id":10},{"text":"4.4. FUNCTION","hash":"function","id":11}]},"47":{"id":47,"text":"During PostgreSQL database development, when writing stored procedures and functions, it is often difficult to discover potential issues such as syntax errors, type mismatches, undefined variables, etc., before runtime. Traditional approaches require waiting until the function is actually executed to discover these errors, which not only increases debugging costs but may also cause unexpected failures in production environments. plpgsql_check is a static code analysis tool (Linter) specifically designed for PostgreSQL’s PL/pgSQL language. It can perform deep checks on the source code of stored procedures and functions without actually executing them. This tool can proactively identify various code quality issues including syntax errors, type mismatches, unused variables, performance problems, security vulnerabilities, etc., helping developers ensure code correctness and robustness during the development phase. For more details, see plpgsql_check official documentation. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 # Download the 2.8.3 source package plpgsql_check-2.8.3.tar.gz from https://github.com/okbob/plpgsql_check/releases/tag/v2.8.3 tar xvf plpgsql_check-2.8.3.tar.gz cd plpgsql_check-2.8.3 # Set pg_config path to PATH environment variable, e.g.: export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make USE_PGXS=1 clean make USE_PGXS=1 all sudo make USE_PGXS=1 install Connect to the database with psql and execute the following commands: ivorysql=# CREATE EXTENSION plpgsql_check; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'plpgsql_check'; name | default_version | installed_version | comment ---------------+-----------------+-------------------+--------------------------------------------------- plpgsql_check | 2.8 | 2.8 | extended check for plpgsql functions (1 row) -- Create a sample function CREATE OR REPLACE FUNCTION test_function(p_id integer) RETURNS text AS $$ DECLARE v_name text; v_unused integer; -- Unused variable BEGIN SELECT name INTO v_name FROM users WHERE id = p_id; RETURN v_naem; -- Spelling error END; $$ LANGUAGE plpgsql; -- Use plpgsql_check to check the function SELECT * FROM plpgsql_check_function('test_function(integer)'); Example check result: plpgsql_check_function -------------------------------------------------------------- error:42601:7:assignment:target variable \"v_naem\" is undefined warning:00000:4:DECLARE:unused variable \"v_unused\" (2 rows) For more detailed usage methods and advanced features, please refer to plpgsql_check official documentation.","title":"plpgsql_check","component":"ivorysql-doc","version":"master","name":"plpgsql_check","url":"/ivorysql-doc/master/ecosystem_components/plpgsql_check.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm plpgsql_check Version","hash":"create-extension-and-confirm-plpgsql_check-version","id":4},{"text":"4. Usage","hash":"usage","id":5},{"text":"4.1. Check Single Function","hash":"check-single-function","id":6}]},"48":{"id":48,"text":"IvorySQL is fully compatible with PostgreSQL, allowing for seamless integration with PostGIS. Users can select the installation method for PostGIS that best suits their development environment from the PostGIS installation page. In addition to the installation methods provided by the PostGIS community, the IvorySQL community also offers a source code installation method, with Ubuntu 24.04 (x86_64) as the installation environment. Please ensure that IvorySQL 5.0 or above is installed in the environment. Install dependencies sudo apt install \\ docbook-xsl-ns \\ gettext \\ libgdal-dev \\ libgeos-dev \\ libjson-c-dev \\ libproj-dev \\ libprotobuf-c-dev \\ libsfcgal-dev \\ libxml2-dev \\ libxml2-utils \\ protobuf-c-compiler \\ xsltproc Install PostGIS $ wget https://download.osgeo.org/postgis/source/postgis-3.5.4.tar.gz $ tar xvf postgis-3.5.4.tar.gz $ cd postgis-3.5.4 $ ./configure --with-pgconfig=/path/to/pg_config eg: /usr/ivory-5/bin/pg_config, if ivorysql installation directory is /usr/ivory-5. $ make $ sudo make install If configure reports PGXS error, please change --with-pgconfig parameter value and confirm the parameter value based on the installation path of IvorySQL in the environment. Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension postgis; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'postgis'; name | default_version | installed_version | comment ---------+-----------------+-------------------+------------------------------------------------------------ postgis | 3.5.4 | 3.5.4 | PostGIS geometry and geography spatial types and functions (1 row) For information about using PostGIS, please refer to the PostGIS 3.5 Official Documentation","title":"PostGIS","component":"ivorysql-doc","version":"master","name":"postgis","url":"/ivorysql-doc/master/ecosystem_components/postgis.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Code Installation","hash":"source-code-installation","id":3},{"text":"3. Create Extension and Verify PostGIS Version","hash":"create-extension-and-verify-postgis-version","id":4},{"text":"4. Using","hash":"using","id":5}]},"49":{"id":49,"text":"system_stats is a Postgres extension that provides functions to access system level statistics that can be used for monitoring. The source code installation environment is Ubuntu 24.04 (x86_64), in which IvorySQL 5 or a later version has been installed. The installation path is /usr/local/ivorysql/ivorysql-5. # download source code package from: https://github.com/EnterpriseDB/system_stats/releases/tag/v3.2 unzip v3.2.zip cd system_stats-3.2 # compile and install the extension make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config install connect the database with psql in Oracle compatible mode and execute the following commands: ivorysql=# CREATE EXTENSION system_stats; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'system_stats'; name | default_version | installed_version | comment --------------+-----------------+-------------------+----------------------------------------------- system_stats | 3.0 | 3.0 | EnterpriseDB system statistics for PostgreSQL (1 row) ivorysql=> select pg_sys_os_info() from dual; pg_sys_os_info -------------------------------------------------------------------------- (\"\"\"Ubuntu 24.04.1 LTS\"\" + \",\"Linux 6.14.0-29-generic\",Ubuntu,\"(none)\",5568,292,532,x86_64,,864122) (1 row) ivorysql=> select pg_sys_cpu_info() from dual; pg_sys_cpu_info ------------------------------------------------------------------------------------------------------------------------------------- (GenuineIntel,\"GenuineIntel model 154 family 6\",\"12th Gen Intel(R) Core(TM) i7-1260P\",,0,2,1,x86_64,2496000000,,,,48,32,1280,18432) (1 row) ivorysql=> select pg_sys_cpu_usage_info() from dual; pg_sys_cpu_usage_info ----------------------- (0,0,0,100,0,0,0,,,,) (1 row) ivorysql=> select pg_sys_memory_info() from dual; pg_sys_memory_info ----------------------------------------------------------------------------------- (4055482368,3911159808,144322560,4055887872,233725952,3822161920,2638602240,,,,,) (1 row) ivorysql=> select pg_sys_io_analysis_info() from dual; pg_sys_io_analysis_info --------------------------------------------------------------- (loop0,15,0,21504,0,1,0) (loop1,1362,0,27916288,0,680,0) (loop2,175,0,3354624,0,131,0) (loop3,141,0,1827840,0,83,0) (loop4,918,0,32752640,0,212,0) (loop5,156,0,2617344,0,67,0) (loop6,3707,0,131992576,0,828,0) (loop7,243,0,4321280,0,95,0) (fd0,0,0,0,0,0,0) (sda,639286,1580583,26547332096,240138948608,402702,2744653) (sda1,561,0,22966272,0,61,0) (sda2,636820,1580583,26491053056,240138948608,402316,2744653) (sr0,87,0,3219456,0,45,0) (sr1,0,0,0,0,0,0) (loop8,228,0,2491392,0,120,0) (loop10,2333,0,36571136,0,1628,0) (loop9,3730,0,66532352,0,3973,0) (loop11,87,0,709632,0,50,0) (loop12,156,0,1948672,0,52,0) (loop14,2579,0,97446912,0,2083,0) (loop13,30,0,102400,0,14,0) (loop15,82,0,1189888,0,117,0) (loop16,1357,0,28335104,0,500,0) (loop17,110,0,2187264,0,93,0) (loop18,129,0,2355200,0,28,0) (loop19,110,0,2202624,0,55,0) (26 rows) ivorysql=> select pg_sys_disk_info() from dual; pg_sys_disk_info ---------------------------------------------------------------------------------- (/,/dev/sda2,,,ext4,105086115840,50903396352,48797392896,6553600,619735,5933865) (1 row) ivorysql=> select pg_sys_load_avg_info() from dual; pg_sys_load_avg_info ---------------------- (0,0,0,) (1 row) ivorysql=> select pg_sys_process_info() from dual; pg_sys_process_info --------------------- (294,1,201,0,0) (1 row) ivorysql=> select pg_sys_network_info() from dual; pg_sys_network_info ----------------------------------------------------------------------------- (lo,127.0.0.1,1071575,5643,0,0,1071575,5643,0,0,0) (ens33,192.168.198.128,619877515,1490961,0,161,5540250106,4345449,0,0,1000) (2 rows) ivorysql=> select pg_sys_cpu_memory_by_process() from dual; pg_sys_cpu_memory_by_process ----------------------------------------------------------------------- (1,\"(systemd)\",864793,0,0.34,13840384) (2,\"(kthreadd)\",864793,0,0,0) (3,\"(pool_workqueue_release)\",864793,0,0,0) (4,\"(kworker/R-rcu_gp)\",864793,0,0,0) ......","title":"system_stats","component":"ivorysql-doc","version":"master","name":"system_stats","url":"/ivorysql-doc/master/ecosystem_components/system_stats.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Code Installation","hash":"source-code-installation","id":3},{"text":"3. Create extension and confirm the version","hash":"create-extension-and-confirm-the-version","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"50":{"id":50,"text":"wal2json is an output plugin for logical decoding. It generates JSON object for each transaction. The source code installation environment is Ubuntu 24.04 (x86_64), in which IvorySQL 5 or a later version has been installed. The installation path is /usr/ivory-5. # download source code package from: https://github.com/eulerto/wal2json/releases/tag/wal2json_2_6 unzip wal2json_2_6.zip cd wal2json_2_6 # compile and install the extension make PG_CONFIG=/usr/ivory-5/bin/pg_config make PG_CONFIG=/usr/ivory-5/bin/pg_config install If there is error \"xlocale.h: No such file or directory\" during compilation, user should remove the line of \"#define HAVE_XLOCALE_H 1\" from file /usr/ivory-5/include/postgresql/server/pg_config.h. Modify the postgresql.conf file to set wal_level as \"logical\", and set max_replication_slots/max_wal_senders. wal_level = logical max_replication_slots = 10 max_wal_senders = 10 Make sure the following content to be in pg_hba.conf. local replication all trust host replication all 127.0.0.1/32 trust host replication all ::1/128 trust Then restart the database. Open the first terminal and execute command: sudo -u ivorysql /usr/ivory-5/bin/pg_recvlogical -d postgres --slot wal2json_slot --create-slot -P wal2json Start monitoring, output the changes in JSON format and in real time. sudo -u ivorysql /usr/ivory-5/bin/pg_recvlogical -d postgres --slot wal2json_slot --start -o pretty-print=1 -f - Connect database in the second terminal: /usr/ivory-5/bin/psql -d postgres -p 1521 Execute the following SQL statement: CREATE TABLE test_cdc (id int primary key, name varchar(50)); INSERT INTO test_cdc VALUES (1, 'test1'); UPDATE test_cdc SET name = 'test1_update' WHERE id = 1; DELETE FROM test_cdc WHERE id = 1; DROP TABLE test_cdc; The following output will appear in the first terminal: { \"change\": [ ] } { \"change\": [ { \"kind\": \"insert\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1\"] } ] } { \"change\": [ { \"kind\": \"update\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1_update\"], \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ { \"kind\": \"delete\", \"schema\": \"public\", \"table\": \"test_cdc\", \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ ] }","title":"wal2json","component":"ivorysql-doc","version":"master","name":"wal2json","url":"/ivorysql-doc/master/ecosystem_components/wal2json.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Code Installation","hash":"source-code-installation","id":3},{"text":"2.2. Modify the configuration file","hash":"modify-the-configuration-file","id":4},{"text":"3. Use","hash":"use","id":5}]},"51":{"id":51,"text":"IvorySQL databases require periodic maintenance known as vacuuming. For many installations, it is sufficient to let vacuuming be performed by the autovacuum daemon. You might need to adjust the autovacuuming parameters described there to obtain best results for your situation. Some database administrators will want to supplement or replace the daemon’s activities with manually-managed VACUUM commands, which typically are executed according to a schedule by cron or Task Scheduler scripts. To set up manually-managed vacuuming properly, it is essential to understand the issues discussed in the next few subsections. Administrators who rely on autovacuuming may still wish to skim this material to help them understand and adjust autovacuuming. IvorySQL’s command has to process each table on a regular basis for several reasons: To recover or reuse disk space occupied by updated or deleted rows. To update data statistics used by the PostgreSQL query planner. To update the visibility map, which speeds up index-only scans. To protect against loss of very old data due to transaction ID wraparound or multixact ID wraparound. Each of these reasons dictates performing VACUUM operations of varying frequency and scope, as explained in the following subsections. There are two variants of VACUUM: standard VACUUM and VACUUM FULL. VACUUM FULL can reclaim more disk space but runs much more slowly. Also, the standard form of VACUUM can run in parallel with production database operations. (Commands such as SELECT, INSERT, UPDATE, and DELETE will continue to function normally, though you will not be able to modify the definition of a table with commands such as ALTER TABLE while it is being vacuumed.) VACUUM FULL requires an ACCESS EXCLUSIVE lock on the table it is working on, and therefore cannot be done in parallel with other use of the table. Generally, therefore, administrators should strive to use standard VACUUM and avoid VACUUM FULL. VACUUM creates a substantial amount of I/O traffic, which can cause poor performance for other active sessions. There are configuration parameters that can be adjusted to reduce the performance impact of background vacuuming. In IvorySQL，an UPDATE or DELETE of a row does not immediately remove the old version of the row. This approach is necessary to gain the benefits of multiversion concurrency control : the row version must not be deleted while it is still potentially visible to other transactions. But eventually, an outdated or deleted row version is no longer of interest to any transaction. The space it occupies must then be reclaimed for reuse by new rows, to avoid unbounded growth of disk space requirements. This is done by running VACUUM. The standard form of VACUUM removes dead row versions in tables and indexes and marks the space available for future reuse. However, it will not return the space to the operating system, except in the special case where one or more pages at the end of a table become entirely free and an exclusive table lock can be easily obtained. In contrast, VACUUM FULL actively compacts tables by writing a complete new version of the table file with no dead space. This minimizes the size of the table, but can take a long time. It also requires extra disk space for the new copy of the table, until the operation completes. The usual goal of routine vacuuming is to do standard VACUUM`s often enough to avoid needing `VACUUM FULL. The autovacuum daemon attempts to work this way, and in fact will never issue VACUUM FULL. In this approach, the idea is not to keep tables at their minimum size, but to maintain steady-state usage of disk space: each table occupies space equivalent to its minimum size plus however much space gets used up between vacuum runs. Although VACUUM FULL can be used to shrink a table back to its minimum size and return the disk space to the operating system, there is not much point in this if the table will just grow again in the future. Thus, moderately-frequent standard VACUUM runs are a better approach than infrequent VACUUM FULL runs for maintaining heavily-updated tables. Some administrators prefer to schedule vacuuming themselves, for example doing all the work at night when load is low. The difficulty with doing vacuuming according to a fixed schedule is that if a table has an unexpected spike in update activity, it may get bloated to the point that VACUUM FULL is really necessary to reclaim space. Using the autovacuum daemon alleviates this problem, since the daemon schedules vacuuming dynamically in response to update activity. It is unwise to disable the daemon completely unless you have an extremely predictable workload. One possible compromise is to set the daemon’s parameters so that it will only react to unusually heavy update activity, thus keeping things from getting out of hand, while scheduled `VACUUM`s are expected to do the bulk of the work when the load is typical. For those not using autovacuum, a typical approach is to schedule a database-wide VACUUM once a day during a low-usage period, supplemented by more frequent vacuuming of heavily-updated tables as necessary. (Some installations with extremely high update rates vacuum their busiest tables as often as once every few minutes.) If you have multiple databases in a cluster, don’t forget to VACUUM each one; the program vacuumdb might be helpful. Tip Plain VACUUM may not be satisfactory when a table contains large numbers of dead row versions as a result of massive update or delete activity. If you have such a table and you need to reclaim the excess disk space it occupies, you will need to use VACUUM FULL, or alternatively CLUSTER or one of the table-rewriting variants of ALTER TABLE. These commands rewrite an entire new copy of the table and build new indexes for it. All these options require an ACCESS EXCLUSIVE lock. Note that they also temporarily use extra disk space approximately equal to the size of the table, since the old copies of the table and indexes can’t be released until the new ones are complete. Tip If you have a table whose entire contents are deleted on a periodic basis, consider doing it with TRUNCATE rather than using DELETE followed by VACUUM. TRUNCATE removes the entire content of the table immediately, without requiring a subsequent VACUUM or VACUUM FULL to reclaim the now-unused disk space. The disadvantage is that strict MVCC semantics are violated. The IvorySQL query planner relies on statistical information about the contents of tables in order to generate good plans for queries. These statistics are gathered by the ANALYZE command, which can be invoked by itself or as an optional step in VACUUM. It is important to have reasonably accurate statistics, otherwise poor choices of plans might degrade database performance. The autovacuum daemon, if enabled, will automatically issue ANALYZE commands whenever the content of a table has changed sufficiently. However, administrators might prefer to rely on manually-scheduled ANALYZE operations, particularly if it is known that update activity on a table will not affect the statistics of “interesting” columns. The daemon schedules ANALYZE strictly as a function of the number of rows inserted or updated; it has no knowledge of whether that will lead to meaningful statistical changes. Tuples changed in partitions and inheritance children do not trigger analyze on the parent table. If the parent table is empty or rarely changed, it may never be processed by autovacuum, and the statistics for the inheritance tree as a whole won’t be collected. It is necessary to run ANALYZE on the parent table manually in order to keep the statistics up to date. As with vacuuming for space recovery, frequent updates of statistics are more useful for heavily-updated tables than for seldom-updated ones. But even for a heavily-updated table, there might be no need for statistics updates if the statistical distribution of the data is not changing much. A simple rule of thumb is to think about how much the minimum and maximum values of the columns in the table change. For example, a timestamp column that contains the time of row update will have a constantly-increasing maximum value as rows are added and updated; such a column will probably need more frequent statistics updates than, say, a column containing URLs for pages accessed on a website. The URL column might receive changes just as often, but the statistical distribution of its values probably changes relatively slowly. It is possible to run ANALYZE on specific tables and even just specific columns of a table, so the flexibility exists to update some statistics more frequently than others if your application requires it. In practice, however, it is usually best to just analyze the entire database, because it is a fast operation. ANALYZE uses a statistically random sampling of the rows of a table rather than reading every single row. Tip Although per-column tweaking of ANALYZE frequency might not be very productive, you might find it worthwhile to do per-column adjustment of the level of detail of the statistics collected by ANALYZE. Columns that are heavily used in WHERE clauses and have highly irregular data distributions might require a finer-grain data histogram than other columns. See ALTER TABLE SET STATISTICS, or change the database-wide default using the default_statistics_target configuration parameter. Tip The autovacuum daemon does not issue ANALYZE commands for foreign tables, since it has no means of determining how often that might be useful. If your queries require statistics on foreign tables for proper planning, it’s a good idea to run manually-managed ANALYZE commands on those tables on a suitable schedule. Tip The autovacuum daemon does not issue ANALYZE commands for partitioned tables. Inheritance parents will only be analyzed if the parent itself is changed - changes to child tables do not trigger autoanalyze on the parent table. If your queries require statistics on parent tables for proper planning, it is necessary to periodically run a manual ANALYZE on those tables to keep the statistics up to date. Vacuum maintains a visibility map for each table to keep track of which pages contain only tuples that are known to be visible to all active transactions (and all future transactions, until the page is again modified). This has two purposes. First, vacuum itself can skip such pages on the next run, since there is nothing to clean up. Second, it allows IvorySQL to answer some queries using only the index, without reference to the underlying table. Since PostgreSQL indexes don’t contain tuple visibility information, a normal index scan fetches the heap tuple for each matching index entry, to check whether it should be seen by the current transaction. An index-only scan, on the other hand, checks the visibility map first. If it’s known that all tuples on the page are visible, the heap fetch can be skipped. This is most useful on large data sets where the visibility map can prevent disk accesses. The visibility map is vastly smaller than the heap, so it can easily be cached even when the heap is very large. IvorySQL’s MVCC transaction semantics depend on being able to compare transaction ID (XID) numbers: a row version with an insertion XID greater than the current transaction’s XID is “in the future” and should not be visible to the current transaction. But since transaction IDs have limited size (32 bits) a cluster that runs for a long time (more than 4 billion transactions) would suffer transaction ID wraparound: the XID counter wraps around to zero, and all of a sudden transactions that were in the past appear to be in the future — which means their output become invisible. In short, catastrophic data loss. (Actually the data is still there, but that’s cold comfort if you cannot get at it.) To avoid this, it is necessary to vacuum every table in every database at least once every two billion transactions. The reason that periodic vacuuming solves the problem is that VACUUM will mark rows as frozen, indicating that they were inserted by a transaction that committed sufficiently far in the past that the effects of the inserting transaction are certain to be visible to all current and future transactions. Normal XIDs are compared using modulo-232 arithmetic. This means that for every normal XID, there are two billion XIDs that are “older” and two billion that are “newer”; another way to say it is that the normal XID space is circular with no endpoint. Therefore, once a row version has been created with a particular normal XID, the row version will appear to be “in the past” for the next two billion transactions, no matter which normal XID we are talking about. If the row version still exists after more than two billion transactions, it will suddenly appear to be in the future. To prevent this, IvorySQL reserves a special XID, FrozenTransactionId, which does not follow the normal XID comparison rules and is always considered older than every normal XID. Frozen row versions are treated as if the inserting XID were FrozenTransactionId, so that they will appear to be “in the past” to all normal transactions regardless of wraparound issues, and so such row versions will be valid until deleted, no matter how long that is. vacuum_freeze_min_age controls how old an XID value has to be before rows bearing that XID will be frozen. Increasing this setting may avoid unnecessary work if the rows that would otherwise be frozen will soon be modified again, but decreasing this setting increases the number of transactions that can elapse before the table must be vacuumed again. VACUUM uses the visibility map to determine which pages of a table must be scanned. Normally, it will skip pages that don’t have any dead row versions even if those pages might still have row versions with old XID values. Therefore, normal VACUUM`s won’t always freeze every old row version in the table. When that happens, `VACUUM will eventually need to perform an aggressive vacuum, which will freeze all eligible unfrozen XID and MXID values, including those from all-visible but not all-frozen pages. In practice most tables require periodic aggressive vacuuming. vacuum_freeze_table_age controls when VACUUM does that: all-visible but not all-frozen pages are scanned if the number of transactions that have passed since the last such scan is greater than vacuum_freeze_table_age minus vacuum_freeze_min_age. Setting vacuum_freeze_table_age to 0 forces VACUUM to always use its aggressive strategy. The maximum time that a table can go unvacuumed is two billion transactions minus the vacuum_freeze_min_age value at the time of the last aggressive vacuum. If it were to go unvacuumed for longer than that, data loss could result. To ensure that this does not happen, autovacuum is invoked on any table that might contain unfrozen rows with XIDs older than the age specified by the configuration parameter autovacuum_freeze_max_age. (This will happen even if autovacuum is disabled.) This implies that if a table is not otherwise vacuumed, autovacuum will be invoked on it approximately once every autovacuum_freeze_max_age minus vacuum_freeze_min_age transactions. For tables that are regularly vacuumed for space reclamation purposes, this is of little importance. However, for static tables (including tables that receive inserts, but no updates or deletes), there is no need to vacuum for space reclamation, so it can be useful to try to maximize the interval between forced autovacuums on very large static tables. Obviously one can do this either by increasing autovacuum_freeze_max_age or decreasing vacuum_freeze_min_age. The effective maximum for vacuum_freeze_table_age is 0.95 * autovacuum_freeze_max_age; a setting higher than that will be capped to the maximum. A value higher than autovacuum_freeze_max_age wouldn’t make sense because an anti-wraparound autovacuum would be triggered at that point anyway, and the 0.95 multiplier leaves some breathing room to run a manual VACUUM before that happens. As a rule of thumb, vacuum_freeze_table_age should be set to a value somewhat below autovacuum_freeze_max_age, leaving enough gap so that a regularly scheduled VACUUM or an autovacuum triggered by normal delete and update activity is run in that window. Setting it too close could lead to anti-wraparound autovacuums, even though the table was recently vacuumed to reclaim space, whereas lower values lead to more frequent aggressive vacuuming. The sole disadvantage of increasing autovacuum_freeze_max_age (and vacuum_freeze_table_age along with it) is that the pg_xact and pg_commit_ts subdirectories of the database cluster will take more space, because it must store the commit status and (if track_commit_timestamp is enabled) timestamp of all transactions back to the autovacuum_freeze_max_age horizon. The commit status uses two bits per transaction, so if autovacuum_freeze_max_age is set to its maximum allowed value of two billion, pg_xact can be expected to grow to about half a gigabyte and pg_commit_ts to about 20GB. If this is trivial compared to your total database size, setting autovacuum_freeze_max_age to its maximum allowed value is recommended. Otherwise, set it depending on what you are willing to allow for pg_xact and pg_commit_ts storage. (The default, 200 million transactions, translates to about 50MB of pg_xact storage and about 2GB of pg_commit_ts storage.) One disadvantage of decreasing vacuum_freeze_min_age is that it might cause VACUUM to do useless work: freezing a row version is a waste of time if the row is modified soon thereafter (causing it to acquire a new XID). So the setting should be large enough that rows are not frozen until they are unlikely to change any more. To track the age of the oldest unfrozen XIDs in a database, VACUUM stores XID statistics in the system tables pg_class and pg_database. In particular, the relfrozenxid column of a table’s pg_class row contains the oldest remaining unfrozen XID at the end of the most recent VACUUM that successfully advanced relfrozenxid (typically the most recent aggressive VACUUM). Similarly, the datfrozenxid column of a database’s pg_database row is a lower bound on the unfrozen XIDs appearing in that database — it is just the minimum of the per-table relfrozenxid values within the database. A convenient way to examine this information is to execute queries such as: SELECT c.oid::regclass as table_name, greatest(age(c.relfrozenxid),age(t.relfrozenxid)) as age FROM pg_class c LEFT JOIN pg_class t ON c.reltoastrelid = t.oid WHERE c.relkind IN ('r', 'm'); SELECT datname, age(datfrozenxid) FROM pg_database; The age column measures the number of transactions from the cutoff XID to the current transaction’s XID. VACUUM normally only scans pages that have been modified since the last vacuum, but relfrozenxid can only be advanced when every page of the table that might contain unfrozen XIDs is scanned. This happens when relfrozenxid is more than vacuum_freeze_table_age transactions old, when VACUUM’s `FREEZE option is used, or when all pages that are not already all-frozen happen to require vacuuming to remove dead row versions. When VACUUM scans every page in the table that is not already all-frozen, it should set age(relfrozenxid) to a value just a little more than the vacuum_freeze_min_age setting that was used (more by the number of transactions started since the VACUUM started). VACUUM will set relfrozenxid to the oldest XID that remains in the table, so it’s possible that the final value will be much more recent than strictly required. If no relfrozenxid-advancing VACUUM is issued on the table until autovacuum_freeze_max_age is reached, an autovacuum will soon be forced for the table. If for some reason autovacuum fails to clear old XIDs from a table, the system will begin to emit warning messages like this when the database’s oldest XIDs reach forty million transactions from the wraparound point: WARNING: database \"mydb\" must be vacuumed within 39985967 transactions HINT: To avoid a database shutdown, execute a database-wide VACUUM in that database. (A manual VACUUM should fix the problem, as suggested by the hint; but note that the VACUUM must be performed by a superuser, else it will fail to process system catalogs and thus not be able to advance the database’s datfrozenxid.) If these warnings are ignored, the system will shut down and refuse to start any new transactions once there are fewer than three million transactions left until wraparound: ERROR: database is not accepting commands to avoid wraparound data loss in database \"mydb\" HINT: Stop the postmaster and vacuum that database in single-user mode. The three-million-transaction safety margin exists to let the administrator recover without data loss, by manually executing the required VACUUM commands. However, since the system will not execute commands once it has gone into the safety shutdown mode, the only way to do this is to stop the server and start the server in single-user mode to execute VACUUM. The shutdown mode is not enforced in single-user mode. See the postgres reference page for details about using single-user mode. Multixact IDs are used to support row locking by multiple transactions. Since there is only limited space in a tuple header to store lock information, that information is encoded as a “multiple transaction ID”, or multixact ID for short, whenever there is more than one transaction concurrently locking a row. Information about which transaction IDs are included in any particular multixact ID is stored separately in the pg_multixact subdirectory, and only the multixact ID appears in the xmax field in the tuple header. Like transaction IDs, multixact IDs are implemented as a 32-bit counter and corresponding storage, all of which requires careful aging management, storage cleanup, and wraparound handling. There is a separate storage area which holds the list of members in each multixact, which also uses a 32-bit counter and which must also be managed. Whenever VACUUM scans any part of a table, it will replace any multixact ID it encounters which is older than vacuum_multixact_freeze_min_age by a different value, which can be the zero value, a single transaction ID, or a newer multixact ID. For each table, pg_class.relminmxid stores the oldest possible multixact ID still appearing in any tuple of that table. If this value is older than vacuum_multixact_freeze_table_age, an aggressive vacuum is forced. As discussed in the previous section, an aggressive vacuum means that only those pages which are known to be all-frozen will be skipped. mxid_age() can be used on pg_class.relminmxid to find its age. Aggressive VACUUM`s, regardless of what causes them, are guaranteed to be able to advance the table’s `relminmxid. Eventually, as all tables in all databases are scanned and their oldest multixact values are advanced, on-disk storage for older multixacts can be removed. As a safety device, an aggressive vacuum scan will occur for any table whose multixact-age is greater than autovacuum_multixact_freeze_max_age. Also, if the storage occupied by multixacts members exceeds 2GB, aggressive vacuum scans will occur more often for all tables, starting with those that have the oldest multixact-age. Both of these kinds of aggressive scans will occur even if autovacuum is nominally disabled. IvorySQL has an optional but highly recommended feature called autovacuum, whose purpose is to automate the execution of VACUUM and ANALYZE commands. When enabled, autovacuum checks for tables that have had a large number of inserted, updated or deleted tuples. These checks use the statistics collection facility; therefore, autovacuum cannot be used unless track_counts is set to true. In the default configuration, autovacuuming is enabled and the related configuration parameters are appropriately set. The “autovacuum daemon” actually consists of multiple processes. There is a persistent daemon process, called the autovacuum launcher, which is in charge of starting autovacuum worker processes for all databases. The launcher will distribute the work across time, attempting to start one worker within each database every autovacuum_naptime seconds. (Therefore, if the installation has N databases, a new worker will be launched every autovacuum_naptime/N seconds.) A maximum of autovacuum_max_workers worker processes are allowed to run at the same time. If there are more than autovacuum_max_workers databases to be processed, the next database will be processed as soon as the first worker finishes. Each worker process will check each table within its database and execute VACUUM and/or ANALYZE as needed. log_autovacuum_min_duration can be set to monitor autovacuum workers' activity. If several large tables all become eligible for vacuuming in a short amount of time, all autovacuum workers might become occupied with vacuuming those tables for a long period. This would result in other tables and databases not being vacuumed until a worker becomes available. There is no limit on how many workers might be in a single database, but workers do try to avoid repeating work that has already been done by other workers. Note that the number of running workers does not count towards max_connections or superuser_reserved_connections limits. Tables whose relfrozenxid value is more than autovacuum_freeze_max_age transactions old are always vacuumed (this also applies to those tables whose freeze max age has been modified via storage parameters; see below). Otherwise, if the number of tuples obsoleted since the last VACUUM exceeds the “vacuum threshold”, the table is vacuumed. The vacuum threshold is defined as: vacuum threshold = vacuum base threshold + vacuum scale factor * number of tuples where the vacuum base threshold is autovacuum_vacuum_threshold, the vacuum scale factor is autovacuum_vacuum_scale_factor, and the number of tuples is pg_class.reltuples. The table is also vacuumed if the number of tuples inserted since the last vacuum has exceeded the defined insert threshold, which is defined as: vacuum insert threshold = vacuum base insert threshold + vacuum insert scale factor * number of tuples where the vacuum insert base threshold is autovacuum_vacuum_insert_threshold, and vacuum insert scale factor is autovacuum_vacuum_insert_scale_factor. Such vacuums may allow portions of the table to be marked as all visible and also allow tuples to be frozen, which can reduce the work required in subsequent vacuums. For tables which receive INSERT operations but no or almost no UPDATE/DELETE operations, it may be beneficial to lower the table’s autovacuum_freeze_min_age as this may allow tuples to be frozen by earlier vacuums. The number of obsolete tuples and the number of inserted tuples are obtained from the cumulative statistics system; it is a semi-accurate count updated by each UPDATE, DELETE and INSERT operation. (It is only semi-accurate because some information might be lost under heavy load.) If the relfrozenxid value of the table is more than vacuum_freeze_table_age transactions old, an aggressive vacuum is performed to freeze old tuples and advance relfrozenxid; otherwise, only pages that have been modified since the last vacuum are scanned. For analyze, a similar condition is used: the threshold, defined as: analyze threshold = analyze base threshold + analyze scale factor * number of tuples is compared to the total number of tuples inserted, updated, or deleted since the last ANALYZE. Partitioned tables are not processed by autovacuum. Statistics should be collected by running a manual ANALYZE when it is first populated, and again whenever the distribution of data in its partitions changes significantly. Temporary tables cannot be accessed by autovacuum. Therefore, appropriate vacuum and analyze operations should be performed via session SQL commands. The default thresholds and scale factors are taken from postgresql.conf, but it is possible to override them (and many other autovacuum control parameters) on a per-table basis; see Storage Parameters for more information. If a setting has been changed via a table’s storage parameters, that value is used when processing that table; otherwise the global settings are used. See Section 20.10 for more details on the global settings. When multiple workers are running, the autovacuum cost delay parameters (see Section 20.4.4) are “balanced” among all the running workers, so that the total I/O impact on the system is the same regardless of the number of workers actually running. However, any workers processing tables whose per-table autovacuum_vacuum_cost_delay or autovacuum_vacuum_cost_limit storage parameters have been set are not considered in the balancing algorithm. Autovacuum workers generally don’t block other commands. If a process attempts to acquire a lock that conflicts with the SHARE UPDATE EXCLUSIVE lock held by autovacuum, lock acquisition will interrupt the autovacuum. For conflicting lock modes, see Table 13.3. However, if the autovacuum is running to prevent transaction ID wraparound (i.e., the autovacuum query name in the pg_stat_activity view ends with (to prevent wraparound)), the autovacuum is not automatically interrupted. Warning Regularly running commands that acquire locks conflicting with a SHARE UPDATE EXCLUSIVE lock (e.g., ANALYZE) can effectively prevent autovacuums from ever completing. In some situations it is worthwhile to rebuild indexes periodically with the REINDEX command or a series of individual rebuilding steps. B-tree index pages that have become completely empty are reclaimed for re-use. However, there is still a possibility of inefficient use of space: if all but a few index keys on a page have been deleted, the page remains allocated. Therefore, a usage pattern in which most, but not all, keys in each range are eventually deleted will see poor use of space. For such usage patterns, periodic reindexing is recommended. The potential for bloat in non-B-tree indexes has not been well researched. It is a good idea to periodically monitor the index’s physical size when using any non-B-tree index type. Also, for B-tree indexes, a freshly-constructed index is slightly faster to access than one that has been updated many times because logically adjacent pages are usually also physically adjacent in a newly built index. (This consideration does not apply to non-B-tree indexes.) It might be worthwhile to reindex periodically just to improve access speed. REINDEX can be used safely and easily in all cases. This command requires an ACCESS EXCLUSIVE lock by default, hence it is often preferable to execute it with its CONCURRENTLY option, which requires only a SHARE UPDATE EXCLUSIVE lock. It is a good idea to save the database server’s log output somewhere, rather than just discarding it via /dev/null. The log output is invaluable when diagnosing problems.Log output tends to be voluminous (especially at higher debug levels) so you won’t want to save it indefinitely. You need to rotate the log files so that new log files are started and old ones removed after a reasonable period of time. If you simply direct the stderr of postgres into a file, you will have log output, but the only way to truncate the log file is to stop and restart the server. This might be acceptable if you are using PostgreSQL in a development environment, but few production servers would find this behavior acceptable. A better approach is to send the server’s stderr output to some type of log rotation program. There is a built-in log rotation facility, which you can use by setting the configuration parameter logging_collector to true in postgresql.conf. You can also use this approach to capture the log data in machine readable CSV (comma-separated values) format. Alternatively, you might prefer to use an external log rotation program if you have one that you are already using with other server software. For example, the rotatelogs tool included in the Apache distribution can be used with PostgreSQL. One way to do this is to pipe the server’s stderr output to the desired program. If you start the server with pg_ctl, then stderr is already redirected to stdout, so you just need a pipe command, for example: pg_ctl start | rotatelogs /var/log/pgsql_log 86400 You can combine these approaches by setting up logrotate to collect log files produced by PostgreSQL built-in logging collector. In this case, the logging collector defines the names and location of the log files, while logrotate periodically archives these files. When initiating log rotation, logrotate must ensure that the application sends further output to the new file. This is commonly done with a postrotate script that sends a SIGHUP signal to the application, which then reopens the log file. In PostgreSQL, you can run pg_ctl with the logrotate option instead. When the server receives this command, the server either switches to a new log file or reopens the existing file, depending on the logging configuration. Note When using static log file names, the server might fail to reopen the log file if the max open file limit is reached or a file table overflow occurs. In this case, log messages are sent to the old log file until a successful log rotation. If logrotate is configured to compress the log file and delete it, the server may lose the messages logged in this time frame. To avoid this issue, you can configure the logging collector to dynamically assign log file names and use a prerotate script to ignore open log files. Another production-grade approach to managing log output is to send it to syslog and let syslog deal with file rotation. To do this, set the configuration parameter log_destination to syslog (to log to syslog only) in postgresql.conf. Then you can send a SIGHUP signal to the syslog daemon whenever you want to force it to start writing a new log file. If you want to automate log rotation, the logrotate program can be configured to work with log files from syslog. On many systems, however, syslog is not very reliable, particularly with large log messages; it might truncate or drop messages just when you need them the most. Also, on Linux, syslog will flush each message to disk, yielding poor performance. (You can use a “-” at the start of the file name in the syslog configuration file to disable syncing.) Note that all the solutions described above take care of starting new log files at configurable intervals, but they do not handle deletion of old, no-longer-useful log files. You will probably want to set up a batch job to periodically delete old log files. Another possibility is to configure the rotation program so that old log files are overwritten cyclically. pgBadger is an external project that does sophisticated log file analysis. check_postgres provides Nagios alerts when important messages appear in the log files, as well as detection of many other extraordinary conditions. Shared disk failover avoids synchronization overhead by having only one copy of the database. It uses a single disk array that is shared by multiple servers. If the main database server fails, the standby server is able to mount and start the database as though it were recovering from a database crash. This allows rapid failover with no data loss. Shared hardware functionality is common in network storage devices. Using a network file system is also possible, though care must be taken that the file system has full POSIX behavior . One significant limitation of this method is that if the shared disk array fails or becomes corrupt, the primary and standby servers are both nonfunctional. Another issue is that the standby server should never access the shared storage while the primary server is running. A modified version of shared hardware functionality is file system replication, where all changes to a file system are mirrored to a file system residing on another computer. The only restriction is that the mirroring must be done in a way that ensures the standby server has a consistent copy of the file system — specifically, writes to the standby must be done in the same order as those on the primary. DRBD is a popular file system replication solution for Linux. Warm and hot standby servers can be kept current by reading a stream of write-ahead log (WAL) records. If the main server fails, the standby contains almost all of the data of the main server, and can be quickly made the new primary database server. This can be synchronous or asynchronous and can only be done for the entire database server. A standby server can be implemented using file-based log shipping or streaming replication, or a combination of both. For information on hot standby Logical replication allows a database server to send a stream of data modifications to another server. IvorySQL logical replication constructs a stream of logical data modifications from the WAL. Logical replication allows replication of data changes on a per-table basis. In addition, a server that is publishing its own changes can also subscribe to changes from another server, allowing data to flow in multiple directions. For more information on logical replication. Through the logical decoding interface , third-party extensions can also provide similar functionality. A trigger-based replication setup typically funnels data modification queries to a designated primary server. Operating on a per-table basis, the primary server sends data changes (typically) asynchronously to the standby servers. Standby servers can answer queries while the primary is running, and may allow some local data changes or write activity. This form of replication is often used for offloading large analytical or data warehouse queries. Slony-I is an example of this type of replication, with per-table granularity, and support for multiple standby servers. Because it updates the standby server asynchronously (in batches), there is possible data loss during fail over. With SQL-based replication middleware, a program intercepts every SQL query and sends it to one or all servers. Each server operates independently. Read-write queries must be sent to all servers, so that every server receives any changes. But read-only queries can be sent to just one server, allowing the read workload to be distributed among them. If queries are simply broadcast unmodified, functions like random(), CURRENT_TIMESTAMP, and sequences can have different values on different servers. This is because each server operates independently, and because SQL queries are broadcast rather than actual data changes. If this is unacceptable, either the middleware or the application must determine such values from a single source and then use those values in write queries. Care must also be taken that all transactions either commit or abort on all servers, perhaps using two-phase commit (PREPARE TRANSACTION and COMMIT PREPARED). Pgpool-II and Continuent Tungsten are examples of this type of replication. For servers that are not regularly connected or have slow communication links, like laptops or remote servers, keeping data consistent among servers is a challenge. Using asynchronous multimaster replication, each server works independently, and periodically communicates with the other servers to identify conflicting transactions. The conflicts can be resolved by users or conflict resolution rules. Bucardo is an example of this type of replication. In synchronous multimaster replication, each server can accept write requests, and modified data is transmitted from the original server to every other server before each transaction commits. Heavy write activity can cause excessive locking and commit delays, leading to poor performance. Read requests can be sent to any server. Some implementations use shared disk to reduce the communication overhead. Synchronous multimaster replication is best for mostly read workloads, though its big advantage is that any server can accept write requests — there is no need to partition workloads between primary and standby servers, and because the data changes are sent from one server to another, there is no problem with non-deterministic functions like random(). IvorySQL does not offer this type of replication, though PostgreSQL two-phase commit (PREPARE TRANSACTION and COMMIT PREPARED) can be used to implement this in application code or middleware. The following table summarizes the capabilities of each of these scenarios. Feature Shared Disk File System Repl. Write-Ahead Log Shipping Logical Repl. Trigger-Based Repl. SQL Repl. Middle-ware Async. MM Repl. Sync. MM Repl. Popular examples NAS DRBD built-in streaming repl. built-in logical repl., pglogical Londiste, Slony pgpool-II Bucardo Comm. method shared disk disk blocks WAL logical decoding table rows SQL table rows table rows and row locks No special hardware required • • • • • • • Allows multiple primary servers • • • • No overhead on primary • • • • No waiting for multiple servers • with sync off with sync off • • Primary failure will never lose data • • with sync on with sync on • • Replicas accept read-only queries with hot standby • • • • • Per-table granularity • • • • No conflict resolution necessary • • • • • • There are a few solutions that do not fit into the above categories: Data Partitioning Data partitioning splits tables into data sets. Each set can be modified by only one server. For example, data can be partitioned by offices, e.g., London and Paris, with a server in each office. If queries combining London and Paris data are necessary, an application can query both servers, or primary/standby replication can be used to keep a read-only copy of the other office's data on each server. Multiple-Server Parallel Query Execution Many of the above solutions allow multiple servers to handle multiple queries, but none allow a single query to use multiple servers to complete faster. This solution allows multiple servers to work concurrently on a single query. It is usually accomplished by splitting the data among servers and having each server execute its part of the query and return results to a central server where they are combined and returned to the user. This can be implemented using the PL/Proxy tool set. It is usually wise to create the primary and standby servers so that they are as similar as possible, at least from the perspective of the database server. In particular, the path names associated with tablespaces will be passed across unmodified, so both primary and standby servers must have the same mount paths for tablespaces if that feature is used. Keep in mind that if CREATE TABLESPACE is executed on the primary, any new mount point needed for it must be created on the primary and all standby servers before the command is executed. Hardware need not be exactly the same, but experience shows that maintaining two identical systems is easier than maintaining two dissimilar ones over the lifetime of the application and system. In any case the hardware architecture must be the same — shipping from, say, a 32-bit to a 64-bit system will not work. In general, log shipping between servers running different major IvorySQL release levels is not possible. It is the policy of the IvorySQL Global Development Group not to make changes to disk formats during minor release upgrades, so it is likely that running different minor release levels on primary and standby servers will work successfully. However, no formal support for that is offered and you are advised to keep primary and standby servers at the same release level as much as possible. When updating to a new minor release, the safest policy is to update the standby servers first — a new minor release is more likely to be able to read WAL files from a previous minor release than vice versa. A server enters standby mode if a standby.signal file exists in the data directory when the server is started. In standby mode, the server continuously applies WAL received from the primary server. The standby server can read WAL from a WAL archive (see restore_command) or directly from the primary over a TCP connection (streaming replication). The standby server will also attempt to restore any WAL found in the standby cluster’s pg_wal directory. That typically happens after a server restart, when the standby replays again WAL that was streamed from the primary before the restart, but you can also manually copy files to pg_wal at any time to have them replayed. At startup, the standby begins by restoring all WAL available in the archive location, calling restore_command. Once it reaches the end of WAL available there and restore_command fails, it tries to restore any WAL available in the pg_wal directory. If that fails, and streaming replication has been configured, the standby tries to connect to the primary server and start streaming WAL from the last valid record found in archive or pg_wal. If that fails or streaming replication is not configured, or if the connection is later disconnected, the standby goes back to step 1 and tries to restore the file from the archive again. This loop of retries from the archive, pg_wal, and via streaming replication goes on until the server is stopped or failover is triggered by a trigger file. Standby mode is exited and the server switches to normal operation when pg_ctl promote is run, pg_promote() is called, or a trigger file is found (promote_trigger_file). Before failover, any WAL immediately available in the archive or in pg_wal will be restored, but no attempt is made to connect to the primary. Set up continuous archiving on the primary to an archive directory accessible from the standby.The archive location should be accessible from the standby even when the primary is down, i.e., it should reside on the standby server itself or another trusted server, not on the primary server. If you want to use streaming replication, set up authentication on the primary server to allow replication connections from the standby server(s); that is, create a role and provide a suitable entry or entries in pg_hba.conf with the database field set to replication. Also ensure max_wal_senders is set to a sufficiently large value in the configuration file of the primary server. If replication slots will be used, ensure that max_replication_slots is set sufficiently high as well. To set up the standby server, restore the base backup taken from primary server . Create a file standby.signal in the standby’s cluster data directory. Set restore_command to a simple command to copy files from the WAL archive. If you plan to have multiple standby servers for high availability purposes, make sure that recovery_target_timeline is set to latest (the default), to make the standby server follow the timeline change that occurs at failover to another standby. Note restore_command should return immediately if the file does not exist; the server will retry the command again if necessary. If you want to use streaming replication, fill in primary_conninfo with a libpq connection string, including the host name (or IP address) and any additional details needed to connect to the primary server. If the primary needs a password for authentication, the password needs to be specified in primary_conninfo as well. If you’re setting up the standby server for high availability purposes, set up WAL archiving, connections and authentication like the primary server, because the standby server will work as a primary server after failover. If you’re using a WAL archive, its size can be minimized using the archive_cleanup_command parameter to remove files that are no longer required by the standby server. The pg_archivecleanup utility is designed specifically to be used with archive_cleanup_command in typical single-standby configurations, see pg_archivecleanup. Note however, that if you’re using the archive for backup purposes, you need to retain files needed to recover from at least the latest base backup, even if they’re no longer needed by the standby. A simple example of configuration is: primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass options=''-c wal_sender_timeout=5000''' restore_command = 'cp /path/to/archive/%f %p' archive_cleanup_command = 'pg_archivecleanup /path/to/archive %r' You can have any number of standby servers, but if you use streaming replication, make sure you set max_wal_senders high enough in the primary to allow them to be connected simultaneously. Streaming replication allows a standby server to stay more up-to-date than is possible with file-based log shipping. The standby connects to the primary, which streams WAL records to the standby as they’re generated, without waiting for the WAL file to be filled. Streaming replication is asynchronous by default , in which case there is a small delay between committing a transaction in the primary and the changes becoming visible in the standby. This delay is however much smaller than with file-based log shipping, typically under one second assuming the standby is powerful enough to keep up with the load. With streaming replication, archive_timeout is not required to reduce the data loss window. If you use streaming replication without file-based continuous archiving, the server might recycle old WAL segments before the standby has received them. If this occurs, the standby will need to be reinitialized from a new base backup. You can avoid this by setting wal_keep_size to a value large enough to ensure that WAL segments are not recycled too early, or by configuring a replication slot for the standby. If you set up a WAL archive that’s accessible from the standby, these solutions are not required, since the standby can always use the archive to catch up provided it retains enough segments. To use streaming replication, set up a file-based log-shipping standby server. The step that turns a file-based log-shipping standby into streaming replication standby is setting the primary_conninfo setting to point to the primary server. Set listen_addresses and authentication options (see pg_hba.conf) on the primary so that the standby server can connect to the replication pseudo-database on the primary server. On systems that support the keepalive socket option, setting tcp_keepalives_idle, tcp_keepalives_interval and tcp_keepalives_count helps the primary promptly notice a broken connection. Set the maximum number of concurrent connections from the standby servers (see max_wal_senders for details). When the standby is started and primary_conninfo is set correctly, the standby will connect to the primary after replaying all WAL files available in the archive. If the connection is established successfully, you will see a walreceiver in the standby, and a corresponding walsender process in the primary. It is very important that the access privileges for replication be set up so that only trusted users can read the WAL stream, because it is easy to extract privileged information from it. Standby servers must authenticate to the primary as an account that has the REPLICATION privilege or a superuser. It is recommended to create a dedicated user account with REPLICATION and LOGIN privileges for replication. While REPLICATION privilege gives very high permissions, it does not allow the user to modify any data on the primary system, which the SUPERUSER privilege does. Client authentication for replication is controlled by a pg_hba.conf record specifying replication in the database field. For example, if the standby is running on host IP 192.168.1.100 and the account name for replication is foo, the administrator can add the following line to the pg_hba.conf file on the primary: # Allow the user \"foo\" from host 192.168.1.100 to connect to the primary # as a replication standby if the user's password is correctly supplied. # # TYPE DATABASE USER ADDRESS METHOD host replication foo 192.168.1.100/32 md5 The host name and port number of the primary, connection user name, and password are specified in the primary_conninfo. The password can also be set in the ~/.pgpass file on the standby (specify replication in the database field). For example, if the primary is running on host IP 192.168.1.50, port 5432, the account name for replication is foo, and the password is foopass, the administrator can add the following line to the postgresql.conf file on the standby: # The standby connects to the primary that is running on host 192.168.1.50 # and port 5432 as the user \"foo\" whose password is \"foopass\". primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' An important health indicator of streaming replication is the amount of WAL records generated in the primary, but not yet applied in the standby. You can calculate this lag by comparing the current WAL write location on the primary with the last WAL location received by the standby. These locations can be retrieved using pg_current_wal_lsn on the primary and pg_last_wal_receive_lsn on the standby, respectively . The last WAL receive location in the standby is also displayed in the process status of the WAL receiver process, displayed using the ps command . You can retrieve a list of WAL sender processes via the pg_stat_replication view. Large differences between pg_current_wal_lsn and the view’s sent_lsn field might indicate that the primary server is under heavy load, while differences between sent_lsn and pg_last_wal_receive_lsn on the standby might indicate network delay, or that the standby is under heavy load. On a hot standby, the status of the WAL receiver process can be retrieved via the pg_stat_wal_receiver view. A large difference between pg_last_wal_replay_lsn and the view’s flushed_lsn indicates that WAL is being received faster than it can be replayed. Replication slots provide an automated way to ensure that the primary does not remove WAL segments until they have been received by all standbys, and that the primary does not remove rows which could cause a recovery conflict even when the standby is disconnected. In lieu of using replication slots, it is possible to prevent the removal of old WAL segments using wal_keep_size, or by storing the segments in an archive using archive_command or archive_library. However, these methods often result in retaining more WAL segments than required, whereas replication slots retain only the number of segments known to be needed. On the other hand, replication slots can retain so many WAL segments that they fill up the space allocated for pg_wal; max_slot_wal_keep_size limits the size of WAL files retained by replication slots. Similarly, hot_standby_feedback and vacuum_defer_cleanup_age provide protection against relevant rows being removed by vacuum, but the former provides no protection during any time period when the standby is not connected, and the latter often needs to be set to a high value to provide adequate protection. Replication slots overcome these disadvantages. Each replication slot has a name, which can contain lower-case letters, numbers, and the underscore character. Existing replication slots and their state can be seen in the pg_replication_slots view. Slots can be created and dropped either via the streaming replication protocol or via SQL functions . You can create a replication slot like this: postgres=# SELECT * FROM pg_create_physical_replication_slot('node_a_slot'); slot_name | lsn -------------+----- node_a_slot | postgres=# SELECT slot_name, slot_type, active FROM pg_replication_slots; slot_name | slot_type | active -------------+-----------+-------- node_a_slot | physical | f (1 row) To configure the standby to use this slot, primary_slot_name should be configured on the standby. Here is a simple example: primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' primary_slot_name = 'node_a_slot' The cascading replication feature allows a standby server to accept replication connections and stream WAL records to other standbys, acting as a relay. This can be used to reduce the number of direct connections to the primary and also to minimize inter-site bandwidth overheads. A standby acting as both a receiver and a sender is known as a cascading standby. Standbys that are more directly connected to the primary are known as upstream servers, while those standby servers further away are downstream servers. Cascading replication does not place limits on the number or arrangement of downstream servers, though each standby connects to only one upstream server which eventually links to a single primary server. A cascading standby sends not only WAL records received from the primary but also those restored from the archive. So even if the replication connection in some upstream connection is terminated, streaming replication continues downstream for as long as new WAL records are available. Cascading replication is currently asynchronous. Synchronous replication settings have no effect on cascading replication at present. Hot standby feedback propagates upstream, whatever the cascaded arrangement. If an upstream standby server is promoted to become the new primary, downstream servers will continue to stream from the new primary if recovery_target_timeline is set to 'latest' (the default). To use cascading replication, set up the cascading standby so that it can accept replication connections (that is, set max_wal_senders and hot_standby, and configure host-based authentication). You will also need to set primary_conninfo in the downstream standby to point to the cascading standby. IvorySQL streaming replication is asynchronous by default. If the primary server crashes then some transactions that were committed may not have been replicated to the standby server, causing data loss. The amount of data loss is proportional to the replication delay at the time of failover. Synchronous replication offers the ability to confirm that all changes made by a transaction have been transferred to one or more synchronous standby servers. This extends that standard level of durability offered by a transaction commit. This level of protection is referred to as 2-safe replication in computer science theory, and group-1-safe (group-safe and 1-safe) when synchronous_commit is set to remote_write. When requesting synchronous replication, each commit of a write transaction will wait until confirmation is received that the commit has been written to the write-ahead log on disk of both the primary and standby server. The only possibility that data can be lost is if both the primary and the standby suffer crashes at the same time. This can provide a much higher level of durability, though only if the sysadmin is cautious about the placement and management of the two servers. Waiting for confirmation increases the user’s confidence that the changes will not be lost in the event of server crashes but it also necessarily increases the response time for the requesting transaction. The minimum wait time is the round-trip time between primary and standby. Read-only transactions and transaction rollbacks need not wait for replies from standby servers. Subtransaction commits do not wait for responses from standby servers, only top-level commits. Long running actions such as data loading or index building do not wait until the very final commit message. All two-phase commit actions require commit waits, including both prepare and commit. A synchronous standby can be a physical replication standby or a logical replication subscriber. It can also be any other physical or logical WAL replication stream consumer that knows how to send the appropriate feedback messages. Besides the built-in physical and logical replication systems, this includes special programs such as pg_receivewal and pg_recvlogical as well as some third-party replication systems and custom programs. Check the respective documentation for details on synchronous replication support. Once streaming replication has been configured, configuring synchronous replication requires only one additional configuration step: synchronous_standby_names must be set to a non-empty value. synchronous_commit must also be set to on, but since this is the default value, typically no change is required.This configuration will cause each commit to wait for confirmation that the standby has written the commit record to durable storage. synchronous_commit can be set by individual users, so it can be configured in the configuration file, for particular users or databases, or dynamically by applications, in order to control the durability guarantee on a per-transaction basis. After a commit record has been written to disk on the primary, the WAL record is then sent to the standby. The standby sends reply messages each time a new batch of WAL data is written to disk, unless wal_receiver_status_interval is set to zero on the standby. In the case that synchronous_commit is set to remote_apply, the standby sends reply messages when the commit record is replayed, making the transaction visible. If the standby is chosen as a synchronous standby, according to the setting of synchronous_standby_names on the primary, the reply messages from that standby will be considered along with those from other synchronous standbys to decide when to release transactions waiting for confirmation that the commit record has been received. These parameters allow the administrator to specify which standby servers should be synchronous standbys. Note that the configuration of synchronous replication is mainly on the primary. Named standbys must be directly connected to the primary; the primary knows nothing about downstream standby servers using cascaded replication. Setting synchronous_commit to remote_write will cause each commit to wait for confirmation that the standby has received the commit record and written it out to its own operating system, but not for the data to be flushed to disk on the standby. This setting provides a weaker guarantee of durability than on does: the standby could lose the data in the event of an operating system crash, though not a PostgreSQL crash. However, it’s a useful setting in practice because it can decrease the response time for the transaction. Data loss could only occur if both the primary and the standby crash and the database of the primary gets corrupted at the same time. Setting synchronous_commit to remote_apply will cause each commit to wait until the current synchronous standbys report that they have replayed the transaction, making it visible to user queries. In simple cases, this allows for load balancing with causal consistency. Users will stop waiting if a fast shutdown is requested. However, as when using asynchronous replication, the server will not fully shutdown until all outstanding WAL records are transferred to the currently connected standby servers. Synchronous replication supports one or more synchronous standby servers; transactions will wait until all the standby servers which are considered as synchronous confirm receipt of their data. The number of synchronous standbys that transactions must wait for replies from is specified in synchronous_standby_names. This parameter also specifies a list of standby names and the method (FIRST and ANY) to choose synchronous standbys from the listed ones. The method FIRST specifies a priority-based synchronous replication and makes transaction commits wait until their WAL records are replicated to the requested number of synchronous standbys chosen based on their priorities. The standbys whose names appear earlier in the list are given higher priority and will be considered as synchronous. Other standby servers appearing later in this list represent potential synchronous standbys. If any of the current synchronous standbys disconnects for whatever reason, it will be replaced immediately with the next-highest-priority standby. An example of synchronous_standby_names for a priority-based multiple synchronous standbys is: synchronous_standby_names = 'FIRST 2 (s1, s2, s3)' In this example, if four standby servers s1, s2, s3 and s4 are running, the two standbys s1 and s2 will be chosen as synchronous standbys because their names appear early in the list of standby names. s3 is a potential synchronous standby and will take over the role of synchronous standby when either of s1 or s2 fails. s4 is an asynchronous standby since its name is not in the list. The method ANY specifies a quorum-based synchronous replication and makes transaction commits wait until their WAL records are replicated to at least the requested number of synchronous standbys in the list. An example of synchronous_standby_names for a quorum-based multiple synchronous standbys is: synchronous_standby_names = 'ANY 2 (s1, s2, s3)' In this example, if four standby servers s1, s2, s3 and s4 are running, transaction commits will wait for replies from at least any two standbys of s1, s2 and s3. s4 is an asynchronous standby since its name is not in the list. The synchronous states of standby servers can be viewed using the pg_stat_replication view. Synchronous replication usually requires carefully planned and placed standby servers to ensure applications perform acceptably. Waiting doesn’t utilize system resources, but transaction locks continue to be held until the transfer is confirmed. As a result, incautious use of synchronous replication will reduce performance for database applications because of increased response times and higher contention. PostgreSQL allows the application developer to specify the durability level required via replication. This can be specified for the system overall, though it can also be specified for specific users or connections, or even individual transactions. For example, an application workload might consist of: 10% of changes are important customer details, while 90% of changes are less important data that the business can more easily survive if it is lost, such as chat messages between users. With synchronous replication options specified at the application level (on the primary) we can offer synchronous replication for the most important changes, without slowing down the bulk of the total workload. Application level options are an important and practical tool for allowing the benefits of synchronous replication for high performance applications. You should consider that the network bandwidth must be higher than the rate of generation of WAL data. synchronous_standby_names specifies the number and names of synchronous standbys that transaction commits made when synchronous_commit is set to on, remote_apply or remote_write will wait for responses from. Such transaction commits may never be completed if any one of synchronous standbys should crash. The best solution for high availability is to ensure you keep as many synchronous standbys as requested. This can be achieved by naming multiple potential synchronous standbys using synchronous_standby_names. In a priority-based synchronous replication, the standbys whose names appear earlier in the list will be used as synchronous standbys. Standbys listed after these will take over the role of synchronous standby if one of current ones should fail. In a quorum-based synchronous replication, all the standbys appearing in the list will be used as candidates for synchronous standbys. Even if one of them should fail, the other standbys will keep performing the role of candidates of synchronous standby. When a standby first attaches to the primary, it will not yet be properly synchronized. This is described as catchup mode. Once the lag between standby and primary reaches zero for the first time we move to real-time streaming state. The catch-up duration may be long immediately after the standby has been created. If the standby is shut down, then the catch-up period will increase according to the length of time the standby has been down. The standby is only able to become a synchronous standby once it has reached streaming state. This state can be viewed using the pg_stat_replication view. If primary restarts while commits are waiting for acknowledgment, those waiting transactions will be marked fully committed once the primary database recovers. There is no way to be certain that all standbys have received all outstanding WAL data at time of the crash of the primary. Some transactions may not show as committed on the standby, even though they show as committed on the primary. The guarantee we offer is that the application will not receive explicit acknowledgment of the successful commit of a transaction until the WAL data is known to be safely received by all the synchronous standbys. If you really cannot keep as many synchronous standbys as requested then you should decrease the number of synchronous standbys that transaction commits must wait for responses from in synchronous_standby_names (or disable it) and reload the configuration file on the primary server. If the primary is isolated from remaining standby servers you should fail over to the best candidate of those other remaining standby servers. If you need to re-create a standby server while transactions are waiting, make sure that the commands pg_backup_start() and pg_backup_stop() are run in a session with synchronous_commit = off, otherwise those requests will wait forever for the standby to appear. When continuous WAL archiving is used in a standby, there are two different scenarios: the WAL archive can be shared between the primary and the standby, or the standby can have its own WAL archive. When the standby has its own WAL archive, set archive_mode to always, and the standby will call the archive command for every WAL segment it receives, whether it’s by restoring from the archive or by streaming replication. The shared archive can be handled similarly, but the archive_command or archive_library must test if the file being archived exists already, and if the existing file has identical contents. This requires more care in the archive_command or archive_library, as it must be careful to not overwrite an existing file with different contents, but return success if the exactly same file is archived twice. And all that must be done free of race conditions, if two servers attempt to archive the same file at the same time. If archive_mode is set to on, the archiver is not enabled during recovery or standby mode. If the standby server is promoted, it will start archiving after the promotion, but will not archive any WAL or timeline history files that it did not generate itself. To get a complete series of WAL files in the archive, you must ensure that all WAL is archived, before it reaches the standby. This is inherently true with file-based log shipping, as the standby can only restore files that are found in the archive, but not if streaming replication is enabled. When a server is not in recovery mode, there is no difference between on and always modes. If the primary server fails then the standby server should begin failover procedures. If the standby server fails then no failover need take place. If the standby server can be restarted, even some time later, then the recovery process can also be restarted immediately, taking advantage of restartable recovery. If the standby server cannot be restarted, then a full new standby server instance should be created. If the primary server fails and the standby server becomes the new primary, and then the old primary restarts, you must have a mechanism for informing the old primary that it is no longer the primary. This is sometimes known as STONITH (Shoot The Other Node In The Head), which is necessary to avoid situations where both systems think they are the primary, which will lead to confusion and ultimately data loss. Many failover systems use just two systems, the primary and the standby, connected by some kind of heartbeat mechanism to continually verify the connectivity between the two and the viability of the primary. It is also possible to use a third system (called a witness server) to prevent some cases of inappropriate failover, but the additional complexity might not be worthwhile unless it is set up with sufficient care and rigorous testing. PostgreSQL does not provide the system software required to identify a failure on the primary and notify the standby database server. Many such tools exist and are well integrated with the operating system facilities required for successful failover, such as IP address migration. Once failover to the standby occurs, there is only a single server in operation. This is known as a degenerate state. The former standby is now the primary, but the former primary is down and might stay down. To return to normal operation, a standby server must be recreated, either on the former primary system when it comes up, or on a third, possibly new, system. The pg_rewind utility can be used to speed up this process on large clusters. Once complete, the primary and standby can be considered to have switched roles. Some people choose to use a third server to provide backup for the new primary until the new standby server is recreated, though clearly this complicates the system configuration and operational processes. So, switching from primary to standby server can be fast but requires some time to re-prepare the failover cluster. Regular switching from primary to standby is useful, since it allows regular downtime on each system for maintenance. This also serves as a test of the failover mechanism to ensure that it will really work when you need it. Written administration procedures are advised. To trigger failover of a log-shipping standby server, run pg_ctl promote, call pg_promote(), or create a trigger file with the file name and path specified by the promote_trigger_file. If you’re planning to use pg_ctl promote or to call pg_promote() to fail over, promote_trigger_file is not required. If you’re setting up the reporting servers that are only used to offload read-only queries from the primary, not for high availability purposes, you don’t need to promote it. Hot standby is the term used to describe the ability to connect to the server and run read-only queries while the server is in archive recovery or standby mode. This is useful both for replication purposes and for restoring a backup to a desired state with great precision. The term hot standby also refers to the ability of the server to move from recovery through to normal operation while users continue running queries and/or keep their connections open. Running queries in hot standby mode is similar to normal query operation, though there are several usage and administrative differences explained below. When the hot_standby parameter is set to true on a standby server, it will begin accepting connections once the recovery has brought the system to a consistent state. All such connections are strictly read-only; not even temporary tables may be written. The data on the standby takes some time to arrive from the primary server so there will be a measurable delay between primary and standby. Running the same query nearly simultaneously on both primary and standby might therefore return differing results. We say that data on the standby is eventually consistent with the primary. Once the commit record for a transaction is replayed on the standby, the changes made by that transaction will be visible to any new snapshots taken on the standby. Snapshots may be taken at the start of each query or at the start of each transaction, depending on the current transaction isolation level. Transactions started during hot standby may issue the following commands: Query access: SELECT, COPY TO Cursor commands: DECLARE, FETCH, CLOSE Settings: SHOW, SET, RESET Transaction management commands: BEGIN, END, ABORT, START TRANSACTION SAVEPOINT, RELEASE, ROLLBACK TO SAVEPOINT EXCEPTION blocks and other internal subtransactions LOCK TABLE, though only when explicitly in one of these modes: ACCESS SHARE, ROW SHARE or ROW EXCLUSIVE. Plans and resources: PREPARE, EXECUTE, DEALLOCATE, DISCARD Plugins and extensions: LOAD UNLISTEN Transactions started during hot standby will never be assigned a transaction ID and cannot write to the system write-ahead log. Therefore, the following actions will produce error messages: Data Manipulation Language (DML): INSERT, UPDATE, DELETE, COPY FROM, TRUNCATE. Note that there are no allowed actions that result in a trigger being executed during recovery. This restriction applies even to temporary tables, because table rows cannot be read or written without assigning a transaction ID, which is currently not possible in a hot standby environment. Data Definition Language (DDL): CREATE, DROP, ALTER, COMMENT. This restriction applies even to temporary tables, because carrying out these operations would require updating the system catalog tables. SELECT …​ FOR SHARE | UPDATE, because row locks cannot be taken without updating the underlying data files. Rules on SELECT statements that generate DML commands. LOCK that explicitly requests a mode higher than ROW EXCLUSIVE MODE. LOCK in short default form, since it requests ACCESS EXCLUSIVE MODE. Transaction management commands that explicitly set non-read-only state: BEGIN READ WRITE, START TRANSACTION READ WRITE SET TRANSACTION READ WRITE, SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE SET transaction_read_only = off Two-phase commit commands: PREPARE TRANSACTION, COMMIT PREPARED, ROLLBACK PREPARED because even read-only transactions need to write WAL in the prepare phase (the first phase of two phase commit). Sequence updates: nextval(), setval() LISTEN, NOTIFY In normal operation, “read-only” transactions are allowed to use LISTEN and NOTIFY, so hot standby sessions operate under slightly tighter restrictions than ordinary read-only sessions. It is possible that some of these restrictions might be loosened in a future release. During hot standby, the parameter transaction_read_only is always true and may not be changed. But as long as no attempt is made to modify the database, connections during hot standby will act much like any other database connection. If failover or switchover occurs, the database will switch to normal processing mode. Sessions will remain connected while the server changes mode. Once hot standby finishes, it will be possible to initiate read-write transactions (even from a session begun during hot standby). Users can determine whether hot standby is currently active for their session by issuing SHOW in_hot_standby. (In server versions before 14, the in_hot_standby parameter did not exist; a workable substitute method for older servers is SHOW transaction_read_only.) In addition, a set of functions allow users to access information about the standby server. These allow you to write programs that are aware of the current state of the database. These can be used to monitor the progress of recovery, or to allow you to write complex programs that restore the database to particular states. The primary and standby servers are in many ways loosely connected. Actions on the primary will have an effect on the standby. As a result, there is potential for negative interactions or conflicts between them. The easiest conflict to understand is performance: if a huge data load is taking place on the primary then this will generate a similar stream of WAL records on the standby, so standby queries may contend for system resources, such as I/O. There are also additional types of conflict that can occur with hot standby. These conflicts are hard conflicts in the sense that queries might need to be canceled and, in some cases, sessions disconnected to resolve them. The user is provided with several ways to handle these conflicts. Conflict cases include: Access Exclusive locks taken on the primary server, including both explicit LOCK commands and various DDL actions, conflict with table accesses in standby queries. Dropping a tablespace on the primary conflicts with standby queries using that tablespace for temporary work files. Dropping a database on the primary conflicts with sessions connected to that database on the standby. Application of a vacuum cleanup record from WAL conflicts with standby transactions whose snapshots can still “see” any of the rows to be removed. Application of a vacuum cleanup record from WAL conflicts with queries accessing the target page on the standby, whether or not the data to be removed is visible. On the primary server, these cases simply result in waiting; and the user might choose to cancel either of the conflicting actions. However, on the standby there is no choice: the WAL-logged action already occurred on the primary so the standby must not fail to apply it. Furthermore, allowing WAL application to wait indefinitely may be very undesirable, because the standby’s state will become increasingly far behind the primary’s. Therefore, a mechanism is provided to forcibly cancel standby queries that conflict with to-be-applied WAL records. An example of the problem situation is an administrator on the primary server running DROP TABLE on a table that is currently being queried on the standby server. Clearly the standby query cannot continue if the DROP TABLE is applied on the standby. If this situation occurred on the primary, the DROP TABLE would wait until the other query had finished. But when DROP TABLE is run on the primary, the primary doesn’t have information about what queries are running on the standby, so it will not wait for any such standby queries. The WAL change records come through to the standby while the standby query is still running, causing a conflict. The standby server must either delay application of the WAL records (and everything after them, too) or else cancel the conflicting query so that the DROP TABLE can be applied. When a conflicting query is short, it’s typically desirable to allow it to complete by delaying WAL application for a little bit; but a long delay in WAL application is usually not desirable. So the cancel mechanism has parameters, max_standby_archive_delay and max_standby_streaming_delay, that define the maximum allowed delay in WAL application. Conflicting queries will be canceled once it has taken longer than the relevant delay setting to apply any newly-received WAL data. There are two parameters so that different delay values can be specified for the case of reading WAL data from an archive (i.e., initial recovery from a base backup or “catching up” a standby server that has fallen far behind) versus reading WAL data via streaming replication. In a standby server that exists primarily for high availability, it’s best to set the delay parameters relatively short, so that the server cannot fall far behind the primary due to delays caused by standby queries. However, if the standby server is meant for executing long-running queries, then a high or even infinite delay value may be preferable. Keep in mind however that a long-running query could cause other sessions on the standby server to not see recent changes on the primary, if it delays application of WAL records. Once the delay specified by max_standby_archive_delay or max_standby_streaming_delay has been exceeded, conflicting queries will be canceled. This usually results just in a cancellation error, although in the case of replaying a DROP DATABASE the entire conflicting session will be terminated. Also, if the conflict is over a lock held by an idle transaction, the conflicting session is terminated (this behavior might change in the future). Canceled queries may be retried immediately (after beginning a new transaction, of course). Since query cancellation depends on the nature of the WAL records being replayed, a query that was canceled may well succeed if it is executed again. Keep in mind that the delay parameters are compared to the elapsed time since the WAL data was received by the standby server. Thus, the grace period allowed to any one query on the standby is never more than the delay parameter, and could be considerably less if the standby has already fallen behind as a result of waiting for previous queries to complete, or as a result of being unable to keep up with a heavy update load. The most common reason for conflict between standby queries and WAL replay is “early cleanup”. Normally, PostgreSQL allows cleanup of old row versions when there are no transactions that need to see them to ensure correct visibility of data according to MVCC rules. However, this rule can only be applied for transactions executing on the primary. So it is possible that cleanup on the primary will remove row versions that are still visible to a transaction on the standby. Experienced users should note that both row version cleanup and row version freezing will potentially conflict with standby queries. Running a manual VACUUM FREEZE is likely to cause conflicts even on tables with no updated or deleted rows. Users should be clear that tables that are regularly and heavily updated on the primary server will quickly cause cancellation of longer running queries on the standby. In such cases the setting of a finite value for max_standby_archive_delay or max_standby_streaming_delay can be considered similar to setting statement_timeout. Remedial possibilities exist if the number of standby-query cancellations is found to be unacceptable. The first option is to set the parameter hot_standby_feedback, which prevents VACUUM from removing recently-dead rows and so cleanup conflicts do not occur. If you do this, you should note that this will delay cleanup of dead rows on the primary, which may result in undesirable table bloat. However, the cleanup situation will be no worse than if the standby queries were running directly on the primary server, and you are still getting the benefit of off-loading execution onto the standby. If standby servers connect and disconnect frequently, you might want to make adjustments to handle the period when hot_standby_feedback feedback is not being provided. For example, consider increasing max_standby_archive_delay so that queries are not rapidly canceled by conflicts in WAL archive files during disconnected periods. You should also consider increasing max_standby_streaming_delay to avoid rapid cancellations by newly-arrived streaming WAL entries after reconnection. Another option is to increase vacuum_defer_cleanup_age on the primary server, so that dead rows will not be cleaned up as quickly as they normally would be. This will allow more time for queries to execute before they are canceled on the standby, without having to set a high max_standby_streaming_delay. However it is difficult to guarantee any specific execution-time window with this approach, since vacuum_defer_cleanup_age is measured in transactions executed on the primary server. The number of query cancels and the reason for them can be viewed using the pg_stat_database_conflicts system view on the standby server. The pg_stat_database system view also contains summary information. Users can control whether a log message is produced when WAL replay is waiting longer than deadlock_timeout for conflicts. This is controlled by the log_recovery_conflict_waits parameter. If hot_standby is on in postgresql.conf (the default value) and there is a standby.signal file present, the server will run in hot standby mode. However, it may take some time for hot standby connections to be allowed, because the server will not accept connections until it has completed sufficient recovery to provide a consistent state against which queries can run. During this period, clients that attempt to connect will be refused with an error message. To confirm the server has come up, either loop trying to connect from the application, or look for these messages in the server logs: LOG: entering standby mode ... then some time later ... LOG: consistent recovery state reached LOG: database system is ready to accept read-only connections Consistency information is recorded once per checkpoint on the primary. It is not possible to enable hot standby when reading WAL written during a period when wal_level was not set to replica or logical on the primary. Reaching a consistent state can also be delayed in the presence of both of these conditions: A write transaction has more than 64 subtransactions Very long-lived write transactions If you are running file-based log shipping (\"warm standby\"), you might need to wait until the next WAL file arrives, which could be as long as the archive_timeout setting on the primary. The settings of some parameters determine the size of shared memory for tracking transaction IDs, locks, and prepared transactions. These shared memory structures must be no smaller on a standby than on the primary in order to ensure that the standby does not run out of shared memory during recovery. For example, if the primary had used a prepared transaction but the standby had not allocated any shared memory for tracking prepared transactions, then recovery could not continue until the standby’s configuration is changed. The parameters affected are: max_connections max_prepared_transactions max_locks_per_transaction max_wal_senders max_worker_processes The easiest way to ensure this does not become a problem is to have these parameters set on the standbys to values equal to or greater than on the primary. Therefore, if you want to increase these values, you should do so on all standby servers first, before applying the changes to the primary server. Conversely, if you want to decrease these values, you should do so on the primary server first, before applying the changes to all standby servers. Keep in mind that when a standby is promoted, it becomes the new reference for the required parameter settings for the standbys that follow it. Therefore, to avoid this becoming a problem during a switchover or failover, it is recommended to keep these settings the same on all standby servers. The WAL tracks changes to these parameters on the primary. If a hot standby processes WAL that indicates that the current value on the primary is higher than its own value, it will log a warning and pause recovery, for example: WARNING: hot standby is not possible because of insufficient parameter settings DETAIL: max_connections = 80 is a lower setting than on the primary server, where its value was 100. LOG: recovery has paused DETAIL: If recovery is unpaused, the server will shut down. HINT: You can then restart the server after making the necessary configuration changes. At that point, the settings on the standby need to be updated and the instance restarted before recovery can continue. If the standby is not a hot standby, then when it encounters the incompatible parameter change, it will shut down immediately without pausing, since there is then no value in keeping it up. It is important that the administrator select appropriate settings for max_standby_archive_delay and max_standby_streaming_delay. The best choices vary depending on business priorities. For example if the server is primarily tasked as a High Availability server, then you will want low delay settings, perhaps even zero, though that is a very aggressive setting. If the standby server is tasked as an additional server for decision support queries then it might be acceptable to set the maximum delay values to many hours, or even -1 which means wait forever for queries to complete. Transaction status \"hint bits\" written on the primary are not WAL-logged, so data on the standby will likely re-write the hints again on the standby. Thus, the standby server will still perform disk writes even though all users are read-only; no changes occur to the data values themselves. Users will still write large sort temporary files and re-generate relcache info files, so no part of the database is truly read-only during hot standby mode. Note also that writes to remote databases using dblink module, and other operations outside the database using PL functions will still be possible, even though the transaction is read-only locally. The following types of administration commands are not accepted during recovery mode: Data Definition Language (DDL): e.g., CREATE INDEX Privilege and Ownership: GRANT, REVOKE, REASSIGN Maintenance commands: ANALYZE, VACUUM, CLUSTER, REINDEX Again, note that some of these commands are actually allowed during \"read only\" mode transactions on the primary. As a result, you cannot create additional indexes that exist solely on the standby, nor statistics that exist solely on the standby. If these administration commands are needed, they should be executed on the primary, and eventually those changes will propagate to the standby. pg_cancel_backend() and pg_terminate_backend() will work on user backends, but not the startup process, which performs recovery. pg_stat_activity does not show recovering transactions as active. As a result, pg_prepared_xacts is always empty during recovery. If you wish to resolve in-doubt prepared transactions, view pg_prepared_xacts on the primary and issue commands to resolve transactions there or resolve them after the end of recovery. pg_locks will show locks held by backends, as normal. pg_locks also shows a virtual transaction managed by the startup process that owns all AccessExclusiveLocks held by transactions being replayed by recovery. Note that the startup process does not acquire locks to make database changes, and thus locks other than AccessExclusiveLocks do not show in pg_locks for the Startup process; they are just presumed to exist. The Nagios plugin check_pgsql will work, because the simple information it checks for exists. The check_postgres monitoring script will also work, though some reported values could give different or confusing results. For example, last vacuum time will not be maintained, since no vacuum occurs on the standby. Vacuums running on the primary do still send their changes to the standby. WAL file control commands will not work during recovery, e.g., pg_backup_start, pg_switch_wal etc. Dynamically loadable modules work, including pg_stat_statements. Advisory locks work normally in recovery, including deadlock detection. Note that advisory locks are never WAL logged, so it is impossible for an advisory lock on either the primary or the standby to conflict with WAL replay. Nor is it possible to acquire an advisory lock on the primary and have it initiate a similar advisory lock on the standby. Advisory locks relate only to the server on which they are acquired. Trigger-based replication systems such as Slony, Londiste and Bucardo won’t run on the standby at all, though they will run happily on the primary server as long as the changes are not sent to standby servers to be applied. WAL replay is not trigger-based so you cannot relay from the standby to any system that requires additional database writes or relies on the use of triggers. New OIDs cannot be assigned, though some UUID generators may still work as long as they do not rely on writing new status to the database. Currently, temporary table creation is not allowed during read-only transactions, so in some cases existing scripts will not run correctly. This restriction might be relaxed in a later release. This is both an SQL standard compliance issue and a technical issue. DROP TABLESPACE can only succeed if the tablespace is empty. Some standby users may be actively using the tablespace via their temp_tablespaces parameter. If there are temporary files in the tablespace, all active queries are canceled to ensure that temporary files are removed, so the tablespace can be removed and WAL replay can continue. Running DROP DATABASE or ALTER DATABASE …​ SET TABLESPACE on the primary will generate a WAL entry that will cause all users connected to that database on the standby to be forcibly disconnected. This action occurs immediately, whatever the setting of max_standby_streaming_delay. Note that ALTER DATABASE …​ RENAME does not disconnect users, which in most cases will go unnoticed, though might in some cases cause a program confusion if it depends in some way upon database name. In normal (non-recovery) mode, if you issue DROP USER or DROP ROLE for a role with login capability while that user is still connected then nothing happens to the connected user — they remain connected. The user cannot reconnect however. This behavior applies in recovery also, so a DROP USER on the primary does not disconnect that user on the standby. The cumulative statistics system is active during recovery. All scans, reads, blocks, index usage, etc., will be recorded normally on the standby. However, WAL replay will not increment relation and database specific counters. I.e. replay will not increment pg_stat_all_tables columns (like n_tup_ins), nor will reads or writes performed by the startup process be tracked in the pg_statio views, nor will associated pg_stat_database columns be incremented. Autovacuum is not active during recovery. It will start normally at the end of recovery. The checkpointer process and the background writer process are active during recovery. The checkpointer process will perform restartpoints (similar to checkpoints on the primary) and the background writer process will perform normal block cleaning activities. This can include updates of the hint bit information stored on the standby server. The CHECKPOINT command is accepted during recovery, though it performs a restartpoint rather than a new checkpoint. On the primary, parameters wal_level and vacuum_defer_cleanup_age can be used. max_standby_archive_delay and max_standby_streaming_delay have no effect if set on the primary. On the standby, parameters hot_standby, max_standby_archive_delay and max_standby_streaming_delay can be used. vacuum_defer_cleanup_age has no effect as long as the server remains in standby mode, though it will become relevant if the standby becomes primary. There are several limitations of hot standby. These can and probably will be fixed in future releases: Full knowledge of running transactions is required before snapshots can be taken. Transactions that use large numbers of subtransactions (currently greater than 64) will delay the start of read-only connections until the completion of the longest running write transaction. If this situation occurs, explanatory messages will be sent to the server log. Valid starting points for standby queries are generated at each checkpoint on the primary. If the standby is shut down while the primary is in a shutdown state, it might not be possible to re-enter hot standby until the primary is started up, so that it generates further starting points in the WAL logs. This situation isn’t a problem in the most common situations where it might happen. Generally, if the primary is shut down and not available anymore, that’s likely due to a serious failure that requires the standby being converted to operate as the new primary anyway. And in situations where the primary is being intentionally taken down, coordinating to make sure the standby becomes the new primary smoothly is also standard procedure. At the end of recovery, AccessExclusiveLocks held by prepared transactions will require twice the normal number of lock table entries. If you plan on running either a large number of concurrent prepared transactions that normally take AccessExclusiveLocks, or you plan on having one large transaction that takes many AccessExclusiveLocks, you are advised to select a larger value of max_locks_per_transaction, perhaps as much as twice the value of the parameter on the primary server. You need not consider this at all if your setting of max_prepared_transactions is 0. The Serializable transaction isolation level is not yet available in hot standby. An attempt to set a transaction to the serializable isolation level in hot standby mode will generate an error.","title":"Maintenance","component":"ivorysql-doc","version":"master","name":"daily_maintenance","url":"/ivorysql-doc/master/getting-started/daily_maintenance.html","titles":[{"text":"1. Routine Vacuuming","hash":"routine-vacuuming","id":1},{"text":"1.1. Vacuuming Basics","hash":"vacuuming-basics","id":2},{"text":"1.2. Recovering Disk Space","hash":"recovering-disk-space","id":3},{"text":"1.3. Updating Planner Statistics","hash":"updating-planner-statistics","id":4},{"text":"1.4. Updating the Visibility Map","hash":"updating-the-visibility-map","id":5},{"text":"1.5. Preventing Transaction ID Wraparound Failures","hash":"preventing-transaction-id-wraparound-failures","id":6},{"text":"1.6. The Autovacuum Daemon","hash":"the-autovacuum-daemon","id":7},{"text":"2. Routine Reindexing","hash":"routine-reindexing","id":8},{"text":"3. Log File Maintenance","hash":"log-file-maintenance","id":9},{"text":"4. High Availability, Load Balancing, and Replication","hash":"high-availability-load-balancing-and-replication","id":10},{"text":"4.1. Comparison of Different Solutions","hash":"comparison-of-different-solutions","id":11},{"text":"4.1.1. Shared Disk Failover","hash":"shared-disk-failover","id":12},{"text":"4.1.2. File System (Block Device) Replication","hash":"file-system-block-device-replication","id":13},{"text":"4.1.3. Write-Ahead Log Shipping","hash":"write-ahead-log-shipping","id":14},{"text":"4.1.4. Logical Replication","hash":"logical-replication","id":15},{"text":"4.1.5. Trigger-Based Primary-Standby Replication","hash":"trigger-based-primary-standby-replication","id":16},{"text":"4.1.6. SQL-Based Replication Middleware","hash":"sql-based-replication-middleware","id":17},{"text":"4.1.7. Asynchronous Multimaster Replication","hash":"asynchronous-multimaster-replication","id":18},{"text":"4.1.8. Synchronous Multimaster Replication","hash":"synchronous-multimaster-replication","id":19},{"text":"4.2. Log-Shipping Standby Servers","hash":"log-shipping-standby-servers","id":20},{"text":"4.2.1. Planning","hash":"planning","id":21},{"text":"4.2.2. Standby Server Operation","hash":"standby-server-operation","id":22},{"text":"4.2.3. Preparing the Primary for Standby Servers","hash":"preparing-the-primary-for-standby-servers","id":23},{"text":"4.2.4. Setting Up a Standby Server","hash":"setting-up-a-standby-server","id":24},{"text":"4.2.5. Streaming Replication","hash":"streaming-replication","id":25},{"text":"4.2.5.1. Authentication","hash":"authentication","id":26},{"text":"4.2.5.2. Monitoring","hash":"monitoring","id":27},{"text":"4.2.6. Replication Slots","hash":"replication-slots","id":28},{"text":"4.2.6.1. Querying And Manipulating Replication Slots","hash":"querying-and-manipulating-replication-slots","id":29},{"text":"4.2.6.2. Configuration Example","hash":"configuration-example","id":30},{"text":"4.2.7. Cascading Replication","hash":"cascading-replication","id":31},{"text":"4.2.8. Synchronous Replication","hash":"synchronous-replication","id":32},{"text":"4.2.8.1. Basic Configuration","hash":"basic-configuration","id":33},{"text":"4.2.8.2. Multiple Synchronous Standbys","hash":"multiple-synchronous-standbys","id":34},{"text":"4.2.8.3. Planning For Performance","hash":"planning-for-performance","id":35},{"text":"4.2.8.4. Planning For High Availability","hash":"planning-for-high-availability","id":36},{"text":"4.2.9. Continuous Archiving in Standby","hash":"continuous-archiving-in-standby","id":37},{"text":"4.3. Failover","hash":"failover","id":38},{"text":"4.4. Hot Standby","hash":"hot-standby","id":39},{"text":"4.4.1. User’s Overview","hash":"users-overview","id":40},{"text":"4.4.2. Handling Query Conflicts","hash":"handling-query-conflicts","id":41},{"text":"4.4.3. Administrator’s Overview","hash":"administrators-overview","id":42},{"text":"4.4.4. Hot Standby Parameter Reference","hash":"hot-standby-parameter-reference","id":43},{"text":"4.4.5. Caveats","hash":"caveats","id":44}]},"52":{"id":52,"text":"On most Unix platforms, IvorySQL modifies its command title as reported by ps, so that individual server processes can readily be identified. A sample display is $ ps auxww | grep ^postgres postgres 15551 0.0 0.1 57536 7132 pts/0 S 18:02 0:00 postgres -i postgres 15554 0.0 0.0 57536 1184 ? Ss 18:02 0:00 postgres: background writer postgres 15555 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: checkpointer postgres 15556 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: walwriter postgres 15557 0.0 0.0 58504 2244 ? Ss 18:02 0:00 postgres: autovacuum launcher postgres 15558 0.0 0.0 17512 1068 ? Ss 18:02 0:00 postgres: stats collector postgres 15582 0.0 0.0 58772 3080 ? Ss 18:04 0:00 postgres: joe runbug 127.0.0.1 idle postgres 15606 0.0 0.0 58772 3052 ? Ss 18:07 0:00 postgres: tgl regression [local] SELECT waiting postgres 15610 0.0 0.0 58772 3056 ? Ss 18:07 0:00 postgres: tgl regression [local] idle in transaction (The appropriate invocation of ps varies across different platforms, as do the details of what is shown. This example is from a recent Linux system.) The first process listed here is the primary server process. The command arguments shown for it are the same ones used when it was launched. The next four processes are background worker processes automatically launched by the primary process. (The “autovacuum launcher” process will not be present if you have set the system not to run autovacuum.) Each of the remaining processes is a server process handling one client connection. Each such process sets its command line display in the form postgres: user database host activity The user, database, and (client) host items remain the same for the life of the client connection, but the activity indicator changes. The activity can be idle (i.e., waiting for a client command), idle in transaction (waiting for client inside a BEGIN block), or a command type name such as SELECT. Also, waiting is appended if the server process is presently waiting on a lock held by another session. In the above example we can infer that process 15606 is waiting for process 15610 to complete its transaction and thereby release some lock. (Process 15610 must be the blocker, because there is no other active session. In more complicated cases it would be necessary to look into the pg_locks system view to determine who is blocking whom.) If cluster_name has been configured the cluster name will also be shown in ps output: $ psql -c 'SHOW cluster_name' cluster_name -------------- server1 (1 row) $ ps aux|grep server1 postgres 27093 0.0 0.0 30096 2752 ? Ss 11:34 0:00 postgres: server1: background writer ... If you have turned off update_process_title then the activity indicator is not updated; the process title is set only once when a new process is launched. On some platforms this saves a measurable amount of per-command overhead; on others it’s insignificant. Tip Solaris requires special handling. You must use /usr/ucb/ps, rather than /bin/ps. You also must use two w flags, not just one. In addition, your original invocation of the postgres command must have a shorter ps status display than that provided by each server process. If you fail to do all three things, the ps output for each server process will be the original postgres command line. IvorySQL’s cumulative statistics system supports collection and reporting of information about server activity. Presently, accesses to tables and indexes in both disk-block and individual-row terms are counted. The total number of rows in each table, and information about vacuum and analyze actions for each table are also counted. If enabled, calls to user-defined functions and the total time spent in each one are counted as well. IvorySQL also supports reporting dynamic information about exactly what is going on in the system right now, such as the exact command currently being executed by other server processes, and which other connections exist in the system. This facility is independent of the cumulative statistics system. Since collection of statistics adds some overhead to query execution, the system can be configured to collect or not collect information. This is controlled by configuration parameters that are normally set in postgresql.conf. The parameter track_activities enables monitoring of the current command being executed by any server process. The parameter track_counts controls whether cumulative statistics are collected about table and index accesses. The parameter track_functions enables tracking of usage of user-defined functions. The parameter track_io_timing enables monitoring of block read and write times. The parameter track_wal_io_timing enables monitoring of WAL write times. Normally these parameters are set in postgresql.conf so that they apply to all server processes, but it is possible to turn them on or off in individual sessions using the SET command. (To prevent ordinary users from hiding their activity from the administrator, only superusers are allowed to change these parameters with SET.) Cumulative statistics are collected in shared memory. Every IvorySQL process collects statistics locally, then updates the shared data at appropriate intervals. When a server, including a physical replica, shuts down cleanly, a permanent copy of the statistics data is stored in the pg_stat subdirectory, so that statistics can be retained across server restarts. In contrast, when starting from an unclean shutdown (e.g., after an immediate shutdown, a server crash, starting from a base backup, and point-in-time recovery), all statistics counters are reset. Several predefined views, listed in Table 1 , are available to show the current state of the system. There are also several other views, listed in Table 2 , available to show the accumulated statistics. Alternatively, one can build custom views using the underlying cumulative statistics functions. When using the cumulative statistics views and functions to monitor collected data, it is important to realize that the information does not update instantaneously. Each individual server process flushes out accumulated statistics to shared memory just before going idle, but not more frequently than once per PGSTAT_MIN_INTERVAL milliseconds (1 second unless altered while building the server); so a query or transaction still in progress does not affect the displayed totals and the displayed information lags behind actual activity. However, current-query information collected by track_activities is always up-to-date. Another important point is that when a server process is asked to display any of the accumulated statistics, accessed values are cached until the end of its current transaction in the default configuration. So the statistics will show static information as long as you continue the current transaction. Similarly, information about the current queries of all sessions is collected when any such information is first requested within a transaction, and the same information will be displayed throughout the transaction. This is a feature, not a bug, because it allows you to perform several queries on the statistics and correlate the results without worrying that the numbers are changing underneath you. When analyzing statistics interactively, or with expensive queries, the time delta between accesses to individual statistics can lead to significant skew in the cached statistics. To minimize skew, stats_fetch_consistency can be set to snapshot, at the price of increased memory usage for caching not-needed statistics data. Conversely, if it’s known that statistics are only accessed once, caching accessed statistics is unnecessary and can be avoided by setting stats_fetch_consistency to none. You can invoke pg_stat_clear_snapshot() to discard the current transaction’s statistics snapshot or cached values (if any). The next use of statistical information will (when in snapshot mode) cause a new snapshot to be built or (when in cache mode) accessed statistics to be cached. A transaction can also see its own statistics (not yet flushed out to the shared memory statistics) in the views pg_stat_xact_all_tables, pg_stat_xact_sys_tables, pg_stat_xact_user_tables, and pg_stat_xact_user_functions. These numbers do not act as stated above; instead they update continuously throughout the transaction. Some of the information in the dynamic statistics views shown in Table 1 is security restricted. Ordinary users can only see all the information about their own sessions (sessions belonging to a role that they are a member of). In rows about other sessions, many columns will be null. Note, however, that the existence of a session and its general properties such as its sessions user and database are visible to all users. Superusers and roles with privileges of built-in role pg_read_all_stats can see all the information about all sessions. Dynamic Statics Views View Name Description pg_stat_activity One row per server process, showing information related to the current activity of that process, such as state and current query. pg_stat_replication One row per WAL sender process, showing statistics about replication to that sender’s connected standby server. pg_stat_wal_receiver Only one row, showing statistics about the WAL receiver from that receiver’s connected server. pg_stat_recovery_prefetch Only one row, showing statistics about blocks prefetched during recovery. pg_stat_subscription At least one row per subscription, showing information about the subscription workers. pg_stat_ssl One row per connection (regular and replication), showing information about SSL used on this connection. pg_stat_gssapi One row per connection (regular and replication), showing information about GSSAPI authentication and encryption used on this connection. pg_stat_progress_analyze One row for each backend (including autovacuum worker processes) running ANALYZE, showing current progress. pg_stat_progress_create_index One row for each backend running CREATE INDEX or REINDEX, showing current progress. pg_stat_progress_vacuum One row for each backend (including autovacuum worker processes) running VACUUM, showing current progress. pg_stat_progress_cluster One row for each backend running CLUSTER or VACUUM FULL, showing current progress. pg_stat_progress_basebackup One row for each WAL sender process streaming a base backup, showing current progress. pg_stat_progress_copy One row for each backend running COPY, showing current progress. Collected Statistics Views View Name Description pg_stat_archiver One row only, showing statistics about the WAL archiver process’s activity. See pg_stat_archiver for details. pg_stat_bgwriter One row only, showing statistics about the background writer process’s activity. See pg_stat_bgwriter for details. pg_stat_wal One row only, showing statistics about WAL activity. See pg_stat_wal for details. pg_stat_database One row per database, showing database-wide statistics. See pg_stat_database for details. pg_stat_database_conflicts One row per database, showing database-wide statistics about query cancels due to conflict with recovery on standby servers. See pg_stat_database_conflicts for details. pg_stat_all_tables One row for each table in the current database, showing statistics about accesses to that specific table. See pg_stat_all_tables for details. pg_stat_sys_tables Same as pg_stat_all_tables, except that only system tables are shown. pg_stat_user_tables Same as pg_stat_all_tables, except that only user tables are shown. pg_stat_xact_all_tables Similar to pg_stat_all_tables, but counts actions taken so far within the current transaction (which are not yet included in pg_stat_all_tables and related views). The columns for numbers of live and dead rows and vacuum and analyze actions are not present in this view. pg_stat_xact_sys_tables Same as pg_stat_xact_all_tables, except that only system tables are shown. pg_stat_xact_user_tables Same as pg_stat_xact_all_tables, except that only user tables are shown. pg_stat_all_indexes One row for each index in the current database, showing statistics about accesses to that specific index. See pg_stat_all_indexes for details. pg_stat_sys_indexes Same as pg_stat_all_indexes, except that only indexes on system tables are shown. pg_stat_user_indexes Same as pg_stat_all_indexes, except that only indexes on user tables are shown. pg_statio_all_tables One row for each table in the current database, showing statistics about I/O on that specific table. See pg_statio_all_tables for details. pg_statio_sys_tables Same as pg_statio_all_tables, except that only system tables are shown. pg_statio_user_tables Same as pg_statio_all_tables, except that only user tables are shown. pg_statio_all_indexes One row for each index in the current database, showing statistics about I/O on that specific index. See pg_statio_all_indexes for details. pg_statio_sys_indexes Same as pg_statio_all_indexes, except that only indexes on system tables are shown. pg_statio_user_indexes Same as pg_statio_all_indexes, except that only indexes on user tables are shown. pg_statio_all_sequences One row for each sequence in the current database, showing statistics about I/O on that specific sequence. See pg_statio_all_sequences for details. pg_statio_sys_sequences Same as pg_statio_all_sequences, except that only system sequences are shown. (Presently, no system sequences are defined, so this view is always empty.) pg_statio_user_sequences Same as pg_statio_all_sequences, except that only user sequences are shown. pg_stat_user_functions One row for each tracked function, showing statistics about executions of that function. See pg_stat_user_functions for details. pg_stat_xact_user_functions Similar to pg_stat_user_functions, but counts only calls during the current transaction (which are not yet included in pg_stat_user_functions). pg_stat_slru One row per SLRU, showing statistics of operations. See pg_stat_slru for details. pg_stat_replication_slots One row per replication slot, showing statistics about the replication slot’s usage. See pg_stat_replication_slots for details. pg_stat_subscription_stats One row per subscription, showing statistics about errors. See pg_stat_subscription_stats for details. The per-index statistics are particularly useful to determine which indexes are being used and how effective they are. The pg_statio_ views are primarily useful to determine the effectiveness of the buffer cache. When the number of actual disk reads is much smaller than the number of buffer hits, then the cache is satisfying most read requests without invoking a kernel call. However, these statistics do not give the entire story: due to the way in which IvorySQL handles disk I/O, data that is not in the IvorySQL buffer cache might still reside in the kernel’s I/O cache, and might therefore still be fetched without requiring a physical read. Users interested in obtaining more detailed information on IvorySQL I/O behavior are advised to use the IvorySQL statistics views in combination with operating system utilities that allow insight into the kernel’s handling of I/O. The pg_stat_activity view will have one row per server process, showing information related to the current activity of that process. pg_stat_activity View Column Type Description datid oid OID of the database this backend is connected to datname name Name of the database this backend is connected to pid integer Process ID of this backend leader_pid integer Process ID of the parallel group leader, if this process is a parallel query worker. NULL if this process is a parallel group leader or does not participate in parallel query. usesysid oid OID of the user logged into this backend usename name Name of the user logged into this backend application_name text Name of the application that is connected to this backend client_addr inet IP address of the client connected to this backend. If this field is null, it indicates either that the client is connected via a Unix socket on the server machine or that this is an internal process such as autovacuum. client_hostname text Host name of the connected client, as reported by a reverse DNS lookup of client_addr. This field will only be non-null for IP connections, and only when log_hostname is enabled. client_port integer TCP port number that the client is using for communication with this backend, or -1 if a Unix socket is used. If this field is null, it indicates that this is an internal server process. backend_start timestamp with time zone Time when this process was started. For client backends, this is the time the client connected to the server. xact_start timestamp with time zone Time when this process' current transaction was started, or null if no transaction is active. If the current query is the first of its transaction, this column is equal to the query_start column. query_start timestamp with time zone Time when the currently active query was started, or if state is not active, when the last query was started state_change timestamp with time zone Time when the state was last changed wait_event_type text The type of event for which the backend is waiting, if any; otherwise NULL. wait_event text Wait event name if backend is currently waiting, otherwise NULL. state text Current overall state of this backend. Possible values are: active: The backend is executing a query. idle: The backend is waiting for a new client command. idle in transaction: The backend is in a transaction, but is not currently executing a query. idle in transaction (aborted): This state is similar to idle in transaction, except one of the statements in the transaction caused an error. fastpath function call: The backend is executing a fast-path function. disabled: This state is reported if track_activities is disabled in this backend. backend_xid xid Top-level transaction identifier of this backend, if any. backend_xmin xid The current backend’s xmin horizon. query_id bigint Identifier of this backend’s most recent query. If state is active this field shows the identifier of the currently executing query. In all other states, it shows the identifier of last query that was executed. Query identifiers are not computed by default so this field will be null unless compute_query_id parameter is enabled or a third-party module that computes query identifiers is configured. query text Text of this backend’s most recent query. If state is active this field shows the currently executing query. In all other states, it shows the last query that was executed. By default the query text is truncated at 1024 bytes; this value can be changed via the parameter track_activity_query_size. backend_type text Type of current backend. Possible types are autovacuum launcher, autovacuum worker, logical replication launcher, logical replication worker, parallel worker, background writer, client backend, checkpointer, archiver, startup, walreceiver, walsender and walwriter. In addition, background workers registered by extensions may have additional types. Note The wait_event and state columns are independent. If a backend is in the active state, it may or may not be waiting on some event. If the state is active and wait_event is non-null, it means that a query is being executed, but is being blocked somewhere in the system. Wait Event Types Wait Event Type Description Activity The server process is idle. This event type indicates a process waiting for activity in its main processing loop. wait_event will identify the specific wait point BufferPin The server process is waiting for exclusive access to a data buffer. Buffer pin waits can be protracted if another process holds an open cursor that last read data from the buffer in question. Client The server process is waiting for activity on a socket connected to a user application. Thus, the server expects something to happen that is independent of its internal processes. wait_event will identify the specific wait point. Extension The server process is waiting for some condition defined by an extension module. IO The server process is waiting for an I/O operation to complete. wait_event will identify the specific wait point. IPC The server process is waiting for some interaction with another server process. wait_event will identify the specific wait point. Lock The server process is waiting for a heavyweight lock. Heavyweight locks, also known as lock manager locks or simply locks, primarily protect SQL-visible objects such as tables. However, they are also used to ensure mutual exclusion for certain internal operations such as relation extension. wait_event will identify the type of lock awaited. LWLock The server process is waiting for a lightweight lock. Most such locks protect a particular data structure in shared memory. wait_event will contain a name identifying the purpose of the lightweight lock. (Some locks have specific names; others are part of a group of locks each with a similar purpose.) . Timeout The server process is waiting for a timeout to expire. wait_event will identify the specific wait point. Wait Events of Type Activity Activity Wait Event Description ArchiverMain Waiting in main loop of archiver process. AutoVacuumMain Waiting in main loop of autovacuum launcher process. BgWriterHibernate Waiting in background writer process, hibernating. BgWriterMain Waiting in main loop of background writer process. CheckpointerMain Waiting in main loop of checkpointer process. LogicalApplyMain Waiting in main loop of logical replication apply process. LogicalLauncherMain Waiting in main loop of logical replication launcher process. RecoveryWalStream Waiting in main loop of startup process for WAL to arrive, during streaming recovery. SysLoggerMain Waiting in main loop of syslogger process. WalReceiverMain Waiting in main loop of WAL receiver process. WalSenderMain Waiting in main loop of WAL sender process. WalWriterMain Waiting in main loop of WAL writer process. Wait Events of Type BufferPin BufferPin Wait Event Description BufferPin Waiting to acquire an exclusive pin on a buffer. Wait Events of Type Client Client Wait Event Description ClientRead Waiting to read data from the client. ClientWrite Waiting to write data to the client. GSSOpenServer Waiting to read data from the client while establishing a GSSAPI session. LibPQWalReceiverConnect Waiting in WAL receiver to establish connection to remote server. LibPQWalReceiverReceive Waiting in WAL receiver to receive data from remote server. SSLOpenServer Waiting for SSL while attempting connection. WalSenderWaitForWAL Waiting for WAL to be flushed in WAL sender process. WalSenderWriteData Waiting for any activity when processing replies from WAL receiver in WAL sender process. Wait Events of Type Extension Extension Wait Event Description Extension Waiting in an extension. Wait Events of Type IO IO Wait Event Description BaseBackupRead Waiting for base backup to read from a file. BufFileRead Waiting for a read from a buffered file. BufFileWrite Waiting for a write to a buffered file. BufFileTruncate Waiting for a buffered file to be truncated. ControlFileRead Waiting for a read from the pg_control file. ControlFileSync Waiting for the pg_control file to reach durable storage. ControlFileSyncUpdate Waiting for an update to the pg_control file to reach durable storage. ControlFileWrite Waiting for a write to the pg_control file. ControlFileWriteUpdate Waiting for a write to update the pg_control file. CopyFileRead Waiting for a read during a file copy operation. CopyFileWrite Waiting for a write during a file copy operation. DSMFillZeroWrite Waiting to fill a dynamic shared memory backing file with zeroes. DataFileExtend Waiting for a relation data file to be extended. DataFileFlush Waiting for a relation data file to reach durable storage. DataFileImmediateSync Waiting for an immediate synchronization of a relation data file to durable storage. DataFilePrefetch Waiting for an asynchronous prefetch from a relation data file. DataFileRead Waiting for a read from a relation data file. DataFileSync Waiting for changes to a relation data file to reach durable storage. DataFileTruncate Waiting for a relation data file to be truncated. DataFileWrite Waiting for a write to a relation data file. LockFileAddToDataDirRead Waiting for a read while adding a line to the data directory lock file. LockFileAddToDataDirSync Waiting for data to reach durable storage while adding a line to the data directory lock file. LockFileAddToDataDirWrite Waiting for a write while adding a line to the data directory lock file. LockFileCreateRead Waiting to read while creating the data directory lock file. LockFileCreateSync Waiting for data to reach durable storage while creating the data directory lock file. LockFileCreateWrite Waiting for a write while creating the data directory lock file. LockFileReCheckDataDirRead Waiting for a read during recheck of the data directory lock file. LogicalRewriteCheckpointSync Waiting for logical rewrite mappings to reach durable storage during a checkpoint. LogicalRewriteMappingSync Waiting for mapping data to reach durable storage during a logical rewrite. LogicalRewriteMappingWrite Waiting for a write of mapping data during a logical rewrite. LogicalRewriteSync Waiting for logical rewrite mappings to reach durable storage. LogicalRewriteTruncate Waiting for truncate of mapping data during a logical rewrite. LogicalRewriteWrite Waiting for a write of logical rewrite mappings. RelationMapRead Waiting for a read of the relation map file. RelationMapSync Waiting for the relation map file to reach durable storage. RelationMapWrite Waiting for a write to the relation map file. ReorderBufferRead Waiting for a read during reorder buffer management. ReorderBufferWrite Waiting for a write during reorder buffer management. ReorderLogicalMappingRead Waiting for a read of a logical mapping during reorder buffer management. ReplicationSlotRead Waiting for a read from a replication slot control file. ReplicationSlotRestoreSync Waiting for a replication slot control file to reach durable storage while restoring it to memory. ReplicationSlotSync Waiting for a replication slot control file to reach durable storage. ReplicationSlotWrite Waiting for a write to a replication slot control file. SLRUFlushSync Waiting for SLRU data to reach durable storage during a checkpoint or database shutdown. SLRURead Waiting for a read of an SLRU page. SLRUSync Waiting for SLRU data to reach durable storage following a page write. SLRUWrite Waiting for a write of an SLRU page. SnapbuildRead Waiting for a read of a serialized historical catalog snapshot. SnapbuildSync Waiting for a serialized historical catalog snapshot to reach durable storage. SnapbuildWrite Waiting for a write of a serialized historical catalog snapshot. TimelineHistoryFileSync Waiting for a timeline history file received via streaming replication to reach durable storage. TimelineHistoryFileWrite Waiting for a write of a timeline history file received via streaming replication. TimelineHistoryRead Waiting for a read of a timeline history file. TimelineHistorySync Waiting for a newly created timeline history file to reach durable storage. TimelineHistoryWrite Waiting for a write of a newly created timeline history file. TwophaseFileRead Waiting for a read of a two phase state file. TwophaseFileSync Waiting for a two phase state file to reach durable storage. TwophaseFileWrite Waiting for a write of a two phase state file. VersionFileWrite Waiting for the version file to be written while creating a database. WALBootstrapSync Waiting for WAL to reach durable storage during bootstrapping. WALBootstrapWrite Waiting for a write of a WAL page during bootstrapping. WALCopyRead Waiting for a read when creating a new WAL segment by copying an existing one. WALCopySync Waiting for a new WAL segment created by copying an existing one to reach durable storage. WALCopyWrite Waiting for a write when creating a new WAL segment by copying an existing one. WALInitSync Waiting for a newly initialized WAL file to reach durable storage. WALInitWrite Waiting for a write while initializing a new WAL file. WALRead Waiting for a read from a WAL file. WALSenderTimelineHistoryRead Waiting for a read from a timeline history file during a walsender timeline command. WALSync Waiting for a WAL file to reach durable storage. WALSyncMethodAssign Waiting for data to reach durable storage while assigning a new WAL sync method. WALWrite Waiting for a write to a WAL file. Wait Events of Type IPC IPC Wait Event Description AppendReady Waiting for subplan nodes of an Append plan node to be ready. ArchiveCleanupCommand Waiting for archive_cleanup_command to complete. ArchiveCommand Waiting for archive_command to complete. BackendTermination Waiting for the termination of another backend. BackupWaitWalArchive Waiting for WAL files required for a backup to be successfully archived. BgWorkerShutdown Waiting for background worker to shut down. BgWorkerStartup Waiting for background worker to start up. BtreePage Waiting for the page number needed to continue a parallel B-tree scan to become available. BufferIO Waiting for buffer I/O to complete. CheckpointDone Waiting for a checkpoint to complete. CheckpointStart Waiting for a checkpoint to start. ExecuteGather Waiting for activity from a child process while executing a Gather plan node. HashBatchAllocate Waiting for an elected Parallel Hash participant to allocate a hash table. HashBatchElect Waiting to elect a Parallel Hash participant to allocate a hash table. HashBatchLoad Waiting for other Parallel Hash participants to finish loading a hash table. HashBuildAllocate Waiting for an elected Parallel Hash participant to allocate the initial hash table. HashBuildElect Waiting to elect a Parallel Hash participant to allocate the initial hash table. HashBuildHashInner Waiting for other Parallel Hash participants to finish hashing the inner relation. HashBuildHashOuter Waiting for other Parallel Hash participants to finish partitioning the outer relation. HashGrowBatchesAllocate Waiting for an elected Parallel Hash participant to allocate more batches. HashGrowBatchesDecide Waiting to elect a Parallel Hash participant to decide on future batch growth. HashGrowBatchesElect Waiting to elect a Parallel Hash participant to allocate more batches. HashGrowBatchesFinish Waiting for an elected Parallel Hash participant to decide on future batch growth. HashGrowBatchesRepartition Waiting for other Parallel Hash participants to finish repartitioning. HashGrowBucketsAllocate Waiting for an elected Parallel Hash participant to finish allocating more buckets. HashGrowBucketsElect Waiting to elect a Parallel Hash participant to allocate more buckets. HashGrowBucketsReinsert Waiting for other Parallel Hash participants to finish inserting tuples into new buckets. LogicalSyncData Waiting for a logical replication remote server to send data for initial table synchronization. LogicalSyncStateChange Waiting for a logical replication remote server to change state. MessageQueueInternal Waiting for another process to be attached to a shared message queue. MessageQueuePutMessage Waiting to write a protocol message to a shared message queue. MessageQueueReceive Waiting to receive bytes from a shared message queue. MessageQueueSend Waiting to send bytes to a shared message queue. ParallelBitmapScan Waiting for parallel bitmap scan to become initialized. ParallelCreateIndexScan Waiting for parallel CREATE INDEX workers to finish heap scan. ParallelFinish Waiting for parallel workers to finish computing. ProcArrayGroupUpdate Waiting for the group leader to clear the transaction ID at end of a parallel operation. ProcSignalBarrier Waiting for a barrier event to be processed by all backends. Promote Waiting for standby promotion. RecoveryConflictSnapshot Waiting for recovery conflict resolution for a vacuum cleanup. RecoveryConflictTablespace Waiting for recovery conflict resolution for dropping a tablespace. RecoveryEndCommand Waiting for recovery_end_command to complete. RecoveryPause Waiting for recovery to be resumed. ReplicationOriginDrop Waiting for a replication origin to become inactive so it can be dropped. ReplicationSlotDrop Waiting for a replication slot to become inactive so it can be dropped. RestoreCommand Waiting for restore_command to complete. SafeSnapshot Waiting to obtain a valid snapshot for a READ ONLY DEFERRABLE transaction. SyncRep Waiting for confirmation from a remote server during synchronous replication. WalReceiverExit Waiting for the WAL receiver to exit. WalReceiverWaitStart Waiting for startup process to send initial data for streaming replication. XactGroupUpdate Waiting for the group leader to update transaction status at end of a parallel operation. Wait Events of Type Lock Lock Wait Event Description advisory Waiting to acquire an advisory user lock. extend Waiting to extend a relation. frozenid Waiting to update pg_database.datfrozenxid and pg_database.datminmxid. object Waiting to acquire a lock on a non-relation database object. page Waiting to acquire a lock on a page of a relation. relation Waiting to acquire a lock on a relation. spectoken Waiting to acquire a speculative insertion lock. transactionid Waiting for a transaction to finish. tuple Waiting to acquire a lock on a tuple. userlock Waiting to acquire a user lock. virtualxid Waiting to acquire a virtual transaction ID lock. Wait Events of Type LWLock LWLock Wait Event Description AddinShmemInit Waiting to manage an extension’s space allocation in shared memory. AutoFile Waiting to update the postgresql.auto.conf file. Autovacuum Waiting to read or update the current state of autovacuum workers. AutovacuumSchedule Waiting to ensure that a table selected for autovacuum still needs vacuuming. BackgroundWorker Waiting to read or update background worker state. BtreeVacuum Waiting to read or update vacuum-related information for a B-tree index. BufferContent Waiting to access a data page in memory. BufferMapping Waiting to associate a data block with a buffer in the buffer pool. CheckpointerComm Waiting to manage fsync requests. CommitTs Waiting to read or update the last value set for a transaction commit timestamp. CommitTsBuffer Waiting for I/O on a commit timestamp SLRU buffer. CommitTsSLRU Waiting to access the commit timestamp SLRU cache. ControlFile Waiting to read or update the pg_control file or create a new WAL file. DynamicSharedMemoryControl Waiting to read or update dynamic shared memory allocation information. LockFastPath Waiting to read or update a process' fast-path lock information. LockManager Waiting to read or update information about “heavyweight” locks. LogicalRepWorker Waiting to read or update the state of logical replication workers. MultiXactGen Waiting to read or update shared multixact state. MultiXactMemberBuffer Waiting for I/O on a multixact member SLRU buffer. MultiXactMemberSLRU Waiting to access the multixact member SLRU cache. MultiXactOffsetBuffer Waiting for I/O on a multixact offset SLRU buffer. MultiXactOffsetSLRU Waiting to access the multixact offset SLRU cache. MultiXactTruncation Waiting to read or truncate multixact information. NotifyBuffer Waiting for I/O on a NOTIFY message SLRU buffer. NotifyQueue Waiting to read or update NOTIFY messages. NotifyQueueTail Waiting to update limit on NOTIFY message storage. NotifySLRU Waiting to access the NOTIFY message SLRU cache. OidGen Waiting to allocate a new OID. OldSnapshotTimeMap Waiting to read or update old snapshot control information. ParallelAppend Waiting to choose the next subplan during Parallel Append plan execution. ParallelHashJoin Waiting to synchronize workers during Parallel Hash Join plan execution. ParallelQueryDSA Waiting for parallel query dynamic shared memory allocation. PerSessionDSA Waiting for parallel query dynamic shared memory allocation. PerSessionRecordType Waiting to access a parallel query’s information about composite types. PerSessionRecordTypmod Waiting to access a parallel query’s information about type modifiers that identify anonymous record types. PerXactPredicateList Waiting to access the list of predicate locks held by the current serializable transaction during a parallel query. PredicateLockManager Waiting to access predicate lock information used by serializable transactions. ProcArray Waiting to access the shared per-process data structures (typically, to get a snapshot or report a session’s transaction ID). RelationMapping Waiting to read or update a pg_filenode.map file (used to track the filenode assignments of certain system catalogs). RelCacheInit Waiting to read or update a pg_internal.init relation cache initialization file. ReplicationOrigin Waiting to create, drop or use a replication origin. ReplicationOriginState Waiting to read or update the progress of one replication origin. ReplicationSlotAllocation Waiting to allocate or free a replication slot. ReplicationSlotControl Waiting to read or update replication slot state. ReplicationSlotIO Waiting for I/O on a replication slot. SerialBuffer Waiting for I/O on a serializable transaction conflict SLRU buffer. SerializableFinishedList Waiting to access the list of finished serializable transactions. SerializablePredicateList Waiting to access the list of predicate locks held by serializable transactions. PgStatsDSA Waiting for stats dynamic shared memory allocator access PgStatsHash Waiting for stats shared memory hash table access PgStatsData Waiting for shared memory stats data access SerializableXactHash Waiting to read or update information about serializable transactions. SerialSLRU Waiting to access the serializable transaction conflict SLRU cache. SharedTidBitmap Waiting to access a shared TID bitmap during a parallel bitmap index scan. SharedTupleStore Waiting to access a shared tuple store during parallel query. ShmemIndex Waiting to find or allocate space in shared memory. SInvalRead Waiting to retrieve messages from the shared catalog invalidation queue. SInvalWrite Waiting to add a message to the shared catalog invalidation queue. SubtransBuffer Waiting for I/O on a sub-transaction SLRU buffer. SubtransSLRU Waiting to access the sub-transaction SLRU cache. SyncRep Waiting to read or update information about the state of synchronous replication. SyncScan Waiting to select the starting location of a synchronized table scan. TablespaceCreate Waiting to create or drop a tablespace. TwoPhaseState Waiting to read or update the state of prepared transactions. WALBufMapping Waiting to replace a page in WAL buffers. WALInsert Waiting to insert WAL data into a memory buffer. WALWrite Waiting for WAL buffers to be written to disk. WrapLimitsVacuum Waiting to update limits on transaction id and multixact consumption. XactBuffer Waiting for I/O on a transaction status SLRU buffer. XactSLRU Waiting to access the transaction status SLRU cache. XactTruncation Waiting to execute pg_xact_status or update the oldest transaction ID available to it. XidGen Waiting to allocate a new transaction ID. Note Extensions can add LWLock types to the list shown in Table 12. In some cases, the name assigned by an extension will not be available in all server processes; so an LWLock wait event might be reported as just “extension” rather than the extension-assigned name. Wait Events of Type Timeout Timeout Wait Event Description BaseBackupThrottle Waiting during base backup when throttling activity. CheckpointWriteDelay Waiting between writes while performing a checkpoint. PgSleep Waiting due to a call to pg_sleep or a sibling function. RecoveryApplyDelay Waiting to apply WAL during recovery because of a delay setting. RecoveryRetrieveRetryInterval Waiting during recovery when WAL data is not available from any source (pg_wal, archive or stream). RegisterSyncRequest Waiting while sending synchronization requests to the checkpointer, because the request queue is full. VacuumDelay Waiting in a cost-based vacuum delay point. VacuumTruncate Waiting to acquire an exclusive lock to truncate off any empty pages at the end of a table vacuumed. Here is an example of how wait events can be viewed: SELECT pid, wait_event_type, wait_event FROM pg_stat_activity WHERE wait_event is NOT NULL; pid | wait_event_type | wait_event ------+-----------------+------------ 2540 | Lock | relation 6644 | LWLock | ProcArray (2 rows) The pg_stat_replication view will contain one row per WAL sender process, showing statistics about replication to that sender’s connected standby server. Only directly connected standbys are listed; no information is available about downstream standby servers. pg_stat_replication View Column Type Description pid integer Process ID of a WAL sender process usesysid oid OID of the user logged into this WAL sender process usename name Name of the user logged into this WAL sender process application_name text Name of the application that is connected to this WAL sender client_addr inet IP address of the client connected to this WAL sender. If this field is null, it indicates that the client is connected via a Unix socket on the server machine. client_hostname text Host name of the connected client, as reported by a reverse DNS lookup of client_addr. This field will only be non-null for IP connections, and only when log_hostname is enabled. client_port integer TCP port number that the client is using for communication with this WAL sender, or -1 if a Unix socket is used backend_start timestamp with time zone Time when this process was started, i.e., when the client connected to this WAL sender backend_xmin xid This standby’s xmin horizon reported by hot_standby_feedback. state text Current WAL sender state. Possible values are: startup: This WAL sender is starting up. catchup: This WAL sender’s connected standby is catching up with the primary. streaming: This WAL sender is streaming changes after its connected standby server has caught up with the primary. backup: This WAL sender is sending a backup. stopping: This WAL sender is stopping. sent_lsn pg_lsn Last write-ahead log location sent on this connection write_lsn pg_lsn Last write-ahead log location written to disk by this standby server flush_lsn pg_lsn Last write-ahead log location flushed to disk by this standby server replay_lsn pg_lsn Last write-ahead log location replayed into the database on this standby server write_lag interval Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written it (but not yet flushed it or applied it). This can be used to gauge the delay that synchronous_commit level remote_write incurred while committing if this server was configured as a synchronous standby. flush_lag interval Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written and flushed it (but not yet applied it). This can be used to gauge the delay that synchronous_commit level on incurred while committing if this server was configured as a synchronous standby. replay_lag interval Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written, flushed and applied it. This can be used to gauge the delay that synchronous_commit level remote_apply incurred while committing if this server was configured as a synchronous standby. sync_priority integer Priority of this standby server for being chosen as the synchronous standby in a priority-based synchronous replication. This has no effect in a quorum-based synchronous replication. sync_state text Synchronous state of this standby server. Possible values are: async: This standby server is asynchronous. potential: This standby server is now asynchronous, but can potentially become synchronous if one of current synchronous ones fails. sync: This standby server is synchronous. quorum: This standby server is considered as a candidate for quorum standbys. reply_time timestamp with time zone Send time of last reply message received from standby server The lag times reported in the pg_stat_replication view are measurements of the time taken for recent WAL to be written, flushed and replayed and for the sender to know about it. These times represent the commit delay that was (or would have been) introduced by each synchronous commit level, if the remote server was configured as a synchronous standby. For an asynchronous standby, the replay_lag column approximates the delay before recent transactions became visible to queries. If the standby server has entirely caught up with the sending server and there is no more WAL activity, the most recently measured lag times will continue to be displayed for a short time and then show NULL. Lag times work automatically for physical replication. Logical decoding plugins may optionally emit tracking messages; if they do not, the tracking mechanism will simply display NULL lag. Note The reported lag times are not predictions of how long it will take for the standby to catch up with the sending server assuming the current rate of replay. Such a system would show similar times while new WAL is being generated, but would differ when the sender becomes idle. In particular, when the standby has caught up completely, pg_stat_replication shows the time taken to write, flush and replay the most recent reported WAL location rather than zero as some users might expect. This is consistent with the goal of measuring synchronous commit and transaction visibility delays for recent write transactions. To reduce confusion for users expecting a different model of lag, the lag columns revert to NULL after a short time on a fully replayed idle system. Monitoring systems should choose whether to represent this as missing data, zero or continue to display the last known value. The pg_stat_replication_slots view will contain one row per logical replication slot, showing statistics about its usage. pg_stat_replication_slots View Column Type Description slot_name text A unique, cluster-wide identifier for the replication slot spill_txns bigint Number of transactions spilled to disk once the memory used by logical decoding to decode changes from WAL has exceeded logical_decoding_work_mem. The counter gets incremented for both top-level transactions and subtransactions. spill_count bigint Number of times transactions were spilled to disk while decoding changes from WAL for this slot. This counter is incremented each time a transaction is spilled, and the same transaction may be spilled multiple times. spill_bytes bigint Amount of decoded transaction data spilled to disk while performing decoding of changes from WAL for this slot. This and other spill counters can be used to gauge the I/O which occurred during logical decoding and allow tuning logical_decoding_work_mem. stream_txns bigint Number of in-progress transactions streamed to the decoding output plugin after the memory used by logical decoding to decode changes from WAL for this slot has exceeded logical_decoding_work_mem. Streaming only works with top-level transactions (subtransactions can’t be streamed independently), so the counter is not incremented for subtransactions. stream_count bigint Number of times in-progress transactions were streamed to the decoding output plugin while decoding changes from WAL for this slot. This counter is incremented each time a transaction is streamed, and the same transaction may be streamed multiple times. stream_bytes bigint Amount of transaction data decoded for streaming in-progress transactions to the decoding output plugin while decoding changes from WAL for this slot. This and other streaming counters for this slot can be used to tune logical_decoding_work_mem. total_txns bigint Number of decoded transactions sent to the decoding output plugin for this slot. This counts top-level transactions only, and is not incremented for subtransactions. Note that this includes the transactions that are streamed and/or spilled. total_bytes bigint Amount of transaction data decoded for sending transactions to the decoding output plugin while decoding changes from WAL for this slot. Note that this includes data that is streamed and/or spilled. stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_wal_receiver view will contain only one row, showing statistics about the WAL receiver from that receiver’s connected server. pg_stat_wal_receiver View Column Type Description pid integer Process ID of the WAL receiver process status text Activity status of the WAL receiver process receive_start_lsn pg_lsn First write-ahead log location used when WAL receiver is started receive_start_tli integer First timeline number used when WAL receiver is started written_lsn pg_lsn Last write-ahead log location already received and written to disk, but not flushed. This should not be used for data integrity checks. flushed_lsn pg_lsn Last write-ahead log location already received and flushed to disk, the initial value of this field being the first log location used when WAL receiver is started received_tli integer Timeline number of last write-ahead log location received and flushed to disk, the initial value of this field being the timeline number of the first log location used when WAL receiver is started last_msg_send_time timestamp with time zone Send time of last message received from origin WAL sender last_msg_receipt_time timestamp with time zone Receipt time of last message received from origin WAL sender latest_end_lsn pg_lsn Last write-ahead log location reported to origin WAL sender latest_end_time timestamp with time zone Time of last write-ahead log location reported to origin WAL sender slot_name text Replication slot name used by this WAL receiver sender_host text Host of the IvorySQL instance this WAL receiver is connected to. This can be a host name, an IP address, or a directory path if the connection is via Unix socket. (The path case can be distinguished because it will always be an absolute path, beginning with /.) sender_port integer Port number of the IvorySQL instance this WAL receiver is connected to. conninfo text Connection string used by this WAL receiver, with security-sensitive fields obfuscated. The pg_stat_recovery_prefetch view will contain only one row. The columns wal_distance, block_distance and io_depth show current values, and the other columns show cumulative counters that can be reset with the pg_stat_reset_shared function. pg_stat_recovery_prefetch View Column Type Description stats_reset timestamp with time zone Time at which these statistics were last reset prefetch bigint Number of blocks prefetched because they were not in the buffer pool hit bigint Number of blocks not prefetched because they were already in the buffer pool skip_init bigint Number of blocks not prefetched because they would be zero-initialized skip_new bigint Number of blocks not prefetched because they didn’t exist yet skip_fpw bigint Number of blocks not prefetched because a full page image was included in the WAL skip_rep bigint Number of blocks not prefetched because they were already recently prefetched wal_distance int How many bytes ahead the prefetcher is looking block_distance int How many blocks ahead the prefetcher is looking io_depth int How many prefetches have been initiated but are not yet known to have completed pg_stat_subscription View Column Type Description subid oid OID of the subscription subname name Name of the subscription pid integer Process ID of the subscription worker process relid oid OID of the relation that the worker is synchronizing; null for the main apply worker received_lsn pg_lsn Last write-ahead log location received, the initial value of this field being 0 last_msg_send_time timestamp with time zone Send time of last message received from origin WAL sender last_msg_receipt_time timestamp with time zone Receipt time of last message received from origin WAL sender latest_end_lsn pg_lsn Last write-ahead log location reported to origin WAL sender latest_end_time timestamp with time zone Time of last write-ahead log location reported to origin WAL sender The pg_stat_subscription_stats view will contain one row per subscription. pg_stat_subscription_stats View Column Type Description subid oid OID of the subscription subname name Name of the subscription apply_error_count bigint Number of times an error occurred while applying changes sync_error_count bigint Number of times an error occurred during the initial table synchronization stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_ssl view will contain one row per backend or WAL sender process, showing statistics about SSL usage on this connection. It can be joined to pg_stat_activity or pg_stat_replication on the pid column to get more details about the connection. pg_stat_ssl View Column Type Description pid integer Process ID of a backend or WAL sender process ssl boolean True if SSL is used on this connection version text Version of SSL in use, or NULL if SSL is not in use on this connection cipher text Name of SSL cipher in use, or NULL if SSL is not in use on this connection bits integer Number of bits in the encryption algorithm used, or NULL if SSL is not used on this connection client_dn text Distinguished Name (DN) field from the client certificate used, or NULL if no client certificate was supplied or if SSL is not in use on this connection. This field is truncated if the DN field is longer than NAMEDATALEN (64 characters in a standard build) client_serial numeric Serial number of the client certificate, or NULL if no client certificate was supplied or if SSL is not in use on this connection. The combination of certificate serial number and certificate issuer uniquely identifies a certificate (unless the issuer erroneously reuses serial numbers) issuer_dn text DN of the issuer of the client certificate, or NULL if no client certificate was supplied or if SSL is not in use on this connection. This field is truncated like client_dn The pg_stat_gssapi view will contain one row per backend, showing information about GSSAPI usage on this connection. It can be joined to pg_stat_activity or pg_stat_replication on the pid column to get more details about the connection. pg_stat_gssapi View Column Type Description pid integer Process ID of a backend gss_authenticated boolean True if GSSAPI authentication was used for this connection principal text Principal used to authenticate this connection, or NULL if GSSAPI was not used. Truncated to NAMEDATALEN (64 characters in a standard build) if longer. encrypted boolean True if GSSAPI encryption is in use on this connection The pg_stat_archiver view will always have a single row, containing data about the archiver process of the cluster. pg_stat_archiver View Column Type Description archived_count bigint Number of WAL files that have been successfully archived last_archived_wal text Name of the WAL file most recently successfully archived last_archived_time timestamp with time zone Time of the most recent successful archive operation failed_count bigint Number of failed attempts for archiving WAL files last_failed_wal text Name of the WAL file of the most recent failed archival operation last_failed_time timestamp with time zone Time of the most recent failed archival operation stats_reset timestamp with time zone Time at which these statistics were last reset Normally, WAL files are archived in order, oldest to newest, but that is not guaranteed, and does not hold under special circumstances like when promoting a standby or after crash recovery. Therefore it is not safe to assume that all files older than last_archived_wal have also been successfully archived. The pg_stat_bgwriter view will always have a single row, containing global data for the cluster. pg_stat_bgwriter View Column Type Description checkpoints_timed bigint Number of scheduled checkpoints that have been performed checkpoints_req bigint Number of requested checkpoints that have been performed checkpoint_write_time double precision Total amount of time that has been spent in the portion of checkpoint processing where files are written to disk, in milliseconds checkpoint_sync_time double precision Total amount of time that has been spent in the portion of checkpoint processing where files are synchronized to disk, in milliseconds buffers_checkpoint bigint Number of buffers written during checkpoints buffers_clean bigint Number of buffers written by the background writer maxwritten_clean bigint Number of times the background writer stopped a cleaning scan because it had written too many buffers buffers_backend bigint Number of buffers written directly by a backend buffers_backend_fsync bigint Number of times a backend had to execute its own fsync call (normally the background writer handles those even when the backend does its own write) buffers_alloc bigint Number of buffers allocated stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_wal view will always have a single row, containing data about WAL activity of the cluster. pg_stat_wal View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table on which the index is being created. index_relid oid OID of the index being created or reindexed. During a non-concurrent CREATE INDEX, this is 0. command text The command that is running: CREATE INDEX, CREATE INDEX CONCURRENTLY, REINDEX, or REINDEX CONCURRENTLY. phase text Current processing phase of index creation. See [Table 1.39](https://www.postgresql.org/docs/current/progress-reporting.html#CREATE-INDEX-PHASES). lockers_total bigint Total number of lockers to wait for, when applicable. lockers_done bigint Number of lockers already waited for. current_locker_pid bigint Process ID of the locker currently being waited for. blocks_total bigint Total number of blocks to be processed in the current phase. blocks_done bigint Number of blocks already processed in the current phase. tuples_total bigint Total number of tuples to be processed in the current phase. tuples_done bigint Number of tuples already processed in the current phase. partitions_total bigint When creating an index on a partitioned table, this column is set to the total number of partitions on which the index is to be created. This field is 0 during a REINDEX. partitions_done bigint When creating an index on a partitioned table, this column is set to the number of partitions on which the index has been created. This field is 0 during a REINDEX. The pg_stat_database view will contain one row for each database in the cluster, plus one for shared objects, showing database-wide statistics. pg_stat_database View Column Type Description datid oid OID of this database, or 0 for objects belonging to a shared relation datname name Name of this database, or NULL for shared objects. numbackends integer Number of backends currently connected to this database, or NULL for shared objects. This is the only column in this view that returns a value reflecting current stateall other columns return the accumulated values since the last reset. xact_commit bigint Number of transactions in this database that have been committed xact_rollback bigint Number of transactions in this database that have been rolled back blks_read bigint Number of disk blocks read in this database blks_hit bigint Number of times disk blocks were found already in the buffer cache, so that a read was not necessary (this only includes hits in the IvorySQL buffer cache, not the operatinsystem’s file system cache) tup_returned bigint Number of live rows fetched by sequential scans and index entries returned by index scans in this database tup_fetched bigint Number of live rows fetched by index scans in this database tup_inserted bigint Number of rows inserted by queries in this database tup_updated bigint Number of rows updated by queries in this database tup_deleted bigint Number of rows deleted by queries in this database conflicts bigint Number of queries canceled due to conflicts with recovery in this database. (Conflicts occur only on standby servers; seepg_stat_database_conflicts](https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-DATABASE-CONFLICTS-VIEW) for details.) temp_files bigint Number of temporary files created by queries in this database. All temporary files are counted, regardless of why the temporary file was created (e.g., sorting or hashing)and regardless of the [log_temp_files](https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-TEMP-FILES) setting. temp_bytes bigint Total amount of data written to temporary files by queries in this database. All temporary files are counted, regardless of why the temporary file was created, and regardlesof the [log_temp_files](https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-TEMP-FILES) setting. deadlocks bigint Number of deadlocks detected in this database checksum_failures bigint Number of data page checksum failures detected in this database (or on a shared object), or NULL if data checksums are not enabled. checksum_last_failure timestamp with time zone Time at which the last data page checksum failure was detected in this database (or on a shared object), or NULL if data checksums are noenabled. blk_read_time double precision Time spent reading data file blocks by backends in this database, in milliseconds (if [track_io_timing](https://www.postgresql.org/docs/currenruntime-config-statistics.html#GUC-TRACK-IO-TIMING) is enabled, otherwise zero) blk_write_time double precision Time spent writing data file blocks by backends in this database, in milliseconds (if [track_io_timing](https://www.postgresql.org/docs/currenruntime-config-statistics.html#GUC-TRACK-IO-TIMING) is enabled, otherwise zero) session_time double precision Time spent by database sessions in this database, in milliseconds (note that statistics are only updated when the state of a session changes, so if sessions havbeen idle for a long time, this idle time won’t be included) active_time double precision Time spent executing SQL statements in this database, in milliseconds (this corresponds to the states active and fastpath function call inpg_stat_activity](https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-ACTIVITY-VIEW)) idle_in_transaction_time double precision Time spent idling while in a transaction in this database, in milliseconds (this corresponds to the states idle in transaction and idle itransaction (aborted) in [pg_stat_activity](https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-ACTIVITY-VIEW)) sessions bigint Total number of sessions established to this database sessions_abandoned bigint Number of database sessions to this database that were terminated because connection to the client was lost sessions_fatal bigint Number of database sessions to this database that were terminated by fatal errors sessions_killed bigint Number of database sessions to this database that were terminated by operator intervention stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_database_conflicts view will contain one row per database, showing database-wide statistics about query cancels occurring due to conflicts with recovery on standby servers. This view will only contain information on standby servers, since conflicts do not occur on primary servers. pg_stat_database_conflicts View Column Type Description datid oid OID of a database datname name Name of this database confl_tablespace bigint Number of queries in this database that have been canceled due to dropped tablespaces confl_lock bigint Number of queries in this database that have been canceled due to lock timeouts confl_snapshot bigint Number of queries in this database that have been canceled due to old snapshots confl_bufferpin bigint Number of queries in this database that have been canceled due to pinned buffers confl_deadlock bigint Number of queries in this database that have been canceled due to deadlocks The pg_stat_all_tables view will contain one row for each table in the current database (including TOAST tables), showing statistics about accesses to that specific table. The pg_stat_user_tables and pg_stat_sys_tables views contain the same information, but filtered to only show user and system tables respectively. pg_stat_all_tables View Column Type Description relid oid OID of a table schemaname name Name of the schema that this table is in relname name Name of this table seq_scan bigint Number of sequential scans initiated on this table seq_tup_read bigint Number of live rows fetched by sequential scans idx_scan bigint Number of index scans initiated on this table idx_tup_fetch bigint Number of live rows fetched by index scans n_tup_ins bigint Number of rows inserted n_tup_upd bigint Number of rows updated (includes HOT updated rows) n_tup_del bigint Number of rows deleted n_tup_hot_upd bigint Number of rows HOT updated (i.e., with no separate index update required) n_live_tup bigint Estimated number of live rows n_dead_tup bigint Estimated number of dead rows n_mod_since_analyze bigint Estimated number of rows modified since this table was last analyzed n_ins_since_vacuum bigint Estimated number of rows inserted since this table was last vacuumed last_vacuum timestamp with time zone Last time at which this table was manually vacuumed (not counting VACUUM FULL) last_autovacuum timestamp with time zone Last time at which this table was vacuumed by the autovacuum daemon last_analyze timestamp with time zone Last time at which this table was manually analyzed last_autoanalyze timestamp with time zone Last time at which this table was analyzed by the autovacuum daemon vacuum_count bigint Number of times this table has been manually vacuumed (not counting VACUUM FULL) autovacuum_count bigint Number of times this table has been vacuumed by the autovacuum daemon analyze_count bigint Number of times this table has been manually analyzed autoanalyze_count bigint Number of times this table has been analyzed by the autovacuum daemon The pg_stat_all_indexes view will contain one row for each index in the current database, showing statistics about accesses to that specific index. The pg_stat_user_indexes and pg_stat_sys_indexes views contain the same information, but filtered to only show user and system indexes respectively. pg_stat_all_indexes View Column Type Description relid oid OID of the table for this index indexrelid oid OID of this index schemaname name Name of the schema this index is in relname name Name of the table for this index indexrelname name Name of this index idx_scan bigint Number of index scans initiated on this index idx_tup_read bigint Number of index entries returned by scans on this index idx_tup_fetch bigint Number of live table rows fetched by simple index scans using this index Indexes can be used by simple index scans, “bitmap” index scans, and the optimizer. In a bitmap scan the output of several indexes can be combined via AND or OR rules, so it is difficult to associate individual heap row fetches with specific indexes when a bitmap scan is used. Therefore, a bitmap scan increments the pg_stat_all_indexes.idx_tup_read count(s) for the index(es) it uses, and it increments the pg_stat_all_tables.idx_tup_fetch count for the table, but it does not affect pg_stat_all_indexes.idx_tup_fetch. The optimizer also accesses indexes to check for supplied constants whose values are outside the recorded range of the optimizer statistics because the optimizer statistics might be stale. Note The idx_tup_read and idx_tup_fetch counts can be different even without any use of bitmap scans, because idx_tup_read counts index entries retrieved from the index while idx_tup_fetch counts live rows fetched from the table. The latter will be less if any dead or not-yet-committed rows are fetched using the index, or if any heap fetches are avoided by means of an index-only scan. The pg_statio_all_tables view will contain one row for each table in the current database (including TOAST tables), showing statistics about I/O on that specific table. The pg_statio_user_tables and pg_statio_sys_tables views contain the same information, but filtered to only show user and system tables respectively. pg_statio_all_tables View Column Type Description relid oid OID of a table schemaname name Name of the schema that this table is in relname name Name of this table heap_blks_read bigint Number of disk blocks read from this table heap_blks_hit bigint Number of buffer hits in this table idx_blks_read bigint Number of disk blocks read from all indexes on this table idx_blks_hit bigint Number of buffer hits in all indexes on this table toast_blks_read bigint Number of disk blocks read from this table’s TOAST table (if any) toast_blks_hit bigint Number of buffer hits in this table’s TOAST table (if any) tidx_blks_read bigint Number of disk blocks read from this table’s TOAST table indexes (if any) tidx_blks_hit bigint Number of buffer hits in this table’s TOAST table indexes (if any) The pg_statio_all_indexes view will contain one row for each index in the current database, showing statistics about I/O on that specific index. The pg_statio_user_indexes and pg_statio_sys_indexes views contain the same information, but filtered to only show user and system indexes respectively. pg_statio_all_indexes View Column Type Description relid oid OID of the table for this index indexrelid oid OID of this index schemaname name Name of the schema this index is in relname name Name of the table for this index indexrelname name Name of this index idx_blks_read bigint Number of disk blocks read from this index idx_blks_hit bigint Number of buffer hits in this index The pg_statio_all_sequences view will contain one row for each sequence in the current database, showing statistics about I/O on that specific sequence. pg_statio_all_sequences View Column Type Description relid oid OID of a sequence schemaname name Name of the schema this sequence is in relname name Name of this sequence blks_read bigint Number of disk blocks read from this sequence blks_hit bigint Number of buffer hits in this sequence The pg_stat_user_functions view will contain one row for each tracked function, showing statistics about executions of that function. The track_functions parameter controls exactly which functions are tracked. pg_stat_user_functions View Column Type Description funcid oid OID of a function schemaname name Name of the schema this function is in funcname name Name of this function calls bigint Number of times this function has been called total_time double precision Total time spent in this function and all other functions called by it, in milliseconds self_time double precision Total time spent in this function itself, not including other functions called by it, in milliseconds IvorySQL accesses certain on-disk information via SLRU (simple least-recently-used) caches. The pg_stat_slru view will contain one row for each tracked SLRU cache, showing statistics about access to cached pages. pg_stat_slru View Column Type Description name text Name of the SLRU blks_zeroed bigint Number of blocks zeroed during initializations blks_hit bigint Number of times disk blocks were found already in the SLRU, so that a read was not necessary (this only includes hits in the SLRU, not the operating system’s file system cache) blks_read bigint Number of disk blocks read for this SLRU blks_written bigint Number of disk blocks written for this SLRU blks_exists bigint Number of blocks checked for existence for this SLRU flushes bigint Number of flushes of dirty data for this SLRU truncates bigint Number of truncates for this SLRU stats_reset timestamp with time zone Time at which these statistics were last reset Other ways of looking at the statistics can be set up by writing queries that use the same underlying statistics access functions used by the standard views shown above. For details such as the functions' names, consult the definitions of the standard views. (For example, in psql you could issue \\d+ pg_stat_activity.) The access functions for per-database statistics take a database OID as an argument to identify which database to report on. The per-table and per-index functions take a table or index OID. The functions for per-function statistics take a function OID. Note that only tables, indexes, and functions in the current database can be seen with these functions. Additional Statistics Functions Function Description pg_backend_pid () → integer Returns the process ID of the server process attached to the current session. pg_stat_get_activity ( integer ) → setof record Returns a record of information about the backend with the specified process ID, or one record for each active backend in the system if NULL is specified. The fields returned are a subset of those in the pg_stat_activity view. pg_stat_get_snapshot_timestamp () → timestamp with time zone Returns the timestamp of the current statistics snapshot, or NULL if no statistics snapshot has been taken. A snapshot is taken the first time cumulative statistics are accessed in a transaction if stats_fetch_consistency is set to snapshot. pg_stat_clear_snapshot () → void Discards the current statistics snapshot or cached information. pg_stat_reset () → void Resets all statistics counters for the current database to zero. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_shared ( text ) → void Resets some cluster-wide statistics counters to zero, depending on the argument. The argument can be bgwriter, archiver, wal, or recovery_prefetch to reset the counters shown in the respective views. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_single_table_counters ( oid ) → void Resets statistics for a single table or index in the current database or shared across all databases in the cluster to zero. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_single_function_counters ( oid ) → void Resets statistics for a single function in the current database to zero. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_slru ( text ) → void Resets statistics to zero for a single SLRU cache, or for all SLRUs in the cluster. If the argument is NULL, all counters shown in the pg_stat_slru view for all SLRU caches are reset. The argument can be one of CommitTs, MultiXactMember, MultiXactOffset, Notify, Serial, Subtrans, or Xact. If the argument is other (or any unrecognized name), then the counters for all other SLRU caches, such as extension-defined caches, are reset. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_replication_slot ( text ) → void Resets statistics of the replication slot defined by the argument. If the argument is NULL, resets statistics for all the replication slots. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_subscription_stats ( oid ) → void Resets statistics for a single subscription shown in the pg_stat_subscription_stats view to zero. If the argument is NULL, reset statistics for all subscriptions. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. Warning Using pg_stat_reset() also resets counters that autovacuum uses to determine when to trigger a vacuum or an analyze. Resetting these counters can cause autovacuum to not perform necessary work, which can cause problems such as table bloat or out-dated table statistics. A database-wide ANALYZE is recommended after the statistics have been reset. pg_stat_get_activity, the underlying function of the pg_stat_activity view, returns a set of records containing all the available information about each backend process. Sometimes it may be more convenient to obtain just a subset of this information. In such cases, an older set of per-backend statistics access functions can be used; These access functions use a backend ID number, which ranges from one to the number of currently active backends. The function pg_stat_get_backend_idset provides a convenient way to generate one row for each active backend for invoking these functions. For example, to show the PIDs and current queries of all backends: SELECT pg_stat_get_backend_pid(s.backendid) AS pid, pg_stat_get_backend_activity(s.backendid) AS query FROM (SELECT pg_stat_get_backend_idset() AS backendid) AS s; Per-Backend Statistics Functions Function Description pg_stat_get_backend_idset () → setof integer Returns the set of currently active backend ID numbers (from 1 to the number of active backends). pg_stat_get_backend_activity ( integer ) → text Returns the text of this backend’s most recent query. pg_stat_get_backend_activity_start ( integer ) → timestamp with time zone Returns the time when the backend’s most recent query was started. pg_stat_get_backend_client_addr ( integer ) → inet Returns the IP address of the client connected to this backend. pg_stat_get_backend_client_port ( integer ) → integer Returns the TCP port number that the client is using for communication. pg_stat_get_backend_dbid ( integer ) → oid Returns the OID of the database this backend is connected to. pg_stat_get_backend_pid ( integer ) → integer Returns the process ID of this backend. pg_stat_get_backend_start ( integer ) → timestamp with time zone Returns the time when this process was started. pg_stat_get_backend_userid ( integer ) → oid Returns the OID of the user logged into this backend. pg_stat_get_backend_wait_event_type ( integer ) → text Returns the wait event type name if this backend is currently waiting, otherwise NULL. pg_stat_get_backend_wait_event ( integer ) → text Returns the wait event name if this backend is currently waiting, otherwise NULL. pg_stat_get_backend_xact_start ( integer ) → timestamp with time zone Returns the time when the backend’s current transaction was started. Another useful tool for monitoring database activity is the pg_locks system table. It allows the database administrator to view information about the outstanding locks in the lock manager. For example, this capability can be used to: View all the locks currently outstanding, all the locks on relations in a particular database, all the locks on a particular relation, or all the locks held by a particular IvorySQL session. Determine the relation in the current database with the most ungranted locks (which might be a source of contention among database clients). Determine the effect of lock contention on overall database performance, as well as the extent to which contention varies with overall database traffic. IvorySQL has the ability to report the progress of certain commands during command execution. Currently, the only commands which support progress reporting are ANALYZE, CLUSTER, CREATE INDEX, VACUUM, COPY, and BASE_BACKUP (i.e., replication command that pg_basebackup issues to take a base backup). This may be expanded in the future. Whenever ANALYZE is running, the pg_stat_progress_analyze view will contain a row for each backend that is currently running that command. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_analyze View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table being analyzed. phase text Current processing phase. See Table 1.37. sample_blks_total bigint Total number of heap blocks that will be sampled. sample_blks_scanned bigint Number of heap blocks scanned. ext_stats_total bigint Number of extended statistics. ext_stats_computed bigint Number of extended statistics computed. This counter only advances when the phase is computing extended statistics. child_tables_total bigint Number of child tables. child_tables_done bigint Number of child tables scanned. This counter only advances when the phase is acquiring inherited sample rows. current_child_table_relid oid OID of the child table currently being scanned. This field is only valid when the phase is acquiring inherited sample rows. ANALYZE Phases Phase Description initializing The command is preparing to begin scanning the heap. This phase is expected to be very brief. acquiring sample rows The command is currently scanning the table given by relid to obtain sample rows. acquiring inherited sample rows The command is currently scanning child tables to obtain sample rows. Columns child_tables_total, child_tables_done, and current_child_table_relid contain the progress information for this phase. computing statistics The command is computing statistics from the sample rows obtained during the table scan. computing extended statistics The command is computing extended statistics from the sample rows obtained during the table scan. finalizing analyze The command is updating pg_class. When this phase is completed, ANALYZE will end. Note Note that when ANALYZE is run on a partitioned table, all of its partitions are also recursively analyzed. In that case, ANALYZE progress is reported first for the parent table, whereby its inheritance statistics are collected, followed by that for each partition. Whenever CREATE INDEX or REINDEX is running, the pg_stat_progress_create_index view will contain one row for each backend that is currently creating indexes. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_create_index View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table on which the index is being created. index_relid oid OID of the index being created or reindexed. During a non-concurrent CREATE INDEX, this is 0. command text The command that is running: CREATE INDEX, CREATE INDEX CONCURRENTLY, REINDEX, or REINDEX CONCURRENTLY. phase text Current processing phase of index creation. See Table 1.39. lockers_total bigint Total number of lockers to wait for, when applicable. lockers_done bigint Number of lockers already waited for. current_locker_pid bigint Process ID of the locker currently being waited for. blocks_total bigint Total number of blocks to be processed in the current phase. blocks_done bigint Number of blocks already processed in the current phase. tuples_total bigint Total number of tuples to be processed in the current phase. tuples_done bigint Number of tuples already processed in the current phase. partitions_total bigint When creating an index on a partitioned table, this column is set to the total number of partitions on which the index is to be created. This field is 0 during a REINDEX. partitions_done bigint When creating an index on a partitioned table, this column is set to the number of partitions on which the index has been created. This field is 0 during a REINDEX. CREATE INDEX Phases Phase Description initializing CREATE INDEX or REINDEX is preparing to create the index. This phase is expected to be very brief. waiting for writers before build CREATE INDEX CONCURRENTLY or REINDEX CONCURRENTLY is waiting for transactions with write locks that can potentially see the table to finish. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. building index The index is being built by the access method-specific code. In this phase, access methods that support progress reporting fill in their own progress data, and the subphase is indicated in this column. Typically, blocks_total and blocks_done will contain progress data, as well as potentially tuples_total and tuples_done. waiting for writers before validation CREATE INDEX CONCURRENTLY or REINDEX CONCURRENTLY is waiting for transactions with write locks that can potentially write into the table to finish. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. index validation: scanning index CREATE INDEX CONCURRENTLY is scanning the index searching for tuples that need to be validated. This phase is skipped when not in concurrent mode. Columns blocks_total (set to the total size of the index) and blocks_done contain the progress information for this phase. index validation: sorting tuples CREATE INDEX CONCURRENTLY is sorting the output of the index scanning phase. index validation: scanning table CREATE INDEX CONCURRENTLY is scanning the table to validate the index tuples collected in the previous two phases. This phase is skipped when not in concurrent mode. Columns blocks_total (set to the total size of the table) and blocks_done contain the progress information for this phase. waiting for old snapshots CREATE INDEX CONCURRENTLY or REINDEX CONCURRENTLY is waiting for transactions that can potentially see the table to release their snapshots. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. waiting for readers before marking dead REINDEX CONCURRENTLY is waiting for transactions with read locks on the table to finish, before marking the old index dead. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. waiting for readers before dropping REINDEX CONCURRENTLY is waiting for transactions with read locks on the table to finish, before dropping the old index. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. Whenever VACUUM is running, the pg_stat_progress_vacuum view will contain one row for each backend (including autovacuum worker processes) that is currently vacuuming. The tables below describe the information that will be reported and provide information about how to interpret it. Progress for VACUUM FULL commands is reported via pg_stat_progress_cluster because both VACUUM FULL and CLUSTER rewrite the table, while regular VACUUM only modifies it in place. pg_stat_progress_vacuum View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table being vacuumed. phase text Current processing phase of vacuum. heap_blks_total bigint Total number of heap blocks in the table. This number is reported as of the beginning of the scan; blocks added later will not be (and need not be) visited by this VACUUM. heap_blks_scanned bigint Number of heap blocks scanned. Because the visibility map is used to optimize scans, some blocks will be skipped without inspection; skipped blocks are included in this total, so that this number will eventually become equal to heap_blks_total when the vacuum is complete. This counter only advances when the phase is scanning heap. heap_blks_vacuumed bigint Number of heap blocks vacuumed. Unless the table has no indexes, this counter only advances when the phase is vacuuming heap. Blocks that contain no dead tuples are skipped, so the counter may sometimes skip forward in large increments. index_vacuum_count bigint Number of completed index vacuum cycles. max_dead_tuples bigint Number of dead tuples that we can store before needing to perform an index vacuum cycle, based on maintenance_work_mem. num_dead_tuples bigint Number of dead tuples collected since the last index vacuum cycle. VACUUM Phases Phase Description initializing VACUUM is preparing to begin scanning the heap. This phase is expected to be very brief. scanning heap VACUUM is currently scanning the heap. It will prune and defragment each page if required, and possibly perform freezing activity. The heap_blks_scanned column can be used to monitor the progress of the scan. vacuuming indexes VACUUM is currently vacuuming the indexes. If a table has any indexes, this will happen at least once per vacuum, after the heap has been completely scanned. It may happen multiple times per vacuum if maintenance_work_mem (or, in the case of autovacuum, autovacuum_work_mem if set) is insufficient to store the number of dead tuples found. vacuuming heap VACUUM is currently vacuuming the heap. Vacuuming the heap is distinct from scanning the heap, and occurs after each instance of vacuuming indexes. If heap_blks_scanned is less than heap_blks_total, the system will return to scanning the heap after this phase is completed; otherwise, it will begin cleaning up indexes after this phase is completed. cleaning up indexes VACUUM is currently cleaning up indexes. This occurs after the heap has been completely scanned and all vacuuming of the indexes and the heap has been completed. truncating heap VACUUM is currently truncating the heap so as to return empty pages at the end of the relation to the operating system. This occurs after cleaning up indexes. performing final cleanup VACUUM is performing final cleanup. During this phase, VACUUM will vacuum the free space map, update statistics in pg_class, and report statistics to the cumulative statistics system. When this phase is completed, VACUUM will end. Whenever CLUSTER or VACUUM FULL is running, the pg_stat_progress_cluster view will contain a row for each backend that is currently running either command. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_cluster View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table being clustered. command text The command that is running. Either CLUSTER or VACUUM FULL. phase text Current processing phase. See Table 1.43. cluster_index_relid oid If the table is being scanned using an index, this is the OID of the index being used; otherwise, it is zero. heap_tuples_scanned bigint Number of heap tuples scanned. This counter only advances when the phase is seq scanning heap, index scanning heap or writing new heap. heap_tuples_written bigint Number of heap tuples written. This counter only advances when the phase is seq scanning heap, index scanning heap or writing new heap. heap_blks_total bigint Total number of heap blocks in the table. This number is reported as of the beginning of seq scanning heap. heap_blks_scanned bigint Number of heap blocks scanned. This counter only advances when the phase is seq scanning heap. index_rebuild_count bigint Number of indexes rebuilt. This counter only advances when the phase is rebuilding index. CLUSTER and VACUUM FULL Phases Phase Description initializing The command is preparing to begin scanning the heap. This phase is expected to be very brief. seq scanning heap The command is currently scanning the table using a sequential scan. index scanning heap CLUSTER is currently scanning the table using an index scan. sorting tuples CLUSTER is currently sorting tuples. writing new heap CLUSTER is currently writing the new heap. swapping relation files The command is currently swapping newly-built files into place. rebuilding index The command is currently rebuilding an index. performing final cleanup The command is performing final cleanup. When this phase is completed, CLUSTER or VACUUM FULL will end. Whenever an application like pg_basebackup is taking a base backup, the pg_stat_progress_basebackup view will contain a row for each WAL sender process that is currently running the BASE_BACKUP replication command and streaming the backup. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_basebackup View Column TypeDescription pid `integer`Process ID of a WAL sender process. phase `text`Current processing phase. backup_total bigint`Total amount of data that will be streamed. This is estimated and reported as of the beginning of `streaming database files phase. Note that this is only an approximation since the database may change during streaming database files phase and WAL log may be included in the backup later. This is always the same value as backup_streamed once the amount of data streamed exceeds the estimated total size. If the estimation is disabled in pg_basebackup (i.e., --no-estimate-size option is specified), this is NULL. backup_streamed bigint`Amount of data streamed. This counter only advances when the phase is `streaming database files or transferring wal files. tablespaces_total `bigint`Total number of tablespaces that will be streamed. tablespaces_streamed bigint`Number of tablespaces streamed. This counter only advances when the phase is `streaming database files. Base Backup Phases Phase Description initializing The WAL sender process is preparing to begin the backup. This phase is expected to be very brief. waiting for checkpoint to finish The WAL sender process is currently performing pg_backup_start to prepare to take a base backup, and waiting for the start-of-backup checkpoint to finish. estimating backup size The WAL sender process is currently estimating the total amount of database files that will be streamed as a base backup. streaming database files The WAL sender process is currently streaming database files as a base backup. waiting for wal archiving to finish The WAL sender process is currently performing pg_backup_stop to finish the backup, and waiting for all the WAL files required for the base backup to be successfully archived. If either --wal-method=none or --wal-method=stream is specified in pg_basebackup, the backup will end when this phase is completed. transferring wal files The WAL sender process is currently transferring all WAL logs generated during the backup. This phase occurs after waiting for wal archiving to finish phase if --wal-method=fetch is specified in pg_basebackup. The backup will end when this phase is completed. Whenever COPY is running, the pg_stat_progress_copy view will contain one row for each backend that is currently running a COPY command. The table below describes the information that will be reported and provides information about how to interpret it. pg_stat_progress_copy View Column TypeDescription pid `integer`Process ID of backend. datid `oid`OID of the database to which this backend is connected. datname `name`Name of the database to which this backend is connected. relid oid`OID of the table on which the `COPY command is executed. It is set to 0 if copying from a SELECT query. command text`The command that is running: `COPY FROM, or COPY TO. type text`The io type that the data is read from or written to: `FILE, PROGRAM, PIPE (for COPY FROM STDIN and COPY TO STDOUT), or CALLBACK (used for example during the initial table synchronization in logical replication). bytes_processed bigint`Number of bytes already processed by `COPY command. bytes_total bigint`Size of source file for `COPY FROM command in bytes. It is set to 0 if not available. tuples_processed bigint`Number of tuples already processed by `COPY command. tuples_excluded bigint`Number of tuples not processed because they were excluded by the `WHERE clause of the COPY command. IvorySQL provides facilities to support dynamic tracing of the database server. This allows an external utility to be called at specific points in the code and thereby trace execution. A number of probes or trace points are already inserted into the source code. These probes are intended to be used by database developers and administrators. By default the probes are not compiled into IvorySQL; the user needs to explicitly tell the configure script to make the probes available. Currently, the DTrace utility is supported, which, at the time of this writing, is available on Solaris, macOS, FreeBSD, NetBSD, and Oracle Linux. The SystemTap project for Linux provides a DTrace equivalent and can also be used. Supporting other dynamic tracing utilities is theoretically possible by changing the definitions for the macros in src/include/utils/probes.h. By default, probes are not available, so you will need to explicitly tell the configure script to make the probes available in IvorySQL. To include DTrace support specify --enable-dtrace to configure. A number of standard probes are provided in the source code, More probes can certainly be added to enhance IvorySQL’s observability. Built-in DTrace Probes Name Parameters Description transaction-start (LocalTransactionId) Probe that fires at the start of a new transaction. arg0 is the transaction ID. transaction-commit (LocalTransactionId) Probe that fires when a transaction completes successfully. arg0 is the transaction ID. transaction-abort (LocalTransactionId) Probe that fires when a transaction completes unsuccessfully. arg0 is the transaction ID. query-start (const char *) Probe that fires when the processing of a query is started. arg0 is the query string. query-done (const char *) Probe that fires when the processing of a query is complete. arg0 is the query string. query-parse-start (const char *) Probe that fires when the parsing of a query is started. arg0 is the query string. query-parse-done (const char *) Probe that fires when the parsing of a query is complete. arg0 is the query string. query-rewrite-start (const char *) Probe that fires when the rewriting of a query is started. arg0 is the query string. query-rewrite-done (const char *) Probe that fires when the rewriting of a query is complete. arg0 is the query string. query-plan-start () Probe that fires when the planning of a query is started. query-plan-done () Probe that fires when the planning of a query is complete. query-execute-start () Probe that fires when the execution of a query is started. query-execute-done () Probe that fires when the execution of a query is complete. statement-status (const char *) Probe that fires anytime the server process updates its pg_stat_activity.status. arg0 is the new status string. checkpoint-start (int) Probe that fires when a checkpoint is started. arg0 holds the bitwise flags used to distinguish different checkpoint types, such as shutdown, immediate or force. checkpoint-done (int, int, int, int, int) Probe that fires when a checkpoint is complete. (The probes listed next fire in sequence during checkpoint processing.) arg0 is the number of buffers written. arg1 is the total number of buffers. arg2, arg3 and arg4 contain the number of WAL files added, removed and recycled respectively. clog-checkpoint-start (bool) Probe that fires when the CLOG portion of a checkpoint is started. arg0 is true for normal checkpoint, false for shutdown checkpoint. clog-checkpoint-done (bool) Probe that fires when the CLOG portion of a checkpoint is complete. arg0 has the same meaning as for clog-checkpoint-start. subtrans-checkpoint-start (bool) Probe that fires when the SUBTRANS portion of a checkpoint is started. arg0 is true for normal checkpoint, false for shutdown checkpoint. subtrans-checkpoint-done (bool) Probe that fires when the SUBTRANS portion of a checkpoint is complete. arg0 has the same meaning as for subtrans-checkpoint-start. multixact-checkpoint-start (bool) Probe that fires when the MultiXact portion of a checkpoint is started. arg0 is true for normal checkpoint, false for shutdown checkpoint. multixact-checkpoint-done (bool) Probe that fires when the MultiXact portion of a checkpoint is complete. arg0 has the same meaning as for multixact-checkpoint-start. buffer-checkpoint-start (int) Probe that fires when the buffer-writing portion of a checkpoint is started. arg0 holds the bitwise flags used to distinguish different checkpoint types, such as shutdown, immediate or force. buffer-sync-start (int, int) Probe that fires when we begin to write dirty buffers during checkpoint (after identifying which buffers must be written). arg0 is the total number of buffers. arg1 is the number that are currently dirty and need to be written. buffer-sync-written (int) Probe that fires after each buffer is written during checkpoint. arg0 is the ID number of the buffer. buffer-sync-done (int, int, int) Probe that fires when all dirty buffers have been written. arg0 is the total number of buffers. arg1 is the number of buffers actually written by the checkpoint process. arg2 is the number that were expected to be written (arg1 of buffer-sync-start); any difference reflects other processes flushing buffers during the checkpoint. buffer-checkpoint-sync-start () Probe that fires after dirty buffers have been written to the kernel, and before starting to issue fsync requests. buffer-checkpoint-done () Probe that fires when syncing of buffers to disk is complete. twophase-checkpoint-start () Probe that fires when the two-phase portion of a checkpoint is started. twophase-checkpoint-done () Probe that fires when the two-phase portion of a checkpoint is complete. buffer-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool) Probe that fires when a buffer read is started. arg0 and arg1 contain the fork and block numbers of the page (but arg1 will be -1 if this is a relation extension request). arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is true for a relation extension request, false for normal read. buffer-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool, bool) Probe that fires when a buffer read is complete. arg0 and arg1 contain the fork and block numbers of the page (if this is a relation extension request, arg1 now contains the block number of the newly added block). arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is true for a relation extension request, false for normal read. arg7 is true if the buffer was found in the pool, false if not. buffer-flush-start (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires before issuing any write request for a shared buffer. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. buffer-flush-done (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires when a write request is complete. (Note that this just reflects the time to pass the data to the kernel; it’s typically not actually been written to disk yet.) The arguments are the same as for buffer-flush-start. buffer-write-dirty-start (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires when a server process begins to write a dirty buffer. (If this happens often, it implies that shared_buffers is too small or the background writer control parameters need adjustment.) arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. buffer-write-dirty-done (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires when a dirty-buffer write is complete. The arguments are the same as for buffer-write-dirty-start. wal-buffer-write-dirty-start () Probe that fires when a server process begins to write a dirty WAL buffer because no more WAL buffer space is available. (If this happens often, it implies that wal_buffers is too small.) wal-buffer-write-dirty-done () Probe that fires when a dirty WAL buffer write is complete. wal-insert (unsigned char, unsigned char) Probe that fires when a WAL record is inserted. arg0 is the resource manager (rmid) for the record. arg1 contains the info flags. wal-switch () Probe that fires when a WAL segment switch is requested. smgr-md-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) Probe that fires when beginning to read a block from a relation. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. smgr-md-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) Probe that fires when a block read is complete. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is the number of bytes actually read, while arg7 is the number requested (if these are different it indicates trouble). smgr-md-write-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) Probe that fires when beginning to write a block to a relation. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. smgr-md-write-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) Probe that fires when a block write is complete. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is the number of bytes actually written, while arg7 is the number requested (if these are different it indicates trouble). sort-start (int, bool, int, int, bool, int) Probe that fires when a sort operation is started. arg0 indicates heap, index or datum sort. arg1 is true for unique-value enforcement. arg2 is the number of key columns. arg3 is the number of kilobytes of work memory allowed. arg4 is true if random access to the sort result is required. arg5 indicates serial when 0, parallel worker when 1, or parallel leader when 2. sort-done (bool, long) Probe that fires when a sort is complete. arg0 is true for external sort, false for internal sort. arg1 is the number of disk blocks used for an external sort, or kilobytes of memory used for an internal sort. lwlock-acquire (char *, LWLockMode) Probe that fires when an LWLock has been acquired. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-release (char *) Probe that fires when an LWLock has been released (but note that any released waiters have not yet been awakened). arg0 is the LWLock’s tranche. lwlock-wait-start (char *, LWLockMode) Probe that fires when an LWLock was not immediately available and a server process has begun to wait for the lock to become available. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-wait-done (char *, LWLockMode) Probe that fires when a server process has been released from its wait for an LWLock (it does not actually have the lock yet). arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-condacquire (char *, LWLockMode) Probe that fires when an LWLock was successfully acquired when the caller specified no waiting. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-condacquire-fail (char *, LWLockMode) Probe that fires when an LWLock was not successfully acquired when the caller specified no waiting. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lock-wait-start (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) Probe that fires when a request for a heavyweight lock (lmgr lock) has begun to wait because the lock is not available. arg0 through arg3 are the tag fields identifying the object being locked. arg4 indicates the type of object being locked. arg5 indicates the lock type being requested. lock-wait-done (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) Probe that fires when a request for a heavyweight lock (lmgr lock) has finished waiting (i.e., has acquired the lock). The arguments are the same as for lock-wait-start. deadlock-found () Probe that fires when a deadlock is found by the deadlock detector. Defined Types Used in Probe Parameters Type Definition LocalTransactionId unsigned int LWLockMode int LOCKMODE int BlockNumber unsigned int Oid unsigned int ForkNumber int bool unsigned char The example below shows a DTrace script for analyzing transaction counts in the system, as an alternative to snapshotting pg_stat_database before and after a performance test: #!/usr/sbin/dtrace -qs postgresql$1:::transaction-start { @start[\"Start\"] = count(); self->ts = timestamp; } postgresql$1:::transaction-abort { @abort[\"Abort\"] = count(); } postgresql$1:::transaction-commit /self->ts/ { @commit[\"Commit\"] = count(); @time[\"Total time (ns)\"] = sum(timestamp - self->ts); self->ts=0; } When executed, the example D script gives output such as: # ./txn_count.d `pgrep -n postgres` or ./txn_count.d <PID> ^C Start 71 Commit 70 Total time (ns) 2312105013 Note SystemTap uses a different notation for trace scripts than DTrace does, even though the underlying trace points are compatible. One point worth noting is that at this writing, SystemTap scripts must reference probe names using double underscores in place of hyphens. This is expected to be fixed in future SystemTap releases. New probes can be defined within the code wherever the developer desires, though this will require a recompilation. Below are the steps for inserting new probes: Decide on probe names and data to be made available through the probes Add the probe definitions to src/backend/utils/probes.d Include pg_trace.h if it is not already present in the module(s) containing the probe points, and insert TRACE_POSTGRESQL probe macros at the desired locations in the source code Recompile and verify that the new probes are available Example: Here is an example of how you would add a probe to trace all new transactions by transaction ID. Decide that the probe will be named transaction-start and requires a parameter of type LocalTransactionId Add the probe definition to src/backend/utils/probes.d: ``` probe transaction__start(LocalTransactionId); ``` Note the use of the double underline in the probe name. In a DTrace script using the probe, the double underline needs to be replaced with a hyphen, so `transaction-start` is the name to document for users. At compile time, transaction__start is converted to a macro called TRACE_POSTGRESQL_TRANSACTION_START (notice the underscores are single here), which is available by including pg_trace.h. Add the macro call to the appropriate location in the source code. In this case, it looks like the following: ``` TRACE_POSTGRESQL_TRANSACTION_START(vxid.localTransactionId); ``` After recompiling and running the new binary, check that your newly added probe is available by executing the following DTrace command. You should see similar output: ``` # dtrace -ln transaction-start ID PROVIDER MODULE FUNCTION NAME 18705 postgresql49878 postgres StartTransactionCommand transaction-start 18755 postgresql49877 postgres StartTransactionCommand transaction-start 18805 postgresql49876 postgres StartTransactionCommand transaction-start 18855 postgresql49875 postgres StartTransactionCommand transaction-start 18986 postgresql49873 postgres StartTransactionCommand transaction-start ``` There are a few things to be careful about when adding trace macros to the C code: You should take care that the data types specified for a probe’s parameters match the data types of the variables used in the macro. Otherwise, you will get compilation errors. On most platforms, if IvorySQL is built with --enable-dtrace, the arguments to a trace macro will be evaluated whenever control passes through the macro, even if no tracing is being done. This is usually not worth worrying about if you are just reporting the values of a few local variables. But beware of putting expensive function calls into the arguments. If you need to do that, consider protecting the macro with a check to see if the trace is actually enabled: ``` if (TRACE_POSTGRESQL_TRANSACTION_START_ENABLED()) TRACE_POSTGRESQL_TRANSACTION_START(some_function(...)); ``` Each trace macro has a corresponding ENABLED macro. Each table has a primary heap disk file where most of the data is stored. If the table has any columns with potentially-wide values, there also might be a TOAST file associated with the table, which is used to store values too wide to fit comfortably in the main table . There will be one valid index on the TOAST table, if present. There also might be indexes associated with the base table. Each table and index is stored in a separate disk file — possibly more than one file, if the file would exceed one gigabyte. You can monitor disk space in three ways: using the SQL functions, using the oid2name module, or using manual inspection of the system catalogs. The SQL functions are the easiest to use and are generally recommended. The remainder of this section shows how to do it by inspection of the system catalogs. Using psql on a recently vacuumed or analyzed database, you can issue queries to see the disk usage of any table: SELECT pg_relation_filepath(oid), relpages FROM pg_class WHERE relname = 'customer'; pg_relation_filepath | relpages ----------------------+---------- base/16384/16806 | 60 (1 row) Each page is typically 8 kilobytes. (Remember, relpages is only updated by VACUUM, ANALYZE, and a few DDL commands such as CREATE INDEX.) The file path name is of interest if you want to examine the table’s disk file directly. To show the space used by TOAST tables, use a query like the following: SELECT relname, relpages FROM pg_class, (SELECT reltoastrelid FROM pg_class WHERE relname = 'customer') AS ss WHERE oid = ss.reltoastrelid OR oid = (SELECT indexrelid FROM pg_index WHERE indrelid = ss.reltoastrelid) ORDER BY relname; relname | relpages ----------------------+---------- pg_toast_16806 | 0 pg_toast_16806_index | 1 You can easily display index sizes, too: SELECT c2.relname, c2.relpages FROM pg_class c, pg_class c2, pg_index i WHERE c.relname = 'customer' AND c.oid = i.indrelid AND c2.oid = i.indexrelid ORDER BY c2.relname; relname | relpages -------------------+---------- customer_id_index | 26 It is easy to find your largest tables and indexes using this information: SELECT relname, relpages FROM pg_class ORDER BY relpages DESC; relname | relpages ----------------------+---------- bigtable | 3290 customer | 3144 The most important disk monitoring task of a database administrator is to make sure the disk doesn’t become full. A filled data disk will not result in data corruption, but it might prevent useful activity from occurring. If the disk holding the WAL files grows full, database server panic and consequent shutdown might occur. If you cannot free up additional space on the disk by deleting other things, you can move some of the database files to other file systems by making use of tablespaces. Tip Some file systems perform badly when they are almost full, so do not wait until the disk is completely full to take action. If your system supports per-user disk quotas, then the database will naturally be subject to whatever quota is placed on the user the server runs as. Exceeding the quota will have the same bad effects as running out of disk space entirely.","title":"Monitoring","component":"ivorysql-doc","version":"master","name":"daily_monitoring","url":"/ivorysql-doc/master/getting-started/daily_monitoring.html","titles":[{"text":"1. Monitoring Database Activity","hash":"monitoring-database-activity","id":1},{"text":"1.1. Standard Unix Tools","hash":"standard-unix-tools","id":2},{"text":"1.2. The Cumulative Statistics System","hash":"the-cumulative-statistics-system","id":3},{"text":"1.2.1. Statistics Collection Configuration","hash":"statistics-collection-configuration","id":4},{"text":"1.2.2. Viewing Statistics","hash":"viewing-statistics","id":5},{"text":"1.2.3. pg_stat_activity","hash":"pg_stat_activity","id":6},{"text":"1.2.4. pg_stat_replication","hash":"pg_stat_replication","id":7},{"text":"1.2.5. pg_stat_replication_slots","hash":"pg_stat_replication_slots","id":8},{"text":"1.2.6. pg_stat_wal_receiver","hash":"pg_stat_wal_receiver","id":9},{"text":"1.2.7. pg_stat_recovery_prefetch","hash":"pg_stat_recovery_prefetch","id":10},{"text":"1.2.8. pg_stat_subscription","hash":"pg_stat_subscription","id":11},{"text":"1.2.9. pg_stat_subscription_stats","hash":"pg_stat_subscription_stats","id":12},{"text":"1.2.10. pg_stat_ssl","hash":"pg_stat_ssl","id":13},{"text":"1.2.11. pg_stat_gssapi","hash":"pg_stat_gssapi","id":14},{"text":"1.2.12. pg_stat_archiver","hash":"pg_stat_archiver","id":15},{"text":"1.2.13. pg_stat_bgwriter","hash":"pg_stat_bgwriter","id":16},{"text":"1.2.14. pg_stat_wal","hash":"pg_stat_wal","id":17},{"text":"1.2.15. pg_stat_database","hash":"pg_stat_database","id":18},{"text":"1.2.16. pg_stat_database_conflicts","hash":"pg_stat_database_conflicts","id":19},{"text":"1.2.17. pg_stat_all_tables","hash":"pg_stat_all_tables","id":20},{"text":"1.2.18. pg_stat_all_indexes","hash":"pg_stat_all_indexes","id":21},{"text":"1.2.19. pg_statio_all_tables","hash":"pg_statio_all_tables","id":22},{"text":"1.2.20. pg_statio_all_indexes","hash":"pg_statio_all_indexes","id":23},{"text":"1.2.21. pg_statio_all_sequences","hash":"pg_statio_all_sequences","id":24},{"text":"1.2.22. pg_stat_user_functions","hash":"pg_stat_user_functions","id":25},{"text":"1.2.23. pg_stat_slru","hash":"pg_stat_slru","id":26},{"text":"1.2.24. Statistics Functions","hash":"statistics-functions","id":27},{"text":"1.3. View Locks","hash":"view-locks","id":28},{"text":"1.4. Progress Reporting","hash":"progress-reporting","id":29},{"text":"1.4.1. ANALYZE Progress Reporting","hash":"analyze-progress-reporting","id":30},{"text":"1.4.2. CREATE INDEX Progress Reporting","hash":"create-index-progress-reporting","id":31},{"text":"1.4.3. VACUUM Progress Reporting","hash":"vacuum-progress-reporting","id":32},{"text":"1.4.4. CLUSTER Progress Reporting","hash":"cluster-progress-reporting","id":33},{"text":"1.4.5. Base Backup Progress Reporting","hash":"base-backup-progress-reporting","id":34},{"text":"1.4.6. COPY Progress Reporting","hash":"copy-progress-reporting","id":35},{"text":"1.5. Dynamic Tracing","hash":"dynamic-tracing","id":36},{"text":"1.5.1. Compiling for Dynamic Tracing","hash":"compiling-for-dynamic-tracing","id":37},{"text":"1.5.2. Built-in Probes","hash":"built-in-probes","id":38},{"text":"1.5.3. Using Probes","hash":"using-probes","id":39},{"text":"1.5.4. Defining New Probes","hash":"defining-new-probes","id":40},{"text":"2. Monitoring Disk Usage","hash":"monitoring-disk-usage","id":41},{"text":"2.1. Determining Disk Usage","hash":"determining-disk-usage","id":42},{"text":"2.2. Disk Full Failure","hash":"disk-full-failure","id":43}]},"53":{"id":53,"text":"Hardware Parameter Minimum Recommended CPU 4 cores 16 cores RAM 4GB 64GB Storage 800MB,HDD 5GB+,SSD or NvMe Network Gigabit network 10G network Software Currently, IvorySQL supports but is not limited to linux(CentOS 8.X/CentOS Stream 9/Ubuntu). The operating system used for the quick start is CentOS Stream 9. Pre-requirements Before getting started, please create an user and grant it root privileges. All the installation steps will be performed by this user. Here we just name it 'ivorysql'. How to create a sudo user installation Create or edit IvorySQL yum repository configuration /etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 After saving and exiting, you can install IvorySQL 4 with the following steps $ sudo dnf install -y ivorysql5-5.0 Setting environment variables Add below contents in ~/.bash_profile file and source to make it effective: PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile Initializing database $ initdb -D /usr/ivory-5/data The -D option specifies the directory where the database cluster should be stored. This is the only information required by initdb, but you can avoid writing it by setting the PGDATA environment variable, which can be convenient since the database server can find the database directory later by the same variable. For more options, refer to initdb --help. Starting IvorySQL service $ pg_ctl -D /usr/ivory-5/data -l ivory.log start The -D option specifies the file system location of the database configuration files. If this option is omitted, the environment variable PGDATA in <<setting-environment-variables>> is used. -l option appends the server log output to filename. If the file does not exist, it is created. For more options, refer to pg_ctl --help. Confirm it’s successfully started: $ ps -ef | grep postgres ivorysql 3214 1 0 20:35 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 3215 3214 0 20:35 ? 00:00:00 postgres: checkpointer ivorysql 3216 3214 0 20:35 ? 00:00:00 postgres: background writer ivorysql 3218 3214 0 20:35 ? 00:00:00 postgres: walwriter ivorysql 3219 3214 0 20:35 ? 00:00:00 postgres: autovacuum launcher ivorysql 3220 3214 0 20:35 ? 00:00:00 postgres: logical replication launcher ivorysql 3238 1551 0 20:35 pts/0 00:00:00 grep --color=auto postgres Get IvorySQL image from Docker Hub $ docker pull ivorysql/ivorysql:5.0-ubi8 Running IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 Check if the IvorySQL container is running successfully $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql Connect to IovrySQL via psql: $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# The -d option specifies the name of the database to connect to. ivorysql is the default database of IvorySQL. However,IvorySQL of lower versions need the users themselves to connect to postgres database at the first connection and then create the ivorysql database.The latest IvorySQL can do all these for users. For more options, refer to psql --help. When running IvorySQL in Docker, additional parameters need to be added, like: psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 Now you can start your journey of IvorySQL! Enjoy! To explore additional installation methods, please refer to the Installation.","title":"Quick Start","component":"ivorysql-doc","version":"master","name":"quick_start","url":"/ivorysql-doc/master/getting-started/quick_start.html","titles":[{"text":"1. Environmental requirements","hash":"environmental-requirements","id":1},{"text":"2. Quick installation","hash":"quick-installation","id":2},{"text":"2.1. yum installation","hash":"yum-installation","id":3},{"text":"2.2. Running IvorySQL in docker","hash":"running-ivorysql-in-docker","id":4},{"text":"3. Connecting to IvorySQL","hash":"connecting-to-ivorysql","id":5}]},"54":{"id":54,"text":"IvorySQL provides compatibility with the Oracle built-in function SYS_CONTEXT('namespace', 'parameter' [, length ]), which returns the value of the parameter associated with the given context at the current moment. It can be used in both SQL and PL/SQL languages. IvorySQL provides the following built-in namespaces: USERENV - Describes the current session. SYS_SESSION_ROLES - Indicates whether a specified role is currently enabled for the session. The implementation principle of SYS_CONTEXT involves dynamically querying system tables and PostgreSQL’s built-in functions to ensure real-time results. It uses SECURITY INVOKER to ensure the function executes with the caller’s permissions, avoiding privilege escalation issues. The implementation logic is as follows: CREATE OR REPLACE FUNCTION sys.sys_context(a varchar2, b varchar2) RETURNS varchar2 AS $$ DECLARE res varchar2; BEGIN IF upper(a) = 'USERENV' THEN CASE upper(b) WHEN 'CURRENT_SCHEMA' THEN SELECT current_schema() INTO res; WHEN 'LANG' THEN SELECT sys.get_lang() INTO res; ... ELSE RAISE EXCEPTION 'invalid USERENV parameter: %', b; END CASE; ELSIF upper(a) = 'SYS_SESSION_ROLES' THEN CASE upper(b) WHEN 'LOGIN' THEN SELECT CASE WHEN rolcanlogin = 't' THEN 'TRUE' ELSE 'FALSE' END INTO res FROM pg_roles WHERE oid = current_user::regrole::oid; ... ELSE RAISE EXCEPTION 'invalid SYS_SESSION_ROLES parameter: %', b; END CASE; ELSE SELECT current_setting(a||'.'||b, true) INTO res; END IF; RETURN res; END; $$ LANGUAGE plisql SECURITY INVOKER; Parameter Name Return Value CURRENT_SCHEMA The name of the currently active default schema. This value may change during the duration of a session through use of an ALTER SESSION SET CURRENT_SCHEMA statement. This may also change during the duration of a session to reflect the owner of any active definer’s rights object. When used directly in the body of a view definition, this returns the default schema used when executing the cursor that is using the view; it does not respect views used in the cursor as being definer’s rights. CURRENT_SCHEMAID Identifier of the currently active default schema. SESSION_USER The name of the session user (the user who logged on). This may change during the duration of a database session as Real Application Security sessions are attached or detached. For enterprise users, returns the schema. For other users, returns the database user name. If a Real Application Security session is currently attached to the database session, returns user XS$NULL. SESSION_USERID The identifier of the session user (the user who logged on). PROXY_USER Name of the database user who opened the current session on behalf of SESSION_USER. PROXY_USERID Identifier of the database user who opened the current session on behalf of SESSION_USER. CURRENT_USER The name of the database user whose privileges are currently active. This may change during the duration of a database session as Real Application Security sessions are attached or detached, or to reflect the owner of any active definer’s rights object. When no definer’s rights object is active, CURRENT_USER returns the same value as SESSION_USER. When used directly in the body of a view definition, this returns the user that is executing the cursor that is using the view; it does not respect views used in the cursor as being definer’s rights. For enterprise users, returns schema. If a Real Application Security user is currently active, returns user XS$NULL. CURRENT_USERID The identifier of the database user whose privileges are currently active. CURRENT_EDITION_NAME The name of the current edition. CLIENT_PROGRAM_NAME The name of the program used for the database session. Parameter Name Return Value IP_ADDRESS The IP address of the client. HOST Name of the host machine from which the client has connected. ISDBA Returns TRUE if the user has been authenticated as having DBA privileges either through the operating system or through a password file. LANG The abbreviated name for the language, a shorter form than the existing 'LANGUAGE' parameter. LANGUAGE The language and territory currently used by your session, along with the database character set, in this form:language_territory.characterset NLS_DATE_FORMAT The date format for the session. PLATFORM_SLASH The slash character that is used as the file path delimiter for your platform. DB_NAME Name of the database as specified in the DB_NAME initialization parameter. SID The session ID. SESSIONID The auditing session identifier. You cannot use this attribute in distributed SQL statements. CLIENT_INFO Returns up to 64 bytes of user session information that can be stored by an application using the DBMS_APPLICATION_INFO package. ENTRYID The current audit entry number. The audit entryid sequence is shared between fine-grained audit records and regular audit records. You cannot use this attribute in distributed SQL statements. The correct auditing entry identifier can be seen only through an audit handler for standard or fine-grained audit. TERMINAL The operating system identifier for the client of the current session. In distributed SQL statements, this attribute returns the identifier for your local session. In a distributed environment, this is supported only for remote SELECT statements, not for remote INSERT, UPDATE, or DELETE operations. (The return length of this parameter may vary by operating system.) Parameter Name Return Value DBA Returns TRUE if the current user is a database administrator. LOGIN Returns TRUE if the current user is a login role. CREATEROLE Returns TRUE if the current session’s user has the privilege to create roles. CREATEDB Returns TRUE if the current session’s user has the privilege to create databases.","title":"Feature Overview","component":"ivorysql-doc","version":"master","name":"sys_context","url":"/ivorysql-doc/master/oracle_builtin_functions/sys_context.html","titles":[{"text":"1. Implementation Principle","hash":"implementation-principle","id":1},{"text":"2. Parameters supported by namespace USERENV","hash":"parameters-supported-by-namespace-userenv","id":2},{"text":"3. Parameters supported by namespace SYS_SESSION_ROLES","hash":"parameters-supported-by-namespace-sys_session_roles","id":3}]},"55":{"id":55,"text":"IvorySQL provides compatibility with Oracle’s built-in function USERENV('parameter'), which is used to return information about the current session. This is a legacy function, and IvorySQL recommends taht you can use the SYS_CONTEXT function with its built-in USERENV namespace for current functionality. USERENV parses function calls through Bison rules, checks parameter validity, and maps them to corresponding SQL functions. The parsing rules are implemented in ora_gram.y, with the following logic: USERENV '(' Sconst ')' { char *normalized_param = downcase_identifier($3, strlen($3), true, true); #define CHECK_AND_CALL(param, func_name) \\ if (strcmp(normalized_param, param) == 0) \\ $$ = (Node *) makeFuncCall(OracleSystemFuncName(func_name), NIL, COERCE_EXPLICIT_CALL, @1); CHECK_AND_CALL(\"client_info\", \"get_client_info\") else CHECK_AND_CALL(\"entryid\", \"get_entryid\") else CHECK_AND_CALL(\"terminal\", \"get_terminal\") else CHECK_AND_CALL(\"isdba\", \"get_isdba\") else CHECK_AND_CALL(\"lang\", \"get_lang\") else CHECK_AND_CALL(\"language\", \"get_language\") else CHECK_AND_CALL(\"sessionid\", \"get_sessionid\") else CHECK_AND_CALL(\"sid\", \"get_sid\") else ereport(ERROR, (errcode(ERRCODE_FEATURE_NOT_SUPPORTED), errmsg(\"invalid USERENV parameter: \\\"%s\\\".\", $3))); #undef CHECK_AND_CALL } The specific functionality is implemented in builtin_functions—​1.0.sql. For example: CREATE OR REPLACE FUNCTION sys.get_language() RETURNS varchar2 AS $$ SELECT (regexp_split_to_array(current_setting('lc_monetary'), '\\\\.'))[1]||'.'||pg_client_encoding(); $$ LANGUAGE sql STRICT; Parameter Name Return Value sid The user ID of the current session. sessionid Returns the audit session identifier. language Returns the language, territory, and character set of the current database session. lang Returns the ISO abbreviation of the language name. isdba Returns \"TRUE\" if the user has been authenticated as an administrator or has administrator privileges through the operating system or password file; otherwise, returns \"FALSE\".","title":"Feature Overview","component":"ivorysql-doc","version":"master","name":"userenv","url":"/ivorysql-doc/master/oracle_builtin_functions/userenv.html","titles":[{"text":"1. Implementation Principle","hash":"implementation-principle","id":1},{"text":"2. Supported parameters","hash":"supported-parameters","id":2}]},"56":{"id":56,"text":"This document is a design document for the PLSQL anonymous block compatible Oracle syntax function, in order to be compatible with Oracle’s anonymous block statements in IvorySQL. Anonymous blocks are PLSQL structures that dynamically create and execute procedural code without the need to persistently store the code as database objects in the system directory. In this implementation, IvorySQL is mainly compatible with the syntax format of PLSQL anonymous blocks, and the parts we mainly deal with include client tool psql, master server and PSQL side support. declare i integer := 10; begin raise notice '%', i; end; / NOTICE: 10 DECLARE grade CHAR(1); BEGIN grade := 'B'; CASE grade WHEN 'A' THEN raise notice 'Excellent'; WHEN 'B' THEN raise notice 'Very Good'; END CASE; EXCEPTION WHEN CASE_NOT_FOUND THEN raise notice 'No such grade'; END; / NOTICE: Very Good","title":"Compatible with Oracle anonymous block","component":"ivorysql-doc","version":"master","name":"anonymous_block","url":"/ivorysql-doc/master/oracle_compatibility/anonymous_block.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function description","hash":"function-description","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"57":{"id":57,"text":"char varchar varchar2 number binary_float binary_double date timestamp timestamp with time zone timestamp with local time zone interval year to month interval day to second raw long sysdate systimestamp add_months last_day next_day months_between current_date current_timestamp new_time tz_offset trunc instr instrb substr substrb trim ltrim rtrim length lengthb rawtohex replace regexp_replace regexp_substr regexp_instr regexp_like to_number to_char to_date to_timestamp to_timestamp_tz to_yminterval to_dsinterval numtodsinterval numtoyminterval localtimestamp from_tz sys_extract_utc sessiontimezone hextoraw uid USERENV asciistr to_multi_byte to_single_byte compose decompose sys_context function: view the corresponding date and time, the test cases are as follows: Query the date of the current system: select sysdate() from dual; sysdate ------------ 2023-07-06 (1 row) Check the date pushed forward by 1 day: select sysdate()-1 from dual; ?column? ------------ 2023-07-05 (1 row) function: return the current system date and time (including microseconds and time zone) on the local database, the test cases are as follows: Date and time to query the current date: select systimestamp() from dual; systimestamp ----------------------------------- 2023-07-06 10:18:31.674322 +08:00 (1 row) function: the function adds a date to the number of months (n), and returns the same day that is n months apart, supporting parameters: date, number; The test cases are as follows: Check the same day of the following month on the current date (July 6): select add_months(sysdate(),1) from dual; add_months ------------ 2023-08-06 (1 row) Query the same day of the previous month for the current date: select add_months(sysdate(),-1) from dual; add_months ------------ 2023-06-06 (1 row) function: return the last day of the month where the specified date is located, support parameters: date, the test cases are as follows: Check the last day of the month in which the day is located: select last_day(sysdate())from dual; last_day ------------ 2023-07-31 (1 row) Query the last day of the month on which a day falls: select last_day(to_date('2019-09-01'))from dual; last_day ------------ 2019-09-30 (1 row) function: return the next date of the specified date. Supported parameters: date, integer /date, text, Note: When the second parameter in the function passes the number of weeks more hours than the existing week, the date of the next week will be returned; When the date passed by the second parameter in the function is greater than the existing number of weeks, the corresponding day of the week of the week is returned. The test cases are as follows: Query the next day of the current date: select next_day(sysdate(),1) from dual; next_day ------------ 2023-07-07 (1 row) Next Friday for the current date: select next_day(sysdate(),'FRIDAY') from dual; next_day ------------ 2023-07-07 (1 row) function: return the month of difference between date1 and date2 of date type, support parameters: date, date, description: if date1 is later than date2, return a positive number; If date1 is earlier than date2, a negative number is returned; If date1 and date2 are the same day of a month, the return result is an integer; If not the same day, results with decimal parts are returned on a monthly basis of 31 days. The test cases are as follows: To find the month that differs between the same day in different months: select months_between(to_date('2023-07-06'),to_date('2023-08-06')) from dual; months_between ---------------- -1 (1 row) Query the month that differs between different days of different months: select months_between(to_date('2023-07-06'),to_date('2023-08-05')) from dual; months_between -------------------- -0.967741935483871 (1 row) function: return the current date of the current time zone, the test cases are as follows: To query the current date in the current time zone: select current_date from dual; current_date -------------- 2023-07-06 (1 row) function: return the current date and current time of the current time zone, including the current time zone information. Support parameters: integer, Note: The returned time can be adjusted with precision. The test cases are as follows: To query the current date and time in the current time zone: select current_timestamp from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:01.440600 +08:00 (1 row) Query the current date and time in the current time zone (the precision is adjusted to the first three decimal places): select current_timestamp(3) from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:14.182000 +08:00 (1 row) function: return the date in another time zone corresponding to a certain time zone, support parameters: date, text, text, the test case is as follows: Returns the date for the current date in another time zone: select sysdate() bj_time,new_time(sysdate(),'PDT','GMT')los_angles from dual; bj_time | los_angles ------------+------------ 2023-07-06 | 2023-07-06 (1 row) function: return the offset of the given time zone and the standard time zone, support parameters: text, the test case is as follows: Returns the offset of a given time zone from the standard time zone: select tz_offset('US/Eastern') from dual; tz_offset ----------- -04:00 (1 row) function: you can intercept the date to get the desired value, such as year, month, day, hour, minute, support parameters: date/date, text, the test case is as follows: Intercept the current date: select trunc(sysdate()) from dual; trunc ------------ 2023-07-06 (1 row) Truncating the year, only the year is correct, and the month and day are not accurate values: select trunc(sysdate(),'yyyy') from dual; trunc ------------ 2023-01-01 (1 row) Intercept the month, the return value only the month is correct, the year and day are not accurate values: select trunc(sysdate(),'mm') from dual; trunc ------------ 2023-07-01 (1 row) function: string search that checks whether the source string contains the target string and returns the match position. Supported parameter forms are as follows: instr(string, str) instr(string, str, start_position, nth_appearance) The following are test cases: Returns the first match by default: SELECT INSTR('database administration', 'data') FROM DUAL; instr ------- 1 (1 row) Specify the starting position and match sequence: SELECT INSTR('database administration', 'i', 1, 2) FROM DUAL; instr ------- 15 (1 row) Supports reverse search from the end of the string: SELECT INSTR('mississippi river', 's', -5, 2) FROM DUAL; instr ------- 6 (1 row) Returns 0 when no match is found: SELECT INSTR('database administration', 'z') FROM DUAL; instr ------- 0 (1 row) instr can also be used for like-style fuzzy matching: select * from tableName where instr(name,'helloworld')>0; Implementation notes: Searches for one string inside another with a classic brute-force algorithm. Reuses the text_instring helper function. Uses the isByte flag to detect multibyte encodings and branch between single-byte and multibyte handling. The sign of position decides forward or backward search and sets loop bounds and step. Iterates byte by byte through the source string, comparing with the pattern until a match is found. function: string lookup function, return the position of the string, support parameters: varchar2, text, number DEFAULT 1, number DEFAULT 1, the following are test cases: RETURNS THE POSITION OF THE STRING IN CORPORATE FLOOR WHEN THE FIRST OR OCCURS BY DEFAULT: SELECT INSTRB('CORPORATE FLOOR','OR') \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 2 (1 row) Returns the position of the string in the corporate floor where the query starts with the fifth character and the second occurrence of or: SELECT INSTRB('CORPORATE FLOOR','OR',5,2) \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 14 (1 row) function: intercept string function, truncated in characters, support parameters: text, integer, test cases are as follows: Intercept the string from the fifth character in 'It is nice today', followed by: SELECT SUBSTR('It is nice today',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- s nice today (1 row) function: intercept string function, intercept in bytes, support parameters: varchar2, number/varchar2, number, number, the test cases are as follows: Intercept the string starting with the fifth byte in 'It’s nice today' and then onwards: SELECT SUBSTRB('It is nice today',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- s nice today (1 row) Intercept the string in 'It is nice today' starting with the fifth byte and ending with the eighth byte: SELECT SUBSTRB('It is nice today',5,8) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- s nice t (1 row) function: remove the left and right spaces or corresponding data of the specified string, support parameters: varchar2 / varchar2, varchar2, the test cases are as follows: Remove the left and right spaces of ' aaa bbb ccc ': select trim(' aaa bbb ccc ')trim from dual; trim ------------- aaa bbb ccc (1 row) Remove aaa from 'aaa bbb ccc': select trim('aaa bbb ccc','aaa')trim from dual; trim ---------- bbb ccc (1 row) function: remove the left space or corresponding data of the specified string, support parameters: varchar2 / varchar2, varchar2, the test cases are as follows: Remove the space to the left of ' abcdefg ': select ltrim(' abcdefg ')ltrim from dual; ltrim ------------ abcdefg (1 row) Traverse from the left side of 'abcdefg', remove it as soon as a character appears in 'fegab', and return the result if it is absent: select ltrim('abcdefg','fegab')ltrim from dual; ltrim ------- cdefg (1 row) function: remove the space on the right side of the specified string, the test case is as follows: Remove the space to the right of ' abcdefg ': select rtrim(' abcdefg ')rtrim from dual; rtrim ---------------- abcdefg (1 row) Traverse from the right side of 'abcdefg', remove it as soon as a character appears in 'fegab', and return the result if it is absent: select rtrim('abcdefg','fegab')rtrim from dual; rtrim ------- abcd (1 row) function: find the length of the specified string character, support parameters: char/integer/varchar2 The test cases are as follows: Query the character length of 223: select length(223) from dual; length -------- 3 (1 row) Query the character length of '223': select length('223') from dual; length -------- 3 (1 row) To query the character length of 'ivorysql database' : select length('ivorysql database') from dual; length -------- 17 (1 row) function: find the length of the specified string byte, support parameters: char/bytea/varchar2 test cases are as follows: Query the byte lengthb of 'ivorysql': select lengthb('ivorysq'::char) from dual; lengthb --------- 1 (1 row) Query the byte lengthb of '0x2C': select lengthb('0x2C'::bytea) from dual; lengthb --------- 4 (1 row) Query the byte lengthb of the 'ivorysql database': select lengthb('ivorysql database') from dual; lengthb --------- 17 (1 row) function: replace the character in the specified string or delete the character, support parameters: text, text, text/varchar2, varchar2, varchar2 DEFAULT NULL::varchar2, test for example: Replace 'j' in 'jack and jue' with 'bl' : select replace('jack and jue','j','bl') from dual; replace ---------------- black and blue (1 row) Remove the 'j' in 'jack and jue' : select replace('jack and jue','j') from dual; replace ------------ ack and ue (1 row) which is an extension of the replace function. Function: Used to perform matching and replacement through regular expressions. Supported parameters: text, text, text /text, text, text, integer/varchar2, varchar2/varchar2, varchar2 varchar2, varchar2 varchar2, for example: Replace the matched number with *#: select regexp_replace('01234abcd56789','[0-9]','*#')from dual; regexp_replace -------------------------- *#*#*#*#*#abcd*#*#*#*#*# (1 row) Start with the second number by replacing the matched number with *#: select regexp_replace('01234abcd56789','[0-9]','*#',2)from dual; regexp_replace ------------------------- 0*#*#*#*#abcd*#*#*#*#*# Delete '01' from '01234abcd56789': select regexp_replace('01234abcd56789','01')from dual; regexp_replace ---------------- 234abcd56789 (1 row) Replace 01234abcd56789' with 'xxx': select regexp_replace('01234abcd56789','012','xxx')from dual; regexp_replace ---------------- xxx34abcd56789 (1 row) function: pick up the character substring described by the regular expression, support parameters: text, text, integer /text, text, integer, integer / text, text, integer, integer, text /varchar2, varchar2, the test cases are as follows: Query the 012 string starting with the first number in '012ab34': select regexp_substr('012ab34', '012',1) from dual; regexp_substr --------------- 012 (1 row) Query the 012 string in '012ab34' starting from the first number of the first group: select regexp_substr('012ab34', '012',1,1) from dual; regexp_substr --------------- 012 (1 row) Query '012a012Ab34' for case-insensitive 012 strings starting from the first number of the first group: select regexp_substr('012a012Ab34', '012A',1,1,'i') from dual; regexp_substr --------------- 012a (1 row) Query '012a012Ab34' for case-sensitive 012 strings starting from the first group of numbers: select regexp_substr('012a012Ab34', '012A',1,1,'c') from dual; regexp_substr --------------- 012A (1 row) Query the 'Database' substring in 'Data': select regexp_substr('Database' , 'Data') from dual; regexp_substr --------------- Data (1 row)s function: used to calibrate the start position of the character substring that conforms to the regular expression, support parameters: text, text, integer /text, text, integer, integer / text, text, integer, integer, text, integer / varchar2, varchar2, the test case is as follows: Query 'abcaBcabc' for the position of the abc substring starting from the first character: SELECT regexp_instr('abcaBcabc', 'abc', 1); regexp_instr -------------- 1 (1 row) Query 'abcaBcabc' starting from the first character, where the abc substring appears for the third time: SELECT regexp_instr('abcaBcabc', 'abc', 1, 3); regexp_instr -------------- 7 (1 row) Query 'abcabcabc' starting from the first character and occurring after the second occurrence of the abc substring: SELECT regexp_instr('abcaBcabc', 'abc', 1, 2,1); regexp_instr -------------- 7 (1 row) Query 'abcaBcabc' from the first character, where it occurs after the first occurrence of the abc substring (case sensitive): SELECT regexp_instr('abcaBcabc', 'abc',1,2,1,'c'); regexp_instr -------------- 7 (1 row) Query the 'Database' substring in 'Data': SELECT regexp_instr('Database', 'Data'); regexp_instr -------------- 1 (1 row) function: similar to like, used for fuzzy queries. Supported parameters: varchar2, varchar2 /varchar2, varchar2 varchar2, First create a regexp_like table for the test case query: create table t_regexp_like ( id varchar(4), value varchar(10) ); insert into t_regexp_like values ('1','1234560'); insert into t_regexp_like values ('2','1234560'); insert into t_regexp_like values ('3','1b3b560'); insert into t_regexp_like values ('4','abc'); insert into t_regexp_like values ('5','abcde'); insert into t_regexp_like values ('6','ADREasx'); insert into t_regexp_like values ('7','123 45'); insert into t_regexp_like values ('8','adc de'); insert into t_regexp_like values ('9','adc,.de'); insert into t_regexp_like values ('10','abcbvbnb'); insert into t_regexp_like values ('11','11114560'); The test cases are as follows: Query t_regexp_like columns with abc in the table: select * from t_regexp_like where regexp_like(value,'abc'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) Query t_regexp_like columns with ABC in the table (not case sensitive): select * from t_regexp_like where regexp_like(value,'ABC','i'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) function: is to change some processed strings arranged in a certain format back to a numeric format, support parameters: text/text, text test cases are as follows: Convert the string '-34,338,492' to numeric format: SELECT to_number('-34,338,492', '99,999,999') from dual; to_number ----------- -34338492 (1 row) Convert the string '5.01-' to numeric format: SELECT to_number('5.01-', '9.99S'); to_number ----------- -5.01 (1 row) function: convert numbers or dates to character types, support parameters: date/date, text/timestamp/timestamp, text test cases are as follows: To convert the current system date to character format: select to_char(sysdate()) from dual; to_char ------------ 2023-07-10 (1 row) Convert current system date to month/day/year character format: select to_char(sysdate(),'mm/dd/yyyy') from dual; to_char ------------ 07/10/2023 (1 row) Converts the timestamp format of the current date to character format SELECT to_char(sysdate()::timestamp); to_char ---------------------------- 2023-07-10 09:46:44.000000 Convert timestamp format of current date to month/date/year character format: SELECT to_char(sysdate()::timestamp,'MM-YYYY-DD'); to_char ------------ 07-2023-10 (1 row) function: convert character type to date type, support parameters: text/text, text test cases are as follows: Convert '2023/07/06' to date type: select to_date('20230706') from dual; to_date ------------ 2023-07-06 (1 row) Convert '-44-02-01' to date type: SELECT to_date('-44,0201','YYYY-MM-DD'); to_date ------------ 0044-02-01 (1 row) function: can store year, month, day, hour, minute, second, and can also store fractional parts of seconds. Supported parameters: text/text, text test cases are as follows: Query '2018-11-02 12:34:56.025' output as a date: SELECT to_timestamp('20181102.12.34.56.025'); to_timestamp ---------------------------- 2018-11-02 12:34:56.025000 (1 row) Query '2011,12,18 11:38' output as a date: SELECT to_timestamp('2011,12,18 11:38 ', 'YYYY-MM-DD HH24:MI:SS'); to_timestamp ---------------------------- 2011-12-18 11:38:00.000000 (1 row) function: according to the time query, the time string has T, Z and milliseconds, time zone. The test cases are as follows: Query '2016-10-9 14:10:10.123000' output as a date: SELECT to_timestamp_tz('2016-10-9 14:10:10.123000') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-10-09 14:10:10.123000 +08:00 (1 row) Query '10-9-2016 14:10:10.123000 +8:30' output as a date: SELECT to_timestamp_tz('10-9-2016 14:10:10.123000 +8:30', 'DD-MM-YYYY HH24:MI:SS.FF TZH:TZM') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-09-10 13:40:10.123000 +08:00 (1 row) function: convert a string type to a year and month time difference type, support parameters: text, The test cases are as follows: Query the date after two years and eight months after '20110101': select to_date('20110101','yyyymmdd')+to_yminterval('02-08') from dual; ?column? ------------ 2013-09-01 (1 row) function: add a date plus a certain hour or number of days into another date, support parameters: text, test cases are as follows: Query the current system time plus the date in 9 and a half hours (currently 2023-07-06, 18:00): select sysdate()+to_dsinterval('0 09:30:00')as newdate from dual; newdate ------------ 2023-07-07 (1 row) function: convert numbers into time interval type data. The supporting parameters: double precision, text test cases are as follows: Convert 100.00 hours to interval type data: SELECT NUMTODSINTERVAL(100.00, 'hour'); numtodsinterval ------------------------------- +000000004 04:00:00.000000000 (1 row) Convert 100 minutes to interval type data: SELECT NUMTODSINTERVAL(100, 'minute'); numtodsinterval ------------------------------- +000000000 01:40:00.000000000 (1 row) function: convert numbers into date interval type data. Convert 1, year to date interval: double precision, text, the test case is as follows: SELECT NUMTOYMINTERVAL(1.00,'year'); numtoyminterval ----------------- +000000001-00 (1 row) Convert 1, mouth to date interval: SELECT NUMTOYMINTERVAL(1,'month'); numtoyminterval ----------------- +000000000-01 (1 row) function: return the date and time in the session, support parameters: integer, add parameters to the function as precision, the test cases are as follows: To return the date and time in the current session: select localtimestamp from dual; localtimestamp ---------------------------- 2023-07-07 09:18:15.896472 (1 row) Returns the date and time in the current session with a precision of 1: select localtimestamp(1) from dual; localtimestamp ---------------------------- 2023-07-07 09:18:16.100000 (1 row) function: convert time from one time zone to another, support parameters; timestamp, text, the test case is as follows: Convert '2000-03-28 08:00:00', '3:00' to the current time zone: SELECT FROM_TZ(TIMESTAMP '2000-03-28 08:00:00', '3:00') FROM DUAL; from_tz ----------------------------------- 2000-03-28 13:00:00.000000 +08:00 (1 row) function: convert a timestamptz to UTC time zone time. Supported parameters: timestamp with time zone The test cases are as follows: Query conversion timestamp '2000-03-28 11:30:00.00 -8:00' to the time after UTC time zone: select sys_extract_utc(timestamp '2000-03-28 11:30:00.00 -8:00') from dual; sys_extract_utc ---------------------------- 2000-03-28 19:30:00.000000 (1 row) function: view time zone details, test cases are as follows: To view the details of the current time zone: select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Shanghai (1 row) After modifying the timezone, check the time zone belief information: set timezone = 'Asia/Hong_Kong'; SET select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Hong_Kong (1 row) function: convert the binary value represented by the string into a RAW value. Support parameters: text, the test cases are as follows: Convert the string 'abcdef' to a raw value: select hextoraw('abcdef')from dual; hextoraw ---------- \\xabcdef (1 row) function: get the instance name of the database. The test cases are as follows: Get the instance name of the current database: select uid() from dual; uid ----- 10 (1 row) function: return the information of the current user environment, the test cases are as follows: Check whether the current user is DBA, and if so, return true: select userenv('isdba')from dual; get_isdba ----------- TRUE (1 row) To view the session flag: select userenv('sessionid')from dual; get_sessionid --------------- 1 (1 row) function: input string, return ASCII characters, the test cases are as follows: string with only ascii chars: select asciistr('Hello, World!') from dual; asciistr --------------- Hello, World! (1 row) string with non-ascii chars: select asciistr('你好') from dual; asciistr ------------ \\4F60\\597D string with mixed ascii and non-ascii: select asciistr('ABÄCDE') from dual; asciistr ------------ AB\\00C4CDE (1 row) function: Convert half-width characters in a string to full-width characters: input half-width characters, Convert to full-width characters: select to_multi_byte('1.2'::text) ; to_multi_byte --------------- １．２ function: Convert full-width characters in a string to half-width characters: input full-width characters, Convert to half-width characters: select to_single_byte('１．２'); to_single_byte ---------------- 1.2 function: Combine base characters and combining marks into a composite Unicode character: input base character 'a' with a combining mark '768', return à： select compose('a'||chr(768)) from dual; compose --------- à (1 row) function: Decompose composite Unicode characters (like those with accents or special symbols) into their base characters and combining marks. input é, return a base character 'e' with a combining mark '301': select asciistr(decompose('é')) from dual; asciistr ---------- e\\0301 which returns the value of the parameter associated with the given context at the current moment. It can be used in both SQL and PL/SQL languages. ivorysql=# select sys_context('USERENV','DB_NAME'); sys_context ------------- ivorysql (1 row)","title":"Built-in data types and built-in functions","component":"ivorysql-doc","version":"master","name":"builtin_types_functions","url":"/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html","titles":[{"text":"1. Built-in data types","hash":"built-in-data-types","id":1},{"text":"2. Built-in functions","hash":"built-in-functions","id":2},{"text":"3. Built-in function descriptions","hash":"built-in-function-descriptions","id":3},{"text":"3.1. sysdate function","hash":"sysdate-function","id":4},{"text":"3.2. systimestamp function","hash":"systimestamp-function","id":5},{"text":"3.3. add_months function","hash":"add_months-function","id":6},{"text":"3.4. last_day function","hash":"last_day-function","id":7},{"text":"3.5. next_day function","hash":"next_day-function","id":8},{"text":"3.6. months_between function","hash":"months_between-function","id":9},{"text":"3.7. current_date function","hash":"current_date-function","id":10},{"text":"3.8. current_timestamp function","hash":"current_timestamp-function","id":11},{"text":"3.9. new_time function","hash":"new_time-function","id":12},{"text":"3.10. tz_offset function","hash":"tz_offset-function","id":13},{"text":"3.11. trunc function","hash":"trunc-function","id":14},{"text":"3.12. instr function","hash":"instr-function","id":15},{"text":"3.13. instrb function","hash":"instrb-function","id":16},{"text":"3.14. substr function","hash":"substr-function","id":17},{"text":"3.15. substrb function","hash":"substrb-function","id":18},{"text":"3.16. trim function","hash":"trim-function","id":19},{"text":"3.17. ltrim function","hash":"ltrim-function","id":20},{"text":"3.18. rtrim function","hash":"rtrim-function","id":21},{"text":"3.19. length function","hash":"length-function","id":22},{"text":"3.20. lengthb function","hash":"lengthb-function","id":23},{"text":"3.21. replace function","hash":"replace-function","id":24},{"text":"3.22. regexp_replace function","hash":"regexp_replace-function","id":25},{"text":"3.23. regexp_substr function","hash":"regexp_substr-function","id":26},{"text":"3.24. regexp_instr function","hash":"regexp_instr-function","id":27},{"text":"3.25. regexp_like function","hash":"regexp_like-function","id":28},{"text":"3.26. to_number function","hash":"to_number-function","id":29},{"text":"3.27. to_char function","hash":"to_char-function","id":30},{"text":"3.28. to_date function","hash":"to_date-function","id":31},{"text":"3.29. to_timestamp function","hash":"to_timestamp-function","id":32},{"text":"3.30. to_timestamp_tz function","hash":"to_timestamp_tz-function","id":33},{"text":"3.31. to_yminterval function","hash":"to_yminterval-function","id":34},{"text":"3.32. to_dsinterval function","hash":"to_dsinterval-function","id":35},{"text":"3.33. numtodsinterval function","hash":"numtodsinterval-function","id":36},{"text":"3.34. numtoyminterval function","hash":"numtoyminterval-function","id":37},{"text":"3.35. localtimestamp function","hash":"localtimestamp-function","id":38},{"text":"3.36. from_tz function","hash":"from_tz-function","id":39},{"text":"3.37. sys_extract_utc function","hash":"sys_extract_utc-function","id":40},{"text":"3.38. sessiontimezone function","hash":"sessiontimezone-function","id":41},{"text":"3.39. hextoraw function","hash":"hextoraw-function","id":42},{"text":"3.40. uid function","hash":"uid-function","id":43},{"text":"3.41. USERENV function","hash":"userenv-function","id":44},{"text":"3.42. ASCIISTR function","hash":"asciistr-function","id":45},{"text":"3.43. TO_MULTI_BYTE function","hash":"to_multi_byte-function","id":46},{"text":"3.44. TO_SINGLE_BYTE function","hash":"to_single_byte-function","id":47},{"text":"3.45. COMPOSE function","hash":"compose-function","id":48},{"text":"3.46. DECOMPOSE function","hash":"decompose-function","id":49},{"text":"3.47. SYS_CONTEXT function","hash":"sys_context-function","id":50}]},"58":{"id":58,"text":"In IvorySQL, the CALL statement supports invoking standalone function stored procedures, as well as functions and stored procedures within packages or object types. The CALL syntax for invoking functions adds an INTO clause, where the insertion target is a host variable. CALL supports invoking standalone functions and stored procedures, as well as those defined within packages. When using CALL to invoke functions, the INTO clause syntax is added, with the insertion target being a host variable. When calling functions/stored procedures with no parameters or all default values, empty parentheses cannot be omitted. CALL supports referencing bind variables in parameters or the INTO clause when invoking functions and stored procedures. The binding variables corresponding to OUT parameters in the CALL statement support validation of precision and data types. Output binding variables are not allowed to be repeatedly bound. -- Create function ivorysql=# create or replace function f_defs(a number default 1314) ivorysql-# return number ivorysql-# is ivorysql-# begin ivorysql-# raise notice '%', a; ivorysql-# return a; ivorysql-# end; ivorysql-# / CREATE FUNCTION -- Declare bind variable ivorysql=# variable x number -- Call the function and retrieve the return value ivorysql=# call f_defs() into :x; NOTICE: 1314 Call completed. ivorysql=# print x X ------ 1314 ivorysql=# create table tb1(c1 int); CREATE TABLE -- Create package specification ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# procedure test_p ; ivorysql-# end; ivorysql-# / CREATE PACKAGE -- Create package body ivorysql=# create or replace package body pkg is ivorysql-# procedure test_p is ivorysql-# begin ivorysql-# insert into tb1 values(1); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 2; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY -- Call the procedure in the package ivorysql=# call pkg.test_p(); CALL ivorysql=# select * from tb1; c1 ----- 1 (1 row) -- Create a function with default parameters ivorysql=# CREATE OR REPLACE FUNCTION default_arg_func(p_num NUMBER DEFAULT 100) RETURN NUMBER AS ivorysql-# BEGIN ivorysql-# RETURN p_num + 5; ivorysql-# END; ivorysql-# / CREATE FUNCTION -- Correctly call a function with default parameters (must include parentheses) ivorysql=# VARIABLE default_result NUMBER; ivorysql=# CALL default_arg_func() INTO :default_result; -- Use the default value of 100 Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 105 -- Call with parameters ivorysql=# CALL default_arg_func(200) INTO :default_result; Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 205 -- Set input bind variables ivorysql=# VARIABLE input_num NUMBER = 7; -- Call a function using bind variables as parameters ivorysql=# VARIABLE func_result NUMBER; ivorysql=# CALL stand_alone_func(:input_num) INTO :func_result; Call completed. ivorysql=# PRINT func_result; FUNC_RESULT ------------- 14 -- Create a procedure with OUT parameters ivorysql=# CREATE OR REPLACE PROCEDURE out_param_proc( ivorysql(# p_in IN VARCHAR2, ivorysql(# p_out OUT VARCHAR2, ivorysql(# p_num_out OUT NUMBER ivorysql(# ) AS ivorysql-# BEGIN ivorysql-# p_out := p_in || ' processed'; ivorysql-# p_num_out := LENGTH(p_in); ivorysql-# END; ivorysql-# / CREATE PROCEDURE -- Test OUT parameter type matching ivorysql=# VARIABLE out_var VARCHAR2(50); ivorysql=# VARIABLE num_var NUMBER; ivorysql=# CALL out_param_proc('Test input', :out_var, :num_var); Call completed. ivorysql=# PRINT out_var; OUT_VAR ---------------------- Test input processed ivorysql=# PRINT num_var; NUM_VAR --------- 10 -- Test insufficient OUT parameter precision (will be truncated) ivorysql=# VARIABLE short_out VARCHAR2(5); ivorysql=# CALL out_param_proc('Long input string', :short_out, :num_var); Call completed. ivorysql=# PRINT short_out; SHORT_OUT ----------- Long -- Test type mismatch (will throw an error) ivorysql=# VARIABLE wrong_type NUMBER; ivorysql=# CALL out_param_proc('Test', :wrong_type, :num_var); ERROR: invalid input syntax for type numeric: \"Test processed\" -- Prepare binding variables ivorysql=# VARIABLE dup_var VARCHAR2(100); -- Attempting duplicate binding (will throw an error) ivorysql=# CALL out_param_func('Test', :dup_var) INTO :dup_var; ERROR: output parameter cannot be a duplicate bind -- Correct approach: Use different binding variables ivorysql=# VARIABLE out1 VARCHAR2(100); ivorysql=# CALL out_param_proc('Correct usage', :out1, :num_var); Call completed. ivorysql=# PRINT out1; OUT1 ------------------------- Correct usage processed","title":"Compatible with Oracle’s CALL INTO","component":"ivorysql-doc","version":"master","name":"compat_call_into","url":"/ivorysql-doc/master/oracle_compatibility/compat_call_into.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3},{"text":"3.1. Call INTO invokes a function and inserts the result into a host variable.","hash":"call-into-invokes-a-function-and-inserts-the-result-into-a-host-variable","id":4},{"text":"3.2. Call the stored procedure in the package","hash":"call-the-stored-procedure-in-the-package","id":5},{"text":"3.3. Call with no parameters or default parameters","hash":"call-with-no-parameters-or-default-parameters","id":6},{"text":"3.4. Reference bind variables in parameters or INTO clauses","hash":"reference-bind-variables-in-parameters-or-into-clauses","id":7},{"text":"3.5. The OUT parameters in CALL statements support precision and data type validation","hash":"the-out-parameters-in-call-statements-support-precision-and-data-type-validation","id":8},{"text":"3.6. Output binding variables do not allow duplicate binding.","hash":"output-binding-variables-do-not-allow-duplicate-binding","id":9}]},"59":{"id":59,"text":"In order to meet the case compatibility of PG and Oracle’s reference identifiers, ivorysql has designed three case conversion modes for reference identifiers. Select the conversion mode via the GUC parameter \"identifier_case_switch\"; If the value of the guc parameter \"identifier_case_switch\" is \"normal\": 1). The letters in the identifier referenced by the double quotation mark are left unchanged. If the value of the guc parameter \"identifier_case_switch\" is \"interchange\": 1). If the letters in the identifier referenced by the double quotation mark are all uppercase, uppercase is converted to lowercase. 2). If the letters in the identifier referenced by the double quotation mark are all lowercase, lowercase is converted to uppercase. 3). If the letters in the identifier enclosed in double quotation marks are mixed-case, the identifier is left unchanged. If the value of the guc parameter \"identifier_case_switch\" is \"lowercase\": 1). If the letters in the identifier referenced by the double quotation mark are all uppercase, uppercase is converted to lowercase. 2). If the letters in the identifier enclosed in double quotation marks are mixed-case, the identifier is left unchanged. Add the -C option to the initdb program to set the case conversion mode, and the corresponding value of -C is: \"normal\" ------ \"0\"synonymy \"interchange\" ------ \"1\"synonymy \"lowercase\" ------ \"2\"synonymy During initialization of the database cluster, the case conversion pattern is saved to the global/pg_control file in the data directory; normal ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = normal; SET ivorysql=# CREATE TABLE \"NORMAL_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Normal_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"normal_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"NORMAL_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Normal_1\"; ERROR: relation \"Normal_1\" does not exist LINE 1: select * from \"Normal_1\"; ivorysql=# select * from \"normal_1\"; ERROR: relation \"normal\" does not exist LINE 1: select * from \"normal\"; ivorysql=# select * from NORMAL_1; ERROR: relation \"normal_1\" does not exist LINE 1: select * from NORMAL_1; ivorysql=# select * from \"Normal_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_2\"; ERROR: relation \"NORMAL_2\" does not exist LINE 1: select * from \"NORMAL_2\"; ivorysql=# select * from \"normal_2\"; ERROR: relation \"normal_2\" does not exist LINE 1: select * from \"normal_2\"; ivorysql=# select * from Normal_2; ERROR: relation \"normal_2\" does not exist LINE 1: select * from Normal_2; ivorysql=# select * from \"normal_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_3\"; ERROR: relation \"NORMAL_3\" does not exist LINE 1: select * from \"NORMAL_3\"; ivorysql=# select * from \"Normal_3\"; ERROR: relation \"Normal_3\" does not exist LINE 1: select * from \"Normal_3\"; ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE interchange ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = interchange; SET ivorysql=# CREATE TABLE \"INTER_CHANGE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Inter_Change_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"inter_change_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"INTER_CHANGE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_1\"; ERROR: relation \"Inter_Change_1\" does not exist LINE 1: select * from \"Inter_Change_1\"; ivorysql=# select * from \"inter_change_1\"; ERROR: relation \"INTER_CHANGE_1\" does not exist LINE 1: select * from \"inter_change_1\"; ivorysql=# select * from INTER_CHANGE_1; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_2\"; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from \"INTER_CHANGE_2\"; ivorysql=# select * from \"inter_change_2\"; ERROR: relation \"INTER_CHANGE_2\" does not exist LINE 1: select * from \"inter_change_2\"; ivorysql=# select * from Inter_Change_2; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from Inter_Change_2; ivorysql=# select * from \"inter_change_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_3\"; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# select * from \"Inter_Change_3\"; ERROR: relation \"Inter_Change_3\" does not exist LINE 1: select * from \"Inter_Change_3\"; ivorysql=# select * from inter_change_3; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# drop table \"INTER_CHANGE_1\"; DROP TABLE ivorysql=# drop table \"Inter_Change_2\"; DROP TABLE ivorysql=# drop table \"inter_change_3\"; DROP TABLE lowercase ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = lowercase; SET ivorysql=# CREATE TABLE \"LOWER_CASE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Lower_Case_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"lower_case_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"LOWER_CASE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_1\"; ERROR: relation \"Lower_Case_1\" does not exist LINE 1: select * from \"Lower_Case_1\"; ivorysql=# select * from \"lower_case_1\"; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from LOWER_CASE_1; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from \"Lower_Case_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"LOWER_CASE_2\"; ivorysql=# select * from \"lower_case_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"lower_case_2\"; ivorysql=# select * from Lower_Case_2; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from Lower_Case_2; ivorysql=# select * from \"lower_case_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_3\"; ERROR: relation \"Lower_Case_3\" does not exist LINE 1: select * from \"Lower_Case_3\"; ivorysql=# select * from LOWER_CASE_3; c1 | c2 ----+---- (0 行记录) ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE","title":"Reference identifier case conversion design","component":"ivorysql-doc","version":"master","name":"compat_case_conversion","url":"/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function","hash":"function","id":2},{"text":"2.1. Three modes of case conversion (interchange by default)","hash":"three-modes-of-case-conversion-interchange-by-default","id":3},{"text":"2.2. When the database cluster is initialized","hash":"when-the-database-cluster-is-initialized","id":4},{"text":"2.3. Use Cases","hash":"use-cases","id":5}]},"60":{"id":60,"text":"In IvorySQL’s Oracle compatibility mode, support converting empty strings to NULL for storage, providing behavior consistent with Oracle Database. Oracle compatibility mode supports converting empty strings to NULL for storage. The feature is controlled by parameter ivorysql.enable_emptystring_to_null, with default value on. When this parameter is enabled, inserting an empty string will automatically convert it to NULL value for storage. NULL values after conversion can be queried using the IS NULL condition. -- Create test table ivorysql=# create table abc (id int); CREATE TABLE -- Check empty string to NULL parameter status ivorysql=# show ivorysql.enable_emptystring_to_null; ivorysql.enable_emptystring_to_NULL ------------------------------------- on (1 row) -- Insert empty string ivorysql=# insert into abc values(''); INSERT 0 1 -- Query table data, displays as NULL ivorysql=# select * from abc; id \\---- (1 row) -- Query using IS NULL condition ivorysql=# select * from abc where id is null; id \\---- (1 row)","title":"Oracle Compatible Empty String to NULL","component":"ivorysql-doc","version":"master","name":"compat_empty_string_to_null","url":"/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3}]},"61":{"id":61,"text":"This document explains the purpose of Force View in IvorySQL and shows how it enables users to create placeholder views when dependencies are not yet ready while keeping the behavior aligned with Oracle. Force View supports migrating views even when dependent tables are not yet ready, and can swiftly revert to regular views through automatic or explicit compilation once dependencies are satisfied. CREATE [OR REPLACE] FORCE VIEW: Creates a view object even when the query references missing tables or functions. The system retains the SQL definition and returns the message WARNING: View created with compilation errors. Automatic compilation: When a Force view is accessed, IvorySQL attempts recompilation. If it succeeds, the view becomes a normal view; if it fails, it raises view \"<schema>.<name>\" has errors. Dependency fallback: When a normal view is invalidated because dependencies are removed or altered, IvorySQL automatically converts it into a Force view, preserving the latest valid definition so it can be restored quickly once dependencies are rebuilt. -- The base table does not exist, but the Force view placeholder is created successfully. CREATE FORCE VIEW fv_customer AS SELECT c_id, c_name FROM missing_customer; -- Expected output: WARNING: View created with compilation errors -- Provide the missing dependency. CREATE TABLE missing_customer( c_id int primary key, c_name text ); INSERT INTO missing_customer VALUES (1, 'Alice'); -- Accessing the Force view triggers automatic compilation. SELECT * FROM fv_customer; -- On success, the view becomes a normal view and returns data. -- Recreate the view definition as a Force view to make it invalid again. CREATE OR REPLACE FORCE VIEW fv_customer AS SELECT c_id, upper(c_name) AS c_name FROM missing_customer_v2; -- Explicit compilation fails because dependencies are still missing. ALTER VIEW fv_customer COMPILE; -- Expected output: WARNING: View altered with compilation errors -- Supply the required dependency and compile again. CREATE TABLE missing_customer_v2( c_id int, c_name text ); ALTER VIEW fv_customer COMPILE; -- Expected output: ALTER VIEW succeeds and the view returns to normal.","title":"Force View","component":"ivorysql-doc","version":"master","name":"compat_force_view","url":"/ivorysql-doc/master/oracle_compatibility/compat_force_view.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3},{"text":"3.1. Create a Force View with Missing Dependencies","hash":"create-a-force-view-with-missing-dependencies","id":4},{"text":"3.2. Automatic Compilation and Recovery","hash":"automatic-compilation-and-recovery","id":5},{"text":"3.3. Explicit Compilation and Failure Fallback","hash":"explicit-compilation-and-failure-fallback","id":6}]},"62":{"id":62,"text":"PostgreSQL supports functions and stored procedures, but there are syntax differences between PostgreSQL and Oracle. To enable Oracle’s PL/SQL statements to run on IvorySQL—i.e., to achieve \"syntax compatibility\"—IvorySQL adopts the following solution: If an Oracle clause has a counterpart clause with the same function in IvorySQL, it is directly mapped to the corresponding IvorySQL clause; otherwise, only the syntax of the Oracle clause is implemented, while its function is not. The specific Oracle compatibility support includes the following aspects: CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer AS BEGIN RETURN 1; END; / CREATE or replace NONEDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / configure --help --with-max-funarg=MAXFUNARG CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE OR REPLACE FUNCTION ora_func (num1 IN int, num2 IN int) RETURN int AS num3 int :=10; num4 int :=10; num5 int; BEGIN num3 := num1 + num2; num4 := num1 * num2; num5 := num3 * num4; RETURN num5; END; / CREATE FUNCTION select ora_func(5,9)from dual; ora_func ---------- 630 (1 row) CREATE OR REPLACE FUNCTION test_nocopy(a IN int, b OUT NOCOPY int, c IN OUT NOCOPY int) RETURN int IS BEGIN b := a; c := a; return 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID CURRENT_USER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON () IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING pg_catalog.int4 IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED TABLE POLYMORPHIC USING pg_catalog.int4 SQL_MACRO IS BEGIN RETURN 1; END; / alter function public.test_func noneditionable; alter function test_func compile; alter function test_func compile debug; alter function test_func compile debug sd = mv; alter function test_func compile debug reuse settings; CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE NONEDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = NONE IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID DEFINER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS p integer := 20; begin raise notice '%', p; end; / alter procedure test_proc editionable; alter procedure public.test_proc noneditionable; alter procedure test_proc compile; alter procedure test_proc compile debug; alter procedure test_proc compile debug sd = mv; alter procedure test_proc compile debug reuse settings; create or replace function f_noparentheses return int is begin return 11; end; / select f_noparentheses from dual; create or replace procedure protest as begin raise notice 'protest'; end; / CALL protest(); They can be found in file contrib/ivorysql_ora/src/sysview/sysview--1.0.sql. Including DBA_PROCEDURES, ALL_PROCEDURES, USER_PROCEDURES, DBA_SOURCE, ALL_SOURCE, USER_SOURCE, DBA_ARGUMENTS, ALL_ARGUMENTS, USER_ARGUMENTS etc.","title":"PL/iSQL function and stored procedure","component":"ivorysql-doc","version":"master","name":"compat_function_procedure","url":"/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Compatibility support","hash":"compatibility-support","id":2},{"text":"2.1. EDITIONABLE/NONEDITIONABLE is supported in CREATE FUNCTION syntax","hash":"editionablenoneditionable-is-supported-in-create-function-syntax","id":3},{"text":"2.2. Keyword 'RETURN' and 'IS' are supported in CREATE FUNCTION syntax","hash":"keyword-return-and-is-are-supported-in-create-function-syntax","id":4},{"text":"2.3. In the CREATE FUNCTION syntax, a function can have no parameters, and no parentheses () after the function name","hash":"in-the-create-function-syntax-a-function-can-have-no-parameters-and-no-parentheses-after-the-function-name","id":5},{"text":"2.4. Increase the number of parameters in CREATE FUNCTION syntax, the max value can be specified by configure command","hash":"increase-the-number-of-parameters-in-create-function-syntax-the-max-value-can-be-specified-by-configure-command","id":6},{"text":"2.5. Psql uses slash (/) as terminator of statement in CREATE FUNCTION syntax","hash":"psql-uses-slash-as-terminator-of-statement-in-create-function-syntax","id":7},{"text":"2.6. No DECLARE keyword before variable in CREATE FUNCTION syntax","hash":"no-declare-keyword-before-variable-in-create-function-syntax","id":8},{"text":"2.7. Support NOCOPY for OUT parameter in CREATE FUNCTION syntax","hash":"support-nocopy-for-out-parameter-in-create-function-syntax","id":9},{"text":"2.8. Support sharing clause in CREATE FUNCTION syntax","hash":"support-sharing-clause-in-create-function-syntax","id":10},{"text":"2.9. Support invoker_rights (AUTHID) in CREATE FUNCTION syntax，and change default permission to be DR（DEFINER）","hash":"support-invoker_rights-authid-in-create-function-syntaxand-change-default-permission-to-be-drdefiner","id":11},{"text":"2.10. Support ACCESSIBLE BY in CREATE FUNCTION syntax","hash":"support-accessible-by-in-create-function-syntax","id":12},{"text":"2.11. Support DEFAULT COLLATION in CREATE FUNCTION syntax","hash":"support-default-collation-in-create-function-syntax","id":13},{"text":"2.12. Support deterministic clause in CREATE FUNCTION syntax","hash":"support-deterministic-clause-in-create-function-syntax","id":14},{"text":"2.13. Support parallel_enable clause in CREATE FUNCTION syntax","hash":"support-parallel_enable-clause-in-create-function-syntax","id":15},{"text":"2.14. Support result_cache clause in CREATE FUNCTION syntax","hash":"support-result_cache-clause-in-create-function-syntax","id":16},{"text":"2.15. Support aggregate clause in CREATE FUNCTION syntax","hash":"support-aggregate-clause-in-create-function-syntax","id":17},{"text":"2.16. Support pipelined clause in CREATE FUNCTION syntax","hash":"support-pipelined-clause-in-create-function-syntax","id":18},{"text":"2.17. Support sql_macro clause in CREATE FUNCTION syntax","hash":"support-sql_macro-clause-in-create-function-syntax","id":19},{"text":"2.18. Compatibility with ALTER FUNCTION syntax","hash":"compatibility-with-alter-function-syntax","id":20},{"text":"2.19. Support EDITIONABLE/NONEDITIONABLE in CREATE PROCEDURE syntax","hash":"support-editionablenoneditionable-in-create-procedure-syntax","id":21},{"text":"2.20. In the CREATE PROCEDURE syntax, a procedure can have no parameters, and no parentheses () after the procedure name","hash":"in-the-create-procedure-syntax-a-procedure-can-have-no-parameters-and-no-parentheses-after-the-procedure-name","id":22},{"text":"2.21. Psql uses slash (/) as terminator of statement in CREATE PROCEDURE syntax","hash":"psql-uses-slash-as-terminator-of-statement-in-create-procedure-syntax","id":23},{"text":"2.22. Support sharing clause in CREATE PROCEDURE syntax","hash":"support-sharing-clause-in-create-procedure-syntax","id":24},{"text":"2.23. Support DEFAULT COLLATION clause in CREATE PROCEDURE syntax","hash":"support-default-collation-clause-in-create-procedure-syntax","id":25},{"text":"2.24. Support invoker_rights clause (AUTHID) in CREATE PROCEDURE syntax","hash":"support-invoker_rights-clause-authid-in-create-procedure-syntax","id":26},{"text":"2.25. Support ACCESSIBLE BY clause in CREATE PROCEDURE syntax","hash":"support-accessible-by-clause-in-create-procedure-syntax","id":27},{"text":"2.26. Compatibility with ALTER PROCEDURE syntax","hash":"compatibility-with-alter-procedure-syntax","id":28},{"text":"2.27. Function and procedure can have no parameter","hash":"function-and-procedure-can-have-no-parameter","id":29},{"text":"2.28. Views related with function and stored procedure","hash":"views-related-with-function-and-stored-procedure","id":30},{"text":"2.29. Support (--) and (/* */)","hash":"support-and","id":31},{"text":"2.30. pg_dump adds one slash (/) at the end of definition of function/procedure when backup SQL file","hash":"pg_dump-adds-one-slash-at-the-end-of-definition-of-functionprocedure-when-backup-sql-file","id":32}]},"63":{"id":63,"text":"This document is intended to provide people using like fuzzy queries with an in-depth understanding of Oracle-compatible fuzzy query like implementations. Database name Like fuzzy queries oracle oracle’s string type is varchar2, which supports fuzzy queries using the Like keyword with wildcards for columns of number, date, and string field types IvorySQL The basic type of IvorySQL’s string is text, so like is based on text, and other IvorySQL types can be implicitly converted to text, so that they can be automatically converted without creating operator create table t_ora_like (id int ,str1 varchar(8), date1 timestamp with time zone, date2 time with time zone, num int, str2 varchar(8)); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123456,'test1','2022-09-26 16:39:20','2022-09-26 16:39:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123457,'test2','2022-09-26 16:40:20','2022-09-26 16:40:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (223456,'test3','2022-09-26 16:41:20','2022-09-26 16:41:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123458,'test4','2022-09-26 16:42:20','2022-09-26 16:42:20'); select * from t_ora_like where str1 like 'test%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date1 like '2022%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date2 like '16%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where id like '123%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (3 rows) select * from t_ora_like where id like null; id | str1 | date1 | date2 | num | str2 ----+------+-------+-------+-----+------ (0 rows)","title":"Compatible with Oracle Like","component":"ivorysql-doc","version":"master","name":"compat_like_operator","url":"/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function description","hash":"function-description","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"64":{"id":64,"text":"Using Oracle-style nested subfunctions in IvorySQL. Allows declaring and invoking subfunctions or subprocedures inside anonymous blocks, functions, or procedures, with scope limited to the parent block. Nested subfunctions can read and update variables defined in the parent scope while introducing their own local variables; the parent scope cannot directly access the subfunction’s internal state. Supports overloading resolution that distinguishes homonymous subfunctions by argument count, data type, or named parameters. DO $$ DECLARE v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; RETURN p_value * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; END; $$ LANGUAGE plisql; DO $$ DECLARE v_base_multiplier integer := 20; v_audit_counter integer := 0; v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; v_audit_counter := v_audit_counter + 1; RETURN v_base_multiplier * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; RAISE NOTICE 'v_audit_counter=%', v_audit_counter; END; $$ LANGUAGE plisql; -- Polymorphic nested function specializing on argument type DO $$ DECLARE v_last_notice text := 'none'; FUNCTION describe_value(p_input anyelement) RETURN text IS BEGIN v_last_notice := format('polymorphic dispatch with %s', pg_typeof(p_input)); RETURN v_last_notice; END; FUNCTION describe_value(p_input anyarray, p_element anyelement) RETURN text IS BEGIN v_last_notice := format('array dispatch with %s', pg_typeof(p_input)::text); RETURN v_last_notice; END; BEGIN RAISE NOTICE '%', describe_value(100); RAISE NOTICE '%', describe_value('IvorySQL'::text); -- explicit cast avoids ambiguous literal RAISE NOTICE '%', describe_value(ARRAY[1,2,3], NULL::int); -- extra arg guides anyarray resolution RAISE NOTICE 'last notice=%', v_last_notice; END; $$ LANGUAGE plisql;","title":"Oracle-Compatible Nested Subfunctions","component":"ivorysql-doc","version":"master","name":"compat_nested_function","url":"/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3}]},"65":{"id":65,"text":"National Language Support(NLS), which refers to the localization support functionality provided by Oracle. IvorySQL offers Oracle-compatible NLS parameter functionality. The following parameters are included: Parameter Name Description ivorysql.datetime_ignore_nls_mask Indicates whether the date format ignores the influence of NLS parameters. Default is 0 . nls_length_semantics Oracle-compatible parameter that specifies whether the size unit for CHAR , VARCHAR , and VARCHAR2 type modifiers is bytes or characters. nls_date_format Specifies the default date format, which can be viewed using the SHOW command. Default is 'YYYY-MM-DD' . nls_timestamp_format Oracle-compatible parameter that controls the format of timestamps. nls_timestamp_tz_format Oracle-compatible parameter that controls the format of timestamps with time zones. nls_territory Oracle-compatible parameter that specifies the default region for the database. nls_iso_currency Oracle-compatible parameter that assigns a unique currency symbol to a specified country or region. nls_currency Oracle-compatible parameter that specifies the symbol for the local currency, corresponding to the placeholder L in numeric string formats. Example: ivorysql=# set ivorysql.datetime_ignore_nls_mask = 0; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ERROR: datetime format picture ends before converting entire input string LINE 1: select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ^ ivorysql=# set ivorysql.datetime_ignore_nls_mask = 2; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) Example: ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) ivorysql=# set nls_timestamp_format=\"pg\"; SET ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; ERROR: date format not recognized LINE 1: select '2025-10-15 11:00:00.102030 '::oratimestamp ; ^ IvorySQL uses the value of the nls_length_semantics parameter to determine the length type, which can be either BYTE or CHAR (default is BYTE )。 Example: ivorysql=# alter session set nls_length_semantics = char; SET ivorysql=# create table character_tb(char_c char(6), char_b varchar2(6 byte), char_v varchar(6)); CREATE TABLE ivorysql=# ivorysql=# insert into character_tb values('abcdef', '123456', 'OK'); INSERT 0 1 ivorysql=# select * from character_tb ; char_c | char_b | char_v --------+--------+-------- abcdef | 123456 | OK (1 row) ivorysql=# select length(char_b), length(char_c), length(char_v) from character_tb; length | length | length --------+--------+-------- 6 | 6 | 2 (1 row) Example: ivorysql=# show ivorysql.identifier_case_switch; ivorysql.identifier_case_switch --------------------------------- interchange (1 row) ivorysql=# set nls_currency to \"CHINA\"; SET ivorysql=# show nls_currency; nls_currency -------------- CHINA (1 row) ivorysql=# set nls_currency to \"China\"; SET ivorysql=# show nls_currency; nls_currency -------------- China (1 row) The behavior of nls_currency and nls_iso_currency is impacted by the Oracle ivorysql.identifier_case_switch compatibility feature.","title":"NLS Parameters","component":"ivorysql-doc","version":"master","name":"compat_nls_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Description","hash":"description","id":2},{"text":"2.1. NLS Date Mask Settings","hash":"nls-date-mask-settings","id":3},{"text":"2.2. Disabling NLS Date or Timestamp Format","hash":"disabling-nls-date-or-timestamp-format","id":4},{"text":"2.3. Parameter nls_length_semantics Settings","hash":"parameter-nls_length_semantics-settings","id":5},{"text":"2.4. NLS Currency Symbols Settings","hash":"nls-currency-symbols-settings","id":6}]},"66":{"id":66,"text":"IvorySQL provides Oracle-compatible OUT parameter functionality, including functions and procedures with OUT parameters, support for OUT parameters in anonymous blocks, and libpq support for OUT parameters. This document aims to introduce the functionality of OUT parameters to users. IvorySQL provides Oracle-compatible OUT parameter functionality, including the following features. Syntax： CREATE [ OR REPLACE ] FUNCTION name ( [ [ argmode ] [ argname ] argtype [ { DEFAULT | = } default_expr ] [, ...] ] ) [ RETURNS rettype | RETURNS TABLE ( column_name column_type [, ...] ) ] { LANGUAGE lang_name | TRANSFORM { FOR TYPE type_name } [, ... ] | WINDOW | { IMMUTABLE | STABLE | VOLATILE } | [ NOT ] LEAKPROOF | { CALLED ON NULL INPUT | RETURNS NULL ON NULL INPUT | STRICT } | { [ EXTERNAL ] SECURITY INVOKER | [ EXTERNAL ] SECURITY DEFINER } | PARALLEL { UNSAFE | RESTRICTED | SAFE } | COST execution_cost | ROWS result_rows | SUPPORT support_function | SET configuration_parameter { TO value | = value | FROM CURRENT } | AS 'definition' | AS 'obj_file', 'link_symbol' | sql_body } ... The argmode can be IN, OUT, INOUT (IN OUT), or VARIADIC. If not specified, the default is IN. The argmode can also be written after the argname. Unlike native PostgreSQL, Oracle-compatible OUT parameters are not associated with the return value data type. Neither IN OUT nor OUT modes can have default values. If there are OUT parameters and the function’s return type is not void, the function body must include a RETURN statement. Supports binding variables in the form of colon placeholders, such as :1, :name. Added new DO+USING syntax： DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, …​] Supports binding variables in libpq by position and by parameter name, providing the system function get_parameter_description, which returns the relationship between variable names and their positions based on the SQL statement. The libpq interface provides functions for preparing, binding, and executing statements, which are similar to the corresponding OCI functions. The usage process is as follows: Use IvyHandleAlloc to allocate statement and error handles. Call IvyStmtPrepare to prepare the statement. Call IvyBindByPos or IvyBindByName to bind parameters. Call IvyStmtExecute to execute, which can be repeated. Call IvyFreeHandle to release the statement and error handles. Additionally, a series of interface functions have been implemented, including Ivyconnectdb, Ivystatus, Ivyexec, IvyresultStatus, IvyCreatePreparedStatement, IvybindOutParameterByPos, IvyexecPreparedStatement, IvyexecPreparedStatement2, Ivynfields, Ivyntuples, and Ivyclear. Example programs can be found in the src/interfaces/libpq/ivytest directory of the source code. OUT parameters are not associated with the return value data type ivorysql=# create or replace function test_return_out(id integer,price out integer,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000; ivorysql-# name := 'test a char out'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / CREATE FUNCTION Neither IN OUT nor OUT modes can have default values ivorysql=# create or replace function test_return_inout(id integer,price in out integer default 100,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000 + price; ivorysql-# name := 'this is a test'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / ERROR: IN OUT formal parameters may have no default expressions If there are OUT parameters and the function’s return type is not void, the function body must include a RETURN statement. --if function's return type is non-void, the function body must has RETURN statement --if there is no RETURN statement, the function can be created, but when it is called, --an error is raised ivorysql=# create or replace function f2(id integer,price out integer) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 2; ivorysql-# end; ivorysql-# / CREATE FUNCTION ivorysql=# declare ivorysql-# a varchar(20); ivorysql-# b int; ivorysql-# begin ivorysql-# a := f2(1, b); ivorysql-# end; ivorysql-# / ERROR: Function returned without value CONTEXT: PL/iSQL function f2(pg_catalog.int4,pg_catalog.int4) line 0 at RETURN PL/iSQL function inline_code_block line 5 at assignment Supports binding variables in the form of colon placeholders and new DO+USING syntax ivorysql=# do $$ ivorysql$# declare ivorysql$# a int; ivorysql$# begin ivorysql$# :x := 1; ivorysql$# :y := 2; ivorysql$# end; $$ using out, out; $1 | $2 ----+---- 1 | 2 (1 row) system function get_parameter_descr() ivorysql=# select * from get_parameter_description('insert into t values(:x,:y)'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows)","title":"out parameter","component":"ivorysql-doc","version":"master","name":"compat_out_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"2.1. functions with OUT parameters","hash":"functions-with-out-parameters","id":3},{"text":"2.2. support for OUT parameters in anonymous blocks","hash":"support-for-out-parameters-in-anonymous-blocks","id":4},{"text":"2.3. calling functions with OUT parameters in libpq","hash":"calling-functions-with-out-parameters-in-libpq","id":5},{"text":"3. test examples","hash":"test-examples","id":6},{"text":"3.1. functions with OUT parameters","hash":"functions-with-out-parameters-2","id":7},{"text":"3.2. support OUT parameters in anonymous block","hash":"support-out-parameters-in-anonymous-block","id":8}]},"67":{"id":67,"text":"IvorySQL provides Oracle-compatible RowID functionality. RowID is a pseudo-column automatically generated by the database when a table is created, returning the address of each row in the database. RowID should have the following characteristics: 1. Logically identifies each row with a unique value 2. Allows quick querying and modification of other columns in the table via ROWID, but cannot be inserted or modified itself 3. Users can control whether this feature is enabled IvorySQL provides multiple ways to enable the RowID functionality. In IvorySQL’s Oracle-compatible mode, the RowID functionality can be enabled by setting ivorysql.default_with_rowids to on. The default value for this parameter is off. Once enabled, tables created will automatically include a RowID column, which can be viewed using \\d+ table_name. ivorysql=# show ivorysql.default_with_rowids; ivorysql.default_with_rowids ------------------------------ off (1 row) ivorysql=# create table t(a int); CREATE TABLE ivorysql=# \\d+ t Table \"public.t\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap Users can choose to include this option for tables that require it; without the WITH ROWID option, a regular table will be created. ivorysql=# create table t2(a int) with rowid; CREATE TABLE ivorysql=# \\d+ t2 Table \"public.t2\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Indexes: \"t2_16432_rowid_idx\" btree (rowid) Access method: heap Has ROWID: yes This approach allows a regular table to add a ROWID column using the ALTER command when ROWID functionality is needed. The ROWID can also be removed using the ALTER TABLE … SET WITHOUT ROWID command. ivorysql=# create table t3(a int); CREATE TABLE ivorysql=# alter table t3 set with rowid; ALTER TABLE ivorysql=# \\d+ t3; Table \"public.t3\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap Has ROWID: yes","title":"RowID","component":"ivorysql-doc","version":"master","name":"compat_rowid","url":"/ivorysql-doc/master/oracle_compatibility/compat_rowid.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Enable the functionality","hash":"enable-the-functionality","id":2},{"text":"2.1. Enable the functionality through GUC parameters","hash":"enable-the-functionality-through-guc-parameters","id":3},{"text":"2.2. Enable the functionality by adding the WITH ROWID option in the table creation statement.","hash":"enable-the-functionality-by-adding-the-with-rowid-option-in-the-table-creation-statement","id":4},{"text":"2.3. Enable the functionality by executing the command ALTER TABLE … SET WITH ROWID on an existing table.","hash":"enable-the-functionality-by-executing-the-command-alter-table-set-with-rowid-on-an-existing-table","id":5}]},"68":{"id":68,"text":"IvorySQL’s sys_guid() is a powerful random number generation function that generates and returns a 16-byte database-level unique identifier (raw value). ivorysql=# select sys_guid() from dual; sys_guid ------------------------------------ \\x3ed9426c8a093442a38bea09a74f44a1 (1 row) create table student ( student_id raw(16) default sys_guid() primary key, student_name varchar2(100) not null ); insert into student(student_name) values ('Steven');","title":"sys_guid() function","component":"ivorysql-doc","version":"master","name":"compat_sys_guid","url":"/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Usage example","hash":"usage-example","id":2},{"text":"3. The sys_guid function can generate default values for primary keys when creating a table","hash":"the-sys_guid-function-can-generate-default-values-for-primary-keys-when-creating-a-table","id":3},{"text":"4. The primary key is automatically populated when new data is added","hash":"the-primary-key-is-automatically-populated-when-new-data-is-added","id":4}]},"69":{"id":69,"text":"IvorySQL provides Oracle-compatible PL/SQL data type functionality, including %TYPE and %ROWTYPE. This document aims to introduce users to the functionality of %TYPE and %ROWTYPE. IvorySQL provides Oracle-compatible %TYPE and %ROWTYPE functionality, including the following content. Create table and function. CREATE TABLE t1(id int, name varchar(20)); --function's parameter datatype is tablename.columnname%TYPE CREATE OR REPLACE FUNCTION fun1(v t1.id%TYPE) RETURN varchar AS BEGIN RETURN v; END; / The state of function is valid and function can be executed successfully. SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) SELECT fun1(1) FROM dual; fun1 ------ 1 (1 row) Modifying the previously defined declaration of a reference causes the function’s status to become invalid, but the function can still execute successfully. ALTER TABLE t1 ALTER COLUMN id TYPE varchar(20); SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --n prostatus ----------- n (1 row) --after changing the column id type from int to varchar, call the function again SELECT fun1('a') FROM dual; --successfully fun1 ------ a (1 row) re-compile the function, its state become valid. ALTER FUNCTION fun1 COMPILE; SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) Example： --the following testcase will fail DECLARE name VARCHAR(25) NOT NULL := 'Niu'; surname name%TYPE ; BEGIN raise notice 'name=%' ,name; raise notice 'surname=%' ,surname; END; / ERROR: variable \"surname\" must have a default value, since it's declared NOT NULL LINE 3: surname name%TYPE ; ^ Example： CREATE TABLE employees(first_name varchar(20) not null, last_name varchar(20) not null, phone_number varchar(50)); INSERT INTO employees VALUES ('Steven','Niu','1-650-555-1234'); CREATE OR REPLACE PROCEDURE p0(v employees%ROWTYPE) AS BEGIN raise notice 'v.first_name = %, v.last_name = %, v.phone_number = %', v.first_name, v.last_name, v.phone_number; END; / DECLARE a employees%ROWTYPE; BEGIN select * into a from employees ; raise notice 'a=%', a; call p0(a); END; / NOTICE: a=(Steven,Niu,1-650-555-1234) NOTICE: v.first_name = Steven, v.last_name = Niu, v.phone_number = 1-650-555-1234 \\df p0 List of functions Schema | Name | Result data type | Argument data types | Type --------+------+------------------+------------------------+------ public | p0 | | IN v employees%ROWTYPE | proc (1 row) Support inserting a variables declared with %TYPE or %ROWTYPE into table. grammar: INSERT INTO table_name VALUES row_variable ; Example: CREATE TABLE t1(id int, name varchar(20)); DECLARE v1 t1%ROWTYPE; BEGIN FOR i IN 1 .. 5 LOOP v1.id := i; v1.name := 'a' || i; INSERT INTO t1 VALUES v1; END LOOP; END; / SELECT * FROM t1; id | name ----+------ 1 | a1 2 | a2 3 | a3 4 | a4 5 | a5 (5 rows) example: CREATE TABLE t1(id int, name varchar(20)); DELETE FROM t1; DECLARE v1 t1%ROWTYPE; v2 t1%ROWTYPE; BEGIN v1.id := 11; v1.name := 'abc'; INSERT INTO t1 VALUES v1; v2.id := 22; v2.name := 'new'; UPDATE t1 SET ROW = v2; END; / SELECT * FROM t1; id | name ----+------ 22 | new (1 row)","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"compat_type_rowtype","url":"/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"2.1. If the reference changes, variables declared with %TYPE or %ROWTYPE will change accordingly.","hash":"if-the-reference-changes-variables-declared-with-type-or-rowtype-will-change-accordingly","id":3},{"text":"2.2. Variables declared with %TYPE inherit the constraints of the referenced variable.","hash":"variables-declared-with-type-inherit-the-constraints-of-the-referenced-variable","id":4},{"text":"2.3. Use table_name%ROWTYPE or view_name%ROWTYPE as the parameter type of a function / stored procedure or the return type of a function","hash":"use-table_namerowtype-or-view_namerowtype-as-the-parameter-type-of-a-function-stored-procedure-or-the-return-type-of-a-function","id":5},{"text":"2.4. Enhancement to INSERT statement","hash":"enhancement-to-insert-statement","id":6},{"text":"2.5. Enhancement to UPDATE statement","hash":"enhancement-to-update-statement","id":7}]},"70":{"id":70,"text":"The introduction of this feature is to accommodate Oracle’s invisible column functionality, making database design simpler and enhancing the flexibility of data management. Users will have better control over the visibility of columns. Invisible columns are also useful during application migration. Making new columns invisible means that they will not be visible to any existing application, but they can still be referenced by any new applications, thus simplifying the online migration of applications. Invisible columns allow users to conceal specific columns during operations like SELECT *. You can use invisible column to make changes to a table without disrupting applications that use the table. Any generic access of a table does not show the invisible columns in the table. Invisible columns must be explicitly referenced by column names in order to be accessed in queries and other operations. Support for invisible columns in psql extended describe (\\d+) is added, in oracle mode only. ivorysql=# CREATE TABLE mytable (a INT, b INT INVISIBLE, c INT); CREATE TABLE You can insert a value into an invisible column only if you explicitly specify the invisible column in the column list for the INSERT statement. Omitting the column list in the INSERT statement is not allowed. Errors will be reported. ivorysql=# INSERT INTO mytable (a, b, c) VALUES (1,2,3); INSERT 0 1 ivorysql=# INSERT INTO mytable VALUES (4,5,6); ERROR: INSERT has more expressions than target columns LINE 1: INSERT INTO mytable VALUES (4,5,6); You can use a SELECT statement to display output for an invisible column only if you explicitly specify the invisible column in the column list. ivorysql=# select * from mytable; a | c ---+--- 1 | 3 (1 row) ivorysql=# select a,b,c from mytable; a | b | c ---+---+--- 1 | 2 | 3 (1 row) ivorysql=# \\d+ mytable Table \"public.mytable\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | b | pg_catalog.int4 | | | | invisible | plain | | | c | pg_catalog.int4 | | | | | plain | | | Access method: heap Modify column is not supported currently. Will be enhanced in later version; In Oracle, there are special considerations for invisible columns and column ordering. When a table contains one or more invisible columns, the invisible columns are not included in the column order for the table. We have not yet addressed this part.","title":"Invisible Columns","component":"ivorysql-doc","version":"master","name":"invisible_column","url":"/ivorysql-doc/master/oracle_compatibility/invisible_column.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"3. Test cases","hash":"test-cases","id":3},{"text":"3.1. Create a table with invisible columns","hash":"create-a-table-with-invisible-columns","id":4},{"text":"3.2. Insert values into invisible columns","hash":"insert-values-into-invisible-columns","id":5},{"text":"3.3. Display output for invisible columns","hash":"display-output-for-invisible-columns","id":6},{"text":"3.4. Support for invisible columns in psql extended describe (\\d+) in Oracle mode","hash":"support-for-invisible-columns-in-psql-extended-describe-d-in-oracle-mode","id":7},{"text":"4. Limitations","hash":"limitations","id":8}]},"71":{"id":71,"text":"IvorySQL provides compatibility for Oracle packages. A package is an encapsulated collection of related program objects stored together in the database. Program objects are procedures, functions, variables, constants, cursors, and exceptions. This document aims to provide a comprehensive understanding of the process of implementing custom packages. IvorySQL provides compatibility for Oracle custom packages, including creation, alteration, and deletion of packages and package bodies. We also add support for package-related commands in the PostgreSQL interactive terminal (psql) with the new \\dk command. The CREATE OR REPLACE PACKAGE statement is used to create or replace the specification of a package. A package is a collection of related stored procedures, functions, and other program objects, stored as a single unit in the database. The package specification declares these objects, and the package body defines them. To create or replace the specification of a package in your own schema, you must have the CREATE PROCEDURE system privilege. If you are creating or replacing a package in another user’s schema, you need the CREATE ANY PROCEDURE system privilege. The CREATE OR REPLACE PACKAGE BODY statement is used to create or replace a package body. To create a package body requires the same privileges as creating the specification of a package, and it is required that the package body and package specification be in the same schema, with the package specification already existing. This statement defines the objects declared in the package specification. When the package specification contains cursors or subprograms, a package body must be present to define them. Otherwise, the package body is optional. The ALTER PACKAGE statement is used to modify the properties of a package.The privileges required to execute the ALTER PACKAGE statement are: you must be the owner of the package or have the ALTER ANY PROCEDURE privilege to modify packages owned by other users. The DROP PACKAGE statement deletes a package in the database. This statement will remove both the package body and the package specification.The DROP PACKAGE BODY statement only deletes the package body. It is not possible to delete a single object within the package using this statement. The privileges required:the package must be in the user’s schema, or the user must have the DROP ANY PROCEDURE system privilege. The DISCARD PACKAGE functionality is implemented for compatibility with PostgreSQL’s DISCARD feature. In psql, \\dk[+] is used to view the definition information of packages and package bodies. Command Descriptions \\dk[+] List the package information currently visible. \\dk[+] xxx List the package specification and package body content of the xxx package. ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# var2 integer; ivorysql-# function test_f(id integer) return integer; ivorysql-# procedure test_p(id integer); ivorysql-# end; ivorysql-# / CREATE PACKAGE ivorysql=# create or replace package body pkg is ivorysql-# var3 integer; ivorysql-# function test_f(id integer) return integer is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg test_f'); ivorysql-# return id; ivorysql-# end; ivorysql-# procedure test_p(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg proc'); ivorysql-# end; ivorysql-# --privite function ivorysql-# function test_piv1(id integer) return integer is ivorysql-# begin ivorysql-# return id; ivorysql-# end; ivorysql-# --privite procedure ivorysql-# procedure test_piv2(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('privite proc'); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 1; ivorysql-# var2 := 2; ivorysql-# var3 := 4; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY ivorysql=# alter package pkg noneditionable; ALTER PACKAGE ivorysql=# Drop package pkg; DROP PACKAGE ivorysql=# Drop package body pkg; DROP PACKAGE BODY ivorysql=# discard package; DISCARD PACKAGES ivorysql=# \\dk List of packages Schema | Name | Owner --------+----------+---------- public | pkg | ivorysql public | test_pkg | ivorysql (2 rows) ivorysql=# \\dk pkg List of packages Schema | Name | Owner --------+------+---------- public | pkg | ivorysql (1 row) ivorysql=# \\dk pkg1 Did not find any package named \"pkg1\". ivorysql=# \\dk+ List of packages Schema | Name | Owner | Security | Editionable | Use Collation | Specification | Package Body --------+----------+----------+----------+-------------+---------------+-----------------------------------------------+----------------------------------------------------- --------- public | pkg | ivorysql | definer | Editionable | default | var1 integer; +| | | | | | | var2 integer; +| | | | | | | function test_f(id integer) return integer; +| | | | | | | procedure test_p(id integer); +| | | | | | | end | public | test_pkg | ivorysql | definer | Editionable | default | var1 integer; +| FUNCTION test_f(id integer) RETURN integer IS + | | | | | | FUNCTION test_f(id integer) RETURN integer;+| BEGIN + | | | | | | end | dbms_output.put_line('invoke function test_pkg.t est_f');+ | | | | | | | RETURN 23; + | | | | | | | end; + | | | | | | | BEGIN + | | | | | | | var1 := 23; + | | | | | | | end (2 rows)","title":"Package","component":"ivorysql-doc","version":"master","name":"package","url":"/ivorysql-doc/master/oracle_compatibility/package.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"2.1. Create the specification of a package","hash":"create-the-specification-of-a-package","id":3},{"text":"2.2. Create package body","hash":"create-package-body","id":4},{"text":"2.3. Alter package","hash":"alter-package","id":5},{"text":"2.4. Drop package and package body","hash":"drop-package-and-package-body","id":6},{"text":"2.5. DISCARD PACKAGE","hash":"discard-package","id":7},{"text":"2.6. Use \\dk[+] in psql for package and package body information","hash":"use-dk-in-psql-for-package-and-package-body-information","id":8},{"text":"3. Test cases","hash":"test-cases","id":9},{"text":"3.1. Create the specification of a package","hash":"create-the-specification-of-a-package-2","id":10},{"text":"3.2. Create package body","hash":"create-package-body-2","id":11},{"text":"3.3. Alter package","hash":"alter-package-2","id":12},{"text":"3.4. Drop package and package body","hash":"drop-package-and-package-body-2","id":13},{"text":"3.5. DISCARD PACKAGE","hash":"discard-package-2","id":14},{"text":"3.6. Use \\dk[+] in psql for package and package body information","hash":"use-dk-in-psql-for-package-and-package-body-information-2","id":15}]},"72":{"id":72,"text":"In order to distinguish the Oracle port, IP and PG port IP. THERE IS NOW A NEED TO INCREASE THE PROCESSING OF ORAPORT AND ORAHOST; Add ivoryhost: You need to add the parameter ivoryhost when connecting, and its function is similar to host; New ivoryport: Compared to host, the function of port is relatively complex. It involves specifying ports in the configure phase and connection phase; ./configure --with-oraport=5555 ./initdb .... ./pg_ctl -D ../data start ./pg_ctl -o \"-p 5433 -o 1522\" -D ../data","title":"Added Oracle compatibility mode ports and IP","component":"ivorysql-doc","version":"master","name":"port_ip","url":"/ivorysql-doc/master/oracle_compatibility/port_ip.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function","hash":"function","id":2},{"text":"3. Test method:","hash":"test-method","id":3}]},"73":{"id":73,"text":"This document is designed for compatibility with Oracle sequence functions, in order to use Oracle’s sequences in IvorySQL. Sequence is a database object, similar to tables and views, that represents an integer sequence that can be used by any table and view in the global database namespace. Sequence values can be accessed using NEXTVAL and CURRVAL. Sequences can be in ascending or descending order. ivorysql=# CREATE sequence seq; CREATE SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 1 (1 row) ivorysql=# ALTER sequence seq restart start with 10; ALTER SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 10 (1 row) ivorysql=# DROP SEQUENCE seq; DROP SEQUENCE","title":"Compatible with Oracle sequence","component":"ivorysql-doc","version":"master","name":"sequence","url":"/ivorysql-doc/master/oracle_compatibility/sequence.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function description","hash":"function-description","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"74":{"id":74,"text":"In Oracle, SQL code often contains XML functions. To ensure consistency in data format and structure when migrating from Oracle to IvorySQL, IvorySQL achieves high compatibility with Oracle XML functions, building upon the foundation of PostgreSQL. This compatibility means that users do not need to make extensive modifications to their existing XML processing logic, thereby ensuring the integrity and accuracy of the data. Furthermore, IvorySQL’s cross-platform compatibility reduces the additional user maintenance and upgrade costs caused by format differences, making data processing and management more efficient, reliable, and flexible. XML (eXtended Markup Language) is a text-based format language used to structure any document that can be tagged. It is a lightweight, extensible, standard, and easy-to-understand language for storing data. IvorySQL achieves compatibility with commonly used XML SQL functions. It maintains consistency with PostgreSQL by utilizing the same underlying processing functions, which are provided by the libxml2 library interface. These XML functions are provided as a sub-plugin of the ivorysql_ora plugin, ensuring compatibility and consistency with PostgreSQL databases in terms of XML processing. Due to Oracle’s XML functions requiring certain parameter types to be XMLType, such as the existsnode() function below: Prototype: EXISTSNODE(XMLType_instance, XPath_string [, namespace_string ]) Demo: SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a') from dual; Therefore, for compatibility purposes, an XMLType data type has been added. Its function is to convert the string provided by the user into an internal XMLType type, allowing SQL statements to be migrated from Oracle to IvorySQL without modification. Additionally, to avoid confusion with the existing keyword \"extract\" in PostgreSQL, IvorySQL has renamed the original keyword to \"PGEXTRACT\" to ensure clarity and accuracy in function calls. When implementing these Oracle-compatible XML functions, IvorySQL adopted two different approaches. Among them, besides the UPDATEXML function, the other functions are implemented using SQL functions. Since the number of parameters for the UPDATEXML function is uncertain, an expression-based approach was used for its implementation. This required writing specialized syntax parsing and executor code to ensure the correctness and flexibility of its functionality. Num Function Name Function Introduction 1 extract(XML) This function is used to return the corresponding content under the XML node path. The parameter XMLType_instance is used to specify the XMLType instance, while Xpath_string is used to specify the XML node path. 2 extractvalue This function provides retrieval functionality for XML content. The extractvalue function can only return one value from one node. 3 existsnode This function is used to check whether the XML content matches the specified path expression. 4 deletexml This function is used to delete XML nodes at specified paths. 5 appendchildxml This function is used to insert child nodes into an XML object. It takes an XML object and an XML fragment as parameters, and inserts the XML fragment as a child node into the XML object. 6 updatexml This function is used to update the content of a specific XML node path. 7 insertxmlbefore This function is used to insert child nodes before a specified path in XML. 8 insertxmlafter This function is used to insert child nodes after a specified path in XML. 9 insertchildxml This function is used to insert child nodes into a specified XML path. 10 insertchildxmlbefore This function is used to insert child nodes before a specified XML path. 11 insertchildxmlafter This function is used to insert child nodes after a specified XML path. 12 xmlisvalid This functions is used to check the xml content is valid or not ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# create table inaf(a int, b xmltype); CREATE TABLE ivorysql=# insert into inaf values(1,xmltype('<a><b>100</b></a>')); INSERT 0 1 ivorysql=# insert into inaf values(2, ''); INSERT 0 1 ivorysql=# select * from inaf; a | b ---+------------------- 1 | <a><b>100</b></a> 2 | (2 rows) ivorysql=# create table xmltest(id int, data XMLType); CREATE TABLE ivorysql=# insert into xmltest values(1, '<value>one</value>'); INSERT 0 1 ivorysql=# insert into xmltest values(2, '<value>two</value>'); INSERT 0 1 ivorysql=# select * from xmltest; id | data ----+-------------------- 1 | <value>one</value> 2 | <value>two</value> (2 rows) ivorysql=# create table xmlnstest(id int, data xmltype); CREATE TABLE ivorysql=# INSERT INTO xmlnstest VALUES(1, xmltype('<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:typ=\"http://www.def.com\" xmlns:web=\"http://www.abc.com\"><soapenv:Body><web:BBB><typ:EEE>41</typ:EEE><typ:FFF>42</typ:FFF></web:BBB></soapenv:Body></soapenv:Envelope>')); INSERT 0 1 ivorysql# SELECT extract(XMLType('<AA><ID>1</ID></AA>'), '/AA/ID') from dual; extract ------------ <ID>1</ID> (1 row) ivorysql# SELECT extractvalue(XMLType('<a><b>100</b></a>'),'/a/b') from dual; extractvalue -------------- 100 (1 row) ivorysql=# SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a/b') from dual; existsnode ------------ 1 (1 row) ivorysql=# SELECT deletexml(XMLType('<test><value>oldnode</value><value>oldnode</value></test>'), '/test/value') from dual; deletexml ----------- <test/> (1 row) ivorysql=# SELECT appendchildxml(XMLType('<test><value></value><value></value></test>'), '/test/value', XMLTYPE('<name>newnode</name>')) from dual; appendchildxml -------------------------- <test> + <value> + <name>newnode</name>+ </value> + <value> + <name>newnode</name>+ </value> + </test> (1 row) ivorysql=# SELECT updatexml(xmltype('<value>one</value>'), '/value', xmltype('<newvalue>1111</newvalue>')) FROM dual; updatexml --------------------------- <newvalue>1111</newvalue> (1 row) ivorysql=# SELECT insertxmlbefore(XMLType('<a>222<b>100</b><b>200</b></a>'), '/a/b', XMLTYPE('<c>88</c>')) from dual; insertxmlbefore -------------------------------------------------- <a>222<c>88</c><b>100</b><c>88</c><b>200</b></a> (1 row) ivorysql=# SELECT insertxmlafter(XMLType('<a><b>100</b></a>'),'/a/b',XMLType('<c>88</c>')) from dual; insertxmlafter ---------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT insertchildxml(XMLType('<a>one<b></b>three<b></b></a>'), '//b', 'name', XMLTYPE('<name>newnode</name>')) from dual; insertchildxml ----------------------------------------------------------------------- <a>one<b><name>newnode</name></b>three<b><name>newnode</name></b></a> (1 row) ivorysql=# SELECT insertchildxmlbefore(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlbefore ---------------------- <a> + <c>88</c> + <b>100</b> + </a> (1 row) ivorysql=# SELECT insertchildxmlafter(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlafter --------------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a>')); xmlisvalid ------------ f (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a/>')); xmlisvalid ------------ t (1 row)","title":"XML","component":"ivorysql-doc","version":"master","name":"xml_functions","url":"/ivorysql-doc/master/oracle_compatibility/xml_functions.html","titles":[{"text":"1. 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IvorySQL","items":[{"content":"Quick Start","url":"/ivorysql-doc/master/getting-started/quick_start.html","urlType":"internal"},{"content":"Monitoring","url":"/ivorysql-doc/master/getting-started/daily_monitoring.html","urlType":"internal"},{"content":"Maintenance","url":"/ivorysql-doc/master/getting-started/daily_maintenance.html","urlType":"internal"}]},{"content":"IvorySQL Advanced Feature","items":[{"content":"Installation","url":"/ivorysql-doc/master/installation_guide.html","urlType":"internal"},{"content":"Cluster","url":"/ivorysql-doc/master/cluster_setup.html","urlType":"internal"},{"content":"Migration","url":"/ivorysql-doc/master/migration_guide.html","urlType":"internal"},{"content":"Developer","url":"/ivorysql-doc/master/developer_guide.html","urlType":"internal"},{"content":"Containerization","items":[{"content":"K8S deployment","url":"/ivorysql-doc/master/containerization/k8s_deployment.html","urlType":"internal"},{"content":"Operator 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is an advanced, full-featured, Oracle open source compatible PostgreSQL with a firm commitment to always remain 100% compatible and a direct replacement for the latest PostgreSQL. IvorySQL adds a GUC parameter called 'ivorysql.compatible_mode' to control the compatibility mode of IvorySQL, which has two values: 'oracle' and 'pg'.When initializing the data directory, specify the compatibility mode of the data directory by specifying the '-m' parameter, and '-m pg' then the data directory is PostgreSQL mode.In this mode, the 'ivorysql.compatible_mode' parameter will be invalidated, and the '-m oracle' or if the '-m' parameter is not specified, the data directory will be compatible with Oracle mode.In this mode, the initial value of the 'ivorysql.compatible_mode' parameter is 'oracle' and does not support some PostgreSQL syntax, and the database can support 100% of PostgreSQL syntax and functions by 'set ivorysql.compatible_mode to pg'. One of the highlights of IvorySQL is the PL/iSQL procedural language, which supports Oracle’s PL/SQL syntax. At the same time, IvorySQL implements Oracle-compatible functions by adding plug-ins ivorysql_ora bound to the kernel, and the functions currently implemented include built-in functions, data types, system views, merges, and the addition of GUC parameters, and will continue to implement new compatible functions in the form of plug-ins bound to the kernel in the future. We are committed to the principles of the open source approach and we strongly believe in building a healthy and inclusive community. We insist that good ideas can come from anywhere. Only by including different perspectives can we make the best decisions. While the first release of IvorySQL focuses on Oracle-compatible features, the future roadmap and feature set will be defined by the community in an open source manner. IvorySQL is developed based on PostgreSQL database and is compatible with Oracle database for strong compatibility. Suitable for PostgreSQL database and Oracle database scenarios. Core Open Source: IvorySQL’s core code including compatible features are all open under the open source protocol, with no vendor restrictions. It is also used in Hankook database company instances and has an active developer community. Oracle compatible: Oracle databases can be migrated to IvorySQL. Customizable: Simply download the code and customize it the way you want. Easy to use: For system administrators, IvorySQL dramatically reduces the cost of administration and maintenance. For developers, IvorySQL provides a simple interface, a minimalist solution, and seamless integration with third-party tools. For data analysis professionals, IvorySQL provides easy access to data. Hemco Support: Powered by the leading PostgreSQL database provider, Hemco. IvorySQL is based on PostgreSQL, with complete SQL, rock-solid reliability and a huge ecosystem. Ivory database’s main application scenarios. Enterprise database For example, ERP, transaction system, financial system involves funds, customers and other information, data cannot be lost and business logic is complex. Choosing IvorySQL as the underlying data storage can help you provide high availability under the premise of data consistency, and you can implement complex business logic with simple programming. Applications with LBS Large-scale games, O2O and other applications need to support world map, nearby businesses, distance between two points and other capabilities. PostGIS adds support for geographic objects, allowing you to run location queries in SQL without complex coding, helping you to rationalize your logic more easily, implement LBS more conveniently, and improve user stickiness. Data Warehousing and Big Data With more data types and powerful computing power, IvorySQL makes it easier for you to build a database warehouse or big data analytics platform to enhance your business operations. Website or App Building IvorySQL's good performance and powerful features can effectively improve website performance and reduce development difficulty. Database Migration If you need to migrate Oracle database to PostgreSQL database, you can directly use IvorySQL database for migration. IvorySQL is a powerful open source object-relational database management system (ORDBMS). Used to store data securely, support best practices, and allow them to be retrieved when requests are processed. In addition, it is also compatible with Oracle’s syntax, which is suitable for scenarios where Oracle is used. Case conversion LIKE operator anonymous block functions and stored procedures Built-in data types and built-in functions ports and IP XML Function sequence Package Invisible Columns RowID Column OUT Parameter %Type & %Rowtype NLS Parameters Force View Nested Subfunctions sys_guid Function Empty String to NULL CALL INTO","title":"","component":"ivorysql-doc","version":"master","name":"about_ivorysql","url":"/ivorysql-doc/master/about_ivorysql.html","titles":[{"text":"1. Introduction to IvorySQL","hash":"introduction-to-ivorysql","id":1},{"text":"1.1. Overview","hash":"overview","id":2},{"text":"1.2. Product Goals and Scope","hash":"product-goals-and-scope","id":3},{"text":"1.3. Core Features","hash":"core-features","id":4},{"text":"1.4. Competitive Advantages","hash":"competitive-advantages","id":5},{"text":"1.5. Technical Ecology","hash":"technical-ecology","id":6},{"text":"1.6. Core Application Scenarios","hash":"core-application-scenarios","id":7},{"text":"2. Main, Basic Features","hash":"main-basic-features","id":8},{"text":"3. Compatibility with Oracle","hash":"compatibility-with-oracle","id":9}]},"2":{"id":2,"text":"For quick database installation by yum, please refer to Quick installation。 For more installation options, please refer to Installation。 The master node database needs to be installed and started. Stop firewall for all the nodes in the cluster to ensure the communication: $ sudo systemctl stop firewalld To create the streaming replication, we need configure the postgresql.conf and pg_hba.conf files on the primary node. postgresql.conf Append the following contents to the end of postgresql.conf: listen_addresses = '*' max_connections = 100 wal_level = replica max_wal_senders = 5 hot_standby = on pg_hba.conf Append the following contents to the end of pg_hba.conf: host all all 0.0.0.0/0 trust host replication all 0.0.0.0/0 trust The configuration of pg_hba in the example is only for demo purposes and testing. This configuration will result in invalidation of the database password. Please configure according to the actual environment. $ pg_ctl restart For quick database installation by yum, please refer to Quick installation。 For more installation options, please refer to Installation。 The standby node database only needs to be installed and not started. Stop firewall for all the nodes in the cluster to ensure the communication: $ sudo systemctl stop firewalld Run below command on the standby node to take base backups of the primary, that is, to build a streaming replication: $ sudo pg_basebackup -F p -P -X fetch -R -h <primary_ip> -p <primary_port> -U ivorysql -D /usr/local/ivorysql/ivorysql-5/data Specifies the host name of the machine on which the server is running; Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults is 5432; User name to connect as; Directory to write the output to. pg_basebackup will create the directory and any parent directories if necessary. The directory may already exist, but it is an error if the directory already exists and is not empty. For more options, refer to pg_basebackup --help. Add below contents in ~/.bash_profile file: PATH=/usr/local/ivorysql/ivorysql-5/bin:$PATH export PATH PGDATA=/usr/local/ivorysql/ivorysql-5/data export PGDATA Source to make it effective: $ source ~/.bash_profile $ sudo pg_ctl -D /usr/local/ivorysql/ivorysql-5/data start Run below command on the primary node, you will see walsender: $ ps -ef |grep postgres ... ivorysql 11176 8067 0 21:54 ? 00:00:00 postgres: walsender ivorysql 192.168.31.102(53416) streaming 0/7000060... while it is walreceiver on standby: $ ps -ef | grep postgres ... ivorysql 6567 6139 0 21:54 ? 00:00:00 postgres: walreceiver streaming 0/7000060 ... On the primary node, connect to IvorySQL and show the status: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# select * from pg_stat_replication; pid | usesysid | usename | application_name | client_addr | client_hostname | client_port | backend_start | backend_ xmin | state | sent_lsn | write_lsn | flush_lsn | replay_lsn | write_lag | flush_lag | replay_lag | sync_priority | sync_state | reply_time -------+----------+----------+------------------+----------------+-----------------+-------------+-------------------------------+--------- -----+-----------+-----------+-----------+-----------+------------+-----------+-----------+------------+---------------+------------+------ ------------------------- 11176 | 10 | ivorysql | walreceiver | 192.168.31.102 | | 53416 | 2024-12-18 21:54:52.041847-05 | | streaming | 0/7000148 | 0/7000148 | 0/7000148 | 0/7000148 | | | | 0 | async | 2024- 12-18 22:52:07.325111-05 (1 row) Here 192.168.31.102 is the ip address of the standby node, and async means the data synchronization method is asynchronous. All writing operations are performed on the primary node, while reading can be on both primary and standby. The data on primary is synchronized to standby through streaming replication. The writing result can be queried on all the nodes in the cluster. Below is an example. Create a new database test on primary and query: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# create database test; CREATE DATABASE ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows) Query on the standby node: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows)","title":"Building an IvorySQL cluster","component":"ivorysql-doc","version":"master","name":"cluster_setup","url":"/ivorysql-doc/master/cluster_setup.html","titles":[{"text":"1. Primary node","hash":"primary-node","id":1},{"text":"1.1. Installing and start database","hash":"installing-and-start-database","id":2},{"text":"1.2. Stopping firewall","hash":"stopping-firewall","id":3},{"text":"1.3. Setting environment variables","hash":"setting-environment-variables","id":4},{"text":"1.4. Restarting IvorySQL sevice","hash":"restarting-ivorysql-sevice","id":5},{"text":"2. Standby node","hash":"standby-node","id":6},{"text":"2.1. Installing database","hash":"installing-database","id":7},{"text":"2.2. Stopping firewall","hash":"stopping-firewall-2","id":8},{"text":"2.3. Building streaming replication","hash":"building-streaming-replication","id":9},{"text":"2.4. Setting environment variables","hash":"setting-environment-variables-2","id":10},{"text":"2.5. Starting IvorySQL sevice","hash":"starting-ivorysql-sevice","id":11},{"text":"3. Experience the IvorySQL cluster","hash":"experience-the-ivorysql-cluster","id":12},{"text":"3.1. Checking cluster status","hash":"checking-cluster-status","id":13},{"text":"3.2. Using the cluster","hash":"using-the-cluster","id":14}]},"3":{"id":3,"text":"lvorySQL provides unique additional functionality on top of the open source PostgreSQL database. IvorySQL is committed to delivering value to its end-users through innovation and building on top of open source based database solutions. Our goal is to deliver a solution with high performance，scalability，reliability，and ease of use for small medium and large-scale enterprises. The extended functionality provided by IvorySQL will enable users to build highly performant and scalable PostgreSQL database clusters with better database compatibility and administration.This simplifies the process of migration to PostgreSQLfrom other DBMS with enhanced database administration experiences. The IvorySQL follows the same general architecture of PostgreSQL with some additions,but it does not deviate from its core philosophy.Thediagram below depicts essentially how IvorySQL operates. The yellow color in the diagram shows the new modules added by IvorySQL on top of existing PostgreSQL while IvorySQL has also made changes to existing modules and logical structures as well. The most noteworthy of those modules that received updates for supporting oracle compatibility are backend parser and system catalogs. The following diagram depicts the changes made to PostgreSQL’s existing directories and the additions that have been made. The first test to see whether you can access the database server is to try to create a database. A running IvorySQL server can manage many databases. Typically, a separate database is used for each project or for each user. Possibly, your site administrator has already created a database for your use. In that case you can omit this step and skip ahead to the next section. To create a new database, in this example named mydb, you use the following command: $ createdb mydb If this produces no response then this step was successful and you can skip over the remainder of this section. If you see a message similar to: createdb: command not found then IvorySQL was not installed properly. Either it was not installed at all or your shell’s search path was not set to include it. Try calling the command with an absolute path instead: $ /usr/local/pgsql/bin/createdb mydb The path at your site might be different. Contact your site administrator or check the installation instructions to correct the situation. Another response could be this: createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory Is the server running locally and accepting connections on that socket? This means that the server was not started, or it is not listening where createdb expects to contact it. Again, check the installation instructions or consult the administrator. Another response could be this: createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: FATAL: role \"joe\" does not exist where your own login name is mentioned. This will happen if the administrator has not created a IvorySQL user account for you. (IvorySQL user accounts are distinct from operating system user accounts.) If you are the administrator, You will need to become the operating system user under which IvorySQL was installed (usually postgres) to create the first user account. It could also be that you were assigned a IvorySQL user name that is different from your operating system user name; in that case you need to use the -U switch or set the PGUSER environment variable to specify your IvorySQL user name. If you have a user account but it does not have the privileges required to create a database, you will see the following: createdb: error: database creation failed: ERROR: permission denied to create database Not every user has authorization to create new databases. If IvorySQL refuses to create databases for you then the site administrator needs to grant you permission to create databases. Consult your site administrator if this occurs. If you installed IvorySQL yourself then you should log in for the purposes of this tutorial under the user account that you started the server as. [1] You can also create databases with other names. IvorySQL allows you to create any number of databases at a given site. Database names must have an alphabetic first character and are limited to 63 bytes in length. A convenient choice is to create a database with the same name as your current user name. Many tools assume that database name as the default, so it can save you some typing. To create that database, simply type: $ createdb If you do not want to use your database anymore you can remove it. For example, if you are the owner (creator) of the database mydb, you can destroy it using the following command: $ dropdb mydb (For this command, the database name does not default to the user account name. You always need to specify it.) This action physically removes all files associated with the database and cannot be undone, so this should only be done with a great deal of forethought. More about createdb and dropdb can be found in createdb and dropdb respectively. You can create a new table by specifying the table name, along with all column names and their types: CREATE TABLE weather ( city varchar(80), temp_lo int, -- low temperature temp_hi int, -- high temperature prcp real, -- precipitation date date ); You can enter this into psql with the line breaks. psql will recognize that the command is not terminated until the semicolon. White space (i.e., spaces, tabs, and newlines) can be used freely in SQL commands. That means you can type the command aligned differently than above, or even all on one line. Two dashes (“--”) introduce comments. Whatever follows them is ignored up to the end of the line. SQL is case insensitive about key words and identifiers, except when identifiers are double-quoted to preserve the case (not done above). varchar(80) specifies a data type that can store arbitrary character strings up to 80 characters in length. int is the normal integer type. real is a type for storing single precision floating-point numbers. date should be self-explanatory. (Yes, the column of type date is also named date. This might be convenient or confusing — you choose.) IvorySQL supports the standard SQL types int, smallint, real, double precision, char(`N), `varchar(`N), `date, time, timestamp, and interval, as well as other types of general utility and a rich set of geometric types. IvorySQL can be customized with an arbitrary number of user-defined data types. Consequently, type names are not key words in the syntax, except where required to support special cases in the SQL standard. The second example will store cities and their associated geographical location: CREATE TABLE cities ( name varchar(80), location point ); The point type is an example of a IvorySQL-specific data type. Finally, it should be mentioned that if you don’t need a table any longer or want to recreate it differently you can remove it using the following command: DROP TABLE tablename; When a table is created, it contains no data. The first thing to do before a database can be of much use is to insert data. Data is inserted one row at a time. You can also insert more than one row in a single command, but it is not possible to insert something that is not a complete row. Even if you know only some column values, a complete row must be created. To create a new row, use the INSERT command. The command requires the table name and column values. CREATE TABLE products ( product_no integer, name text, price numeric ); CREATE TABLE new_products ( product_no int , name varchar(255), price DECIMAL(10, 2), release_date DATE ); An example command to insert a row would be: INSERT INTO products VALUES (1, 'Cheese', 9.99); The data values are listed in the order in which the columns appear in the table, separated by commas. Usually, the data values will be literals (constants), but scalar expressions are also allowed. The above syntax has the drawback that you need to know the order of the columns in the table. To avoid this you can also list the columns explicitly. For example, both of the following commands have the same effect as the one above: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99); INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1); Many users consider it good practice to always list the column names. If you don’t have values for all the columns, you can omit some of them. In that case, the columns will be filled with their default values. For example: INSERT INTO products (product_no, name) VALUES (1, 'Cheese'); INSERT INTO products VALUES (1, 'Cheese'); The second form is a IvorySQL extension. It fills the columns from the left with as many values as are given, and the rest will be defaulted. For clarity, you can also request default values explicitly, for individual columns or for the entire row: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', DEFAULT); INSERT INTO products DEFAULT VALUES; You can insert multiple rows in a single command: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99), (2, 'Bread', 1.99), (3, 'Milk', 2.99); INSERT INTO new_products (product_no, name, price, release_date) VALUES (1, 'A', 100.00, '2025-05-29'), (2, 'B', 150.50, '2024-11-20'), (3, 'C', 200.75, '2025-05-29'); It is also possible to insert the result of a query (which might be no rows, one row, or many rows): INSERT INTO products (product_no, name, price) SELECT product_no, name, price FROM new_products WHERE release_date = '2025-05-29'; This provides the full power of the SQL query mechanism for computing the rows to be inserted. Tip When inserting a lot of data at the same time, consider using the COPY command. It is not as flexible as the INSERT command, but is more efficient. The results of two queries can be combined using the set operations union, intersection, and difference. The syntax is query1 UNION [ALL] query2 query1 INTERSECT [ALL] query2 query1 EXCEPT [ALL] query2 where query1 and query2 are queries that can use any of the features discussed up to this point. UNION effectively appends the result of query2 to the result of query1 (although there is no guarantee that this is the order in which the rows are actually returned). Furthermore, it eliminates duplicate rows from its result, in the same way as DISTINCT, unless UNION ALL is used. INTERSECT returns all rows that are both in the result of query1 and in the result of query2. Duplicate rows are eliminated unless INTERSECT ALL is used. EXCEPT returns all rows that are in the result of query1 but not in the result of query2. (This is sometimes called the difference between two queries.) Again, duplicates are eliminated unless EXCEPT ALL is used. In order to calculate the union, intersection, or difference of two queries, the two queries must be “union compatible”, which means that they return the same number of columns and the corresponding columns have compatible data types. Set operations can be combined, for example query1 UNION query2 EXCEPT query3 which is equivalent to (query1 UNION query2) EXCEPT query3 As shown here, you can use parentheses to control the order of evaluation. Without parentheses, UNION and EXCEPT associate left-to-right, but INTERSECT binds more tightly than those two operators. Thus query1 UNION query2 INTERSECT query3 means query1 UNION (query2 INTERSECT query3) You can also surround an individual query with parentheses. This is important if the query needs to use any of the clauses discussed in following sections, such as LIMIT. Without parentheses, you’ll get a syntax error, or else the clause will be understood as applying to the output of the set operation rather than one of its inputs. For example, SELECT a FROM b UNION SELECT x FROM y LIMIT 10 is accepted, but it means (SELECT a FROM b UNION SELECT x FROM y) LIMIT 10 not SELECT a FROM b UNION (SELECT x FROM y LIMIT 10) When the optimizer determines that parallel query is the fastest execution strategy for a particular query, it will create a query plan that includes a Gather or Gather Merge node. Here is a simple example: EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%'; QUERY PLAN ------------------------------------------------------------------------------------- Gather (cost=1000.00..217018.43 rows=1 width=97) Workers Planned: 2 -> Parallel Seq Scan on pgbench_accounts (cost=0.00..216018.33 rows=1 width=97) Filter: (filler ~~ '%x%'::text) (4 rows) In all cases, the Gather or Gather Merge node will have exactly one child plan, which is the portion of the plan that will be executed in parallel. If the Gather or Gather Merge node is at the very top of the plan tree, then the entire query will execute in parallel. If it is somewhere else in the plan tree, then only the portion of the plan below it will run in parallel. In the example above, the query accesses only one table, so there is only one plan node other than the Gather node itself; since that plan node is a child of the Gather node, it will run in parallel. Using EXPLAIN, you can see the number of workers chosen by the planner. When the Gather node is reached during query execution, the process that is implementing the user’s session will request a number of background worker processes equal to the number of workers chosen by the planner. The number of background workers that the planner will consider using is limited to at most max_parallel_workers_per_gather. The total number of background workers that can exist at any one time is limited by both max_worker_processes and max_parallel_workers. Therefore, it is possible for a parallel query to run with fewer workers than planned, or even with no workers at all. The optimal plan may depend on the number of workers that are available, so this can result in poor query performance. If this occurrence is frequent, consider increasing max_worker_processes and max_parallel_workers so that more workers can be run simultaneously or alternatively reducing max_parallel_workers_per_gather so that the planner requests fewer workers. Every background worker process that is successfully started for a given parallel query will execute the parallel portion of the plan. The leader will also execute that portion of the plan, but it has an additional responsibility: it must also read all of the tuples generated by the workers. When the parallel portion of the plan generates only a small number of tuples, the leader will often behave very much like an additional worker, speeding up query execution. Conversely, when the parallel portion of the plan generates a large number of tuples, the leader may be almost entirely occupied with reading the tuples generated by the workers and performing any further processing steps that are required by plan nodes above the level of the Gather node or Gather Merge node. In such cases, the leader will do very little of the work of executing the parallel portion of the plan. When the node at the top of the parallel portion of the plan is Gather Merge rather than Gather, it indicates that each process executing the parallel portion of the plan is producing tuples in sorted order, and that the leader is performing an order-preserving merge. In contrast, Gather reads tuples from the workers in whatever order is convenient, destroying any sort order that may have existed. There are several settings that can cause the query planner not to generate a parallel query plan under any circumstances. In order for any parallel query plans whatsoever to be generated, the following settings must be configured as indicated. max_parallel_workers_per_gather must be set to a value that is greater than zero. This is a special case of the more general principle that no more workers should be used than the number configured via max_parallel_workers_per_gather. In addition, the system must not be running in single-user mode. Since the entire database system is running as a single process in this situation, no background workers will be available. Even when it is in general possible for parallel query plans to be generated, the planner will not generate them for a given query if any of the following are true: The query writes any data or locks any database rows. If a query contains a data-modifying operation either at the top level or within a CTE, no parallel plans for that query will be generated. As an exception, the following commands, which create a new table and populate it, can use a parallel plan for the underlying SELECT part of the query: CREATE TABLE …​ AS SELECT INTO CREATE MATERIALIZED VIEW REFRESH MATERIALIZED VIEW The query might be suspended during execution. In any situation in which the system thinks that partial or incremental execution might occur, no parallel plan is generated. For example, a cursor created using DECLARE CURSOR will never use a parallel plan. Similarly, a PL/pgSQL loop of the form FOR x IN query LOOP .. END LOOP will never use a parallel plan, because the parallel query system is unable to verify that the code in the loop is safe to execute while parallel query is active. The query uses any function marked PARALLEL UNSAFE. Most system-defined functions are PARALLEL SAFE, but user-defined functions are marked PARALLEL UNSAFE by default. The query is running inside of another query that is already parallel. For example, if a function called by a parallel query issues an SQL query itself, that query will never use a parallel plan. This is a limitation of the current implementation, but it may not be desirable to remove this limitation, since it could result in a single query using a very large number of processes. Even when parallel query plan is generated for a particular query, there are several circumstances under which it will be impossible to execute that plan in parallel at execution time. If this occurs, the leader will execute the portion of the plan below the Gather node entirely by itself, almost as if the Gather node were not present. This will happen if any of the following conditions are met: No background workers can be obtained because of the limitation that the total number of background workers cannot exceed max_worker_processes. No background workers can be obtained because of the limitation that the total number of background workers launched for purposes of parallel query cannot exceed max_parallel_workers. The client sends an Execute message with a non-zero fetch count. See the discussion of the extended query protocol. Since libpq currently provides no way to send such a message, this can only occur when using a client that does not rely on libpq. If this is a frequent occurrence, it may be a good idea to set max_parallel_workers_per_gather to zero in sessions where it is likely, so as to avoid generating query plans that may be suboptimal when run serially. Because each worker executes the parallel portion of the plan to completion, it is not possible to simply take an ordinary query plan and run it using multiple workers. Each worker would produce a full copy of the output result set, so the query would not run any faster than normal but would produce incorrect results. Instead, the parallel portion of the plan must be what is known internally to the query optimizer as a partial plan; that is, it must be constructed so that each process that executes the plan will generate only a subset of the output rows in such a way that each required output row is guaranteed to be generated by exactly one of the cooperating processes. Generally, this means that the scan on the driving table of the query must be a parallel-aware scan. The following types of parallel-aware table scans are currently supported. In a parallel sequential scan, the table’s blocks will be divided into ranges and shared among the cooperating processes. Each worker process will complete the scanning of its given range of blocks before requesting an additional range of blocks. In a parallel bitmap heap scan, one process is chosen as the leader. That process performs a scan of one or more indexes and builds a bitmap indicating which table blocks need to be visited. These blocks are then divided among the cooperating processes as in a parallel sequential scan. In other words, the heap scan is performed in parallel, but the underlying index scan is not. In a parallel index scan or parallel index-only scan, the cooperating processes take turns reading data from the index. Currently, parallel index scans are supported only for btree indexes. Each process will claim a single index block and will scan and return all tuples referenced by that block; other processes can at the same time be returning tuples from a different index block. The results of a parallel btree scan are returned in sorted order within each worker process. Other scan types, such as scans of non-btree indexes, may support parallel scans in the future. Just as in a non-parallel plan, the driving table may be joined to one or more other tables using a nested loop, hash join, or merge join. The inner side of the join may be any kind of non-parallel plan that is otherwise supported by the planner provided that it is safe to run within a parallel worker. Depending on the join type, the inner side may also be a parallel plan. In a nested loop join, the inner side is always non-parallel. Although it is executed in full, this is efficient if the inner side is an index scan, because the outer tuples and thus the loops that look up values in the index are divided over the cooperating processes. In a merge join, the inner side is always a non-parallel plan and therefore executed in full. This may be inefficient, especially if a sort must be performed, because the work and resulting data are duplicated in every cooperating process. In a hash join (without the \"parallel\" prefix), the inner side is executed in full by every cooperating process to build identical copies of the hash table. This may be inefficient if the hash table is large or the plan is expensive. In a parallel hash join, the inner side is a parallel hash that divides the work of building a shared hash table over the cooperating processes. IvorySQL supports parallel aggregation by aggregating in two stages. First, each process participating in the parallel portion of the query performs an aggregation step, producing a partial result for each group of which that process is aware. This is reflected in the plan as a Partial Aggregate node. Second, the partial results are transferred to the leader via Gather or Gather Merge. Finally, the leader re-aggregates the results across all workers in order to produce the final result. This is reflected in the plan as a Finalize Aggregate node. Because the Finalize Aggregate node runs on the leader process, queries that produce a relatively large number of groups in comparison to the number of input rows will appear less favorable to the query planner. For example, in the worst-case scenario the number of groups seen by the Finalize Aggregate node could be as many as the number of input rows that were seen by all worker processes in the Partial Aggregate stage. For such cases, there is clearly going to be no performance benefit to using parallel aggregation. The query planner takes this into account during the planning process and is unlikely to choose parallel aggregate in this scenario. Parallel aggregation is not supported in all situations. Each aggregate must be safe for parallelism and must have a combine function. If the aggregate has a transition state of type internal, it must have serialization and deserialization functions. See CREATE AGGREGATE for more details. Parallel aggregation is not supported if any aggregate function call contains DISTINCT or ORDER BY clause and is also not supported for ordered set aggregates or when the query involves GROUPING SETS. It can only be used when all joins involved in the query are also part of the parallel portion of the plan. Whenever IvorySQL needs to combine rows from multiple sources into a single result set, it uses an Append or MergeAppend plan node. This commonly happens when implementing UNION ALL or when scanning a partitioned table. Such nodes can be used in parallel plans just as they can in any other plan. However, in a parallel plan, the planner may instead use a Parallel Append node. When an Append node is used in a parallel plan, each process will execute the child plans in the order in which they appear, so that all participating processes cooperate to execute the first child plan until it is complete and then move to the second plan at around the same time. When a Parallel Append is used instead, the executor will instead spread out the participating processes as evenly as possible across its child plans, so that multiple child plans are executed simultaneously. This avoids contention, and also avoids paying the startup cost of a child plan in those processes that never execute it. Also, unlike a regular Append node, which can only have partial children when used within a parallel plan, a Parallel Append node can have both partial and non-partial child plans. Non-partial children will be scanned by only a single process, since scanning them more than once would produce duplicate results. Plans that involve appending multiple results sets can therefore achieve coarse-grained parallelism even when efficient partial plans are not available. For example, consider a query against a partitioned table that can only be implemented efficiently by using an index that does not support parallel scans. The planner might choose a Parallel Append of regular Index Scan plans; each individual index scan would have to be executed to completion by a single process, but different scans could be performed at the same time by different processes. enable_parallel_append can be used to disable this feature. If a query that is expected to do so does not produce a parallel plan, you can try reducing parallel_setup_cost or parallel_tuple_cost. Of course, this plan may turn out to be slower than the serial plan that the planner preferred, but this will not always be the case. If you don’t get a parallel plan even with very small values of these settings (e.g., after setting them both to zero), there may be some reason why the query planner is unable to generate a parallel plan for your query. When executing a parallel plan, you can use EXPLAIN (ANALYZE, VERBOSE) to display per-worker statistics for each plan node. This may be useful in determining whether the work is being evenly distributed between all plan nodes and more generally in understanding the performance characteristics of the plan. ABORT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ABORT rolls back the current transaction and causes all the updates made by the transaction to be discarded. This command is identical in behavior to the standard SQL command ROLLBACK, and is present only for historical reasons. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use COMMIT to successfully terminate a transaction. Issuing ABORT outside of a transaction block emits a warning and otherwise has no effect. To abort all changes: ABORT; This command is a IvorySQL extension present for historical reasons. ROLLBACK is the equivalent standard SQL command. BEGIN [ WORK | TRANSACTION ] [ transaction_mode [, ...] ] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE BEGIN initiates a transaction block, that is, all statements after a BEGIN command will be executed in a single transaction until an explicit COMMIT or ROLLBACK is given. By default (without BEGIN), IvorySQL executes transactions in “autocommit” mode, that is, each statement is executed in its own transaction and a commit is implicitly performed at the end of the statement (if execution was successful, otherwise a rollback is done). Statements are executed more quickly in a transaction block, because transaction start/commit requires significant CPU and disk activity. Execution of multiple statements inside a transaction is also useful to ensure consistency when making several related changes: other sessions will be unable to see the intermediate states wherein not all the related updates have been done. If the isolation level, read/write mode, or deferrable mode is specified, the new transaction has those characteristics, as if SET TRANSACTION was executed. WORK TRANSACTION Optional key words. They have no effect. Refer to SET TRANSACTION for information on the meaning of the other parameters to this statement. START TRANSACTION has the same functionality as BEGIN. Use COMMIT or ROLLBACK to terminate a transaction block. Issuing BEGIN when already inside a transaction block will provoke a warning message. The state of the transaction is not affected. To nest transactions within a transaction block, use savepoints (see SAVEPOINT). For reasons of backwards compatibility, the commas between successive transaction_modes can be omitted. To begin a transaction block: BEGIN; BEGIN is a IvorySQL language extension. It is equivalent to the SQL-standard command START TRANSACTION, whose reference page contains additional compatibility information. The DEFERRABLE transaction_mode is a IvorySQL language extension. Incidentally, the BEGIN key word is used for a different purpose in embedded SQL. You are advised to be careful about the transaction semantics when porting database applications. COMMIT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] COMMIT commits the current transaction. All changes made by the transaction become visible to others and are guaranteed to be durable if a crash occurs. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use ROLLBACK to abort a transaction. Issuing COMMIT when not inside a transaction does no harm, but it will provoke a warning message. COMMIT AND CHAIN when not inside a transaction is an error. To commit the current transaction and make all changes permanent: COMMIT; The command COMMIT conforms to the SQL standard. The form COMMIT TRANSACTION is a IvorySQL extension. COMMIT PREPARED transaction_id COMMIT PREPARED commits a transaction that is in prepared state. transaction_id The transaction identifier of the transaction that is to be committed. To commit a prepared transaction, you must be either the same user that executed the transaction originally, or a superuser. But you do not have to be in the same session that executed the transaction. This command cannot be executed inside a transaction block. The prepared transaction is committed immediately. All currently available prepared transactions are listed in the pg_prepared_xacts system view. Commit the transaction identified by the transaction identifier foobar: COMMIT PREPARED 'foobar'; COMMIT PREPARED is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. END [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] END commits the current transaction. All changes made by the transaction become visible to others and are guaranteed to be durable if a crash occurs. This command is a IvorySQL extension that is equivalent to COMMIT. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use ROLLBACK to abort a transaction. Issuing END when not inside a transaction does no harm, but it will provoke a warning message. To commit the current transaction and make all changes permanent: END; END is a IvorySQL extension that provides functionality equivalent to COMMIT, which is specified in the SQL standard. PREPARE TRANSACTION transaction_id PREPARE TRANSACTION prepares the current transaction for two-phase commit. After this command, the transaction is no longer associated with the current session; instead, its state is fully stored on disk, and there is a very high probability that it can be committed successfully, even if a database crash occurs before the commit is requested. Once prepared, a transaction can later be committed or rolled back with COMMIT PREPARED or ROLLBACK PREPARED, respectively. Those commands can be issued from any session, not only the one that executed the original transaction. From the point of view of the issuing session, PREPARE TRANSACTION is not unlike a ROLLBACK command: after executing it, there is no active current transaction, and the effects of the prepared transaction are no longer visible. (The effects will become visible again if the transaction is committed.) If the PREPARE TRANSACTION command fails for any reason, it becomes a ROLLBACK: the current transaction is canceled. transaction_id An arbitrary identifier that later identifies this transaction for COMMIT PREPARED or ROLLBACK PREPARED. The identifier must be written as a string literal, and must be less than 200 bytes long. It must not be the same as the identifier used for any currently prepared transaction. PREPARE TRANSACTION is not intended for use in applications or interactive sessions. Its purpose is to allow an external transaction manager to perform atomic global transactions across multiple databases or other transactional resources. Unless you’re writing a transaction manager, you probably shouldn’t be using PREPARE TRANSACTION. This command must be used inside a transaction block. Use BEGIN to start one. It is not currently allowed to PREPARE a transaction that has executed any operations involving temporary tables or the session’s temporary namespace, created any cursors WITH HOLD, or executed LISTEN, UNLISTEN, or NOTIFY. Those features are too tightly tied to the current session to be useful in a transaction to be prepared. If the transaction modified any run-time parameters with SET (without the LOCAL option), those effects persist after PREPARE TRANSACTION, and will not be affected by any later COMMIT PREPARED or ROLLBACK PREPARED. Thus, in this one respect PREPARE TRANSACTION acts more like COMMIT than ROLLBACK. All currently available prepared transactions are listed in the pg_prepared_xacts system view. It is unwise to leave transactions in the prepared state for a long time. This will interfere with the ability of VACUUM to reclaim storage, and in extreme cases could cause the database to shut down to prevent transaction ID wraparound (see Section 25.1.5). Keep in mind also that the transaction continues to hold whatever locks it held. The intended usage of the feature is that a prepared transaction will normally be committed or rolled back as soon as an external transaction manager has verified that other databases are also prepared to commit. If you have not set up an external transaction manager to track prepared transactions and ensure they get closed out promptly, it is best to keep the prepared-transaction feature disabled by setting max_prepared_transactions to zero. This will prevent accidental creation of prepared transactions that might then be forgotten and eventually cause problems. Prepare the current transaction for two-phase commit, using foobar as the transaction identifier: PREPARE TRANSACTION 'foobar'; PREPARE TRANSACTION is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. ROLLBACK [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ROLLBACK rolls back the current transaction and causes all the updates made by the transaction to be discarded. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use COMMIT to successfully terminate a transaction. Issuing ROLLBACK outside of a transaction block emits a warning and otherwise has no effect. ROLLBACK AND CHAIN outside of a transaction block is an error. To abort all changes: ROLLBACK; The command ROLLBACK conforms to the SQL standard. The form ROLLBACK TRANSACTION is a IvorySQL extension. ROLLBACK PREPARED transaction_id ROLLBACK PREPARED rolls back a transaction that is in prepared state. transaction_id The transaction identifier of the transaction that is to be rolled back. To roll back a prepared transaction, you must be either the same user that executed the transaction originally, or a superuser. But you do not have to be in the same session that executed the transaction. This command cannot be executed inside a transaction block. The prepared transaction is rolled back immediately. All currently available prepared transactions are listed in the pg_prepared_xacts system view. Roll back the transaction identified by the transaction identifier foobar: ROLLBACK PREPARED 'foobar'; ROLLBACK PREPARED is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. SAVEPOINT savepoint_name SAVEPOINT establishes a new savepoint within the current transaction. A savepoint is a special mark inside a transaction that allows all commands that are executed after it was established to be rolled back, restoring the transaction state to what it was at the time of the savepoint. savepoint_name The name to give to the new savepoint. If savepoints with the same name already exist, they will be inaccessible until newer identically-named savepoints are released. Use ROLLBACK TO to rollback to a savepoint. Use RELEASE SAVEPOINT to destroy a savepoint, keeping the effects of commands executed after it was established. Savepoints can only be established when inside a transaction block. There can be multiple savepoints defined within a transaction. To establish a savepoint and later undo the effects of all commands executed after it was established: BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); ROLLBACK TO SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); COMMIT; The above transaction will insert the values 1 and 3, but not 2. To establish and later destroy a savepoint: BEGIN; INSERT INTO table1 VALUES (3); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (4); RELEASE SAVEPOINT my_savepoint; COMMIT; The above transaction will insert both 3 and 4. To use a single savepoint name: BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); -- rollback to the second savepoint ROLLBACK TO SAVEPOINT my_savepoint; SELECT * FROM table1; -- shows rows 1 and 2 -- release the second savepoint RELEASE SAVEPOINT my_savepoint; -- rollback to the first savepoint ROLLBACK TO SAVEPOINT my_savepoint; SELECT * FROM table1; -- shows only row 1 COMMIT; The above transaction shows row 3 being rolled back first, then row 2. SQL requires a savepoint to be destroyed automatically when another savepoint with the same name is established. In IvorySQL, the old savepoint is kept, though only the more recent one will be used when rolling back or releasing. (Releasing the newer savepoint with RELEASE SAVEPOINT will cause the older one to again become accessible to ROLLBACK TO SAVEPOINT and RELEASE SAVEPOINT.) Otherwise, SAVEPOINT is fully SQL conforming. SET CONSTRAINTS { ALL | name [, ...] } { DEFERRED | IMMEDIATE } SET CONSTRAINTS sets the behavior of constraint checking within the current transaction. IMMEDIATE constraints are checked at the end of each statement. DEFERRED constraints are not checked until transaction commit. Each constraint has its own IMMEDIATE or DEFERRED mode. Upon creation, a constraint is given one of three characteristics: DEFERRABLE INITIALLY DEFERRED, DEFERRABLE INITIALLY IMMEDIATE, or NOT DEFERRABLE. The third class is always IMMEDIATE and is not affected by the SET CONSTRAINTS command. The first two classes start every transaction in the indicated mode, but their behavior can be changed within a transaction by SET CONSTRAINTS. SET CONSTRAINTS with a list of constraint names changes the mode of just those constraints (which must all be deferrable). Each constraint name can be schema-qualified. The current schema search path is used to find the first matching name if no schema name is specified. SET CONSTRAINTS ALL changes the mode of all deferrable constraints. When SET CONSTRAINTS changes the mode of a constraint from DEFERRED to IMMEDIATE, the new mode takes effect retroactively: any outstanding data modifications that would have been checked at the end of the transaction are instead checked during the execution of the SET CONSTRAINTS command. If any such constraint is violated, the SET CONSTRAINTS fails (and does not change the constraint mode). Thus, SET CONSTRAINTS can be used to force checking of constraints to occur at a specific point in a transaction. Currently, only UNIQUE, PRIMARY KEY, REFERENCES (foreign key), and EXCLUDE constraints are affected by this setting. NOT NULL and CHECK constraints are always checked immediately when a row is inserted or modified (not at the end of the statement). Uniqueness and exclusion constraints that have not been declared DEFERRABLE are also checked immediately. The firing of triggers that are declared as “constraint triggers” is also controlled by this setting — they fire at the same time that the associated constraint should be checked. Because IvorySQL does not require constraint names to be unique within a schema (but only per-table), it is possible that there is more than one match for a specified constraint name. In this case SET CONSTRAINTS will act on all matches. For a non-schema-qualified name, once a match or matches have been found in some schema in the search path, schemas appearing later in the path are not searched. This command only alters the behavior of constraints within the current transaction. Issuing this outside of a transaction block emits a warning and otherwise has no effect. This command complies with the behavior defined in the SQL standard, except for the limitation that, in IvorySQL, it does not apply to NOT NULL and CHECK constraints. Also, IvorySQL checks non-deferrable uniqueness constraints immediately, not at end of statement as the standard would suggest. SET TRANSACTION transaction_mode [, ...] SET TRANSACTION SNAPSHOT snapshot_id SET SESSION CHARACTERISTICS AS TRANSACTION transaction_mode [, ...] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE The SET TRANSACTION command sets the characteristics of the current transaction. It has no effect on any subsequent transactions. SET SESSION CHARACTERISTICS sets the default transaction characteristics for subsequent transactions of a session. These defaults can be overridden by SET TRANSACTION for an individual transaction. The available transaction characteristics are the transaction isolation level, the transaction access mode (read/write or read-only), and the deferrable mode. In addition, a snapshot can be selected, though only for the current transaction, not as a session default. The isolation level of a transaction determines what data the transaction can see when other transactions are running concurrently: READ COMMITTED A statement can only see rows committed before it began. This is the default. REPEATABLE READ All statements of the current transaction can only see rows committed before the first query or data-modification statement was executed in this transaction. SERIALIZABLE All statements of the current transaction can only see rows committed before the first query or data-modification statement was executed in this transaction. If a pattern of reads and writes among concurrent serializable transactions would create a situation which could not have occurred for any serial (one-at-a-time) execution of those transactions, one of them will be rolled back with a serialization_failure error. The SQL standard defines one additional level, READ UNCOMMITTED. In IvorySQL READ UNCOMMITTED is treated as READ COMMITTED. The transaction isolation level cannot be changed after the first query or data-modification statement (SELECT, INSERT, DELETE, UPDATE, FETCH, or COPY) of a transaction has been executed. See Chapter 13 for more information about transaction isolation and concurrency control. The transaction access mode determines whether the transaction is read/write or read-only. Read/write is the default. When a transaction is read-only, the following SQL commands are disallowed: INSERT, UPDATE, DELETE, and COPY FROM if the table they would write to is not a temporary table; all CREATE, ALTER, and DROP commands; COMMENT, GRANT, REVOKE, TRUNCATE; and EXPLAIN ANALYZE and EXECUTE if the command they would execute is among those listed. This is a high-level notion of read-only that does not prevent all writes to disk. The DEFERRABLE transaction property has no effect unless the transaction is also SERIALIZABLE and READ ONLY. When all three of these properties are selected for a transaction, the transaction may block when first acquiring its snapshot, after which it is able to run without the normal overhead of a SERIALIZABLE transaction and without any risk of contributing to or being canceled by a serialization failure. This mode is well suited for long-running reports or backups. The SET TRANSACTION SNAPSHOT command allows a new transaction to run with the same snapshot as an existing transaction. The pre-existing transaction must have exported its snapshot with the pg_export_snapshot function. That function returns a snapshot identifier, which must be given to SET TRANSACTION SNAPSHOT to specify which snapshot is to be imported. The identifier must be written as a string literal in this command, for example '00000003-0000001B-1'. SET TRANSACTION SNAPSHOT can only be executed at the start of a transaction, before the first query or data-modification statement (SELECT, INSERT, DELETE, UPDATE, FETCH, or COPY) of the transaction. Furthermore, the transaction must already be set to SERIALIZABLE or REPEATABLE READ isolation level (otherwise, the snapshot would be discarded immediately, since READ COMMITTED mode takes a new snapshot for each command). If the importing transaction uses SERIALIZABLE isolation level, then the transaction that exported the snapshot must also use that isolation level. Also, a non-read-only serializable transaction cannot import a snapshot from a read-only transaction. If SET TRANSACTION is executed without a prior START TRANSACTION or BEGIN, it emits a warning and otherwise has no effect. It is possible to dispense with SET TRANSACTION by instead specifying the desired transaction_modes in BEGIN or START TRANSACTION. But that option is not available for SET TRANSACTION SNAPSHOT. The session default transaction modes can also be set or examined via the configuration parameters default_transaction_isolation, default_transaction_read_only, and default_transaction_deferrable. (In fact SET SESSION CHARACTERISTICS is just a verbose equivalent for setting these variables with SET.) This means the defaults can be set in the configuration file, via ALTER DATABASE, etc. Consult Chapter 20 for more information. The current transaction’s modes can similarly be set or examined via the configuration parameters transaction_isolation, transaction_read_only, and transaction_deferrable. Setting one of these parameters acts the same as the corresponding SET TRANSACTION option, with the same restrictions on when it can be done. However, these parameters cannot be set in the configuration file, or from any source other than live SQL. To begin a new transaction with the same snapshot as an already existing transaction, first export the snapshot from the existing transaction. That will return the snapshot identifier, for example: BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SELECT pg_export_snapshot(); pg_export_snapshot --------------------- 00000003-0000001B-1 (1 row) Then give the snapshot identifier in a SET TRANSACTION SNAPSHOT command at the beginning of the newly opened transaction: BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SET TRANSACTION SNAPSHOT '00000003-0000001B-1'; These commands are defined in the SQL standard, except for the DEFERRABLE transaction mode and the SET TRANSACTION SNAPSHOT form, which are IvorySQL extensions. SERIALIZABLE is the default transaction isolation level in the standard. In IvorySQL the default is ordinarily READ COMMITTED, but you can change it as mentioned above. In the SQL standard, there is one other transaction characteristic that can be set with these commands: the size of the diagnostics area. This concept is specific to embedded SQL, and therefore is not implemented in the IvorySQL server. The SQL standard requires commas between successive transaction_modes, but for historical reasons IvorySQL allows the commas to be omitted. START TRANSACTION [ transaction_mode [, ...] ] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE This command begins a new transaction block. If the isolation level, read/write mode, or deferrable mode is specified, the new transaction has those characteristics, as if SET TRANSACTION was executed. This is the same as the BEGIN command. Refer to SET TRANSACTION for information on the meaning of the parameters to this statement. In the standard, it is not necessary to issue START TRANSACTION to start a transaction block: any SQL command implicitly begins a block. IvorySQL’s behavior can be seen as implicitly issuing a COMMIT after each command that does not follow START TRANSACTION (or BEGIN), and it is therefore often called “autocommit”. Other relational database systems might offer an autocommit feature as a convenience. The DEFERRABLE transaction_mode is a IvorySQL language extension. The SQL standard requires commas between successive transaction_modes, but for historical reasons IvorySQL allows the commas to be omitted. See also the compatibility section of SET TRANSACTION. SQL input consists of a sequence of commands. A command is composed of a sequence of tokens, terminated by a semicolon (“;”). The end of the input stream also terminates a command. Which tokens are valid depends on the syntax of the particular command. A token can be a key word, an identifier, a quoted identifier, a literal (or constant), or a special character symbol. Tokens are normally separated by whitespace (space, tab, newline), but need not be if there is no ambiguity (which is generally only the case if a special character is adjacent to some other token type). For example, the following is (syntactically) valid SQL input: SELECT * FROM MY_TABLE; UPDATE MY_TABLE SET A = 5; INSERT INTO MY_TABLE VALUES (3, 'hi there'); This is a sequence of three commands, one per line (although this is not required; more than one command can be on a line, and commands can usefully be split across lines). Additionally, comments can occur in SQL input. They are not tokens, they are effectively equivalent to whitespace. The SQL syntax is not very consistent regarding what tokens identify commands and which are operands or parameters. The first few tokens are generally the command name, so in the above example we would usually speak of a “SELECT”, an “UPDATE”, and an “INSERT” command. But for instance the UPDATE command always requires a SET token to appear in a certain position, and this particular variation of INSERT also requires a VALUES in order to be complete. Tokens such as SELECT, UPDATE, or VALUES in the example above are examples of key words, that is, words that have a fixed meaning in the SQL language. The tokens MY_TABLE and A are examples of identifiers. They identify names of tables, columns, or other database objects, depending on the command they are used in. Therefore they are sometimes simply called “names”. Key words and identifiers have the same lexical structure, meaning that one cannot know whether a token is an identifier or a key word without knowing the language. A complete list of key words can be found in Appendix C. SQL identifiers and key words must begin with a letter (a-z, but also letters with diacritical marks and non-Latin letters) or an underscore (_). Subsequent characters in an identifier or key word can be letters, underscores, digits (0-9), or dollar signs ($). Note that dollar signs are not allowed in identifiers according to the letter of the SQL standard, so their use might render applications less portable. The SQL standard will not define a key word that contains digits or starts or ends with an underscore, so identifiers of this form are safe against possible conflict with future extensions of the standard. The system uses no more than NAMEDATALEN-1 bytes of an identifier; longer names can be written in commands, but they will be truncated. By default, NAMEDATALEN is 64 so the maximum identifier length is 63 bytes. If this limit is problematic, it can be raised by changing the NAMEDATALEN constant in src/include/pg_config_manual.h. Key words and unquoted identifiers are case insensitive. Therefore: UPDATE MY_TABLE SET A = 5; can equivalently be written as: uPDaTE my_TabLE SeT a = 5; A convention often used is to write key words in upper case and names in lower case, e.g.: UPDATE my_table SET a = 5; There is a second kind of identifier: the delimited identifier or quoted identifier. It is formed by enclosing an arbitrary sequence of characters in double-quotes (\"). A delimited identifier is always an identifier, never a key word. So \"select\" could be used to refer to a column or table named “select”, whereas an unquoted select would be taken as a key word and would therefore provoke a parse error when used where a table or column name is expected. The example can be written with quoted identifiers like this: UPDATE \"my_table\" SET \"a\" = 5; Quoted identifiers can contain any character, except the character with code zero. (To include a double quote, write two double quotes.) This allows constructing table or column names that would otherwise not be possible, such as ones containing spaces or ampersands. The length limitation still applies. Quoting an identifier also makes it case-sensitive, whereas unquoted names are always folded to lower case. For example, the identifiers FOO, foo, and \"foo\" are considered the same by IvorySQL, but \"Foo\" and \"FOO\" are different from these three and each other. (The folding of unquoted names to lower case in IvorySQL is incompatible with the SQL standard, which says that unquoted names should be folded to upper case. Thus, foo should be equivalent to \"FOO\" not \"foo\" according to the standard. If you want to write portable applications you are advised to always quote a particular name or never quote it.) A variant of quoted identifiers allows including escaped Unicode characters identified by their code points. This variant starts with U& (upper or lower case U followed by ampersand) immediately before the opening double quote, without any spaces in between, for example U&\"foo\". (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the identifier \"data\" could be written as U&\"d\\0061t\\+000061\" The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters: U&\"\\0441\\043B\\043E\\043D\" If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example: U&\"d!0061t!+000061\" UESCAPE '!' The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character. Note that the escape character is written in single quotes, not double quotes, after UESCAPE. To include the escape character in the identifier literally, write it twice. Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.) If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that’s not possible. There are three kinds of implicitly-typed constants in IvorySQL: strings, bit strings, and numbers. Constants can also be specified with explicit types, which can enable more accurate representation and more efficient handling by the system. These alternatives are discussed in the following subsections. A string constant in SQL is an arbitrary sequence of characters bounded by single quotes ('), for example 'This is a string'. To include a single-quote character within a string constant, write two adjacent single quotes, e.g., 'Dianne''s horse'. Note that this is not the same as a double-quote character (\"). Two string constants that are only separated by whitespace with at least one newline are concatenated and effectively treated as if the string had been written as one constant. For example: SELECT 'foo' 'bar'; is equivalent to: SELECT 'foobar'; but: SELECT 'foo' 'bar'; is not valid syntax. (This slightly bizarre behavior is specified by SQL; IvorySQL is following the standard.) IvorySQL also accepts “escape” string constants, which are an extension to the SQL standard. An escape string constant is specified by writing the letter E (upper or lower case) just before the opening single quote, e.g., E’foo'. (When continuing an escape string constant across lines, write E only before the first opening quote.) Within an escape string, a backslash character (\\) begins a C-like backslash escape sequence, in which the combination of backslash and following character(s) represent a special byte value. Table 5.1. Backslash Escape Sequences Backslash Escape Sequence Interpretation \\b backspace \\f form feed \\n newline \\r carriage return \\t tab *`o*, `*`oo*, `*`ooo*` (o = 0–7) octal byte value \\x*`h, `\\x`hh` (h* = 0–9, A–F) hexadecimal byte value \\u*`xxxx, `\\U`xxxxxxxx` (x* = 0–9, A–F) 16 or 32-bit hexadecimal Unicode character value Any other character following a backslash is taken literally. Thus, to include a backslash character, write two backslashes (\\\\). Also, a single quote can be included in an escape string by writing \\', in addition to the normal way of ''. It is your responsibility that the byte sequences you create, especially when using the octal or hexadecimal escapes, compose valid characters in the server character set encoding. A useful alternative is to use Unicode escapes or the alternative Unicode escape syntax, ; then the server will check that the character conversion is possible. Caution If the configuration parameter standard_conforming_strings is off, then IvorySQL recognizes backslash escapes in both regular and escape string constants. However, as of IvorySQL, the default is on, meaning that backslash escapes are recognized only in escape string constants. This behavior is more standards-compliant, but might break applications which rely on the historical behavior, where backslash escapes were always recognized. As a workaround, you can set this parameter to off, but it is better to migrate away from using backslash escapes. If you need to use a backslash escape to represent a special character, write the string constant with an E.In addition to standard_conforming_strings, the configuration parameters escape_string_warning and backslash_quote govern treatment of backslashes in string constants.The character with the code zero cannot be in a string constant. IvorySQL also supports another type of escape syntax for strings that allows specifying arbitrary Unicode characters by code point. A Unicode escape string constant starts with U& (upper or lower case letter U followed by ampersand) immediately before the opening quote, without any spaces in between, for example U&'foo'. (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the string 'data' could be written as U&'d\\0061t\\+000061' The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters: U&'\\0441\\043B\\043E\\043D' If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example: U&'d!0061t!+000061' UESCAPE '!' The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character. To include the escape character in the string literally, write it twice. Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.) If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that’s not possible. Also, the Unicode escape syntax for string constants only works when the configuration parameter standard_conforming_strings is turned on. This is because otherwise this syntax could confuse clients that parse the SQL statements to the point that it could lead to SQL injections and similar security issues. If the parameter is set to off, this syntax will be rejected with an error message. While the standard syntax for specifying string constants is usually convenient, it can be difficult to understand when the desired string contains many single quotes or backslashes, since each of those must be doubled. To allow more readable queries in such situations, IvorySQL provides another way, called “dollar quoting”, to write string constants. A dollar-quoted string constant consists of a dollar sign ($), an optional “tag” of zero or more characters, another dollar sign, an arbitrary sequence of characters that makes up the string content, a dollar sign, the same tag that began this dollar quote, and a dollar sign. For example, here are two different ways to specify the string “Dianne’s horse” using dollar quoting: $$Dianne's horse$$ $SomeTag$Dianne's horse$SomeTag$ Notice that inside the dollar-quoted string, single quotes can be used without needing to be escaped. Indeed, no characters inside a dollar-quoted string are ever escaped: the string content is always written literally. Backslashes are not special, and neither are dollar signs, unless they are part of a sequence matching the opening tag. It is possible to nest dollar-quoted string constants by choosing different tags at each nesting level. This is most commonly used in writing function definitions. For example: $function$ BEGIN RETURN ($1 ~ $q$[\\t\\r\\n\\v\\\\]$q$); END; $function$ Here, the sequence $q$[\\t\\r\\n\\v\\\\]$q$ represents a dollar-quoted literal string [\\t\\r\\n\\v\\\\], which will be recognized when the function body is executed by IvorySQL. But since the sequence does not match the outer dollar quoting delimiter $function$, it is just some more characters within the constant so far as the outer string is concerned. The tag, if any, of a dollar-quoted string follows the same rules as an unquoted identifier, except that it cannot contain a dollar sign. Tags are case sensitive, so $tag$String content$tag$ is correct, but $TAG$String content$tag$ is not. A dollar-quoted string that follows a keyword or identifier must be separated from it by whitespace; otherwise the dollar quoting delimiter would be taken as part of the preceding identifier. Dollar quoting is not part of the SQL standard, but it is often a more convenient way to write complicated string literals than the standard-compliant single quote syntax. It is particularly useful when representing string constants inside other constants, as is often needed in procedural function definitions. With single-quote syntax, each backslash in the above example would have to be written as four backslashes, which would be reduced to two backslashes in parsing the original string constant, and then to one when the inner string constant is re-parsed during function execution. Bit-string constants look like regular string constants with a B (upper or lower case) immediately before the opening quote (no intervening whitespace), e.g., B'1001'. The only characters allowed within bit-string constants are 0 and 1. Alternatively, bit-string constants can be specified in hexadecimal notation, using a leading X (upper or lower case), e.g., X'1FF'. This notation is equivalent to a bit-string constant with four binary digits for each hexadecimal digit. Both forms of bit-string constant can be continued across lines in the same way as regular string constants. Dollar quoting cannot be used in a bit-string constant. Numeric constants are accepted in these general forms: digits digits.[digits][e[+-]digits] [digits].digits[e[+-]digits] digitse[+-]digits where digits is one or more decimal digits (0 through 9). At least one digit must be before or after the decimal point, if one is used. At least one digit must follow the exponent marker (e), if one is present. There cannot be any spaces or other characters embedded in the constant. Note that any leading plus or minus sign is not actually considered part of the constant; it is an operator applied to the constant. These are some examples of valid numeric constants: 42 3.5 4. .001 5e2 1.925e-3 A numeric constant that contains neither a decimal point nor an exponent is initially presumed to be type integer if its value fits in type integer (32 bits); otherwise it is presumed to be type bigint if its value fits in type bigint (64 bits); otherwise it is taken to be type numeric. Constants that contain decimal points and/or exponents are always initially presumed to be type numeric. The initially assigned data type of a numeric constant is just a starting point for the type resolution algorithms. In most cases the constant will be automatically coerced to the most appropriate type depending on context. When necessary, you can force a numeric value to be interpreted as a specific data type by casting it. For example, you can force a numeric value to be treated as type real (float4) by writing: REAL '1.23' -- string style 1.23::REAL -- IvorySQL (historical) style These are actually just special cases of the general casting notations discussed next. A constant of an arbitrary type can be entered using any one of the following notations: type 'string' 'string'::type CAST ( 'string' AS type ) The string constant’s text is passed to the input conversion routine for the type called type. The result is a constant of the indicated type. The explicit type cast can be omitted if there is no ambiguity as to the type the constant must be (for example, when it is assigned directly to a table column), in which case it is automatically coerced. The string constant can be written using either regular SQL notation or dollar-quoting. It is also possible to specify a type coercion using a function-like syntax: typename ( 'string' ) but not all type names can be used in this way. The ::, CAST(), and function-call syntaxes can also be used to specify run-time type conversions of arbitrary expressions. To avoid syntactic ambiguity, the `type 'string'` syntax can only be used to specify the type of a simple literal constant. Another restriction on the `type 'string'` syntax is that it does not work for array types; use :: or CAST() to specify the type of an array constant. The CAST() syntax conforms to SQL. The `type 'string'` syntax is a generalization of the standard: SQL specifies this syntax only for a few data types, but IvorySQL allows it for all types. The syntax with :: is historical IvorySQL usage, as is the function-call syntax. An operator name is a sequence of up to NAMEDATALEN-1 (63 by default) characters from the following list: \\+ - * / < > = ~ ! @ # % ^ & | ` ? There are a few restrictions on operator names, however: -- and /* cannot appear anywhere in an operator name, since they will be taken as the start of a comment. A multiple-character operator name cannot end in + or -, unless the name also contains at least one of these characters: ~ ! @ # % ^ & | ` ? For example, @- is an allowed operator name, but *- is not. This restriction allows IvorySQL to parse SQL-compliant queries without requiring spaces between tokens. When working with non-SQL-standard operator names, you will usually need to separate adjacent operators with spaces to avoid ambiguity. For example, if you have defined a prefix operator named @, you cannot write X*@Y; you must write X* @Y to ensure that IvorySQL reads it as two operator names not one. Some characters that are not alphanumeric have a special meaning that is different from being an operator. Details on the usage can be found at the location where the respective syntax element is described. This section only exists to advise the existence and summarize the purposes of these characters. A dollar sign ($) followed by digits is used to represent a positional parameter in the body of a function definition or a prepared statement. In other contexts the dollar sign can be part of an identifier or a dollar-quoted string constant. Parentheses (()) have their usual meaning to group expressions and enforce precedence. In some cases parentheses are required as part of the fixed syntax of a particular SQL command. Brackets ([]) are used to select the elements of an array. Commas (,) are used in some syntactical constructs to separate the elements of a list. The semicolon (;) terminates an SQL command. It cannot appear anywhere within a command, except within a string constant or quoted identifier. The colon (:) is used to select “slices” from arrays. In certain SQL dialects (such as Embedded SQL), the colon is used to prefix variable names. The asterisk (*) is used in some contexts to denote all the fields of a table row or composite value. It also has a special meaning when used as the argument of an aggregate function, namely that the aggregate does not require any explicit parameter. The period (.) is used in numeric constants, and to separate schema, table, and column names. A comment is a sequence of characters beginning with double dashes and extending to the end of the line, e.g.: -- This is a standard SQL comment Alternatively, C-style block comments can be used: /* multiline comment * with nesting: /* nested block comment */ */ where the comment begins with / and extends to the matching occurrence of /. These block comments nest, as specified in the SQL standard but unlike C, so that one can comment out larger blocks of code that might contain existing block comments. A comment is removed from the input stream before further syntax analysis and is effectively replaced by whitespace. Table 5.2 shows the precedence and associativity of the operators in IvorySQL. Most operators have the same precedence and are left-associative. The precedence and associativity of the operators is hard-wired into the parser. Add parentheses if you want an expression with multiple operators to be parsed in some other way than what the precedence rules imply. Table 5.2. Operator Precedence (highest to lowest) Operator/Element Associativity Description . left table/column name separator :: left IvorySQL-style typecast [ ] left array element selection + - right unary plus, unary minus ^ left exponentiation * / % left multiplication, division, modulo + - left addition, subtraction (any other operator) left all other native and user-defined operators BETWEEN IN LIKE ILIKE SIMILAR range containment, set membership, string matching < > = ⇐ >= <> comparison operators IS ISNULL NOTNULL IS TRUE, IS FALSE, IS NULL, IS DISTINCT FROM, etc. NOT right logical negation AND left logical conjunction OR left logical disjunction Note that the operator precedence rules also apply to user-defined operators that have the same names as the built-in operators mentioned above. For example, if you define a “” operator for some custom data type it will have the same precedence as the built-in “” operator, no matter what yours does. When a schema-qualified operator name is used in the OPERATOR syntax, as for example in: SELECT 3 OPERATOR(pg_catalog.+) 4; the OPERATOR construct is taken to have the default precedence shown in Table 5.2 for “any other operator”. This is true no matter which specific operator appears inside OPERATOR(). Note In particular, ⇐ >= and <> used to be treated as generic operators; IS tests used to have higher priority; and NOT BETWEEN and related constructs acted inconsistently, being taken in some cases as having the precedence of NOT rather than BETWEEN. These rules were changed for better compliance with the SQL standard and to reduce confusion from inconsistent treatment of logically equivalent constructs. In most cases, these changes will result in no behavioral change, or perhaps in “no such operator” failures which can be resolved by adding parentheses. However there are corner cases in which a query might change behavior without any parsing error being reported. Value expressions are used in a variety of contexts, such as in the target list of the SELECT command, as new column values in INSERT or UPDATE, or in search conditions in a number of commands. The result of a value expression is sometimes called a scalar, to distinguish it from the result of a table expression (which is a table). Value expressions are therefore also called scalar expressions (or even simply expressions). The expression syntax allows the calculation of values from primitive parts using arithmetic, logical, set, and other operations. A value expression is one of the following: A constant or literal value A column reference A positional parameter reference, in the body of a function definition or prepared statement A subscripted expression A field selection expression An operator invocation A function call An aggregate expression A window function call A type cast A collation expression A scalar subquery An array constructor A row constructor Another value expression in parentheses (used to group subexpressions and override precedence) In addition to this list, there are a number of constructs that can be classified as an expression but do not follow any general syntax rules. These generally have the semantics of a function or operator . An example is the IS NULL clause. A column can be referenced in the form: correlation.columnname correlation is the name of a table (possibly qualified with a schema name), or an alias for a table defined by means of a FROM clause. The correlation name and separating dot can be omitted if the column name is unique across all the tables being used in the current query. A positional parameter reference is used to indicate a value that is supplied externally to an SQL statement. Parameters are used in SQL function definitions and in prepared queries. Some client libraries also support specifying data values separately from the SQL command string, in which case parameters are used to refer to the out-of-line data values. The form of a parameter reference is: $number For example, consider the definition of a function, dept, as: CREATE FUNCTION dept(text) RETURNS dept AS $$ SELECT * FROM dept WHERE name = $1 $$ LANGUAGE SQL; Here the $1 references the value of the first function argument whenever the function is invoked. If an expression yields a value of an array type, then a specific element of the array value can be extracted by writing expression[subscript] or multiple adjacent elements (an “array slice”) can be extracted by writing expression[lower_subscript:upper_subscript] (Here, the brackets [ ] are meant to appear literally.) Each subscript is itself an expression, which will be rounded to the nearest integer value. In general the array expression must be parenthesized, but the parentheses can be omitted when the expression to be subscripted is just a column reference or positional parameter. Also, multiple subscripts can be concatenated when the original array is multidimensional. For example: mytable.arraycolumn[4] mytable.two_d_column[17][34] $1[10:42] (arrayfunction(a,b))[42] The parentheses in the last example are required. If an expression yields a value of a composite type (row type), then a specific field of the row can be extracted by writing expression.fieldname In general the row expression must be parenthesized, but the parentheses can be omitted when the expression to be selected from is just a table reference or positional parameter. For example: mytable.mycolumn $1.somecolumn (rowfunction(a,b)).col3 (Thus, a qualified column reference is actually just a special case of the field selection syntax.) An important special case is extracting a field from a table column that is of a composite type: (compositecol).somefield (mytable.compositecol).somefield The parentheses are required here to show that compositecol is a column name not a table name, or that mytable is a table name not a schema name in the second case. You can ask for all fields of a composite value by writing .*: (compositecol).* This notation behaves differently depending on context. There are two possible syntaxes for an operator invocation: expression operator expression (binary infix operator) operator expression (unary prefix operator) where the operator token follows the syntax rules , or is one of the key words AND, OR, and NOT, or is a qualified operator name in the form: OPERATOR(schema.operatorname) Which particular operators exist and whether they are unary or binary depends on what operators have been defined by the system or the user. The syntax for a function call is the name of a function (possibly qualified with a schema name), followed by its argument list enclosed in parentheses: function_name ([expression [, expression ... ]] ) For example, the following computes the square root of 2: sqrt(2) Other functions can be added by the user. When issuing queries in a database where some users mistrust other users, The arguments can optionally have names attached. Note A function that takes a single argument of composite type can optionally be called using field-selection syntax, and conversely field selection can be written in functional style. That is, the notations col(table) and table.col are interchangeable. This behavior is not SQL-standard but is provided in IvorySQL because it allows use of functions to emulate “computed fields”. An aggregate expression represents the application of an aggregate function across the rows selected by a query. An aggregate function reduces multiple inputs to a single output value, such as the sum or average of the inputs. The syntax of an aggregate expression is one of the following: aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ FILTER ( WHERE filter_clause ) ] where aggregate_name is a previously defined aggregate (possibly qualified with a schema name) and expression is any value expression that does not itself contain an aggregate expression or a window function call. The optional order_by_clause and filter_clause are described below. The first form of aggregate expression invokes the aggregate once for each input row. The second form is the same as the first, since ALL is the default. The third form invokes the aggregate once for each distinct value of the expression (or distinct set of values, for multiple expressions) found in the input rows. The fourth form invokes the aggregate once for each input row; since no particular input value is specified, it is generally only useful for the count() aggregate function. The last form is used with *ordered-set aggregate functions, which are described below. Most aggregate functions ignore null inputs, so that rows in which one or more of the expression(s) yield null are discarded. This can be assumed to be true, unless otherwise specified, for all built-in aggregates. For example, count(*) yields the total number of input rows; count(f1) yields the number of input rows in which f1 is non-null, since count ignores nulls; and count(distinct f1) yields the number of distinct non-null values of f1. Ordinarily, the input rows are fed to the aggregate function in an unspecified order. In many cases this does not matter; for example, min produces the same result no matter what order it receives the inputs in. However, some aggregate functions (such as array_agg and string_agg) produce results that depend on the ordering of the input rows. When using such an aggregate, the optional order_by_clause can be used to specify the desired ordering. The order_by_clause has the same syntax as for a query-level ORDER BY clause, except that its expressions are always just expressions and cannot be output-column names or numbers. For example: SELECT array_agg(a ORDER BY b DESC) FROM table; When dealing with multiple-argument aggregate functions, note that the ORDER BY clause goes after all the aggregate arguments. For example, write this: SELECT string_agg(a, ',' ORDER BY a) FROM table; not this: SELECT string_agg(a ORDER BY a, ',') FROM table; -- incorrect The latter is syntactically valid, but it represents a call of a single-argument aggregate function with two ORDER BY keys (the second one being rather useless since it’s a constant). If DISTINCT is specified in addition to an order_by_clause, then all the ORDER BY expressions must match regular arguments of the aggregate; that is, you cannot sort on an expression that is not included in the DISTINCT list. Note The ability to specify both DISTINCT and ORDER BY in an aggregate function is a IvorySQL extension. Placing ORDER BY within the aggregate’s regular argument list, as described so far, is used when ordering the input rows for general-purpose and statistical aggregates, for which ordering is optional. There is a subclass of aggregate functions called ordered-set aggregates for which an order_by_clause is required, usually because the aggregate’s computation is only sensible in terms of a specific ordering of its input rows. Typical examples of ordered-set aggregates include rank and percentile calculations. For an ordered-set aggregate, the order_by_clause is written inside WITHIN GROUP (…​), as shown in the final syntax alternative above. The expressions in the order_by_clause are evaluated once per input row just like regular aggregate arguments, sorted as per the order_by_clause's requirements, and fed to the aggregate function as input arguments. (This is unlike the case for a non-WITHIN GROUP order_by_clause, which is not treated as argument(s) to the aggregate function.) The argument expressions preceding WITHIN GROUP, if any, are called direct arguments to distinguish them from the aggregated arguments listed in the order_by_clause. Unlike regular aggregate arguments, direct arguments are evaluated only once per aggregate call, not once per input row. This means that they can contain variables only if those variables are grouped by GROUP BY; this restriction is the same as if the direct arguments were not inside an aggregate expression at all. Direct arguments are typically used for things like percentile fractions, which only make sense as a single value per aggregation calculation. The direct argument list can be empty; in this case, write just () not (*). (IvorySQL will actually accept either spelling, but only the first way conforms to the SQL standard.) An example of an ordered-set aggregate call is: SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_cont ----------------- 50489 which obtains the 50th percentile, or median, value of the income column from table households. Here, 0.5 is a direct argument; it would make no sense for the percentile fraction to be a value varying across rows. If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the aggregate function; other rows are discarded. For example: SELECT count(*) AS unfiltered, count(*) FILTER (WHERE i < 5) AS filtered FROM generate_series(1,10) AS s(i); unfiltered | filtered ------------+---------- 10 | 4 (1 row) Other aggregate functions can be added by the user. An aggregate expression can only appear in the result list or HAVING clause of a SELECT command. It is forbidden in other clauses, such as WHERE, because those clauses are logically evaluated before the results of aggregates are formed. When an aggregate expression appears in a subquery,the aggregate is normally evaluated over the rows of the subquery. But an exception occurs if the aggregate’s arguments (and filter_clause if any) contain only outer-level variables: the aggregate then belongs to the nearest such outer level, and is evaluated over the rows of that query. The aggregate expression as a whole is then an outer reference for the subquery it appears in, and acts as a constant over any one evaluation of that subquery. The restriction about appearing only in the result list or HAVING clause applies with respect to the query level that the aggregate belongs to. A window function call represents the application of an aggregate-like function over some portion of the rows selected by a query. Unlike non-window aggregate calls, this is not tied to grouping of the selected rows into a single output row — each row remains separate in the query output. However the window function has access to all the rows that would be part of the current row’s group according to the grouping specification (PARTITION BY list) of the window function call. The syntax of a window function call is one of the following: function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) where window_definition has the syntax [ existing_window_name ] [ PARTITION BY expression [, ...] ] [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ] [ frame_clause ] The optional frame_clause can be one of { RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ] { RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end [ frame_exclusion ] where frame_start and frame_end can be one of UNBOUNDED PRECEDING offset PRECEDING CURRENT ROW offset FOLLOWING UNBOUNDED FOLLOWING and frame_exclusion can be one of EXCLUDE CURRENT ROW EXCLUDE GROUP EXCLUDE TIES EXCLUDE NO OTHERS Here, expression represents any value expression that does not itself contain window function calls. window_name is a reference to a named window specification defined in the query’s WINDOW clause. Alternatively, a full window_definition can be given within parentheses, using the same syntax as for defining a named window in the WINDOW clause; see the SELECT reference page for details. It’s worth pointing out that OVER wname is not exactly equivalent to OVER (wname …​); the latter implies copying and modifying the window definition, and will be rejected if the referenced window specification includes a frame clause. The PARTITION BY clause groups the rows of the query into partitions, which are processed separately by the window function. PARTITION BY works similarly to a query-level GROUP BY clause, except that its expressions are always just expressions and cannot be output-column names or numbers. Without PARTITION BY, all rows produced by the query are treated as a single partition. The ORDER BY clause determines the order in which the rows of a partition are processed by the window function. It works similarly to a query-level ORDER BY clause, but likewise cannot use output-column names or numbers. Without ORDER BY, rows are processed in an unspecified order. The frame_clause specifies the set of rows constituting the window frame, which is a subset of the current partition, for those window functions that act on the frame instead of the whole partition. The set of rows in the frame can vary depending on which row is the current row. The frame can be specified in RANGE, ROWS or GROUPS mode; in each case, it runs from the frame_start to the frame_end. If frame_end is omitted, the end defaults to CURRENT ROW. A frame_start of UNBOUNDED PRECEDING means that the frame starts with the first row of the partition, and similarly a frame_end of UNBOUNDED FOLLOWING means that the frame ends with the last row of the partition. In RANGE or GROUPS mode, a frame_start of CURRENT ROW means the frame starts with the current row’s first peer row (a row that the window’s ORDER BY clause sorts as equivalent to the current row), while a frame_end of CURRENT ROW means the frame ends with the current row’s last peer row. In ROWS mode, CURRENT ROW simply means the current row. In the offset PRECEDING and offset FOLLOWING frame options, the offset must be an expression not containing any variables, aggregate functions, or window functions. The meaning of the offset depends on the frame mode: In ROWS mode, the offset must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of rows before or after the current row. In GROUPS mode, the offset again must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of peer groups before or after the current row’s peer group, where a peer group is a set of rows that are equivalent in the ORDER BY ordering. (There must be an ORDER BY clause in the window definition to use GROUPS mode.) In RANGE mode, these options require that the ORDER BY clause specify exactly one column. The offset specifies the maximum difference between the value of that column in the current row and its value in preceding or following rows of the frame. The data type of the offset expression varies depending on the data type of the ordering column. For numeric ordering columns it is typically of the same type as the ordering column, but for datetime ordering columns it is an interval. For example, if the ordering column is of type date or timestamp, one could write RANGE BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING. The offset is still required to be non-null and non-negative, though the meaning of “non-negative” depends on its data type. In any case, the distance to the end of the frame is limited by the distance to the end of the partition, so that for rows near the partition ends the frame might contain fewer rows than elsewhere. Notice that in both ROWS and GROUPS mode, 0 PRECEDING and 0 FOLLOWING are equivalent to CURRENT ROW. This normally holds in RANGE mode as well, for an appropriate data-type-specific meaning of “zero”. The frame_exclusion option allows rows around the current row to be excluded from the frame, even if they would be included according to the frame start and frame end options. EXCLUDE CURRENT ROW excludes the current row from the frame. EXCLUDE GROUP excludes the current row and its ordering peers from the frame. EXCLUDE TIES excludes any peers of the current row from the frame, but not the current row itself. EXCLUDE NO OTHERS simply specifies explicitly the default behavior of not excluding the current row or its peers. The default framing option is RANGE UNBOUNDED PRECEDING, which is the same as RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. With ORDER BY, this sets the frame to be all rows from the partition start up through the current row’s last ORDER BY peer. Without ORDER BY, this means all rows of the partition are included in the window frame, since all rows become peers of the current row. Restrictions are that frame_start cannot be UNBOUNDED FOLLOWING, frame_end cannot be UNBOUNDED PRECEDING, and the frame_end choice cannot appear earlier in the above list of frame_start and frame_end options than the frame_start choice does — for example RANGE BETWEEN CURRENT ROW AND `offset PRECEDING` is not allowed. But, for example, ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING is allowed, even though it would never select any rows. If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the window function; other rows are discarded. Only window functions that are aggregates accept a FILTER clause. Other window functions can be added by the user. Also, any built-in or user-defined general-purpose or statistical aggregate can be used as a window function. (Ordered-set and hypothetical-set aggregates cannot presently be used as window functions.) The syntaxes using are used for calling parameter-less aggregate functions as window functions, for example count() OVER (PARTITION BY x ORDER BY y). The asterisk (*) is customarily not used for window-specific functions. Window-specific functions do not allow DISTINCT or ORDER BY to be used within the function argument list. Window function calls are permitted only in the SELECT list and the ORDER BY clause of the query. A type cast specifies a conversion from one data type to another. IvorySQL accepts two equivalent syntaxes for type casts: CAST ( expression AS type ) expression::type The CAST syntax conforms to SQL; the syntax with :: is historical IvorySQL usage. When a cast is applied to a value expression of a known type, it represents a run-time type conversion. The cast will succeed only if a suitable type conversion operation has been defined. Notice that this is subtly different from the use of casts with constants. A cast applied to an unadorned string literal represents the initial assignment of a type to a literal constant value, and so it will succeed for any type (if the contents of the string literal are acceptable input syntax for the data type). An explicit type cast can usually be omitted if there is no ambiguity as to the type that a value expression must produce (for example, when it is assigned to a table column); the system will automatically apply a type cast in such cases. However, automatic casting is only done for casts that are marked “OK to apply implicitly” in the system catalogs. Other casts must be invoked with explicit casting syntax. This restriction is intended to prevent surprising conversions from being applied silently. It is also possible to specify a type cast using a function-like syntax: typename ( expression ) However, this only works for types whose names are also valid as function names. For example, double precision cannot be used this way, but the equivalent float8 can. Also, the names interval, time, and timestamp can only be used in this fashion if they are double-quoted, because of syntactic conflicts. Therefore, the use of the function-like cast syntax leads to inconsistencies and should probably be avoided. Note The function-like syntax is in fact just a function call. When one of the two standard cast syntaxes is used to do a run-time conversion, it will internally invoke a registered function to perform the conversion. By convention, these conversion functions have the same name as their output type, and thus the “function-like syntax” is nothing more than a direct invocation of the underlying conversion function. Obviously, this is not something that a portable application should rely on. For further details see CREATE CAST. The COLLATE clause overrides the collation of an expression. It is appended to the expression it applies to: expr COLLATE collation where collation is a possibly schema-qualified identifier. The COLLATE clause binds tighter than operators; parentheses can be used when necessary. If no collation is explicitly specified, the database system either derives a collation from the columns involved in the expression, or it defaults to the default collation of the database if no column is involved in the expression. The two common uses of the COLLATE clause are overriding the sort order in an ORDER BY clause, for example: SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE \"C\"; and overriding the collation of a function or operator call that has locale-sensitive results, for example: SELECT * FROM tbl WHERE a > 'foo' COLLATE \"C\"; Note that in the latter case the COLLATE clause is attached to an input argument of the operator we wish to affect. It doesn’t matter which argument of the operator or function call the COLLATE clause is attached to, because the collation that is applied by the operator or function is derived by considering all arguments, and an explicit COLLATE clause will override the collations of all other arguments. (Attaching non-matching COLLATE clauses to more than one argument, however, is an error.) Thus, this gives the same result as the previous example: SELECT * FROM tbl WHERE a COLLATE \"C\" > 'foo'; But this is an error: SELECT * FROM tbl WHERE (a > 'foo') COLLATE \"C\"; because it attempts to apply a collation to the result of the > operator, which is of the non-collatable data type boolean. A scalar subquery is an ordinary SELECT query in parentheses that returns exactly one row with one column. The SELECT query is executed and the single returned value is used in the surrounding value expression. It is an error to use a query that returns more than one row or more than one column as a scalar subquery. (But if, during a particular execution, the subquery returns no rows, there is no error; the scalar result is taken to be null.) The subquery can refer to variables from the surrounding query, which will act as constants during any one evaluation of the subquery. For example, the following finds the largest city population in each state: SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name) FROM states; An array constructor is an expression that builds an array value using values for its member elements. A simple array constructor consists of the key word ARRAY, a left square bracket [, a list of expressions (separated by commas) for the array element values, and finally a right square bracket ]. For example: SELECT ARRAY[1,2,3+4]; array --------- {1,2,7} (1 row) By default, the array element type is the common type of the member expressions, determined using the same rules as for UNION or CASE constructs. You can override this by explicitly casting the array constructor to the desired type, for example: SELECT ARRAY[1,2,22.7]::integer[]; array ---------- {1,2,23} (1 row) This has the same effect as casting each expression to the array element type individually. Multidimensional array values can be built by nesting array constructors. In the inner constructors, the key word ARRAY can be omitted. For example, these produce the same result: SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]]; array --------------- {{1,2},{3,4}} (1 row) SELECT ARRAY[[1,2],[3,4]]; array --------------- {{1,2},{3,4}} (1 row) Since multidimensional arrays must be rectangular, inner constructors at the same level must produce sub-arrays of identical dimensions. Any cast applied to the outer ARRAY constructor propagates automatically to all the inner constructors. Multidimensional array constructor elements can be anything yielding an array of the proper kind, not only a sub-ARRAY construct. For example: CREATE TABLE arr(f1 int[], f2 int[]); INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]); SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr; array ------------------------------------------------ {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}} (1 row) You can construct an empty array, but since it’s impossible to have an array with no type, you must explicitly cast your empty array to the desired type. For example: SELECT ARRAY[]::integer[]; array ------- {} (1 row) It is also possible to construct an array from the results of a subquery. In this form, the array constructor is written with the key word ARRAY followed by a parenthesized (not bracketed) subquery. For example: SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%'); array ------------------------------------------------------------------ {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412} (1 row) SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i)); array ---------------------------------- {{1,2},{2,4},{3,6},{4,8},{5,10}} (1 row) The subquery must return a single column. If the subquery’s output column is of a non-array type, the resulting one-dimensional array will have an element for each row in the subquery result, with an element type matching that of the subquery’s output column. If the subquery’s output column is of an array type, the result will be an array of the same type but one higher dimension; in this case all the subquery rows must yield arrays of identical dimensionality, else the result would not be rectangular. The subscripts of an array value built with ARRAY always begin with one. A row constructor is an expression that builds a row value (also called a composite value) using values for its member fields. A row constructor consists of the key word ROW, a left parenthesis, zero or more expressions (separated by commas) for the row field values, and finally a right parenthesis. For example: SELECT ROW(1,2.5,'this is a test'); The key word ROW is optional when there is more than one expression in the list. A row constructor can include the syntax rowvalue., which will be expanded to a list of the elements of the row value, just as occurs when the . syntax is used at the top level of a SELECT list .For example, if table t has columns f1 and f2, these are the same: SELECT ROW(t.*, 42) FROM t; SELECT ROW(t.f1, t.f2, 42) FROM t; Note Before PostgreSQL 8.2, the . syntax was not expanded in row constructors, so that writing ROW(t., 42) created a two-field row whose first field was another row value. The new behavior is usually more useful. If you need the old behavior of nested row values, write the inner row value without .*, for instance ROW(t, 42). By default, the value created by a ROW expression is of an anonymous record type. If necessary, it can be cast to a named composite type — either the row type of a table, or a composite type created with CREATE TYPE AS. An explicit cast might be needed to avoid ambiguity. For example: CREATE TABLE mytable(f1 int, f2 float, f3 text); CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- No cast needed since only one getf1() exists SELECT getf1(ROW(1,2.5,'this is a test')); getf1 ------- 1 (1 row) CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric); CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- Now we need a cast to indicate which function to call: SELECT getf1(ROW(1,2.5,'this is a test')); ERROR: function getf1(record) is not unique SELECT getf1(ROW(1,2.5,'this is a test')::mytable); getf1 ------- 1 (1 row) SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype)); getf1 ------- 11 (1 row) Row constructors can be used to build composite values to be stored in a composite-type table column, or to be passed to a function that accepts a composite parameter. Also, it is possible to compare two row values or test a row with IS NULL or IS NOT NULL, for example: SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows The order of evaluation of subexpressions is not defined. In particular, the inputs of an operator or function are not necessarily evaluated left-to-right or in any other fixed order. Furthermore, if the result of an expression can be determined by evaluating only some parts of it, then other subexpressions might not be evaluated at all. For instance, if one wrote: SELECT true OR somefunc(); then somefunc() would (probably) not be called at all. The same would be the case if one wrote: SELECT somefunc() OR true; Note that this is not the same as the left-to-right “short-circuiting” of Boolean operators that is found in some programming languages. As a consequence, it is unwise to use functions with side effects as part of complex expressions. It is particularly dangerous to rely on side effects or evaluation order in WHERE and HAVING clauses, since those clauses are extensively reprocessed as part of developing an execution plan. Boolean expressions (AND/OR/NOT combinations) in those clauses can be reorganized in any manner allowed by the laws of Boolean algebra. When it is essential to force evaluation order, a CASE construct can be used. For example, this is an untrustworthy way of trying to avoid division by zero in a WHERE clause: SELECT ... WHERE x > 0 AND y/x > 1.5; But this is safe: SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END; A CASE construct used in this fashion will defeat optimization attempts, so it should only be done when necessary. (In this particular example, it would be better to sidestep the problem by writing y > 1.5*x instead.) CASE is not a cure-all for such issues, however. One limitation of the technique illustrated above is that it does not prevent early evaluation of constant subexpressions. As described in Section 38.7, functions and operators marked IMMUTABLE can be evaluated when the query is planned rather than when it is executed. Thus for example SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab; is likely to result in a division-by-zero failure due to the planner trying to simplify the constant subexpression, even if every row in the table has x > 0 so that the ELSE arm would never be entered at run time. While that particular example might seem silly, related cases that don’t obviously involve constants can occur in queries executed within functions, since the values of function arguments and local variables can be inserted into queries as constants for planning purposes. Within PL/pgSQL functions, for example, using an IF-THEN-ELSE statement to protect a risky computation is much safer than just nesting it in a CASE expression. Another limitation of the same kind is that a CASE cannot prevent evaluation of an aggregate expression contained within it, because aggregate expressions are computed before other expressions in a SELECT list or HAVING clause are considered. For example, the following query can cause a division-by-zero error despite seemingly having protected against it: SELECT CASE WHEN min(employees) > 0 THEN avg(expenses / employees) END FROM departments; The min() and avg() aggregates are computed concurrently over all the input rows, so if any row has employees equal to zero, the division-by-zero error will occur before there is any opportunity to test the result of min(). Instead, use a WHERE or FILTER clause to prevent problematic input rows from reaching an aggregate function in the first place. IvorySQL allows functions that have named parameters to be called using either positional or named notation. Named notation is especially useful for functions that have a large number of parameters, since it makes the associations between parameters and actual arguments more explicit and reliable. In positional notation, a function call is written with its argument values in the same order as they are defined in the function declaration. In named notation, the arguments are matched to the function parameters by name and can be written in any order. In either notation, parameters that have default values given in the function declaration need not be written in the call at all. But this is particularly useful in named notation, since any combination of parameters can be omitted; while in positional notation parameters can only be omitted from right to left. IvorySQL also supports mixed notation, which combines positional and named notation. In this case, positional parameters are written first and named parameters appear after them. The following examples will illustrate the usage of all three notations, using the following function definition: CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false) RETURNS text AS $$ SELECT CASE WHEN $3 THEN UPPER($1 || ' ' || $2) ELSE LOWER($1 || ' ' || $2) END; $$ LANGUAGE SQL IMMUTABLE STRICT; Function concat_lower_or_upper has two mandatory parameters, a and b. Additionally there is one optional parameter uppercase which defaults to false. The a and b inputs will be concatenated, and forced to either upper or lower case depending on the uppercase parameter. The remaining details of this function definition are not important here . Positional notation is the traditional mechanism for passing arguments to functions in IvorySQL. An example is: SELECT concat_lower_or_upper('Hello', 'World', true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) All arguments are specified in order. The result is upper case since uppercase is specified as true. Another example is: SELECT concat_lower_or_upper('Hello', 'World'); concat_lower_or_upper ----------------------- hello world (1 row) Here, the uppercase parameter is omitted, so it receives its default value of false, resulting in lower case output. In positional notation, arguments can be omitted from right to left so long as they have defaults. In named notation, each argument’s name is specified using ⇒ to separate it from the argument expression. For example: SELECT concat_lower_or_upper(a => 'Hello', b => 'World'); concat_lower_or_upper ----------------------- hello world (1 row) Again, the argument uppercase was omitted so it is set to false implicitly. One advantage of using named notation is that the arguments may be specified in any order, for example: SELECT concat_lower_or_upper(a => 'Hello', b => 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) SELECT concat_lower_or_upper(a => 'Hello', uppercase => true, b => 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) An older syntax based on \":=\" is supported for backward compatibility: SELECT concat_lower_or_upper(a := 'Hello', uppercase := true, b := 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) The mixed notation combines positional and named notation. However, as already mentioned, named arguments cannot precede positional arguments. For example: SELECT concat_lower_or_upper('Hello', 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) In the above query, the arguments a and b are specified positionally, while uppercase is specified by name. In this example, that adds little except documentation. With a more complex function having numerous parameters that have default values, named or mixed notation can save a great deal of writing and reduce chances for error. Note Named and mixed call notations currently cannot be used when calling an aggregate function (but they do work when an aggregate function is used as a window function). ALTER TABLE [ IF EXISTS ] [ ONLY ] name [ * ] action; action: ADD ( add_coldef [, ... ] ) | MODIFY ( modify_coldef [, ... ] ) | DROP [ COLUMN ] ( column_name [, ... ] ) add_coldef: column_name data_type modify_coldef: column_name data_type alter_using alter_using: USING expression name Table name. column_name Column name. data_type Column type. expression The value expression. ADD keyword Adds a column to the table, either one or more columns. MODIFY keyword Modify a column of the table, you can modify one or more columns. DROP keyword Deletes a column of a table, you can delete one or more columns. USING keyword Modifies the value of a column. ADD: create table tb_test1(id int, flg char(10)); alter table tb_test1 add (name varchar); ALTER TABLE tb_test1 ADD adress varchar, ADD num int, ADD flg1 char; \\d tb_test1 Table \"public.tb_test1\" Column | Type | Collation | Nullable | Default --------+-----------------+-----------+----------+--------- id | pg_catalog.int4 | | | flg | char(10) | | | name | varchar2(4000) | | | adress | varchar2(4000) | | | num | pg_catalog.int4 | | | flg1 | char(1) | | | MODIFY: create table tb_test2(id int, flg char(10), num varchar); insert into tb_test2 values('1', 2, '3'); ALTER TABLE tb_test2 ALTER COLUMN id TYPE char; \\d tb_test2 Table \"public.tb_test2\" Column | Type | Collation | Nullable | Default --------+----------------+-----------+----------+--------- id | char(1) | | | flg | char(10) | | | num | varchar2(4000) | | | DROP: create table tb_test3(id int, flg1 char(10), flg2 char(11), flg3 char(12), flg4 char(13), flg5 char(14), flg6 char(15)); ALTER TABLE tb_test3 DROP id; \\d tb_test3 Table \"public.tb_test3\" Column | Type | Collation | Nullable | Default --------+----------+-----------+----------+--------- flg1 | char(10) | | | flg2 | char(11) | | | flg3 | char(12) | | | flg4 | char(13) | | | flg5 | char(14) | | | flg6 | char(15) | | | [ WITH [ RECURSIVE ] with_query [, ...] ] DELETE [ FROM ] [ ONLY ] table_name [ * ] [ [ AS ] alias ] [ USING using_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name The name of the table. alias The table alias. using_list A list of table expressions that allow columns from other tables to appear in the WHERE condition. condition An expression that returns a boolean type value. cursor_name The name of the cursor to be used in the WHERE CURRENT OF case. output_expression An expression that is calculated by DELETE and returned after each row is deleted. output_name The name of the returned column. create table tb_test4(id int, flg char(10)); insert into tb_test4 values(1, '2'), (5, '6'); delete from tb_test4 where id = 1; table tb_test4; id | flg ----+------------ 5 | 6 (1 row) [ WITH [ RECURSIVE ] with_query [, ...] ] UPDATE [ ONLY ] table_name [ * ] [ [ AS ] alias ] SET { [ table_name | alias ] column_name = { expression | DEFAULT } | ( [ table_name | alias ] column_name [, ...] ) = [ ROW ] ( { expression | DEFAULT } [, ...] ) | ( [ table_name | alias ] column_name [, ...] ) = ( sub-SELECT ) } [, ...] [ FROM from_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name Table name. alias Table alias. column_name Column name. expression Value expression. sub-SELECT select clause. from_list Table expression. condition An expression that returns a value of type boolean. cursor_name The name of the cursor to be used in the WHERE CURRENT OF case. output_expression An expression that is computed by DELETE and returned after each row is deleted. output_name The name of the column being returned. create table tb_test5(id int, flg char(10)); insert into tb_test5 values(1, '2'), (3, '4'), (5, '6'); update tb_test5 a set a.id = 33 where a.id = 3; table tb_test5; Id | flg ----+------------ 1 | 2 5 | 6 33 | 4 (3 rows) set ivorysql.compatible_mode to oracle; create table students(student_id varchar(20) primary key , student_name varchar(40), student_pid int); select student_id,student_name from students group by student_id; ERROR: column \"students.student_name\" must appear in the GROUP BY clause or be used in an aggregate function SELECT 100 AS value FROM DUAL UNION SELECT 200 AS value FROM DUAL UNION SELECT 100 AS value FROM DUAL; value ------- 100 200 (2 rows) select_statement MINUS [ ALL | DISTINCT ] select_statement; select_statement Any SELECT statement without the ORDER BY, LIMIT, FOR NO KEY UPDATE, FOR UPDATE, FOR SHARE, and FOR KEY SHARE clauses. ALL keyword contains duplicate row results. DISTINCT keyword shows the elimination of duplicate rows. select * from generate_series(1, 3) g(i) MINUS select * from generate_series(1, 3) g(i) where i = 1; i --- 2 3 (2 rows) Use q\\' to escape special characters. q\\' escaped characters are usually used after ! [] {} () \\<> and other escaping characters, you can also use \\, letters, numbers, \\=, +, -, *, \\&, \\$, \\%, #, etc., no spaces are allowed. select q''' is goog ''; ?column? ------------ ' is goog (1 row) SELECT [ database {schema} | schema ] sequence {nextval | currval}; `sequence`Sequence Name. create sequence sq; select sq.nextval; nextval ----------- 1 (1 row) select sq.currval; nextval ----------- 1 (1 row) Convert the given timestamp without time zone to the specified timestamp with time zone, or return NULL if the specified time zone or timestamp is NULL. Parameters Description day Timestamp without time zone tz Specified time zone select from_tz('2021-11-08 09:12:39','Asia/Shanghai') from dual; from_tz ----------------------------------- 2021-11-08 09:12:39 Asia/Shanghai (1 row) select from_tz('2021-11-08 09:12:39','SAST') from dual; from_tz -------------------------- 2021-11-08 09:12:39 SAST select from_tz(NULL,'SAST') from dual; from_tz --------- (1 row) select from_tz('2021-11-08 09:12:31',NULL) from dual; from_tz --------- (1 row) Get the timestamp of the current database system. select oracle.systimestamp(); systimestamp ------------------------------- 2021-12-02 14:38:59.879642+08 (1 row) select systimestamp; statement_timestamp ------------------------------- 2021-12-02 14:39:33.262828+08 Converts the given timestamp with time zone to UTC time without time zone. Parameters Description day Need to convert time stamp with time zone select sys_extract_utc('2018-03-28 11:30:00.00 +09:00'::timestamptz) from dual; sys_extract_utc --------------------- 2018-03-28 02:30:00 (1 row) select oracle.sys_extract_utc(NULL) from dual; sys_extract_utc ----------------- (1 row) Gets the time zone of the current session. select sessiontimezone() from dual; sessiontimezone ----------------- PRC (1 row) set timezone to UTC; select oracle.sessiontimezone(); sessiontimezone ----------------- UTC (1 row) next_day returns the date of the first weekday with the same format name, which is later than the current date. The return type is always DATE, regardless of the date's data type. The return value has the same hour, minute, and second parts as the Parameters date. Parameters Description value Start Timestamp weekday The day of the week, can be \"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" or 0,1,2,3,4,5,6,0 for Sunday select next_day(to_timestamp('2020-02-29 14:40:50', 'YYYY-MM-DD HH24:MI:SS'), 'Tuesday') from dual; next_day --------------------- 2020-03-03 14:40:50 (1 row) select next_day('2020-07-01 19:43:51 +8'::timestamptz, 1) from dual; next_day --------------------- 2020-07-05 19:43:51 (1 row) last_day returns the last day of the month in which the slot date falls. Parameters Description value Specified timestamp select last_day(timestamp '2020-05-17 13:27:19') from dual; last_day --------------------- 2020-05-31 13:27:19 (1 row) select last_day('2020-11-29 19:20:40 +08'::timestamptz) from dual; last_day --------------------- 2020-11-30 19:20:40 (1 row) add_months returns the date plus an integer month. date Parameters can be date-time values or any value that can be implicitly converted to DATE. integer Parameters can be an integer or any value that can be implicitly converted to an integer. Parameters Description day oracle.date type, the timestamp that needs to be changed value A shaping data, the number of months to be added select add_months(date '2020-02-15',7) from dual; add_months --------------------- 2020-09-15 00:00:00 (1 row) select add_months(timestamp '2018-12-15 19:12:09',12) from dual; add_months --------------------- 2019-12-15 19:12:09 (1 row) sysdate returns the operating system time of the database server. select sysdate; statement_sysdate --------------------- 2021-12-09 16:20:34 (1 row) select sysdate(); sysdate --------------------- 2021-12-09 16:21:39 (1 row) Convert the time of the first time zone to the time of the second time zone. The time zones include \"ast\", \"adt\", \"bst\", \"bdt\", \"cst\", \"cdt\", \"est\", \"edt\", \"gmt\", \"hst\", \"hdt\", \"mst\", \"mdt\", \"nst\", \"pst\", \"pdt\", \"yst\", \"ydt\". Parameters Description day Timestamp to be converted tz1 Timestamped time zones tz2 Target time zone select new_time(timestamp '2020-12-12 17:45:18', 'AST', 'ADT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'BST', 'BDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'CST', 'CDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) The trunc function returns a date, truncated in the specified format. fmt includes \"Y\", \"YY\", \"YYYY\", \"YYYY\", \"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \" MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". Parameters Description value The specified date (oracle.date, timestamp, timestamptz) fmt The specified format, if omitted, defaults to \"DDD\" select trunc(timestamp '2020-07-28 19:16:12', 'Q'); trunc --------------------- 2020-07-01 00:00:00 (1 row) select trunc(timestamptz '2020-09-27 18:30:21 + 08', 'MONTH'); trunc ------------------------ 2020-09-01 00:00:00+08 (1 row) The round function returns a date, rounded to the specified format. fmt includes \"Y\", \"YY\", \"YYYY\", \"YYYY\", \"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D \", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". Parameters Description value The date being converted (oracle.date, timestamp, timestamptz) fmt The specified format, if omitted, defaults to \"DDD\" select round(timestamp '2050-06-12 16:40:55', 'IYYY'); round --------------------- 2050-01-03 00:00:00 (1 row) TO_CHAR (str,[fmt]) Converts the input Parameters to a TEXT data type value according to the given format. If fmt is omitted, the data will be converted to a TEXT value in the system default format. If str is null, the function returns null. str Input Parameters (any type). fmt Input format Parameters, see format fmt for details. select to_char('3 2:20:05' ); to_char ----------------- 3 days 02:20:05 (1 row) select to_char('4.00'::numeric); to_char --------- 4 (1 row) select to_char(NULL); to_char --------- (1 row) select to_char(123,'xx'); to_char --------- 7b (1 row) TO_NUMBER(str,[fmt1]) Converts the input Parameters str to a value of the NUMREIC data type according to the given format. If fmt1 is omitted, the data will be converted to a NUMERIC value in the system default format. If str is NUMERIC, the function returns str. If str calculates to null, the function returns null. If it cannot be converted to the NUMERIC data type, the function returns an error. str input Parameters include the following data types (double precision, numeric, text, integer, etc., but must be implicitly converted to numeric). fmt1 Input format Parameters, see format fmt1 for details. select to_number(1210.73::numeric, 9999.99::numeric); to_number ----------- 1210.73 (1 row) select to_number(NULL); to_number ----------- (1 row) select to_number('123'::text); to_number ----------- 123 (1 row) TO_DATE(str,[fmt]) Converts the input Parameters str to a date data type value according to the given format. If fmt is omitted, the data will be converted to a date value in the system default format. If str is null, the function returns null. If fmt is J, for Julian, then char must be an integer. The function returns an error if it cannot be converted to DATE. str input Parameters (integer, text, can be implicitly converted to the above type, string that matches the date format). fmt input format Parameters, see format fmt for details. select to_date('50-11-28 ','RR-MM-dd '); to_date --------------------- 1950-11-28 00:00:00 (1 row) select to_date(2454336, 'J'); to_date --------------------- 2007-08-23 00:00:00 (1 row) select to_date('2019/11/22', 'yyyy-mm-dd'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('20-11-28 10:14:22','YY-MM-dd hh24:mi:ss'); to_date --------------------- 2020-11-28 10:14:22 (1 row) select to_date('2019/11/22'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('2019/11/27 10:14:22'); to_date --------------------- 2019-11-27 10:14:22 (1 row) select to_date('2020','RR'); to_date --------------------- 2020-01-01 00:00:00 (1 row) select to_date(NULL); to_date --------- (1 row) select to_date('-4712-07-23 14:31:23', 'syyyy-mm-dd hh24:mi:ss'); to_date ---------------------- -4712-07-23 14:31:23 (1 row) TO_TIMESTAMP(str,[fmt]) Converts the input Parameters str to a timestamp without a time zone according to the given format. If fmt is omitted, the data is converted to a timestamp with no time zone value in the system default format. If str is null, the function returns null. If it cannot be converted to a timestamp without a time zone, the function returns an error. str input Parameters (double precision,text, which can be implicitly converted to the above type). fmt Input format Parameters, see format fmt for details. select to_timestamp(1212121212.55::numeric); to_timestamp --------------------------- 2008-05-30 12:20:12.55 (1 row) select to_timestamp('2020/03/03 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp ------------------------ 2020-03-03 13:13:18 (1 row) select to_timestamp(NULL,NULL); to_timestamp -------------- (1 row) TO_YMINTERVAL(str) Converts the input Parameters str time interval to a time interval in the year-to-month range. Only the year and month are processed, other parts are omitted. If the input Parameters is NULL, the function returns NULL, and if the input Parameters is in the wrong format, the function returns an error. str Input Parameters (text, can be implicitly converted to text type, must be in time interval format. (SQL interval format compatible with SQL standard, ISO duration format compatible with ISO 8601:2004 standard). select to_yminterval('P1Y-2M2D'); to_yminterval --------------- +000000000-10 (1 row) select to_yminterval('P1Y2M2D'); to_yminterval --------------- +000000001-02 (1 row) select to_yminterval('-01-02'); to_yminterval --------------- -000000001-02 (1 row) TO_DSINTERVAL(str) converts the time interval of the input Parameters str to a time interval in the range of days to seconds. Input Parameters include: day, hour, minute, second and microsecond. If the input Parameters is NULL, the function returns NULL, and if the input Parameters contains the year and month or is in the wrong format, the function returns an error. str Input Parameters (text, can be implicitly converted to text type, must be in time interval format. (SQL interval format compatible with SQL standard, ISO duration format compatible with ISO 8601:2004 standard). select to_dsinterval('100 00 :02 :00'); to_dsinterval ------------------------------- +000000100 00:02:00.000000000 (1 row) select to_dsinterval('-100 00:02:00'); to_dsinterval ------------------------------- -000000100 00:02:00.000000000 (1 row) select to_dsinterval(NULL); to_dsinterval --------------- (1 row) TO_TIMESTAMP_TZ(str,[fmt]) Converts the input Parameters str to a timestamp with a time zone according to the given format. If fmt is omitted, the data will be converted to a timestamp with a time zone value in the system default format. If str is null, the function returns null. If it cannot be converted to a timestamp with a time zone, the function returns an error. str input Parameters (text, which can be implicitly converted to a text type). fmt Enter format Parameters, see format fmt for details. select to_timestamp_tz('2019','yyyy'); to_timestamp_tz ------------------------ 2019-01-01 00:00:00+08 (1 row) select to_timestamp_tz('2019-11','yyyy-mm'); to_timestamp_tz ------------------------ 2019-11-01 00:00:00+08 (1 row) select to_timestamp_tz('2003/12/13 10:13:18 +7:00'); to_timestamp_tz ------------------------ 2003-12-13 11:13:18+08 (1 row) select to_timestamp_tz('2019/12/13 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp_tz ------------------------ 2019-12-13 13:13:18+08 (1 row) select to_timestamp_tz(NULL); to_timestamp_tz ----------------- (1 row) GREATEST(expr1,expr2,…​) Gets the maximum value in the input list of one or more expressions. If the result of any expr calculation is NULL, the function returns NULL. expr1` Enter Parameters (of any type). `expr2` Enter Parameters (of any type). `... select greatest('a','b','A','B'); greatest ---------- b (1 row) select greatest(',','.','/',';','!','@','?'); greatest ---------- @ (1 row) select greatest('瀚','高','数','据','库'); greatest ---------- 高 (1 row) SELECT greatest('HARRY', 'HARRIOT', 'HARRA'); greatest ---------- HARRY (1 row) SELECT greatest('HARRY', 'HARRIOT', NULL); greatest ---------- (1 row) SELECT greatest(1.1, 2.22, 3.33); greatest ---------- 3.33 (1 row) SELECT greatest('A', 6, 7, 5000, 'E', 'F','G') A; a --- G (1 row) LEAST(expr1,expr2,…​) Gets the smallest value in the input list of one or more expressions. If the result of any expr calculation is NULL, the function returns NULL. expr1` Enter Parameters (of any type). `expr2` Enter Parameters (of any type). `... SELECT least(1,' 2', '3' ); least ------- 1 (1 row) SELECT least(NULL, NULL, NULL); least ------- (1 row) SELECT least('A', 6, 7, 5000, 'E', 'F','G') A; a ------ 5000 (1 row) select least(1,3,5,10); least ------- 1 (1 row) select least('a','A','b','B'); least ------- A (1 row) select least(',','.','/',';','!','@'); least ------- ! (1 row) select least('瀚','高','据','库'); least ------- 库 (1 row) SELECT least('HARRY', 'HARRIOT', NULL); least ------- (1 row) NLS_LENGTH_SEMANTICS enables you to create CHAR and VARCHAR2 columns using byte or character length semantics. Existing columns are not affected. In this case, the default semantics is BYTE. SET NLS_LENGTH_SEMANTICS TO [NONE | BYTE | CHAR]; BYTE: The data is stored in byte length. CHAR:Data is stored in character length. NONE:Data is stored using native IvorySQL storage. create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 SET NLS_LENGTH_SEMANTICS TO BYTE; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- byte (1 row) insert into test values ('Hello,Mr.li'); 2021-12-14 15:28:11.906 HKT [6774] ERROR: value too long for type varchar2(5 byte) 2021-12-14 15:28:11.906 HKT [6774] STATEMENT: insert into test values ('Hello,Mr.li'); ERROR: value too long for type varchar2(5 byte) Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character. VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 PL/iSQL is IvorySQL’s procedural language for writing custom functions, procedures and packages for IvorySQL. PL/iSQL is derived from IvorySQL’s PL/pgsql with some added features, but syntactically PL/iSQL is closer to Oracle’s PL/SQL. This document Describes the basic structure and construction of PL/iSQL programs. iSQL is a procedural block structure language that supports four different program types, PACKAGES, PROCEDURES, FUNCTIONS, and TRIGGERS. iSQL supports four different program types, PACKAGES, PROCEDURES, FUNCTIONS, and TRIGGERS. iSQL uses the same block structure for each type of supported program. A block consists of up to three parts: a declaration part, an executable, and an exception part. The declaration and exception sections are optional. [DECLARE declarations] BEGIN statements [ EXCEPTION WHEN <exception_condition> THEN statements] END; A block can consist of at least one executable section Contains one or more iSQL statements in the BEGIN and END keywords. CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS BEGIN NULL; END; / All keywords are case-insensitive. Identifiers are implicitly converted to lowercase unless double-quoted, just as they are in normal SQL commands. The declaration section can be used to declare variables and cursors, and depending on the context in which the block is used, the declaration section can begin with the keyword DECLARE. CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS DECLARE quantity integer := 30; c_row pg_class%ROWTYPE; r_cursor refcursor; CURSOR c1 RETURN pg_proc%ROWTYPE; BEGIN NULL; end; / An optional exception section can also be included in a BEGIN - END block. The exception section begins with the keyword EXCEPTION and continues until the end of the block in which it appears. If a statement within the block throws an exception, program control goes to the exception section, which may or may not handle the thrown exception, depending on the contents of the exception and exception sections. CREATE OR REPLACE FUNCTION reraise_test() RETURN void AS BEGIN BEGIN RAISE syntax_error; EXCEPTION WHEN syntax_error THEN BEGIN raise notice 'exception % thrown in inner block, reraising', sqlerrm; RAISE; EXCEPTION WHEN OTHERS THEN raise notice 'RIGHT - exception % caught in inner block', sqlerrm; END; END; EXCEPTION WHEN OTHERS THEN raise notice 'WRONG - exception % caught in outer block', sqlerrm; END; / Note Like PL/pgSQL, PL/iSQL uses BEGIN/END to group statements, and do not confuse them with the SQL commands of the same name used for transaction control. PL/iSQL’s BEGIN/END are used only for grouping; they do not start or end transactions To create a PL/iSQL program from a psql client, you can use a syntax similar to PL/pgSQL’s $$ CREATE FUNCTION func() RETURNS void as $$ .. end$$ language plisql; Alternatively, you can use the Oracle-compliant syntax of references and language specifications without $$ and end the program definition with / (forward slash). The */ (forward slash) must be on the newline character CREATE FUNCTION func() RETURN void AS … END; / CREATE [OR REPLACE] PROCEDURE procedure_name [(parameter_list)] is [DECLARE] -- variable declaration BEGIN -- stored procedure body END; / CREATE [OR REPLACE] FUNCTION function_name ([parameter_list]) RETURN return_type AS [DECLARE] -- variable declaration BEGIN -- function body return statement END; / CREATE [ OR REPLACE ] PACKAGE [schema.] *package_name* [invoker_rights_clause] [IS | AS] item_list[, item_list ...] END [*package_name*]; invoker_rights_clause: AUTHID [CURRENT_USER | DEFINER] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] function_declaration: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype; procedure_declaration: PROCEDURE procedure_name [(parameter_declaration[, ...])] type_definition: record_type_definition | ref_cursor_type_definition cursor_declaration: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype; item_declaration: cursor_declaration | cursor_variable_declaration | record_variable_declaration | variable_declaration | record_type_definition: TYPE record_type IS RECORD ( variable_declaration [, variable_declaration]... ) ; ref_cursor_type_definition: TYPE type IS REF CURSOR [ RETURN type%ROWTYPE ]; cursor_variable_declaration: curvar curtype; record_variable_declaration: recvar { record_type | rowtype_attribute | record_type%TYPE }; variable_declaration: varname datatype [ [ NOT NULL ] := expr ] parameter_declaration: parameter_name [IN] datatype [[:= | DEFAULT] expr] CREATE [ OR REPLACE ] PACKAGE BODY [schema.] package_name [IS | AS] [item_list[, item_list ...]] | item_list_2 [, item_list_2 ...] [initialize_section] END [package_name]; initialize_section: BEGIN statement[, ...] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] item_list_2: [ function_declaration function_definition procedure_declaration procedure_definition cursor_definition ] function_definition: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype [IS | AS] [declare_section] body; procedure_definition: PROCEDURE procedure_name [(parameter_declaration[, ...])] [IS | AS] [declare_section] body; cursor_definition: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype IS select_statement; body: BEGIN statement[, ...] END [name]; statement: [<<LABEL>>] pl_statments[, ...]; { CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... [ START WITH condition ] | START WITH condition CONNECT BY [ NOCYCLE ] [PRIOR] condition [AND [PRIOR] condition]... } The CONNECT BY query syntax begins with the CONNECT BY keywords, which define hierarchical interdependencies between parent and child rows. The result must be further qualified by specifying the PRIOR keyword in the conditional part of the CONNECT BY clause. The PRIOR PRIOR keyword is a unary operator that relates the previous row to the current row. This keyword can be used to the left or right of the equality condition. START WITH This clause specifies the line from which the hierarchy begins. NOCYCLE No operation statement. Currently only supported by the syntax. This clause indicates that data is returned even if a loop exists. LEVEL Returns the level of the current row in the hierarchy, starting at 1 at the root node and incrementing by 1 at each level thereafter. CONNECT_BY_ROOT expr Returns the parent column of the current row in the hierarchy. SYS_CONNECT_BY_PATH(col, chr) It is a function that returns the value of the column from the root to the current node, separated by the character \"chr\". This function currently has the following limitations. Additional columns can be used for most expressions, such as function calls, CASE statements and general expressions, but there are some unsupported columns, such as ROW, TYPECAST, COLLATE, GROUPING clauses, etc. In case two or more columns are the same, you may need to output the column name, Example such as SELECT CONNECT_BY_ROOT col AS \"col1\", CONNECT_BY_ROOT col AS \"col2\" …​. Indirect operators or \"*\" are not supported Loop detection is not supported CREATE UNIQUE INDEX [IF NOT EXISTS] name ON table_name [USING method] (columns) GLOBAL CREATE UNIQUE INDEX myglobalindex on mytable(bid) GLOBAL; During the creation of a globally unique index, the system performs an index scan on all existing partitions and raises an error if it finds duplicate entries from other partitions than the current one. Example. Command create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); create table gidxpart2 partition of gidxpart for values from (100000) to (199999); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); insert into gidxpart (a, b, c) values (150000, 572814, 'inserted second on gidxpart2'); create unique index on gidxpart (b) global; Output ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated. During global unique index creation, the system performs an index scan on all existing partitions and raises an error if duplicate items are found in other partitions than the current one. Command create table gidx_part (a int, b int, c text) partition by range (a); create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (10); create table gidxpart2 partition of gidxpart for values from (10) to (100); create unique index gidx_u on gidxpart using btree(b) global; insert into gidxpart values (1, 1, 'first'); insert into gidxpart values (11, 11, 'eleventh'); insert into gidxpart values (2, 11, 'duplicated (b)=(11) on other partition'); Output ERROR: duplicate key value violates unique constraint \"gidxpart2_b_idx\" DETAIL: Key (b)=(11) already exists. When appending a new table to a partitioned table with a globally unique index, the system performs a duplicate check on all existing partitions. If a duplicate item is found in an existing partition that matches a tuple in the appended table, an error is raised and the append fails. Appending requires a SHARE LOCK on all existing partitions. If one of the partitions is doing a concurrent INSERT, the append will wait for it to complete first. This can be improved in a future release Command create table gidxpart (a int, b int, c text) partition by range (a); create table gidxpart1 partition of gidxpart for values from (0) to (100000); insert into gidxpart (a, b, c) values (42, 572814, 'inserted first on gidxpart1'); create unique index on gidxpart (b) global; create table gidxpart2 (a int, b int, c text); insert into gidxpart2 (a, b, c) values (150000, 572814, 'dup inserted on gidxpart2'); alter table gidxpart attach partition gidxpart2 for values from (100000) to (199999); Output ERROR: could not create unique index \"gidxpart1_b_idx\" DETAIL: Key (b)=(572814) is duplicated.","title":"Developer Guide","component":"ivorysql-doc","version":"master","name":"developer_guide","url":"/ivorysql-doc/master/developer_guide.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"1.1. Architecture Overview","hash":"architecture-overview","id":2},{"text":"1.2. 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Append and detach","hash":"append-and-detach","id":273},{"text":"8.3.1. Global uniqueness guarantee for append statements","hash":"global-uniqueness-guarantee-for-append-statements","id":274},{"text":"8.3.2. Example","hash":"example-31","id":275}]},"4":{"id":4,"text":"PostgreSQL provides comprehensive server-side support for the GB18030 character set. GB18030 is a Chinese national standard designed to include all Chinese characters and various minority scripts, aiming for alignment with Unicode. Proper configuration and use of the GB18030 character set within PostgreSQL are essential for processing and storing Chinese data that must comply with this standard. Server-side GB18030 support should provide the following features: 1. Support for GB18030 as a server encoding: initdb -E GB18030 is available, and SHOW server_encoding displays GB18030. 2. Provide bidirectional conversion between GB18030 and UTF8. 3. Support for multibyte character boundary determination. PostgreSQL has historically supported the GB18030-2000 standard as a client-side encoding. Support for conversion between the GB18030_2022 character set and UTF-8 is provided via an extension. To enable GB18030 as a server encoding, modifications are made to pg_enc, and new low-level functions are added to the PostgreSQL encoding framework for invocation by the core system. A global variable, is_load_gb18030_2022, is introduced with a default value of true. When the -E option is used during initdb, the get_encoding_id function checks the specified encoding name. If the name is gb18030_2022, it is internally mapped to the gb18030 encoding ID, and the is_load_gb18030_2022 flag is set to true. If the -E option is GB18030, the flag is set to false. At the appropriate stage in the startup process, the system checks this flag to determine if the extension should be loaded. If required, the load_gb18030_2022 function is executed, and the gb18030_2022 extension is added to the shared_preload_libraries parameter in ivorysql.conf. if (encoding_name && *encoding_name) { encoding_name_modify = pg_strdup(encoding_name); if(pg_strcasecmp(encoding_name,\"gb18030_2022\") == 0) { encoding_name_modify = pg_strdup(\"gb18030\"); is_load_gb18030_2022 = true; } else if(pg_strcasecmp(encoding_name,\"gb18030\") == 0) is_load_gb18030_2022 = false; if ((enc = pg_valid_server_encoding((const char *)encoding_name_modify)) >= 0) return enc; } Function pointers for GB18030 are added in wchar.c: pg_gb180302wchar_with_len(const unsigned char *from, pg_wchar *to, int len) gb18030 → wchar pg_wchar2gb18030_with_len(const pg_wchar *from, unsigned char *to, int len) wchar → gb18030 pg_gb18030_mblen(const unsigned char *s)：Returns 1/2/4. pg_gb18030_dsplen(const unsigned char *s)：Calculates the display width of a character. ASCII characters have a width of 1, while others are also treated as having a width of 1 . pg_gb18030_verifier(const unsigned char *s, int len)：Verifies that a byte sequence is a valid GB18030 character, rejecting illegal sequences. Receiving Data When a client using UTF-8 encoding connects to the server, the server, upon receiving data, invokes its internal utf8_to_gb18030 function. This converts the data to the GB18030 format, which is then validated and stored. Sending Data When the same client executes a SELECT query, the server reads the native GB18030 data from disk or memory. It then calls the gb18030_to_utf8 function to convert the data to UTF-8 format before sending it to the client via the network protocol. A new data file, GB18030-2022.xml, is introduced. This file is parsed by a Perl script to generate mapping files that provide the logic for the gb18030_to_utf8() and utf8_to_gb18030() conversion functions. The implementation prioritizes a table-driven approach, falling back to algorithmic mapping for ranges not covered by the tables. static inline uint32 unicode_to_utf8word(uint32 c) { uint32 word; if (c <= 0x7F) { word = c; } else if (c <= 0x7FF) { word = (0xC0 | ((c >> 6) & 0x1F)) << 8; word |= 0x80 | (c & 0x3F); } else if (c <= 0xFFFF) { word = (0xE0 | ((c >> 12) & 0x0F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } else { word = (0xF0 | ((c >> 18) & 0x07)) << 24; word |= (0x80 | ((c >> 12) & 0x3F)) << 16; word |= (0x80 | ((c >> 6) & 0x3F)) << 8; word |= 0x80 | (c & 0x3F); } return word; } static uint32 conv_18030_2022_to_utf8(uint32 code) { #define conv18030(minunicode, mincode, maxcode) \\ if (code >= mincode && code <= maxcode) \\ return unicode_to_utf8word(gb_linear(code) - gb_linear(mincode) + minunicode) conv18030(0x0452, 0x8130D330, 0x8136A531); conv18030(0x2643, 0x8137A839, 0x8138FD38); conv18030(0x361B, 0x8230A633, 0x8230F237); conv18030(0x3CE1, 0x8231D438, 0x8232AF32); conv18030(0x4160, 0x8232C937, 0x8232F837); conv18030(0x44D7, 0x8233A339, 0x8233C931); conv18030(0x478E, 0x8233E838, 0x82349638); conv18030(0x49B8, 0x8234A131, 0x8234E733); conv18030(0x9FA6, 0x82358F33, 0x8336C738); conv18030(0xE865, 0x8336D030, 0x84308534); conv18030(0xFA2A, 0x84309C38, 0x84318537); conv18030(0xFFE6, 0x8431A234, 0x8431A439); conv18030(0x10000, 0x90308130, 0xE3329A35); /* No mapping exists */ return 0; }","title":"Server-Side Support for the GB18030 Character Set","component":"ivorysql-doc","version":"master","name":"gb18030","url":"/ivorysql-doc/master/gb18030.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Implementation Details","hash":"implementation-details","id":2},{"text":"2.1. Specifying GB18030 or GB18030_2022 with the -E Option during initdb","hash":"specifying-gb18030-or-gb18030_2022-with-the-e-option-during-initdb","id":3},{"text":"2.2. Multibyte Character Handling","hash":"multibyte-character-handling","id":4},{"text":"2.3. Client-Server Interaction","hash":"client-server-interaction","id":5}]},"5":{"id":5,"text":"The installation methods for IvorySQL include the following five: Yum installation Docker installation rpm installation Source code installation deb installation This chapter will provide detailed instructions on the installation, execution, and uninstallation processes for each method. For a quicker access to IvorySQL, please refer to Quick installation. Before getting started, please create an user and grant it root privileges. All the installation steps will be performed by this user. Here we just name it 'ivorysql'. Create or edit IvorySQL yum repository configuration /etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 After saving and exiting, you can install IvorySQL 4 with the following steps $ sudo dnf install -y ivorysql5-5.0 Get IvorySQL image from Docker Hub $ docker pull ivorysql/ivorysql:5.0-ubi8 Run IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 -e Parameter Explanation Parameter Name Required Description IVORYSQL_USER No Database user, default is ivorysql IVORYSQL_PASSWORD yes Database password IVORYSQL_DB no Database name,default is ivorysql POSTGRES_HOST_AUTH_METHOD no Modify host authentication method,reference value:md5 POSTGRES_INITDB_ARGS no Add additional parameters to initdb,reference value:\"--data-checksums\" PGDATA no Define the data directory to be located in another path or folder (e.g., subdirectory), defaulting to /var/lib/ivorysql/data POSTGRES_INITDB_WALDIR no Define the IvorySQL transaction folder path, which defaults to a subdirectory within the data directory (PGDATA) It is not recommended to set the POSTGRES_HOST_AUTH_METHOD parameter to trust, as this will make the IVORYSQL_PASSWORD setting ineffective. If the POSTGRES_HOST_AUTH_METHOD parameter is set to scram-sha-256, it is also necessary to set POSTGRES_INITDB_ARGS to --auth-host=scram-sha-256 to ensure proper initialization of the database. Installing dependencies $ sudo dnf install -y lz4 libicu libxslt python3 Getting rpms $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-9d890e9-20251120.x86_64.rpm Installing rpms Use the following command to install all the rpms: $ sudo yum --disablerepo=* localinstall *.rpm IvorySQL then will be installed in the /usr/ivory-5/ directory. Installing dependencies $ sudo dnf install -y bison readline-devel zlib-devel openssl-devel uuid-devel $ sudo dnf groupinstall -y 'Development Tools' Getting source code $ git clone https://github.com/IvorySQL/IvorySQL.git $ cd IvorySQL $ git checkout -b IVORY_REL_5_STABLE origin/IVORY_REL_5_STABLE Configuring In the IvorySQL directory run the following command with --prefix to specify the directory where you want the database to be installed: $ ./configure --prefix=/usr/local/ivorysql/ivorysql-5 Compiling Run the following command to compile the source code: $ make When the compilation is completed, you can test the result with 'make check' or 'make all-check-world' before your installation. Installing Run the following command to install the database system, IvorySQL then will be installed in the directory specified by --prefix: $ sudo make install Installing dependencies $ sudo apt -y install pkg-config libreadline-dev libicu-dev libldap2-dev uuid-dev tcl-dev libperl-dev python3-dev bison flex openssl libssl-dev libpam-dev libxml2-dev libxslt-dev libossp-uuid-dev libselinux-dev gettext Getting deb $ sudo wget https://github.com/IvorySQL/IvorySQL/releases/download/IvorySQL_5.0/IvorySQL-5.0-a50789d-20250304.amd64.deb Installing deb $ sudo dpkg -i IvorySQL-5.0-a50789d-20250304.amd64.deb IvorySQL will then be installed in the /usr/ivory-5/ directory. Users following the instructions in Yum installation, rpm installation, Source code installation and deb installation need to manually start the database. Granting permissions Execute the following command to grant permissions to the installation user. The example user is ivorysql, and the installation directory is /usr/ivory-5/: $ sudo chown -R ivorysql:ivorysql /usr/ivory-5/ Setting environment variables Add below contents in ~/.bash_profile file and source to make it effective: PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile Initializing database $ mkdir /usr/ivory-5/data $ initdb -D /usr/ivory-5/data The -D option specifies the directory where the database cluster should be stored. This is the only information required by initdb, but you can avoid writing it by setting the PGDATA environment variable, which can be convenient since the database server can find the database directory later by the same variable. For more options, refer to initdb --help. Starting IvorySQL service $ pg_ctl -D /usr/ivory-5/data -l ivory.log start The -D option specifies the file system location of the database configuration files. If this option is omitted, the environment variable PGDATA in [setting-environment-variables] is used. -l option appends the server log output to filename. If the file does not exist, it is created. For more options, refer to pg_ctl --help. Confirm it’s successfully started: $ ps -ef | grep postgres ivorysql 130427 1 0 02:45 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 130428 130427 0 02:45 ? 00:00:00 postgres: checkpointer ivorysql 130429 130427 0 02:45 ? 00:00:00 postgres: background writer ivorysql 130431 130427 0 02:45 ? 00:00:00 postgres: walwriter ivorysql 130432 130427 0 02:45 ? 00:00:00 postgres: autovacuum launcher ivorysql 130433 130427 0 02:45 ? 00:00:00 postgres: logical replication launcher ivorysql 130445 130274 0 02:45 pts/1 00:00:00 grep --color=auto postgres Connect to IovrySQL via psql: $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# The -d option specifies the name of the database to connect to. ivorysql is the default database of IvorySQL. However,IvorySQL of lower versions need the users themselves to connect to postgres database at the first connection and then create the ivorysql database.The latest IvorySQL can do all these for users. For more options, refer to psql --help. When running IvorySQL in Docker, additional parameters need to be added, like: psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 No matter which method is used for the uninstallation, make sure the service has been stopped cleanly and your data has been backed up safely. Run the following commands in turn and clean the residual folders: $ sudo dnf remove -y ivorysql5-5.0 Stop IvorySQL container and remove IvorySQL image: $ docker stop ivorysql $ docker rm ivorysql $ docker rmi ivorysql/ivorysql:5.0-ubi8 Uninstall the rpms and clear the residual folders: $ sudo yum remove --disablerepo=* ivorysql5\\* $ sudo rm -rf IvorySQL-5.0 Uninstall the database system, then clear the residual folders: $ sudo make uninstall $ make clean $ sudo rm -rf IvorySQL-5.0 Uninstall the database system, then clear the residual folders: $ sudo dpkg -P IvorySQL-5.0 $ sudo rm -rf IvorySQL-5.0","title":"Installation","component":"ivorysql-doc","version":"master","name":"installation_guide","url":"/ivorysql-doc/master/installation_guide.html","titles":[{"text":"1. Introduction","hash":"introduction","id":1},{"text":"2. Yum installation","hash":"Yum-installation","id":2},{"text":"3. Docker installation","hash":"Docker-installation","id":3},{"text":"4. rpm installation","hash":"Rpm-installation","id":4},{"text":"5. Source code installation","hash":"Source-code-installation","id":5},{"text":"6. deb installation","hash":"deb-installation","id":6},{"text":"7. Start Database","hash":"start-database","id":7},{"text":"8. Connecting to IvorySQL","hash":"connecting-to-ivorysql","id":8},{"text":"9. Uninstallation","hash":"uninstallation","id":9},{"text":"9.1. Uninstallation for yum installation","hash":"uninstallation-for-yum-installation","id":10},{"text":"9.2. Uninstallation for docker installation","hash":"uninstallation-for-docker-installation","id":11},{"text":"9.3. Uninstallation for rpm installation","hash":"uninstallation-for-rpm-installation","id":12},{"text":"9.4. Uninstallation for source code installation","hash":"uninstallation-for-source-code-installation","id":13},{"text":"9.5. Uninstallation for deb installation","hash":"uninstallation-for-deb-installation","id":14}]},"6":{"id":6,"text":"Database migration refers to the process of transferring data from one database to another, and the databases at both ends may be PostgreSql, IvoySQL, MySQL, Oracle, SQL Server, etc. The migration process is a challenging, complex process that requires a thorough understanding of how databases work and their characteristics. If the application has been deployed to the production environment and is in a normal operating state, a smooth application migration is required after database migration to maintain uninterrupted business operation and no data loss. After migration, databases and systems should meet the following requirements: The migrated database system should fully host the data of the original database system. Avoid data loss during migration that causes incomplete data to the new database system. The migrated database system should fully adapt to the functions of the original database. Avoid the inability to run or throw errors of the entire business system due to data types, syntax, and functions that are not supported after migration, and there is no alternative. The migrated database should be adapted to the upstream and downstream of the entire business system to ensure the stable and reliable operation of the entire business system. The comprehensive performance of the migrated database cannot be weaker than that of the original database, providing performance guarantee for the entire business system. Ora2Pg is a free tool for migrating Oracle databases to an IvorySQL-compatible schema. It connects to your Oracle database, automatically scans and extracts its structure or data, and then generates SQL scripts that can be loaded into an IvorySQL database. Ora2Pg can migrate from a reverse-engineered Oracle database to a large enterprise database, or simply copy some Oracle data into an IvorySQL database. It is very easy to use and does not require any Oracle database knowledge without providing the parameters required to connect to Oracle Database. Ora2Pg consists of a Perl script (ora2pg) and a Perl module(Ora2Pg.pm), the only thing that needs to be done is to modify its configuration file, ora2pg.conf, set the DSN to connect to the Oracle database, and an optional SCHEMA name. ONCE THAT’S DONE, YOU ONLY NEED TO SET THE EXPORTED TYPE: TABLE (INCLUDING CONSTRAINTS AND INDEXES), VIEW, MVIEW, TABLESPACE, SEQUENCE, INDEXES, TRIGGER, GRANT, FUNCTION, PROCEDURE, PACKAGE, PARTITION, TYPE, INSERT OR COPY, FDW, QUERY, KETTLE, AND SYNONYM. By default, Ora2Pg exports an SQL file, which can be executed through the IvorySQL client tool psql. When performing data migration, you can set the DSN of the target database in the configuration file to import data directly from Oracle into the IvorySQL database. Object Whether ora2pg is supported view yes trigger yes，In some cases, you need to modify the script manually sequence yes function yes procedure yes，In some cases, you need to modify the script manually type yes，In some cases, you need to modify the script manually materialized view yes，In some cases, you need to modify the script manually linux Oracle Version IvorySQL Version Centos Stream 9 19.0.0.0 4.2 # dnf install -y perl perl-ExtUtils-CBuilder perl-ExtUtils-MakeMaker # perl -v This is perl 5, version 16, subversion 3 (v5.16.3) built for x86_64-linux-thread-multi (with 44 registered patches, see perl -V for more detail) Copyright 1987-2012, Larry Wall Perl may be copied only under the terms of either the Artistic License or the GNU General Public License, which may be found in the Perl 5 source kit. Complete documentation for Perl, including FAQ lists, should be found on this system using \"man perl\" or \"perldoc perl\". If you have access to the Internet, point your browser at http://www.perl.org/, the Perl Home Page. DBI，Database Independent Interface，is the interface of the Perl language to connect to the database Download address: https://cpan.metacpan.org/authors/id/T/TI/TIMB/DBI-1.643.tar.gz # tar zxvf DBI-1.643.tar.gz # cd DBI-1.643/ # perl Makefile.PL # make # make install Download address：https://sourceforge.net/projects/ora2pg/ Set environment variables; Load environment variables; Because ORACLE must be defined_ HOME environment variable; This example configures environment variables under the ivorysql user export ORACLE_HOME=/usr/lib/oracle/18.3/client64 # tar -zxvf DBD-Oracle-1.76.tar.gz # source /home/postgres/.bashrc # cd DBD-Oracle-1.76 # perl Makefile.PL # make # make install Download address：https://metacpan.org/release/DBD-Pg/ Set environment variables： export POSTGRES_HOME=/opt/ivorysql/3.2 # tar -zxvf DBD-Pg-3.80.tar.gz # source /home/ivorysql/.bashrc # cd DBD-Pg-3.8.0 # perl Makefile.PL # make # make install Download address：https://sourceforge.net/projects/ora2pg/ # tar -xjf ora2pg-24.0.tar.bz2 # cd ora2pg-xx/ # perl Makefile.PL PREFIX=<your_install_dir> # make && make install Installed in/usr/local/bin/directory by default Check the software environment: # vi check.pl #!/usr/bin/perl use strict; use ExtUtils::Installed; my $inst= ExtUtils::Installed->new(); my @modules = $inst->modules(); foreach(@modules) { my $ver = $inst->version($_) || \"???\"; printf(\"%-12s -- %s\\n\", $_, $ver); } exit; # perl check.pl DBD::Oracle -- 1.76 DBD::Pg -- 3.8.0 DBI -- 1.643 Ora2Pg -- 24.0 Perl -- 5.16.3 Set environment variables export PERL5LIB=<your_install_dir> #export PERL5LIB=/usr/local/bin/ Update oracle statistics to improve performance BEGIN DBMS_STATS.GATHER_SCHEMA_STATS('SH'); DBMS_STATS.GATHER_SCHEMA_STATS('SCOTT'); DBMS_STATS.GATHER_SCHEMA_STATS('HR'); DBMS_STATS.GATHER_DATABASE_STATS ; DBMS_STATS.GATHER_DICTIONARY_STATS; END;/ Query the source end object pair type SYS@PROD1>set pagesize 200 SYS@PROD1>select distinct OBJECT_TYPE from dba_objects where OWNER in ('SH','SCOTT','HR'); OBJECT_TYPE ------------------- INDEX PARTITION TABLE PARTITION SEQUENCE PROCEDURE LOB X TRIGGER DIMENSION X MATERIALIZED VIEW TABLE INDEX VIEW 11 rows selected. Configure ora2pg.conf By default, Ora2Pg will find the/etc/ora2pg/ora2pg.conf configuration file. If the file exists, you only need to execute:/usr/local/bin/ora2pg cat /etc/ora2pg/ora2pg.conf.dist | grep -v ^# |grep -v ^$ >ora2pg.conf vi ora2pg.conf # cat ora2pg.conf ORACLE_HOME /opt/oracle/product/19c/dbhome_1 ORACLE_DSN dbi:Oracle:host=localhost;sid=ORCLCDB;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 SKIP fkeys ukeys checks TYPE TABLE,VIEW,GRANT,SEQUENCE,TABLESPACE,PROCEDURE,TRIGGER,FUNCTION,PACKAGE,PARTITION,TYPE,MVIEW,QUERY,DBLINK,SYNONYM,DIRECTORY,TEST,TEST_VIEW NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh.sql Only one type of export can be executed at the same time, so the TYPE instruction must be unique. If you have more than one, only the last one will be found in the file. But I can export multiple types at the same time. Please note that you can link multiple exports by providing a comma-separated list of export types to the TYPE directive, but in this case, you cannot use COPY or INSERT with other export types. Some export types cannot or should not be directly loaded into the IvorySQL database, and still require little manual editing. This is the case for GRANT, TABLESPACE, TRIGGER, FUNCTION, PROCEDURE, TYPE, QUERY and PACKAGE export types, especially if you have PLSQL code or Oracle specific SQL. For TABLESPACE, you must ensure that the file path exists on the system. For SYNONYM, you can ensure that the owner and schema of the object correspond to the new PostgreSQL database design. It is recommended to export the table structure one type at a time to avoid other errors affecting each other. After setting the Oracle database DSN, you can execute ora2pg to check whether it is valid： # ora2pg -t SHOW_VERSION -c config/ora2pg.conf Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 It is not easy to estimate the cost of the migration process from Oracle to PostgreSQL. In order to obtain a good evaluation of the migration cost, Ora2Pg will check all database objects, all functions and stored procedures to detect whether there are still some objects and PL/SQL code that cannot be automatically converted by Ora2Pg. Ora2Pg has a content analysis mode, which checks the Oracle database to generate a text report about the content contained in the Oracle database and the content that cannot be exported. # ora2pg -t SHOW_REPORT --estimate_cost -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 11/11 objects types (100.0%) end of objects auditing. ------------------------------------------------------------------------------- Ora2Pg v24.0 - Database Migration Report ------------------------------------------------------------------------------- Version Oracle Database 19c Enterprise Edition Release 19.0.0.0.0 Schema SH Size 287.25 MB ------------------------------------------------------------------------------- Object Number Invalid Estimated cost Comments Details ------------------------------------------------------------------------------- DATABASE LINK 0 0 0 Database links will be exported as SQL/MED IvorySQL's Foreign Data Wrapper (FDW) extensions using oracle_fdw. DIMENSION 5 0 0 GLOBAL TEMPORARY TABLE 0 0 0 Global temporary table are not supported by PostgreSQL and will not be exported. You will have to rewrite some application code to match the PostgreSQL temporary table behavior. INDEX 20 0 3.4 14 index(es) are concerned by the export, others are automatically generated and will do so on PostgreSQL. Bitmap will be exported as btree_gin index(es) and hash index(es) will be exported as b-tree index(es) if any. Domain index are exported as b-tree but commented to be edited to mainly use FTS. Cluster, bitmap join and IOT indexes will not be exported at all. Reverse indexes are not exported too, you may use a trigram-based index (see pg_trgm) or a reverse() function based index and search. Use 'varchar_pattern_ops', 'text_pattern_ops' or 'bpchar_pattern_ops' operators in your indexes to improve search with the LIKE operator respectively into varchar, text or char columns. 11 bitmap index(es). 1 domain index(es). 2 b-tree index(es). INDEX PARTITION 196 0 0 Only local indexes partition are exported, they are build on the column used for the partitioning. JOB 0 0 0 Job are not exported. You may set external cron job with them. MATERIALIZED VIEW 2 0 6 All materialized view will be exported as snapshot materialized views, they are only updated when fully refreshed. SYNONYM 0 0 0 SYNONYMs will be exported as views. SYNONYMs do not exists with PostgreSQL but a common workaround is to use views or set the PostgreSQL search_path in your session to access object outside the current schema. TABLE 11 0 1.1 1 external table(s) will be exported as standard table. See EXTERNAL_TO_FDW configuration directive to export as file_fdw foreign tables or use COPY in your code if you just want to load data from external files. Total number of rows: 1063384. Top 10 of tables sorted by number of rows:. sales has 918843 rows. costs has 82112 rows. customers has 55500 rows. supplementary_demographics has 4500 rows. times has 1826 rows. promotions has 503 rows. products has 72 rows. countries has 23 rows. channels has 5 rows. sales_transactions_ext has 0 rows. Top 10 of largest tables:. TABLE PARTITION 56 0 5.6 Partitions are exported using table inheritance and check constraint. Hash and Key partitions are not supported by PostgreSQL and will not be exported. 56 RANGE partitions.. VIEW 1 0 1 Views are fully supported but can use specific functions. ------------------------------------------------------------------------------- Total 291 0 17.10 17.10 cost migration units means approximatively 1 man-day(s). The migration unit was set to 5 minute(s) ------------------------------------------------------------------------------ Migration level : A-1 ------------------------------------------------------------------------------- Migration levels: A - Migration that might be run automatically B - Migration with code rewrite and a human-days cost up to 5 days C - Migration with code rewrite and a human-days cost above 5 days Technical levels: 1 = trivial: no stored functions and no triggers 2 = easy: no stored functions but with triggers, no manual rewriting 3 = simple: stored functions and/or triggers, no manual rewriting 4 = manual: no stored functions but with triggers or views with code rewriting 5 = difficult: stored functions and/or triggers with code rewriting ------------------------------------------------------------------------------- # ora2pg -c ora2pg.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 12/12 tables (100.0%) end of table export. [========================>] 1/1 views (100.0%) end of output. [========================>] 0/0 sequences (100.0%) end of output. [========================>] 0/0 procedures (100.0%) end of procedures export. [========================>] 0/0 triggers (100.0%) end of output. [========================>] 0/0 functions (100.0%) end of functions export. [========================>] 0/0 packages (100.0%) end of output. [========================>] 56/56 partitions (100.0%) end of output. [========================>] 0/0 types (100.0%) end of output. [========================>] 2/2 materialized views (100.0%) end of output. [========================>] 0/0 dblink (100.0%) end of output. [========================>] 0/0 synonyms (100.0%) end of output. [========================>] 2/2 directory (100.0%) end of output. Fixing function calls in output files.... Configure the type of ora2pg.conf as COPY or INSERT # cp ora2pg.conf sh_data.conf # vi sh_data.conf ORACLE_HOME /usr/lib/oracle/18.3/client64 ORACLE_DSN dbi:Oracle:host=10.85.10.6 ;sid=PROD1;port=1521 ORACLE_USER system ORACLE_PWD oracle SCHEMA SH EXPORT_SCHEMA 1 DISABLE_UNLOGGED 1 SKIP fkeys ukeys checks TYPE COPY NLS_LANG AMERICAN_AMERICA.UTF8 OUTPUT sh_data.sql Export Data # ora2pg -c sh_data.conf [========================>] 11/11 tables (100.0%) end of scanning. [========================>] 5/5 rows (100.0%) Table CHANNELS (5 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1995 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_H1_1997 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). [> ] 0/82112 rows (0.0%) Table COSTS_1996 (0 recs/sec) [> ] 5/1063384 total rows (0.0%) - (0 sec., avg: 5 recs/sec). …………………………………………………………… [========================>] 4500/4500 rows (100.0%) Table SUPPLEMENTARY_DEMOGRAPHICS (4500 recs/sec) [=======================> ] 1061558/1063384 total rows (99.8%) - (45 sec., avg: 23590 recs/sec). [========================>] 1826/1826 rows (100.0%) Table TIMES (1826 recs/sec) [========================>] 1063384/1063384 total rows (100.0%) - (45 sec., avg: 23630 recs/sec). [========================>] 1063384/1063384 rows (100.0%) on total estimated data (45 sec., avg: 23630 recs/sec) Fixing function calls in output files... To view the exported file: # ls -lrt *.sql -rw-r--r-- 1 root root 15716 Jul 2 21:21 TABLE_sh.sql -rw-r--r-- 1 root root 858 Jul 2 21:21 VIEW_sh.sql -rw-r--r-- 1 root root 2026 Jul 2 21:21 TABLESPACE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 SEQUENCE_sh.sql -rw-r--r-- 1 root root 2382 Jul 2 21:21 GRANT_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 TRIGGER_sh.sql -rw-r--r-- 1 root root 346 Jul 2 21:21 PROCEDURE_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 PACKAGE_sh.sql -rw-r--r-- 1 root root 345 Jul 2 21:21 FUNCTION_sh.sql -rw-r--r-- 1 root root 6771 Jul 2 21:21 PARTITION_sh.sql -rw-r--r-- 1 root root 341 Jul 2 21:21 TYPE_sh.sql -rw-r--r-- 1 root root 342 Jul 2 21:21 QUERY_sh.sql -rw-r--r-- 1 root root 950 Jul 2 21:21 MVIEW_sh.sql -rw-r--r-- 1 root root 344 Jul 2 21:21 SYNONYM_sh.sql -rw-r--r-- 1 root root 926 Jul 2 21:21 DIRECTORY_sh.sql -rw-r--r-- 1 root root 343 Jul 2 21:21 DBLINK_sh.sql -rw-r--r-- 1 root root 55281235 Jul 2 17:11 sh_data.sql Export HR and SCOTT user data in the same way. Create ORCL database # su - ivorysql Last login: Tue Jul 2 20:04:30 CST 2019 on pts/3 $ createdb orcl $ psql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Collate | Ctype | ICU Locale | Locale Provider | Access privileges -----------+----------+-----------+---------+-------+------------+-----------------+----------------------- ivorysql | ivorysql | SQL_ASCII | C | C | | libc | orcl | ivorysql | SQL_ASCII | C | C | | libc | postgres | ivorysql | SQL_ASCII | C | C | | libc | template0 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | SQL_ASCII | C | C | | libc | =c/ivorysql + | | | | | | | ivorysql=CTc/ivorysql (5 rows) ivorysql=# Create SH, HR, SCOTT users: $ psql orcl psql (18.0) Type \"help\" for help. orcl=# orcl=# create user sh with password 'sh'; CREATE ROLE Because of the materialized view, in TABLE_ The sh.sql contains the index of the materialized view, which will fail to create. You need to first create a table, then create a materialized view, and finally create an index. Cancel the materialized view index and create it separately later: CREATE INDEX fw_psc_s_mv_chan_bix ON fweek_pscat_sales_mv (channel_id); CREATE INDEX fw_psc_s_mv_promo_bix ON fweek_pscat_sales_mv (promo_id); CREATE INDEX fw_psc_s_mv_subcat_bix ON fweek_pscat_sales_mv (prod_subcategory); CREATE INDEX fw_psc_s_mv_wd_bix ON fweek_pscat_sales_mv (week_ending_day); CREATE TEXT SEARCH CONFIGURATION en (COPY = pg_catalog.english); ALTER TEXT SEARCH CONFIGURATION en ALTER MAPPING FOR hword, hword_part, word WITH unaccent, english_stem; psql orcl -f tab.sql.sql ALTER TABLE PARTITION sh.sales OWNER TO sh; COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT COMMENT ALTER TABLE ALTER TABLE ALTER TABLE ……………………………… cat psql orcl -f GRANT_sh.sql CREATE USER SH WITH PASSWORD 'change_my_secret' LOGIN; ALTER TABLE sh.fweek_pscat_sales_mv OWNER TO sh; GRANT ALL ON sh.fweek_pscat_sales_mv TO sh; Materialized views require relevant query permissions, so import permissions. Please keep up with users here $ psql orcl sh -f MVIEW_sh.sql SELECT 0 SELECT 0 CREATE INDEX CREATE INDEX CREATE INDEX CREATE INDEX $ psql orcl -f VIEW_sh.sql SET SET SET CREATE VIEW $ psql orcl -f PARTITION_sh.sql SET SET SET CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE CREATE TABLE ………………………… $ psql orcl -f sh_data.sql SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 0 SET COPY 4500 SET COPY 1826 COMMIT Source database and target side extract part of objects for comparison： SYS@PROD1>select count(*) from sh.products; COUNT(*) ---------- 72 orcl=# select count(*) from sh.products; count ------- 72 (1 row) --------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.channels; COUNT(*) ---------- 5 orcl=# select count(*) from sh.channels; count ------- 5 (1 row) -------------------------------------------------------------------------- SYS@PROD1>select count(*) from sh.customers ; COUNT(*) ---------- 55500 orcl=# select count(*) from sh.customers ; count ------- 55500 (1 row) When using, there are two options — project_ Base and — init_ Project indicates to ora2pg that he must create a project template, which contains the work tree, configuration files and scripts for exporting all objects from the Oracle database. Generate a generic configuration file. 1. Create script export_ Schema.sh to automatically perform all exports. 2. Create script import_ All.sh to automatically perform all imports. example： mkdir -p /ora2pg/migration # ora2pg --project_base /ora2pg/migration/ --init_project test_project Creating project test_project. /ora2pg/migration//test_project/ schema/ dblinks/ directories/ functions/ grants/ mviews/ packages/ partitions/ procedures/ sequences/ synonyms/ tables/ tablespaces/ triggers/ types/ views/ sources/ functions/ mviews/ packages/ partitions/ procedures/ triggers/ types/ views/ data/ config/ reports/ Generating generic configuration file Creating script export_schema.sh to automate all exports. Creating script import_all.sh to automate all imports.","title":"Migration guide","component":"ivorysql-doc","version":"master","name":"migration_guide","url":"/ivorysql-doc/master/migration_guide.html","titles":[{"text":"1. Migration overview","hash":"migration-overview","id":1},{"text":"2. Migration tool——Ora2Pg","hash":"migration-toolora2pg","id":2},{"text":"3. Migrate Oracle Database to IvorySQL","hash":"migrate-oracle-database-to-ivorysql","id":3},{"text":"3.1. Environment preparation","hash":"environment-preparation","id":4},{"text":"3.2. Environment-dependent installation","hash":"environment-dependent-installation","id":5},{"text":"3.2.1. Install Perl","hash":"install-perl","id":6},{"text":"3.2.2. Install the DBI module","hash":"install-the-dbi-module","id":7},{"text":"3.2.3. Install DBD-Oracle","hash":"install-dbd-oracle","id":8},{"text":"3.2.4. Install DBD-PG (optional)","hash":"install-dbd-pg-optional","id":9},{"text":"3.3. Install Ora2pg","hash":"install-ora2pg","id":10},{"text":"3.4. Source side preparation","hash":"source-side-preparation","id":11},{"text":"3.5. Ora2pg export table structure","hash":"ora2pg-export-table-structure","id":12},{"text":"3.5.1. Test connection","hash":"test-connection","id":13},{"text":"3.5.2. Migration cost assessment","hash":"migration-cost-assessment","id":14},{"text":"3.5.3. Export SH table structure","hash":"export-sh-table-structure","id":15},{"text":"3.5.4. Export SH user data","hash":"export-sh-user-data","id":16},{"text":"3.6. Create orcl library in IvorySQL environment","hash":"create-orcl-library-in-ivorysql-environment","id":17},{"text":"4. Migration Portal","hash":"migration-portal","id":18},{"text":"4.1. Import table structure","hash":"import-table-structure","id":19},{"text":"4.2. Authorize objects","hash":"authorize-objects","id":20},{"text":"4.3. Import materialized view structure","hash":"import-materialized-view-structure","id":21},{"text":"4.4. Import View","hash":"import-view","id":22},{"text":"4.5. Import partition table","hash":"import-partition-table","id":23},{"text":"4.6. Import data","hash":"import-data","id":24},{"text":"5. Data validation","hash":"data-validation","id":25},{"text":"6. Generate migration template","hash":"generate-migration-template","id":26}]},"7":{"id":7,"text":"Since IvorySQL is based on PostgreSQL, it is recommended that when reading and understanding this section, O&M staff also refer to doc。 The IvorySQL version number consists of a major version and a minor version. For example, 3 in IvorySQL 3.2 is the major version and 2 is the minor version. Releasing a minor version is not going to change the internal storage format, so it is always compatible with the same major version. For example, IvorySQL 3.4 is compatible with Ivory SQL 3.0 and the subsequent Ivory SQL 3.x. Upgrading for these compatible versions is as simple as shutting down the database service, installing a replacement binary executable, and restarting the service. ​ Next, we focus on cross-version upgrades of IvorySQL, for example, from IvorySQL 2.3 to IvorySQL 3.2. Major version upgrades may modify the internal data storage format and therefore require additional operations to be performed. The common cross-version upgrade methods and applicable scenarios are as follows. Upgrade method Applicable scenarios Shutdown time pg_dumpall Small to medium sized databases, e.g. less than 100GB to support cross-platform data migration Depends on the size of the database pg_upgrade Large and medium-sized databases, e.g., >100GB local upgrade A few minutes Logical Replication Large and medium-sized databases, e.g. >100GB cross-platform support A few seconds Attention： New major releases usually introduce some user-visible incompatibilities and may therefore require application programming changes. All user-visible changes are listed in description，Please pay special attention to the section labeled \"Migration\". Although you may upgrade from one major version to another without upgrading an intermediate version, you should read the major release notes for all intermediate versions. The traditional cross-version upgrade method uses pg_dump/pg_dumpall to logically backup the database and then restore it in the new version via pg_restore. It is recommended to use the new version of pg_dump/pg_dumpall tool when exporting the old version of the database. You can take advantage of its latest parallel export and restore features, while reducing database bloat problems. Logical backup and restore is very simple but slow. Downtime depends on the size of the database, so it is suitable for small to medium sized database upgrades. ​The following describes how this upgrade method works. If the current IvorySQL software installation directory is located in /usr/local/pgsql and the data directory is located in /usr/local/pgsql/data, we do the upgrade on the same server. 1.Stop the application before performing a logical backup and make sure that no data is updated, as updates after the backup has started will not be exported. If necessary, modify the /usr/local/pgsql/data/pg_hba.conf file to disable others from accessing the database. Then backup the database. pg_dumpall > outputfile If you have installed a new version of IvorySQL, you can use the new version of the pg_dumpall command to back up the old version of the database. 2.Stop the backend services of older versions. pg_ctl stop Or stop the background service by other means. 3.If the installation directory does not contain a specific version identifier, the directory can be renamed and modified back if necessary. Directories can be renamed using a command similar to the following. mv /usr/local/pgsql /usr/local/pgsql.old 4.Install the new version of IvorySQL software, if the installation directory is still /usr/local/pgsql. 5.Initializing a new database cluster requires the use of a database specific user (usually postgres; if upgrading the version, this user should already exist) to perform the operation. /usr/local/pgsql/bin/initdb -D /usr/local/pgsql/data 6.Change the old configuration file pg_hba.conf, postgresql.conf, etc. in the corresponding new configuration file. 7.To start a new version of the backend service using a dedicated database user. /usr/local/pgsql/bin/postgres -D /usr/local/pgsql/data 8.Finally, using the new version of the psql command to restore the data. /usr/local/pgsql/bin/psql -d postgres -f outputfile To reduce downtime, you can install the new version of IvorySQL to another directory, while starting the service using a different port. Then perform both the export and import of the database. pg_dumpall -p 5432 | psql -d postgres -p 5433 When the above operation is executed, the old and new versions of the backend service run simultaneously, with the new version using port 5433 and the old version using port 5432. The pg_upgrade tool is a built-in cross-version upgrade utility in PostgreSQL that enables in-place database upgrades without requiring export and import operations. Since IvorySQL is derived from PostgreSQL, it can also use the pg_upgrade tool for major version upgrades. Below is a brief introduction on how to use pg_upgrade to upgrade IvorySQL to the latest 5.0 version on the CentOS8 platform. pg_upgrade provides a pre-upgrade compatibility check (using the -c or --check option), which can identify issues such as plugin or data type incompatibilities. If the --link option is specified, the new version service can directly use the existing database files without copying, typically completing the upgrade in just a few minutes. Commonly used parameters include: -b bindir, --old-bindir=bindir: The directory of the old IvorySQL executable files; -B bindir, --new-bindir=bindir: The directory of the new IvorySQL executable files; -d configdir, --old-datadir=configdir: The data directory of the old version; -D configdir, --new-datadir=configdir: The data directory of the new version; -c, --check: Only check upgrade compatibility without modifying any data; -k, --link: Upgrade using hard link method; Upgrade preparation: First, stop the old version of the IvorySQL 4.6 database: /usr/ivory-4/bin/pg_ctl -D ./data stop Then install the new version of the IvorySQL 5.0 database: dnf install -y ivorysql5-5.0 Initialize the new IvorySQL 5.0 data directory: /usr/ivory-5/bin/initdb -D ./data Check version compatibility: /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ --check The appearance of \"Clusters are compatible\" at the end indicates that there are no compatibility issues between the two versions of data, and the upgrade can proceed. Official upgrade: /usr/ivory-5/bin/pg_upgrade --old-datadir=/home/ivorysql/test/4.6/data --new-datadir=/home/ivorysql/test/5.0/data --old-bindir=/usr/ivory-4/bin/ --new-bindir=/usr/ivory-5/bin/ Seeing \"Upgrade Complete\" indicates that the upgrade has been successfully completed. Update statistics: pg_upgrade creates new system tables and reuses old data for the upgrade. However, statistics are not migrated during the upgrade process. Therefore, before enabling the new version, you should first recollect statistics to avoid incorrect query plans caused by missing statistics. Start the new version of the database. /usr/ivory-5/bin/pg_ctl -D ./data -l logfile start Manually run the vacuum command vacuum --all --analyze-in-stage -h 127.0.0.1 -p 1521 Cleanup after upgrade rm -rf /home/ivorysql/test/4.6/data pg_upgrade Documentoutlines the steps required above. We can also create a fallback server using logical replication of an updated version of IvorySQL, which supports replication between different major versions of IvorySQL. The fallback server can be on the same computer or on a different computer. Once it is synchronized with the primary server (running an older version of IvorySQL), you can switch hosts and use the backup server as the host, and then shut down the older database instance. Such a switchover allows an upgrade with only a few seconds of downtime. This upgrade method can be used with built-in logical replication tools and external logical replication systems such as pglogical, Slony, Londiste, and Bucardo. pg_upgrade supports upgrading native PostgreSQL clusters to IvorySQL. When the source cluster is native PostgreSQL and the target is an IvorySQL cluster, the -g（--using-ora-pg）parameter must be used. Upgrading from PostgreSQL to IvorySQL is considered a \"cross-product\" migration. It is recommended to complete comprehensive testing and validation before performing the operation in a production environment, and to develop a detailed rollback plan. parameter description -g / --using-ora-pg Indicates that the source cluster is PostgreSQL and the target is an IvorySQL cluster. When this option is enabled, pg_upgrade uniformly uses the PostgreSQL standard port when connecting to both clusters, ensuring proper handling of the upgrade. # Use IvorySQL's initdb to initialize the new cluster /opt/ivorysql/bin/initdb -D /data/ivorysql/data /usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop Use -c to perform compatibility checks only without modifying any data: /opt/ivorysql/bin/pg_upgrade \\ -b /usr/lib/postgresql/16/bin \\ # Source PG executable directory -B /opt/ivorysql/bin \\ # Target IvorySQL executable directory -d /data/pg/data \\ # Source PG data directory -D /data/ivorysql/data \\ # Target IvorySQL data directory -g \\ # Source is PostgreSQL, target is IvorySQL -c # Check only, do not upgrade /opt/ivorysql/bin/pg_upgrade \\ -b /usr/lib/postgresql/16/bin \\ -B /opt/ivorysql/bin \\ -d /data/pg/data \\ -D /data/ivorysql/data \\ -g /opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start # Verify if the database is functioning properly /opt/ivorysql/bin/psql -p 5432 -c \"SELECT version();\" After the upgrade is completed,pg_upgrade will generate a cleanup script: ./delete_old_cluster.sh Parameters Long options Description -b --old-bindir Source cluster executable directory -B --new-bindir Target cluster executable directory -d --old-datadir Source cluster data directory -D --new-datadir Target cluster data directory -g --using-ora-pg Upgrade from PostgreSQL to IvorySQL -p --old-port Source cluster port -P --new-port Target cluster PG port -q --old-oraport Source cluster Oracle port -Q --new-oraport Target cluster Oracle port -j --jobs Number of parallel processes -k --link Use hard links instead of file copying -c --check Check compatibility only, do not perform the upgrade -v --verbose Output detailed logs The -g parameter is only used when the source is a pure PostgreSQL cluster、and the target is IvorySQL . If the source cluster is already IvorySQL, this parameter is not needed. Both the source and target clusters must be in a stopped state during the upgrade. Be sure to perform a complete backup of the source cluster before upgrading. IvorySQL is based on PostgreSQL and is updated at the same frequency as PostgreSQL, with one major release per year and one minor release per quarter. IvorySQL 5.0 is based on PostgreSQL 18.0, and all versions of IvorySQL are backward compatible.The relevant version features can be viewed by looking at Official Website。 IvorySQL uses the concept of Roles to manage database access rights. A role can be thought of as a database user or a group of database users, depending on how the role is set. Roles can own database objects (for example, tables and functions) and can delegate permissions on those objects to other roles to control who can access which objects. In addition, membership in one role can be granted to another role, allowing member roles to use the permissions granted to another role. The concept of roles encompasses the concepts of \"user\" and \"group\". The database roles are conceptually completely separate from the operating system users. It may in fact be easier to maintain a correspondence, but this is not necessary. Database roles are global within a database cluster installation (and not within a separate database). To create a role, use the CREATE ROLE SQL command. CREATE ROLE name; Name follows the rules of SQL identifiers: either unadorned with no special characters or surrounded by double quotes (in fact, you will always have to add additional options to the command, such as LOGIN. see below for more details). To remove an existing role, use the similar DROP ROLE command. DROP ROLE name; For convenience, the createuser and dropuser programs are provided as wrappers for these SQL commands, which can be invoked from the shell command line at createuser name dropuser name To determine the set of existing roles, check the pg_roles system directory, e.g. SELECT rolname FROM pg_roles; The \\du meta command of the psql program can also be used to list existing roles. To bootstrap a database system, a system that has just been initialized always contains a predefined role. This role is always a \"superuser\" and by default (unless changed when running initdb) it is named the same as the OS user who initialized the database cluster. By convention, this role will be named postgres. In order to create more roles, you must first connect as the initial role. Each connection to the database server is established using a particular role name, and this role determines the initial access rights to the command that initiates the connection. The role name to use for a particular database connection is indicated by the client, which initiates the connection request in an application-related style. For example, the psql program uses the -U command line option to specify which role to connect under. Many applications assume that this name is the current operating system user by default (including createuser and psql). Therefore it is often convenient to maintain a name correspondence between the role and the OS user. The set of database roles that a given client connection can use to connect to is determined by the authentication settings of that client, so a client is not limited to connecting with a role that matches its OS user, just as a person’s login name does not need to match her real name. Because role identity determines the set of permissions available to a connected client, be careful when setting up a multi-user environment to configure permissions. A database role can have a number of attributes that define the permissions of the role and interact with the client authentication system. It is often convenient to group users together to facilitate the management of permissions: in this way, permissions can be granted to or reclaimed from an entire group. This is done in IvorySQL by creating a role that represents a group, and then granting membership in that group role to individual user roles. Since roles can own database objects and hold privileges to access other objects, deleting a role is often not a one-time DROP ROLE solution. Any objects owned by that user must first be deleted or transferred to another owner, and any privileges that have been granted to that role must be withdrawn. For more details on database access management, refers to doc. IvorySQL is based on PostgreSQL and has a full SQL with defined data objects that can be referred to doc.On top of that, IvorySQL has done some Oracle proprietary data object compatibility for Oracle compatibility. Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character. VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 IvorySQL is developed based on PostgreSQL, with full SQL, query data specific operations can be referred to doc. IvorySQL implements portions of the SQL/MED specification, allowing you to access data that resides outside IvorySQL using regular SQL queries. Such data is referred to as foreign data. (Note that this usage is not to be confused with foreign keys, which are a type of constraint within the database.) Foreign data is accessed with help from a foreign data wrapper. A foreign data wrapper is a library that can communicate with an external data source, hiding the details of connecting to the data source and obtaining data from it. There are some foreign data wrappers available as contrib modules;see Appendix F. Other kinds of foreign data wrappers might be found as third party products. If none of the existing foreign data wrappers suit your needs, you can write your own; see doc. To access foreign data, you need to create a foreign server object, which defines how to connect to a particular external data source according to the set of options used by its supporting foreign data wrapper. Then you need to create one or more foreign tables, which define the structure of the remote data. A foreign table can be used in queries just like a normal table, but a foreign table has no storage in the IvorySQL server. Whenever it is used, IvorySQL asks the foreign data wrapper to fetch data from the external source, or transmit data to the external source in the case of update commands. Accessing remote data may require authenticating to the external data source. This information can be provided by a user mapping, which can provide additional data such as user names and passwords based on the current IvorySQL role. As with everything that contains valuable data, IvorySQL databases should be backed up regularly. While the procedure is essentially simple, it is important to have a clear understanding of the underlying techniques and assumptions. There are three fundamentally different approaches to backing up IvorySQL data: SQL dump File system level backup Continuous archiving The idea behind this dump method is to generate a file with SQL commands that, when fed back to the server, will recreate the database in the same state as it was at the time of the dump. IvorySQL provides the utility program pg_dumpfor this purpose. The basic usage of this command is: pg_dump dbname > dumpfile As you see, pg_dump writes its result to the standard output. We will see below how this can be useful. While the above command creates a text file, pg_dump can create files in other formats that allow for parallelism and more fine-grained control of object restoration. pg_dump is a regular IvorySQL client application (albeit a particularly clever one). This means that you can perform this backup procedure from any remote host that has access to the database. pg_dump must have read access to all tables that you want to back up, so in order to back up the entire database you almost always have to run it as a database superuser. (If you do not have sufficient privileges to back up the entire database, you can still back up portions of the database to which you do have access using options such as -n `schema` or -t `table`.) ​To specify which database server pg_dump should contact, use the command line options -h `host` and -p `port. The default host is the local host or whatever your `HOST environment variable specifies. Similarly, the default port is indicated by the PORT environment variable or, failing that, by the compiled-in default. (Conveniently, the server will normally have the same compiled-in default.) pg_dump will by default connect with the database user name that is equal to the current operating system user name. To override this, either specify the -U option or set the environment variable PGUSER. Remember that pg_dump connections are subject to the normal client authentication mechanisms 。 ​ An important advantage of pg_dump over the other backup methods described later is that pg_dump’s output can generally be re-loaded into newer versions of IvorySQL, whereas file-level backups and continuous archiving are both extremely server-version-specific. pg_dump is also the only method that will work when transferring a database to a different machine architecture, such as going from a 32-bit to a 64-bit server. ​ Dumps created by pg_dump are internally consistent, meaning, the dump represents a snapshot of the database at the time pg_dump began running. pg_dump does not block other operations on the database while it is working. (Exceptions are those operations that need to operate with an exclusive lock, such as most forms of ALTER TABLE.) Text files created by pg_dump are intended to be read in by the psql program. The general command form to restore a dump is psql dbname < dumpfile where dumpfile is the file output by the pg_dump command. The database dbname will not be created by this command, so you must create it yourself from template0 before executing psql (e.g., with createdb -T template0 `dbname`). psql supports options similar to pg_dump for specifying the database server to connect to and the user name to use. See the psql reference page for more information. Non-text file dumps are restored using the pg_restore utility. Before restoring an SQL dump, all the users who own objects or were granted permissions on objects in the dumped database must already exist. If they do not, the restore will fail to recreate the objects with the original ownership and/or permissions. (Sometimes this is what you want, but usually it is not.) By default, the psql script will continue to execute after an SQL error is encountered. You might wish to run psql with the ON_ERROR_STOP variable set to alter that behavior and have psql exit with an exit status of 3 if an SQL error occurs: psql --set ON_ERROR_STOP=on dbname < infile Either way, you will only have a partially restored database. Alternatively, you can specify that the whole dump should be restored as a single transaction, so the restore is either fully completed or fully rolled back. This mode can be specified by passing the -1 or --single-transaction command-line options to psql. When using this mode, be aware that even a minor error can rollback a restore that has already run for many hours. However, that might still be preferable to manually cleaning up a complex database after a partially restored dump. The ability of pg_dump and psql to write to or read from pipes makes it possible to dump a database directly from one server to another, for example: pg_dump -h host1 dbname | psql -h host2 dbname Important：The dumps produced by pg_dump are relative to template0. This means that any languages, procedures, etc. added via template1 will also be dumped by pg_dump. As a result, when restoring, if you are using a customized template1, you must create the empty database from template0, as in the example above. After restoring a backup, it is wise to run ANALYZE on each database so the query optimizer has useful statistics. pg_dump dumps only a single database at a time, and it does not dump information about roles or tablespaces (because those are cluster-wide rather than per-database). To support convenient dumping of the entire contents of a database cluster, the pg_dumpall program is provided. pg_dumpall backs up each database in a given cluster, and also preserves cluster-wide data such as role and tablespace definitions. The basic usage of this command is: pg_dumpall > dumpfile The resulting dump can be restored with psql: psql -f dumpfile ivorysql (Actually, you can specify any existing database name to start from, but if you are loading into an empty cluster then ivorysql should usually be used.) It is always necessary to have database superuser access when restoring a pg_dumpall dump, as that is required to restore the role and tablespace information. If you use tablespaces, make sure that the tablespace paths in the dump are appropriate for the new installation. pg_dumpall works by emitting commands to re-create roles, tablespaces, and empty databases, then invoking pg_dump for each database. This means that while each database will be internally consistent, the snapshots of different databases are not synchronized. Cluster-wide data can be dumped alone using the pg_dumpall --globals-only option. This is necessary to fully backup the cluster if running the pg_dump command on individual databases. Some operating systems have maximum file size limits that cause problems when creating large pg_dump output files. Fortunately, pg_dump can write to the standard output, so you can use standard Unix tools to work around this potential problem. There are several possible methods: Use compressed dumps. You can use your favorite compression program, for example gzip: pg_dump dbname | gzip > filename.gz Reload with： gunzip -c filename.gz | psql dbname or: cat filename.gz | gunzip | psql dbname Use split. The split command allows you to split the output into smaller files that are acceptable in size to the underlying file system. For example, to make 2 gigabyte chunks: pg_dump dbname | split -b 2G - filename Reload with: cat filename* | psql dbname If using GNU split, it is possible to use it and gzip together: pg_dump dbname | split -b 2G -−filter='gzip > $FILE.gz' It can be restored using zcat. Use pg_dump’s custom dump format. If IvorySQL was built on a system with the zlib compression library installed, the custom dump format will compress data as it writes it to the output file. This will produce dump file sizes similar to using gzip, but it has the added advantage that tables can be restored selectively. The following command dumps a database using the custom dump format: pg_dump -Fc dbname > filename A custom-format dump is not a script for psql, but instead must be restored with pg_restore, for example: pg_restore -d dbname filename See the pg_dump and pg_restore reference pages for details. For very large databases, you might need to combine split with one of the other two approaches. Use pg_dump’s parallel dump feature. To speed up the dump of a large database, you can use pg_dump’s parallel mode. This will dump multiple tables at the same time. You can control the degree of parallelism with the -j parameter. Parallel dumps are only supported for the \"directory\" archive format. pg_dump -j num -F d -f out.dir dbname You can use pg_restore -j to restore a dump in parallel. This will work for any archive of either the \"custom\" or the \"directory\" archive mode, whether or not it has been created with pg_dump -j. An alternative backup strategy is to directly copy the files that IvorySQL uses to store the data in the database，You can use whatever method you prefer for doing file system backups; for example: tar -cf backup.tar /usr/local/pgsql/data There are two restrictions, however, which make this method impractical, or at least inferior to the pg_dump method: The database server must be shut down in order to get a usable backup. Half-way measures such as disallowing all connections will not work (in part because tar and similar tools do not take an atomic snapshot of the state of the file system, but also because of internal buffering within the server).Needless to say, you also need to shut down the server before restoring the data. If you have dug into the details of the file system layout of the database, you might be tempted to try to back up or restore only certain individual tables or databases from their respective files or directories. This will not work because the information contained in these files is not usable without the commit log files, pg_xact/*, which contain the commit status of all transactions. A table file is only usable with this information. Of course it is also impossible to restore only a table and the associated pg_xact data because that would render all other tables in the database cluster useless. So file system backups only work for complete backup and restoration of an entire database cluster. ​ An alternative file-system backup approach is to make a “consistent snapshot” of the data directory, if the file system supports that functionality (and you are willing to trust that it is implemented correctly). The typical procedure is to make a “frozen snapshot” of the volume containing the database, then copy the whole data directory (not just parts, see above) from the snapshot to a backup device, then release the frozen snapshot. This will work even while the database server is running. However, a backup created in this way saves the database files in a state as if the database server was not properly shut down; therefore, when you start the database server on the backed-up data, it will think the previous server instance crashed and will replay the WAL log. This is not a problem; just be aware of it (and be sure to include the WAL files in your backup). You can perform a CHECKPOINT before taking the snapshot to reduce recovery time. ​ If your database is spread across multiple file systems, there might not be any way to obtain exactly-simultaneous frozen snapshots of all the volumes. For example, if your data files and WAL log are on different disks, or if tablespaces are on different file systems, it might not be possible to use snapshot backup because the snapshots must be simultaneous. Read your file system documentation very carefully before trusting the consistent-snapshot technique in such situations. ​ If simultaneous snapshots are not possible, one option is to shut down the database server long enough to establish all the frozen snapshots. Another option is to perform a continuous archiving base backup,because such backups are immune to file system changes during the backup. This requires enabling continuous archiving just during the backup process; restore is done using continuous archive recovery ​ Another option is to use rsync to perform a file system backup. This is done by first running rsync while the database server is running, then shutting down the database server long enough to do an rsync --checksum. (--checksum is necessary because rsync only has file modification-time granularity of one second.) The second rsync will be quicker than the first, because it has relatively little data to transfer, and the end result will be consistent because the server was down. This method allows a file system backup to be performed with minimal downtime. Note that a file system backup will typically be larger than an SQL dump. (pg_dump does not need to dump the contents of indexes for example, just the commands to recreate them.) However, taking a file system backup might be faster. At all times,IvorySQLmaintains a write ahead log (WAL) in the pg_wal/ subdirectory of the cluster’s data directory. The log records every change made to the database’s data files. This log exists primarily for crash-safety purposes: if the system crashes, the database can be restored to consistency by “replaying” the log entries made since the last checkpoint. However, the existence of the log makes it possible to use a third strategy for backing up databases: we can combine a file-system-level backup with backup of the WAL files. If recovery is needed, we restore the file system backup and then replay from the backed-up WAL files to bring the system to a current state. This approach is more complex to administer than either of the previous approaches, but it has some significant benefits: We do not need a perfectly consistent file system backup as the starting point. Any internal inconsistency in the backup will be corrected by log replay (this is not significantly different from what happens during crash recovery). So we do not need a file system snapshot capability, just tar or a similar archiving tool. Since we can combine an indefinitely long sequence of WAL files for replay, continuous backup can be achieved simply by continuing to archive the WAL files. This is particularly valuable for large databases, where it might not be convenient to take a full backup frequently. It is not necessary to replay the WAL entries all the way to the end. We could stop the replay at any point and have a consistent snapshot of the database as it was at that time. Thus, this technique supports point-in-time recovery: it is possible to restore the database to its state at any time since your base backup was taken. If we continuously feed the series of WAL files to another machine that has been loaded with the same base backup file, we have a warm standby system: at any point we can bring up the second machine and it will have a nearly-current copy of the database. Note: pg_dump and pg_dumpall do not produce file-system-level backups and cannot be used as part of a continuous-archiving solution. Such dumps are logical and do not contain enough information to be used by WAL replay. ​ As with the plain file-system-backup technique, this method can only support restoration of an entire database cluster, not a subset. Also, it requires a lot of archival storage: the base backup might be bulky, and a busy system will generate many megabytes of WAL traffic that have to be archived. Still, it is the preferred backup technique in many situations where high reliability is needed. ​ To recover successfully using continuous archiving (also called “online backup” by many database vendors), you need a continuous sequence of archived WAL files that extends back at least as far as the start time of your backup. So to get started, you should set up and test your procedure for archiving WAL files before you take your first base backup. Accordingly, we first discuss the mechanics of archiving WAL files.For more information on how to create archives and backups and the key points during operation, please refer to doc。 COPY moves data between IvorySQL tables and standard file-system files. COPY TO copies the contents of a table to a file, while COPY FROM copies data from a file to a table (appending the data to whatever is in the table already). COPY TO can also copy the results of a SELECT query. If a column list is specified, COPY TO copies only the data in the specified columns to the file. For COPY FROM, each field in the file is inserted, in order, into the specified column. Table columns not specified in the COPY FROM column list will receive their default values. COPY with a file name instructs the IvorySQL server to directly read from or write to a file. The file must be accessible by the IvorySQL user (the user ID the server runs as) and the name must be specified from the viewpoint of the server. When PROGRAM is specified, the server executes the given command and reads from the standard output of the program, or writes to the standard input of the program. The command must be specified from the viewpoint of the server, and be executable by the IvorySQL user. When STDIN or STDOUT is specified, data is transmitted via the connection between the client and the server. ​ Each backend running COPY will report its progress in the pg_stat_progress_copy view. COPY table_name [ ( column_name [, ...] ) ] FROM { 'filename' | PROGRAM 'command' | STDIN } [ [ WITH ] ( option [, ...] ) ] [ WHERE condition ] COPY { table_name [ ( column_name [, ...] ) ] | ( query ) } TO { 'filename' | PROGRAM 'command' | STDOUT } [ [ WITH ] ( option [, ...] ) ] where option can be one of: FORMAT format_name FREEZE [ boolean ] DELIMITER 'delimiter_character' NULL 'null_string' HEADER [ boolean ] QUOTE 'quote_character' ESCAPE 'escape_character' FORCE_QUOTE { ( column_name [, ...] ) | * } FORCE_NOT_NULL ( column_name [, ...] ) FORCE_NULL ( column_name [, ...] ) ENCODING 'encoding_name' For detailed parameter settings, please refer to doc. On successful completion, a COPY command returns a command tag of the form COPY count The count is the number of rows copied. Note： psql will print this command tag only if the command was not COPY …​ TO STDOUT, or the equivalent psql meta-command \\copy …​ to stdout. This is to prevent confusing the command tag with the data that was just printed. COPY TO can be used only with plain tables, not views, and does not copy rows from child tables or child partitions. For example, COPY `table TO` copies the same rows as SELECT * FROM ONLY `table. The syntax `COPY (SELECT * FROM `table) TO …​` can be used to dump all of the rows in an inheritance hierarchy, partitioned table, or view. COPY FROM can be used with plain, foreign, or partitioned tables or with views that have INSTEAD OF INSERT triggers. You must have select privilege on the table whose values are read by COPY TO, and insert privilege on the table into which values are inserted by COPY FROM. It is sufficient to have column privileges on the column(s) listed in the command. If row-level security is enabled for the table, the relevant SELECT policies will apply to COPY `table TO` statements. Currently, COPY FROM is not supported for tables with row-level security. Use equivalent INSERT statements instead. ​ Files named in a COPY command are read or written directly by the server, not by the client application. Therefore, they must reside on or be accessible to the database server machine, not the client. They must be accessible to and readable or writable by the IvorySQL user (the user ID the server runs as), not the client. Similarly, the command specified with PROGRAM is executed directly by the server, not by the client application, must be executable by the IvorySQL user. COPY naming a file or command is only allowed to database superusers or users who are granted one of the roles pg_read_server_files, pg_write_server_files, or pg_execute_server_program, since it allows reading or writing any file or running a program that the server has privileges to access. ​ Do not confuse COPY with the psql instruction \\copy. \\copy invokes COPY FROM STDIN or COPY TO STDOUT, and then fetches/stores the data in a file accessible to the psql client. Thus, file accessibility and access rights depend on the client rather than the server when \\copy is used. It is recommended that the file name used in COPY always be specified as an absolute path. This is enforced by the server in the case of COPY TO, but for COPY FROM you do have the option of reading from a file specified by a relative path. The path will be interpreted relative to the working directory of the server process (normally the cluster’s data directory), not the client’s working directory. Executing a command with PROGRAM might be restricted by the operating system’s access control mechanisms, such as SELinux. COPY FROM will invoke any triggers and check constraints on the destination table. However, it will not invoke rules. For identity columns, the COPY FROM command will always write the column values provided in the input data, like the INSERT option OVERRIDING SYSTEM VALUE. COPY input and output is affected by DateStyle. To ensure portability to other IvorySQL installations that might use non-default DateStyle settings, DateStyle should be set to ISO before using COPY TO. It is also a good idea to avoid dumping data with IntervalStyle set to sql_standard, because negative interval values might be misinterpreted by a server that has a different setting for IntervalStyle. Input data is interpreted according to ENCODING option or the current client encoding, and output data is encoded in ENCODING or the current client encoding, even if the data does not pass through the client but is read from or written to a file directly by the server. COPY stops operation at the first error. This should not lead to problems in the event of a COPY TO, but the target table will already have received earlier rows in a COPY FROM. These rows will not be visible or accessible, but they still occupy disk space. This might amount to a considerable amount of wasted disk space if the failure happened well into a large copy operation. You might wish to invoke VACUUM to recover the wasted space. FORCE_NULL and FORCE_NOT_NULL can be used simultaneously on the same column. This results in converting quoted null strings to null values and unquoted null strings to empty strings. When the text format is used, the data read or written is a text file with one line per table row. Columns in a row are separated by the delimiter character. The column values themselves are strings generated by the output function, or acceptable to the input function, of each attribute’s data type. The specified null string is used in place of columns that are null. COPY FROM will raise an error if any line of the input file contains more or fewer columns than are expected. End of data can be represented by a single line containing just backslash-period (\\.). An end-of-data marker is not necessary when reading from a file, since the end of file serves perfectly well; it is needed only when copying data to or from client applications using pre-3.0 client protocol. Backslash characters (\\) can be used in the COPY data to quote data characters that might otherwise be taken as row or column delimiters. In particular, the following characters must be preceded by a backslash if they appear as part of a column value: backslash itself, newline, carriage return, and the current delimiter character. The specified null string is sent by COPY TO without adding any backslashes; conversely, COPY FROM matches the input against the null string before removing backslashes. Therefore, a null string such as \\N cannot be confused with the actual data value \\N (which would be represented as \\\\N). The following special backslash sequences are recognized by COPY FROM: Sequence Represents \\b Backspace (ASCII 8) \\f Form feed (ASCII 12) \\n Newline (ASCII 10) \\r Carriage return (ASCII 13) \\t Tab (ASCII 9) \\v Vertical tab (ASCII 11) \\digits Backslash followed by one to three octal digits specifies the byte with that numeric code \\xdigits Backslash x followed by one or two hex digits specifies the byte with that numeric code Presently, COPY TO will never emit an octal or hex-digits backslash sequence, but it does use the other sequences listed above for those control characters. Any other backslashed character that is not mentioned in the above table will be taken to represent itself. However, beware of adding backslashes unnecessarily, since that might accidentally produce a string matching the end-of-data marker (\\.) or the null string (\\N by default). These strings will be recognized before any other backslash processing is done. It is strongly recommended that applications generating COPY data convert data newlines and carriage returns to the \\n and \\r sequences respectively. At present it is possible to represent a data carriage return by a backslash and carriage return, and to represent a data newline by a backslash and newline. However, these representations might not be accepted in future releases. They are also highly vulnerable to corruption if the COPY file is transferred across different machines (for example, from Unix to Windows or vice versa). All backslash sequences are interpreted after encoding conversion. The bytes specified with the octal and hex-digit backslash sequences must form valid characters in the database encoding. COPY TO will terminate each row with a Unix-style newline (“\\n”). Servers running on Microsoft Windows instead output carriage return/newline (“\\r\\n”), but only for COPY to a server file; for consistency across platforms, COPY TO STDOUT always sends “\\n” regardless of server platform. COPY FROM can handle lines ending with newlines, carriage returns, or carriage return/newlines. To reduce the risk of error due to un-backslashed newlines or carriage returns that were meant as data, COPY FROM will complain if the line endings in the input are not all alike. This format option is used for importing and exporting the Comma Separated Value (CSV) file format used by many other programs, such as spreadsheets. Instead of the escaping rules used by IvorySQL’s standard text format, it produces and recognizes the common CSV escaping mechanism. The values in each record are separated by the DELIMITER character. If the value contains the delimiter character, the QUOTE character, the NULL string, a carriage return, or line feed character, then the whole value is prefixed and suffixed by the QUOTE character, and any occurrence within the value of a QUOTE character or the ESCAPE character is preceded by the escape character. You can also use FORCE_QUOTE to force quotes when outputting non-NULL values in specific columns. The CSV format has no standard way to distinguish a NULL value from an empty string. IvorySQL’s COPY handles this by quoting. A NULL is output as the NULL parameter string and is not quoted, while a non-NULL value matching the NULL parameter string is quoted. For example, with the default settings, a NULL is written as an unquoted empty string, while an empty string data value is written with double quotes (\"\"). Reading values follows similar rules. You can use FORCE_NOT_NULL to prevent NULL input comparisons for specific columns. You can also use FORCE_NULL to convert quoted null string data values to NULL. Because backslash is not a special character in the CSV format, \\., the end-of-data marker, could also appear as a data value. To avoid any misinterpretation, a \\. data value appearing as a lone entry on a line is automatically quoted on output, and on input, if quoted, is not interpreted as the end-of-data marker. If you are loading a file created by another application that has a single unquoted column and might have a value of \\., you might need to quote that value in the input file. Note CSV format, all characters are significant. A quoted value surrounded by white space, or any characters other than DELIMITER, will include those characters. This can cause errors if you import data from a system that pads CSV lines with white space out to some fixed width. If such a situation arises you might need to preprocess the CSV file to remove the trailing white space, before importing the data into IvorySQL. Note CSV format will both recognize and produce CSV files with quoted values containing embedded carriage returns and line feeds. Thus the files are not strictly one line per table row like text-format files. Note Many programs produce strange and occasionally perverse CSV files, so the file format is more a convention than a standard. Thus you might encounter some files that cannot be imported using this mechanism, and COPY might produce files that other programs cannot process. The binary format option causes all data to be stored/read as binary format rather than as text. It is somewhat faster than the text and CSV formats, but a binary-format file is less portable across machine architectures and IvorySQL versions. Also, the binary format is very data type specific; for example it will not work to output binary data from a smallint column and read it into an integer column, even though that would work fine in text format. The binary file format consists of a file header, zero or more tuples containing the row data, and a file trailer. Headers and data are in network byte order. File Header The file header consists of 15 bytes of fixed fields, followed by a variable-length header extension area. The fixed fields are: Signature 11-byte sequence PGCOPY\\n\\377\\r\\n\\0 — note that the zero byte is a required part of the signature. (The signature is designed to allow easy identification of files that have been munged by a non-8-bit-clean transfer. This signature will be changed by end-of-line-translation filters, dropped zero bytes, dropped high bits, or parity changes.) Flags field 32-bit integer bit mask to denote important aspects of the file format. Bits are numbered from 0 (LSB) to 31 (MSB). Note that this field is stored in network byte order (most significant byte first), as are all the integer fields used in the file format. Bits 16–31 are reserved to denote critical file format issues; a reader should abort if it finds an unexpected bit set in this range. Bits 0–15 are reserved to signal backwards-compatible format issues; a reader should simply ignore any unexpected bits set in this range. Currently only one flag bit is defined, and the rest must be zero: Bit 16 If 1, OIDs are included in the data; if 0, not. Oid system columns are not supported in IvorySQL anymore, but the format still contains the indicator. Header extension area length 32-bit integer, length in bytes of remainder of header, not including self. Currently, this is zero, and the first tuple follows immediately. Future changes to the format might allow additional data to be present in the header. A reader should silently skip over any header extension data it does not know what to do with. ​The header extension area is envisioned to contain a sequence of self-identifying chunks. The flags field is not intended to tell readers what is in the extension area. Specific design of header extension contents is left for a later release. This design allows for both backwards-compatible header additions (add header extension chunks, or set low-order flag bits) and non-backwards-compatible changes (set high-order flag bits to signal such changes, and add supporting data to the extension area if needed). Tuples Each tuple begins with a 16-bit integer count of the number of fields in the tuple. (Presently, all tuples in a table will have the same count, but that might not always be true.) Then, repeated for each field in the tuple, there is a 32-bit length word followed by that many bytes of field data. (The length word does not include itself, and can be zero.) As a special case, -1 indicates a NULL field value. No value bytes follow in the NULL case. ​ There is no alignment padding or any other extra data between fields. Presently, all data values in a binary-format file are assumed to be in binary format (format code one). It is anticipated that a future extension might add a header field that allows per-column format codes to be specified. To determine the appropriate binary format for the actual tuple data you should consult the PostgreSQL source, in particular the *send and *recv functions for each column’s data type (typically these functions are found in the src/backend/utils/adt/ directory of the source distribution). If OIDs are included in the file, the OID field immediately follows the field-count word. It is a normal field except that it’s not included in the field-count. Note that oid system columns are not supported in current versions of IvorySQL. The file trailer consists of a 16-bit integer word containing -1. This is easily distinguished from a tuple’s field-count word. ​A reader should report an error if a field-count word is neither -1 nor the expected number of columns. This provides an extra check against somehow getting out of sync with the data. The following example copies a table to the client using the vertical bar (|) as the field delimiter: COPY country TO STDOUT (DELIMITER '|'); ​ To copy data from a file into the country table: COPY country TO STDOUT (DELIMITER '|'); To copy into a file just the countries whose names start with 'A': COPY (SELECT * FROM country WHERE country_name LIKE 'A%') TO '/usr1/proj/bray/sql/a_list_countries.copy'; ​ To copy into a compressed file, you can pipe the output through an external compression program: COPY country TO PROGRAM 'gzip > /usr1/proj/bray/sql/country_data.gz'; ​ Here is a sample of data suitable for copying into a table from STDIN: AF AFGHANISTAN AL ALBANIA DZ ALGERIA ZM ZAMBIA ZW ZIMBABWE Note that the white space on each line is actually a tab character. ​ The following is the same data, output in binary format. The data is shown after filtering through the Unix utility od -c. The table has three columns; the first has type char(2), the second has type text, and the third has type integer. All the rows have a null value in the third column. 0000000 P G C O P Y \\n 377 \\r \\n \\0 \\0 \\0 \\0 \\0 \\0 0000020 \\0 \\0 \\0 \\0 003 \\0 \\0 \\0 002 A F \\0 \\0 \\0 013 A 0000040 F G H A N I S T A N 377 377 377 377 \\0 003 0000060 \\0 \\0 \\0 002 A L \\0 \\0 \\0 007 A L B A N I 0000100 A 377 377 377 377 \\0 003 \\0 \\0 \\0 002 D Z \\0 \\0 \\0 0000120 007 A L G E R I A 377 377 377 377 \\0 003 \\0 \\0 0000140 \\0 002 Z M \\0 \\0 \\0 006 Z A M B I A 377 377 0000160 377 377 \\0 003 \\0 \\0 \\0 002 Z W \\0 \\0 \\0 \\b Z I 0000200 M B A B W E 377 377 377 377 377 377 The remaining details can see doc. Query performance can be affected by a variety of factors. Some of these factors can be controlled by the user, while others are fundamentals of the system’s lower-level design. IvorySQL devises a query plan for each query it receives. Choosing the right plan to match the query structure and the properties of the data is absolutely critical for good performance, so the system includes a complex planner that tries to choose good plans. You can use the EXPLAIN command to see what query plan the planner creates for any query. Plan-reading is an art that requires some experience to master, but this section attempts to cover the basics. ​ The examples use `EXPLAIN’s default “text” output format, which is compact and convenient for humans to read. If you want to feed `EXPLAIN’s output to a program for further analysis, you should use one of its machine-readable output formats (XML, JSON, or YAML) instead. The structure of a query plan is a tree of plan nodes. Nodes at the bottom level of the tree are scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. There are also non-table row sources, such as VALUES clauses and set-returning functions in FROM, which have their own scan node types. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes above the scan nodes to perform these operations. Again, there is usually more than one possible way to do these operations, so different node types can appear here too. The output of EXPLAIN has one line for each node in the plan tree, showing the basic node type plus the cost estimates that the planner made for the execution of that plan node. Additional lines might appear, indented from the node’s summary line, to show additional properties of the node. The very first line (the summary line for the topmost node) has the estimated total execution cost for the plan; it is this number that the planner seeks to minimize. Here is a trivial example, just to show what the output looks like: EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) Since this query has no WHERE clause, it must scan all the rows of the table, so the planner has chosen to use a simple sequential scan plan. The numbers that are quoted in parentheses are (left to right): Estimated start-up cost. This is the time expended before the output phase can begin, e.g., time to do the sorting in a sort node. Estimated total cost. This is stated on the assumption that the plan node is run to completion, i.e., all available rows are retrieved. In practice a node’s parent node might stop short of reading all available rows (see the LIMIT example below). Estimated number of rows output by this plan node. Again, the node is assumed to be run to completion. Estimated average width of rows output by this plan node (in bytes). ​ The costs are measured in arbitrary units determined by the planner’s cost parameters .Traditional practice is to measure the costs in units of disk page fetches; that is, seq_page_cost is conventionally set to 1.0 and the other cost parameters are set relative to that. The examples in this section are run with the default cost parameters. ​ It’s important to understand that the cost of an upper-level node includes the cost of all its child nodes. It’s also important to realize that the cost only reflects things that the planner cares about. In particular, the cost does not consider the time spent transmitting result rows to the client, which could be an important factor in the real elapsed time; but the planner ignores it because it cannot change it by altering the plan. (Every correct plan will output the same row set, we trust.) The rows value is a little tricky because it is not the number of rows processed or scanned by the plan node, but rather the number emitted by the node. This is often less than the number scanned, as a result of filtering by any WHERE-clause conditions that are being applied at the node. Ideally the top-level rows estimate will approximate the number of rows actually returned, updated, or deleted by the query. Returning to our example: EXPLAIN SELECT * FROM tenk1; QUERY PLAN ------------------------------------------------------------- Seq Scan on tenk1 (cost=0.00..458.00 rows=10000 width=244) ​ These numbers are derived very straightforwardly. If you do: SELECT relpages, reltuples FROM pg_class WHERE relname = 'tenk1'; ​ you will find that tenk1 has 358 disk pages and 10000 rows. The estimated cost is computed as (disk pages read * seq_page_cost) + (rows scanned * cpu_tuple_cost). By default, seq_page_cost is 1.0 and cpu_tuple_cost is 0.01, so the estimated cost is (358 * 1.0) + (10000 * 0.01) = 458. Now let’s modify the query to add a WHERE condition: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 7000; QUERY PLAN ------------------------------------------------------------ Seq Scan on tenk1 (cost=0.00..483.00 rows=7001 width=244) Filter: (unique1 < 7000) ​ Notice that the EXPLAIN output shows the WHERE clause being applied as a “filter” condition attached to the Seq Scan plan node. This means that the plan node checks the condition for each row it scans, and outputs only the ones that pass the condition. The estimate of output rows has been reduced because of the WHERE clause. However, the scan will still have to visit all 10000 rows, so the cost hasn’t decreased; in fact it has gone up a bit (by 10000 * cpu_operator_cost, to be exact) to reflect the extra CPU time spent checking the WHERE condition. The actual number of rows this query would select is 7000, but the rows estimate is only approximate. If you try to duplicate this experiment, you will probably get a slightly different estimate; moreover, it can change after each ANALYZE command, because the statistics produced by ANALYZE are taken from a randomized sample of the table. Now, let’s make the condition more restrictive: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) ​ Here the planner has decided to use a two-step plan: the child plan node visits an index to find the locations of rows matching the index condition, and then the upper plan node actually fetches those rows from the table itself. Fetching rows separately is much more expensive than reading them sequentially, but because not all the pages of the table have to be visited, this is still cheaper than a sequential scan. (The reason for using two plan levels is that the upper plan node sorts the row locations identified by the index into physical order before reading them, to minimize the cost of separate fetches. The “bitmap” mentioned in the node names is the mechanism that does the sorting.) Now let’s add another condition to the WHERE clause: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND stringu1 = 'xxx'; QUERY PLAN -------------------------------------------------------------------​----------- Bitmap Heap Scan on tenk1 (cost=5.04..229.43 rows=1 width=244) Recheck Cond: (unique1 < 100) Filter: (stringu1 = 'xxx'::name) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) ​ The added condition stringu1 = 'xxx' reduces the output row count estimate, but not the cost because we still have to visit the same set of rows. Notice that the stringu1 clause cannot be applied as an index condition, since this index is only on the unique1 column. Instead it is applied as a filter on the rows retrieved by the index. Thus the cost has actually gone up slightly to reflect this extra checking. In some cases the planner will prefer a “simple” index scan plan: EXPLAIN SELECT * FROM tenk1 WHERE unique1 = 42; QUERY PLAN -------------------------------------------------------------------​----------- Index Scan using tenk1_unique1 on tenk1 (cost=0.29..8.30 rows=1 width=244) Index Cond: (unique1 = 42) ​ In this type of plan the table rows are fetched in index order, which makes them even more expensive to read, but there are so few that the extra cost of sorting the row locations is not worth it. You’ll most often see this plan type for queries that fetch just a single row. It’s also often used for queries that have an ORDER BY condition that matches the index order, because then no extra sorting step is needed to satisfy the ORDER BY. In this example, adding ORDER BY unique1 would use the same plan because the index already implicitly provides the requested ordering. The planner may implement an ORDER BY clause in several ways. The above example shows that such an ordering clause may be implemented implicitly. The planner may also add an explicit sort step: EXPLAIN SELECT * FROM tenk1 ORDER BY unique1; QUERY PLAN ------------------------------------------------------------------- Sort (cost=1109.39..1134.39 rows=10000 width=244) Sort Key: unique1 -> Seq Scan on tenk1 (cost=0.00..445.00 rows=10000 width=244) If a part of the plan guarantees an ordering on a prefix of the required sort keys, then the planner may instead decide to use an `incremental sort` step: EXPLAIN SELECT * FROM tenk1 ORDER BY four, ten LIMIT 100; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Limit (cost=521.06..538.05 rows=100 width=244) -> Incremental Sort (cost=521.06..2220.95 rows=10000 width=244) Sort Key: four, ten Presorted Key: four -> Index Scan using index_tenk1_on_four on tenk1 (cost=0.29..1510.08 rows=10000 width=244) ​ Compared to regular sorts, sorting incrementally allows returning tuples before the entire result set has been sorted, which particularly enables optimizations with LIMIT queries. It may also reduce memory usage and the likelihood of spilling sorts to disk, but it comes at the cost of the increased overhead of splitting the result set into multiple sorting batches. If there are separate indexes on several of the columns referenced in WHERE, the planner might choose to use an AND or OR combination of the indexes: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​------------------ Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) Index Cond: (unique2 > 9000) ​ But this requires visiting both indexes, so it’s not necessarily a win compared to using just one index and treating the other condition as a filter. If you vary the ranges involved you’ll see the plan change accordingly. Here is an example showing the effects of LIMIT: EXPLAIN SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------ Limit (cost=0.29..14.48 rows=2 width=244) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..71.27 rows=10 width=244) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) ​ This is the same query as above, but we added a LIMIT so that not all the rows need be retrieved, and the planner changed its mind about what to do. Notice that the total cost and row count of the Index Scan node are shown as if it were run to completion. However, the Limit node is expected to stop after retrieving only a fifth of those rows, so its total cost is only a fifth as much, and that’s the actual estimated cost of the query. This plan is preferred over adding a Limit node to the previous plan because the Limit could not avoid paying the startup cost of the bitmap scan, so the total cost would be something over 25 units with that approach. Let’s try joining two tables, using the columns we have been discussing: EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..118.62 rows=10 width=488) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) Index Cond: (unique2 = t1.unique2) ​ In this plan, we have a nested-loop join node with two table scans as inputs, or children. The indentation of the node summary lines reflects the plan tree structure. The join’s first, or “outer”, child is a bitmap scan similar to those we saw before. Its cost and row count are the same as we’d get from SELECT …​ WHERE unique1 < 10 because we are applying the WHERE clause unique1 < 10 at that node. The t1.unique2 = t2.unique2 clause is not relevant yet, so it doesn’t affect the row count of the outer scan. The nested-loop join node will run its second, or “inner” child once for each row obtained from the outer child. Column values from the current outer row can be plugged into the inner scan; here, the t1.unique2 value from the outer row is available, so we get a plan and costs similar to what we saw above for a simple SELECT …​ WHERE t2.unique2 = `constant` case. (The estimated cost is actually a bit lower than what was seen above, as a result of caching that’s expected to occur during the repeated index scans on t2.) The costs of the loop node are then set on the basis of the cost of the outer scan, plus one repetition of the inner scan for each outer row (10 * 7.91, here), plus a little CPU time for join processing. In this example the join’s output row count is the same as the product of the two scans' row counts, but that’s not true in all cases because there can be additional WHERE clauses that mention both tables and so can only be applied at the join point, not to either input scan. Here’s an example: EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t2.unique2 < 10 AND t1.hundred < t2.hundred; QUERY PLAN -------------------------------------------------------------------​------------------ Nested Loop (cost=4.65..49.46 rows=33 width=488) Join Filter: (t1.hundred < t2.hundred) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) Index Cond: (unique1 < 10) -> Materialize (cost=0.29..8.51 rows=10 width=244) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..8.46 rows=10 width=244) Index Cond: (unique2 < 10) ​ The condition t1.hundred < t2.hundred can’t be tested in the tenk2_unique2 index, so it’s applied at the join node. This reduces the estimated output row count of the join node, but does not change either input scan. Notice that here the planner has chosen to “materialize” the inner relation of the join, by putting a Materialize plan node atop it. This means that the t2 index scan will be done just once, even though the nested-loop join node needs to read that data ten times, once for each row from the outer relation. The Materialize node saves the data in memory as it’s read, and then returns the data from memory on each subsequent pass. When dealing with outer joins, you might see join plan nodes with both “Join Filter” and plain “Filter” conditions attached. Join Filter conditions come from the outer join’s ON clause, so a row that fails the Join Filter condition could still get emitted as a null-extended row. But a plain Filter condition is applied after the outer-join rules and so acts to remove rows unconditionally. In an inner join there is no semantic difference between these types of filters. If we change the query’s selectivity a bit, we might get a very different join plan: EXPLAIN SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Hash Join (cost=230.47..713.98 rows=101 width=488) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) -> Hash (cost=229.20..229.20 rows=101 width=244) -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) Index Cond: (unique1 < 100) Here, the planner has chosen to use a hash join, in which rows of one table are entered into an in-memory hash table, after which the other table is scanned and the hash table is probed for matches to each row. Again note how the indentation reflects the plan structure: the bitmap scan on tenk1 is the input to the Hash node, which constructs the hash table. That’s then returned to the Hash Join node, which reads rows from its outer child plan and searches the hash table for each one. Another possible type of join is a merge join, illustrated here: EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​------------------ Merge Join (cost=198.11..268.19 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Sort (cost=197.83..200.33 rows=1000 width=244) Sort Key: t2.unique2 -> Seq Scan on onek t2 (cost=0.00..148.00 rows=1000 width=244) ​ Merge join requires its input data to be sorted on the join keys. In this plan the tenk1 data is sorted by using an index scan to visit the rows in the correct order, but a sequential scan and sort is preferred for onek, because there are many more rows to be visited in that table. (Sequential-scan-and-sort frequently beats an index scan for sorting many rows, because of the nonsequential disk access required by the index scan.) One way to look at variant plans is to force the planner to disregard whatever strategy it thought was the cheapest, using the enable/disable flags .For example, if we’re unconvinced that sequential-scan-and-sort is the best way to deal with table onek in the previous example, we could try SET enable_sort = off; EXPLAIN SELECT * FROM tenk1 t1, onek t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​----------------------- Merge Join (cost=0.56..292.65 rows=10 width=488) Merge Cond: (t1.unique2 = t2.unique2) -> Index Scan using tenk1_unique2 on tenk1 t1 (cost=0.29..656.28 rows=101 width=244) Filter: (unique1 < 100) -> Index Scan using onek_unique2 on onek t2 (cost=0.28..224.79 rows=1000 width=244) ​ which shows that the planner thinks that sorting onek by index-scanning is about 12% more expensive than sequential-scan-and-sort. Of course, the next question is whether it’s right about that. We can investigate that using EXPLAIN ANALYZE, as discussed below. It is possible to check the accuracy of the planner’s estimates by using EXPLAIN’s `ANALYZE option. With this option, EXPLAIN actually executes the query, and then displays the true row counts and true run time accumulated within each plan node, along with the same estimates that a plain EXPLAIN shows. For example, we might get a result like this: EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 10 AND t1.unique2 = t2.unique2; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Nested Loop (cost=4.65..118.62 rows=10 width=488) (actual time=0.128..0.377 rows=10 loops=1) -> Bitmap Heap Scan on tenk1 t1 (cost=4.36..39.47 rows=10 width=244) (actual time=0.057..0.121 rows=10 loops=1) Recheck Cond: (unique1 < 10) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..4.36 rows=10 width=0) (actual time=0.024..0.024 rows=10 loops=1) Index Cond: (unique1 < 10) -> Index Scan using tenk2_unique2 on tenk2 t2 (cost=0.29..7.91 rows=1 width=244) (actual time=0.021..0.022 rows=1 loops=10) Index Cond: (unique2 = t1.unique2) Planning time: 0.181 ms Execution time: 0.501 ms Note that the “actual time” values are in milliseconds of real time, whereas the cost estimates are expressed in arbitrary units; so they are unlikely to match up. The thing that’s usually most important to look for is whether the estimated row counts are reasonably close to reality. In this example the estimates were all dead-on, but that’s quite unusual in practice. ​ In some query plans, it is possible for a subplan node to be executed more than once. For example, the inner index scan will be executed once per outer row in the above nested-loop plan. In such cases, the loops value reports the total number of executions of the node, and the actual time and rows values shown are averages per-execution. This is done to make the numbers comparable with the way that the cost estimates are shown. Multiply by the loops value to get the total time actually spent in the node. In the above example, we spent a total of 0.220 milliseconds executing the index scans on tenk2. In some cases EXPLAIN ANALYZE shows additional execution statistics beyond the plan node execution times and row counts. For example, Sort and Hash nodes provide extra information: EXPLAIN ANALYZE SELECT * FROM tenk1 t1, tenk2 t2 WHERE t1.unique1 < 100 AND t1.unique2 = t2.unique2 ORDER BY t1.fivethous; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------------​------ Sort (cost=717.34..718.09 rows=101 width=488) (actual time=7.761..7.774 rows=100 loops=1) Sort Key: t1.fivethous Sort Method: quicksort Memory: 77kB -> Hash Join (cost=230.47..713.98 rows=101 width=488) (actual time=0.711..7.427 rows=100 loops=1) Hash Cond: (t2.unique2 = t1.unique2) -> Seq Scan on tenk2 t2 (cost=0.00..445.00 rows=10000 width=244) (actual time=0.007..2.583 rows=10000 loops=1) -> Hash (cost=229.20..229.20 rows=101 width=244) (actual time=0.659..0.659 rows=100 loops=1) Buckets: 1024 Batches: 1 Memory Usage: 28kB -> Bitmap Heap Scan on tenk1 t1 (cost=5.07..229.20 rows=101 width=244) (actual time=0.080..0.526 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.049..0.049 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.194 ms Execution time: 8.008 ms ​ The Sort node shows the sort method used (in particular, whether the sort was in-memory or on-disk) and the amount of memory or disk space needed. The Hash node shows the number of hash buckets and batches as well as the peak amount of memory used for the hash table. (If the number of batches exceeds one, there will also be disk space usage involved, but that is not shown.) Another type of extra information is the number of rows removed by a filter condition: EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE ten < 7; QUERY PLAN -------------------------------------------------------------------​-------------------------------------- Seq Scan on tenk1 (cost=0.00..483.00 rows=7000 width=244) (actual time=0.016..5.107 rows=7000 loops=1) Filter: (ten < 7) Rows Removed by Filter: 3000 Planning time: 0.083 ms Execution time: 5.905 ms These counts can be particularly valuable for filter conditions applied at join nodes. The “Rows Removed” line only appears when at least one scanned row, or potential join pair in the case of a join node, is rejected by the filter condition. A case similar to filter conditions occurs with “lossy” index scans. For example, consider this search for polygons containing a specific point: EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​----------------------------------- Seq Scan on polygon_tbl (cost=0.00..1.05 rows=1 width=32) (actual time=0.044..0.044 rows=0 loops=1) Filter: (f1 @> '((0.5,2))'::polygon) Rows Removed by Filter: 4 Planning time: 0.040 ms Execution time: 0.083 ms The planner thinks (quite correctly) that this sample table is too small to bother with an index scan, so we have a plain sequential scan in which all the rows got rejected by the filter condition. But if we force an index scan to be used, we see: SET enable_seqscan TO off; EXPLAIN ANALYZE SELECT * FROM polygon_tbl WHERE f1 @> polygon '(0.5,2.0)'; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------- Index Scan using gpolygonind on polygon_tbl (cost=0.13..8.15 rows=1 width=32) (actual time=0.062..0.062 rows=0 loops=1) Index Cond: (f1 @> '((0.5,2))'::polygon) Rows Removed by Index Recheck: 1 Planning time: 0.034 ms Execution time: 0.144 ms ​ Here we can see that the index returned one candidate row, which was then rejected by a recheck of the index condition. This happens because a GiST index is “lossy” for polygon containment tests: it actually returns the rows with polygons that overlap the target, and then we have to do the exact containment test on those rows. EXPLAIN has a BUFFERS option that can be used with ANALYZE to get even more run time statistics: EXPLAIN (ANALYZE, BUFFERS) SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000; QUERY PLAN -------------------------------------------------------------------​-------------------------------------------------------------- Bitmap Heap Scan on tenk1 (cost=25.08..60.21 rows=10 width=244) (actual time=0.323..0.342 rows=10 loops=1) Recheck Cond: ((unique1 < 100) AND (unique2 > 9000)) Buffers: shared hit=15 -> BitmapAnd (cost=25.08..25.08 rows=10 width=0) (actual time=0.309..0.309 rows=0 loops=1) Buffers: shared hit=7 -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Buffers: shared hit=2 -> Bitmap Index Scan on tenk1_unique2 (cost=0.00..19.78 rows=999 width=0) (actual time=0.227..0.227 rows=999 loops=1) Index Cond: (unique2 > 9000) Buffers: shared hit=5 Planning time: 0.088 ms Execution time: 0.423 ms ​ The numbers provided by BUFFERS help to identify which parts of the query are the most I/O-intensive. Keep in mind that because EXPLAIN ANALYZE actually runs the query, any side-effects will happen as usual, even though whatever results the query might output are discarded in favor of printing the EXPLAIN data. If you want to analyze a data-modifying query without changing your tables, you can roll the command back afterwards, for example: BEGIN; EXPLAIN ANALYZE UPDATE tenk1 SET hundred = hundred + 1 WHERE unique1 < 100; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------- Update on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=14.628..14.628 rows=0 loops=1) -> Bitmap Heap Scan on tenk1 (cost=5.07..229.46 rows=101 width=250) (actual time=0.101..0.439 rows=100 loops=1) Recheck Cond: (unique1 < 100) -> Bitmap Index Scan on tenk1_unique1 (cost=0.00..5.04 rows=101 width=0) (actual time=0.043..0.043 rows=100 loops=1) Index Cond: (unique1 < 100) Planning time: 0.079 ms Execution time: 14.727 ms ROLLBACK; ​ As seen in this example, when the query is an INSERT, UPDATE, or DELETE command, the actual work of applying the table changes is done by a top-level Insert, Update, or Delete plan node. The plan nodes underneath this node perform the work of locating the old rows and/or computing the new data. So above, we see the same sort of bitmap table scan we’ve seen already, and its output is fed to an Update node that stores the updated rows. It’s worth noting that although the data-modifying node can take a considerable amount of run time (here, it’s consuming the lion’s share of the time), the planner does not currently add anything to the cost estimates to account for that work. That’s because the work to be done is the same for every correct query plan, so it doesn’t affect planning decisions. When an UPDATE or DELETE command affects an inheritance hierarchy, the output might look like this: EXPLAIN UPDATE parent SET f2 = f2 + 1 WHERE f1 = 101; QUERY PLAN -------------------------------------------------------------------​---------------- Update on parent (cost=0.00..24.53 rows=4 width=14) Update on parent Update on child1 Update on child2 Update on child3 -> Seq Scan on parent (cost=0.00..0.00 rows=1 width=14) Filter: (f1 = 101) -> Index Scan using child1_f1_key on child1 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child2_f1_key on child2 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) -> Index Scan using child3_f1_key on child3 (cost=0.15..8.17 rows=1 width=14) Index Cond: (f1 = 101) ​ In this example the Update node needs to consider three child tables as well as the originally-mentioned parent table. So there are four input scanning subplans, one per table. For clarity, the Update node is annotated to show the specific target tables that will be updated, in the same order as the corresponding subplans. The Planning time shown by EXPLAIN ANALYZE is the time it took to generate the query plan from the parsed query and optimize it. It does not include parsing or rewriting. The Execution time shown by EXPLAIN ANALYZE includes executor start-up and shut-down time, as well as the time to run any triggers that are fired, but it does not include parsing, rewriting, or planning time. Time spent executing BEFORE triggers, if any, is included in the time for the related Insert, Update, or Delete node; but time spent executing AFTER triggers is not counted there because AFTER triggers are fired after completion of the whole plan. The total time spent in each trigger (either BEFORE or AFTER) is also shown separately. Note that deferred constraint triggers will not be executed until end of transaction and are thus not considered at all by EXPLAIN ANALYZE. There are two significant ways in which run times measured by EXPLAIN ANALYZE can deviate from normal execution of the same query. First, since no output rows are delivered to the client, network transmission costs and I/O conversion costs are not included. Second, the measurement overhead added by EXPLAIN ANALYZE can be significant, especially on machines with slow gettimeofday() operating-system calls. You can use the pg_test_timing tool to measure the overhead of timing on your system. EXPLAIN results should not be extrapolated to situations much different from the one you are actually testing; for example, results on a toy-sized table cannot be assumed to apply to large tables. The planner’s cost estimates are not linear and so it might choose a different plan for a larger or smaller table. An extreme example is that on a table that only occupies one disk page, you’ll nearly always get a sequential scan plan whether indexes are available or not. The planner realizes that it’s going to take one disk page read to process the table in any case, so there’s no value in expending additional page reads to look at an index. (We saw this happening in the polygon_tbl example above.) There are cases in which the actual and estimated values won’t match up well, but nothing is really wrong. One such case occurs when plan node execution is stopped short by a LIMIT or similar effect. For example, in the LIMIT query we used before, EXPLAIN ANALYZE SELECT * FROM tenk1 WHERE unique1 < 100 AND unique2 > 9000 LIMIT 2; QUERY PLAN -------------------------------------------------------------------​------------------------------------------------------------ Limit (cost=0.29..14.71 rows=2 width=244) (actual time=0.177..0.249 rows=2 loops=1) -> Index Scan using tenk1_unique2 on tenk1 (cost=0.29..72.42 rows=10 width=244) (actual time=0.174..0.244 rows=2 loops=1) Index Cond: (unique2 > 9000) Filter: (unique1 < 100) Rows Removed by Filter: 287 Planning time: 0.096 ms Execution time: 0.336 ms ​ the estimated cost and row count for the Index Scan node are shown as though it were run to completion. But in reality the Limit node stopped requesting rows after it got two, so the actual row count is only 2 and the run time is less than the cost estimate would suggest. This is not an estimation error, only a discrepancy in the way the estimates and true values are displayed. Merge joins also have measurement artifacts that can confuse the unwary. A merge join will stop reading one input if it’s exhausted the other input and the next key value in the one input is greater than the last key value of the other input; in such a case there can be no more matches and so no need to scan the rest of the first input. This results in not reading all of one child, with results like those mentioned for LIMIT. Also, if the outer (first) child contains rows with duplicate key values, the inner (second) child is backed up and rescanned for the portion of its rows matching that key value. EXPLAIN ANALYZE counts these repeated emissions of the same inner rows as if they were real additional rows. When there are many outer duplicates, the reported actual row count for the inner child plan node can be significantly larger than the number of rows that are actually in the inner relation. BitmapAnd and BitmapOr nodes always report their actual row counts as zero, due to implementation limitations. Normally, EXPLAIN will display every plan node created by the planner. However, there are cases where the executor can determine that certain nodes need not be executed because they cannot produce any rows, based on parameter values that were not available at planning time. (Currently this can only happen for child nodes of an Append or MergeAppend node that is scanning a partitioned table.) When this happens, those plan nodes are omitted from the EXPLAIN output and a Subplans Removed: `N` annotation appears instead. As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates. One component of the statistics is the total number of entries in each table and index, as well as the number of disk blocks occupied by each table and index. This information is kept in the table pg_class, in the columns reltuples and relpages. We can look at it with queries similar to this one: SELECT relname, relkind, reltuples, relpages FROM pg_class WHERE relname LIKE 'tenk1%'; relname | relkind | reltuples | relpages ----------------------+---------+-----------+---------- tenk1 | r | 10000 | 358 tenk1_hundred | i | 10000 | 30 tenk1_thous_tenthous | i | 10000 | 30 tenk1_unique1 | i | 10000 | 30 tenk1_unique2 | i | 10000 | 30 (5 rows) ​ Here we can see that tenk1 contains 10000 rows, as do its indexes, but the indexes are (unsurprisingly) much smaller than the table. For efficiency reasons, reltuples and relpages are not updated on-the-fly, and so they usually contain somewhat out-of-date values. They are updated by VACUUM, ANALYZE, and a few DDL commands such as CREATE INDEX. A VACUUM or ANALYZE operation that does not scan the entire table (which is commonly the case) will incrementally update the reltuples count on the basis of the part of the table it did scan, resulting in an approximate value. In any case, the planner will scale the values it finds in pg_class to match the current physical table size, thus obtaining a closer approximation. Most queries retrieve only a fraction of the rows in a table, due to WHERE clauses that restrict the rows to be examined. The planner thus needs to make an estimate of the selectivity of WHERE clauses, that is, the fraction of rows that match each condition in the WHERE clause. The information used for this task is stored in the pg_statistic system catalog. Entries in pg_statistic are updated by the ANALYZE and VACUUM ANALYZE commands, and are always approximate even when freshly updated. Rather than look at pg_statistic directly, it’s better to look at its view pg_stats when examining the statistics manually. pg_stats is designed to be more easily readable. Furthermore, pg_stats is readable by all, whereas pg_statistic is only readable by a superuser. (This prevents unprivileged users from learning something about the contents of other people’s tables from the statistics. The pg_stats view is restricted to show only rows about tables that the current user can read.) For example, we might do: SELECT attname, inherited, n_distinct, array_to_string(most_common_vals, E'\\n') as most_common_vals FROM pg_stats WHERE tablename = 'road'; attname | inherited | n_distinct | most_common_vals ---------+-----------+------------+------------------------------------ name | f | -0.363388 | I- 580 Ramp+ | | | I- 880 Ramp+ | | | Sp Railroad + | | | I- 580 + | | | I- 680 Ramp name | t | -0.284859 | I- 880 Ramp+ | | | I- 580 Ramp+ | | | I- 680 Ramp+ | | | I- 580 + | | | State Hwy 13 Ramp (2 rows) Note that two rows are displayed for the same column, one corresponding to the complete inheritance hierarchy starting at the road table (inherited=t), and another one including only the road table itself (inherited=f). The amount of information stored in pg_statistic by ANALYZE, in particular the maximum number of entries in the most_common_vals and histogram_bounds arrays for each column, can be set on a column-by-column basis using the ALTER TABLE SET STATISTICS command, or globally by setting the default_statistics_target configuration variable. The default limit is presently 100 entries. Raising the limit might allow more accurate planner estimates to be made, particularly for columns with irregular data distributions, at the price of consuming more space in pg_statistic and slightly more time to compute the estimates. Conversely, a lower limit might be sufficient for columns with simple data distributions. Further details about the planner’s use of statistics can be found in doc. It is common to see slow queries running bad execution plans because multiple columns used in the query clauses are correlated. The planner normally assumes that multiple conditions are independent of each other, an assumption that does not hold when column values are correlated. Regular statistics, because of their per-individual-column nature, cannot capture any knowledge about cross-column correlation. However, IvorySQL has the ability to compute multivariate statistics, which can capture such information. Because the number of possible column combinations is very large, it’s impractical to compute multivariate statistics automatically. Instead, extended statistics objects, more often called just statistics objects, can be created to instruct the server to obtain statistics across interesting sets of columns. Statistics objects are created using the CREATE STATISTICS command. Creation of such an object merely creates a catalog entry expressing interest in the statistics. Actual data collection is performed by ANALYZE (either a manual command, or background auto-analyze). The collected values can be examined in the pg_statistic_ext_data catalog. ANALYZE computes extended statistics based on the same sample of table rows that it takes for computing regular single-column statistics. Since the sample size is increased by increasing the statistics target for the table or any of its columns (as described in the previous section), a larger statistics target will normally result in more accurate extended statistics, as well as more time spent calculating them. The following subsections describe the kinds of extended statistics that are currently supported. The simplest kind of extended statistics tracks functional dependencies, a concept used in definitions of database normal forms. We say that column b is functionally dependent on column a if knowledge of the value of a is sufficient to determine the value of b, that is there are no two rows having the same value of a but different values of b. In a fully normalized database, functional dependencies should exist only on primary keys and superkeys. However, in practice many data sets are not fully normalized for various reasons; intentional denormalization for performance reasons is a common example. Even in a fully normalized database, there may be partial correlation between some columns, which can be expressed as partial functional dependency. The existence of functional dependencies directly affects the accuracy of estimates in certain queries. If a query contains conditions on both the independent and the dependent column(s), the conditions on the dependent columns do not further reduce the result size; but without knowledge of the functional dependency, the query planner will assume that the conditions are independent, resulting in underestimating the result size. To inform the planner about functional dependencies, ANALYZE can collect measurements of cross-column dependency. Assessing the degree of dependency between all sets of columns would be prohibitively expensive, so data collection is limited to those groups of columns appearing together in a statistics object defined with the dependencies option. It is advisable to create dependencies statistics only for column groups that are strongly correlated, to avoid unnecessary overhead in both ANALYZE and later query planning. Here is an example of collecting functional-dependency statistics: CREATE STATISTICS stts (dependencies) ON city, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxname, stxkeys, stxddependencies FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts'; stxname | stxkeys | stxddependencies ---------+---------+------------------------------------------ stts | 1 5 | {\"1 => 5\": 1.000000, \"5 => 1\": 0.423130} (1 row) ​ Here it can be seen that column 1 (zip code) fully determines column 5 (city) so the coefficient is 1.0, while city only determines zip code about 42% of the time, meaning that there are many cities (58%) that are represented by more than a single ZIP code. When computing the selectivity for a query involving functionally dependent columns, the planner adjusts the per-condition selectivity estimates using the dependency coefficients so as not to produce an underestimate. Functional dependencies are currently only applied when considering simple equality conditions that compare columns to constant values, and IN clauses with constant values. They are not used to improve estimates for equality conditions comparing two columns or comparing a column to an expression, nor for range clauses, LIKE or any other type of condition. When estimating with functional dependencies, the planner assumes that conditions on the involved columns are compatible and hence redundant. If they are incompatible, the correct estimate would be zero rows, but that possibility is not considered. For example, given a query like SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '94105'; ​ the planner will disregard the city clause as not changing the selectivity, which is correct. However, it will make the same assumption about SELECT * FROM zipcodes WHERE city = 'San Francisco' AND zip = '90210'; ​ even though there will really be zero rows satisfying this query. Functional dependency statistics do not provide enough information to conclude that, however. In many practical situations, this assumption is usually satisfied; for example, there might be a GUI in the application that only allows selecting compatible city and ZIP code values to use in a query. But if that’s not the case, functional dependencies may not be a viable option. Single-column statistics store the number of distinct values in each column. Estimates of the number of distinct values when combining more than one column (for example, for GROUP BY a, b) are frequently wrong when the planner only has single-column statistical data, causing it to select bad plans. To improve such estimates, ANALYZE can collect n-distinct statistics for groups of columns. As before, it’s impractical to do this for every possible column grouping, so data is collected only for those groups of columns appearing together in a statistics object defined with the ndistinct option. Data will be collected for each possible combination of two or more columns from the set of listed columns. Continuing the previous example, the n-distinct counts in a table of ZIP codes might look like the following: CREATE STATISTICS stts2 (ndistinct) ON city, state, zip FROM zipcodes; ANALYZE zipcodes; SELECT stxkeys AS k, stxdndistinct AS nd FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid) WHERE stxname = 'stts2'; -[ RECORD 1 ]------------------------------------------------------​-- k | 1 2 5 nd | {\"1, 2\": 33178, \"1, 5\": 33178, \"2, 5\": 27435, \"1, 2, 5\": 33178} (1 row) ​ This indicates that there are three combinations of columns that have 33178 distinct values: ZIP code and state; ZIP code and city; and ZIP code, city and state (the fact that they are all equal is expected given that ZIP code alone is unique in this table). On the other hand, the combination of city and state has only 27435 distinct values. It’s advisable to create ndistinct statistics objects only on combinations of columns that are actually used for grouping, and for which misestimation of the number of groups is resulting in bad plans. Otherwise, the ANALYZE cycles are just wasted. Another type of statistic stored for each column are most-common value lists. This allows very accurate estimates for individual columns, but may result in significant misestimates for queries with conditions on multiple columns. To improve such estimates, ANALYZE can collect MCV lists on combinations of columns. Similarly to functional dependencies and n-distinct coefficients, it’s impractical to do this for every possible column grouping. Even more so in this case, as the MCV list (unlike functional dependencies and n-distinct coefficients) does store the common column values. So data is collected only for those groups of columns appearing together in a statistics object defined with the mcv option. Continuing the previous example, the MCV list for a table of ZIP codes might look like the following (unlike for simpler types of statistics, a function is required for inspection of MCV contents): CREATE STATISTICS stts3 (mcv) ON city, state FROM zipcodes; ANALYZE zipcodes; SELECT m.* FROM pg_statistic_ext join pg_statistic_ext_data on (oid = stxoid), pg_mcv_list_items(stxdmcv) m WHERE stxname = 'stts3'; index | values | nulls | frequency | base_frequency -------+------------------------+-------+-----------+---------------- 0 | {Washington, DC} | {f,f} | 0.003467 | 2.7e-05 1 | {Apo, AE} | {f,f} | 0.003067 | 1.9e-05 2 | {Houston, TX} | {f,f} | 0.002167 | 0.000133 3 | {El Paso, TX} | {f,f} | 0.002 | 0.000113 4 | {New York, NY} | {f,f} | 0.001967 | 0.000114 5 | {Atlanta, GA} | {f,f} | 0.001633 | 3.3e-05 6 | {Sacramento, CA} | {f,f} | 0.001433 | 7.8e-05 7 | {Miami, FL} | {f,f} | 0.0014 | 6e-05 8 | {Dallas, TX} | {f,f} | 0.001367 | 8.8e-05 9 | {Chicago, IL} | {f,f} | 0.001333 | 5.1e-05 ... (99 rows) ​ This indicates that the most common combination of city and state is Washington in DC, with actual frequency (in the sample) about 0.35%. The base frequency of the combination (as computed from the simple per-column frequencies) is only 0.0027%, resulting in two orders of magnitude under-estimates. It’s advisable to create MCV statistics objects only on combinations of columns that are actually used in conditions together, and for which misestimation of the number of groups is resulting in bad plans. Otherwise, the ANALYZE and planning cycles are just wasted. It is possible to control the query planner to some extent by using the explicit JOIN syntax. To see why this matters, we first need some background. In a simple join query, such as: SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; ​ the planner is free to join the given tables in any order. For example, it could generate a query plan that joins A to B, using the WHERE condition a.id = b.id, and then joins C to this joined table, using the other WHERE condition. Or it could join B to C and then join A to that result. Or it could join A to C and then join them with B — but that would be inefficient, since the full Cartesian product of A and C would have to be formed, there being no applicable condition in the WHERE clause to allow optimization of the join. (All joins in the IvorySQL executor happen between two input tables, so it’s necessary to build up the result in one or another of these fashions.) The important point is that these different join possibilities give semantically equivalent results but might have hugely different execution costs. Therefore, the planner will explore all of them to try to find the most efficient query plan. When a query only involves two or three tables, there aren’t many join orders to worry about. But the number of possible join orders grows exponentially as the number of tables expands. Beyond ten or so input tables it’s no longer practical to do an exhaustive search of all the possibilities, and even for six or seven tables planning might take an annoyingly long time. When there are too many input tables, the IvorySQL planner will switch from exhaustive search to a genetic probabilistic search through a limited number of possibilities. (The switch-over threshold is set by the geqo_threshold run-time parameter.) The genetic search takes less time, but it won’t necessarily find the best possible plan. When the query involves outer joins, the planner has less freedom than it does for plain (inner) joins. For example, consider: SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); ​ Although this query’s restrictions are superficially similar to the previous example, the semantics are different because a row must be emitted for each row of A that has no matching row in the join of B and C. Therefore the planner has no choice of join order here: it must join B to C and then join A to that result. Accordingly, this query takes less time to plan than the previous query. In other cases, the planner might be able to determine that more than one join order is safe. For example, given: SELECT * FROM a LEFT JOIN b ON (a.bid = b.id) LEFT JOIN c ON (a.cid = c.id); it is valid to join A to either B or C first. Currently, only FULL JOIN completely constrains the join order. Most practical cases involving LEFT JOIN or RIGHT JOIN can be rearranged to some extent. Explicit inner join syntax (INNER JOIN, CROSS JOIN, or unadorned JOIN) is semantically the same as listing the input relations in FROM, so it does not constrain the join order. Even though most kinds of JOIN don’t completely constrain the join order, it is possible to instruct the IvorySQL query planner to treat all JOIN clauses as constraining the join order anyway. For example, these three queries are logically equivalent: SELECT * FROM a, b, c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id; SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id); ​ But if we tell the planner to honor the JOIN order, the second and third take less time to plan than the first. This effect is not worth worrying about for only three tables, but it can be a lifesaver with many tables. To force the planner to follow the join order laid out by explicit `JOIN`s, set the join_collapse_limit run-time parameter to 1. (Other possible values are discussed below.) You do not need to constrain the join order completely in order to cut search time, because it’s OK to use JOIN operators within items of a plain FROM list. For example, consider: SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...; ​ With join_collapse_limit = 1, this forces the planner to join A to B before joining them to other tables, but doesn’t constrain its choices otherwise. In this example, the number of possible join orders is reduced by a factor of 5. Constraining the planner’s search in this way is a useful technique both for reducing planning time and for directing the planner to a good query plan. If the planner chooses a bad join order by default, you can force it to choose a better order via JOIN syntax — assuming that you know of a better order, that is. Experimentation is recommended. A closely related issue that affects planning time is collapsing of subqueries into their parent query. For example, consider: SELECT * FROM x, y, (SELECT * FROM a, b, c WHERE something) AS ss WHERE somethingelse; This situation might arise from use of a view that contains a join; the view’s SELECT rule will be inserted in place of the view reference, yielding a query much like the above. Normally, the planner will try to collapse the subquery into the parent, yielding: SELECT * FROM x, y, a, b, c WHERE something AND somethingelse; ​ This usually results in a better plan than planning the subquery separately. (For example, the outer WHERE conditions might be such that joining X to A first eliminates many rows of A, thus avoiding the need to form the full logical output of the subquery.) But at the same time, we have increased the planning time; here, we have a five-way join problem replacing two separate three-way join problems. Because of the exponential growth of the number of possibilities, this makes a big difference. The planner tries to avoid getting stuck in huge join search problems by not collapsing a subquery if more than from_collapse_limit FROM items would result in the parent query. You can trade off planning time against quality of plan by adjusting this run-time parameter up or down. from_collapse_limit and join_collapse_limit are similarly named because they do almost the same thing: one controls when the planner will “flatten out” subqueries, and the other controls when it will flatten out explicit joins. Typically you would either set join_collapse_limit equal to from_collapse_limit (so that explicit joins and subqueries act similarly) or set join_collapse_limit to 1 (if you want to control join order with explicit joins). But you might set them differently if you are trying to fine-tune the trade-off between planning time and run time. One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible. When using multiple INSERT`s, turn off autocommit and just do one commit at the end. (In plain SQL, this means issuing `BEGIN at the start and COMMIT at the end. Some client libraries might do this behind your back, in which case you need to make sure the library does it when you want it done.) If you allow each insertion to be committed separately, IvorySQL is doing a lot of work for each row that is added. An additional benefit of doing all insertions in one transaction is that if the insertion of one row were to fail then the insertion of all rows inserted up to that point would be rolled back, so you won’t be stuck with partially loaded data. Use COPY to load all the rows in one command, instead of using a series of INSERT commands. The COPY command is optimized for loading large numbers of rows; it is less flexible than INSERT, but incurs significantly less overhead for large data loads. Since COPY is a single command, there is no need to disable autocommit if you use this method to populate a table. If you cannot use COPY, it might help to use PREPARE to create a prepared INSERT statement, and then use EXECUTE as many times as required. This avoids some of the overhead of repeatedly parsing and planning INSERT. Different interfaces provide this facility in different ways; look for “prepared statements” in the interface documentation. Note that loading a large number of rows using COPY is almost always faster than using INSERT, even if PREPARE is used and multiple insertions are batched into a single transaction. COPY is fastest when used within the same transaction as an earlier CREATE TABLE or TRUNCATE command. In such cases no WAL needs to be written, because in case of an error, the files containing the newly loaded data will be removed anyway. However, this consideration only applies when wal_level is minimal as all commands must write WAL otherwise. If you are loading a freshly created table, the fastest method is to create the table, bulk load the table’s data using COPY, then create any indexes needed for the table. Creating an index on pre-existing data is quicker than updating it incrementally as each row is loaded. If you are adding large amounts of data to an existing table, it might be a win to drop the indexes, load the table, and then recreate the indexes. Of course, the database performance for other users might suffer during the time the indexes are missing. One should also think twice before dropping a unique index, since the error checking afforded by the unique constraint will be lost while the index is missing. Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing. What’s more, when you load data into a table with existing foreign key constraints, each new row requires an entry in the server’s list of pending trigger events (since it is the firing of a trigger that checks the row’s foreign key constraint). Loading many millions of rows can cause the trigger event queue to overflow available memory, leading to intolerable swapping or even outright failure of the command. Therefore it may be necessary, not just desirable, to drop and re-apply foreign keys when loading large amounts of data. If temporarily removing the constraint isn’t accept. Temporarily increasing the maintenance_work_mem configuration variable when loading large amounts of data can lead to improved performance. This will help to speed up CREATE INDEX commands and ALTER TABLE ADD FOREIGN KEY commands. It won’t do much for COPY itself, so this advice is only useful when you are using one or both of the above techniques. Temporarily increasing the max_wal_size configuration variable can also make large data loads faster. This is because loading a large amount of data into IvorySQL will cause checkpoints to occur more often than the normal checkpoint frequency (specified by the checkpoint_timeout configuration variable). Whenever a checkpoint occurs, all dirty pages must be flushed to disk. By increasing max_wal_size temporarily during bulk data loads, the number of checkpoints that are required can be reduced. When loading large amounts of data into an installation that uses WAL archiving or streaming replication, it might be faster to take a new base backup after the load has completed than to process a large amount of incremental WAL data. To prevent incremental WAL logging while loading, disable archiving and streaming replication, by setting wal_level to minimal, archive_mode to off, and max_wal_senders to zero. But note that changing these settings requires a server restart, and makes any base backups taken before unavailable for archive recovery and standby server, which may lead to data loss. Aside from avoiding the time for the archiver or WAL sender to process the WAL data, doing this will actually make certain commands faster, because they do not to write WAL at all if wal_level is minimal and the current subtransaction (or top-level transaction) created or truncated the table or index they change. (They can guarantee crash safety more cheaply by doing an fsync at the end than by writing WAL.) Whenever you have significantly altered the distribution of data within a table, running ANALYZE is strongly recommended. This includes bulk loading large amounts of data into the table. Running ANALYZE (or VACUUM ANALYZE) ensures that the planner has up-to-date statistics about the table. With no statistics or obsolete statistics, the planner might make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics. Note that if the autovacuum daemon is enabled, it might run ANALYZE automatically. Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To restore a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while restoring a dump, not while creating it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.) By default, pg_dump uses COPY, and when it is generating a complete schema-and-data dump, it is careful to load data before creating indexes and foreign keys. So in this case several guidelines are handled automatically. What is left for you to do is to: Set appropriate (i.e., larger than normal) values for maintenance_work_mem and max_wal_size. If using WAL archiving or streaming replication, consider disabling them during the restore. To do that, set archive_mode to off, wal_level to minimal, and max_wal_senders to zero before loading the dump. Afterwards, set them back to the right values and take a fresh base backup. Experiment with the parallel dump and restore modes of both pg_dump and pg_restore and find the optimal number of concurrent jobs to use. Dumping and restoring in parallel by means of the -j option should give you a significantly higher performance over the serial mode. Consider whether the whole dump should be restored as a single transaction. To do that, pass the -1 or --single-transaction command-line option to psql or pg_restore. When using this mode, even the smallest of errors will rollback the entire restore, possibly discarding many hours of processing. Depending on how interrelated the data is, that might seem preferable to manual cleanup, or not. COPY commands will run fastest if you use a single transaction and have WAL archiving turned off. If multiple CPUs are available in the database server, consider using pg_restore’s --jobs option. This allows concurrent data loading and index creation. Run ANALYZE afterwards. Durability is a database feature that guarantees the recording of committed transactions even if the server crashes or loses power. However, durability adds significant database overhead, so if your site does not require such a guarantee, IvorySQL can be configured to run much faster. The following are configuration changes you can make to improve performance in such cases. Except as noted below, durability is still guaranteed in case of a crash of the database software; only an abrupt operating system crash creates a risk of data loss or corruption when these settings are used. Place the database cluster’s data directory in a memory-backed file system (i.e., RAM disk). This eliminates all database disk I/O, but limits data storage to the amount of available memory (and perhaps swap). Turn off fsync; there is no need to flush data to disk. Turn off synchronous_commit; there might be no need to force WAL writes to disk on every commit. This setting does risk transaction loss (though not data corruption) in case of a crash of the database. Turn off full_page_writes; there is no need to guard against partial page writes. Increase max_wal_size and checkpoint_timeout; this reduces the frequency of checkpoints, but increases the storage requirements of /pg_wal. Create unlogged tables to avoid WAL writes, though it makes the tables non-crash-safe.","title":"Operation and maintenance management guide","component":"ivorysql-doc","version":"master","name":"operation_guide","url":"/ivorysql-doc/master/operation_guide.html","titles":[{"text":"1. 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Increase max_wal_size","hash":"increase-max_wal_size","id":62},{"text":"10.4.7. Disable WAL Archival and Streaming Replication","hash":"disable-wal-archival-and-streaming-replication","id":63},{"text":"10.4.8. Run ANALYZE Afterwards","hash":"run-analyze-afterwards","id":64},{"text":"10.4.9. Some Notes about pg_dump","hash":"some-notes-about-pg_dump","id":65},{"text":"10.5. Non-Durable Settings","hash":"non-durable-settings","id":66}]},"8":{"id":8,"text":"[Release Date: Nov 25, 2025] IvorySQL 5.0, based on PostgreSQL 18.0, introduces significant Oracle compatibility improvements, PL/iSQL enhancements, and new globalization capabilities while refreshing packaging, automation, and tooling. For a comprehensive list of updates, please visit our documentation site. PostgreSQL 18.0 An asynchronous I/O (AIO) subsystem that can improve performance of sequential scans, bitmap heap scans, vacuums, and other operations. pg_upgrade now retains optimizer statistics. Support for \"skip scan\" lookups that allow using multicolumn B-tree indexes in more cases. uuidv7() function for generating timestamp-ordered UUIDs. Virtual generated columns that compute their values during read operations. This is now the default for generated columns. OAuth authentication support. OLD and NEW support for RETURNING clauses in INSERT, UPDATE, DELETE, and MERGE commands. Temporal constraints, or constraints over ranges, for PRIMARY KEY, UNIQUE, and FOREIGN KEY constraints. For further details, visit PostgreSQL’s release notes. Oracle-compatible ROWID support: Feature #126 Ensures IvorySQL row identifiers align with Oracle semantics for seamless cross-database tooling. PL/iSQL CALL invocation syntax: Feature #764 Adds the Oracle-style CALL entry point so stored procedures can be invoked consistently. PL/iSQL %ROWTYPE support: Feature #765 Allows variables to mirror entire table or cursor rows for concise PL/iSQL coding. PL/iSQL %TYPE support: Feature #766 Enables variables to adopt the data type of existing columns or variables to reduce drift. Case-sensitive compatibility switch: Feature #767 Preserves identifier case to match Oracle behavior when required. NLS parameter compatibility: Feature #768 Honors Oracle-style NLS settings such as NLS_DATE_FORMAT and NLS_TIMESTAMP_FORMAT. Empty string to NULL translation: Feature #769 Converts zero-length strings to NULL to match Oracle compatibility rules. Parser switching capability: Feature #770 Adds toggles between Oracle and PostgreSQL parsers for per-session flexibility. GB18030 database encoding: Feature #771 Provides GB18030 initialization and create-database options for full Chinese market coverage. Oracle-compatible SYS_GUID: Feature #773 Implements the Oracle SYS_GUID function to generate RAW-based GUIDs. Oracle-compatible SYS_CONTEXT: Feature #774 Delivers the Oracle SYS_CONTEXT API for querying session and environment metadata. Oracle-compatible USERENV: Feature #775 Adds the USERENV function so sessions can inspect Oracle-style contextual details. Oracle-compatible function syntax: Feature #776 Supports Oracle constructs such as EDITIONABLE/NONEDITIONABLE, RETURN, IS, and OUT …​ NOCOPY options. Oracle-compatible procedure syntax: Feature #777 Enables procedure DDL with Oracle options, EXEC invocation, and ALTER PROCEDURE support. libpq OUT parameter plumbing: Feature #778 Extends client protocol handling so OUT parameters can be consumed like OCI. Procedure OUT parameter support: Feature #779 Allows stored procedures to declare IN, OUT, and IN OUT modes per Oracle conventions. Function OUT parameter support: Feature #780 Permits Oracle-style OUT parameters within functions, including IN OUT combinations. Nested subprograms: Feature #781 Introduces support for declaring functions or procedures within other subprograms, including overloading. Oracle-compatible INSTR: Feature #782 Matches Oracle INSTR behavior for substring searches and position checks. Oracle-compatible FORCE VIEW: Feature #783 Lets views be created even when referenced objects do not yet exist, mirroring Oracle’s FORCE option. Oracle-compatible LIKE operator: Feature #784 Aligns pattern-matching semantics with Oracle for predictable wildcard behavior. Online Experience: IvorySQL v5.0: Featuare #887 An interactive, browser-based environment will be launched to allow users to explore and evaluate IvorySQL v5.0 in real time — no installation required. Packaging for All Platforms: Featuare #949 Provides multi-platform media packages for X86、ARM、MIPS、LoongArch architecture. Containerized Deployment Support (Docker Compose & Docker Swarm): Supports deployment of standalone IvorySQL databases and high-availability clusters in Docker Swarm and Docker Compose environments. Containerized Deployment Support (Kubernetes): Supports deployment of standalone IvorySQL databases and high-availability clusters on Kubernetes (K8S) using Helm. IvorySQL Operator v5 released (Kubernetes): The IvorySQL Operator v5 has been adapted to support IvorySQL v5.0, with upgrades to system component versions and database extension versions. IvorySQL Cloud v5 released (Unified Lifecycle & Visual Control Plane): Offers a fully managed, graphical control plane that handles IvorySQL v5 database subscriptions, orchestrates end-to-end lifecycle operations, and integrates surrounding ecosystem services. pg_cron: Feature #882 Scheduled job execution within the database layer will be available through pg_cron integration. pgAudit: Feature #929 Provides detailed session and/or object audit logging via the standard PostgreSQL logging facility. PostGIS: Feature #880 Spatial data processing and geospatial analytics will be enabled through PostGIS compatibility. pgRouting: Feature #881 Network and routing analysis features will be introduced with pgRouting support. PGroonga: Feature #879 Full-text search capabilities will be enhanced via planned PGroonga support. ddlx: Feature #877 Support for ddlx to enable advanced schema introspection and automated DDL generation. pgsql-http: Feature #883 Allow the database to initiate HTTP/HTTPS requests internally, enabling seamless communication between the database and external web services. system_stats： Feature #946 System level statistics will be provided by system_stats support. plpgsql_check: Feature #915 Static code analysis on PL/pgSQL functions to identify errors, warnings, and potential issues before runtime execution pgvector: Feature #878 Integration with pgvector to empower AI/ML workloads through native vector similarity search. Repaired unused_oids and duplicate_oids catalog tooling so header scans correctly detect conflicts without false positives: Issue #841 Added .gitignore coverage for libpq/ivytest artifacts to prevent generated binaries and logs from polluting developer trees: Issue #843 Extended GitHub workflow regression runs to cover builds configured with --with-libnuma, preventing future breakages on NUMA-enabled hosts: Issue #869 Enabled psql users to access CREATE PACKAGE syntax help via \\h create package, closing the CLI documentation gap for PL/iSQL packages: Issue #936 Eliminated the MainLoop dangling-pointer scenario that triggered intermittent segmentation faults under concurrency stress: Issue #898 Re-enabled oracle_test/modules/*/sql cases by fixing harness assumptions so Oracle-compatibility suites execute end-to-end again: Issue #897 Updated README.md and README_CN.md to reflect IvorySQL v5 feature set, packaging, and onboarding instructions: Issue #896 Corrected globally unique index enforcement so related regression tests now pass reliably across supported platforms: Issue #894 IvorySQL’s development is maintained across four main repositories: IvorySQL database source code: https://github.com/IvorySQL/IvorySQL IvorySQL official website: https://github.com/IvorySQL/Ivory-www IvorySQL documentation: https://github.com/IvorySQL/IvorySQL-docs IvorySQL Docker: https://github.com/IvorySQL/docker_library The following individuals (in alphabetical order) have contributed to this release as patch authors, committers, reviewers, testers, or reporters of issues. Carlos Chong ccwxl Cédric Villemain elodiefb Fawei Zhao Ge Sui Grant Zhou Imran Zaheer jerome-peng Jiaoshun Tian luss Martin Gerhardy msdnchina omstack otegami rophy Shaolin Chu Shawn Yan Shihua Yang Shiji Niu Shuisen Tong shlei6067 sjw1933 Xiangyu Liang Xiaohui Liu Xinjie Lv xuexiaoganghs Xueyu Gao yangchunwanwusheng Yanliang Lei Yasir Hussain Shah Yuan Li Zheng Tao Zhenhao Pan Zhuoyan Shi","title":"","component":"ivorysql-doc","version":"master","name":"release_notes","url":"/ivorysql-doc/master/release_notes.html","titles":[{"text":"1. Version Overview","hash":"version-overview","id":1},{"text":"2. Enhancements","hash":"enhancements","id":2},{"text":"3. New Features","hash":"new-features","id":3},{"text":"3.1. 21 New Oracle Compatibility Features","hash":"21-new-oracle-compatibility-features","id":4},{"text":"3.2. Online Trial and Multi-Platform Distribution Packages","hash":"online-trial-and-multi-platform-distribution-packages","id":5},{"text":"3.3. Cloud-Native & Containerized","hash":"cloud-native-containerized","id":6},{"text":"3.4. Support for 10 Additional PostgreSQL Extensions","hash":"support-for-10-additional-postgresql-extensions","id":7},{"text":"4. Fixed Issues","hash":"fixed-issues","id":8},{"text":"5. Source Code","hash":"source-code","id":9},{"text":"6. Contributors","hash":"contributors","id":10}]},"9":{"id":9,"text":"This part contains reference information for IvorySQL client applications and utilities. Not all of these commands are of general utility; some might require special privileges. The common feature of Client Applications is that they can be run on any host, independent of where the database server resides. When specified on the command line, user and database names have their case preserved — the presence of spaces or special characters might require quoting. Table names and other identifiers do not have their case preserved, except where documented, and might require quoting. category Tool name Description Client Applications clusterdb clusterdb is a utility for reclustering tables in a IvorySQL database. It finds tables that have previously been clustered, and clusters them again on the same index that was last used. Tables that have never been clustered are not affected.clusterdb is a wrapper around the SQL command CLUSTER. There is no effective difference between clustering databases via this utility and via other methods for accessing the server. createdb createdb creates a new IvorySQL database.Normally, the database user who executes this command becomes the owner of the new database. However, a different owner can be specified via the -O option, if the executing user has appropriate privileges.createdb is a wrapper around the SQL command CREATE DATABASE. There is no effective difference between creating databases via this utility and via other methods for accessing the server. createuser createuser creates a new IvorySQL user (or more precisely, a role). Only superusers and users with CREATEROLE privilege can create new users, so createuser must be invoked by someone who can connect as a superuser or a user with CREATEROLE privilege. createuser is a wrapper around the SQL command CREATE ROLE. There is no effective difference between creating users via this utility and via other methods for accessing the server. dropdb dropdb destroys an existing IvorySQL database. The user who executes this command must be a database superuser or the owner of the database.dropdb is a wrapper around the SQL command DROP DATABASE. There is no effective difference between dropping databases via this utility and via other methods for accessing the server. dropuser dropuser removes an existing IvorySQL user. Only superusers and users with the CREATEROLE privilege can remove IvorySQL users. (To remove a superuser, you must yourself be a superuser.)dropuser is a wrapper around the SQL command DROP ROLE. There is no effective difference between dropping users via this utility and via other methods for accessing the server. ecpg ecpg is the embedded SQL preprocessor for C programs. It converts C programs with embedded SQL statements to normal C code by replacing the SQL invocations with special function calls. The output files can then be processed with any C compiler tool chain.ecpg will convert each input file given on the command line to the corresponding C output file. If an input file name does not have any extension, .pgc is assumed. The file’s extension will be replaced by .c to construct the output file name. But the output file name can be overridden using the -o option.If an input file name is just -, ecpg reads the program from standard input (and writes to standard output, unless that is overridden with -o).This reference page does not describe the embedded SQL language. pg_amcheck pg_amcheck supports running amcheck's corruption checking functions against one or more databases, with options to select which schemas, tables and indexes to check, which kinds of checking to perform, and whether to perform the checks in parallel, and if so, the number of parallel connections to establish and use. pg_basebackup pg_basebackup is used to take a base backup of a running IvorySQL database cluster. The backup is taken without affecting other clients of the database, and can be used both for point-in-time recovery and as the starting point for a log-shipping or streaming-replication standby server.pg_basebackup makes an exact copy of the database cluster’s files, while making sure the server is put into and out of backup mode automatically. Backups are always taken of the entire database cluster; it is not possible to back up individual databases or database objects. For selective backups, another tool such as pg_dump must be used.The backup is made over a regular IvorySQL connection that uses the replication protocol. The connection must be made with a user ID that has REPLICATION permissions or is a superuser, and pg_hba.conf must permit the replication connection. The server must also be configured with max_wal_senders set high enough to provide at least one walsender for the backup plus one for WAL streaming (if used).There can be multiple pg_basebackup`s running at the same time, but it is usually better from a performance point of view to take only one backup, and copy the result.pg_basebackup can make a base backup from not only a primary server but also a standby. To take a backup from a standby, set up the standby so that it can accept replication connections (that is, set `max_wal_senders and hot_standby, and configure its pg_hba.conf appropriately). You will also need to enable full_page_writes on the primary. pgbench pgbench is a simple program for running benchmark tests on IvorySQL. It runs the same sequence of SQL commands over and over, possibly in multiple concurrent database sessions, and then calculates the average transaction rate (transactions per second). By default, pgbench tests a scenario that is loosely based on TPC-B, involving five SELECT, UPDATE, and INSERT commands per transaction. However, it is easy to test other cases by writing your own transaction script files. pg_config The pg_config utility prints configuration parameters of the currently installed version of IvorySQL. It is intended, for example, to be used by software packages that want to interface to IvorySQL to facilitate finding the required header files and libraries. pg_dump pg_dump is a utility for backing up a IvorySQL database. It makes consistent backups even if the database is being used concurrently. pg_dump does not block other users accessing the database (readers or writers).pg_dump only dumps a single database. To back up an entire cluster, or to back up global objects that are common to all databases in a cluster (such as roles and tablespaces), use pg_dumpall. Dumps can be output in script or archive file formats. Script dumps are plain-text files containing the SQL commands required to reconstruct the database to the state it was in at the time it was saved. To restore from such a script, feed it to psql. Script files can be used to reconstruct the database even on other machines and other architectures; with some modifications, even on other SQL database products.The alternative archive file formats must be used with pg_restore to rebuild the database. They allow pg_restore to be selective about what is restored, or even to reorder the items prior to being restored. The archive file formats are designed to be portable across architectures.When used with one of the archive file formats and combined with pg_restore, pg_dump provides a flexible archival and transfer mechanism. pg_dump can be used to backup an entire database, then pg_restore can be used to examine the archive and/or select which parts of the database are to be restored. The most flexible output file formats are the “custom” format ( -Fc ) and the “directory” format ( -Fd ). They allow for selection and reordering of all archived items, support parallel restoration, and are compressed by default. The “directory” format is the only format that supports parallel dumps.While running pg_dump, one should examine the output for any warnings (printed on standard error), especially in light of the limitations listed below. pg_dumpall pg_dumpall is a utility for writing out (“dumping”) all IvorySQL databases of a cluster into one script file. The script file contains SQL commands that can be used as input to psql to restore the databases. It does this by calling pg_dump for each database in the cluster. pg_dumpall also dumps global objects that are common to all databases, namely database roles, tablespaces, and privilege grants for configuration parameters. (pg_dump does not save these objects.)Since pg_dumpall reads tables from all databases you will most likely have to connect as a database superuser in order to produce a complete dump. Also you will need superuser privileges to execute the saved script in order to be allowed to add roles and create databases.The SQL script will be written to the standard output. Use the -f/--file option or shell operators to redirect it into a file.pg_dumpall needs to connect several times to the IvorySQL server (once per database). If you use password authentication it will ask for a password each time. It is convenient to have a ~/.pgpass file in such cases. pg_isready pg_isready is a utility for checking the connection status of a IvorySQL database server. The exit status specifies the result of the connection check. pg_receivewal pg_receivewal is used to stream the write-ahead log from a running IvorySQL cluster. The write-ahead log is streamed using the streaming replication protocol, and is written to a local directory of files. This directory can be used as the archive location for doing a restore using point-in-time recovery.pg_receivewal streams the write-ahead log in real time as it’s being generated on the server, and does not wait for segments to complete like archive_command does. For this reason, it is not necessary to set archive_timeout when using pg_receivewal.Unlike the WAL receiver of a IvorySQL standby server, pg_receivewal by default flushes WAL data only when a WAL file is closed. The option --synchronous must be specified to flush WAL data in real time. Since pg_receivewal does not apply WAL, you should not allow it to become a synchronous standby when synchronous_commit equals remote_apply. If it does, it will appear to be a standby that never catches up, and will cause transaction commits to block. To avoid this, you should either configure an appropriate value for synchronous_standby_names, or specify application_name for pg_receivewal that does not match it, or change the value of synchronous_commit to something other than remote_apply.The write-ahead log is streamed over a regular IvorySQL connection and uses the replication protocol. The connection must be made with a user having REPLICATION permissions or a superuser, and pg_hba.conf must permit the replication connection. The server must also be configured with max_wal_senders set high enough to leave at least one session available for the stream. pg_recvlogical pg_recvlogical controls logical decoding replication slots and streams data from such replication slots.It creates a replication-mode connection, so it is subject to the same constraints as pg_receivewal, plus those for logical replication .pg_recvlogical has no equivalent to the logical decoding SQL interface’s peek and get modes. It sends replay confirmations for data lazily as it receives it and on clean exit. To examine pending data on a slot without consuming it, use pg_logical_slot_peek_changes. pg_restore pg_restore is a utility for restoring a IvorySQL database from an archive created by pg_dump in one of the non-plain-text formats. It will issue the commands necessary to reconstruct the database to the state it was in at the time it was saved. The archive files also allow pg_restore to be selective about what is restored, or even to reorder the items prior to being restored. The archive files are designed to be portable across architectures.pg_restore can operate in two modes. If a database name is specified, pg_restore connects to that database and restores archive contents directly into the database. Otherwise, a script containing the SQL commands necessary to rebuild the database is created and written to a file or standard output. This script output is equivalent to the plain text output format of pg_dump. Some of the options controlling the output are therefore analogous to pg_dump options.Obviously, pg_restore cannot restore information that is not present in the archive file. For instance, if the archive was made using the “dump data as INSERT commands” option, pg_restore will not be able to load the data using COPY statements. pg_verifybackup pg_verifybackup is used to check the integrity of a database cluster backup taken using pg_basebackup against a backup_manifest generated by the server at the time of the backup. The backup must be stored in the \"plain\" format; a \"tar\" format backup can be checked after extracting it.It is important to note that the validation which is performed by pg_verifybackup does not and cannot include every check which will be performed by a running server when attempting to make use of the backup. Even if you use this tool, you should still perform test restores and verify that the resulting databases work as expected and that they appear to contain the correct data. However, pg_verifybackup can detect many problems that commonly occur due to storage problems or user error.Backup verification proceeds in four stages. First, pg_verifybackup reads the backup_manifest file. If that file does not exist, cannot be read, is malformed, or fails verification against its own internal checksum, pg_verifybackup will terminate with a fatal error. psql psql is a terminal-based front-end to IvorySQL. It enables you to type in queries interactively, issue them to IvorySQL, and see the query results. Alternatively, input can be from a file or from command line arguments. In addition, psql provides a number of meta-commands and various shell-like features to facilitate writing scripts and automating a wide variety of tasks. reindexdb reindexdb is a utility for rebuilding indexes in a IvorySQL database.reindexdb is a wrapper around the SQL command REINDEX. There is no effective difference between reindexing databases via this utility and via other methods for accessing the server. vacuumdb vacuumdb is a utility for cleaning a IvorySQL database. vacuumdb will also generate internal statistics used by the IvorySQL query optimizer. Server Applications initdb(1) initdb creates a new IvorySQL database cluster. A database cluster is a collection of databases that are managed by a single server instance.Creating a database cluster consists of creating the directories in which the database data will live, generating the shared catalog tables (tables that belong to the whole cluster rather than to any particular database), and creating the postgres, template1, and template0 databases. The postgres database is a default database meant for use by users, utilities and third party applications. template1 and template0 are meant as source databases to be copied by later CREATE DATABASE commands. template0 should never be modified, but you can add objects to template1, which by default will be copied into databases created later.Although initdb will attempt to create the specified data directory, it might not have permission if the parent directory of the desired data directory is root-owned. To initialize in such a setup, create an empty data directory as root, then use chown to assign ownership of that directory to the database user account, then su to become the database user to run initdb. Server Applications initdb(2) initdb must be run as the user that will own the server process, because the server needs to have access to the files and directories that initdb creates. Since the server cannot be run as root, you must not run initdb as root either. (It will in fact refuse to do so.)For security reasons the new cluster created by initdb will only be accessible by the cluster owner by default. The --allow-group-access option allows any user in the same group as the cluster owner to read files in the cluster. This is useful for performing backups as a non-privileged user.initdb initializes the database cluster’s default locale and character set encoding. These can also be set separately for each database when it is created. initdb determines those settings for the template databases, which will serve as the default for all other databases. By default, initdb uses the locale provider libc, takes the locale settings from the environment, and determines the encoding from the locale settings. This is almost always sufficient, unless there are special requirements.To choose a different locale for the cluster, use the option --locale. There are also individual options --lc-* (see below) to set values for the individual locale categories. Note that inconsistent settings for different locale categories can give nonsensical results, so this should be used with care.Alternatively, the ICU library can be used to provide locale services. (Again, this only sets the default for subsequently created databases.) To select this option, specify --locale-provider=icu. To choose the specific ICU locale ID to apply, use the option --icu-locale. Note that for implementation reasons and to support legacy code, initdb will still select and initialize libc locale settings when the ICU locale provider is used.When initdb runs, it will print out the locale settings it has chosen. If you have complex requirements or specified multiple options, it is advisable to check that the result matches what was intended. pg_archivecleanup pg_archivecleanup is designed to be used as an archive_cleanup_command to clean up WAL file archives when running as a standby server .pg_archivecleanup can also be used as a standalone program to clean WAL file archives. pg_checksums pg_checksums checks, enables or disables data checksums in a IvorySQL cluster. The server must be shut down cleanly before running pg_checksums. When verifying checksums, the exit status is zero if there are no checksum errors, and nonzero if at least one checksum failure is detected. When enabling or disabling checksums, the exit status is nonzero if the operation failed.When verifying checksums, every file in the cluster is scanned. When enabling checksums, each relation file block with a changed checksum is rewritten in-place. Disabling checksums only updates the file pg_control. pg_controldata pg_controldata prints information initialized during initdb, such as the catalog version. It also shows information about write-ahead logging and checkpoint processing. This information is cluster-wide, and not specific to any one database.This utility can only be run by the user who initialized the cluster because it requires read access to the data directory. You can specify the data directory on the command line, or use the environment variable PGDATA. This utility supports the options -V and --version, which print the pg_controldata version and exit. It also supports options -? and --help, which output the supported arguments. pg_ctl pg_ctl is a utility for initializing a IvorySQL database cluster, starting, stopping, or restarting the IvorySQL database server (postgres), or displaying the status of a running server. Although the server can be started manually, pg_ctl encapsulates tasks such as redirecting log output and properly detaching from the terminal and process group. It also provides convenient options for controlled shutdown. pg_resetwal pg_resetwal clears the write-ahead log (WAL) and optionally resets some other control information stored in the pg_control file. This function is sometimes needed if these files have become corrupted. It should be used only as a last resort, when the server will not start due to such corruption.After running this command, it should be possible to start the server, but bear in mind that the database might contain inconsistent data due to partially-committed transactions. You should immediately dump your data, run initdb, and restore. After restore, check for inconsistencies and repair as needed.This utility can only be run by the user who installed the server, because it requires read/write access to the data directory. For safety reasons, you must specify the data directory on the command line. pg_resetwal does not use the environment variable PGDATA.If pg_resetwal complains that it cannot determine valid data for pg_control, you can force it to proceed anyway by specifying the -f (force) option. In this case plausible values will be substituted for the missing data. Most of the fields can be expected to match, but manual assistance might be needed for the next OID, next transaction ID and epoch, next multitransaction ID and offset, and WAL starting location fields. These fields can be set using the options discussed below. If you are not able to determine correct values for all these fields, -f can still be used, but the recovered database must be treated with even more suspicion than usual: an immediate dump and restore is imperative. Do not execute any data-modifying operations in the database before you dump, as any such action is likely to make the corruption worse. pg_rewind(1) pg_rewind is a tool for synchronizing a IvorySQL cluster with another copy of the same cluster, after the clusters' timelines have diverged. A typical scenario is to bring an old primary server back online after failover as a standby that follows the new primary.After a successful rewind, the state of the target data directory is analogous to a base backup of the source data directory. Unlike taking a new base backup or using a tool like rsync, pg_rewind does not require comparing or copying unchanged relation blocks in the cluster. Only changed blocks from existing relation files are copied; all other files, including new relation files, configuration files, and WAL segments, are copied in full. As such the rewind operation is significantly faster than other approaches when the database is large and only a small fraction of blocks differ between the clusters.pg_rewind examines the timeline histories of the source and target clusters to determine the point where they diverged, and expects to find WAL in the target cluster’s pg_wal directory reaching all the way back to the point of divergence. The point of divergence can be found either on the target timeline, the source timeline, or their common ancestor. In the typical failover scenario where the target cluster was shut down soon after the divergence, this is not a problem, but if the target cluster ran for a long time after the divergence, its old WAL files might no longer be present. In this case, you can manually copy them from the WAL archive to the pg_wal directory, or run pg_rewind with the -c option to automatically retrieve them from the WAL archive. The use of pg_rewind is not limited to failover, e.g., a standby server can be promoted, run some write transactions, and then rewound to become a standby again.After running pg_rewind, WAL replay needs to complete for the data directory to be in a consistent state. pg_rewind(2) When the target server is started again it will enter archive recovery and replay all WAL generated in the source server from the last checkpoint before the point of divergence. If some of the WAL was no longer available in the source server when pg_rewind was run, and therefore could not be copied by the pg_rewind session, it must be made available when the target server is started. This can be done by creating a recovery.signal file in the target data directory and by configuring a suitable restore_command in IvorySQL.conf.pg_rewind requires that the target server either has the wal_log_hints option enabled in IvorySQL.conf or data checksums enabled when the cluster was initialized with initdb. Neither of these are currently on by default. full_page_writes must also be set to on, but is enabled by default. pg_test_fsync pg_test_fsync is intended to give you a reasonable idea of what the fastest wal_sync_method is on your specific system, as well as supplying diagnostic information in the event of an identified I/O problem. However, differences shown by pg_test_fsync might not make any significant difference in real database throughput, especially since many database servers are not speed-limited by their write-ahead logs. pg_test_fsync reports average file sync operation time in microseconds for each wal_sync_method, which can also be used to inform efforts to optimize the value of commit_delay. pg_test_timing pg_test_timing is a tool to measure the timing overhead on your system and confirm that the system time never moves backwards. Systems that are slow to collect timing data can give less accurate EXPLAIN ANALYZE results. pg_upgrade Major IvorySQL releases regularly add new features that often change the layout of the system tables, but the internal data storage format rarely changes. pg_upgrade uses this fact to perform rapid upgrades by creating new system tables and simply reusing the old user data files. If a future major release ever changes the data storage format in a way that makes the old data format unreadable, pg_upgrade will not be usable for such upgrades. (The community will attempt to avoid such situations.)pg_upgrade does its best to make sure the old and new clusters are binary-compatible, e.g., by checking for compatible compile-time settings, including 32/64-bit binaries. It is important that any external modules are also binary compatible, though this cannot be checked by pg_upgrade. pg_waldump pg_waldump displays the write-ahead log (WAL) and is mainly useful for debugging or educational purposes.This utility can only be run by the user who installed the server, because it requires read-only access to the data directory. postgres postgres is the IvorySQL database server. In order for a client application to access a database it connects (over a network or locally) to a running postgres instance. The postgres instance then starts a separate server process to handle the connection. clusterdb [connection-option…​] [ --verbose | -v ] [ --table | -t table ] …​ [dbname] clusterdb` [*`connection-option`*...] [ `--verbose` | `-v` ] `--all` | `-a clusterdb accepts the following command-line arguments: -a --all Cluster all databases. [-d] dbname dbname Specifies the name of the database to be clustered, when -a / --all is not used. If this is not specified, the database name is read from the environment variable PGDATABASE. If that is not set, the user name specified for the connection is used. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --echo Echo the commands that clusterdb generates and sends to the server. -q --quiet Do not display progress messages. -t table --table=table Cluster table only. Multiple tables can be clustered by writing multiple -t switches. -v --verbose Print detailed information during processing. -V --version Print the clusterdb version and exit. -? --help Show help about clusterdb command line arguments, and exit.clusterdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force clusterdb to prompt for a password before connecting to a database.This option is never essential, since clusterdb will automatically prompt for a password if the server demands password authentication. However, clusterdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to to discover which databases should be clustered, when -a / --all is used. If not specified, the postgres database will be used, or if that does not exist, template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. Also, connection string parameters other than the database name itself will be re-used when connecting to other databases. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see CLUSTER and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To cluster the database test: $ clusterdb test To cluster a single table foo in a database named xyzzy: $ clusterdb --table=foo xyzzy createdb — create a new IvorySQL database createdb [connection-option…​] [option…​] [dbname [description]] createdb accepts the following command-line arguments: dbname Specifies the name of the database to be created. The name must be unique among all IvorySQL databases in this cluster. The default is to create a database with the same name as the current system user. description Specifies a comment to be associated with the newly created database. -D tablespace --tablespace=tablespace Specifies the default tablespace for the database. (This name is processed as a double-quoted identifier.) -e --echo Echo the commands that createdb generates and sends to the server. -E encoding --encoding=encoding Specifies the character encoding scheme to be used in this database. -l locale --locale=locale Specifies the locale to be used in this database. This is equivalent to specifying both --lc-collate and --lc-ctype. --lc-collate=`locale` Specifies the LC_COLLATE setting to be used in this database. --lc-ctype=locale Specifies the LC_CTYPE setting to be used in this database. --icu-locale=`locale` Specifies the ICU locale ID to be used in this database, if the ICU locale provider is selected. --locale-provider={libc|icu} Specifies the locale provider for the database’s default collation. -O `owner` --owner=`owner` Specifies the database user who will own the new database. (This name is processed as a double-quoted identifier.) -T template --template=template Specifies the template database from which to build this database. (This name is processed as a double-quoted identifier.) -V --version Print the createdb version and exit. -? --help Show help about createdb command line arguments, and exit. The options -D, -l, -E, -O, and -T correspond to options of the underlying SQL command CREATE DATABASE; see there for more information about them. createdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or the local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force createdb to prompt for a password before connecting to a database.This option is never essential, since createdb will automatically prompt for a password if the server demands password authentication. However, createdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=`dbname` Specifies the name of the database to connect to when creating the new database. If not specified, the postgres database will be used; if that does not exist (or if it is the name of the new database being created), template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. PGDATABASE If set, the name of the database to create, unless overridden on the command line. PGHOST PGPORT PGUSER Default connection parameters. PGUSER also determines the name of the database to create, if it is not specified on the command line or by PGDATABASE. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see CREATE DATABASE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To create the database demo using the default database server: $ createdb demo To create the database demo using the server on host eden, port 5000, using the template0 template database, here is the command-line command and the underlying SQL command: $ createdb -p 5000 -h eden -T template0 -e demo CREATE DATABASE demo TEMPLATE template0; createuser — define a new IvorySQL user account createuser [connection-option…​] [option…​] [username] createuser accepts the following command-line arguments: username Specifies the name of the IvorySQL user to be created. -c number --connection-limit=number Set a maximum number of connections for the new user. The default is to set no limit. -d --createdb The new user will be allowed to create databases. -D --no-createdb The new user will not be allowed to create databases. This is the default. -e --echo Echo the commands that createuser generates and sends to the server. -E --encrypted This option is obsolete but still accepted for backward compatibility. -g role --role=role Indicates role to which this role will be added immediately as a new member. Multiple roles to which this role will be added as a member can be specified by writing multiple -g switches. -i --inherit The new role will automatically inherit privileges of roles it is a member of. This is the default. -I --no-inherit The new role will not automatically inherit privileges of roles it is a member of. --interactive Prompt for the user name if none is specified on the command line, and also prompt for whichever of the options -d / -D, -r / -R, -s / -S is not specified on the command line. -l --login The new user will be allowed to log in (that is, the user name can be used as the initial session user identifier). This is the default. -L --no-login The new user will not be allowed to log in. (A role without login privilege is still useful as a means of managing database permissions.) -P --pwprompt If given, createuser will issue a prompt for the password of the new user. This is not necessary if you do not plan on using password authentication. -r --createrole The new user will be allowed to create new roles. That is, this user will have CREATEROLE privilege. -R --no-createrole The new user will not be allowed to create new roles. This is the default. -s --superuser The new user will be a superuser. -S --no-superuser The new user will not be a superuser. This is the default. -V --version Print the createuser version and exit. --replication The new user will have the REPLICATION privilege, which is described more fully in the documentation for CREATE ROLE. --no-replication The new user will not have the REPLICATION privilege, which is described more fully in the documentation for CREATE ROLE. -? --help Show help about createuser command line arguments, and exit. createuser also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as (not the user name to create). -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force createuser to prompt for a password (for connecting to the server, not for the password of the new user).This option is never essential, since createuser will automatically prompt for a password if the server demands password authentication. However, createuser will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see CREATE ROLE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To create a user joe on the default database server: $ createuser joe To create a user joe on the default database server with prompting for some additional attributes: $ createuser --interactive joe Shall the new role be a superuser? (y/n) n Shall the new role be allowed to create databases? (y/n) n Shall the new role be allowed to create more new roles? (y/n) n To create the same user joe using the server on host eden, port 5000, with attributes explicitly specified, taking a look at the underlying command: $ createuser -h eden -p 5000 -S -D -R -e joe CREATE ROLE joe NOSUPERUSER NOCREATEDB NOCREATEROLE INHERIT LOGIN; To create the user joe as a superuser, and assign a password immediately: $ createuser -P -s -e joe Enter password for new role: xyzzy Enter it again: xyzzy CREATE ROLE joe PASSWORD 'md5b5f5ba1a423792b526f799ae4eb3d59e' SUPERUSER CREATEDB CREATEROLE INHERIT LOGIN; In the above example, the new password isn’t actually echoed when typed, but we show what was typed for clarity. As you see, the password is encrypted before it is sent to the client. dropdb — remove a IvorySQL database dropdb [connection-option…​] [option…​] dbname dropdb accepts the following command-line arguments: dbname Specifies the name of the database to be removed. -e --echo Echo the commands that dropdb generates and sends to the server. -f --force Attempt to terminate all existing connections to the target database before dropping it. See DROP DATABASE for more information on this option. -i --interactive Issues a verification prompt before doing anything destructive. -V --version Print the dropdb version and exit. --if-exists Do not throw an error if the database does not exist. A notice is issued in this case. -? --help Show help about dropdb command line arguments, and exit. dropdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force dropdb to prompt for a password before connecting to a database.This option is never essential, since dropdb will automatically prompt for a password if the server demands password authentication. However, dropdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to in order to drop the target database. If not specified, the postgres database will be used; if that does not exist (or is the database being dropped), template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq . In case of difficulty, see DROP DATABASE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To destroy the database demo on the default database server: $ dropdb demo To destroy the database demo using the server on host eden, port 5000, with verification and a peek at the underlying command: $ dropdb -p 5000 -h eden -i -e demo Database \"demo\" will be permanently deleted. Are you sure? (y/n) y DROP DATABASE demo; dropuser — remove a IvorySQL user account dropuser [connection-option…​] [option…​] [username] dropuser accepts the following command-line arguments: username Specifies the name of the IvorySQL user to be removed. You will be prompted for a name if none is specified on the command line and the -i / --interactive option is used. -e --echo Echo the commands that dropuser generates and sends to the server. -i --interactive Prompt for confirmation before actually removing the user, and prompt for the user name if none is specified on the command line. -V --version Print the dropuser version and exit. --if-exists Do not throw an error if the user does not exist. A notice is issued in this case. -? --help Show help about dropuser command line arguments, and exit. dropuser also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as (not the user name to drop). -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force dropuser to prompt for a password before connecting to a database.This option is never essential, since dropuser will automatically prompt for a password if the server demands password authentication. However, dropuser will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq In case of difficulty, see DROP ROLE and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. To remove user joe from the default database server: $ dropuser joe To remove user joe using the server on host eden, port 5000, with verification and a peek at the underlying command: $ dropuser -p 5000 -h eden -i -e joe Role \"joe\" will be permanently removed. Are you sure? (y/n) y DROP ROLE joe; ecpg — embedded SQL C preprocessor ecpg [option…​] file…​ ecpg accepts the following command-line arguments: -c Automatically generate certain C code from SQL code. Currently, this works for EXEC SQL TYPE. -C mode Set a compatibility mode. mode can be INFORMIX, INFORMIX_SE, or ORACLE. -D `symbol` Define a C preprocessor symbol. -h Process header files. When this option is specified, the output file extension becomes .h not .c, and the default input file extension is .pgh not .pgc. Also, the -c option is forced on. -i Parse system include files as well. -I `directory` Specify an additional include path, used to find files included via EXEC SQL INCLUDE. Defaults are . (current directory), /usr/local/include, the IvorySQL include directory which is defined at compile time (default: /usr/local/pgsql/include), and /usr/include, in that order. -o `filename` Specifies that ecpg should write all its output to the given filename. Write -o - to send all output to standard output. -r `option` Selects run-time behavior. Option can be one of the following:`no_indicator`Do not use indicators but instead use special values to represent null values. Historically there have been databases using this approach.prepare`Prepare all statements before using them. Libecpg will keep a cache of prepared statements and reuse a statement if it gets executed again. If the cache runs full, libecpg will free the least used statement.`questionmarks Allow question mark as placeholder for compatibility reasons. This used to be the default long ago. -t Turn on autocommit of transactions. In this mode, each SQL command is automatically committed unless it is inside an explicit transaction block. In the default mode, commands are committed only when EXEC SQL COMMIT is issued. -v Print additional information including the version and the \"include\" path. --version Print the ecpg version and exit. -? --help Show help about ecpg command line arguments, and exit. When compiling the preprocessed C code files, the compiler needs to be able to find the ECPG header files in the IvorySQL include directory. Therefore, you might have to use the -I option when invoking the compiler (e.g., -I/usr/local/pgsql/include). Programs using C code with embedded SQL have to be linked against the libecpg library, for example using the linker options -L/usr/local/pgsql/lib -lecpg. The value of either of these directories that is appropriate for the installation can be found out using pg_config. If you have an embedded SQL C source file named prog1.pgc, you can create an executable program using the following sequence of commands: ecpg prog1.pgc cc -I/usr/local/pgsql/include -c prog1.c cc -o prog1 prog1.o -L/usr/local/pgsql/lib -lecpg pg_amcheck — checks for corruption in one or more IvorySQL databases pg_amcheck [option…​] [dbname] The following command-line options control what is checked: -a --all Check all databases, except for any excluded via --exclude-database. -d pattern --database=pattern Check databases matching the specified pattern, except for any excluded by --exclude-database. This option can be specified more than once. -D pattern --exclude-database=pattern Exclude databases matching the given pattern. This option can be specified more than once. -i pattern --index=pattern Check indexes matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.This is similar to the --relation option, except that it applies only to indexes, not to other relation types. -I pattern --exclude-index=pattern Exclude indexes matching the specified pattern. This option can be specified more than once.This is similar to the --exclude-relation option, except that it applies only to indexes, not other relation types. -r `pattern` --relation=`pattern` Check relations matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.Patterns may be unqualified, e.g. myrel*, or they may be schema-qualified, e.g. myschema*.myrel* or database-qualified and schema-qualified, e.g. mydb*.myscheam*.myrel*. A database-qualified pattern will add matching databases to the list of databases to be checked. -R pattern --exclude-relation=pattern Exclude relations matching the specified pattern. This option can be specified more than once.As with --relation, the pattern may be unqualified, schema-qualified, or database- and schema-qualified. -s pattern --schema=pattern Check tables and indexes in schemas matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.To select only tables in schemas matching a particular pattern, consider using something like --table=SCHEMAPAT.* --no-dependent-indexes. To select only indexes, consider using something like --index=SCHEMAPAT..A schema pattern may be database-qualified. For example, you may write --schema=mydb.myschema* to select schemas matching myschema* in databases matching mydb*. -S pattern --exclude-schema=pattern Exclude tables and indexes in schemas matching the specified pattern. This option can be specified more than once.As with --schema, the pattern may be database-qualified. -t pattern --table=pattern Check tables matching the specified pattern, unless they are otherwise excluded. This option can be specified more than once.This is similar to the --relation option, except that it applies only to tables, materialized views, and sequences, not to indexes. -T pattern --exclude-table=pattern Exclude tables matching the specified pattern. This option can be specified more than once.This is similar to the --exclude-relation option, except that it applies only to tables, materialized views, and sequences, not to indexes. --no-dependent-indexes By default, if a table is checked, any btree indexes of that table will also be checked, even if they are not explicitly selected by an option such as --index or --relation. This option suppresses that behavior. --no-dependent-toast By default, if a table is checked, its toast table, if any, will also be checked, even if it is not explicitly selected by an option such as --table or --relation. This option suppresses that behavior. --no-strict-names By default, if an argument to --database, --table, --index, or --relation matches no objects, it is a fatal error. This option downgrades that error to a warning. The following command-line options control checking of tables: --exclude-toast-pointers By default, whenever a toast pointer is encountered in a table, a lookup is performed to ensure that it references apparently-valid entries in the toast table. These checks can be quite slow, and this option can be used to skip them. --on-error-stop After reporting all corruptions on the first page of a table where corruption is found, stop processing that table relation and move on to the next table or index.Note that index checking always stops after the first corrupt page. This option only has meaning relative to table relations. --skip=`option` If all-frozen is given, table corruption checks will skip over pages in all tables that are marked as all frozen.If all-visible is given, table corruption checks will skip over pages in all tables that are marked as all visible.By default, no pages are skipped. This can be specified as none, but since this is the default, it need not be mentioned. --startblock=`block` Start checking at the specified block number. An error will occur if the table relation being checked has fewer than this number of blocks. This option does not apply to indexes, and is probably only useful when checking a single table relation. See --endblock for further caveats. --endblock=`block` End checking at the specified block number. An error will occur if the table relation being checked has fewer than this number of blocks. This option does not apply to indexes, and is probably only useful when checking a single table relation. If both a regular table and a toast table are checked, this option will apply to both, but higher-numbered toast blocks may still be accessed while validating toast pointers, unless that is suppressed using --exclude-toast-pointers. The following command-line options control checking of B-tree indexes: --heapallindexed For each index checked, verify the presence of all heap tuples as index tuples in the index using amcheck's heapallindexed option. --parent-check For each btree index checked, use amcheck's bt_index_parent_check function, which performs additional checks of parent/child relationships during index checking.The default is to use amcheck’s bt_index_check function, but note that use of the --rootdescend option implicitly selects bt_index_parent_check. --rootdescend For each index checked, re-find tuples on the leaf level by performing a new search from the root page for each tuple using amcheck's rootdescend option.Use of this option implicitly also selects the --parent-check option.This form of verification was originally written to help in the development of btree index features. It may be of limited use or even of no use in helping detect the kinds of corruption that occur in practice. It may also cause corruption checking to take considerably longer and consume considerably more resources on the server. The extra checks performed against B-tree indexes when the --parent-check option or the --rootdescend option is specified require relatively strong relation-level locks. These checks are the only checks that will block concurrent data modification from INSERT, UPDATE, and DELETE commands. The following command-line options control the connection to the server: -h hostname --host=hostname Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_amcheck to prompt for a password before connecting to a database.This option is never essential, since pg_amcheck will automatically prompt for a password if the server demands password authentication. However, pg_amcheck will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies a database or connection string to be used to discover the list of databases to be checked. If neither --all nor any option including a database pattern is used, no such connection is required and this option does nothing. Otherwise, any connection string parameters other than the database name which are included in the value for this option will also be used when connecting to the databases being checked. If this option is omitted, the default is postgres or, if that fails, template1. Other options are also available: -e --echo Echo to stdout all SQL sent to the server. -j `num` --jobs=`num` Use num concurrent connections to the server, or one per object to be checked, whichever is less.The default is to use a single connection. -P --progress Show progress information. Progress information includes the number of relations for which checking has been completed, and the total size of those relations. It also includes the total number of relations that will eventually be checked, and the estimated size of those relations. -v --verbose Print more messages. In particular, this will print a message for each relation being checked, and will increase the level of detail shown for server errors. -V --version Print the pg_amcheck version and exit. --install-missing --install-missing=`schema` Install any missing extensions that are required to check the database(s). If not yet installed, each extension’s objects will be installed into the given schema, or if not specified into schema pg_catalog.At present, the only required extension is amcheck. -? --help Show help about pg_amcheck command line arguments, and exit. pg_basebackup — take a base backup of a IvorySQL cluster pg_basebackup [option…​] The following command-line options control the location and format of the output: -D directory --pgdata=directory Sets the target directory to write the output to. pg_basebackup will create this directory (and any missing parent directories) if it does not exist. If it already exists, it must be empty.When the backup is in tar format, the target directory may be specified as - (dash), causing the tar file to be written to stdout.This option is required. -F format --format=format Selects the format for the output. format can be one of the following: p plain Write the output as plain files, with the same layout as the source server’s data directory and tablespaces. When the cluster has no additional tablespaces, the whole database will be placed in the target directory. If the cluster contains additional tablespaces, the main data directory will be placed in the target directory, but all other tablespaces will be placed in the same absolute path as they have on the source server. (See --tablespace-mapping to change that.)This is the default format. t tar Write the output as tar files in the target directory. The main data directory’s contents will be written to a file named base.tar, and each other tablespace will be written to a separate tar file named after that tablespace’s OID.If the target directory is specified as - (dash), the tar contents will be written to standard output, suitable for piping to (for example) gzip. This is only allowed if the cluster has no additional tablespaces and WAL streaming is not used. -R --write-recovery-conf Creates a standby.signal file and appends connection settings to the IvorySQL.auto.conf file in the target directory (or within the base archive file when using tar format). This eases setting up a standby server using the results of the backup.The IvorySQL.auto.conf file will record the connection settings and, if specified, the replication slot that pg_basebackup is using, so that streaming replication will use the same settings later on. -t target --target=target Instructs the server where to place the base backup. The default target is client, which specifies that the backup should be sent to the machine where pg_basebackup is running. If the target is instead set to server:/some/path, the backup will be stored on the machine where the server is running in the /some/path directory. Storing a backup on the server requires superuser privileges or having privileges of the pg_write_server_files role. If the target is set to blackhole, the contents are discarded and not stored anywhere. This should only be used for testing purposes, as you will not end up with an actual backup.Since WAL streaming is implemented by pg_basebackup rather than by the server, this option cannot be used together with -Xstream. Since that is the default, when this option is specified, you must also specify either -Xfetch or -Xnone. -T olddir=newdir --tablespace-mapping=olddir=newdir Relocates the tablespace in directory olddir to newdir during the backup. To be effective, olddir must exactly match the path specification of the tablespace as it is defined on the source server. (But it is not an error if there is no tablespace in olddir on the source server.) Meanwhile newdir is a directory in the receiving host’s filesystem. As with the main target directory, newdir need not exist already, but if it does exist it must be empty. Both olddir and newdir must be absolute paths. If either path needs to contain an equal sign (=), precede that with a backslash. This option can be specified multiple times for multiple tablespaces.If a tablespace is relocated in this way, the symbolic links inside the main data directory are updated to point to the new location. So the new data directory is ready to be used for a new server instance with all tablespaces in the updated locations.Currently, this option only works with plain output format; it is ignored if tar format is selected. --waldir=waldir Sets the directory to write WAL (write-ahead log) files to. By default WAL files will be placed in the pg_wal subdirectory of the target directory, but this option can be used to place them elsewhere. waldir must be an absolute path. As with the main target directory, waldir need not exist already, but if it does exist it must be empty. This option can only be specified when the backup is in plain format. -X method --wal-method=method Includes the required WAL (write-ahead log) files in the backup. This will include all write-ahead logs generated during the backup. Unless the method none is specified, it is possible to start a postmaster in the target directory without the need to consult the log archive, thus making the output a completely standalone backup.The following methods for collecting the write-ahead logs are supported: n none Don’t include write-ahead logs in the backup. f fetch`The write-ahead log files are collected at the end of the backup. Therefore, it is necessary for the source server’s wal_keep_size parameter to be set high enough that the required log data is not removed before the end of the backup. If the required log data has been recycled before it’s time to transfer it, the backup will fail and be unusable.When tar format is used, the write-ahead log files will be included in the `base.tar file.s stream`Stream write-ahead log data while the backup is being taken. This method will open a second connection to the server and start streaming the write-ahead log in parallel while running the backup. Therefore, it will require two replication connections not just one. As long as the client can keep up with the write-ahead log data, using this method requires no extra write-ahead logs to be saved on the source server.When tar format is used, the write-ahead log files will be written to a separate file named `pg_wal.tar (if the server is a version earlier than 10, the file will be named pg_xlog.tar).This value is the default. -z --gzip Enables gzip compression of tar file output, with the default compression level. Compression is only available when using the tar format, and the suffix .gz will automatically be added to all tar filenames. -Z level -Z method[:*detail*] --compress=level --compress=method[:*detail*] Requests compression of the backup. If client or server is included, it specifies where the compression is to be performed. Compressing on the server will reduce transfer bandwidth but will increase server CPU consumption. The default is client except when --target is used. In that case, the backup is not being sent to the client, so only server compression is sensible. When -Xstream, which is the default, is used, server-side compression will not be applied to the WAL. To compress the WAL, use client-side compression, or specify -Xfetch.The compression method can be set to gzip, lz4, zstd, or none for no compression. A compression detail string can optionally be specified. If the detail string is an integer, it specifies the compression level. Otherwise, it should be a comma-separated list of items, each of the form keyword or keyword=value. Currently, the supported keywords are level and workers.If no compression level is specified, the default compression level will be used. If only a level is specified without mentioning an algorithm, gzip compression will be used if the level is greater than 0, and no compression will be used if the level is 0.When the tar format is used with gzip, lz4, or zstd, the suffix .gz, .lz4, or .zst, respectively, will be automatically added to all tar filenames. When the plain format is used, client-side compression may not be specified, but it is still possible to request server-side compression. If this is done, the server will compress the backup for transmission, and the client will decompress and extract it.When this option is used in combination with -Xstream, pg_wal.tar will be compressed using gzip if client-side gzip compression is selected, but will not be compressed if any other compression algorithm is selected, or if server-side compression is selected. The following command-line options control the generation of the backup and the invocation of the program: -c {fast|spread} --checkpoint={fast|spread} Sets checkpoint mode to fast (immediate) or spread (the default) . -C --create-slot Specifies that the replication slot named by the --slot option should be created before starting the backup. An error is raised if the slot already exists. -l label --label=label Sets the label for the backup. If none is specified, a default value of “pg_basebackup base backup” will be used. -n --no-clean By default, when pg_basebackup aborts with an error, it removes any directories it might have created before discovering that it cannot finish the job (for example, the target directory and write-ahead log directory). This option inhibits tidying-up and is thus useful for debugging.Note that tablespace directories are not cleaned up either way. -N --no-sync By default, pg_basebackup will wait for all files to be written safely to disk. This option causes pg_basebackup to return without waiting, which is faster, but means that a subsequent operating system crash can leave the base backup corrupt. Generally, this option is useful for testing but should not be used when creating a production installation. -P --progress Enables progress reporting. Turning this on will deliver an approximate progress report during the backup. Since the database may change during the backup, this is only an approximation and may not end at exactly 100%. In particular, when WAL log is included in the backup, the total amount of data cannot be estimated in advance, and in this case the estimated target size will increase once it passes the total estimate without WAL. -r rate --max-rate=rate Sets the maximum transfer rate at which data is collected from the source server. This can be useful to limit the impact of pg_basebackup on the server. Values are in kilobytes per second. Use a suffix of M to indicate megabytes per second. A suffix of k is also accepted, and has no effect. Valid values are between 32 kilobytes per second and 1024 megabytes per second.This option always affects transfer of the data directory. Transfer of WAL files is only affected if the collection method is fetch. -S slotname --slot=slotname This option can only be used together with -X stream. It causes WAL streaming to use the specified replication slot. If the base backup is intended to be used as a streaming-replication standby using a replication slot, the standby should then use the same replication slot name as primary_slot_name. This ensures that the primary server does not remove any necessary WAL data in the time between the end of the base backup and the start of streaming replication on the new standby.The specified replication slot has to exist unless the option -C is also used.If this option is not specified and the server supports temporary replication slots (version 10 and later), then a temporary replication slot is automatically used for WAL streaming. -v --verbose Enables verbose mode. Will output some extra steps during startup and shutdown, as well as show the exact file name that is currently being processed if progress reporting is also enabled. --manifest-checksums=algorithm Specifies the checksum algorithm that should be applied to each file included in the backup manifest. Currently, the available algorithms are NONE, CRC32C, SHA224, SHA256, SHA384, and SHA512. The default is CRC32C.If NONE is selected, the backup manifest will not contain any checksums. Otherwise, it will contain a checksum of each file in the backup using the specified algorithm. In addition, the manifest will always contain a SHA256 checksum of its own contents. The SHA algorithms are significantly more CPU-intensive than CRC32C, so selecting one of them may increase the time required to complete the backup.Using a SHA hash function provides a cryptographically secure digest of each file for users who wish to verify that the backup has not been tampered with, while the CRC32C algorithm provides a checksum that is much faster to calculate; it is good at catching errors due to accidental changes but is not resistant to malicious modifications. Note that, to be useful against an adversary who has access to the backup, the backup manifest would need to be stored securely elsewhere or otherwise verified not to have been modified since the backup was taken. pg_verifybackup can be used to check the integrity of a backup against the backup manifest. --manifest-force-encode Forces all filenames in the backup manifest to be hex-encoded. If this option is not specified, only non-UTF8 filenames are hex-encoded. This option is mostly intended to test that tools which read a backup manifest file properly handle this case. --no-estimate-size Prevents the server from estimating the total amount of backup data that will be streamed, resulting in the backup_total column in the pg_stat_progress_basebackup view always being NULL.Without this option, the backup will start by enumerating the size of the entire database, and then go back and send the actual contents. This may make the backup take slightly longer, and in particular it will take longer before the first data is sent. This option is useful to avoid such estimation time if it’s too long.This option is not allowed when using --progress. --no-manifest Disables generation of a backup manifest. If this option is not specified, the server will generate and send a backup manifest which can be verified using pg_verifybackup. The manifest is a list of every file present in the backup with the exception of any WAL files that may be included. It also stores the size, last modification time, and an optional checksum for each file. --no-slot Prevents the creation of a temporary replication slot for the backup.By default, if log streaming is selected but no slot name is given with the -S option, then a temporary replication slot is created (if supported by the source server).The main purpose of this option is to allow taking a base backup when the server has no free replication slots. Using a replication slot is almost always preferred, because it prevents needed WAL from being removed by the server during the backup. --no-verify-checksums Disables verification of checksums, if they are enabled on the server the base backup is taken from.By default, checksums are verified and checksum failures will result in a non-zero exit status. However, the base backup will not be removed in such a case, as if the --no-clean option had been used. Checksum verification failures will also be reported in the pg_stat_database view. The following command-line options control the connection to the source server: -d connstr --dbname=connstr Specifies parameters used to connect to the server, as a connection string; these will override any conflicting command line options.The option is called --dbname for consistency with other client applications, but because pg_basebackup doesn’t connect to any particular database in the cluster, any database name in the connection string will be ignored. -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for a Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -s interval --status-interval=interval Specifies the number of seconds between status packets sent back to the source server. Smaller values allow more accurate monitoring of backup progress from the server. A value of zero disables periodic status updates completely, although an update will still be sent when requested by the server, to avoid timeout-based disconnects. The default value is 10 seconds. -U username --username=username Specifies the user name to connect as. -w --no-password Prevents issuing a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Forces pg_basebackup to prompt for a password before connecting to the source server.This option is never essential, since pg_basebackup will automatically prompt for a password if the server demands password authentication. However, pg_basebackup will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. Other options are also available: -V --version Prints the pg_basebackup version and exits. -? --help Shows help about pg_basebackup command line arguments, and exits. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. At the beginning of the backup, a checkpoint needs to be performed on the source server. This can take some time (especially if the option --checkpoint=fast is not used), during which pg_basebackup will appear to be idle. The backup will include all files in the data directory and tablespaces, including the configuration files and any additional files placed in the directory by third parties, except certain temporary files managed by IvorySQL. But only regular files and directories are copied, except that symbolic links used for tablespaces are preserved. Symbolic links pointing to certain directories known to IvorySQL are copied as empty directories. Other symbolic links and special device files are skipped. In plain format, tablespaces will be backed up to the same path they have on the source server, unless the option --tablespace-mapping is used. Without this option, running a plain format base backup on the same host as the server will not work if tablespaces are in use, because the backup would have to be written to the same directory locations as the original tablespaces. When tar format is used, it is the user’s responsibility to unpack each tar file before starting a IvorySQL server that uses the data. If there are additional tablespaces, the tar files for them need to be unpacked in the correct locations. In this case the symbolic links for those tablespaces will be created by the server according to the contents of the tablespace_map file that is included in the base.tar file. pg_basebackup works with servers of the same or an older major version. pg_basebackup will preserve group permissions for data files if group permissions are enabled on the source cluster. To create a base backup of the server at mydbserver and store it in the local directory /usr/local/pgsql/data: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data To create a backup of the local server with one compressed tar file for each tablespace, and store it in the directory backup, showing a progress report while running: $ pg_basebackup -D backup -Ft -z -P To create a backup of a single-tablespace local database and compress this with bzip2: $ pg_basebackup -D - -Ft -X fetch | bzip2 > backup.tar.bz2 (This command will fail if there are multiple tablespaces in the database.) To create a backup of a local database where the tablespace in /opt/ts is relocated to ./backup/ts: $ pg_basebackup -D backup/data -T /opt/ts=$(pwd)/backup/ts To create a backup of a local server with one tar file for each tablespace compressed with gzip at level 9, stored in the directory backup: $ pg_basebackup -D backup -Ft --compress=gzip:9 pgbench — run a benchmark test on IvorySQL pgbench -i [option…​] [dbname] pgbench [option…​] [dbname] pgbench -i creates four tables pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers, destroying any existing tables of these names. Be very careful to use another database if you have tables having these names! At the default “scale factor” of 1, the tables initially contain this many rows: table # of rows --------------------------------- pgbench_branches 1 pgbench_tellers 10 pgbench_accounts 100000 pgbench_history 0 You can (and, for most purposes, probably should) increase the number of rows by using the -s (scale factor) option. The -F (fillfactor) option might also be used at this point. Once you have done the necessary setup, you can run your benchmark with a command that doesn’t include -i, that is pgbench [ options ] dbname In nearly all cases, you’ll need some options to make a useful test. The most important options are -c (number of clients), -t (number of transactions), -T (time limit), and -f (specify a custom script file). See below for a full list. The following is divided into three subsections. Different options are used during database initialization and while running benchmarks, but some options are useful in both cases. pgbench accepts the following command-line initialization arguments: dbname Specifies the name of the database to test in. If this is not specified, the environment variable PGDATABASE is used. If that is not set, the user name specified for the connection is used. -i --initialize Required to invoke initialization mode. -I init_steps --init-steps=init_steps Perform just a selected set of the normal initialization steps. init_steps specifies the initialization steps to be performed, using one character per step. Each step is invoked in the specified order. The default is dtgvp. The available steps are:`d` (Drop)Drop any existing pgbench tables.t (create Tables)Create the tables used by the standard pgbench scenario, namely pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers.g or G (Generate data, client-side or server-side)Generate data and load it into the standard tables, replacing any data already present.With g (client-side data generation), data is generated in pgbench client and then sent to the server. This uses the client/server bandwidth extensively through a COPY. pgbench uses the FREEZE option with version 14 or later of IvorySQL to speed up subsequent VACUUM, unless partitions are enabled. Using g causes logging to print one message every 100,000 rows while generating data for the pgbench_accounts table.With G (server-side data generation), only small queries are sent from the pgbench client and then data is actually generated in the server. No significant bandwidth is required for this variant, but the server will do more work. Using G causes logging not to print any progress message while generating data.The default initialization behavior uses client-side data generation (equivalent to g).v (Vacuum)Invoke VACUUM on the standard tables.p (create Primary keys)Create primary key indexes on the standard tables.f (create Foreign keys)Create foreign key constraints between the standard tables. (Note that this step is not performed by default.) -F fillfactor --fillfactor= fillfactor Create the pgbench_accounts, pgbench_tellers and pgbench_branches tables with the given fillfactor. Default is 100. -n --no-vacuum Perform no vacuuming during initialization. (This option suppresses the v initialization step, even if it was specified in -I.) -q --quiet Switch logging to quiet mode, producing only one progress message per 5 seconds. The default logging prints one message each 100,000 rows, which often outputs many lines per second (especially on good hardware).This setting has no effect if G is specified in -I. -s scale_factor --scale=scale_factor Multiply the number of rows generated by the scale factor. For example, -s 100 will create 10,000,000 rows in the pgbench_accounts table. Default is 1. When the scale is 20,000 or larger, the columns used to hold account identifiers (aid columns) will switch to using larger integers (bigint), in order to be big enough to hold the range of account identifiers. --foreign-keys Create foreign key constraints between the standard tables. (This option adds the f step to the initialization step sequence, if it is not already present.) --index-tablespace=index_tablespace Create indexes in the specified tablespace, rather than the default tablespace. --partition-method=`NAME` Create a partitioned pgbench_accounts table with NAME method. Expected values are range or hash. This option requires that --partitions is set to non-zero. If unspecified, default is range. --partitions=NUM Create a partitioned pgbench_accounts table with NUM partitions of nearly equal size for the scaled number of accounts. Default is 0, meaning no partitioning. --tablespace=`tablespace` Create tables in the specified tablespace, rather than the default tablespace. --unlogged-tables Create all tables as unlogged tables, rather than permanent tables. pgbench accepts the following command-line benchmarking arguments: -b scriptname[@weight] --builtin=scriptname[@weight] Add the specified built-in script to the list of scripts to be executed. Available built-in scripts are: tpcb-like, simple-update and select-only. Unambiguous prefixes of built-in names are accepted. With the special name list, show the list of built-in scripts and exit immediately.Optionally, write an integer weight after @ to adjust the probability of selecting this script versus other ones. The default weight is 1. See below for details. -c clients --client=clients Number of clients simulated, that is, number of concurrent database sessions. Default is 1. -C --connect Establish a new connection for each transaction, rather than doing it just once per client session. This is useful to measure the connection overhead. -d --debug Print debugging output. -D varname=value --define=varname=value Define a variable for use by a custom script (see below). Multiple -D options are allowed. -f filename[@weight] --file=filename[@weight] Add a transaction script read from filename to the list of scripts to be executed.Optionally, write an integer weight after @ to adjust the probability of selecting this script versus other ones. The default weight is 1. (To use a script file name that includes an @ character, append a weight so that there is no ambiguity, for example filen@me@1.) See below for details. -j threads --jobs=threads Number of worker threads within pgbench. Using more than one thread can be helpful on multi-CPU machines. Clients are distributed as evenly as possible among available threads. Default is 1. -l --log Write information about each transaction to a log file. See below for details. -L limit --latency-limit=limit Transactions that last more than limit milliseconds are counted and reported separately, as late.When throttling is used (--rate=…​), transactions that lag behind schedule by more than limit ms, and thus have no hope of meeting the latency limit, are not sent to the server at all. They are counted and reported separately as skipped.When the --max-tries option is used, a transaction which fails due to a serialization anomaly or from a deadlock will not be retried if the total time of all its tries is greater than limit ms. To limit only the time of tries and not their number, use --max-tries=0. By default, the option --max-tries is set to 1 and transactions with serialization/deadlock errors are not retried. -M querymode --protocol=querymode Protocol to use for submitting queries to the server:`simple`: use simple query protocol. extended : use extended query protocol. prepared : use extended query protocol with prepared statements.In the prepared mode, pgbench reuses the parse analysis result starting from the second query iteration, so pgbench runs faster than in other modes.The default is simple query protocol. -n --no-vacuum Perform no vacuuming before running the test. This option is necessary if you are running a custom test scenario that does not include the standard tables pgbench_accounts, pgbench_branches, pgbench_history, and pgbench_tellers. -N --skip-some-updates Run built-in simple-update script. Shorthand for -b simple-update. -P sec --progress=sec Show progress report every sec seconds. The report includes the time since the beginning of the run, the TPS since the last report, and the transaction latency average, standard deviation, and the number of failed transactions since the last report. Under throttling (-R), the latency is computed with respect to the transaction scheduled start time, not the actual transaction beginning time, thus it also includes the average schedule lag time. When --max-tries is used to enable transaction retries after serialization/deadlock errors, the report includes the number of retried transactions and the sum of all retries. -r --report-per-command Report the following statistics for each command after the benchmark finishes: the average per-statement latency (execution time from the perspective of the client), the number of failures, and the number of retries after serialization or deadlock errors in this command. The report displays retry statistics only if the --max-tries option is not equal to 1. -R rate --rate=rate Execute transactions targeting the specified rate instead of running as fast as possible (the default). The rate is given in transactions per second. If the targeted rate is above the maximum possible rate, the rate limit won’t impact the results.The rate is targeted by starting transactions along a Poisson-distributed schedule time line. The expected start time schedule moves forward based on when the client first started, not when the previous transaction ended. That approach means that when transactions go past their original scheduled end time, it is possible for later ones to catch up again.When throttling is active, the transaction latency reported at the end of the run is calculated from the scheduled start times, so it includes the time each transaction had to wait for the previous transaction to finish. The wait time is called the schedule lag time, and its average and maximum are also reported separately. The transaction latency with respect to the actual transaction start time, i.e., the time spent executing the transaction in the database, can be computed by subtracting the schedule lag time from the reported latency.If --latency-limit is used together with --rate, a transaction can lag behind so much that it is already over the latency limit when the previous transaction ends, because the latency is calculated from the scheduled start time. Such transactions are not sent to the server, but are skipped altogether and counted separately.A high schedule lag time is an indication that the system cannot process transactions at the specified rate, with the chosen number of clients and threads. When the average transaction execution time is longer than the scheduled interval between each transaction, each successive transaction will fall further behind, and the schedule lag time will keep increasing the longer the test run is. When that happens, you will have to reduce the specified transaction rate. -s scale_factor --scale=scale_factor Report the specified scale factor in pgbench’s output. With the built-in tests, this is not necessary; the correct scale factor will be detected by counting the number of rows in the pgbench_branches table. However, when testing only custom benchmarks (-f option), the scale factor will be reported as 1 unless this option is used. -S --select-only Run built-in select-only script. Shorthand for -b select-only. -t transactions --transactions=transactions Number of transactions each client runs. Default is 10. -T seconds --time=seconds Run the test for this many seconds, rather than a fixed number of transactions per client. -t and -T are mutually exclusive. -v --vacuum-all Vacuum all four standard tables before running the test. With neither -n nor -v, pgbench will vacuum the pgbench_tellers and pgbench_branches tables, and will truncate pgbench_history. --aggregate-interval=`seconds` Length of aggregation interval (in seconds). May be used only with -l option. With this option, the log contains per-interval summary data, as described below. --failures-detailed Report failures in per-transaction and aggregation logs, as well as in the main and per-script reports, grouped by the following types:serialization failures;deadlock failures. --log-prefix=`prefix` Set the filename prefix for the log files created by --log. The default is pgbench_log. --max-tries=`number_of_tries` Enable retries for transactions with serialization/deadlock errors and set the maximum number of these tries. This option can be combined with the --latency-limit option which limits the total time of all transaction tries; moreover, you cannot use an unlimited number of tries (--max-tries=0) without --latency-limit or --time. The default value is 1 and transactions with serialization/deadlock errors are not retried. --progress-timestamp When showing progress (option -P), use a timestamp (Unix epoch) instead of the number of seconds since the beginning of the run. The unit is in seconds, with millisecond precision after the dot. This helps compare logs generated by various tools. --random-seed=seed Set random generator seed. Seeds the system random number generator, which then produces a sequence of initial generator states, one for each thread. Values for seed may be: time (the default, the seed is based on the current time), rand (use a strong random source, failing if none is available), or an unsigned decimal integer value. The random generator is invoked explicitly from a pgbench script (random…​ functions) or implicitly (for instance option --rate uses it to schedule transactions). When explicitly set, the value used for seeding is shown on the terminal. Any value allowed for seed may also be provided through the environment variable PGBENCH_RANDOM_SEED. To ensure that the provided seed impacts all possible uses, put this option first or use the environment variable.Setting the seed explicitly allows to reproduce a pgbench run exactly, as far as random numbers are concerned. As the random state is managed per thread, this means the exact same pgbench run for an identical invocation if there is one client per thread and there are no external or data dependencies. From a statistical viewpoint reproducing runs exactly is a bad idea because it can hide the performance variability or improve performance unduly, e.g., by hitting the same pages as a previous run. However, it may also be of great help for debugging, for instance re-running a tricky case which leads to an error. Use wisely. --sampling-rate=`rate` Sampling rate, used when writing data into the log, to reduce the amount of log generated. If this option is given, only the specified fraction of transactions are logged. 1.0 means all transactions will be logged, 0.05 means only 5% of the transactions will be logged.Remember to take the sampling rate into account when processing the log file. For example, when computing TPS values, you need to multiply the numbers accordingly (e.g., with 0.01 sample rate, you’ll only get 1/100 of the actual TPS). --show-script=scriptname Show the actual code of builtin script scriptname on stderr, and exit immediately. --verbose-errors Print messages about all errors and failures (errors without retrying) including which limit for retries was exceeded and how far it was exceeded for the serialization/deadlock failures. (Note that in this case the output can be significantly increased.). pgbench also accepts the following common command-line arguments for connection parameters: -h hostname --host=hostname The database server’s host name -p port --port=port The database server’s port number -U login --username=login The user name to connect as -V --version Print the pgbench version and exit. -? --help Show help about pgbench command line arguments, and exit. A successful run will exit with status 0. Exit status 1 indicates static problems such as invalid command-line options or internal errors which are supposed to never occur. Early errors that occur when starting benchmark such as initial connection failures also exit with status 1. Errors during the run such as database errors or problems in the script will result in exit status 2. In the latter case, pgbench will print partial results. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_config — retrieve information about the installed version of IvorySQL pg_config [option…​] To use pg_config, supply one or more of the following options: --bindir Print the location of user executables. Use this, for example, to find the psql program. This is normally also the location where the pg_config program resides. --docdir Print the location of documentation files. --htmldir Print the location of HTML documentation files. --includedir Print the location of C header files of the client interfaces. --pkgincludedir Print the location of other C header files. --includedir-server Print the location of C header files for server programming. --libdir Print the location of object code libraries. --pkglibdir Print the location of dynamically loadable modules, or where the server would search for them. (Other architecture-dependent data files might also be installed in this directory.) --localedir Print the location of locale support files. (This will be an empty string if locale support was not configured when IvorySQL was built.) --mandir Print the location of manual pages. --sharedir Print the location of architecture-independent support files. --sysconfdir Print the location of system-wide configuration files. --pgxs Print the location of extension makefiles. --configure Print the options that were given to the configure script when IvorySQL was configured for building. This can be used to reproduce the identical configuration, or to find out with what options a binary package was built. (Note however that binary packages often contain vendor-specific custom patches.) See also the examples below. --cc Print the value of the CC variable that was used for building IvorySQL. This shows the C compiler used. --cppflags Print the value of the CPPFLAGS variable that was used for building IvorySQL. This shows C compiler switches needed at preprocessing time (typically, -I switches). --cflags Print the value of the CFLAGS variable that was used for building IvorySQL. This shows C compiler switches. --cflags_sl Print the value of the CFLAGS_SL variable that was used for building IvorySQL. This shows extra C compiler switches used for building shared libraries. --ldflags Print the value of the LDFLAGS variable that was used for building IvorySQL. This shows linker switches. --ldflags_ex Print the value of the LDFLAGS_EX variable that was used for building IvorySQL. This shows linker switches used for building executables only. --ldflags_sl Print the value of the LDFLAGS_SL variable that was used for building IvorySQL. This shows linker switches used for building shared libraries only. --libs Print the value of the LIBS variable that was used for building IvorySQL. This normally contains -l switches for external libraries linked into IvorySQL. --version Print the version of IvorySQL. -? --help Show help about pg_config command line arguments, and exit. If more than one option is given, the information is printed in that order, one item per line. If no options are given, all available information is printed, with labels. To reproduce the build configuration of the current IvorySQL installation, run the following command: eval ./configure `pg_config --configure` The output of pg_config --configure contains shell quotation marks so arguments with spaces are represented correctly. Therefore, using eval is required for proper results. pg_dump — extract a IvorySQL database into a script file or other archive file pg_dump [connection-option…​] [option…​] [dbname] The following command-line options control the content and format of the output. dbname Specifies the name of the database to be dumped. If this is not specified, the environment variable `PGDATABASE` is used. If that is not set, the user name specified for the connection is used. -a --data-only Dump only the data, not the schema (data definitions). Table data, large objects, and sequence values are dumped.This option is similar to, but for historical reasons not identical to, specifying `--section=data`. -b --blobs Include large objects in the dump. This is the default behavior except when `--schema`, `--table`, or `--schema-only` is specified. The `-b` switch is therefore only useful to add large objects to dumps where a specific schema or table has been requested. Note that blobs are considered data and therefore will be included when `--data-only` is used, but not when `--schema-only` is. -B --no-blobs Exclude large objects in the dump.When both `-b` and `-B` are given, the behavior is to output large objects, when data is being dumped, see the `-b` documentation. -c --clean Output commands to clean (drop) database objects prior to outputting the commands for creating them. (Unless `--if-exists` is also specified, restore might generate some harmless error messages, if any objects were not present in the destination database.)This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. -C --create Begin the output with a command to create the database itself and reconnect to the created database. (With a script of this form, it doesn't matter which database in the destination installation you connect to before running the script.) If `--clean` is also specified, the script drops and recreates the target database before reconnecting to it.With `--create`, the output also includes the database's comment if any, and any configuration variable settings that are specific to this database, that is, any `ALTER DATABASE ... SET ...` and `ALTER ROLE ... IN DATABASE ... SET ...` commands that mention this database. Access privileges for the database itself are also dumped, unless `--no-acl` is specified.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. -e `pattern` --extension=`pattern` Dump only extensions matching *`pattern`*. When this option is not specified, all non-system extensions in the target database will be dumped. Multiple extensions can be selected by writing multiple `-e` switches. The *`pattern`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands , so multiple extensions can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards.Any configuration relation registered by `pg_extension_config_dump` is included in the dump if its extension is specified by `--extension`.NoteWhen `-e` is specified, pg_dump makes no attempt to dump any other database objects that the selected extension(s) might depend upon. Therefore, there is no guarantee that the results of a specific-extension dump can be successfully restored by themselves into a clean database. -E `encoding` --encoding=`encoding` Create the dump in the specified character set encoding. By default, the dump is created in the database encoding. (Another way to get the same result is to set the `PGCLIENTENCODING` environment variable to the desired dump encoding.) -f `file` --file=`file` Send output to the specified file. This parameter can be omitted for file based output formats, in which case the standard output is used. It must be given for the directory output format however, where it specifies the target directory instead of a file. In this case the directory is created by `pg_dump` and must not exist before. -F `format` --format=`format` Selects the format of the output. *`format`* can be one of the following:`p` `plain`Output a plain-text SQL script file (the default).`c` `custom`Output a custom-format archive suitable for input into pg_restore. Together with the directory output format, this is the most flexible output format in that it allows manual selection and reordering of archived items during restore. This format is also compressed by default.`d` `directory`Output a directory-format archive suitable for input into pg_restore. This will create a directory with one file for each table and blob being dumped, plus a so-called Table of Contents file describing the dumped objects in a machine-readable format that pg_restore can read. A directory format archive can be manipulated with standard Unix tools; for example, files in an uncompressed archive can be compressed with the gzip tool. This format is compressed by default and also supports parallel dumps.`t` `tar`Output a `tar`-format archive suitable for input into pg_restore. The tar format is compatible with the directory format: extracting a tar-format archive produces a valid directory-format archive. However, the tar format does not support compression. Also, when using tar format the relative order of table data items cannot be changed during restore. -j `njobs` --jobs=`njobs` Run the dump in parallel by dumping *`njobs`* tables simultaneously. This option may reduce the time needed to perform the dump but it also increases the load on the database server. You can only use this option with the directory output format because this is the only output format where multiple processes can write their data at the same time.pg_dump will open *`njobs`* + 1 connections to the database, so make sure your http://www.postgresql.org/docs/17/runtime-config-connection.html#GUC-MAX-CONNECTIONS[max_connections] setting is high enough to accommodate all connections.Requesting exclusive locks on database objects while running a parallel dump could cause the dump to fail. The reason is that the pg_dump leader process requests shared locks on the objects that the worker processes are going to dump later in order to make sure that nobody deletes them and makes them go away while the dump is running. If another client then requests an exclusive lock on a table, that lock will not be granted but will be queued waiting for the shared lock of the leader process to be released. Consequently any other access to the table will not be granted either and will queue after the exclusive lock request. This includes the worker process trying to dump the table. Without any precautions this would be a classic deadlock situation. To detect this conflict, the pg_dump worker process requests another shared lock using the `NOWAIT` option. If the worker process is not granted this shared lock, somebody else must have requested an exclusive lock in the meantime and there is no way to continue with the dump, so pg_dump has no choice but to abort the dump.To perform a parallel dump, the database server needs to support synchronized snapshots, a feature that was introduced in IvorySQL for primary servers and 10 for standbys. With this feature, database clients can ensure they see the same data set even though they use different connections. `pg_dump -j` uses multiple database connections; it connects to the database once with the leader process and once again for each worker job. Without the synchronized snapshot feature, the different worker jobs wouldn't be guaranteed to see the same data in each connection, which could lead to an inconsistent backup. -n `pattern` --schema=`pattern` Dump only schemas matching *`pattern`*; this selects both the schema itself, and all its contained objects. When this option is not specified, all non-system schemas in the target database will be dumped. Multiple schemas can be selected by writing multiple `-n` switches. The *`pattern`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands, so multiple schemas can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards. NoteWhen `-n` is specified, pg_dump makes no attempt to dump any other database objects that the selected schema(s) might depend upon. Therefore, there is no guarantee that the results of a specific-schema dump can be successfully restored by themselves into a clean database.NoteNon-schema objects such as blobs are not dumped when `-n` is specified. You can add blobs back to the dump with the `--blobs` switch. -N `pattern` --exclude-schema=`pattern` Do not dump any schemas matching *`pattern`*. The pattern is interpreted according to the same rules as for `-n`. `-N` can be given more than once to exclude schemas matching any of several patterns.When both `-n` and `-N` are given, the behavior is to dump just the schemas that match at least one `-n` switch but no `-N` switches. If `-N` appears without `-n`, then schemas matching `-N` are excluded from what is otherwise a normal dump. -O --no-owner Do not output commands to set ownership of objects to match the original database. By default, pg_dump issues `ALTER OWNER` or `SET SESSION AUTHORIZATION` statements to set ownership of created database objects. These statements will fail when the script is run unless it is started by a superuser (or the same user that owns all of the objects in the script). To make a script that can be restored by any user, but will give that user ownership of all the objects, specify `-O`.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. -R --no-reconnect This option is obsolete but still accepted for backwards compatibility. -s --schema-only Dump only the object definitions (schema), not data.This option is the inverse of `--data-only`. It is similar to, but for historical reasons not identical to, specifying `--section=pre-data --section=post-data`.(Do not confuse this with the `--schema` option, which uses the word “schema” in a different meaning.)To exclude table data for only a subset of tables in the database, see `--exclude-table-data`. -S `username` --superuser=`username` Specify the superuser user name to use when disabling triggers. This is relevant only if `--disable-triggers` is used. (Usually, it's better to leave this out, and instead start the resulting script as superuser.) -t `pattern` --table=`pattern` Dump only tables with names matching *`pattern`*. Multiple tables can be selected by writing multiple `-t` switches. The *`pattern`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands , so multiple tables can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards; As well as tables, this option can be used to dump the definition of matching views, materialized views, foreign tables, and sequences. It will not dump the contents of views or materialized views, and the contents of foreign tables will only be dumped if the corresponding foreign server is specified with `--include-foreign-data`.The `-n` and `-N` switches have no effect when `-t` is used, because tables selected by `-t` will be dumped regardless of those switches, and non-table objects will not be dumped.NoteWhen `-t` is specified, pg_dump makes no attempt to dump any other database objects that the selected table(s) might depend upon. Therefore, there is no guarantee that the results of a specific-table dump can be successfully restored by themselves into a clean database. -T `pattern` --exclude-table=`pattern` Do not dump any tables matching *`pattern`*. The pattern is interpreted according to the same rules as for `-t`. `-T` can be given more than once to exclude tables matching any of several patterns.When both `-t` and `-T` are given, the behavior is to dump just the tables that match at least one `-t` switch but no `-T` switches. If `-T` appears without `-t`, then tables matching `-T` are excluded from what is otherwise a normal dump. -v --verbose Specifies verbose mode. This will cause pg_dump to output detailed object comments and start/stop times to the dump file, and progress messages to standard error. Repeating the option causes additional debug-level messages to appear on standard error. -V --version Print the pg_dump version and exit. -x --no-privileges --no-acl Prevent dumping of access privileges (grant/revoke commands). -Z `0..9` --compress=`0..9` Specify the compression level to use. Zero means no compression. For the custom and directory archive formats, this specifies compression of individual table-data segments, and the default is to compress at a moderate level. For plain text output, setting a nonzero compression level causes the entire output file to be compressed, as though it had been fed through gzip; but the default is not to compress. The tar archive format currently does not support compression at all. --binary-upgrade This option is for use by in-place upgrade utilities. Its use for other purposes is not recommended or supported. The behavior of the option may change in future releases without notice. --column-inserts --attribute-inserts Dump data as `INSERT` commands with explicit column names (`INSERT INTO *`table`* (*`column`*, ...) VALUES ...`). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. Any error during restoring will cause only rows that are part of the problematic `INSERT` to be lost, rather than the entire table contents. --disable-dollar-quoting This option disables the use of dollar quoting for function bodies, and forces them to be quoted using SQL standard string syntax. --disable-triggers This option is relevant only when creating a data-only dump. It instructs pg_dump to include commands to temporarily disable triggers on the target tables while the data is restored. Use this if you have referential integrity checks or other triggers on the tables that you do not want to invoke during data restore.Presently, the commands emitted for `--disable-triggers` must be done as superuser. So, you should also specify a superuser name with `-S`, or preferably be careful to start the resulting script as a superuser.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. --enable-row-security This option is relevant only when dumping the contents of a table which has row security. By default, pg_dump will set http://www.postgresql.org/docs/17/runtime-config-client.html#GUC-ROW-SECURITY[row_security] to off, to ensure that all data is dumped from the table. If the user does not have sufficient privileges to bypass row security, then an error is thrown. This parameter instructs pg_dump to set http://www.postgresql.org/docs/17/runtime-config-client.html#GUC-ROW-SECURITY[row_security] to on instead, allowing the user to dump the parts of the contents of the table that they have access to.Note that if you use this option currently, you probably also want the dump be in `INSERT` format, as the `COPY FROM` during restore does not support row security. --exclude-table-data=`pattern` Do not dump data for any tables matching *`pattern`*. The pattern is interpreted according to the same rules as for `-t`. `--exclude-table-data` can be given more than once to exclude tables matching any of several patterns. This option is useful when you need the definition of a particular table even though you do not need the data in it.To exclude data for all tables in the database, see `--schema-only`. --extra-float-digits=`ndigits` Use the specified value of `extra_float_digits` when dumping floating-point data, instead of the maximum available precision. Routine dumps made for backup purposes should not use this option. --if-exists Use conditional commands (i.e., add an `IF EXISTS` clause) when cleaning database objects. This option is not valid unless `--clean` is also specified. --include-foreign-data=`foreignserver` Dump the data for any foreign table with a foreign server matching *`foreignserver`* pattern. Multiple foreign servers can be selected by writing multiple `--include-foreign-data` switches. Also, the *`foreignserver`* parameter is interpreted as a pattern according to the same rules used by psql's `\\d` commands, so multiple foreign servers can also be selected by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent the shell from expanding the wildcards; The only exception is that an empty pattern is disallowed.NoteWhen `--include-foreign-data` is specified, pg_dump does not check that the foreign table is writable. Therefore, there is no guarantee that the results of a foreign table dump can be successfully restored. --inserts Dump data as `INSERT` commands (rather than `COPY`). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. Any error during restoring will cause only rows that are part of the problematic `INSERT` to be lost, rather than the entire table contents. Note that the restore might fail altogether if you have rearranged column order. The `--column-inserts` option is safe against column order changes, though even slower. --load-via-partition-root When dumping data for a table partition, make the `COPY` or `INSERT` statements target the root of the partitioning hierarchy that contains it, rather than the partition itself. This causes the appropriate partition to be re-determined for each row when the data is loaded. This may be useful when restoring data on a server where rows do not always fall into the same partitions as they did on the original server. That could happen, for example, if the partitioning column is of type text and the two systems have different definitions of the collation used to sort the partitioning column.It is best not to use parallelism when restoring from an archive made with this option, because pg_restore will not know exactly which partition(s) a given archive data item will load data into. This could result in inefficiency due to lock conflicts between parallel jobs, or perhaps even restore failures due to foreign key constraints being set up before all the relevant data is loaded. --lock-wait-timeout=`timeout` Do not wait forever to acquire shared table locks at the beginning of the dump. Instead fail if unable to lock a table within the specified *`timeout`*. The timeout may be specified in any of the formats accepted by `SET statement_timeout` --no-comments Do not dump comments. --no-publications Do not dump publications. --no-security-labels Do not dump security labels. --no-subscriptions Do not dump subscriptions. --no-sync By default, `pg_dump` will wait for all files to be written safely to disk. This option causes `pg_dump` to return without waiting, which is faster, but means that a subsequent operating system crash can leave the dump corrupt. Generally, this option is useful for testing but should not be used when dumping data from production installation. --no-tablespaces Do not output commands to select tablespaces. With this option, all objects will be created in whichever tablespace is the default during restore.This option is ignored when emitting an archive (non-text) output file. For the archive formats, you can specify the option when you call `pg_restore`. --no-toast-compression Do not output commands to set TOAST compression methods. With this option, all columns will be restored with the default compression setting. --no-unlogged-table-data Do not dump the contents of unlogged tables and sequences. This option has no effect on whether or not the table and sequence definitions (schema) are dumped; it only suppresses dumping the table and sequence data. Data in unlogged tables and sequences is always excluded when dumping from a standby server. --on-conflict-do-nothing Add `ON CONFLICT DO NOTHING` to `INSERT` commands. This option is not valid unless `--inserts`, `--column-inserts` or `--rows-per-insert` is also specified. --quote-all-identifiers Force quoting of all identifiers. This option is recommended when dumping a database from a server whose IvorySQL major version is different from pg_dump's, or when the output is intended to be loaded into a server of a different major version. By default, pg_dump quotes only identifiers that are reserved words in its own major version. This sometimes results in compatibility issues when dealing with servers of other versions that may have slightly different sets of reserved words. Using `--quote-all-identifiers` prevents such issues, at the price of a harder-to-read dump script. --rows-per-insert=`nrows` Dump data as `INSERT` commands (rather than `COPY`). Controls the maximum number of rows per `INSERT` command. The value specified must be a number greater than zero. Any error during restoring will cause only rows that are part of the problematic `INSERT` to be lost, rather than the entire table contents. --section=`sectionname` Only dump the named section. The section name can be `pre-data`, `data`, or `post-data`. This option can be specified more than once to select multiple sections. The default is to dump all sections.The data section contains actual table data, large-object contents, and sequence values. Post-data items include definitions of indexes, triggers, rules, and constraints other than validated check constraints. Pre-data items include all other data definition items. --serializable-deferrable Use a `serializable` transaction for the dump, to ensure that the snapshot used is consistent with later database states; but do this by waiting for a point in the transaction stream at which no anomalies can be present, so that there isn't a risk of the dump failing or causing other transactions to roll back with a `serialization_failure`.This option is not beneficial for a dump which is intended only for disaster recovery. It could be useful for a dump used to load a copy of the database for reporting or other read-only load sharing while the original database continues to be updated. Without it the dump may reflect a state which is not consistent with any serial execution of the transactions eventually committed. For example, if batch processing techniques are used, a batch may show as closed in the dump without all of the items which are in the batch appearing.This option will make no difference if there are no read-write transactions active when pg_dump is started. If read-write transactions are active, the start of the dump may be delayed for an indeterminate length of time. Once running, performance with or without the switch is the same. --snapshot=`snapshotname` Use the specified synchronized snapshot when making a dump of the database.This option is useful when needing to synchronize the dump with a logical replication slot or with a concurrent session.In the case of a parallel dump, the snapshot name defined by this option is used rather than taking a new snapshot. --strict-names Require that each extension (`-e`/`--extension`), schema (`-n`/`--schema`) and table (`-t`/`--table`) qualifier match at least one extension/schema/table in the database to be dumped. Note that if none of the extension/schema/table qualifiers find matches, pg_dump will generate an error even without `--strict-names`.This option has no effect on `-N`/`--exclude-schema`, `-T`/`--exclude-table`, or `--exclude-table-data`. An exclude pattern failing to match any objects is not considered an error. --use-set-session-authorization Output SQL-standard `SET SESSION AUTHORIZATION` commands instead of `ALTER OWNER` commands to determine object ownership. This makes the dump more standards-compatible, but depending on the history of the objects in the dump, might not restore properly. Also, a dump using `SET SESSION AUTHORIZATION` will certainly require superuser privileges to restore correctly, whereas `ALTER OWNER` requires lesser privileges. -? --help Show help about pg_dump command line arguments, and exit. The following command-line options control the database connection parameters. -d `dbname` --dbname=`dbname` Specifies the name of the database to connect to. This is equivalent to specifying *`dbname`* as the first non-option argument on the command line. The *`dbname`* can be a http://www.postgresql.org/docs/17/libpq-connect.html#LIBPQ-CONNSTRING[connection string]. If so, connection string parameters will override any conflicting command line options. -h `host` --host=`host` Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the `PGHOST` environment variable, if set, else a Unix domain socket connection is attempted. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the `PGPORT` environment variable, if set, or a compiled-in default. -U `username` --username=`username` User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a `.pgpass` file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_dump to prompt for a password before connecting to a database.This option is never essential, since pg_dump will automatically prompt for a password if the server demands password authentication. However, pg_dump will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing `-W` to avoid the extra connection attempt. --role=`rolename` Specifies a role name to be used to create the dump. This option causes pg_dump to issue a `SET ROLE` *`rolename`* command after connecting to the database. It is useful when the authenticated user (specified by `-U`) lacks privileges needed by pg_dump, but can switch to a role with the required rights. Some installations have a policy against logging in directly as a superuser, and use of this option allows dumps to be made without violating the policy. PGDATABASE PGHOST PGOPTIONS PGPORT PGUSER Default connection parameters. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are `always`, `auto` and `never`. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq. pg_dump internally executes SELECT statements. If you have problems running pg_dump, make sure you are able to select information from the database using, for example, psql. Also, any default connection settings and environment variables used by the libpq front-end library will apply. The database activity of pg_dump is normally collected by the cumulative statistics system. If this is undesirable, you can set parameter track_counts to false via PGOPTIONS or the ALTER USER command. If your database cluster has any local additions to the template1 database, be careful to restore the output of pg_dump into a truly empty database; otherwise you are likely to get errors due to duplicate definitions of the added objects. To make an empty database without any local additions, copy from template0 not template1, for example: CREATE DATABASE foo WITH TEMPLATE template0; When a data-only dump is chosen and the option --disable-triggers is used, pg_dump emits commands to disable triggers on user tables before inserting the data, and then commands to re-enable them after the data has been inserted. If the restore is stopped in the middle, the system catalogs might be left in the wrong state. The dump file produced by pg_dump does not contain the statistics used by the optimizer to make query planning decisions. Therefore, it is wise to run ANALYZE after restoring from a dump file to ensure optimal performance. When dumping logical replication subscriptions, pg_dump will generate CREATE SUBSCRIPTION commands that use the connect = false option, so that restoring the subscription does not make remote connections for creating a replication slot or for initial table copy. That way, the dump can be restored without requiring network access to the remote servers. It is then up to the user to reactivate the subscriptions in a suitable way. If the involved hosts have changed, the connection information might have to be changed. It might also be appropriate to truncate the target tables before initiating a new full table copy. If users intend to copy initial data during refresh they must create the slot with two_phase = false. After the initial sync, the two_phase option will be automatically enabled by the subscriber if the subscription had been originally created with two_phase = true option. To dump a database called mydb into an SQL-script file: $ pg_dump mydb > db.sql To reload such a script into a (freshly created) database named newdb: $ psql -d newdb -f db.sql To dump a database into a custom-format archive file: $ pg_dump -Fc mydb > db.dump To dump a database into a directory-format archive: $ pg_dump -Fd mydb -f dumpdir To dump a database into a directory-format archive in parallel with 5 worker jobs: $ pg_dump -Fd mydb -j 5 -f dumpdir To reload an archive file into a (freshly created) database named newdb: $ pg_restore -d newdb db.dump To reload an archive file into the same database it was dumped from, discarding the current contents of that database: $ pg_restore -d postgres --clean --create db.dump To dump a single table named mytab: $ pg_dump -t mytab mydb > db.sql To dump all tables whose names start with emp in the detroit schema, except for the table named employee_log: $ pg_dump -t 'detroit.emp*' -T detroit.employee_log mydb > db.sql To dump all schemas whose names start with east or west and end in gsm, excluding any schemas whose names contain the word test: $ pg_dump -n 'east*gsm' -n 'west*gsm' -N '*test*' mydb > db.sql The same, using regular expression notation to consolidate the switches: $ pg_dump -n '(east|west)*gsm' -N '*test*' mydb > db.sql To dump all database objects except for tables whose names begin with ts_: $ pg_dump -T 'ts_*' mydb > db.sql To specify an upper-case or mixed-case name in -t and related switches, you need to double-quote the name; else it will be folded to lower case.But double quotes are special to the shell, so in turn they must be quoted. Thus, to dump a single table with a mixed-case name, you need something like $ pg_dump -t \"\\\"MixedCaseName\\\"\" mydb > mytab.sql pg_dumpall — extract a IvorySQL database cluster into a script file pg_dumpall [connection-option…​] [option…​] The following command-line options control the content and format of the output. -a --data-only Dump only the data, not the schema (data definitions). -c --clean Include SQL commands to clean (drop) databases before recreating them. DROP commands for roles and tablespaces are added as well. -E encoding --encoding=encoding Create the dump in the specified character set encoding. By default, the dump is created in the database encoding. (Another way to get the same result is to set the PGCLIENTENCODING environment variable to the desired dump encoding.) -f filename --file=filename Send output to the specified file. If this is omitted, the standard output is used. -g --globals-only Dump only global objects (roles and tablespaces), no databases. -O --no-owner Do not output commands to set ownership of objects to match the original database. By default, pg_dumpall issues ALTER OWNER or SET SESSION AUTHORIZATION statements to set ownership of created schema elements. These statements will fail when the script is run unless it is started by a superuser (or the same user that owns all of the objects in the script). To make a script that can be restored by any user, but will give that user ownership of all the objects, specify -O. -r --roles-only Dump only roles, no databases or tablespaces. -s --schema-only Dump only the object definitions (schema), not data. -S username --superuser=username Specify the superuser user name to use when disabling triggers. This is relevant only if --disable-triggers is used. (Usually, it’s better to leave this out, and instead start the resulting script as superuser.) -t --tablespaces-only Dump only tablespaces, no databases or roles. -v --verbose Specifies verbose mode. This will cause pg_dumpall to output start/stop times to the dump file, and progress messages to standard error. Repeating the option causes additional debug-level messages to appear on standard error. The option is also passed down to pg_dump. -V --version Print the pg_dumpall version and exit. -x --no-privileges --no-acl Prevent dumping of access privileges (grant/revoke commands). --binary-upgrade This option is for use by in-place upgrade utilities. Its use for other purposes is not recommended or supported. The behavior of the option may change in future releases without notice. --column-inserts --attribute-inserts Dump data as INSERT commands with explicit column names (INSERT INTO table (column, …​) VALUES …​). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. --disable-dollar-quoting This option disables the use of dollar quoting for function bodies, and forces them to be quoted using SQL standard string syntax. --disable-triggers This option is relevant only when creating a data-only dump. It instructs pg_dumpall to include commands to temporarily disable triggers on the target tables while the data is restored. Use this if you have referential integrity checks or other triggers on the tables that you do not want to invoke during data restore.Presently, the commands emitted for --disable-triggers must be done as superuser. So, you should also specify a superuser name with -S, or preferably be careful to start the resulting script as a superuser. --exclude-database=`pattern` Do not dump databases whose name matches pattern. Multiple patterns can be excluded by writing multiple --exclude-database switches. The pattern parameter is interpreted as a pattern according to the same rules used by psql’s \\d commands, so multiple databases can also be excluded by writing wildcard characters in the pattern. When using wildcards, be careful to quote the pattern if needed to prevent shell wildcard expansion. --extra-float-digits=`ndigits` Use the specified value of extra_float_digits when dumping floating-point data, instead of the maximum available precision. Routine dumps made for backup purposes should not use this option. --if-exists Use conditional commands (i.e., add an IF EXISTS clause) to drop databases and other objects. This option is not valid unless --clean is also specified. --inserts Dump data as INSERT commands (rather than COPY). This will make restoration very slow; it is mainly useful for making dumps that can be loaded into non-IvorySQL databases. Note that the restore might fail altogether if you have rearranged column order. The --column-inserts option is safer, though even slower. --load-via-partition-root When dumping data for a table partition, make the COPY or INSERT statements target the root of the partitioning hierarchy that contains it, rather than the partition itself. This causes the appropriate partition to be re-determined for each row when the data is loaded. This may be useful when restoring data on a server where rows do not always fall into the same partitions as they did on the original server. That could happen, for example, if the partitioning column is of type text and the two systems have different definitions of the collation used to sort the partitioning column. --lock-wait-timeout=`timeout` Do not wait forever to acquire shared table locks at the beginning of the dump. Instead, fail if unable to lock a table within the specified timeout. The timeout may be specified in any of the formats accepted by SET statement_timeout. --no-comments Do not dump comments. --no-publications Do not dump publications. --no-role-passwords Do not dump passwords for roles. When restored, roles will have a null password, and password authentication will always fail until the password is set. Since password values aren’t needed when this option is specified, the role information is read from the catalog view pg_roles instead of pg_authid. Therefore, this option also helps if access to pg_authid is restricted by some security policy. --no-security-labels Do not dump security labels. --no-subscriptions Do not dump subscriptions. --no-sync By default, pg_dumpall will wait for all files to be written safely to disk. This option causes pg_dumpall to return without waiting, which is faster, but means that a subsequent operating system crash can leave the dump corrupt. Generally, this option is useful for testing but should not be used when dumping data from production installation. --no-table-access-method Do not output commands to select table access methods. With this option, all objects will be created with whichever table access method is the default during restore. --no-tablespaces Do not output commands to create tablespaces nor select tablespaces for objects. With this option, all objects will be created in whichever tablespace is the default during restore. --no-toast-compression Do not output commands to set TOAST compression methods. With this option, all columns will be restored with the default compression setting. --no-unlogged-table-data Do not dump the contents of unlogged tables. This option has no effect on whether or not the table definitions (schema) are dumped; it only suppresses dumping the table data. --on-conflict-do-nothing Add ON CONFLICT DO NOTHING to INSERT commands. This option is not valid unless --inserts or --column-inserts is also specified. --quote-all-identifiers Force quoting of all identifiers. This option is recommended when dumping a database from a server whose IvorySQL major version is different from pg_dumpall’s, or when the output is intended to be loaded into a server of a different major version. By default, pg_dumpall quotes only identifiers that are reserved words in its own major version. This sometimes results in compatibility issues when dealing with servers of other versions that may have slightly different sets of reserved words. Using --quote-all-identifiers prevents such issues, at the price of a harder-to-read dump script. --rows-per-insert=`nrows` Dump data as INSERT commands (rather than COPY). Controls the maximum number of rows per INSERT command. The value specified must be a number greater than zero. Any error during restoring will cause only rows that are part of the problematic INSERT to be lost, rather than the entire table contents. --use-set-session-authorization Output SQL-standard SET SESSION AUTHORIZATION commands instead of ALTER OWNER commands to determine object ownership. This makes the dump more standards compatible, but depending on the history of the objects in the dump, might not restore properly. -? --help Show help about pg_dumpall command line arguments, and exit. The following command-line options control the database connection parameters. -d connstr --dbname=connstr Specifies parameters used to connect to the server, as a connection string; these will override any conflicting command line options.The option is called --dbname for consistency with other client applications, but because pg_dumpall needs to connect to many databases, the database name in the connection string will be ignored. Use the -l option to specify the name of the database used for the initial connection, which will dump global objects and discover what other databases should be dumped. -h host --host=host Specifies the host name of the machine on which the database server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -l dbname --database=dbname Specifies the name of the database to connect to for dumping global objects and discovering what other databases should be dumped. If not specified, the postgres database will be used, and if that does not exist, template1 will be used. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_dumpall to prompt for a password before connecting to a database.This option is never essential, since pg_dumpall will automatically prompt for a password if the server demands password authentication. However, pg_dumpall will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt.Note that the password prompt will occur again for each database to be dumped. Usually, it’s better to set up a ~/.pgpass file than to rely on manual password entry. --role=`rolename` Specifies a role name to be used to create the dump. This option causes pg_dumpall to issue a SET ROLE rolename command after connecting to the database. It is useful when the authenticated user (specified by -U) lacks privileges needed by pg_dumpall, but can switch to a role with the required rights. Some installations have a policy against logging in directly as a superuser, and use of this option allows dumps to be made without violating the policy. PGHOST PGOPTIONS PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq Since pg_dumpall calls pg_dump internally, some diagnostic messages will refer to pg_dump. The --clean option can be useful even when your intention is to restore the dump script into a fresh cluster. Use of --clean authorizes the script to drop and re-create the built-in postgres and template1 databases, ensuring that those databases will retain the same properties (for instance, locale and encoding) that they had in the source cluster. Without the option, those databases will retain their existing database-level properties, as well as any pre-existing contents. Once restored, it is wise to run ANALYZE on each database so the optimizer has useful statistics. You can also run vacuumdb -a -z to analyze all databases. The dump script should not be expected to run completely without errors. In particular, because the script will issue CREATE ROLE for every role existing in the source cluster, it is certain to get a “role already exists” error for the bootstrap superuser, unless the destination cluster was initialized with a different bootstrap superuser name. This error is harmless and should be ignored. Use of the --clean option is likely to produce additional harmless error messages about non-existent objects, although you can minimize those by adding --if-exists. pg_dumpall requires all needed tablespace directories to exist before the restore; otherwise, database creation will fail for databases in non-default locations. To dump all databases: $ pg_dumpall > db.out To restore database(s) from this file, you can use: $ psql -f db.out postgres It is not important to which database you connect here since the script file created by pg_dumpall will contain the appropriate commands to create and connect to the saved databases. An exception is that if you specified --clean, you must connect to the postgres database initially; the script will attempt to drop other databases immediately, and that will fail for the database you are connected to. pg_isready — check the connection status of a IvorySQL server pg_isready [connection-option…​] [option…​] -d dbname --dbname=dbname Specifies the name of the database to connect to. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -h hostname --host=hostname Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix-domain socket. -p port --port=port Specifies the TCP port or the local Unix-domain socket file extension on which the server is listening for connections. Defaults to the value of the PGPORT environment variable or, if not set, to the port specified at compile time, usually 5432. -q --quiet Do not display status message. This is useful when scripting. -t seconds --timeout=seconds The maximum number of seconds to wait when attempting connection before returning that the server is not responding. Setting to 0 disables. The default is 3 seconds. -U username --username=username Connect to the database as the user username instead of the default. -V --version Print the pg_isready version and exit. -? --help Show help about pg_isready command line arguments, and exit. pg_isready returns 0 to the shell if the server is accepting connections normally, 1 if the server is rejecting connections (for example during startup), 2 if there was no response to the connection attempt, and 3 if no attempt was made (for example due to invalid parameters). pg_isready, like most other IvorySQL utilities, also uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. It is not necessary to supply correct user name, password, or database name values to obtain the server status; however, if incorrect values are provided, the server will log a failed connection attempt. Standard Usage: $ pg_isready /tmp:5432 - accepting connections $ echo $? 0 Running with connection parameters to a IvorySQL cluster in startup: $ pg_isready -h localhost -p 5433 localhost:5433 - rejecting connections $ echo $? 1 Running with connection parameters to a non-responsive IvorySQL cluster: $ pg_isready -h someremotehost someremotehost:5432 - no response $ echo $? 2 pg_receivewal — stream write-ahead logs from a IvorySQL server pg_receivewal [option…​] -D directory --directory=directory Directory to write the output to.This parameter is required. -E lsn --endpos=lsn Automatically stop replication and exit with normal exit status 0 when receiving reaches the specified LSN.If there is a record with LSN exactly equal to lsn, the record will be processed. --if-not-exists Do not error out when --create-slot is specified and a slot with the specified name already exists. -n --no-loop Don’t loop on connection errors. Instead, exit right away with an error. --no-sync This option causes pg_receivewal to not force WAL data to be flushed to disk. This is faster, but means that a subsequent operating system crash can leave the WAL segments corrupt. Generally, this option is useful for testing but should not be used when doing WAL archiving on a production deployment.This option is incompatible with --synchronous. -s interval --status-interval=interval Specifies the number of seconds between status packets sent back to the server. This allows for easier monitoring of the progress from server. A value of zero disables the periodic status updates completely, although an update will still be sent when requested by the server, to avoid timeout disconnect. The default value is 10 seconds. -S slotname --slot=slotname Require pg_receivewal to use an existing replication slot, When this option is used, pg_receivewal will report a flush position to the server, indicating when each segment has been synchronized to disk so that the server can remove that segment if it is not otherwise needed.When the replication client of pg_receivewal is configured on the server as a synchronous standby, then using a replication slot will report the flush position to the server, but only when a WAL file is closed. Therefore, that configuration will cause transactions on the primary to wait for a long time and effectively not work satisfactorily. The option --synchronous (see below) must be specified in addition to make this work correctly. --synchronous Flush the WAL data to disk immediately after it has been received. Also send a status packet back to the server immediately after flushing, regardless of --status-interval.This option should be specified if the replication client of pg_receivewal is configured on the server as a synchronous standby, to ensure that timely feedback is sent to the server. -v --verbose Enables verbose mode. -Z level -Z method[:*detail*] --compress=level --compress=method[:*detail*] Enables compression of write-ahead logs.The compression method can be set to gzip, lz4 (if IvorySQL was compiled with --with-lz4) or none for no compression. A compression detail string can optionally be specified. If the detail string is an integer, it specifies the compression level. Otherwise, it should be a comma-separated list of items, each of the form keyword or keyword=value. Currently, the only supported keyword is level.If no compression level is specified, the default compression level will be used. If only a level is specified without mentioning an algorithm, gzip compression will be used if the level is greater than 0, and no compression will be used if the level is 0.The suffix .gz will automatically be added to all filenames when using gzip, and the suffix .lz4 is added when using lz4. The following command-line options control the database connection parameters. -d connstr --dbname=connstr Specifies parameters used to connect to the server, as a connection string; these will override any conflicting command line options.The option is called --dbname for consistency with other client applications, but because pg_receivewal doesn’t connect to any particular database in the cluster, database name in the connection string will be ignored. -h `host` --host=`host` Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U `username` --username=`username` User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_receivewal to prompt for a password before connecting to a database.This option is never essential, since pg_receivewal will automatically prompt for a password if the server demands password authentication. However, pg_receivewal will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. pg_receivewal can perform one of the two following actions in order to control physical replication slots: --create-slot Create a new physical replication slot with the name specified in --slot, then exit. --drop-slot Drop the replication slot with the name specified in --slot, then exit. Other options are also available: -V --version Print the pg_receivewal version and exit. -? --help Show help about pg_receivewal command line arguments, and exit. pg_receivewal will exit with status 0 when terminated by the SIGINT signal. (That is the normal way to end it. Hence it is not an error.) For fatal errors or other signals, the exit status will be nonzero. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. When using pg_receivewal instead of archive_command as the main WAL backup method, it is strongly recommended to use replication slots. Otherwise, the server is free to recycle or remove write-ahead log files before they are backed up, because it does not have any information, either from archive_command or the replication slots, about how far the WAL stream has been archived. Note, however, that a replication slot will fill up the server’s disk space if the receiver does not keep up with fetching the WAL data. pg_receivewal will preserve group permissions on the received WAL files if group permissions are enabled on the source cluster. To stream the write-ahead log from the server at mydbserver and store it in the local directory /usr/local/pgsql/archive: $ pg_receivewal -h mydbserver -D /usr/local/pgsql/archive pg_recvlogical — control IvorySQL logical decoding streams pg_recvlogical [option…​] At least one of the following options must be specified to select an action: --create-slot Create a new logical replication slot with the name specified by --slot, using the output plugin specified by --plugin, for the database specified by --dbname.The --two-phase can be specified with --create-slot to enable decoding of prepared transactions. --drop-slot Drop the replication slot with the name specified by --slot, then exit. --start Begin streaming changes from the logical replication slot specified by --slot, continuing until terminated by a signal. If the server side change stream ends with a server shutdown or disconnect, retry in a loop unless --no-loop is specified.The stream format is determined by the output plugin specified when the slot was created.The connection must be to the same database used to create the slot. --create-slot and --start can be specified together. --drop-slot cannot be combined with another action. The following command-line options control the location and format of the output and other replication behavior: -E lsn --endpos=lsn In --start mode, automatically stop replication and exit with normal exit status 0 when receiving reaches the specified LSN. If specified when not in --start mode, an error is raised.If there’s a record with LSN exactly equal to lsn, the record will be output.The --endpos option is not aware of transaction boundaries and may truncate output partway through a transaction. Any partially output transaction will not be consumed and will be replayed again when the slot is next read from. Individual messages are never truncated. -f filename --file=filename Write received and decoded transaction data into this file. Use - for stdout. -F interval_seconds --fsync-interval=interval_seconds Specifies how often pg_recvlogical should issue fsync() calls to ensure the output file is safely flushed to disk.The server will occasionally request the client to perform a flush and report the flush position to the server. This setting is in addition to that, to perform flushes more frequently.Specifying an interval of 0 disables issuing fsync() calls altogether, while still reporting progress to the server. In this case, data could be lost in the event of a crash. -I lsn --startpos=lsn In --start mode, start replication from the given LSN. For details on the effect of this. --if-not-exists Do not error out when --create-slot is specified and a slot with the specified name already exists. -n --no-loop When the connection to the server is lost, do not retry in a loop, just exit. -o name[=value] --option=name[=value] Pass the option name to the output plugin with, if specified, the option value value. Which options exist and their effects depends on the used output plugin. -P plugin --plugin=plugin When creating a slot, use the specified logical decoding output plugin. This option has no effect if the slot already exists. -s interval_seconds --status-interval=interval_seconds This option has the same effect as the option of the same name in pg_receivewal. See the description there. -S slot_name --slot=slot_name In --start mode, use the existing logical replication slot named slot_name. In --create-slot mode, create the slot with this name. In --drop-slot mode, delete the slot with this name. -t --two-phase Enables decoding of prepared transactions. This option may only be specified with --create-slot -v --verbose Enables verbose mode. The following command-line options control the database connection parameters. -d dbname --dbname=dbname The database to connect to. See the description of the actions for what this means in detail. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. Defaults to the user name. -h hostname-or-ip --host=hostname-or-ip Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U user --username=user User name to connect as. Defaults to current operating system user name. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_recvlogical to prompt for a password before connecting to a database.This option is never essential, since pg_recvlogical will automatically prompt for a password if the server demands password authentication. However, pg_recvlogical will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. The following additional options are available: -V --version Print the pg_recvlogical version and exit. -? --help Show help about pg_recvlogical command line arguments, and exit. This utility, like most other IvorySQL utilities, uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_recvlogical will preserve group permissions on the received WAL files if group permissions are enabled on the source cluster. pg_restore — restore a IvorySQL database from an archive file created by pg_dump pg_restore [connection-option…​] [option…​] [filename] pg_restore accepts the following command line arguments. filename Specifies the location of the archive file (or directory, for a directory-format archive) to be restored. If not specified, the standard input is used. -a --data-only Restore only the data, not the schema (data definitions). Table data, large objects, and sequence values are restored, if present in the archive.This option is similar to, but for historical reasons not identical to, specifying --section=data. -c --clean Clean (drop) database objects before recreating them. (Unless --if-exists is used, this might generate some harmless error messages, if any objects were not present in the destination database.) -C --create Create the database before restoring into it. If --clean is also specified, drop and recreate the target database before connecting to it.With --create, pg_restore also restores the database’s comment if any, and any configuration variable settings that are specific to this database, that is, any ALTER DATABASE …​ SET …​ and ALTER ROLE …​ IN DATABASE …​ SET …​ commands that mention this database. Access privileges for the database itself are also restored, unless --no-acl is specified.When this option is used, the database named with -d is used only to issue the initial DROP DATABASE and CREATE DATABASE commands. All data is restored into the database name that appears in the archive. -d dbname --dbname=dbname Connect to database dbname and restore directly into the database. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --exit-on-error Exit if an error is encountered while sending SQL commands to the database. The default is to continue and to display a count of errors at the end of the restoration. -f `filename` --file=`filename` Specify output file for generated script, or for the listing when used with -l. Use - for stdout. -F format --format=format Specify format of the archive. It is not necessary to specify the format, since pg_restore will determine the format automatically. If specified, it can be one of the following:`c` custom`The archive is in the custom format of pg_dump.`d directory`The archive is a directory archive.`t tar`The archive is a `tar archive. -I index --index=index Restore definition of named index only. Multiple indexes may be specified with multiple -I switches. -j number-of-jobs --jobs=number-of-jobs Run the most time-consuming steps of pg_restore — those that load data, create indexes, or create constraints — concurrently, using up to number-of-jobs concurrent sessions. This option can dramatically reduce the time to restore a large database to a server running on a multiprocessor machine. This option is ignored when emitting a script rather than connecting directly to a database server.Each job is one process or one thread, depending on the operating system, and uses a separate connection to the server.The optimal value for this option depends on the hardware setup of the server, of the client, and of the network. Factors include the number of CPU cores and the disk setup. A good place to start is the number of CPU cores on the server, but values larger than that can also lead to faster restore times in many cases. Of course, values that are too high will lead to decreased performance because of thrashing.Only the custom and directory archive formats are supported with this option. The input must be a regular file or directory (not, for example, a pipe or standard input). Also, multiple jobs cannot be used together with the option --single-transaction. -l --list List the table of contents of the archive. The output of this operation can be used as input to the -L option. Note that if filtering switches such as -n or -t are used with -l, they will restrict the items listed. -L list-file --use-list=list-file Restore only those archive elements that are listed in list-file, and restore them in the order they appear in the file. Note that if filtering switches such as -n or -t are used with -L, they will further restrict the items restored.list-file is normally created by editing the output of a previous -l operation. Lines can be moved or removed, and can also be commented out by placing a semicolon (;) at the start of the line. See below for examples. -n schema --schema=schema Restore only objects that are in the named schema. Multiple schemas may be specified with multiple -n switches. This can be combined with the -t option to restore just a specific table. -N schema --exclude-schema=schema Do not restore objects that are in the named schema. Multiple schemas to be excluded may be specified with multiple -N switches.When both -n and -N are given for the same schema name, the -N switch wins and the schema is excluded. -O --no-owner Do not output commands to set ownership of objects to match the original database. By default, pg_restore issues ALTER OWNER or SET SESSION AUTHORIZATION statements to set ownership of created schema elements. These statements will fail unless the initial connection to the database is made by a superuser (or the same user that owns all of the objects in the script). With -O, any user name can be used for the initial connection, and this user will own all the created objects. -P function-name(argtype [, …​]) --function=function-name(argtype [, …​]) Restore the named function only. Be careful to spell the function name and arguments exactly as they appear in the dump file’s table of contents. Multiple functions may be specified with multiple -P switches. -R --no-reconnect This option is obsolete but still accepted for backwards compatibility. -s --schema-only Restore only the schema (data definitions), not data, to the extent that schema entries are present in the archive.This option is the inverse of --data-only. It is similar to, but for historical reasons not identical to, specifying --section=pre-data --section=post-data.(Do not confuse this with the --schema option, which uses the word “schema” in a different meaning.) -S username --superuser=username Specify the superuser user name to use when disabling triggers. This is relevant only if --disable-triggers is used. -t table --table=table Restore definition and/or data of only the named table. For this purpose, “table” includes views, materialized views, sequences, and foreign tables. Multiple tables can be selected by writing multiple -t switches. This option can be combined with the -n option to specify table(s) in a particular schema.NoteWhen -t is specified, pg_restore makes no attempt to restore any other database objects that the selected table(s) might depend upon. Therefore, there is no guarantee that a specific-table restore into a clean database will succeed.NoteThis flag does not behave identically to the -t flag of pg_dump. There is not currently any provision for wild-card matching in pg_restore, nor can you include a schema name within its -t. And, while pg_dump’s -t flag will also dump subsidiary objects (such as indexes) of the selected table(s), pg_restore’s -t flag does not include such subsidiary objects. -T trigger --trigger=trigger Restore named trigger only. Multiple triggers may be specified with multiple -T switches. -v --verbose Specifies verbose mode. This will cause pg_restore to output detailed object comments and start/stop times to the output file, and progress messages to standard error. Repeating the option causes additional debug-level messages to appear on standard error. -V --version Print the pg_restore version and exit. -x --no-privileges --no-acl Prevent restoration of access privileges (grant/revoke commands). -1 --single-transaction Execute the restore as a single transaction (that is, wrap the emitted commands in BEGIN / COMMIT). This ensures that either all the commands complete successfully, or no changes are applied. This option implies --exit-on-error. --disable-triggers This option is relevant only when performing a data-only restore. It instructs pg_restore to execute commands to temporarily disable triggers on the target tables while the data is restored. Use this if you have referential integrity checks or other triggers on the tables that you do not want to invoke during data restore.Presently, the commands emitted for --disable-triggers must be done as superuser. So you should also specify a superuser name with -S or, preferably, run pg_restore as a IvorySQL superuser. --enable-row-security This option is relevant only when restoring the contents of a table which has row security. By default, pg_restore will set row_security to off, to ensure that all data is restored in to the table. If the user does not have sufficient privileges to bypass row security, then an error is thrown. This parameter instructs pg_restore to set row_security to on instead, allowing the user to attempt to restore the contents of the table with row security enabled. This might still fail if the user does not have the right to insert the rows from the dump into the table.Note that this option currently also requires the dump be in INSERT format, as COPY FROM does not support row security. --if-exists Use conditional commands (i.e., add an IF EXISTS clause) to drop database objects. This option is not valid unless --clean is also specified. --no-comments Do not output commands to restore comments, even if the archive contains them. --no-data-for-failed-tables By default, table data is restored even if the creation command for the table failed (e.g., because it already exists). With this option, data for such a table is skipped. This behavior is useful if the target database already contains the desired table contents. For example, auxiliary tables for IvorySQL extensions such as PostGIS might already be loaded in the target database; specifying this option prevents duplicate or obsolete data from being loaded into them.This option is effective only when restoring directly into a database, not when producing SQL script output. --no-publications Do not output commands to restore publications, even if the archive contains them. --no-security-labels Do not output commands to restore security labels, even if the archive contains them. --no-subscriptions Do not output commands to restore subscriptions, even if the archive contains them. --no-table-access-method Do not output commands to select table access methods. With this option, all objects will be created with whichever access method is the default during restore. --no-tablespaces Do not output commands to select tablespaces. With this option, all objects will be created in whichever tablespace is the default during restore. --section=`sectionname` Only restore the named section. The section name can be pre-data, data, or post-data. This option can be specified more than once to select multiple sections. The default is to restore all sections.The data section contains actual table data as well as large-object definitions. Post-data items consist of definitions of indexes, triggers, rules and constraints other than validated check constraints. Pre-data items consist of all other data definition items. --strict-names Require that each schema (-n/--schema) and table (-t/--table) qualifier match at least one schema/table in the backup file. --use-set-session-authorization Output SQL-standard SET SESSION AUTHORIZATION commands instead of ALTER OWNER commands to determine object ownership. This makes the dump more standards-compatible, but depending on the history of the objects in the dump, might not restore properly. -? --help Show help about pg_restore command line arguments, and exit. pg_restore also accepts the following command line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. The default is taken from the PGHOST environment variable, if set, else a Unix domain socket connection is attempted. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults to the PGPORT environment variable, if set, or a compiled-in default. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force pg_restore to prompt for a password before connecting to a database.This option is never essential, since pg_restore will automatically prompt for a password if the server demands password authentication. However, pg_restore will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --role=`rolename` Specifies a role name to be used to perform the restore. This option causes pg_restore to issue a SET ROLE rolename command after connecting to the database. It is useful when the authenticated user (specified by -U) lacks privileges needed by pg_restore, but can switch to a role with the required rights. Some installations have a policy against logging in directly as a superuser, and use of this option allows restores to be performed without violating the policy. PGHOST PGOPTIONS PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq. However, it does not read PGDATABASE when a database name is not supplied. When a direct database connection is specified using the -d option, pg_restore internally executes SQL statements. If you have problems running pg_restore, make sure you are able to select information from the database using, for example, psql. Also, any default connection settings and environment variables used by the libpq front-end library will apply. If your installation has any local additions to the template1 database, be careful to load the output of pg_restore into a truly empty database; otherwise you are likely to get errors due to duplicate definitions of the added objects. To make an empty database without any local additions, copy from template0 not template1, for example: CREATE DATABASE foo WITH TEMPLATE template0; The limitations of pg_restore are detailed below. When restoring data to a pre-existing table and the option --disable-triggers is used, pg_restore emits commands to disable triggers on user tables before inserting the data, then emits commands to re-enable them after the data has been inserted. If the restore is stopped in the middle, the system catalogs might be left in the wrong state. pg_restore cannot restore large objects selectively; for instance, only those for a specific table. If an archive contains large objects, then all large objects will be restored, or none of them if they are excluded via -L, -t, or other options. Assume we have dumped a database called mydb into a custom-format dump file: $ pg_dump -Fc mydb > db.dump To drop the database and recreate it from the dump: $ dropdb mydb $ pg_restore -C -d postgres db.dump The database named in the -d switch can be any database existing in the cluster; pg_restore only uses it to issue the CREATE DATABASE command for mydb. With -C, data is always restored into the database name that appears in the dump file. To restore the dump into a new database called newdb: $ createdb -T template0 newdb $ pg_restore -d newdb db.dump Notice we don’t use -C, and instead connect directly to the database to be restored into. Also note that we clone the new database from template0 not template1, to ensure it is initially empty. To reorder database items, it is first necessary to dump the table of contents of the archive: $ pg_restore -l db.dump > db.list The listing file consists of a header and one line for each item, e.g.: ; ; Archive created at Mon Sep 14 13:55:39 2009 ; dbname: DBDEMOS ; TOC Entries: 81 ; Compression: 9 ; Dump Version: 1.10-0 ; Format: CUSTOM ; Integer: 4 bytes ; Offset: 8 bytes ; Dumped from database version: 8.3.5 ; Dumped by pg_dump version: 8.3.8 ; ; ; Selected TOC Entries: ; 3; 2615 2200 SCHEMA - public pasha 1861; 0 0 COMMENT - SCHEMA public pasha 1862; 0 0 ACL - public pasha 317; 1247 17715 TYPE public composite pasha 319; 1247 25899 DOMAIN public domain0 pasha Semicolons start a comment, and the numbers at the start of lines refer to the internal archive ID assigned to each item. Lines in the file can be commented out, deleted, and reordered. For example: 10; 145433 TABLE map_resolutions postgres ;2; 145344 TABLE species postgres ;4; 145359 TABLE nt_header postgres 6; 145402 TABLE species_records postgres ;8; 145416 TABLE ss_old postgres could be used as input to pg_restore and would only restore items 10 and 6, in that order: $ pg_restore -L db.list db.dump pg_verifybackup — verify the integrity of a base backup of a IvorySQL cluster pg_verifybackup [option…​] pg_verifybackup accepts the following command-line arguments: -e --exit-on-error Exit as soon as a problem with the backup is detected. If this option is not specified, pg_verifybackup will continue checking the backup even after a problem has been detected, and will report all problems detected as errors. -i path --ignore=path Ignore the specified file or directory, which should be expressed as a relative path name, when comparing the list of data files actually present in the backup to those listed in the backup_manifest file. If a directory is specified, this option affects the entire subtree rooted at that location. Complaints about extra files, missing files, file size differences, or checksum mismatches will be suppressed if the relative path name matches the specified path name. This option can be specified multiple times. -m path --manifest-path=path Use the manifest file at the specified path, rather than one located in the root of the backup directory. -n --no-parse-wal Don’t attempt to parse write-ahead log data that will be needed to recover from this backup. -q --quiet Don’t print anything when a backup is successfully verified. -s --skip-checksums Do not verify data file checksums. The presence or absence of files and the sizes of those files will still be checked. This is much faster, because the files themselves do not need to be read. -w path --wal-directory=path Try to parse WAL files stored in the specified directory, rather than in pg_wal. This may be useful if the backup is stored in a separate location from the WAL archive. Other options are also available: -V --version Print the pg_verifybackup version and exit. -? --help Show help about pg_verifybackup command line arguments, and exit. To create a base backup of the server at mydbserver and verify the integrity of the backup: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ pg_verifybackup /usr/local/pgsql/data To create a base backup of the server at mydbserver, move the manifest somewhere outside the backup directory, and verify the backup: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/backup1234 $ mv /usr/local/pgsql/backup1234/backup_manifest /my/secure/location/backup_manifest.1234 $ pg_verifybackup -m /my/secure/location/backup_manifest.1234 /usr/local/pgsql/backup1234 To verify a backup while ignoring a file that was added manually to the backup directory, and also skipping checksum verification: $ pg_basebackup -h mydbserver -D /usr/local/pgsql/data $ edit /usr/local/pgsql/data/note.to.self $ pg_verifybackup --ignore=note.to.self --skip-checksums /usr/local/pgsql/data psql — IvorySQL interactive terminal psql [option…​] [dbname [username]] -a --echo-all Print all nonempty input lines to standard output as they are read. (This does not apply to lines read interactively.) This is equivalent to setting the variable ECHO to all. -A --no-align Switches to unaligned output mode. (The default output mode is aligned.) This is equivalent to \\pset format unaligned. -b --echo-errors Print failed SQL commands to standard error output. This is equivalent to setting the variable ECHO to errors. -c command --command=command Specifies that psql is to execute the given command string, command. This option can be repeated and combined in any order with the -f option. When either -c or -f is specified, psql does not read commands from standard input; instead it terminates after processing all the -c and -f options in sequence.command must be either a command string that is completely parsable by the server (i.e., it contains no psql-specific features), or a single backslash command. Thus you cannot mix SQL and psql meta-commands within a -c option. To achieve that, you could use repeated -c options or pipe the string into psql, for example:`psql -c '\\x' -c 'SELECT * FROM foo;' or`echo '\\x \\\\ SELECT * FROM foo;' | psql `(\\\\` is the separator meta-command.)Each SQL command string passed to -c is sent to the server as a single request. Because of this, the server executes it as a single transaction even if the string contains multiple SQL commands, unless there are explicit BEGIN/COMMIT commands included in the string to divide it into multiple transactions.If having several commands executed in one transaction is not desired, use repeated -c commands or feed multiple commands to psql’s standard input, either using echo as illustrated above, or via a shell here-document, for example:`psql <<EOF \\x SELECT * FROM foo; EOF ` --csv Switches to CSV (Comma-Separated Values) output mode. This is equivalent to \\pset format csv. -d dbname --dbname=dbname Specifies the name of the database to connect to. This is equivalent to specifying dbname as the first non-option argument on the command line. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --echo-queries Copy all SQL commands sent to the server to standard output as well. This is equivalent to setting the variable ECHO to queries. -E --echo-hidden Echo the actual queries generated by \\d and other backslash commands. You can use this to study psql’s internal operations. This is equivalent to setting the variable ECHO_HIDDEN to on. -f filename --file=filename Read commands from the file filename, rather than standard input. This option can be repeated and combined in any order with the -c option. When either -c or -f is specified, psql does not read commands from standard input; instead it terminates after processing all the -c and -f options in sequence. Except for that, this option is largely equivalent to the meta-command \\i.If filename is - (hyphen), then standard input is read until an EOF indication or \\q meta-command. This can be used to intersperse interactive input with input from files. Note however that Readline is not used in this case (much as if -n had been specified).Using this option is subtly different from writing psql < `filename. In general, both will do what you expect, but using `-f enables some nice features such as error messages with line numbers. There is also a slight chance that using this option will reduce the start-up overhead. On the other hand, the variant using the shell’s input redirection is (in theory) guaranteed to yield exactly the same output you would have received had you entered everything by hand. -F separator --field-separator=separator Use separator as the field separator for unaligned output. This is equivalent to \\pset fieldsep or \\f. -h hostname --host=hostname Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix-domain socket. -H --html Switches to HTML output mode. This is equivalent to \\pset format html or the \\H command. -l --list List all available databases, then exit. Other non-connection options are ignored. This is similar to the meta-command \\list.When this option is used, psql will connect to the database postgres, unless a different database is named on the command line (option -d or non-option argument, possibly via a service entry, but not via an environment variable). -L filename --log-file=filename Write all query output into file filename, in addition to the normal output destination. -n --no-readline Do not use Readline for line editing and do not use the command history -o filename --output=filename Put all query output into file filename. This is equivalent to the command \\o. -p port --port=port Specifies the TCP port or the local Unix-domain socket file extension on which the server is listening for connections. Defaults to the value of the PGPORT environment variable or, if not set, to the port specified at compile time, usually 5432. -P assignment --pset=assignment Specifies printing options, in the style of \\pset. Note that here you have to separate name and value with an equal sign instead of a space. For example, to set the output format to LaTeX, you could write -P format=latex. -q --quiet Specifies that psql should do its work quietly. By default, it prints welcome messages and various informational output. If this option is used, none of this happens. This is useful with the -c option. This is equivalent to setting the variable QUIET to on. -R separator --record-separator=separator Use separator as the record separator for unaligned output. This is equivalent to \\pset recordsep. -s --single-step Run in single-step mode. That means the user is prompted before each command is sent to the server, with the option to cancel execution as well. Use this to debug scripts. -S --single-line Runs in single-line mode where a newline terminates an SQL command, as a semicolon does.NoteThis mode is provided for those who insist on it, but you are not necessarily encouraged to use it. In particular, if you mix SQL and meta-commands on a line the order of execution might not always be clear to the inexperienced user. -t --tuples-only Turn off printing of column names and result row count footers, etc. This is equivalent to \\t or \\pset tuples_only. -T table_options --table-attr=table_options Specifies options to be placed within the HTML table tag. See \\pset tableattr for details. -U `username` --username=`username` Connect to the database as the user username instead of the default. (You must have permission to do so, of course.) -v assignment --set=assignment --variable=assignment Perform a variable assignment, like the \\set meta-command. Note that you must separate name and value, if any, by an equal sign on the command line. To unset a variable, leave off the equal sign. To set a variable with an empty value, use the equal sign but leave off the value. These assignments are done during command line processing, so variables that reflect connection state will get overwritten later. -V --version Print the psql version and exit. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available from other sources such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password.Note that this option will remain set for the entire session, and so it affects uses of the meta-command \\connect as well as the initial connection attempt. -W --password Force psql to prompt for a password before connecting to a database, even if the password will not be used.If the server requires password authentication and a password is not available from other sources such as a .pgpass file, psql will prompt for a password in any case. However, psql will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt.Note that this option will remain set for the entire session, and so it affects uses of the meta-command \\connect as well as the initial connection attempt. -x --expanded Turn on the expanded table formatting mode. This is equivalent to \\x or \\pset expanded. -X, --no-psqlrc Do not read the start-up file (neither the system-wide psqlrc file nor the user’s ~/.psqlrc file). -z --field-separator-zero Set the field separator for unaligned output to a zero byte. This is equivalent to \\pset fieldsep_zero. -0 --record-separator-zero Set the record separator for unaligned output to a zero byte. This is useful for interfacing, for example, with xargs -0. This is equivalent to \\pset recordsep_zero. -1 --single-transaction This option can only be used in combination with one or more -c and/or -f options. It causes psql to issue a BEGIN command before the first such option and a COMMIT command after the last one, thereby wrapping all the commands into a single transaction. If any of the commands fails and the variable ON_ERROR_STOP was set, a ROLLBACK command is sent instead. This ensures that either all the commands complete successfully, or no changes are applied.If the commands themselves contain BEGIN, COMMIT, or ROLLBACK, this option will not have the desired effects. Also, if an individual command cannot be executed inside a transaction block, specifying this option will cause the whole transaction to fail. -? --help[=`topic]` Show help about psql and exit. The optional topic parameter (defaulting to options) selects which part of psql is explained: commands describes psql’s backslash commands; options describes the command-line options that can be passed to psql; and variables shows help about psql configuration variables. psql returns 0 to the shell if it finished normally, 1 if a fatal error of its own occurs (e.g., out of memory, file not found), 2 if the connection to the server went bad and the session was not interactive, and 3 if an error occurred in a script and the variable ON_ERROR_STOP was set. reindexdb — reindex a IvorySQL database reindexdb [connection-option…​] [option…​] [ -S | --schema schema ] …​ [ -t | --table table ] …​ [ -i | --index index ] …​ [dbname] reindexdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all reindexdb [connection-option…​] [option…​] -s | --system [dbname] reindexdb accepts the following command-line arguments: -a --all Reindex all databases. --concurrently Use the CONCURRENTLY option. See REINDEX, where all the caveats of this option are explained in detail. [-d] dbname dbname Specifies the name of the database to be reindexed, when -a/--all is not used. If this is not specified, the database name is read from the environment variable PGDATABASE. If that is not set, the user name specified for the connection is used. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. -e --echo Echo the commands that reindexdb generates and sends to the server. -i index --index=index Recreate index only. Multiple indexes can be recreated by writing multiple -i switches. -j njobs --jobs=njobs Execute the reindex commands in parallel by running njobs commands simultaneously. This option may reduce the processing time but it also increases the load on the database server.reindexdb will open njobs connections to the database, so make sure your max_connections setting is high enough to accommodate all connections.Note that this option is incompatible with the --index and --system options. -q --quiet Do not display progress messages. -s --system Reindex database’s system catalogs only. -S schema --schema=schema Reindex schema only. Multiple schemas can be reindexed by writing multiple -S switches. -t table --table=table Reindex table only. Multiple tables can be reindexed by writing multiple -t switches. --tablespace=tablespace Specifies the tablespace where indexes are rebuilt. (This name is processed as a double-quoted identifier.) -v --verbose Print detailed information during processing. -V --version Print the reindexdb version and exit. -? --help Show help about reindexdb command line arguments, and exit. reindexdb also accepts the following command-line arguments for connection parameters: -h host --host=host Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p port --port=port Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U username --username=username User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force reindexdb to prompt for a password before connecting to a database.This option is never essential, since reindexdb will automatically prompt for a password if the server demands password authentication. However, reindexdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to to discover which databases should be reindexed, when -a / --all is used. If not specified, the postgres database will be used, or if that does not exist, template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. Also, connection string parameters other than the database name itself will be re-used when connecting to other databases. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq . In case of difficulty, see REINDEX and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. reindexdb might need to connect several times to the IvorySQL server, asking for a password each time. It is convenient to have a ~/.pgpass file in such cases. To reindex the database test: $ reindexdb test To reindex the table foo and the index bar in a database named abcd: $ reindexdb --table=foo --index=bar abcd vacuumdb — garbage-collect and analyze a IvorySQL database vacuumdb [connection-option…​] [option…​] [ -t | --table table [( column [,…​] )] ] …​ [dbname] vacuumdb` [*`connection-option`*...] [*`option`*...] `-a` | `--all vacuumdb accepts the following command-line arguments: -a --all Vacuum all databases. [-d] `dbname` `dbname` Specifies the name of the database to be cleaned or analyzed, when -a/--all is not used. If this is not specified, the database name is read from the environment variable PGDATABASE. If that is not set, the user name specified for the connection is used. The dbname can be a connection string. If so, connection string parameters will override any conflicting command line options. --disable-page-skipping Disable skipping pages based on the contents of the visibility map. -e --echo Echo the commands that vacuumdb generates and sends to the server. -f --full Perform “full” vacuuming. -F --freeze Aggressively “freeze” tuples. --force-index-cleanup Always remove index entries pointing to dead tuples. -j njobs --jobs=njobs Execute the vacuum or analyze commands in parallel by running njobs commands simultaneously. This option may reduce the processing time but it also increases the load on the database server.vacuumdb will open njobs connections to the database, so make sure your max_connections setting is high enough to accommodate all connections.Note that using this mode together with the -f (FULL) option might cause deadlock failures if certain system catalogs are processed in parallel. --min-mxid-age mxid_age Only execute the vacuum or analyze commands on tables with a multixact ID age of at least mxid_age. This setting is useful for prioritizing tables to process to prevent multixact ID wraparound .For the purposes of this option, the multixact ID age of a relation is the greatest of the ages of the main relation and its associated TOAST table, if one exists. Since the commands issued by vacuumdb will also process the TOAST table for the relation if necessary, it does not need to be considered separately. --min-xid-age xid_age Only execute the vacuum or analyze commands on tables with a transaction ID age of at least xid_age. This setting is useful for prioritizing tables to process to prevent transaction ID wraparound.For the purposes of this option, the transaction ID age of a relation is the greatest of the ages of the main relation and its associated TOAST table, if one exists. Since the commands issued by vacuumdb will also process the TOAST table for the relation if necessary, it does not need to be considered separately. --no-index-cleanup Do not remove index entries pointing to dead tuples. --no-process-toast Skip the TOAST table associated with the table to vacuum, if any. --no-truncate Do not truncate empty pages at the end of the table. -P parallel_workers --parallel=parallel_workers Specify the number of parallel workers for parallel vacuum. This allows the vacuum to leverage multiple CPUs to process indexes. See VACUUM. -q --quiet Do not display progress messages. --skip-locked Skip relations that cannot be immediately locked for processing -t `table [ (column [,…​]) ]` --table=`table [ (column [,…​]) ]` Clean or analyze table only. Column names can be specified only in conjunction with the --analyze or --analyze-only options. Multiple tables can be vacuumed by writing multiple -t switches.TipIf you specify columns, you probably have to escape the parentheses from the shell. (See examples below.) -v --verbose Print detailed information during processing. -V --version Print the vacuumdb version and exit. -z --analyze Also calculate statistics for use by the optimizer. -Z --analyze-only Only calculate statistics for use by the optimizer (no vacuum). --analyze-in-stages Only calculate statistics for use by the optimizer (no vacuum), like --analyze-only. Run three stages of analyze; the first stage uses the lowest possible statistics target and subsequent stages build the full statistics.This option is only useful to analyze a database that currently has no statistics or has wholly incorrect ones, such as if it is newly populated from a restored dump or by pg_upgrade. Be aware that running with this option in a database with existing statistics may cause the query optimizer choices to become transiently worse due to the low statistics targets of the early stages. -? --help Show help about vacuumdb command line arguments, and exit. vacuumdb also accepts the following command-line arguments for connection parameters: -h `host` --host=`host` Specifies the host name of the machine on which the server is running. If the value begins with a slash, it is used as the directory for the Unix domain socket. -p `port` --port=`port` Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. -U `username` --username=`username` User name to connect as. -w --no-password Never issue a password prompt. If the server requires password authentication and a password is not available by other means such as a .pgpass file, the connection attempt will fail. This option can be useful in batch jobs and scripts where no user is present to enter a password. -W --password Force vacuumdb to prompt for a password before connecting to a database.This option is never essential, since vacuumdb will automatically prompt for a password if the server demands password authentication. However, vacuumdb will waste a connection attempt finding out that the server wants a password. In some cases it is worth typing -W to avoid the extra connection attempt. --maintenance-db=dbname Specifies the name of the database to connect to to discover which databases should be vacuumed, when -a / --all is used. If not specified, the postgres database will be used, or if that does not exist, template1 will be used. This can be a connection string. If so, connection string parameters will override any conflicting command line options. Also, connection string parameters other than the database name itself will be re-used when connecting to other databases. PGDATABASE PGHOST PGPORT PGUSER Default connection parameters PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq. In case of difficulty, see VACUUM and psql for discussions of potential problems and error messages. The database server must be running at the targeted host. Also, any default connection settings and environment variables used by the libpq front-end library will apply. vacuumdb might need to connect several times to the IvorySQL server, asking for a password each time. It is convenient to have a ~/.pgpass file in such cases. To clean the database test: $ vacuumdb test To clean and analyze for the optimizer a database named bigdb: $ vacuumdb --analyze bigdb To clean a single table foo in a database named xyzzy, and analyze a single column bar of the table for the optimizer: $ vacuumdb --analyze --verbose --table='foo(bar)' xyzzy initdb — create a new IvorySQL database cluster initdb [option…​] [ --pgdata | -D ] directory -A authmethod --auth=authmethod This option specifies the default authentication method for local users used in pg_hba.conf (host and local lines). Do not use trust unless you trust all local users on your system. trust is the default for ease of installation. --auth-host=`authmethod` This option specifies the authentication method for local users via TCP/IP connections used in pg_hba.conf (host lines). --auth-local=`authmethod` This option specifies the authentication method for local users via Unix-domain socket connections used in pg_hba.conf (local lines). -D directory --pgdata=directory This option specifies the directory where the database cluster should be stored. This is the only information required by initdb, but you can avoid writing it by setting the PGDATA environment variable, which can be convenient since the database server (postgres) can find the database directory later by the same variable. -E `encoding` --encoding=`encoding` Selects the encoding of the template databases. This will also be the default encoding of any database you create later, unless you override it then. The default is derived from the locale, if the libc locale provider is used, or UTF8 if the ICU locale provider is used. -g --allow-group-access Allows users in the same group as the cluster owner to read all cluster files created by initdb. This option is ignored on Windows as it does not support POSIX-style group permissions. --icu-locale=locale Specifies the ICU locale ID, if the ICU locale provider is used. -k --data-checksums Use checksums on data pages to help detect corruption by the I/O system that would otherwise be silent. Enabling checksums may incur a noticeable performance penalty. If set, checksums are calculated for all objects, in all databases. All checksum failures will be reported in the pg_stat_database view. --locale=locale Sets the default locale for the database cluster. If this option is not specified, the locale is inherited from the environment that initdb runs in. --lc-collate=locale --lc-ctype=locale --lc-messages=locale --lc-monetary=locale --lc-numeric=locale --lc-time=locale Like --locale, but only sets the locale in the specified category. --no-locale Equivalent to --locale=C. --locale-provider={libc|icu} This option sets the locale provider for databases created in the new cluster. It can be overridden in the CREATE DATABASE command when new databases are subsequently created. The default is libc. -N --no-sync By default, initdb will wait for all files to be written safely to disk. This option causes initdb to return without waiting, which is faster, but means that a subsequent operating system crash can leave the data directory corrupt. Generally, this option is useful for testing, but should not be used when creating a production installation. --no-instructions By default, initdb will write instructions for how to start the cluster at the end of its output. This option causes those instructions to be left out. This is primarily intended for use by tools that wrap initdb in platform-specific behavior, where those instructions are likely to be incorrect. --pwfile=filename Makes initdb read the database superuser’s password from a file. The first line of the file is taken as the password. -S --sync-only Safely write all database files to disk and exit. This does not perform any of the normal initdb operations. -T config --text-search-config=config Sets the default text search configuration. -U username --username=username Selects the user name of the database superuser. This defaults to the name of the effective user running initdb. It is really not important what the superuser’s name is, but one might choose to keep the customary name postgres, even if the operating system user’s name is different. -W --pwprompt Makes initdb prompt for a password to give the database superuser. If you don’t plan on using password authentication, this is not important. Otherwise you won’t be able to use password authentication until you have a password set up. -X directory --waldir=directory This option specifies the directory where the write-ahead log should be stored. --wal-segsize=size Set the WAL segment size, in megabytes. This is the size of each individual file in the WAL log. The default size is 16 megabytes. The value must be a power of 2 between 1 and 1024 (megabytes). This option can only be set during initialization, and cannot be changed later.It may be useful to adjust this size to control the granularity of WAL log shipping or archiving. Also, in databases with a high volume of WAL, the sheer number of WAL files per directory can become a performance and management problem. Increasing the WAL file size will reduce the number of WAL files. Other, less commonly used, options are also available: -d --debug Print debugging output from the bootstrap backend and a few other messages of lesser interest for the general public. The bootstrap backend is the program initdb uses to create the catalog tables. This option generates a tremendous amount of extremely boring output. --discard-caches Run the bootstrap backend with the debug_discard_caches=1 option. This takes a very long time and is only of use for deep debugging. -L directory Specifies where initdb should find its input files to initialize the database cluster. This is normally not necessary. You will be told if you need to specify their location explicitly. -n --no-clean By default, when initdb determines that an error prevented it from completely creating the database cluster, it removes any files it might have created before discovering that it cannot finish the job. This option inhibits tidying-up and is thus useful for debugging. Other options: -V --version Print the initdb version and exit. -? --help Show help about initdb command line arguments, and exit. PGDATA Specifies the directory where the database cluster is to be stored; can be overridden using the -D option. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. TZ Specifies the default time zone of the created database cluster. The value should be a full time zone name This utility, like most other IvorySQL utilities, also uses the environment variables supported by libpq initdb can also be invoked via pg_ctl initdb. pg_archivecleanup — clean up IvorySQL WAL archive files pg_archivecleanup [option…​] archivelocation oldestkeptwalfile pg_archivecleanup accepts the following command-line arguments: -d Print lots of debug logging output on stderr. -n Print the names of the files that would have been removed on stdout (performs a dry run). -V --version Print the pg_archivecleanup version and exit. -x extension Provide an extension that will be stripped from all file names before deciding if they should be deleted. This is typically useful for cleaning up archives that have been compressed during storage, and therefore have had an extension added by the compression program. For example: -x .gz. -? --help Show help about pg_archivecleanup command line arguments, and exit. The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. On Linux or Unix systems, you might use: archive_cleanup_command = 'pg_archivecleanup -d /mnt/standby/archive %r 2>>cleanup.log' where the archive directory is physically located on the standby server, so that the archive_command is accessing it across NFS, but the files are local to the standby. This will: produce debugging output in cleanup.log remove no-longer-needed files from the archive directory pg_checksums — enable, disable or check data checksums in a IvorySQL database cluster pg_checksums [option…​] [[ -D | --pgdata ] datadir] The following command-line options are available: -D directory --pgdata=directory Specifies the directory where the database cluster is stored. -c --check Checks checksums. This is the default mode if nothing else is specified. -d --disable Disables checksums. -e --enable Enables checksums. -f filenode --filenode=filenode Only validate checksums in the relation with filenode filenode. -N --no-sync By default, pg_checksums will wait for all files to be written safely to disk. This option causes pg_checksums to return without waiting, which is faster, but means that a subsequent operating system crash can leave the updated data directory corrupt. Generally, this option is useful for testing but should not be used on a production installation. This option has no effect when using --check. -P --progress Enable progress reporting. Turning this on will deliver a progress report while checking or enabling checksums. -v --verbose Enable verbose output. Lists all checked files. -V --version Print the pg_checksums version and exit. -? --help Show help about pg_checksums command line arguments, and exit. PGDATA Specifies the directory where the database cluster is stored; can be overridden using the -D option. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. Enabling checksums in a large cluster can potentially take a long time. During this operation, the cluster or other programs that write to the data directory must not be started or else data loss may occur. When using a replication setup with tools which perform direct copies of relation file blocks (for example pg_rewind), enabling or disabling checksums can lead to page corruptions in the shape of incorrect checksums if the operation is not done consistently across all nodes. When enabling or disabling checksums in a replication setup, it is thus recommended to stop all the clusters before switching them all consistently. Destroying all standbys, performing the operation on the primary and finally recreating the standbys from scratch is also safe. If pg_checksums is aborted or killed while enabling or disabling checksums, the cluster’s data checksum configuration remains unchanged, and pg_checksums can be re-run to perform the same operation. pg_controldata — display control information of a IvorySQL database cluster pg_controldata [option] [[ -D | --pgdata ] datadir] PGDATA Default data directory location PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_ctl — initialize, start, stop, or control a IvorySQL server pg_ctl init[db] [-D datadir] [-s] [-o initdb-options] pg_ctl start [-D datadir] [-l filename] [-W] [-t seconds] [-s] [-o options] [-p path] [-c] pg_ctl stop [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] pg_ctl restart [-D datadir] [-m s[mart] | f[ast] | i[mmediate] ] [-W] [-t seconds] [-s] [-o options] [-c] pg_ctl reload [-D datadir] [-s] pg_ctl status [-D datadir] pg_ctl promote [-D datadir] [-W] [-t seconds] [-s] pg_ctl logrotate [-D datadir] [-s] pg_ctl kill signal_name process_id On Microsoft Windows, also: pg_ctl register [-D datadir] [-N servicename] [-U username] [-P password] [-S a[uto] | d[emand] ] [-e source] [-W] [-t seconds] [-s] [-o options] pg_ctl unregister [-N servicename] -c --core-files Attempt to allow server crashes to produce core files, on platforms where this is possible, by lifting any soft resource limit placed on core files. This is useful in debugging or diagnosing problems by allowing a stack trace to be obtained from a failed server process. -D datadir --pgdata=datadir Specifies the file system location of the database configuration files. If this option is omitted, the environment variable PGDATA is used. -l filename --log=filename Append the server log output to filename. If the file does not exist, it is created. The umask is set to 077, so access to the log file is disallowed to other users by default. -m mode --mode=mode Specifies the shutdown mode. mode can be smart, fast, or immediate, or the first letter of one of these three. If this option is omitted, fast is the default. -o options --options=options Specifies options to be passed directly to the postgres command. -o can be specified multiple times, with all the given options being passed through.The options should usually be surrounded by single or double quotes to ensure that they are passed through as a group. -o initdb-options --options=initdb-options Specifies options to be passed directly to the initdb command. -o can be specified multiple times, with all the given options being passed through.The initdb-options should usually be surrounded by single or double quotes to ensure that they are passed through as a group. -p path Specifies the location of the postgres executable. By default the postgres executable is taken from the same directory as pg_ctl, or failing that, the hard-wired installation directory. It is not necessary to use this option unless you are doing something unusual and get errors that the postgres executable was not found.In init mode, this option analogously specifies the location of the initdb executable. -s --silent Print only errors, no informational messages. -t seconds --timeout=seconds Specifies the maximum number of seconds to wait when waiting for an operation to complete (see option -w). Defaults to the value of the PGCTLTIMEOUT environment variable or, if not set, to 60 seconds. -V --version Print the pg_ctl version and exit. -w --wait Wait for the operation to complete. This is supported for the modes start, stop, restart, promote, and register, and is the default for those modes.When waiting, pg_ctl repeatedly checks the server’s PID file, sleeping for a short amount of time between checks. Startup is considered complete when the PID file indicates that the server is ready to accept connections. Shutdown is considered complete when the server removes the PID file. pg_ctl returns an exit code based on the success of the startup or shutdown.If the operation does not complete within the timeout (see option -t), then pg_ctl exits with a nonzero exit status. But note that the operation might continue in the background and eventually succeed. -W --no-wait Do not wait for the operation to complete. This is the opposite of the option -w.If waiting is disabled, the requested action is triggered, but there is no feedback about its success. In that case, the server log file or an external monitoring system would have to be used to check the progress and success of the operation.In prior releases of IvorySQL, this was the default except for the stop mode. -? --help Show help about pg_ctl command line arguments, and exit. If an option is specified that is valid, but not relevant to the selected operating mode, pg_ctl ignores it. -e source Name of the event source for pg_ctl to use for logging to the event log when running as a Windows service. The default is IvorySQL. Note that this only controls messages sent from pg_ctl itself; once started, the server will use the event source specified by its event_source parameter. Should the server fail very early in startup, before that parameter has been set, it might also log using the default event source name IvorySQL. -N `servicename` Name of the system service to register. This name will be used as both the service name and the display name. The default is IvorySQL. -P `password` Password for the user to run the service as. -S start-type Start type of the system service. start-type can be auto, or demand, or the first letter of one of these two. If this option is omitted, auto is the default. -U username User name for the user to run the service as. For domain users, use the format DOMAIN\\username. PGCTLTIMEOUT Default limit on the number of seconds to wait when waiting for startup or shutdown to complete. If not set, the default is 60 seconds. PGDATA Default data directory location. Most pg_ctl modes require knowing the data directory location; therefore, the -D option is required unless PGDATA is set. pg_ctl, like most other IvorySQL utilities, also uses the environment variables supported by libpq. postmaster.pid pg_ctl examines this file in the data directory to determine whether the server is currently running. postmaster.opts If this file exists in the data directory, pg_ctl (in restart mode) will pass the contents of the file as options to postgres, unless overridden by the -o option. The contents of this file are also displayed in status mode. To start the server, waiting until the server is accepting connections: $ pg_ctl start To start the server using port 5433, and running without fsync, use: $ pg_ctl -o \"-F -p 5433\" start To stop the server, use: $ pg_ctl stop The -m option allows control over how the server shuts down: $ pg_ctl stop -m smart Restarting the server is almost equivalent to stopping the server and starting it again, except that by default, pg_ctl saves and reuses the command line options that were passed to the previously-running instance. To restart the server using the same options as before, use: $ pg_ctl restart But if -o is specified, that replaces any previous options. To restart using port 5433, disabling fsync upon restart: $ pg_ctl -o \"-F -p 5433\" restart Here is sample status output from pg_ctl: $ pg_ctl status pg_ctl: server is running (PID: 13718) /usr/local/pgsql/bin/postgres \"-D\" \"/usr/local/pgsql/data\" \"-p\" \"5433\" \"-B\" \"128\" The second line is the command that would be invoked in restart mode. pg_resetwal — reset the write-ahead log and other control information of a IvorySQL database cluster pg_resetwal [ -f | --force ] [ -n | --dry-run ] [option…​] [ -D | --pgdata ] datadir -f --force Force pg_resetwal to proceed even if it cannot determine valid data for pg_control, as explained above. -n --dry-run The -n / --dry-run option instructs pg_resetwal to print the values reconstructed from pg_control and values about to be changed, and then exit without modifying anything. This is mainly a debugging tool, but can be useful as a sanity check before allowing pg_resetwal to proceed for real. -V --version Display version information, then exit. -? --help Show help, then exit. The following options are only needed when pg_resetwal is unable to determine appropriate values by reading pg_control. Safe values can be determined as described below. For values that take numeric arguments, hexadecimal values can be specified by using the prefix 0x. -c xid,xid --commit-timestamp-ids=xid,xid Manually set the oldest and newest transaction IDs for which the commit time can be retrieved.A safe value for the oldest transaction ID for which the commit time can be retrieved (first part) can be determined by looking for the numerically smallest file name in the directory pg_commit_ts under the data directory. Conversely, a safe value for the newest transaction ID for which the commit time can be retrieved (second part) can be determined by looking for the numerically greatest file name in the same directory. The file names are in hexadecimal. -e xid_epoch --epoch=xid_epoch Manually set the next transaction ID’s epoch.The transaction ID epoch is not actually stored anywhere in the database except in the field that is set by pg_resetwal, so any value will work so far as the database itself is concerned. You might need to adjust this value to ensure that replication systems such as Slony-I and Skytools work correctly — if so, an appropriate value should be obtainable from the state of the downstream replicated database. -l walfile --next-wal-file=walfile Manually set the WAL starting location by specifying the name of the next WAL segment file.The name of next WAL segment file should be larger than any WAL segment file name currently existing in the directory pg_wal under the data directory. These names are also in hexadecimal and have three parts. The first part is the “timeline ID” and should usually be kept the same. For example, if 00000001000000320000004A is the largest entry in pg_wal, use -l 00000001000000320000004B or higher.Note that when using nondefault WAL segment sizes, the numbers in the WAL file names are different from the LSNs that are reported by system functions and system views. This option takes a WAL file name, not an LSN.Note`pg_resetwal` itself looks at the files in pg_wal and chooses a default -l setting beyond the last existing file name. Therefore, manual adjustment of -l should only be needed if you are aware of WAL segment files that are not currently present in pg_wal, such as entries in an offline archive; or if the contents of pg_wal have been lost entirely. -m mxid,mxid --multixact-ids=mxid,mxid Manually set the next and oldest multitransaction ID.A safe value for the next multitransaction ID (first part) can be determined by looking for the numerically largest file name in the directory pg_multixact/offsets under the data directory, adding one, and then multiplying by 65536 (0x10000). Conversely, a safe value for the oldest multitransaction ID (second part of -m) can be determined by looking for the numerically smallest file name in the same directory and multiplying by 65536. The file names are in hexadecimal, so the easiest way to do this is to specify the option value in hexadecimal and append four zeroes. -o oid --next-oid=oid Manually set the next OID.There is no comparably easy way to determine a next OID that’s beyond the largest one in the database, but fortunately it is not critical to get the next-OID setting right. -O mxoff --multixact-offset=mxoff Manually set the next multitransaction offset.A safe value can be determined by looking for the numerically largest file name in the directory pg_multixact/members under the data directory, adding one, and then multiplying by 52352 (0xCC80). The file names are in hexadecimal. There is no simple recipe such as the ones for other options of appending zeroes. --wal-segsize=wal_segment_size Set the new WAL segment size, in megabytes. The value must be set to a power of 2 between 1 and 1024 (megabytes). See the same option of initdb for more information.NoteWhile pg_resetwal will set the WAL starting address beyond the latest existing WAL segment file, some segment size changes can cause previous WAL file names to be reused. It is recommended to use -l together with this option to manually set the WAL starting address if WAL file name overlap will cause problems with your archiving strategy. -u xid --oldest-transaction-id=xid Manually set the oldest unfrozen transaction ID.A safe value can be determined by looking for the numerically smallest file name in the directory pg_xact under the data directory and then multiplying by 1048576 (0x100000). Note that the file names are in hexadecimal. It is usually easiest to specify the option value in hexadecimal too. For example, if 0007 is the smallest entry in pg_xact, -u 0x700000 will work (five trailing zeroes provide the proper multiplier). -x xid --next-transaction-id=xid Manually set the next transaction ID.A safe value can be determined by looking for the numerically largest file name in the directory pg_xact under the data directory, adding one, and then multiplying by 1048576 (0x100000). Note that the file names are in hexadecimal. It is usually easiest to specify the option value in hexadecimal too. For example, if 0011 is the largest entry in pg_xact, -x 0x1200000 will work (five trailing zeroes provide the proper multiplier). PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. This command must not be used when the server is running. pg_resetwal will refuse to start up if it finds a server lock file in the data directory. If the server crashed then a lock file might have been left behind; in that case you can remove the lock file to allow pg_resetwal to run. But before you do so, make doubly certain that there is no server process still alive. pg_resetwal works only with servers of the same major version. pg_rewind — synchronize a IvorySQL data directory with another data directory that was forked from it pg_rewind [option…​] { -D | --target-pgdata } directory { --source-pgdata=`directory` | --source-server=`connstr` } If pg_rewind fails while processing, then the data folder of the target is likely not in a state that can be recovered. In such a case, taking a new fresh backup is recommended. As pg_rewind copies configuration files entirely from the source, it may be required to correct the configuration used for recovery before restarting the target server, especially if the target is reintroduced as a standby of the source. If you restart the server after the rewind operation has finished but without configuring recovery, the target may again diverge from the primary. pg_rewind will fail immediately if it finds files it cannot write directly to. This can happen for example when the source and the target server use the same file mapping for read-only SSL keys and certificates. If such files are present on the target server it is recommended to remove them before running pg_rewind. After doing the rewind, some of those files may have been copied from the source, in which case it may be necessary to remove the data copied and restore back the set of links used before the rewind. pg_rewind accepts the following command-line arguments: -D directory --target-pgdata=directory This option specifies the target data directory that is synchronized with the source. The target server must be shut down cleanly before running pg_rewind --source-pgdata=directory Specifies the file system path to the data directory of the source server to synchronize the target with. This option requires the source server to be cleanly shut down. --source-server=connstr Specifies a libpq connection string to connect to the source IvorySQL server to synchronize the target with. The connection must be a normal (non-replication) connection with a role having sufficient permissions to execute the functions used by pg_rewind on the source server (see Notes section for details) or a superuser role. This option requires the source server to be running and accepting connections. -R --write-recovery-conf Create standby.signal and append connection settings to IvorySQL.auto.conf in the output directory. --source-server is mandatory with this option. -n --dry-run Do everything except actually modifying the target directory. -N --no-sync By default, pg_rewind will wait for all files to be written safely to disk. This option causes pg_rewind to return without waiting, which is faster, but means that a subsequent operating system crash can leave the data directory corrupt. Generally, this option is useful for testing but should not be used on a production installation. -P --progress Enables progress reporting. Turning this on will deliver an approximate progress report while copying data from the source cluster. -c --restore-target-wal Use restore_command defined in the target cluster configuration to retrieve WAL files from the WAL archive if these files are no longer available in the pg_wal directory. --config-file=filename Use the specified main server configuration file for the target cluster. This affects pg_rewind when it uses internally the postgres command for the rewind operation on this cluster (when retrieving restore_command with the option -c/--restore-target-wal and when forcing a completion of crash recovery). --debug Print verbose debugging output that is mostly useful for developers debugging pg_rewind. --no-ensure-shutdown pg_rewind requires that the target server is cleanly shut down before rewinding. By default, if the target server is not shut down cleanly, pg_rewind starts the target server in single-user mode to complete crash recovery first, and stops it. By passing this option, pg_rewind skips this and errors out immediately if the server is not cleanly shut down. Users are expected to handle the situation themselves in that case. -V --version Display version information, then exit. -? --help Show help, then exit. When --source-server option is used, pg_rewind also uses the environment variables supported by libpq . The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. When executing pg_rewind using an online cluster as source, a role having sufficient permissions to execute the functions used by pg_rewind on the source cluster can be used instead of a superuser. Here is how to create such a role, named rewind_user here: CREATE USER rewind_user LOGIN; GRANT EXECUTE ON function pg_catalog.pg_ls_dir(text, boolean, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_stat_file(text, boolean) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text) TO rewind_user; GRANT EXECUTE ON function pg_catalog.pg_read_binary_file(text, bigint, bigint, boolean) TO rewind_user; When executing pg_rewind using an online cluster as source which has been recently promoted, it is necessary to execute a CHECKPOINT after promotion such that its control file reflects up-to-date timeline information, which is used by pg_rewind to check if the target cluster can be rewound using the designated source cluster. The basic idea is to copy all file system-level changes from the source cluster to the target cluster: 1.Scan the WAL log of the target cluster, starting from the last checkpoint before the point where the source cluster’s timeline history forked off from the target cluster. For each WAL record, record each data block that was touched. This yields a list of all the data blocks that were changed in the target cluster, after the source cluster forked off. If some of the WAL files are no longer available, try re-running pg_rewind with the -c option to search for the missing files in the WAL archive. 2.Copy all those changed blocks from the source cluster to the target cluster, either using direct file system access (--source-pgdata) or SQL (--source-server). Relation files are now in a state equivalent to the moment of the last completed checkpoint prior to the point at which the WAL timelines of the source and target diverged plus the current state on the source of any blocks changed on the target after that divergence. 3.Copy all other files, including new relation files, WAL segments, pg_xact, and configuration files from the source cluster to the target cluster. Similarly to base backups, the contents of the directories pg_dynshmem/, pg_notify/, pg_replslot/, pg_serial/, pg_snapshots/, pg_stat_tmp/, and pg_subtrans/ are omitted from the data copied from the source cluster. The files backup_label, tablespace_map, pg_internal.init, postmaster.opts, and postmaster.pid, as well as any file or directory beginning with pgsql_tmp, are omitted. 4.Create a backup_label file to begin WAL replay at the checkpoint created at failover and configure the pg_control file with a minimum consistency LSN defined as the result of pg_current_wal_insert_lsn() when rewinding from a live source or the last checkpoint LSN when rewinding from a stopped source. 5.When starting the target, IvorySQL replays all the required WAL, resulting in a data directory in a consistent state. pg_test_fsync — determine fastest wal_sync_method for IvorySQL pg_test_fsync [option…​] pg_test_fsync accepts the following command-line options: -f --filename Specifies the file name to write test data in. This file should be in the same file system that the pg_wal directory is or will be placed in. (pg_wal contains the WAL files.) The default is pg_test_fsync.out in the current directory. -s --secs-per-test Specifies the number of seconds for each test. The more time per test, the greater the test’s accuracy, but the longer it takes to run. The default is 5 seconds, which allows the program to complete in under 2 minutes. -V --version Print the pg_test_fsync version and exit. -? --help Show help about pg_test_fsync command line arguments, and exit. The environment variable PG_COLOR specifies whether to use color in diagnostic messages. Possible values are always, auto and never. pg_test_timing — measure timing overhead pg_test_timing [option…​] pg_test_timing accepts the following command-line options: -d `duration` --duration=`duration` Specifies the test duration, in seconds. Longer durations give slightly better accuracy, and are more likely to discover problems with the system clock moving backwards. The default test duration is 3 seconds. -V --version Print the pg_test_timing version and exit. -? --help Show help about pg_test_timing command line arguments, and exit. Good results will show most (>90%) individual timing calls take less than one microsecond. Average per loop overhead will be even lower, below 100 nanoseconds. This example from an Intel i7-860 system using a TSC clock source shows excellent performance: Testing timing overhead for 3 seconds. Per loop time including overhead: 35.96 ns Histogram of timing durations: < us % of total count 1 96.40465 80435604 2 3.59518 2999652 4 0.00015 126 8 0.00002 13 16 0.00000 2 Note that different units are used for the per loop time than the histogram. The loop can have resolution within a few nanoseconds (ns), while the individual timing calls can only resolve down to one microsecond (us). When the query executor is running a statement using EXPLAIN ANALYZE, individual operations are timed as well as showing a summary. The overhead of your system can be checked by counting rows with the psql program: CREATE TABLE t AS SELECT * FROM generate_series(1,100000); \\timing SELECT COUNT(*) FROM t; EXPLAIN ANALYZE SELECT COUNT(*) FROM t; The i7-860 system measured runs the count query in 9.8 ms while the EXPLAIN ANALYZE version takes 16.6 ms, each processing just over 100,000 rows. That 6.8 ms difference means the timing overhead per row is 68 ns, about twice what pg_test_timing estimated it would be. Even that relatively small amount of overhead is making the fully timed count statement take almost 70% longer. On more substantial queries, the timing overhead would be less problematic. On some newer Linux systems, it’s possible to change the clock source used to collect timing data at any time. A second example shows the slowdown possible from switching to the slower acpi_pm time source, on the same system used for the fast results above: # cat /sys/devices/system/clocksource/clocksource0/available_clocksource tsc hpet acpi_pm # echo acpi_pm > /sys/devices/system/clocksource/clocksource0/current_clocksource # pg_test_timing Per loop time including overhead: 722.92 ns Histogram of timing durations: < us % of total count 1 27.84870 1155682 2 72.05956 2990371 4 0.07810 3241 8 0.01357 563 16 0.00007 3 In this configuration, the sample EXPLAIN ANALYZE above takes 115.9 ms. That’s 1061 ns of timing overhead, again a small multiple of what’s measured directly by this utility. That much timing overhead means the actual query itself is only taking a tiny fraction of the accounted for time, most of it is being consumed in overhead instead. In this configuration, any EXPLAIN ANALYZE totals involving many timed operations would be inflated significantly by timing overhead. FreeBSD also allows changing the time source on the fly, and it logs information about the timer selected during boot: # dmesg | grep \"Timecounter\" Timecounter \"ACPI-fast\" frequency 3579545 Hz quality 900 Timecounter \"i8254\" frequency 1193182 Hz quality 0 Timecounters tick every 10.000 msec Timecounter \"TSC\" frequency 2531787134 Hz quality 800 # sysctl kern.timecounter.hardware=TSC kern.timecounter.hardware: ACPI-fast -> TSC Other systems may only allow setting the time source on boot. On older Linux systems the \"clock\" kernel setting is the only way to make this sort of change. And even on some more recent ones, the only option you’ll see for a clock source is \"jiffies\". Jiffies are the older Linux software clock implementation, which can have good resolution when it’s backed by fast enough timing hardware, as in this example: $ cat /sys/devices/system/clocksource/clocksource0/available_clocksource jiffies $ dmesg | grep time.c time.c: Using 3.579545 MHz WALL PM GTOD PIT/TSC timer. time.c: Detected 2400.153 MHz processor. $ pg_test_timing Testing timing overhead for 3 seconds. Per timing duration including loop overhead: 97.75 ns Histogram of timing durations: < us % of total count 1 90.23734 27694571 2 9.75277 2993204 4 0.00981 3010 8 0.00007 22 16 0.00000 1 32 0.00000 1 Collecting accurate timing information is normally done on computers using hardware clocks with various levels of accuracy. With some hardware the operating systems can pass the system clock time almost directly to programs. A system clock can also be derived from a chip that simply provides timing interrupts, periodic ticks at some known time interval. In either case, operating system kernels provide a clock source that hides these details. But the accuracy of that clock source and how quickly it can return results varies based on the underlying hardware. Inaccurate time keeping can result in system instability. Test any change to the clock source very carefully. Operating system defaults are sometimes made to favor reliability over best accuracy. And if you are using a virtual machine, look into the recommended time sources compatible with it. Virtual hardware faces additional difficulties when emulating timers, and there are often per operating system settings suggested by vendors. The Time Stamp Counter (TSC) clock source is the most accurate one available on current generation CPUs. It’s the preferred way to track the system time when it’s supported by the operating system and the TSC clock is reliable. There are several ways that TSC can fail to provide an accurate timing source, making it unreliable. Older systems can have a TSC clock that varies based on the CPU temperature, making it unusable for timing. Trying to use TSC on some older multicore CPUs can give a reported time that’s inconsistent among multiple cores. This can result in the time going backwards, a problem this program checks for. And even the newest systems can fail to provide accurate TSC timing with very aggressive power saving configurations. Newer operating systems may check for the known TSC problems and switch to a slower, more stable clock source when they are seen. If your system supports TSC time but doesn’t default to that, it may be disabled for a good reason. And some operating systems may not detect all the possible problems correctly, or will allow using TSC even in situations where it’s known to be inaccurate. The High Precision Event Timer (HPET) is the preferred timer on systems where it’s available and TSC is not accurate. The timer chip itself is programmable to allow up to 100 nanosecond resolution, but you may not see that much accuracy in your system clock. Advanced Configuration and Power Interface (ACPI) provides a Power Management (PM) Timer, which Linux refers to as the acpi_pm. The clock derived from acpi_pm will at best provide 300 nanosecond resolution. Timers used on older PC hardware include the 8254 Programmable Interval Timer (PIT), the real-time clock (RTC), the Advanced Programmable Interrupt Controller (APIC) timer, and the Cyclone timer. These timers aim for millisecond resolution. pg_upgrade — upgrade a IvorySQL server instance pg_upgrade -b oldbindir [-B newbindir] -d oldconfigdir -D newconfigdir [option…​] pg_upgrade accepts the following command-line arguments: -b bindir --old-bindir=bindir the old IvorySQL executable directory; environment variable PGBINOLD -B bindir --new-bindir=bindir the new IvorySQL executable directory; default is the directory where pg_upgrade resides; environment variable PGBINNEW -c --check check clusters only, don’t change any data -d configdir --old-datadir=configdir the old database cluster configuration directory; environment variable PGDATAOLD -D configdir --new-datadir=configdir the new database cluster configuration directory; environment variable PGDATANEW -j `njobs` --jobs=`njobs` number of simultaneous processes or threads to use -k --link use hard links instead of copying files to the new cluster -N --no-sync By default, pg_upgrade will wait for all files of the upgraded cluster to be written safely to disk. This option causes pg_upgrade to return without waiting, which is faster, but means that a subsequent operating system crash can leave the data directory corrupt. Generally, this option is useful for testing but should not be used on a production installation. -o options --old-options options options to be passed directly to the old postgres command; multiple option invocations are appended -O options --new-options options options to be passed directly to the new postgres command; multiple option invocations are appended -p port --old-port=port the old cluster port number; environment variable PGPORTOLD -P port --new-port=port the new cluster port number; environment variable PGPORTNEW -r --retain retain SQL and log files even after successful completion -s dir --socketdir=dir directory to use for postmaster sockets during upgrade; default is current working directory; environment variable PGSOCKETDIR -U username --username=username cluster’s install user name; environment variable PGUSER -v --verbose enable verbose internal logging -V --version display version information, then exit --clone Use efficient file cloning (also known as “reflinks” on some systems) instead of copying files to the new cluster. This can result in near-instantaneous copying of the data files, giving the speed advantages of -k/--link while leaving the old cluster untouched.File cloning is only supported on some operating systems and file systems. If it is selected but not supported, the pg_upgrade run will error. At present, it is supported on Linux (kernel 4.5 or later) with Btrfs and XFS (on file systems created with reflink support), and on macOS with APFS. -? --help show help, then exit These are the steps to perform an upgrade with pg_upgrade: 1.Optionally move the old cluster If your installation directory is not version-specific, e.g., /usr/local/pgsql, it is necessary to move the current IvorySQL install directory so it does not interfere with the new IvorySQL installation. Once the current IvorySQL server is shut down, it is safe to rename the IvorySQL installation directory; assuming the old directory is /usr/local/pgsql, you can do: mv /usr/local/pgsql /usr/local/pgsql.old to rename the directory. 2.For source installs, build the new version Build the new IvorySQL source with configure flags that are compatible with the old cluster. pg_upgrade will check pg_controldata to make sure all settings are compatible before starting the upgrade. 3.Install the new IvorySQL binaries Install the new server’s binaries and support files. pg_upgrade is included in a default installation. For source installs, if you wish to install the new server in a custom location, use the prefix variable: make prefix=/usr/local/pgsql.new install 4.Initialize the new IvorySQL cluster Initialize the new cluster using initdb. Again, use compatible initdb flags that match the old cluster. Many prebuilt installers do this step automatically. There is no need to start the new cluster. 5.Install extension shared object files Many extensions and custom modules, whether from contrib or another source, use shared object files (or DLLs), e.g., pgcrypto.so. If the old cluster used these, shared object files matching the new server binary must be installed in the new cluster, usually via operating system commands. Do not load the schema definitions, e.g., CREATE EXTENSION pgcrypto, because these will be duplicated from the old cluster. If extension updates are available, pg_upgrade will report this and create a script that can be run later to update them. 6.Copy custom full-text search files Copy any custom full text search files (dictionary, synonym, thesaurus, stop words) from the old to the new cluster. 7.Adjust authentication pg_upgrade will connect to the old and new servers several times, so you might want to set authentication to peer in pg_hba.conf or use a ~/.pgpass file . 8.Stop both servers Make sure both database servers are stopped using, on Unix, e.g.: pg_ctl -D /opt/IvorySQL/1.5 stop pg_ctl -D /opt/IvorySQL/2.1 stop or on Windows, using the proper service names: NET STOP IvorySQL-1.5 NET STOP IvorySQL-2.1 Streaming replication and log-shipping standby servers can remain running until a later step. 9.Prepare for standby server upgrades Verify that the old standby servers are caught up by running pg_controldata against the old primary and standby clusters. Verify that the “Latest checkpoint location” values match in all clusters. (There will be a mismatch if old standby servers were shut down before the old primary or if the old standby servers are still running.) Also, make sure wal_level is not set to minimal in the IvorySQL.conf file on the new primary cluster. 10.Run pg_upgrade Always run the pg_upgrade binary of the new server, not the old one. pg_upgrade requires the specification of the old and new cluster’s data and executable (bin) directories. You can also specify user and port values, and whether you want the data files linked or cloned instead of the default copy behavior. If you use link mode, the upgrade will be much faster (no file copying) and use less disk space, but you will not be able to access your old cluster once you start the new cluster after the upgrade. Link mode also requires that the old and new cluster data directories be in the same file system. (Tablespaces and pg_wal can be on different file systems.) Clone mode provides the same speed and disk space advantages but does not cause the old cluster to be unusable once the new cluster is started. Clone mode also requires that the old and new data directories be in the same file system. This mode is only available on certain operating systems and file systems. The --jobs option allows multiple CPU cores to be used for copying/linking of files and to dump and restore database schemas in parallel; a good place to start is the maximum of the number of CPU cores and tablespaces. This option can dramatically reduce the time to upgrade a multi-database server running on a multiprocessor machine. For Windows users, you must be logged into an administrative account, and then start a shell as the postgres user and set the proper path: RUNAS /USER:postgres \"CMD.EXE\" SET PATH=%PATH%;C:\\Program Files\\IvorySQL\\15\\bin; and then run pg_upgrade with quoted directories, e.g.: pg_upgrade.exe --old-datadir \"C:/Program Files/IvorySQL/1.5/data\" --new-datadir \"C:/Program Files/IvorySQL/2.1/data\" --old-bindir \"C:/Program Files/IvorySQL/1.5/bin\" --new-bindir \"C:/Program Files/IvorySQL/2.1/bin\" Once started, pg_upgrade will verify the two clusters are compatible and then do the upgrade. You can use pg_upgrade --check to perform only the checks, even if the old server is still running. pg_upgrade --check will also outline any manual adjustments you will need to make after the upgrade. If you are going to be using link or clone mode, you should use the option --link or --clone with --check to enable mode-specific checks. pg_upgrade requires write permission in the current directory. Obviously, no one should be accessing the clusters during the upgrade. pg_upgrade defaults to running servers on port 50432 to avoid unintended client connections. You can use the same port number for both clusters when doing an upgrade because the old and new clusters will not be running at the same time. However, when checking an old running server, the old and new port numbers must be different. If an error occurs while restoring the database schema, pg_upgrade will exit and you will have to revert to the old cluster .To try pg_upgrade again, you will need to modify the old cluster so the pg_upgrade schema restore succeeds. If the problem is a contrib module, you might need to uninstall the contrib module from the old cluster and install it in the new cluster after the upgrade, assuming the module is not being used to store user data. 11.Upgrade streaming replication and log-shipping standby servers If you used link mode and have Streaming Replication or Log-Shipping standby servers, you can follow these steps to quickly upgrade them. You will not be running pg_upgrade on the standby servers, but rather rsync on the primary. Do not start any servers yet. If you did not use link mode, do not have or do not want to use rsync, or want an easier solution, skip the instructions in this section and simply recreate the standby servers once pg_upgrade completes and the new primary is running. Install the new IvorySQL binaries on standby servers Make sure the new binaries and support files are installed on all standby servers. Make sure the new standby data directories do *not\\* exist Make sure the new standby data directories do *not* exist or are empty. If initdb was run, delete the standby servers' new data directories. Install extension shared object files Install the same extension shared object files on the new standbys that you installed in the new primary cluster. Stop standby servers If the standby servers are still running, stop them now using the above instructions. Save configuration files Save any configuration files from the old standbys' configuration directories you need to keep, e.g., `IvorySQL.conf` (and any files included by it), `IvorySQL.auto.conf`, `pg_hba.conf`, because these will be overwritten or removed in the next step. Run rsync When using link mode, standby servers can be quickly upgraded using rsync. To accomplish this, from a directory on the primary server that is above the old and new database cluster directories, run this on the *primary* for each standby server: ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive old_cluster new_cluster remote_dir ``` where `old_cluster` and `new_cluster` are relative to the current directory on the primary, and `remote_dir` is *above* the old and new cluster directories on the standby. The directory structure under the specified directories on the primary and standbys must match. Consult the rsync manual page for details on specifying the remote directory, e.g., ``` rsync --archive --delete --hard-links --size-only --no-inc-recursive /opt/IvorySQL/1.5 \\ /opt/IvorySQL/2.1 standby.example.com:/opt/IvorySQL ``` You can verify what the command will do using rsync's `--dry-run` option. While rsync must be run on the primary for at least one standby, it is possible to run rsync on an upgraded standby to upgrade other standbys, as long as the upgraded standby has not been started. What this does is to record the links created by pg_upgrade's link mode that connect files in the old and new clusters on the primary server. It then finds matching files in the standby's old cluster and creates links for them in the standby's new cluster. Files that were not linked on the primary are copied from the primary to the standby. (They are usually small.) This provides rapid standby upgrades. Unfortunately, rsync needlessly copies files associated with temporary and unlogged tables because these files don't normally exist on standby servers. If you have relocated `pg_wal` outside the data directories, rsync must be run on those directories too. Configure streaming replication and log-shipping standby servers Configure the servers for log shipping. (You do not need to run `pg_backup_start()` and `pg_backup_stop()` or take a file system backup as the standbys are still synchronized with the primary.) Replication slots are not copied and must be recreated. 12.Restore pg_hba.conf If you modified pg_hba.conf, restore its original settings. It might also be necessary to adjust other configuration files in the new cluster to match the old cluster, e.g., IvorySQL.conf (and any files included by it), IvorySQL.auto.conf. 13.Start the new server The new server can now be safely started, and then any rsync’ed standby servers. 14.Post-upgrade processing If any post-upgrade processing is required, pg_upgrade will issue warnings as it completes. It will also generate script files that must be run by the administrator. The script files will connect to each database that needs post-upgrade processing. Each script should be run using: psql --username=postgres --file=script.sql postgres The scripts can be run in any order and can be deleted once they have been run. Caution In general it is unsafe to access tables referenced in rebuild scripts until the rebuild scripts have run to completion; doing so could yield incorrect results or poor performance. Tables not referenced in rebuild scripts can be accessed immediately. 15.Statistics Because optimizer statistics are not transferred by pg_upgrade, you will be instructed to run a command to regenerate that information at the end of the upgrade. You might need to set connection parameters to match your new cluster. 16.Delete old cluster Once you are satisfied with the upgrade, you can delete the old cluster’s data directories by running the script mentioned when pg_upgrade completes. (Automatic deletion is not possible if you have user-defined tablespaces inside the old data directory.) You can also delete the old installation directories (e.g., bin, share). 17.Reverting to old cluster If, after running pg_upgrade, you wish to revert to the old cluster, there are several options: If the --check option was used, the old cluster was unmodified; it can be restarted. If the --link option was not used, the old cluster was unmodified; it can be restarted. If the --link option was used, the data files might be shared between the old and new cluster: If pg_upgrade aborted before linking started, the old cluster was unmodified; it can be restarted. If you did not start the new cluster, the old cluster was unmodified except that, when linking started, a .old suffix was appended to $PGDATA/global/pg_control. To reuse the old cluster, remove the .old suffix from $PGDATA/global/pg_control; you can then restart the old cluster. If you did start the new cluster, it has written to shared files and it is unsafe to use the old cluster. The old cluster will need to be restored from backup in this case. pg_upgrade creates various working files, such as schema dumps, stored within pg_upgrade_output.d in the directory of the new cluster. Each run creates a new subdirectory named with a timestamp formatted as per ISO 8601 (%Y%m%dT%H%M%S), where all its generated files are stored. pg_upgrade_output.d and its contained files will be removed automatically if pg_upgrade completes successfully; but in the event of trouble, the files there may provide useful debugging information. pg_upgrade launches short-lived postmasters in the old and new data directories. Temporary Unix socket files for communication with these postmasters are, by default, made in the current working directory. In some situations the path name for the current directory might be too long to be a valid socket name. In that case you can use the -s option to put the socket files in some directory with a shorter path name. For security, be sure that that directory is not readable or writable by any other users. (This is not supported on Windows.) All failure, rebuild, and reindex cases will be reported by pg_upgrade if they affect your installation; post-upgrade scripts to rebuild tables and indexes will be generated automatically. If you are trying to automate the upgrade of many clusters, you should find that clusters with identical database schemas require the same post-upgrade steps for all cluster upgrades; this is because the post-upgrade steps are based on the database schemas, and not user data. For deployment testing, create a schema-only copy of the old cluster, insert dummy data, and upgrade that. pg_upgrade does not support upgrading of databases containing table columns using these reg* OID-referencing system data types: regcollation regconfig regdictionary regnamespace regoper regoperator regproc regprocedure (regclass, regrole, and regtype can be upgraded.) If you want to use link mode and you do not want your old cluster to be modified when the new cluster is started, consider using the clone mode. If that is not available, make a copy of the old cluster and upgrade that in link mode. To make a valid copy of the old cluster, use rsync to create a dirty copy of the old cluster while the server is running, then shut down the old server and run rsync --checksum again to update the copy with any changes to make it consistent. (--checksum is necessary because rsync only has file modification-time granularity of one second.) . If your file system supports file system snapshots or copy-on-write file copies, you can use that to make a backup of the old cluster and tablespaces, though the snapshot and copies must be created simultaneously or while the database server is down. pg_waldump — display a human-readable rendering of the write-ahead log of a IvorySQL database cluster pg_waldump [option…​] [startseg [endseg]] The following command-line options control the location and format of the output: startseg Start reading at the specified log segment file. This implicitly determines the path in which files will be searched for, and the timeline to use. endseg Stop after reading the specified log segment file. -b --bkp-details Output detailed information about backup blocks. -B block --block=block Only display records that modify the given block. The relation must also be provided with --relation or -R. -e end --end=end Stop reading at the specified WAL location, instead of reading to the end of the log stream. -f --follow After reaching the end of valid WAL, keep polling once per second for new WAL to appear. -F fork --fork=fork If provided, only display records that modify blocks in the given fork. The valid values are main for the main fork, fsm for the free space map, vm for the visibility map, and init for the init fork. -n limit --limit=limit Display the specified number of records, then stop. -p path --path=path Specifies a directory to search for log segment files or a directory with a pg_wal subdirectory that contains such files. The default is to search in the current directory, the pg_wal subdirectory of the current directory, and the pg_wal subdirectory of PGDATA. -q --quiet Do not print any output, except for errors. This option can be useful when you want to know whether a range of WAL records can be successfully parsed but don’t care about the record contents. -r rmgr --rmgr=rmgr Only display records generated by the specified resource manager. You can specify the option multiple times to select multiple resource managers. If list is passed as name, print a list of valid resource manager names, and exit.Extensions may define custom resource managers, but pg_waldump does not load the extension module and therefore does not recognize custom resource managers by name. Instead, you can specify the custom resource managers as custom where “” is the three-digit resource manager ID. Names of this form will always be considered valid. -R tblspc / db / rel --relation=tblspc / db / rel Only display records that modify blocks in the given relation. The relation is specified with tablespace OID, database OID, and relfilenode separated by slashes, for example 1234/12345/12345. This is the same format used for relations in the program’s output. -s start --start=start WAL location at which to start reading. The default is to start reading the first valid log record found in the earliest file found. -t timeline --timeline=timeline Timeline from which to read log records. The default is to use the value in startseg, if that is specified; otherwise, the default is 1. -V --version Print the pg_waldump version and exit. -w --fullpage Only display records that include full page images. -x xid --xid=xid Only display records marked with the given transaction ID. -z --stats[=record] Display summary statistics (number and size of records and full-page images) instead of individual records. Optionally generate statistics per-record instead of per-rmgr.If pg_waldump is terminated by signal SIGINT (Control + C), the summary of the statistics computed is displayed up to the termination point. This operation is not supported on Windows. -? --help Show help about pg_waldump command line arguments, and exit. PGDATA Data directory; see also the -p option. PG_COLOR Specifies whether to use color in diagnostic messages. Possible values are always, auto and never. Can give wrong results when the server is running. Only the specified timeline is displayed (or the default, if none is specified). Records in other timelines are ignored. pg_waldump cannot read WAL files with suffix .partial. If those files need to be read, .partial suffix needs to be removed from the file name. postgres — IvorySQL database server postgres [option…​] postgres accepts the following command-line arguments. You can save typing most of these options by setting up a configuration file. Some (safe) options can also be set from the connecting client in an application-dependent way to apply only for that session. For example, if the environment variable PGOPTIONS is set, then libpq-based clients will pass that string to the server, which will interpret it as postgres command-line options. -B nbuffers Sets the number of shared buffers for use by the server processes. The default value of this parameter is chosen automatically by initdb. Specifying this option is equivalent to setting the shared_buffers configuration parameter. -c name=value Sets a named run-time parameter. Most of the other command line options are in fact short forms of such a parameter assignment. -c can appear multiple times to set multiple parameters. -C name Prints the value of the named run-time parameter, and exits. (See the -c option above for details.) This returns values from postgresql.conf, modified by any parameters supplied in this invocation. It does not reflect parameters supplied when the cluster was started.This can be used on a running server for most parameters. This option is meant for other programs that interact with a server instance, such as pg_ctl, to query configuration parameter values. User-facing applications should instead use SHOW or the pg_settings view. -d debug-level Sets the debug level. The higher this value is set, the more debugging output is written to the server log. Values are from 1 to 5. It is also possible to pass -d 0 for a specific session, which will prevent the server log level of the parent postgres process from being propagated to this session. -D datadir Specifies the file system location of the database configuration files. -e Sets the default date style to “European”, that is DMY ordering of input date fields. This also causes the day to be printed before the month in certain date output formats. -F Disables fsync calls for improved performance, at the risk of data corruption in the event of a system crash. Specifying this option is equivalent to disabling the fsync configuration parameter. Read the detailed documentation before using this! -h hostname Specifies the IP host name or address on which postgres is to listen for TCP/IP connections from client applications. The value can also be a comma-separated list of addresses, or * to specify listening on all available interfaces. An empty value specifies not listening on any IP addresses, in which case only Unix-domain sockets can be used to connect to the server. Defaults to listening only on localhost. Specifying this option is equivalent to setting the listen_addresses configuration parameter. -i Allows remote clients to connect via TCP/IP (Internet domain) connections. Without this option, only local connections are accepted. This option is equivalent to setting listen_addresses to * in IvorySQL.conf or via -h.This option is deprecated since it does not allow access to the full functionality of listen_addresses. It’s usually better to set listen_addresses directly. -k directory Specifies the directory of the Unix-domain socket on which postgres is to listen for connections from client applications. The value can also be a comma-separated list of directories. An empty value specifies not listening on any Unix-domain sockets, in which case only TCP/IP sockets can be used to connect to the server. The default value is normally /tmp, but that can be changed at build time. Specifying this option is equivalent to setting the unix_socket_directories configuration parameter. -l Enables secure connections using SSL. IvorySQL must have been compiled with support for SSL for this option to be available. -N max-connections Sets the maximum number of client connections that this server will accept. The default value of this parameter is chosen automatically by initdb. Specifying this option is equivalent to setting the max_connections configuration parameter. -p port Specifies the TCP/IP port or local Unix domain socket file extension on which postgres is to listen for connections from client applications. Defaults to the value of the PGPORT environment variable, or if PGPORT is not set, then defaults to the value established during compilation (normally 5432). If you specify a port other than the default port, then all client applications must specify the same port using either command-line options or PGPORT. -s Print time information and other statistics at the end of each command. This is useful for benchmarking or for use in tuning the number of buffers. -S work-mem Specifies the base amount of memory to be used by sorts and hash tables before resorting to temporary disk files. -V --version Print the postgres version and exit. --name=value Sets a named run-time parameter; a shorter form of -c. --describe-config This option dumps out the server’s internal configuration variables, descriptions, and defaults in tab-delimited COPY format. It is designed primarily for use by administration tools. -? --help Show help about postgres command line arguments, and exit. The options described here are used mainly for debugging purposes, and in some cases to assist with recovery of severely damaged databases. There should be no reason to use them in a production database setup. They are listed here only for use by IvorySQL system developers. Furthermore, these options might change or be removed in a future release without notice. -f { s | i | o | b | t | n | m | h } Forbids the use of particular scan and join methods: s and i disable sequential and index scans respectively, o, b and t disable index-only scans, bitmap index scans, and TID scans respectively, while n, m, and h disable nested-loop, merge and hash joins respectively.Neither sequential scans nor nested-loop joins can be disabled completely; the -fs and -fn options simply discourage the optimizer from using those plan types if it has any other alternative. -n This option is for debugging problems that cause a server process to die abnormally. The ordinary strategy in this situation is to notify all other server processes that they must terminate and then reinitialize the shared memory and semaphores. This is because an errant server process could have corrupted some shared state before terminating. This option specifies that postgres will not reinitialize shared data structures. A knowledgeable system programmer can then use a debugger to examine shared memory and semaphore state. -O Allows the structure of system tables to be modified. This is used by initdb. -P Ignore system indexes when reading system tables, but still update the indexes when modifying the tables. This is useful when recovering from damaged system indexes. -t pa[rser] | pl[anner] | e[xecutor] Print timing statistics for each query relating to each of the major system modules. This option cannot be used together with the -s option. -T This option is for debugging problems that cause a server process to die abnormally. The ordinary strategy in this situation is to notify all other server processes that they must terminate and then reinitialize the shared memory and semaphores. This is because an errant server process could have corrupted some shared state before terminating. This option specifies that postgres will stop all other server processes by sending the signal SIGSTOP, but will not cause them to terminate. This permits system programmers to collect core dumps from all server processes by hand. -v protocol Specifies the version number of the frontend/backend protocol to be used for a particular session. This option is for internal use only. -W seconds A delay of this many seconds occurs when a new server process is started, after it conducts the authentication procedure. This is intended to give an opportunity to attach to the server process with a debugger. The following options only apply to the single-user mode (see Single-User Mode below). --single Selects the single-user mode. This must be the first argument on the command line. database Specifies the name of the database to be accessed. This must be the last argument on the command line. If it is omitted it defaults to the user name. -E Echo all commands to standard output before executing them. -j Use semicolon followed by two newlines, rather than just newline, as the command entry terminator. -r filename Send all server log output to filename. This option is only honored when supplied as a command-line option. PGCLIENTENCODING Default character encoding used by clients. (The clients can override this individually.) This value can also be set in the configuration file. PGDATA Default data directory location PGDATESTYLE Default value of the DateStyle run-time parameter. (The use of this environment variable is deprecated.) PGPORT Default port number (preferably set in the configuration file) A failure message mentioning semget or shmget probably indicates you need to configure your kernel to provide adequate shared memory and semaphores. You might be able to postpone reconfiguring your kernel by decreasing shared_buffers to reduce the shared memory consumption of IvorySQL, and/or by reducing max_connections to reduce the semaphore consumption. A failure message suggesting that another server is already running should be checked carefully, for example by using the command $ ps ax | grep postgres or $ ps -ef | grep postgres depending on your system. If you are certain that no conflicting server is running, you can remove the lock file mentioned in the message and try again. A failure message indicating inability to bind to a port might indicate that that port is already in use by some non-IvorySQL process. You might also get this error if you terminate postgres and immediately restart it using the same port; in this case, you must simply wait a few seconds until the operating system closes the port before trying again. Finally, you might get this error if you specify a port number that your operating system considers to be reserved. For example, many versions of Unix consider port numbers under 1024 to be “trusted” and only permit the Unix superuser to access them. The utility command pg_ctl can be used to start and shut down the postgres server safely and comfortably. If at all possible, do not use SIGKILL to kill the main postgres server. Doing so will prevent postgres from freeing the system resources (e.g., shared memory and semaphores) that it holds before terminating. This might cause problems for starting a fresh postgres run. To terminate the postgres server normally, the signals SIGTERM, SIGINT, or SIGQUIT can be used. The first will wait for all clients to terminate before quitting, the second will forcefully disconnect all clients, and the third will quit immediately without proper shutdown, resulting in a recovery run during restart. The SIGHUP signal will reload the server configuration files. It is also possible to send SIGHUP to an individual server process, but that is usually not sensible. To cancel a running query, send the SIGINT signal to the process running that command. To terminate a backend process cleanly, send SIGTERM to that process. See also pg_cancel_backend. The postgres server uses SIGQUIT to tell subordinate server processes to terminate without normal cleanup. This signal should not be used by users. It is also unwise to send SIGKILL to a server process — the main postgres process will interpret this as a crash and will force all the sibling processes to quit as part of its standard crash-recovery procedure. The -- options will not work on FreeBSD or OpenBSD. Use -c instead. This is a bug in the affected operating systems; a future release of IvorySQL will provide a workaround if this is not fixed. To start a single-user mode server, use a command like postgres --single -D /usr/local/pgsql/data other-options my_database Provide the correct path to the database directory with -D, or make sure that the environment variable PGDATA is set. Also specify the name of the particular database you want to work in. Normally, the single-user mode server treats newline as the command entry terminator; there is no intelligence about semicolons, as there is in psql. To continue a command across multiple lines, you must type backslash just before each newline except the last one. The backslash and adjacent newline are both dropped from the input command. Note that this will happen even when within a string literal or comment. But if you use the -j command line switch, a single newline does not terminate command entry; instead, the sequence semicolon-newline-newline does. That is, type a semicolon immediately followed by a completely empty line. Backslash-newline is not treated specially in this mode. Again, there is no intelligence about such a sequence appearing within a string literal or comment. In either input mode, if you type a semicolon that is not just before or part of a command entry terminator, it is considered a command separator. When you do type a command entry terminator, the multiple statements you’ve entered will be executed as a single transaction. To quit the session, type EOF (Control+D, usually). If you’ve entered any text since the last command entry terminator, then EOF will be taken as a command entry terminator, and another EOF will be needed to exit. Note that the single-user mode server does not provide sophisticated line-editing features (no command history, for example). Single-user mode also does not do any background processing, such as automatic checkpoints or replication. To start postgres in the background using default values, type: $ nohup postgres >logfile 2>&1 </dev/null & To start postgres with a specific port, e.g., 1234: $ postgres -p 1234 To connect to this server using psql, specify this port with the -p option: $ psql -p 1234 or set the environment variable PGPORT: $ export PGPORT=1234 $ psql Named run-time parameters can be set in either of these styles: $ postgres -c work_mem=1234 $ postgres --work-mem=1234 Either form overrides whatever setting might exist for work_mem in IvorySQL.conf. Notice that underscores in parameter names can be written as either underscore or dash on the command line. Except for short-term experiments, it’s probably better practice to edit the setting in IvorySQL.conf than to rely on a command-line switch to set a parameter.","title":"Tool Reference","component":"ivorysql-doc","version":"master","name":"tools_reference","url":"/ivorysql-doc/master/tools_reference.html","titles":[{"text":"1. List of tools","hash":"list-of-tools","id":1},{"text":"2. Client Applications","hash":"client-applications","id":2},{"text":"2.1. clusterdb","hash":"clusterdb","id":3},{"text":"2.1.1. Synopsis","hash":"synopsis","id":4},{"text":"2.1.2. Options","hash":"options","id":5},{"text":"2.1.3. Environment","hash":"environment","id":6},{"text":"2.1.4. Diagnostics","hash":"diagnostics","id":7},{"text":"2.1.5. Examples","hash":"examples","id":8},{"text":"2.2. createdb","hash":"createdb","id":9},{"text":"2.2.1. Synopsis","hash":"synopsis-2","id":10},{"text":"2.2.2. Options","hash":"options-2","id":11},{"text":"2.2.3. Environment","hash":"environment-2","id":12},{"text":"2.2.4. Diagnostics","hash":"diagnostics-2","id":13},{"text":"2.2.5. Examples","hash":"examples-2","id":14},{"text":"2.3. createuser","hash":"createuser","id":15},{"text":"2.3.1. Synopsis","hash":"synopsis-3","id":16},{"text":"2.3.2. Options","hash":"options-3","id":17},{"text":"2.3.3. Environment","hash":"environment-3","id":18},{"text":"2.3.4. Diagnostics","hash":"diagnostics-3","id":19},{"text":"2.3.5. Examples","hash":"examples-3","id":20},{"text":"2.4. dropdb","hash":"dropdb","id":21},{"text":"2.4.1. Synopsis","hash":"synopsis-4","id":22},{"text":"2.4.2. Options","hash":"options-4","id":23},{"text":"2.4.3. Environment","hash":"environment-4","id":24},{"text":"2.4.4. Diagnostics","hash":"diagnostics-4","id":25},{"text":"2.4.5. Examples","hash":"examples-4","id":26},{"text":"2.5. dropuser","hash":"dropuser","id":27},{"text":"2.5.1. Synopsis","hash":"synopsis-5","id":28},{"text":"2.5.2. Options","hash":"options-5","id":29},{"text":"2.5.3. Environment","hash":"environment-5","id":30},{"text":"2.5.4. Diagnostics","hash":"diagnostics-5","id":31},{"text":"2.5.5. Examples","hash":"examples-5","id":32},{"text":"2.6. ecpg","hash":"ecpg","id":33},{"text":"2.6.1. Synopsis","hash":"synopsis-6","id":34},{"text":"2.6.2. Options","hash":"options-6","id":35},{"text":"2.6.3. Notes","hash":"notes","id":36},{"text":"2.6.4. Examples","hash":"examples-6","id":37},{"text":"2.7. pg_amcheck","hash":"pg_amcheck","id":38},{"text":"2.7.1. Synopsis","hash":"synopsis-7","id":39},{"text":"2.7.2. Options","hash":"options-7","id":40},{"text":"2.7.3. Warning","hash":"warning","id":41},{"text":"2.8. pg_basebackup","hash":"pg_basebackup","id":42},{"text":"2.8.1. Synopsis","hash":"synopsis-8","id":43},{"text":"2.8.2. Options","hash":"options-8","id":44},{"text":"2.8.3. Environment","hash":"environment-6","id":45},{"text":"2.8.4. Notes","hash":"notes-2","id":46},{"text":"2.8.5. Examples","hash":"examples-7","id":47},{"text":"2.9. pgbench","hash":"pgbench","id":48},{"text":"2.9.1. Synopsis","hash":"synopsis-9","id":49},{"text":"2.9.2. Caution","hash":"caution","id":50},{"text":"2.9.3. Options","hash":"options-9","id":51},{"text":"2.9.4. Initialization Options","hash":"initialization-options","id":52},{"text":"2.9.5. Benchmarking Options","hash":"benchmarking-options","id":53},{"text":"2.9.6. Common Options","hash":"common-options","id":54},{"text":"2.9.7. Exit Status","hash":"exit-status","id":55},{"text":"2.9.8. Environment","hash":"environment-7","id":56},{"text":"2.10. pg_config","hash":"pg_config","id":57},{"text":"2.10.1. Synopsis","hash":"synopsis-10","id":58},{"text":"2.10.2. Options","hash":"options-10","id":59},{"text":"2.10.3. Example","hash":"example","id":60},{"text":"2.11. pg_dump","hash":"pg_dump","id":61},{"text":"2.11.1. Synopsis","hash":"synopsis-11","id":62},{"text":"2.11.2. Options","hash":"options-11","id":63},{"text":"2.11.3. Environment","hash":"environment-8","id":64},{"text":"2.11.4. Diagnostics","hash":"diagnostics-6","id":65},{"text":"2.11.5. Notes","hash":"notes-3","id":66},{"text":"2.11.6. Examples","hash":"examples-8","id":67},{"text":"2.12. pg_dumpall","hash":"pg_dumpall","id":68},{"text":"2.12.1. Synopsis","hash":"synopsis-12","id":69},{"text":"2.12.2. Options","hash":"options-12","id":70},{"text":"2.12.3. Environment","hash":"environment-9","id":71},{"text":"2.12.4. Notes","hash":"notes-4","id":72},{"text":"2.12.5. Examples","hash":"examples-9","id":73},{"text":"2.13. pg_isready","hash":"pg_isready","id":74},{"text":"2.13.1. Synopsis","hash":"synopsis-13","id":75},{"text":"2.13.2. Options","hash":"options-13","id":76},{"text":"2.13.3. Exit Status","hash":"exit-status-2","id":77},{"text":"2.13.4. Environment","hash":"environment-10","id":78},{"text":"2.13.5. Notes","hash":"notes-5","id":79},{"text":"2.13.6. Examples","hash":"examples-10","id":80},{"text":"2.14. pg_receivewal","hash":"pg_receivewal","id":81},{"text":"2.14.1. Synopsis","hash":"synopsis-14","id":82},{"text":"2.14.2. Options","hash":"options-14","id":83},{"text":"2.14.3. Exit Status","hash":"exit-status-3","id":84},{"text":"2.14.4. Environment","hash":"environment-11","id":85},{"text":"2.14.5. Notes","hash":"notes-6","id":86},{"text":"2.14.6. Examples","hash":"examples-11","id":87},{"text":"2.15. pg_recvlogical","hash":"pg_recvlogical","id":88},{"text":"2.15.1. Synopsis","hash":"synopsis-15","id":89},{"text":"2.15.2. Options","hash":"options-15","id":90},{"text":"2.15.3. Environment","hash":"environment-12","id":91},{"text":"2.15.4. Notes","hash":"notes-7","id":92},{"text":"2.16. pg_restore","hash":"pg_restore","id":93},{"text":"2.16.1. Synopsis","hash":"synopsis-16","id":94},{"text":"2.16.2. Options","hash":"options-16","id":95},{"text":"2.16.3. Environment","hash":"environment-13","id":96},{"text":"2.16.4. Diagnostics","hash":"diagnostics-7","id":97},{"text":"2.16.5. Notes","hash":"notes-8","id":98},{"text":"2.16.6. Examples","hash":"examples-12","id":99},{"text":"2.17. pg_verifybackup","hash":"pg_verifybackup","id":100},{"text":"2.17.1. Synopsis","hash":"synopsis-17","id":101},{"text":"2.17.2. Options","hash":"options-17","id":102},{"text":"2.17.3. Examples","hash":"examples-13","id":103},{"text":"2.18. psql","hash":"psql","id":104},{"text":"2.18.1. Synopsis","hash":"synopsis-18","id":105},{"text":"2.18.2. Options","hash":"options-18","id":106},{"text":"2.18.3. Exit Status","hash":"exit-status-4","id":107},{"text":"2.19. reindexdb","hash":"reindexdb","id":108},{"text":"2.19.1. Synopsis","hash":"synopsis-19","id":109},{"text":"2.19.2. Options","hash":"options-19","id":110},{"text":"2.19.3. Environment","hash":"environment-14","id":111},{"text":"2.19.4. Diagnostics","hash":"diagnostics-8","id":112},{"text":"2.19.5. Notes","hash":"notes-9","id":113},{"text":"2.19.6. Examples","hash":"examples-14","id":114},{"text":"2.20. vacuumdb","hash":"vacuumdb","id":115},{"text":"2.20.1. Synopsis","hash":"synopsis-20","id":116},{"text":"2.20.2. Options","hash":"options-20","id":117},{"text":"2.20.3. Environment","hash":"environment-15","id":118},{"text":"2.20.4. Diagnostics","hash":"diagnostics-9","id":119},{"text":"2.20.5. Notes","hash":"notes-10","id":120},{"text":"2.20.6. Examples","hash":"examples-15","id":121},{"text":"3. Server Applications","hash":"server-applications","id":122},{"text":"3.1. initdb","hash":"initdb","id":123},{"text":"3.1.1. Synopsis","hash":"synopsis-21","id":124},{"text":"3.1.2. Options","hash":"options-21","id":125},{"text":"3.1.3. Environment","hash":"environment-16","id":126},{"text":"3.1.4. Notes","hash":"notes-11","id":127},{"text":"3.2. pg_archivecleanup","hash":"pg_archivecleanup","id":128},{"text":"3.2.1. Synopsis","hash":"synopsis-22","id":129},{"text":"3.2.2. Options","hash":"options-22","id":130},{"text":"3.2.3. Environment","hash":"environment-17","id":131},{"text":"3.2.4. Examples","hash":"examples-16","id":132},{"text":"3.3. pg_checksums","hash":"pg_checksums","id":133},{"text":"3.3.1. Synopsis","hash":"synopsis-23","id":134},{"text":"3.3.2. Options","hash":"options-23","id":135},{"text":"3.3.3. Environment","hash":"environment-18","id":136},{"text":"3.3.4. Notes","hash":"notes-12","id":137},{"text":"3.4. pg_controldata","hash":"pg_controldata","id":138},{"text":"3.4.1. Synopsis","hash":"synopsis-24","id":139},{"text":"3.4.2. Environment","hash":"environment-19","id":140},{"text":"3.5. pg_ctl","hash":"pg_ctl","id":141},{"text":"3.5.1. Synopsis","hash":"synopsis-25","id":142},{"text":"3.5.2. Options","hash":"options-24","id":143},{"text":"3.5.3. Options for Windows","hash":"options-for-windows","id":144},{"text":"3.5.4. Environment","hash":"environment-20","id":145},{"text":"3.5.5. Files","hash":"files","id":146},{"text":"3.5.6. Examples","hash":"examples-17","id":147},{"text":"3.5.6.1. Starting the Server","hash":"starting-the-server","id":148},{"text":"3.5.6.2. Stopping the Server","hash":"stopping-the-server","id":149},{"text":"3.5.6.3. Restarting the Server","hash":"restarting-the-server","id":150},{"text":"3.5.6.4. Showing the Server Status","hash":"showing-the-server-status","id":151},{"text":"3.6. pg_resetwal","hash":"pg_resetwal","id":152},{"text":"3.6.1. Synopsis","hash":"synopsis-26","id":153},{"text":"3.6.2. Options","hash":"options-25","id":154},{"text":"3.6.3. Environment","hash":"environment-21","id":155},{"text":"3.6.4. Notes","hash":"notes-13","id":156},{"text":"3.7. pg_rewind","hash":"pg_rewind","id":157},{"text":"3.7.1. Synopsis","hash":"synopsis-27","id":158},{"text":"3.7.2. Warning","hash":"warning-2","id":159},{"text":"3.7.3. Options","hash":"options-26","id":160},{"text":"3.7.4. Environment","hash":"environment-22","id":161},{"text":"3.7.5. Notes","hash":"notes-14","id":162},{"text":"3.7.6. How It Works","hash":"how-it-works","id":163},{"text":"3.8. pg_test_fsync","hash":"pg_test_fsync","id":164},{"text":"3.8.1. Synopsis","hash":"synopsis-28","id":165},{"text":"3.8.2. Options","hash":"options-27","id":166},{"text":"3.8.3. Environment","hash":"environment-23","id":167},{"text":"3.9. pg_test_timing","hash":"pg_test_timing","id":168},{"text":"3.9.1. Synopsis","hash":"synopsis-29","id":169},{"text":"3.9.2. Options","hash":"options-28","id":170},{"text":"3.9.3. Usage","hash":"usage","id":171},{"text":"3.9.3.1. Interpreting Results","hash":"interpreting-results","id":172},{"text":"3.9.3.2. Measuring Executor Timing Overhead","hash":"measuring-executor-timing-overhead","id":173},{"text":"3.9.3.3. Changing Time Sources","hash":"changing-time-sources","id":174},{"text":"3.9.3.4. Clock Hardware and Timing Accuracy","hash":"clock-hardware-and-timing-accuracy","id":175},{"text":"3.10. pg_upgrade","hash":"pg_upgrade","id":176},{"text":"3.10.1. Synopsis","hash":"synopsis-30","id":177},{"text":"3.10.2. Options","hash":"options-29","id":178},{"text":"3.10.3. Usage","hash":"usage-2","id":179},{"text":"3.10.4. Notes","hash":"notes-15","id":180},{"text":"3.11. pg_waldump","hash":"pg_waldump","id":181},{"text":"3.11.1. Synopsis","hash":"synopsis-31","id":182},{"text":"3.11.2. Options","hash":"options-30","id":183},{"text":"3.11.3. Environment","hash":"environment-24","id":184},{"text":"3.11.4. Notes","hash":"notes-16","id":185},{"text":"3.12. postgres","hash":"postgres","id":186},{"text":"3.12.1. Synopsis","hash":"synopsis-32","id":187},{"text":"3.12.2. Options","hash":"options-31","id":188},{"text":"3.12.3. General Purpose","hash":"general-purpose","id":189},{"text":"3.12.4. Semi-Internal Options","hash":"semi-internal-options","id":190},{"text":"3.12.5. Options for Single-User Mode","hash":"options-for-single-user-mode","id":191},{"text":"3.12.6. Environment","hash":"environment-25","id":192},{"text":"3.12.7. Diagnostics","hash":"diagnostics-10","id":193},{"text":"3.12.8. Notes","hash":"notes-17","id":194},{"text":"3.12.9. Bugs","hash":"bugs","id":195},{"text":"3.12.10. Single-User Mode","hash":"single-user-mode","id":196},{"text":"3.12.11. Examples","hash":"examples-18","id":197}]},"10":{"id":10,"text":"IvorySQL is the only Oracle compatible open source PostgreSQL. Get started by downloading the code from Github. Go to IvorySQL Release Page. IvorySQL project is an open source project proposed by Highgo Software to add the Oracle compatibility features into the popular PostgreSQL database. It is Apache licensed Open Source and always free to use. Any comments please contact support@ivorysql.org IvorySQL v5.0 pdf documentation","title":"Welcome to IvorySQL","component":"ivorysql-doc","version":"master","name":"welcome","url":"/ivorysql-doc/master/welcome.html","titles":[{"text":"Getting Started","hash":"getting-started","id":1},{"text":"Releases","hash":"releases","id":2},{"text":"About IvorySQL","hash":"about-ivorysql","id":3},{"text":"Docs Download","hash":"docs-download","id":4}]},"11":{"id":11,"text":"In order to support both the PG mode and Oracle-compatible mode, IvorySQL allows specifying the -m parameter during initdb to obtain either a PG mode database or an Oracle-compatible mode database. If the -m parameter is not specified, it defaults to Oracle-compatible mode. If the -m parameter is specified as pg, the database will no longer be compatible with Oracle syntax. initdb -m initialization requires judgment of different modes, among which oracle mode requires the execution of SQL statements postgres_oracle.bki. The default is Oracle compatibility mode, and the process is as follows: Startup: When starting, it determines whether it is an Oracle compatibility mode based on the initialization mode; Description: database_mode: Used to indicate initialization mode; database_mode=DB_PG，PG mode, and cannot be switched; database_mode=DB_ORACLE， oracle compatibility mode; Initialize the PG mode: ./initdb -D ../data -m pg Initialize the Oracle compatibility mode: ./initdb -D ../data -m oracle or ./initdb -D ../data","title":"Dual-mode design","component":"ivorysql-doc","version":"master","name":"dual_mode_design","url":"/ivorysql-doc/master/architecture/dual_mode_design.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function","hash":"function","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"12":{"id":12,"text":"The dual parser framework is divided into two main parts: the SQL side and the server-side programming language. The basic approach is to introduce a new set of Oracle-compatible syntax and lexical rules. When Oracle compatibility is enabled, the parser follows Oracle-style syntax analysis to generate the corresponding syntax tree. Implementation Details: Under src/backend/, create a new directory named oracle_parser. Copy scan.l and gram.y from src/backend/parser/ to the oracle_parser directory, renaming them to ora_gram.y and ora_scan.l. Add Oracle-style syntax and lexical analysis code to these files. Copy keywords.c to the oracle_parser directory to store Oracle-specific keywords. Compile the oracle_parser directory into a dynamic library named libparser_oracle.so. When Oracle compatibility is enabled, the ivorysql.conf file is appended to the end of the postgresql.conf file. Add liboracle_parser to the shared_preload_libraries parameter in ivorysql.conf to ensure the dynamic library is loaded automatically when the database starts. A new function pointer ora_raw_parser is introduced. When the libparser_oracle.so library is loaded, the _PG_init() function assigns the address of oracle_raw_parser() to ora_raw_parser. The _PG_fini() function resets ora_raw_parser to NULL when the compatibility mode is switched. Each backend process calls the BackendInitialize() function, which sets port→connmode based on the port number the backend process is connected to. If the port is an Oracle-compatible port, connmode is set to 'o'; otherwise, it is set to 'p'. After this, PostgresMain() calls InitIvorysql(). If port→connmode is 'o', the function SetConfigOption(\"ivorysql.compatible_mode\", \"oracle\", PGC_USERSET, PGC_S_OVERRIDE) is called. Since this parameter has an assign_hook, executing assign_hook() within SetConfigOption() is equivalent to calling assign_compatible_mode(), which sets sql_raw_parser = ora_raw_parser. During SQL statement analysis, the function pg_parse_query()→raw_parser() calls either standard_parser() or ora_raw_parser() through the function pointer sql_raw_parser. The diagram below illustrates what happens during SQL statement analysis. A new language, pliSQL, is added to the system table pg_language. Implementation Details: Copy the plpgsql directory from the PostgreSQL source code and rename it to plisql. Rename all files in the plisql directory to start with plisql. Since plpgsql is a language, the registration functions for plpgsql (e.g., plpgsql_validator, plpgsql_call_handler, plpgsql_inline_handler) are renamed to start with plisql. All other functions in the directory are also renamed to start with plisql. The plisql directory is built as a plugin. If the database mode is Oracle during initdb, this plugin is created. The plugin registers the pliSQL language in the database system tables. The pliSQL side does not have its own lexical rules; it relies on the lexical rules of the SQL side. Therefore, for pliSQL, Oracle-compatible lexical rules are enforced. The main modification is in the plisql_scanner_init function, which calls ora_scanner_init(). The internal_yylex() function in the plisql directory calls ora_core_yylex. The syntax rules for pliSQL are defined in plisql/src/pl_gram.y, where the syntax rules for Oracle-compatible PL/SQL blocks are implemented. When creating a function on the SQL side, if no language is specified: In Oracle compatibility mode, the default language is plisql. In PostgreSQL compatibility mode, the default language is plpgsql. In oracle_parser, ora_gram.y defaults to plisql, while in the PostgreSQL parser, gram.y defaults to plpgsql. For anonymous blocks, if no language is specified: In Oracle compatibility mode, the default language is plisql. In PostgreSQL compatibility mode, the default language is plpgsql. The ExecuteDoStmt function also determines the default language based on the compatibility mode.","title":"Dual Parser Framework for Oracle Compatibility","component":"ivorysql-doc","version":"master","name":"dual_parser","url":"/ivorysql-doc/master/architecture/dual_parser.html","titles":[{"text":"1. Lexical and Syntax Separation on the SQL Side","hash":"lexical-and-syntax-separation-on-the-sql-side","id":1},{"text":"2. Lexical and Syntax Separation for Server-Side Programming Language","hash":"lexical-and-syntax-separation-for-server-side-programming-language","id":2}]},"13":{"id":13,"text":"In order to minimize changes to the original PostgreSQL, it is compatible with Oracle. We need to implement a framework for dual-parser, dual-port, modal PL/PGSQL to implement PL/iSQL. The implementation flowchart is as follows: The Ivorysql port 5432 is kept compatible with the original PostgreSQL situation, so Ivorysql uses another separate port to log in, which defaults to 1521. Log in from this port, and the Oracle compatibility mode is used by default. If you need to log in from port 5432 and also enter compatibility mode, you must configure it using SET ivorysql.compatible_mode=oracle. In order to minimize the interference between Oracle syntax and PostgreSQL syntax, a parser module has been added to handle oracle-related syntax. Added PL/iSQL procedural language compatible with Oracle’s PL/SQL procedural language.","title":"IVORYSQL framework design","component":"ivorysql-doc","version":"master","name":"framework_design","url":"/ivorysql-doc/master/architecture/framework_design.html","titles":[{"text":"1. Function","hash":"function","id":1},{"text":"1.1. Dual-port design","hash":"dual-port-design","id":2},{"text":"1.2. Parser module design","hash":"parser-module-design","id":3},{"text":"1.3. Added PL/iSQL procedural language","hash":"added-plisql-procedural-language","id":4}]},"14":{"id":14,"text":"In order to be compatible with Oracle, it is necessary to add some variables for controlling the database execution results on the basis of the original GUC variables, so as to achieve the same behavior as Oracle. In order to better add compatible GUC parameters and to minimize changes to the PG kernel source code, we need to design a framework to add GUC to a unified location. When adding guc parameters, we need to add them uniformly in the ivy_guc.c. Where Ivy_ConfigureNamesBool, Ivy_ConfigureNamesInt, Ivy_ConfigureNamesString, Ivy_ConfigureNamesReal and Ivy_ConfigureNamesEnum represent 5 different types of guc parameters. When adding guc parameters, simply add the value of guc to the corresponding array. Variable Description ivorysql.compatible_mode Indicates which database (pg/oracle) is currently compatible with, which can be viewed through the show command. The set command changes this variable, and the reset command resets it to the database mode at the time of connection. Resetting all will affect this variable ivorysql.database_mode Indicates the current database schema (pg/oracle), which can be viewed through the show command. The set/reset/reset all command does not affect this variable ivorysql.datetime_ignore_nls_mask Indicates whether the date format will be affected by the NLS parameter. The default value is 0, which can be set using the set command. The reset command resets the date format, and the reset all command resets the variable ivorysql.enable_emptystring_to_NULL The value is (on/off), and when this variable is on, it will convert the inserted empty string into a NULL value for storage ivorysql.identifier_case_switch Set character case conversion mode ivorysql.listen_address Indicates the address for compatibility mode listening. When initializing the database, read the configuration from the ivorysql.conf file, modify the value in the configuration file, and restart the database to take effect. This can be viewed through the show command ivorysql.port Indicates the port number for connecting in compatibility mode. When initializing the database, read the configuration from the ivorysql.conf file and modify the value in the configuration file. To take effect, restart the database and view it through the show command nls_date_format Represents the default date format, which can be viewed through the show command and defaults to 'YYYY-MM-DD'. It can be set through the set command and reset back to the default value through the reset command. The reset all command will reset this variable nls_length_semantics Compatible with oracle parameters of the same name, controlling the size of memory occupied by a character nls_timestamp_format Compatible with oracle parameters of the same name, controlling date format with time nls_timestamp_tz_format Compatible with oracle parameters of the same name, controlling the date format with time zone shared_preload_libraries When initializing the database, read from the ivorysql.conf file and view it through the show command. Modify the value in the configuration file and restart the database to take effect. The optional values ​​of this GUC variable are from 0 to 15. optional values Types not formatted through NLS 0 Does not block any types, all time formats are formatted by NLS. 1 date 2 timestamp 3 date、timestamp 4 timestamptz 5 date、timestamptz 6 timestamp、timestamptz 7 date、timestamp、timestamptz 8 timestampltz 9 date、timestampltz 10 timestamp、timestampltz 11 date、timestamp、timestampltz 12 timestamptz、timestampltz 13 date、timestamptz、timestampltz 14 timestamp、timestamptz、timestampltz 15 date、timestamp、timestamptz、timestampltz Usage Example(date) check value of nls_date_format and datetime_ignore_nls_mask ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# show nls_date_format; nls_date_format ----------------- YYYY-MM-DD (1 row) ivorysql=# show ivorysql.datetime_ignore_nls_mask; ivorysql.datetime_ignore_nls_mask ----------------------------------- 0 (1 row) create a table for testing ivorysql=# create table test_nls_date(a int, created_at date); CREATE TABLE insert data ivorysql=# insert into test_nls_date values(1, '2024/04/05'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 2024-04-05 (1 row) modify nls_date_format ivorysql=# set nls_date_format to 'yy-mm-dd'; SET Insert NLS formatted data and view, insert successfully. ivorysql=# insert into test_nls_date values(2, '24/04/15'); INSERT 0 1 ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows) Changing the date type to not undergo NLS processing, and inserting the same data, such as changing it to 1 (3, 5, 7, etc.), will result in an error upon data insertion. NLS formatting will not affect the query results for dates. ivorysql=# set ivorysql.datetime_ignore_nls_mask to 1; SET ivorysql=# insert into test_nls_date values(3, '24/05/15'); ERROR: date/time field value out of range: \"24/05/15\" LINE 1: insert into test_nls_date values(3, '24/05/15'); ^ HINT: Perhaps you need a different \"datestyle\" setting. ivorysql=# select * from test_nls_date; a | created_at ---+------------ 1 | 24-04-05 2 | 24-04-15 (2 rows)","title":"GUC Framework","component":"ivorysql-doc","version":"master","name":"guc_framework","url":"/ivorysql-doc/master/architecture/guc_framework.html","titles":[{"text":"1. New GUC variables","hash":"new-guc-variables","id":1},{"text":"1.1. Achieve","hash":"achieve","id":2},{"text":"1.2. New variables(currently)","hash":"new-variablescurrently","id":3},{"text":"1.3. Example","hash":"example","id":4},{"text":"1.3.1. ivorysql.datetime_ignore_nls_mask","hash":"ivorysql-datetime_ignore_nls_mask","id":5}]},"15":{"id":15,"text":"IvorySQL supports two database modes during the initialization process: PG Mode: Maintains compatibility with native PostgreSQL. Oracle Mode (default): Provides Oracle syntax compatibility and enhanced features. Users can specify the initialization mode using the initdb command parameters to meet compatibility requirements for different scenarios. During the initial phase, initdb parses the input command-line parameters. Parameter Description Options Default Value -m Specifies the database mode oracle/pg oracle -C Sets the case conversion mode for Oracle compatibility interchange/normal/lowercase interchange Parameter Parsing Workflow: 1.Inherits PostgreSQL’s existing parameter processing mechanism. 2.Adds parsing logic for the mode selection parameter -m. 3.Introduces a processing module for the case conversion parameter -C. The setup_data_file_paths() function is executed to configure critical file paths: if (DB_PG == database_mode) set_input(&bki_file, \"postgres.bki\"); else set_input(&bki_file, \"postgres_oracle.bki\"); Path Verification Mechanism: 1.Verifies the existence of the BKI files (postgres_oracle.bki / postgres.bki). 2.Confirms the availability of the configuration file templates. 3.Establishes the system directory structure corresponding to the database mode. The core initialization operations are performed by the initialize_data_directory() function: Calls create_data_directory() to create the main data directory (PGDATA). Uses create_xlog_or_symlink() to establish the WAL log directory. Iteratively creates standard subdirectories such as base and global. Calls set_null_conf() to create an empty postgresql.conf file. In Oracle mode, additionally creates the ivorysql.conf configuration file. Calls setup_config() to write configuration information to postgresql.conf. In Oracle mode, additional configuration information is written to ivorysql.conf. Executes bootstrap_template1() to load the corresponding BKI file and initialize the template1 template database. IvorySQL additionally sets the database mode (oracle/pg) and case conversion mode for the template1 template database. load_plisql(): Installs the PL/iSQL procedural language for Oracle PL/SQL compatibility. load_ivorysql_ora(): Loads the core Oracle compatibility layer extension. make_ivorysql(): Creates the default ivorysql database. When the database mode is Oracle, usernames are forcibly converted to lowercase. Note In PostgreSQL, \"bki\" stands for \"Bootstrap Kit.\" The Bootstrap Kit is a collection of tools and files used internally by PostgreSQL to bootstrap the database system. The Bootstrap Kit is primarily used to initialize the foundational structure of the PostgreSQL database system, including the system catalog, system tables, and other necessary system objects. These system catalogs and tables store metadata information about the PostgreSQL database system, such as database objects, table structures, and index information. The genbki.pl script processes system table files like pg_class.h, pg_namespace.h, and pg_proc.h, and generates postgres.bki for initdb to load. This primarily involves loading initialization data, such as creating system tables, system functions, and predefined types. During the compilation phase before initdb, genbki.pl generates both postgres.bki and postgres_ora.bki. While postgres.bki remains unchanged from PostgreSQL, postgres_ora.bki includes additional system objects for Oracle compatibility.","title":"initdb Process","component":"ivorysql-doc","version":"master","name":"initdb_process","url":"/ivorysql-doc/master/architecture/initdb_process.html","titles":[{"text":"1. Parameter Parsing and Processing","hash":"parameter-parsing-and-processing","id":1},{"text":"2. File Path Initialization","hash":"file-path-initialization","id":2},{"text":"3. Data Directory Initialization Process","hash":"data-directory-initialization-process","id":3},{"text":"3.1. Directory Structure Creation","hash":"directory-structure-creation","id":4},{"text":"3.2. Configuration File Initialization","hash":"configuration-file-initialization","id":5},{"text":"3.3. Template Database Bootstrapping","hash":"template-database-bootstrapping","id":6},{"text":"3.4. Oracle-Compatible Username Handling","hash":"oracle-compatible-username-handling","id":7}]},"16":{"id":16,"text":"The IvorySQL Cloud platform is a comprehensive solution that integrates the IvorySQL database and its surrounding ecosystem to deliver end-to-end database and resource management capabilities. Before starting the installation, compile and install the following projects from GitHub: Frontend: https://github.com/IvorySQL/ivory-cloud-web Backend: https://github.com/IvorySQL/ivory-cloud Prepare a Kubernetes cluster (version 1.23) and install ivory-operator on the master node: https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE The IvorySQL Cloud platform currently supports installation on Linux systems. The required packages are listed below. Component Package Frontend dist Backend cloudnative-1.0-SNAPSHOT.jar K8S cluster docker.io/ivorysql/ivory-operator:v5.0 docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 In addition, install the following supporting components: Backend database: Stores and manages all data related to cloud resources, user information, access control, billing, and more. Use a PostgreSQL-compatible database such as PostgreSQL, HighGo DB, or IvorySQL. NGINX: Hosts the web user interface of the cloud platform. Complete the preparation steps on every server before installation. Deploy IvorySQL Cloud on a Kubernetes (1.23) cluster that already has a default storage class. Disable the firewall on every server to ensure full connectivity. systemctl stop firewalld.service systemctl disable firewalld.service Install the backend database yourself by following the instructions on the IvorySQL official website. # Clone the code git clone https://github.com/IvorySQL/ivory-cloud.git # Go to the project root cd ivory-cloud Ensure that every .sh file in ivory-cloud\\cloudnative\\src\\main\\resources\\monitor and its subdirectories uses the Unix format. If not, run dos2unix to convert them. dos2unix cloudnative\\src\\main\\resources\\monitor\\* # Build mvn clean mvn package -D maven.test.skip=true The packaged artifact `cloudnative-1.0-SNAPSHOT.jar` can be found under `ivory-cloud/cloudnative/target`. Execute the following steps on the Kubernetes server: # Create a working directory mkdir -p /home/ivory # Upload `ivory-cloud/cloudnative/target/cloudnative-1.0-SNAPSHOT.jar` to the directory created above # Configuration files ## Create a configuration directory mkdir -p /home/ivory/config ## Upload configuration files Copy the following files from `ivory-cloud/cloudnative/src/main/resources` to `/home/ivory/config`: application.yaml application-native.yaml spring_pro_logback.xml ## Update the configuration Replace `url`, `username`, and `password` with the database information configured in <<Backend database>>. ## /home/ivory/config/application-native.yaml datasource: druid: db-type: com.alibaba.druid.pool.DruidDataSource driver-class-name: org.postgresql.Driver url: jdbc:postgresql://127.0.0.1:5432/ivorysql username: ivorysql password: \"ivory@123\" # Install JDK 1.8 yum install -y java-1.8.0-openjdk.x86_64 [root@cloud ivory]# pwd /home/ivory/ [root@cloud ivory]# nohup java -jar cloudnative-1.0-SNAPSHOT.jar > log_native 2>&1 & [root@cloud ivory]# ps -ef | grep java root 77494 1 0 Oct09 ? 00:03:07 java -jar cloudnative-1.0-SNAPSHOT.jar ## Fetch the code git clone https://github.com/IvorySQL/ivory-cloud-web.git ## Go to the project root cd ivorysql-cloud-web ## Install dependencies npm install ## Build for production npm run build:prod # Create a deployment directory [root@cloud opt]# mkdir -p /opt/cloud/web # Copy the generated `dist` folder to /opt/cloud/web # Grant permissions [root@cloud web]# chmod 755 /opt/cloud/web/dist [root@cloud web]# chmod -R 777 /opt/cloud/web/dist Edit the configuration file: [root@cloud dist]# pwd /home/cloud/web/dist [root@cloud dist]# vi config.js var PLATFROM_CONFIG = {}; // Replace with the IP address of the current server PLATFROM_CONFIG.baseUrl = \"http://192.168.31.43:8081/cloudapi/api/v1\" // true: show the “Register” button on the login page // false: hide the “Register” button on the login page globalShowRegister = true // Hide the cloud-native database? true: hide; false: show disableNative = false // Database type dbtype = \"IvorySQL\" dbversion = \"5.0\" The IvorySQL Cloud host must have NGINX installed to serve the web interface. Users can select any installation method; the following steps are provided for reference. [root@cloud web]# wget https://nginx.org/download/nginx-1.20.1.tar.gz [root@cloud web]# ls -lrt total 3924 -rwxrwxr-x. 1 root root 1061461 May 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 Oct 9 16:43 dist.tar.gz drwxrwxrwx. 4 root root 103 Oct 21 13:20 dist [root@host30 cloud]# yum -y install pcre-devel [root@host30 cloud]# yum -y install openssl openssl-devel NGINX is installed under the directory specified by --prefix during configure. The example below installs it to /opt/cloud/nginx. ## Extract nginx-1.20.1.tar.gz [root@cloud web]# tar -zxvf nginx-1.20.1.tar.gz ## Verify that nginx-1.20.1 was created [root@cloud web]# ls -lrt total 3924 -rwxrwxr-x. 1 root root 1061461 May 25 2021 nginx-1.20.1.tar.gz -rwxrwxr-x. 1 root root 2943732 Oct 9 16:43 dist.tar.gz drwxrwxr-x. 9 1001 1001 186 Oct 9 16:53 nginx-1.20.1 drwxrwxrwx. 4 root root 103 Oct 21 13:20 dist ## Configure [root@cloud web]# cd nginx-1.20.1 [root@cloud nginx-1.20.1]# ./configure --prefix=/opt/cloud/nginx --with-http_ssl_module ## Compile and install [root@cloud nginx-1.20.1]# make [root@cloud nginx-1.20.1]# make install The configuration file is stored under /opt/cloud/nginx. Adjust it according to the README on GitHub, and replace the IP with the address of the current server. server { listen 9104; server_name 192.168.31.43; location / { root /opt/cloud/web/dist; index index.html index.htm; } error_page 500 502 503 504 /50x.html; location = /50x.html { root html; } } [root@cloud sbin]# pwd /opt/cloud/nginx/sbin [root@cloud sbin]# ./nginx -c /opt/cloud/nginx/conf/nginx.conf [root@cloud sbin]# ps -ef | grep nginx root 2179 131037 0 09:46 pts/1 00:00:00 grep --color=auto nginx root 55047 1 0 Oct21 ? 00:00:00 nginx: master process ./nginx -c /opt/cloud/nginx/conf/nginx.conf nobody 55048 55047 0 Oct21 ? 00:00:00 nginx: worker process Set up the Kubernetes cluster yourself. This section describes how to install ivory-operator on the cluster and preload container images. Refer to the README on GitHub: https://github.com/IvorySQL/ivory-operator/tree/IVYO_REL_5_STABLE If your servers have direct access to Docker Hub, you can skip this step. Otherwise, preload the following images on every node in the Kubernetes cluster. docker.io/ivorysql/pgadmin:ubi8-9.9-5.0-1 docker.io/ivorysql/pgbackrest:ubi8-2.56.0-5.0-1 docker.io/ivorysql/pgbouncer:ubi8-1.23.0-5.0-1 docker.io/ivorysql/postgres-exporter:ubi8-0.17.0-5.0-1 docker.io/ivorysql/ivorysql:ubi8-5.0-5.0-1 docker.io/prom/prometheus:v2.33.5 docker.io/prom/alertmanager:v0.22.2 docker.io/grafana/grafana:8.5.10","title":"Installation Guide","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_installation","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_installation.html","titles":[{"text":"1. IvorySQL Cloud Platform Installation","hash":"ivorysql-cloud-platform-installation","id":1},{"text":"2. Pre-installation Checklist","hash":"pre-installation-checklist","id":2},{"text":"2.1. Disable the firewall","hash":"disable-the-firewall","id":3},{"text":"2.2. Backend deployment","hash":"backend-deployment","id":4},{"text":"2.2.1. Backend database","hash":"backend-db","id":5},{"text":"2.2.2. Backend services","hash":"backend-services","id":6},{"text":"2.2.2.1. Compile the backend service","hash":"compile-the-backend-service","id":7},{"text":"2.2.2.2. Deploy the service","hash":"deploy-the-service","id":8},{"text":"2.2.3. Start the backend service","hash":"start-the-backend-service","id":9},{"text":"2.3. Frontend deployment","hash":"frontend-deployment","id":10},{"text":"2.3.1. Compile the frontend","hash":"compile-the-frontend","id":11},{"text":"2.3.2. Update directory and file permissions","hash":"update-directory-and-file-permissions","id":12},{"text":"2.3.3. Update config.js","hash":"update-config-js","id":13},{"text":"2.4. Install and configure NGINX","hash":"install-and-configure-nginx","id":14},{"text":"2.4.1. Download the NGINX source package","hash":"download-the-nginx-source-package","id":15},{"text":"2.4.2. Install dependencies","hash":"install-dependencies","id":16},{"text":"2.4.3. Build and install NGINX","hash":"build-and-install-nginx","id":17},{"text":"2.4.4. Update nginx.conf","hash":"update-nginx-conf","id":18},{"text":"2.4.5. Start NGINX","hash":"start-nginx","id":19},{"text":"2.5. Operator deployment","hash":"operator-deployment","id":20},{"text":"2.5.1. Install ivory-operator","hash":"install-ivory-operator","id":21},{"text":"2.5.2. Load container images","hash":"load-container-images","id":22}]},"17":{"id":17,"text":"IvorySQL Cloud is a web-based service platform that can be accessed from any computer through a browser. After installing the cloud service platform on the server with IP 192.168.31.43, open a browser and enter http://192.168.31.43:9104/ (9104 is the port configured in nginx.conf.default) to reach the login page: On the login page, enter the prompted information to access the IvorySQL Cloud service platform: Click the avatar in the upper-right corner to display the current username and the Log Out action. Click Log Out to exit; click the username to stay on the current page: After signing in as the admin user, click K8S Cluster Management in the left navigation bar to open the cluster list. Click Add Kubernetes Cluster in the upper-left corner, fill in the cluster information, and submit. The cluster management page lists details for each cluster and allows you to edit or delete them. Sign in with the demo user. Click Database Subscription in the left navigation, fill in the database parameters, and click Next: Confirm. Review the information and click Confirm. After confirming, the page automatically redirects to Database Management to show the subscription task. Displays all databases managed by the cloud service platform. Sign in with the demo user. Go to Database Management, select a database, click More in the Actions column, and choose Restart. Review the information and click Confirm. Sign in with the demo user. Go to Database Management, select a database, and click its Instance ID. On the database details page, click the password icon. Enter a new password and click Confirm. Sign in with the demo user. Go to Database Management, select a database, click More in the Actions column, and choose Delete Instance. Review the confirmation window and click Confirm. This feature requires additional plug-ins such as TopoLVM. Sign in with the demo user. Click Storage Expansion, select a database, then click Edit in the Actions column; alternatively, go to Database Management, click More, and choose Storage Expansion. Enter the expanded storage size and click Confirm. Sign in with the demo user. Click Specification Change, select a database, then click Edit in the Actions column; or go to Database Management, click More, and choose Specification Change. Enter the new specification and click Confirm. Sign in with the demo user. Go to Database Backup, select a database, and click Backup in the Actions column; or go to Database Management, click More, and choose Backup. Enter a backup name and click Confirm. Sign in with the demo user. Go to Database Restore, select a database, and click View in the Actions column; or go to Database Management, click More, and choose Restore. Select the backup file and click Restore in the Actions column. Enter the target database information. Use the database password from before the backup. Continue following the workflow described in “4.1 Database Subscription”. Sign in with the demo user. Click Monitoring Tools in the left navigation and select the cluster where the database resides; or go to Database Management, select a database, click More, and choose Monitoring. After the monitoring stack is created, repeat step (2) to open the monitoring page, then sign in with admin/admin and click Login. Click the magnifying glass icon to view monitoring metrics. Sign in with the demo user. Go to Database Management, select a database, click More in the Actions column, and choose Login. On the new page, enter the database account sysdba@ivyo.com and the database password, then click Login. Once the connection is established, you can operate on the database.","title":"User Guide","component":"ivorysql-doc","version":"master","name":"ivorysql_cloud_usage","url":"/ivorysql-doc/master/cloud_platform/ivorysql_cloud_usage.html","titles":[{"text":"1. Sign In and Sign Out","hash":"sign-in-and-sign-out","id":1},{"text":"1.1. Sign In","hash":"sign-in","id":2},{"text":"1.2. Sign Out","hash":"sign-out","id":3},{"text":"2. Administrator Features","hash":"administrator-features","id":4},{"text":"2.1. Add a Cluster","hash":"add-a-cluster","id":5},{"text":"2.2. Manage Clusters","hash":"manage-clusters","id":6},{"text":"3. demo User Features","hash":"demo-user-features","id":7},{"text":"3.1. Database Subscription","hash":"database-subscription","id":8},{"text":"3.2. Database Management","hash":"database-management","id":9},{"text":"3.3. Restart a Database","hash":"restart-a-database","id":10},{"text":"3.4. Change the Password","hash":"change-the-password","id":11},{"text":"3.5. Delete an Instance","hash":"delete-an-instance","id":12},{"text":"3.6. Storage Expansion","hash":"storage-expansion","id":13},{"text":"3.7. Specification Change","hash":"specification-change","id":14},{"text":"3.8. Database Backup","hash":"database-backup","id":15},{"text":"3.9. Database Restore","hash":"database-restore","id":16},{"text":"3.10. Database Monitoring","hash":"database-monitoring","id":17},{"text":"3.11. Visual Login Tool","hash":"visual-login-tool","id":18}]},"18":{"id":18,"text":"Currently, PostgreSQL’s CALL statement has the following limitations: Does not support the INTO clause; Cannot call functions that return values; Cannot assign results to client-side variables (i.e., Oracle’s binding variables / host variables). To enhance compatibility with Oracle, IvorySQL has implemented support for the CALL func(…​) INTO :var; syntax, allowing users to receive function return values through binding variables (e.g., :x). This behavior (including precision checks and error handling) aligns with Oracle’s standards. Since PostgreSQL/IvorySQL inherently does not support direct assignment to client-side variables at the SQL level, this solution adopts a \"client-side rewriting + server-side collaboration\" approach: When the CALL statement includes binding variables (e.g., :x): The client rewrites it into a special anonymous PL block (DO $$ ... $$); Sends it using the extended query protocol to transmit parameter type and precision information; The server executes this anonymous block and returns the result to the client; The client then writes the result to the corresponding binding variable. When the CALL statement does not contain binding variables: The behavior remains fully consistent with native PostgreSQL, using the simple query protocol without any rewriting. To ensure compatibility with the CALL [INTO] statement in the interface, it must be converted into an anonymous PL/iSQL block, leveraging the support for OUT parameters in anonymous blocks to achieve functional equivalence. This requires the get_parameter_description function to correctly identify CALL statements and, when encountering CALL INTO, return the rewritten PL statement. Correspondingly, the get_hostvariables routine needs to store this information (such as whether it is a CALL statement, whether it includes INTO, the rewritten statement, etc.) in the HostVariable structure. The definition of HostVariable is as follows: typedef struct HostVariable { HostVariableEntry *hostvars; int length; bool isdostmt; bool iscallstmt; // Whether it is from a CALL statement char *convertcall; // Rewritten statement } HostVariable; On the server side, modifications are required in the syntax parser section. Add CALL INTO grammar rules in ora_gram.y, and generate rewritten PL statements in the action section, such as \"x := add(1,2);\". CallStmt: CALL func_application { CallStmt *n = makeNode(CallStmt); n->funccall = castNode(FuncCall, $2); $$ = (Node *)n; } | CALL func_application INTO ORAPARAM { CallStmt *n = makeNode(CallStmt); OraParamRef *hostvar = makeNode(OraParamRef); char *callstr = NULL; n->funccall = castNode(FuncCall, $2); hostvar->number = 0; hostvar->location = @4; hostvar->name = $4; n->hostvariable = hostvar; callstr = pnstrdup(pg_yyget_extra(yyscanner)->core_yy_extra.scanbuf + @2, @3 - @2); n->callinto = psprintf(\"%s := %s;\", $4, callstr); pfree(callstr); $$ = (Node *)n; } ; The CallStmt structure needs to store the INTO clause and the converted PL statement. typedef struct CallStmt { NodeTag type; FuncCall *funccall; /* from the parser */ FuncExpr *funcexpr; /* transformed call, with only input args */ List *outargs; /* transformed output-argument expressions */ OraParamRef *hostvariable; /* only used for get_parameter_description() */ char *callinto; /* rewrite CALL INTO to a PL assign stmt */ } CallStmt; To distinguish between regular DO statements and anonymous blocks converted from CALL statements, a new keyword GENERATED FROM CALL is added to the syntax. opt_do_from_where: GENERATED FROM CALL { $$ = true; } | /*EMPTY*/ { $$ = false; } ; The generated DoStmt node will set do_from_call = true for executor identification. typedef struct DoStmt { NodeTag type; List *args; /* List of DefElem nodes */ List *paramsmode; /* List of parameters mode */ List *paramslen; /* List of length for parameter datatypes */ bool do_from_call; /* True if DoStmt is come from CallStmt */ } DoStmt; In the IVY interface, placeholder information is obtained through a Set-Returning Function (SRF) called get_parameter_description. This function needs to identify the type of input statement and return the rewritten PL/iSQL assignment statement when encountering CALL INTO statements. To achieve this, IvorySQL has extended the return structure (TupleDesc) of this function: a new hint field has been added specifically to return the rewritten PL code for CALL INTO statements; for other types of statements, this field remains NULL. Additionally, the first field of the first tuple in the original function result set previously used only true/false to distinguish whether the statement was an anonymous block. To more accurately identify statement types (especially CALL statements), it has now been modified to return the corresponding parse tree’s CommandTag. All these metadata details are ultimately encapsulated into a user context structure to enable efficient passing and reuse across multiple invocations of the SRF function. { OraParamExtralData *extral; const char *cmdtag; char *callintoexpr; } outparam_fctx; The IVY-prefixed interfaces involved in CALL include: IvyStmtExecute IvyStmtExecute2 IvyexecPreparedStatement IvyexecPreparedStatement2 In the aforementioned interfaces, user-provided CALL [INTO] statements are rewritten into a \"special\" anonymous block statement. To clearly identify such anonymous blocks converted from CALL statements, a dedicated type has been added to the statement type definitions of the interface. The purpose of this type is to correctly recognize such statements in IvyHandleDostmt and generate execution statements in the form of:DO ... USING … — GENERATED FROM CALL typedef enum IvyStmtType { IVY_STMT_UNKNOW, IVY_STMT_DO, IVY_STMT_DOFROMCALL, /* new statement type */ IVY_STMT_DOHANDLED, IVY_STMT_OTHERS } IvyStmtType; When rewriting CALL statements, if encountering a CALL INTO statement that invokes a function, the interface needs to internally adjust the order of bound variables. This adjustment is completely transparent to users: when binding parameters, users only need to follow the order in which the variables appear in the CALL statement—that is, the variable in the INTO clause is positioned last in the original statement. However, in the rewritten special anonymous block, this INTO variable will appear as the left-hand side (i.e., the first parameter) of an assignment expression. Therefore, the interface must internally adjust the binding order correctly to ensure the execution logic matches the user’s expectations. All interface routines involved in this logic must implement this handling. The relevant routines are as follows: Ivyreplacenamebindtoposition Ivyreplacenamebindtoposition2 Ivyreplacenamebindtoposition3 In interfaces such as IvyexecPreparedStatement and IvyexecPreparedStatement2, users must explicitly provide paramvalues, paramlengths, paramformats, and parammode for each parameter. For CALL statements, the order of elements in these parameter arrays must be adjusted according to the rewritten anonymous block structure to ensure binding consistency with the execution logic. Among them, IvyexecPreparedStatement2 is more specialized: it requires users to additionally provide an output binding list of type IvyBindOutInfo*. This list is not only used to bind OUT parameters but is also utilized by IvyAssignPLISQLOutParameter to identify the data type of each OUT parameter when retrieving PL/iSQL procedure results. Therefore, when processing CALL statements, the interface first reorders the user-provided IvyBindOutInfo* list (moving the INTO-bound output variable to the first position) and then writes it into the IvyPreparedStatement statement handle for subsequent assignment. Regarding precision handling for output parameters: When there is a mismatch between the precision of an output binding variable in a CALL statement and the actual returned value, the system may either raise an error or automatically truncate—the specific behavior depends on whether the data type of the binding variable exactly matches the parameter type declared in the procedure/function. In the PL/iSQL inline handler, the precise data type of each OUT parameter can be obtained through ParamListInfo during the binding phase. If the currently executed anonymous block is a special DoStmt converted from a CALL statement, the system performs the following checks during assignment: If the type recorded in ParamListInfo exactly matches the formal parameter type of the function/stored procedure, a forced type conversion is applied for assignment. Otherwise, an implicit type conversion is used for assignment. This mechanism is designed to be compatible with Oracle’s behavior, ensuring safe and reasonable assignment even when types do not fully match. -- Original CALL statement: CALL my_func(:in1, :in2) INTO :out; -- Rewritten as: do $$BEGIN :out := my_func(:in1, :in2); END$$ using out INOUT, in1 INOUT, in2 INOUT paramslength -1,-1,-1 GENERATED FROM CALL;","title":"CALL INTO","component":"ivorysql-doc","version":"master","name":"call_into","url":"/ivorysql-doc/master/compatibility_features_design/call_into.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Overall Design Approach","hash":"overall-design-approach","id":2},{"text":"3. Implementation Details","hash":"implementation-details","id":3},{"text":"3.1. psql","hash":"psql","id":4},{"text":"3.2. Server-side","hash":"server-side","id":5},{"text":"3.3. Interface layer","hash":"interface-layer","id":6}]},"19":{"id":19,"text":"To meet Oracle’s quoted identifier case compatibility requirements, IvorySQL has designed three case conversion modes for quoted identifiers. If the parameter -C is appended during database initialization, with values of normal/interchange/lowercase, the Initdb.c→main() function in the code will process this parameter and set the global variable caseswitchmode according to the parameter value. Then the initdb command will start a postgres process in -boot mode to set up the template1 template database, while passing the parameter -C ivorysql.identifier_case_switch=caseswitchmode to the new process. This newly started backend process will write the identifier_case_switch information to the pg_control file through the following code: BootstrapModeMain() -> BootStrapXLOG(); /* save database compatible level value */ ControlFile->dbmode = bootstrap_database_mode; ControlFile->casemode = identifier_case_switch; /* some additional ControlFile fields are set in WriteControlFile() */ WriteControlFile(); When a user starts the database using the pg_ctl command, the postmaster process will read the contents of the pg_control file. The code call path is: PostmasterMain()-->SetCaseGucOption()-->GetCaseSwitchModeFromControl() After reading the parameter value, the SetConfigOption() function is called to assign the value. When each new backend process starts, since it is forked from the postmaster process, it automatically has the same ivorysql.identifier_case_switch parameter value. First, it processes the startup packet, and if it contains database or user parameters, the parameter values are processed accordingly based on identifier_case_switch. The source code is: BackendMain()->BackendInitialize()-->ProcessStartupPacket() Additionally, when processing user SQL statements, if they contain identifiers, the same processing is performed. The code is distributed in: SplitIdentifierString(), quoteOneName() and SplitGUCList() functions.","title":"Quoted Identifier Case Conversion","component":"ivorysql-doc","version":"master","name":"case_conversion","url":"/ivorysql-doc/master/compatibility_features_design/case_conversion.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation Details","hash":"implementation-details","id":2}]},"20":{"id":20,"text":"In Oracle databases, empty strings ('') are treated as NULL values, which is an important Oracle feature. To maintain compatibility with this Oracle behavior, IvorySQL provides the empty string to NULL conversion functionality. When this feature is enabled, empty strings in SQL statements are automatically converted to NULL values, ensuring behavioral consistency for Oracle applications running on IvorySQL. The parameter ivorysql.enable_emptystring_to_NULL corresponds to the GUC variable enable_emptystring_to_NULL. In the file ora_scan.l, you can see how this variable is used: case xe: yylval->str = litbufdup(yyscanner); if (strcmp(yylval->str, \"\") == 0 && ORA_PARSER == compatible_db && enable_emptystring_to_NULL) { return NULL_P; } return SCONST; Where xe represents strings enclosed in quotes: <xe> extended quoted strings (support backslash escape sequences) The logic of the above code is that during lexical analysis, if an empty string is encountered and the empty-to-NULL conversion feature is enabled, it returns NULL_P; otherwise, it returns SCONST. In the grammar analysis file ora_gram.y, the statement insert into abc values(''); is parsed as follows: values_clause: VALUES '(' expr_list ')' { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1($3); $$ = (Node *) n; } expr_list: a_expr { $$ = list_make1($1); } a_expr: c_expr { $$ = $1; } c_expr: AexprConst { $$ = $1; } AexprConst: Iconst | Sconst { $$ = makeStringConst($1, @1); } | NULL_P { $$ = makeNullAConst(@1); } The above code constructs corresponding nodes to handle NULL_P or SCONST returned from the lexical analysis.","title":"Empty String to NULL Conversion","component":"ivorysql-doc","version":"master","name":"empty_string_to_null","url":"/ivorysql-doc/master/compatibility_features_design/empty_string_to_null.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation","hash":"implementation","id":2}]},"21":{"id":21,"text":"Force View offers Oracle-compatible CREATE [OR REPLACE] FORCE VIEW and ALTER VIEW …​ COMPILE behavior, allowing developers to persist a placeholder view even when dependencies are missing and then switch it back to a normal view once dependencies are available. The system records the original SQL text and identifier case mode in a dedicated catalog entry so that automatic or manual recompilation can restore the view with Oracle-style warnings and error reporting. In PostgreSQL, a base table referenced by a view cannot be dropped unless CASCADE is used. Oracle, however, allows the base table to be dropped while retaining the Force View and marking it as invalid. To align with Oracle semantics, IvorySQL introduces new grammar rules and a catalog that stores Force View metadata so that the Force state can be shared across sessions. Register the Force View keywords in src/backend/oracle_parser/ora_gram.y. Set ViewStmt→force during the grammar phase, and generate the AT_ForceViewCompile option when parsing AlterTableStmt. Preserve ViewStmt→stmt_literal for later reuse when the placeholder is materialized. /* Insert or update the pg_force_view catalog as needed */ if (need_store) { ...... StoreForceViewQuery(address.objectId, stmt->replace, ident_case, stmt->stmt_literal ? stmt->stmt_literal : queryString); } Add bool force and char *stmt_literal to ViewStmt in src/include/nodes/parsenodes.h. Define AT_ForceViewCompile consistently between parsenodes.h and tablecmds.c so that ALTER VIEW …​ COMPILE flows into the regular alter-table machinery. parse_analyze still follows the native path; Force mode intervenes only after a parsing error occurs. DefineView() in src/backend/commands/view.c now handles both normal and Force views. The function first attempts normal parsing inside a PG_TRY/PG_CATCH; if the semantic analysis fails, it checks stmt→force to decide whether to enter the Force View branch. On success it continues to StoreViewQuery(); on failure it falls back to the Force View logic so that the view object still exists. src/include/catalog/pg_force_view.h defines the catalog table, whose fields include the view OID (fvoid), identifier case (ident_case), and the original SQL text (source). The unique index pg_force_view_fvoid_index plus the syscache FORCEVIEWOID (declared in src/include/catalog/syscache_info.h) enable lookups by view OID. The catalog enables TOAST so lengthy SQL definitions are preserved in full, covering complex migration scripts. CATALOG(pg_force_view,9120,ForceViewRelationId) { /* oid of force view */ Oid fvoid; /* see IDENT_CASE__xxx constants below */ char ident_case; #ifdef CATALOG_VARLEN /* variable-length fields start here */ /* sql definition */ text source; #endif } FormData_pg_force_view; bool rel_is_force_view(Oid relid) in src/backend/commands/view.c determines whether a view is in Force state by checking whether the _RETURN rule exists. Force views still register as relkind = RELKIND_VIEW, avoiding extra compatibility branches. pg_class.relhasrules is set to false while in placeholder mode, which becomes part of the detection logic. After successful parsing, DefineView() calls DefineVirtualRelation() to populate pg_class, pg_attribute, and related catalogs. StoreViewQuery() generates the _RETURN rule and records dependencies. This path never touches pg_force_view; the view is immediately ready for use. CreateForceVirtualPlaceholder() in src/backend/commands/view.c creates or reuses a placeholder view: If the view does not exist, it calls DefineVirtualRelation() to create the base object without a _RETURN rule. If a Force View already exists, it reuses the current record and updates column definitions or cleans up legacy metadata. If a normal view exists and OR REPLACE is specified, it invokes make_view_invalid() to invalidate the old definition before installing the placeholder. StoreForceViewQuery() persists stmt_literal and the current ivorysql.identifier_case_switch to pg_force_view so identifier case semantics can be restored later. After the placeholder is created, the client receives WARNING: View created with compilation errors, indicating the view cannot yet be used. make_view_invalid() is triggered when dependencies are dropped, altered, or otherwise compromised. The routine removes the _RETURN rule, clears pg_depend entries, resets pg_class.relhasrules, and truncates pg_attribute column metadata. It also captures CREATE FORCE VIEW …​ AS <pg_get_viewdef> and saves it in pg_force_view, while setting ident_case to IDENT_CASE_UNDEFINE to indicate the SQL is system-generated. The view remains visible in metadata catalogs but runtime access detects the Force flag. Because _RETURN is missing, the executor calls compile_force_view() when opening the view; if compilation fails, it raises view \"<schema>.<name>\" has errors. Users can recover by issuing ALTER VIEW …​ COMPILE or CREATE OR REPLACE FORCE VIEW once dependencies are ready. addRangeTableEntry() in parse_relation.c and the target-relation open logic in parse_clause.c call compile_force_view() after detecting a Force view. The function reruns raw_parser and parse_analyze; on success it reinstalls the _RETURN rule, and on failure it aborts the statement with the encountered error. AT_ForceViewCompile executes during phase 2 in tablecmds.c, acquiring AccessExclusiveLock before invoking compile_force_view(). Successful compilation behaves like a normal ALTER VIEW; failures emit WARNING: View altered with compilation errors, and the view stays in placeholder mode. compile_force_view() reads pg_force_view.source, rebuilds a ViewStmt, and calls compile_force_view_internal(). The routine uses checkViewColumns() to compare legacy columns, allowing additions but rejecting incompatible type changes; new columns are applied through AT_AddColumnToView(). _RETURN is regenerated via StoreViewQuery(), and DeleteForceView() removes the catalog record so the view becomes a standard one again.","title":"Force View","component":"ivorysql-doc","version":"master","name":"force_view","url":"/ivorysql-doc/master/compatibility_features_design/force_view.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation Description","hash":"implementation-description","id":2},{"text":"2.1. Grammar and Parsing Entry Points","hash":"grammar-and-parsing-entry-points","id":3},{"text":"2.1.1. Force View Syntax Support","hash":"force-view-syntax-support","id":4},{"text":"2.1.2. AST Field Extensions","hash":"ast-field-extensions","id":5},{"text":"2.1.3. Parsing Entry","hash":"parsing-entry","id":6},{"text":"2.2. Force View Metadata","hash":"force-view-metadata","id":7},{"text":"2.2.1. pg_force_view Catalog","hash":"pg_force_view-catalog","id":8},{"text":"2.2.2. View State","hash":"view-state","id":9},{"text":"2.3. Creation and Replacement Flow","hash":"creation-and-replacement-flow","id":10},{"text":"2.3.1. Normal View","hash":"normal-view","id":11},{"text":"2.3.2. Force View","hash":"force-view","id":12},{"text":"2.4. Dependency Invalidations and Rollback","hash":"dependency-invalidations-and-rollback","id":13},{"text":"2.4.1. Active Invalidation Logic","hash":"active-invalidation-logic","id":14},{"text":"2.4.2. Observable Behavior After Invalidation","hash":"observable-behavior-after-invalidation","id":15},{"text":"2.5. Automatic and Manual Compilation","hash":"automatic-and-manual-compilation","id":16},{"text":"2.5.1. Automatic Compilation Triggers","hash":"automatic-compilation-triggers","id":17},{"text":"2.5.2. Manual Compilation","hash":"manual-compilation","id":18},{"text":"2.5.3. Column Checks and Metadata Updates","hash":"column-checks-and-metadata-updates","id":19}]},"22":{"id":22,"text":"PostgreSQL supports functions and stored procedures, but there are syntax differences between PostgreSQL and Oracle. To enable Oracle’s PL/SQL statements to run on IvorySQL—i.e., to achieve \"syntax compatibility\"—IvorySQL adopts the following solution: If an Oracle clause has a counterpart clause with the same function in IvorySQL, it is directly mapped to the corresponding IvorySQL clause; otherwise, only the syntax of the Oracle clause is implemented, while its function is not. PL/iSQL is the name of the procedural language in IvorySQL, specifically designed to be compatible with Oracle’s PL/SQL statements. To achieve compatibility with Oracle-style syntax for functions and stored procedures, corresponding adjustments need to be made to the psql client tool, the SQL layer, and the PL/iSQL layer. Oracle’s sqlplus tool uses a slash (/) to terminate functions and stored procedures. IvorySQL’s client tool, psql, needs to be compatible with the same syntax. This means that the conventional mechanism—where statements are sent to the server upon encountering a semicolon—becomes ineffective when dealing with Oracle-style function and stored procedure commands; instead, a slash (/) is used to send the commands. To this end the following fields are added to the PsqlScanStateData structure: bool cancel_semicolon_terminator; /* not send command when semicolon found */ /* * State to track boundaries of Oracle ANONYMOUS BLOCK. * Case 1: Statements starting with << ident >> is Oracle anonymous block. */ int token_count; /* # of tokens, not blank or newline since start of statement */ bool anonymous_label_start; /* T if the first token is \"<<\" */ bool anonymous_label_ident; /* T if the second token is an identifier */ bool anonymous_label_end; /* T if the third token is \">>\" */ /* * Case 2: DECLARE BEGIN ... END is Oracle anonymous block syntax. * DECLARE can also be a PostgreSQL cursor declaration statement, we need to tell this. */ bool maybe_anonymous_declare_start; /* T if the first token is DECLARE */ int token_cursor_idx; /* the position of keyword CURSOR in SQL statement */ /* * Case 3: DECLARE BEGIN ... END is Oracle anonymous block syntax. * BEGIN can also be a PostgreSQL transaction statement. */ bool maybe_anonymous_begin_start; /* T if the first token is BEGIN */ Meanwhile, modify ora_psqlscan.l and add or update the corresponding lexical rules. Below is a code snippet example: { if (is_oracle_slash(cur_state, cur_state->scanline)) { /* Terminate lexing temporarily */ cur_state->cancel_semicolon_terminator = false; cur_state->maybe_anonymous_declare_start = false; cur_state->maybe_anonymous_begin_start = false; cur_state->anonymous_label_start = false; cur_state->anonymous_label_ident = false; cur_state->anonymous_label_end = false; cur_state->start_state = YY_START; cur_state->token_count = 0; cur_state->token_cursor_idx = 0; cur_state->identifier_count = 0; cur_state->begin_depth = 0; cur_state->ora_plsql_expect_end_symbol = END_SYMBOL_INVALID; return LEXRES_SEMI; } ECHO; The psql tool needs to detect the meaning of the slash (/), to avoid identifying slashes in comments and other parts as terminators. To this end, a separate interface is_oracle_slash is added in the oracle_fe_utils/ora_psqlscan.l file for detection. bool is_oracle_slash(PsqlScanState state, const char *line) { bool result = false; switch (state->start_state) { case INITIAL: case xqs: /* treat these like INITIAL */ { int len, i; bool has_slash = false; len = strlen(line); for (i = 0; i < len; i++) { /* allow special char */ if (line[i] == '\\t' || line[i] == '\\n' || line[i] == '\\r' || line[i] == ' ') continue; if (line[i] == '/') { if (has_slash) break; has_slash = true; continue; } /* others */ break; } if (i == len && has_slash) result = true; } break; default: break; } return result; } The SQL layer needs to be able to recognize the creation syntax for functions and stored procedures, and this is achieved by modifying ora_base_yylex. This function prefetches and caches tokens: if the token follows Oracle syntax, it organizes an SCONST and sends it to the PL/SQL layer; otherwise, it retrieves the previously preread tokens from the stack and processes them according to the native PostgreSQL logic. The following fields are added to the ora_base_yy_extra_type structure: /* * The native PG only cache one-token info include yylloc, yylval and token * number in yyextra, IvorySQL cache multiple tokens info using two arrays. */ int max_pushbacks; /* the max size of cache array */ int loc_pushback; /* # of used tokens */ int num_pushbacks; /* # of cached tokens */ int *pushback_token; /* token number array */ TokenAuxData *pushback_auxdata; /* auxdata array */ OraBodyStyle body_style; int body_start; int body_level; Add operation interfaces for the token stack: push_back_token forward_token ora_internal_yylex internal_yylex  In the ora_base_yylex function, when creating functions, procedures, or anonymous blocks, some tokens are preread. These tokens are cached into the stack using the aforementioned structure, and this is done to construct an SCONST that conforms to Oracle PL/SQL syntax and send it to the PL/iSQL layer for processing. For details, please refer to the source code. This part mainly modifies the pl_gram.y file to achieve compatibility with the syntax of PL/SQL functions and stored procedures. It enables compatibility with Oracle PL/SQL syntax forms without affecting PostgreSQL’s native PL/pgSQL. Below is a code example for the compatibility of the DECLARE section; for more details, please refer to the IvorySQL source code. /* * The declaration section of the outermost block in Oracle does not have the DECLARE keyword. */ ora_outermost_pl_block: ora_decl_sect K_BEGIN proc_sect exception_sect K_END opt_label { PLiSQL_stmt_block *new; new = palloc0(sizeof(PLiSQL_stmt_block)); new->cmd_type = PLISQL_STMT_BLOCK; new->lineno = plisql_location_to_lineno(@2); new->stmtid = ++plisql_curr_compile->nstatements; new->label = $1.label; new->n_initvars = $1.n_initvars; new->initvarnos = $1.initvarnos; new->body = $3; new->exceptions = $4; check_labels($1.label, $6, @6); plisql_ns_pop(); $$ = (PLiSQL_stmt *)new; } ; ora_decl_sect: opt_block_label opt_ora_decl_start opt_ora_decl_stmts { if ($2) { if ($1 == NULL) { plisql_ns_push(NULL, PLISQL_LABEL_BLOCK); } } } opt_ora_decl_stmts { if ($4) { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; /* Remember variables declared in decl_stmts */ $$.n_initvars = plisql_add_initdatums(&($$.initvarnos)); } else { plisql_IdentifierLookup = IDENTIFIER_LOOKUP_NORMAL; $$.label = ($1 == NULL ? plisql_curr_compile->namelabel : $1); $$.n_initvars = 0; if ($2 && $1 == NULL) $$.popname = true; else $$.popname = false; $$.initvarnos = NULL; } } ; opt_ora_decl_start: K_DECLARE { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = true; } | /*EMPTY*/ { /* Forget any variables created before block */ plisql_add_initdatums(NULL); /* * Disable scanner lookup of identifiers while * we process the decl_stmts */ plisql_IdentifierLookup = IDENTIFIER_LOOKUP_DECLARE; $$ = false; } ; opt_ora_decl_stmts: ora_decl_stmts { $$ = true; } | /*EMPTY*/ { $$ = false; } ora_decl_stmts: ora_decl_stmts ora_decl_stmt | ora_decl_stmt ; ora_decl_stmt: decl_statement ;","title":"PL/iSQL function and stored procedure","component":"ivorysql-doc","version":"master","name":"function_procedure","url":"/ivorysql-doc/master/compatibility_features_design/function_procedure.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation description","hash":"implementation-description","id":2},{"text":"2.1. Client tool psql","hash":"client-tool-psql","id":3},{"text":"2.2. SQL layer","hash":"sql-layer","id":4},{"text":"2.3. PL/iSQL layer","hash":"plisql-layer","id":5}]},"23":{"id":23,"text":"The LIKE syntax in Oracle and IvorySQL is identical. Their difference lies in the expression types: Oracle supports using the LIKE keyword with wildcards for fuzzy queries on columns of numeric, date, and string types. Native PostgreSQL only supports string types, not date or numeric types. IvorySQL achieves Oracle-compatible LIKE operator functionality by extending data type support and operator overloading. In PostgreSQL, the fundamental string type is text, so LIKE is text-based. Other PostgreSQL types implicitly convert to text, enabling automatic conversion without creating operators. In IvorySQL, the Oracle-compatible string type is varchar2. Therefore, a LIKE operator for varchar2 is created, and other Oracle types also utilize the LIKE operator by implicitly converting to varchar2 without requiring additional operator creation. In the previous implementation of Oracle-compatible data types, IvorySQL established implicit conversions from certain data types (such as integer, float8, float4) to varchar2, but not directly to text. To achieve LIKE operator compatibility for these types, there are two approaches: 1、Add a separate LIKE operator for each individual type. 2、Implement a base LIKE operator for varchar2. In the second approach, since IvorySQL already supports implicit conversions from float8, integer, number, etc., to varchar2, these data types can share the same operator. Thus, only a single LIKE operator for varchar2 needs to be created. You can use the following SQL statement to check existing implicit type conversions. -- Check existing implicit conversion paths (where 9503 is the OID of varchar2) SELECT t1.typname AS source_type, t2.typname AS target_type FROM pg_cast C JOIN pg_type t1 ON C.castsource = t1.OID JOIN pg_type t2 ON C.casttarget = t2.OID WHERE C.casttarget = 9503; After reviewing, it can be observed that all the types requiring compatibility have implicit conversions defined in this table. Therefore, these types can be directly converted to the text type for fuzzy query operations. CREATE OR REPLACE FUNCTION sys.varchar2like(varchar2, varchar2) RETURNS bool AS $$ SELECT $1::text LIKE $2::text; $$ LANGUAGE SQL IMMUTABLE STRICT; CREATE OPERATOR ~~ ( PROCEDURE = sys.varchar2like, LEFTARG = varchar2, RIGHTARG = varchar2 ); The first code segment defines a function named sys.varchar2like, which accepts two parameters of type varchar2. By converting them to PostgreSQL’s native text type, it performs standard LIKE pattern matching and ultimately returns a boolean value indicating whether the match is successful. The second code segment creates an operator named ~~, which uses the previously defined varchar2like function as its implementation. It specifies that both the left and right operands of this operator must be of type varchar2. This establishes an Oracle-compatible LIKE operator in IvorySQL. When users employ the ~~ operator, it essentially invokes PostgreSQL’s native LIKE functionality after type conversion, thereby achieving semantic compatibility with Oracle’s LIKE behavior.","title":"Functional Overview","component":"ivorysql-doc","version":"master","name":"like_operator","url":"/ivorysql-doc/master/compatibility_features_design/like_operator.html","titles":[{"text":"1. Implementation Principle","hash":"implementation-principle","id":1},{"text":"1.1. Type Conversion","hash":"type-conversion","id":2},{"text":"1.2. Core Function Implementation","hash":"core-function-implementation","id":3}]},"24":{"id":24,"text":"Nested subfunctions refer to functions or procedures defined inside another function, stored procedure, or anonymous block; they are also called subprocs or inner functions. Parent functions are the outer functions, stored procedures, or anonymous blocks that host nested subfunctions and are responsible for invoking them during execution. When a DECLARE block contains a function …​ is/as begin …​ end construct, pl_gram.y calls plisql_build_subproc_function() (similar to creating a regular function and updating the entry in pg_proc): Create a PLiSQL_subproc_function entry in the parent PLiSQL_function’s `subprocfuncs[] array to store the name, arguments, return type, and other attributes, and record the index fno as the identifier of this subfunction. Call plisql_check_subprocfunc_properties() to validate the combination of declaration and definition attributes. Nested subfunctions share the parent’s datum table. During compilation, PLiSQL_function→datums describes variables and record fields inside the subfunction, while PLiSQL_execstate→datums keeps the runtime values. If the subfunction uses polymorphic parameters, the parser stores its source code in subprocfunc→src and sets has_poly_argument to true so that the executor can recompile it for each distinct argument type. The parent PLiSQL_function gains a subprocfuncs array, each element being the PLiSQL_subproc_function created earlier. Each PLiSQL_subproc_function has a HTAB *poly_tab pointer that is initialized on the first compilation when has_poly_argument is true. The hash key is PLiSQL_func_hashkey, which records the subfunction’s fno and input argument types; the value is the compiled PLiSQL_function * execution context. PostgreSQL builds a ParseState structure during compilation. plisql_subprocfunc_ref() locates the parent PLiSQL_function through ParseState→p_subprocfunc_hook() and calls plisql_ns_lookup() to gather all fno values for subfunctions sharing the same name, then selects the best match based on argument count and types. When FuncExpr nodes are created, the subfunction call is tagged for later execution: function_from = FUNC_FROM_SUBPROCFUNC, parent_func points to the parent PLiSQL_function, and funcid = fno. In plisql_call_handler(), when function_from == FUNC_FROM_SUBPROCFUNC, the runtime fetches the appropriate PLiSQL_subproc_function via the pair (parent_func, fno): For non-polymorphic subfunctions, reuse the precompiled action tree stored in subprocfunc→function. For polymorphic subfunctions, probe poly_tab; if there is no cached plan, call plisql_dynamic_compile_subproc() to compile one and store it in the cache. Before execution, plisql_init_subprocfunc_globalvar() forks relevant entries from the parent’s datum table so the subfunction can access the latest parent variables without polluting the parent scope. After execution, plisql_assign_out_subprocfunc_globalvar() writes back the necessary variables. pl_gram.y adds productions for subprocedure declarations and nested definitions, and records metadata such as lastoutvardno and subprocedure descriptors. Nested subfunctions can reference variables from the parent scope, other subprocedures, and user-defined types. Whenever a function …​ is/as begin …​ end construct is seen inside a DECLARE block, pl_gram.y invokes plisql_build_subproc_function(): Insert a PLiSQL_subproc_function entry into the parent PLiSQL_function→subprocfuncs[], storing the name, arguments, return type, and other attributes, and assign an index fno. Call plisql_check_subprocfunc_properties() to verify that declarations and definitions are consistent and to prevent duplicate or missing declarations from introducing semantic errors. The parent program’s datum tables hold the variables accessible to nested subfunctions during compilation and execution: PLiSQL_function→datums preserves the variable and record metadata visible during compilation. PLiSQL_execstate→datums carries the live values at runtime. When a subfunction contains polymorphic arguments, the parser will: Copy the subfunction source text into subprocfunc→src. Set has_poly_argument = true to prepare for dynamic recompilation based on actual argument types. The parent PLiSQL_function includes a subprocfuncs array, with each element corresponding to a PLiSQL_subproc_function. Each PLiSQL_subproc_function maintains an optional HTAB *poly_tab; when has_poly_argument is true, the cache is initialized on the first compile. Keys are PLiSQL_func_hashkey (subfunction fno plus argument types), and values are the compiled PLiSQL_function plans. During compilation, PostgreSQL creates a ParseState. plisql_subprocfunc_ref() plugs into ParseState→p_subprocfunc_hook, reusing the namespace lookup logic to gather candidates. plisql_get_subprocfunc_detail() then chooses the best match based on argument count, types, and named parameters, enabling overloaded dispatch. When constructing FuncExpr nodes, the compiler attaches metadata so the executor can recognize nested calls: function_from = FUNC_FROM_SUBPROCFUNC. parent_func references the owning PLiSQL_function. funcid = fno, enabling direct lookup of the subfunction definition. plisql_subprocfunc_ref() implements ParseState→p_subprocfunc_hook and reuses the namespace search to find nested subfunctions. plisql_get_subprocfunc_detail() applies matching rules for argument count, type, and naming to pick the optimal overload. plisql_call_handler() checks function_from; if it is a nested subfunction, the handler locates PLiSQL_subproc_function via (parent_func, fno). For regular subfunctions, reuse the cached plan stored in subprocfunc→function. For polymorphic subfunctions, consult poly_tab; on a miss, call plisql_dynamic_compile_subproc() to build and cache a specialized plan. plisql_init_subprocfunc_globalvar() copies the relevant entries from the parent datum table before the subfunction runs to expose the latest state. plisql_assign_out_subprocfunc_globalvar() writes back OUT/INOUT variables after execution to keep parent and child scopes consistent without mutual pollution. psqlscan.l adjusts the push/pop logic of proc_func_define_level and begin_depth so the nested subfunction body is transmitted to the SQL engine as a whole. Statements are sent only when the nesting depth returns to zero and a semicolon is reached, avoiding partial dispatch of subfunction blocks. Regular functions obtain metadata via funcid from pg_proc; nested subfunctions rely on FuncExpr.parent_func, which holds the parent PLiSQL_function. A set of callback pointers (registered through plisql_register_internal_func()) allows the SQL layer to fetch nested subfunction names, return types, and OUT parameter information on demand.","title":"Nested Subfunctions","component":"ivorysql-doc","version":"master","name":"nested_function","url":"/ivorysql-doc/master/compatibility_features_design/nested_function.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Implementation Notes","hash":"implementation-notes","id":2},{"text":"2.1. Syntax Recognition for Nested Subfunctions","hash":"syntax-recognition-for-nested-subfunctions","id":3},{"text":"2.1.1. Detecting Nested Definitions","hash":"detecting-nested-definitions","id":4},{"text":"2.1.2. Storing Datum Entries","hash":"storing-datum-entries","id":5},{"text":"2.1.3. Preserving Polymorphic Templates","hash":"preserving-polymorphic-templates","id":6},{"text":"2.2. Recompiling the Parent Program","hash":"recompiling-the-parent-program","id":7},{"text":"2.3. Name Resolution During Invocation","hash":"name-resolution-during-invocation","id":8},{"text":"3. Module Design","hash":"module-design","id":9},{"text":"3.1. PL/iSQL Grammar Extensions","hash":"plisql-grammar-extensions","id":10},{"text":"3.2. Datum Storage","hash":"datum-storage","id":11},{"text":"3.3. Polymorphic Templates","hash":"polymorphic-templates","id":12},{"text":"3.4. Parent Recompilation","hash":"parent-recompilation","id":13},{"text":"3.5. Parser Hooks","hash":"parser-hooks","id":14},{"text":"3.6. FuncExpr Annotation","hash":"funcexpr-annotation","id":15},{"text":"3.7. Nested Subfunction Lookup","hash":"nested-subfunction-lookup","id":16},{"text":"3.8. Execution Path","hash":"execution-path","id":17},{"text":"3.9. Variable Synchronization","hash":"variable-synchronization","id":18},{"text":"3.10. Statement Dispatch in psql","hash":"statement-dispatch-in-psql","id":19},{"text":"3.11. Retrieving Return Information on the SQL Side","hash":"retrieving-return-information-on-the-sql-side","id":20}]},"25":{"id":25,"text":"IvorySQL provides Oracle-compatible NLS (National Language Support) parameter functionality, including the following parameters: Parameter Name Description ivorysql.datetime_ignore_nls_mask Indicates whether the date format ignores the influence of NLS parameters. Default is 0. nls_length_semantics Oracle-compatible parameter that specifies whether the size unit for CHAR, VARCHAR, and VARCHAR2 type modifiers is bytes or characters. nls_date_format Specifies the default date format, which can be viewed using the SHOW command. Default is 'YYYY-MM-DD'. nls_timestamp_format Oracle-compatible parameter that controls the format of timestamps. nls_timestamp_tz_format Oracle-compatible parameter that controls the format of timestamps with time zones. nls_territory Oracle-compatible parameter that specifies the default region for the database. nls_iso_currency Oracle-compatible parameter that assigns a unique currency symbol to a specified country or region. nls_currency Oracle-compatible parameter that specifies the symbol for the local currency, corresponding to the placeholder L in numeric string formats. In IvorySQL, data types have an attribute modifier called typmod, which provides additional information about the type. For example, in VARCHAR(n), n is the type modifier. When creating or modifying table columns, you can specify the length type, such as: ivorysql=# create table t1(name varchar2(2 byte)); For columns of type CHAR, VARCHAR, or VARCHAR2, if the length type is not explicitly specified, IvorySQL uses the value of the nls_length_semantics parameter to determine the length type. The possible values are BYTE and CHAR, with BYTE being the default. Note that the nls_length_semantics parameter only affects newly created columns and has no impact on existing columns. In the syntax parsing file ora_gram.y, the following code converts the original CHAR, VARCHAR, or VARCHAR2 types to oracharchar or oracharbyte based on nls_length_semantics. CharacterWithLength: character '(' Iconst ')' { if (ORA_PARSER == compatible_db) { if (strcmp($1, \"bpchar\")) { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oravarcharchar\"; else $1 = \"oravarcharbyte\"; } else { if (nls_length_semantics == NLS_LENGTH_CHAR) $1 = \"oracharchar\"; else $1 = \"oracharbyte\"; } $$ = OracleSystemTypeName($1); $$->typmods = list_make1(makeIntConst($3, @3)); $$->location = @1; } else { ... } } ; The input/output functions for the oracharchar and oracharbyte data types in IvorySQL include: oravarcharchartypmodout() oravarcharbytetypmodout() oracharbytetypmodout() oracharchartypmodout() These functions call the C-language function anychar_typmodout(), which adjusts the output to include BYTE or CHAR based on the value of nls_length_semantics. Another role of nls_length_semantics is to limit column lengths in tables. If the original VARCHAR type is converted to oracharchar, the function oravarcharchar() is called, and pg_mbcharcliplen() calculates the character length instead of the byte length. Datum oravarcharchar(PG_FUNCTION_ARGS) { VarChar *source = PG_GETARG_VARCHAR_PP(0); int32 typmod = PG_GETARG_INT32(1); bool isExplicit = PG_GETARG_BOOL(2); int32 len, maxlen; size_t maxmblen; char *s_data; len = VARSIZE_ANY_EXHDR(source); s_data = VARDATA_ANY(source); maxlen = typmod - VARHDRSZ; /* No work if typmod is invalid or supplied data fits it already */ if (maxlen < 0 || len <= maxlen) PG_RETURN_VARCHAR_P(source); maxmblen = pg_mbcharcliplen(s_data, len, maxlen); ... } This parameter is defined as an int value, where the lower four bits indicate whether to ignore NLS parameter influence on the corresponding date-time formats. The mask definitions are: #define ORADATE_MASK 0x01 #define ORATIMESTAMP_MASK 0x02 #define ORATIMESTAMPTZ_MASK 0x04 #define ORATIMESTAMPLTZ_MASK 0x08 In the source code, this GUC parameter is used in the following functions: oradate_in() oratimestamp_in() oratimestampltz_in() oratimestamptz_in() If the corresponding mask is set, the native PostgreSQL processing function is called; otherwise, the compatibility code is invoked, and NLS formats are ignored. These three GUC parameters serve as format strings in the function ora_do_to_timestamp() for checking and parsing input strings. Their default values are: char *nls_date_format = \"YYYY-MM-DD\"; char *nls_timestamp_format = \"YYYY-MM-DD HH24:MI:SS.FF6\"; char *nls_timestamp_tz_format = \"YYYY-MM-DD HH24:MI:SS.FF6 TZH:TZM\"; Setting these values to \"pg\" disables NLS-specific behavior, reverting to PostgreSQL’s default behavior. Currently, nls_territory and nls_iso_currency support the values CHINA and AMERICA. The default values are: char *nls_territory = \"AMERICA\"; char *nls_currency = \"$\"; char *nls_iso_currency = \"AMERICA\"; These parameters will be used in the Oracle-compatible function to_number(). The to_number() function has not yet been implemented.","title":"Functional Overview","component":"ivorysql-doc","version":"master","name":"nls_parameter","url":"/ivorysql-doc/master/compatibility_features_design/nls_parameter.html","titles":[{"text":"1. Implementation Principles","hash":"implementation-principles","id":1},{"text":"1.1. Parameter nls_length_semantics","hash":"parameter-nls_length_semantics","id":2},{"text":"1.2. GUC Parameter datetime_ignore_nls_mask","hash":"guc-parameter-datetime_ignore_nls_mask","id":3},{"text":"1.3. GUC Parameters nls_date_format/nls_timestamp_format/nls_timestamp_tz_format","hash":"guc-parameters-nls_date_formatnls_timestamp_formatnls_timestamp_tz_format","id":4},{"text":"1.4. GUC Parameters nls_currency/nls_iso_currency/nls_territory","hash":"guc-parameters-nls_currencynls_iso_currencynls_territory","id":5}]},"26":{"id":26,"text":"IvorySQL provides Oracle-compatible OUT parameter functionality, including functions and procedures with OUT parameters, support for OUT parameters in anonymous blocks, and libpq support for OUT parameters. For PL/pgSQL functions, when creating a function, the system table pg_proc stores the total number of parameters (including OUT parameters) and their corresponding data types. In the interpret_function_parameter_list() function, which processes function parameters, it checks the parameter mode. If the mode is IN OUT, the parameter cannot have a default value. In the make_return_stmt function, the error handling for detecting OUT parameters has been removed. By modifying the FuncnameGetCandidates function, function lookup now matches all parameters, including OUT parameters. During function compilation, a row variable is constructed to hold OUT parameter variables and the return value variable. The compiled function’s return type (function→fn_rettype) is modified to RECORDOID. During function execution, the ExecInitFunc function calls the new ExecInitFuncOutParams function to construct OUT parameter computation nodes. The plisql_out_param function separates the function return value and OUT parameter values from the tuple and assigns values to the OUT parameters externally. To support binding variables in the form of colon placeholders, the ora_scan.l file was modified to add syntax that returns ORAPARAM when a colon placeholder is encountered. In the ora_gram.y file, handling for ORAPARAM was added to the c_expr and plassign_target assignment syntax, constructing an OraParamRef node. New DO + USING syntax was added: DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, ...] The ora_gram.y file was modified to support the DO+USING syntax. The DoStmt structure now includes a paramsmode field to store a list of binding variable modes and other information for anonymous blocks. Modifications to the PBE (Parse, Bind, Execute) process include: In the exec_parse_message function, the parameter mode is identified based on the parameter type OID passed from the application interface. In the exec_bind_message function, for anonymous blocks with DO+USING, the parameter modes following USING are identified, and parameter information is passed to the executor. Execution of anonymous blocks with OUT parameters: 1. In the PortalStart function, for anonymous block statements, the CreateTupleDescFromParams function is called to construct parameter description information. A new value, PROKIND_ANONYMOUS_BLOCK, was added to the PLiSQL_function member fn_prokind to indicate an anonymous block. In the plisql_exec_function function, anonymous blocks with OUT parameters are evaluated. The plisql_anonymous_return_out_parameter function is called to construct a PLiSQL_row type variable for OUT parameters, and the evaluated row type variable is used as the return value of the anonymous block function. The libpq interface was modified to support binding by position and by parameter name, involving changes to the SQL layer, PL/pgSQL layer, and libpq interface layer. SQL Layer: A system function get_parameter_description was implemented on the server side. This function returns the relationship between variable names and their positions based on the SQL statement. It is used in libpq interface functions. The first row of the result shows the SQL type in the name column, followed by rows displaying placeholder names and position information. ivorysql=# select * from get_parameter_description('insert into t values(:x, :y);'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows) Support for anonymous block statements is not yet implemented. PL/pgSQL Layer: The PL/pgSQL block adjusts the internal identification of parameters based on their position or name. The execution function retrieves parameter values and type information from the binding handle. For OUT parameters, a special handling of return column names is applied. If a parameter is an OUT parameter, its column name is formatted as _column_xxx, where xxx is the position of the OUT parameter. This allows assigning values to OUT parameters from the result set based on the binding position and return position. At the PLiSQL execution layer, parameter names are converted to internal identifiers (e.g., $number) based on their position. When returning to the client, description information is sent to libpq, ensuring that the returned column names are constructed from parameter names. The libpq side assigns values to OUT parameters based on these column names. libpq Interface Layer: Provides functions for preparing, binding, and executing statements, similar to the corresponding OCI functions. The general calling process is as follows: Use IvyHandleAlloc to allocate statement and error handles. Call IvyStmtPrepare to prepare the statement. Call IvyBindByPos or IvyBindByName to bind parameters. Call IvyStmtExecute to execute, which can be repeated. Call IvyFreeHandle to release the statement and error handles. Additionally, a series of interface functions have been implemented, including Ivyconnectdb, Ivystatus, Ivyexec, IvyresultStatus, IvyCreatePreparedStatement, IvybindOutParameterByPos, IvyexecPreparedStatement, IvyexecPreparedStatement2, Ivynfields, Ivyntuples, and Ivyclear.","title":"Functional Overview","component":"ivorysql-doc","version":"master","name":"out_parameter","url":"/ivorysql-doc/master/compatibility_features_design/out_parameter.html","titles":[{"text":"1. Implementation Principles","hash":"implementation-principles","id":1},{"text":"1.1. Functions with OUT Parameters","hash":"functions-with-out-parameters","id":2},{"text":"1.2. Support for OUT Parameters in Anonymous Blocks","hash":"support-for-out-parameters-in-anonymous-blocks","id":3},{"text":"1.3. Calling Functions with OUT Parameters in libpq","hash":"calling-functions-with-out-parameters-in-libpq","id":4}]},"27":{"id":27,"text":"IvorySQL provides Oracle-compatible RowID functionality. RowID is a pseudo-column automatically generated by the database when a table is created, returning the address of each row in the database. RowID should have the following characteristics: 1. Logically identifies each row with a unique value 2. Allows quick querying and modification of other columns in the table via ROWID, but cannot be inserted or modified itself 3. Users can control whether this feature is enabled In IvorySQL, the system column ctid represents the physical location of a data row in a table, also known as the tuple identifier, which consists of a pair of values (block number and row index). The ctid allows for quick lookup of data rows in a table, behaving similarly to Oracle’s RowID. However, the ctid value may change (e.g., during UPDATE or VACUUM FULL), making it unsuitable as a long-term row identifier. We chose a composite type consisting of the table’s OID and a sequence value as the RowID value, where the sequence is a system column. If the RowID functionality is enabled, a sequence named table-id_rowid_seq is created simultaneously with the table. Additionally, in the heap_form_tuple constructor, the length of HeapTupleHeaderData is increased by 8 bytes, and the td→t_infomask = HEAP_HASROWID bit is set to indicate the presence of the RowID. When the RowID functionality is enabled via the GUC parameter, or by the WITH ROWID option in table creation, or by executing ALTER TABLE … SET WITH ROWID on an existing table, a sequence is created by adding a sequence creation command. /* * Build a CREATE SEQUENCE command to create the sequence object, * and add it to the list of things to be done before this CREATE/ALTER TABLE */ seqstmt = makeNode(CreateSeqStmt); seqstmt->with_rowid = true; seqstmt->sequence = makeRangeVar(snamespace, sname, -1); seqstmt->options = lcons(makeDefElem(\"as\", (Node *) makeTypeNameFromOid(INT8OID, -1), -1), seqstmt->options); seqstmt->options = lcons(makeDefElem(\"nocache\", NULL, -1), seqstmt->options); To enable fast querying of a row using the RowID pseudo-column, a UNIQUE index is automatically created on the RowID column of the table by default to provide efficient query performance. The implementation of the RowID column as a system attribute column is achieved by adding a new system column in heap.c. /* * Compatible Oracle ROWID pseudo column. */ static const FormData_pg_attribute a7 = { .attname = {\"rowid\"}, .atttypid = ROWIDOID, .attlen = -1, .attnum = RowIdAttributeNumber, .attcacheoff = -1, .atttypmod = -1, .attbyval = false, .attalign = TYPALIGN_SHORT, .attstorage = TYPSTORAGE_PLAIN, .attnotnull = true, .attislocal = true, }; A boolean field relhasrowid has been added to the pg_class system table to indicate whether the WITH ROWID option was specified during table creation. If the WITH ROWID option is included when creating a table, relhasrowid is set to t; otherwise, it is set to f. This value is also updated when a user executes the ALTER TABLE … SET WITH ROWID or ALTER TABLE … SET WITHOUT ROWID command. /* T if we generate ROWIDs for rows of rel */ bool relhasrowid BKI_DEFAULT(f); Regarding RowID storage, if the RowID pseudo-column functionality is enabled, the heap_form_tuple function will add 8 bytes to the HeapTupleHeaderData to store the sequence value, depending on whether tdhasrowid in the TupleDesc parameter is true. In the heap_prepare_insert function, the nextval of the sequence is obtained and stored in the corresponding position in the HeapTupleHeader. if (relation->rd_rel->relhasrowid) { // Get the sequence next value seqnum = nextval_internal(relation->rd_rowdSeqid, true); // Set the HeapTupleHeader HeapTupleSetRowId(tup, seqnum); }","title":"RowID","component":"ivorysql-doc","version":"master","name":"rowid","url":"/ivorysql-doc/master/compatibility_features_design/rowid.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Implementation Principles","hash":"implementation-principles","id":2}]},"28":{"id":28,"text":"IvorySQL’s sys_guid() is a powerful random number generation function that generates and returns a 16-byte database-level unique identifier (raw value). The sys_guid() function of IvorySQL is implemented by modifying the code of the uuid-ossp plugin. To make full use of various underlying libraries of uuid, the following logic is adopted: If the uuid-ossp exists in the system, uuid_make() will be used; If the uuid-e2fs exists in the system, uuid_generate_random() will be used； Otherwise use arc4random()； Meanwhile modify the code so that IvorySQL can load uuid-ossp extension automatically.","title":"sys_guid() function","component":"ivorysql-doc","version":"master","name":"sys_guid_function","url":"/ivorysql-doc/master/compatibility_features_design/sys_guid_function.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation Description","hash":"implementation-description","id":2}]},"29":{"id":29,"text":"IvorySQL provides Oracle-compatible PL/SQL data type functionality, including %TYPE and %ROWTYPE. This is a passive process. The current implementation records the dependency between functions (stored procedures) and tablename.columnname. When the referenced type changes, the function (stored procedure) cache is invalidated based on this dependency. As a result, when the function is called, it undergoes forced compilation, ensuring that the function retrieves the latest variable type. A field named prostatus is added to the system table pg_proc to indicate the status of a function (stored procedure), with three possible states: validate (v), invalidate (i), and N/A (n). After a function is successfully compiled, this status is set to valid. When parsing function content, the functions plisql_parse_cwordtype, plisql_parse_wordrowtype, and plisql_parse_cwordrowtype identify objects referenced by %TYPE and %ROWTYPE, record them in the plisql_referenced_object linked list, and finally add them to the pg_depend system table. Add a new dependency type, DEPENDENCY_TYPE = 't', to represent %TYPE or %ROWTYPE dependencies. When adding object reference relationships to the pg_depend system table, set the dependency type to 't'. When performing operations on a table (such as modifying the table type or deleting the table), check the pg_depend system table. If there exists a dependency type deptype='t' and the dependent object is a function, call the plisql_free_function function to clear the function cache and update the function status prostatus in the pg_proc system table to N/A (n). Add bool notnull member in structure PLiSQL_type; /* * Postgres data type */ typedef struct PLiSQL_type { bool notnull; /* the type is built by variable%type, * isnull or notnull of the variable */ In the plisql_parse_wordtype or plisql_parse_cwordtype functions responsible for parsing %TYPE type functions, determine if the referenced variable type specifies a NOT NULL constraint, and set the bool notnull attribute of the returned datatype member to true. In the decl_statement grammar of the pl_gram.y file, assign the notnull attribute of the PLiSQL_variable *var variable based on the bool notnull member of PLiSQL_type. This way, the constraints of the referenced variable are inherited. Add support of %ROWTYPE for func_type in ora_gram.y | type_function_name attrs '%' ROWTYPE { $$ = makeTypeNameFromNameList(lcons(makeString($1), $2)); $$->row_type = true; $$->location = @1; } Add a member bool row_type to the TypeName struct to indicate whether %ROWTYPE is specified. typedef struct TypeName { bool pct_type; /* %TYPE specified? */ bool row_type; /* %ROWTYPE specified? */ In the LookupTypeName function, if the row_type member of TypeName is TRUE, obtain the schema name and table name from the names member of TypeName, and then retrieve the table’s typeoid. In ora_gram.y, add new syntax to support VALUES without requiring parentheses '(' afterward. values_clause_no_parens: VALUES columnref { SelectStmt *n = makeNode(SelectStmt); n->valuesLists = list_make1(list_make1($2)); n->valuesIsrow = true; $$ = (Node *) n; } Add a field bool valuesIsrow to the SelectStmt struct to indicate that values is a row. When the transformInsertStmt function processes the INSERT …​ VALUES statement, if valuesIsrow is true, calls the new function transformRowExpression to convert row_variable into the equivalent row_variable.field1, …​, row_variable.fieldN. When transforming an UPDATE statement, i.e., when calling the transformUpdateStmt function, if in Oracle compatibility mode, invoke the newly added transformIvyUpdateTargetList function. In this new function, for cases where the origTlist (i.e., targetList) does not contain a name of row, execute the transformUpdateTargetList function following the original UPDATE transform process. For cases where the origTlist parameter contains a name row, since row can be used as a column name in PostgreSQL but is a reserved keyword in Oracle and cannot be used as a column name, it is necessary to determine whether row is a column in the table being updated. If row is not a column in the table to be updated, call the new function transformUpdateRowTargetList to convert the sql statement UPDATE table_name SET ROW = row_variable [WHERE …]; into equivalent UPDATE table_name SET table_name.column1 = row_variable.column1, table_name.column2 = row_variable.filed2,… table_name.columnN = row_variable.columnN [WHERE …]; If the variable row_variable in the statement UPDATE table_name SET ROW = row_variable is not a composite type, execute the transformUpdateTargetList function following the original UPDATE transform process. If the variable row_variable in the statement is a composite type, the column named row in the table is also a composite type, and their type OIDs match, execute the transformUpdateTargetList function following the original UPDATE transform process. In all other cases, call the new function transformUpdateRowTargetList for processing.","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"type_rowtype","url":"/ivorysql-doc/master/compatibility_features_design/type_rowtype.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Implementation description","hash":"implementation-description","id":2},{"text":"2.1. If the reference changes, variables declared with %TYPE or %ROWTYPE will change accordingly","hash":"if-the-reference-changes-variables-declared-with-type-or-rowtype-will-change-accordingly","id":3},{"text":"2.2. Variables declared with %TYPE inherit the constraints of the referenced variable.","hash":"variables-declared-with-type-inherit-the-constraints-of-the-referenced-variable","id":4},{"text":"2.3. Use table_name%ROWTYPE or view_name%ROWTYPE as the parameter type of a function / stored procedure or the return type of a function","hash":"use-table_namerowtype-or-view_namerowtype-as-the-parameter-type-of-a-function-stored-procedure-or-the-return-type-of-a-function","id":5},{"text":"2.4. Enhancement to INSERT statement","hash":"enhancement-to-insert-statement","id":6},{"text":"2.5. Enhancement to UPDATE statement","hash":"enhancement-to-update-statement","id":7}]},"30":{"id":30,"text":"Get IvorySQL image from Docker Hub $ docker pull ivorysql/ivorysql:5.0-ubi8 Running IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 Check if the IvorySQL container is running successfully $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql Pull IvorySQL Image from Docker Hub [highgo@manager-node1 ~]$ podman pull ivorysql/ivorysql:5.0-ubi8 ✔ docker.io/ivorysql/ivorysql:5.0-ubi8 Trying to pull docker.io/ivorysql/ivorysql:5.0-ubi8... Getting image source signatures Copying blob 5885448c5c88 done | Copying blob 6c502b378234 done | Copying blob 8b4f2b90d6b6 done | Copying blob 9b000f2935f6 done | Copying blob 806f782da874 done | Copying blob e4c51845a9eb done | Copying blob dcb1e9a04275 done | Copying blob 285a279173f8 done | Copying blob 1f6f247b9ae0 done | Copying blob 3cc81bed8614 done | Copying blob 863c87bf25eb done | Copying blob 4f4fb700ef54 done | Copying config 88e1bbeda8 done | Writing manifest to image destination 88e1bbeda81c51d88e12cbd2b19730498f1343d1c64bb3dddc8ffcb08a1f965f Run IvorySQL Container $ podman run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=123456 -d ivorysql/ivorysql:5.0-ubi8 Check if IvorySQL Container is Running Successfully [highgo@manager-node1 ~]$ podman ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 368dee58d5ef docker.io/ivorysql/ivorysql:5.0-ubi8 postgres 20 seconds ago Up 20 seconds 0.0.0.0:5434->5432/tcp, 1521/tcp, 5866/tcp ivorysql [highgo@manager-node1 ~]$ podman exec -it ivorysql /bin/bash [root@8cc631eb413d /]# ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=#","title":"Docker & Podman deployment IvorySQL","component":"ivorysql-doc","version":"master","name":"docker_podman_deployment","url":"/ivorysql-doc/master/containerization/docker_podman_deployment.html","titles":[{"text":"1. Running IvorySQL in docker","hash":"running-ivorysql-in-docker","id":1},{"text":"2. Running with Podman","hash":"running-with-podman","id":2}]},"31":{"id":31,"text":"Prepare three servers with network connectivity and set up a Swarm cluster. The test cluster names and corresponding IP addresses are as follows: manager-node1: 192.168.21.205 manager-node2: 192.168.21.164 manager-node3: 192.168.21.51 [root@manager-node1 docker-swarm]# docker node ls ID HOSTNAME STATUS AVAILABILITY MANAGER STATUS ENGINE VERSION y9d9wd9t2ncy4t9bvw6bg9sjs * manager-node1 Ready Active Reachable 26.1.4 iv17o6m9t9e06vd9iu1o6damd manager-node2 Ready Active Leader 25.0.4 vjnax76qj812mlvut6cv4qotl manager-node3 Ready Active Reachable 24.0.6 Download the source code [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-swarm Deploy a three-node etcd cluster [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-etcd.yml ivoryhac-etcd Creating network ivoryhac-etcd_etcd-net Creating service ivoryhac-etcd_etcd3 Creating service ivoryhac-etcd_etcd1 Creating service ivoryhac-etcd_etcd2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 You can customize the external database directory by modifying the volumes section in docker-swarm-ivypatroni.yml. After making changes, adjust the directory permissions and ownership accordingly. Example as follows: mkdir -p /home/ivorysql/{data,patroni} chown -R 1000:1000 /home/ivorysql/{data,patroni} chmod 700 /home/ivorysql/{data,patroni} Deploy an IvorySQL High Availability Cluster [root@manager-node1 docker-swarm]# docker stack deploy -c docker-swarm-ivypatroni.yml ivoryhac-patroni Since --detach=false was not specified, tasks will be created in the background. In a future release, --detach=false will become the default. Creating service ivoryhac-patroni_ivypatroni1 Creating service ivoryhac-patroni_ivypatroni2 [root@manager-node1 docker-swarm]# docker service ls ID NAME MODE REPLICAS IMAGE PORTS 1jst0mva8o5n ivoryhac-etcd_etcd1 replicated 1/1 quay.io/coreos/etcd:v3.5.8 *:2379-2380->2379-2380/tcp sosag5017cis ivoryhac-etcd_etcd2 replicated 1/1 quay.io/coreos/etcd:v3.5.8 8twpgkzo2mnx ivoryhac-etcd_etcd3 replicated 1/1 quay.io/coreos/etcd:v3.5.8 uzdvjq5j2gwt ivorysql-hac_ivypatroni1 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1521->1521/tcp, *:5866->5866/tcp fr0m9chu3ce8 ivorysql-hac_ivypatroni2 replicated 1/1 ivorysql/docker-swarm-ha-cluster:5.0-4.0.6-ubi8 *:1522->1521/tcp, *:5867->5866/tcp Connect to the database using psql via Oracle port and PostgreSQL port [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) Uninstall the IvorySQL cluster [root@manager-node1 ~] docker stack rm ivoryhac-patroni [root@manager-node1 ~] docker stack rm ivoryhac-etcd Download the source code [root@manager-node1 ~]# git clone https://github.com/IvorySQL/docker_library.git [root@manager-node1 ~]# cd docker_library/docker-cluster/docker-compose Copy files to another server Copy the etcd and ivypatroni Docker Compose files to other servers respectively. For example, to the test server: 192.168.21.205 will host etcd1+ivorypatroni1， 192.168.21.164 will host etcd2+ivorypatroni2， 192.168.21.51 will host etcd3+ivorypatroni3 Deploy a three-node etcd cluster, taking node1 as an example [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-etcd1.yml up -d [+] Running 1/1 ✔ Container etcd Started 0.1s Deploy an IvorySQL high-availability cluster. Deploy the ivyhac service on each node, using node1 as an example. [root@manager-node1 docker-compose]# docker-compose -f ./docker-compose-ivypatroni_1.yml up -d [+] Running 1/1 ✔ Container ivyhac1 Started 0.1s [root@manager-node1 docker-compose]# docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 736c0d188bdd ivorysql/docker-compose-ha-cluster:5.0-4.0.6-ubi8 \"/bin/sh /docker-ent…\" 18 seconds ago Up 17 seconds ivyhac1 9d8e04e4f819 quay.io/coreos/etcd:v3.5.8 \"/usr/local/bin/etcd\" 24 minutes ago Up 24 minutes etcd At this point, the one-primary-two-standby cluster setup is complete. Connect to the database using psql via Oracle-compatible ports and PostgreSQL ports. [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p1521 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# exit [root@manager-node1 docker-swarm]# psql -h127.0.0.1 -p5432 -U ivorysql -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) Uninstall the IvorySQL cluster, using node1 as an example. [root@manager-node1 ~] docker-compose -f ./docker-compose-ivypatroni_1.yml down [root@manager-node1 ~] docker-compose -f ./docker-compose-etcd1.yml down","title":"Docker Swarm & Docker Compose Deploying IvorySQL High Availability Cluster","component":"ivorysql-doc","version":"master","name":"docker_swarm_compose_deployment","url":"/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html","titles":[{"text":"1. Building IvorySQL HA Cluster using Docker Swarm","hash":"building-ivorysql-ha-cluster-using-docker-swarm","id":1},{"text":"2. Set up an IvorySQL HA Cluster using Docker Compose","hash":"set-up-an-ivorysql-ha-cluster-using-docker-compose","id":2}]},"32":{"id":32,"text":"On the master node of the k8s cluster, create a namespace named ivorysql. [root@k8s-master ~]# kubectl create ns ivorysql Download the latest docker_library code. [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git Enter the single-node directory [root@k8s-master ~]# cd docker_library/k8s-cluster/single [root@k8s-master single]# vim statefulset.yaml #Update the PVC information and database password in the StatefulSet to match your actual environment. Use statefulset.yaml to create a single-node pod. [root@k8s-master single]# kubectl apply -f statefulset.yaml service/ivorysql-svc created statefulset.apps/ivorysql created Wait for the single-node pod to be successfully created. [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 0/1 ContainerCreating 0 47s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 47s NAME READY AGE statefulset.apps/ivorysql 0/1 47s [root@k8s-master single]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-0 1/1 Running 0 2m39s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-svc NodePort 10.108.178.236 <none> 5432:32106/TCP,1521:31887/TCP 2m39s NAME READY AGE statefulset.apps/ivorysql 1/1 2m39s Connect to IvorySQL via its PostgreSQL port using the psql [root@k8s-master single]# psql -U ivorysql -p 32106 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# exit Connect to IvorySQL’s Oracle-compatible port using psql. [root@k8s-master single]# psql -U ivorysql -p 31887 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# select version(); version ------------------------------------------------------------------------------------------------------------------------ PostgreSQL 18.0 (IvorySQL 5.0) on x86_64-pc-linux-gnu, compiled by gcc (GCC) 8.5.0 20210514 (Red Hat 8.5.0-28), 64-bit (1 row) ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) Uninstall Single-node container [root@k8s-master single]# kubectl delete -f statefulset.yaml Access the master node of the k8s cluster and create a namespace named ivorysql. [root@k8s-master ~]# kubectl create ns ivorysql Download the latest docker_library code. [root@k8s-master ~]# git clone https://github.com/IvorySQL/docker_library.git Enter the high-availability cluster directory. [root@k8s-master ~]# cd docker_library/k8s-cluster/ha-cluster/helm_charts [root@k8s-master single]# vim values.yaml #Adjust the PVC settings, cluster size, and other configurations in values.yaml according to your environment. For the database password, check templates/secret.yaml and modify it as needed. Deploy the high-availability cluster using Helm commands. [root@k8s-master helm_charts]# helm install ivorysql-ha-cluster -n ivorysql . NAME: ivorysql-ha-cluster LAST DEPLOYED: Wed Sep 10 09:45:36 2025 NAMESPACE: ivorysql STATUS: deployed REVISION: 1 TEST SUITE: None [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 42s pod/ivorysql-patroni-hac-1 0/1 Running 0 18s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 42s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 42s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 42s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 1/3 42s Wait until all pods are running successfully, indicating the cluster deployment is complete. [root@k8s-master helm_charts]# kubectl get all -n ivorysql NAME READY STATUS RESTARTS AGE pod/ivorysql-patroni-hac-0 1/1 Running 0 88s pod/ivorysql-patroni-hac-1 1/1 Running 0 64s pod/ivorysql-patroni-hac-2 1/1 Running 0 41s NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE service/ivorysql-patroni-hac NodePort 10.96.119.203 <none> 5432:32391/TCP,1521:32477/TCP 88s service/ivorysql-patroni-hac-config ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-pods ClusterIP None <none> <none> 88s service/ivorysql-patroni-hac-repl NodePort 10.100.122.0 <none> 5432:30111/TCP,1521:32654/TCP 88s NAME READY AGE statefulset.apps/ivorysql-patroni-hac 3/3 88s Connect to the PostgreSQL and Oracle ports of the cluster’s primary node using psql. [root@k8s-master helm_charts]# psql -U ivorysql -p 32391 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32477 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- f (1 row) ivorysql=# Use psql to connect to the PostgreSQL and Oracle ports of the cluster’s standby node. [root@k8s-master helm_charts]# psql -U ivorysql -p 30111 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- pg (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# exit [root@k8s-master helm_charts]# psql -U ivorysql -p 32654 -h 127.0.0.1 -d ivorysql Password for user ivorysql: ivorysql=# show ivorysql.compatible_mode; ivorysql.compatible_mode -------------------------- oracle (1 row) ivorysql=# SELECT pg_is_in_recovery(); pg_is_in_recovery ------------------- t (1 row) ivorysql=# Uninstall high-availability cluster [root@k8s-master helm_charts]# helm uninstall ivorysql-ha-cluster -n ivorysql Remove PVC [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc ivyhac-config-ivorysql-patroni-hac-2 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-0 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-1 -n ivorysql [root@k8s-master helm_charts]# kubectl delete pvc pgdata-ivorysql-patroni-hac-2 -n ivorysql","title":"Deploying single-node containers and high-availability clusters on k8s","component":"ivorysql-doc","version":"master","name":"k8s_deployment","url":"/ivorysql-doc/master/containerization/k8s_deployment.html","titles":[{"text":"1. Single-node container","hash":"single-node-container","id":1},{"text":"2. High Availability Cluster","hash":"high-availability-cluster","id":2}]},"33":{"id":33,"text":"Fork ivory-operator repository and clone it to your host machine： YOUR_GITHUB_UN=\"<your GitHub username>\" git clone --depth 1 \"git@github.com:${YOUR_GITHUB_UN}/ivory-operator.git\" cd ivory-operator Run the following commands： kubectl apply -k examples/kustomize/install/namespace kubectl apply --server-side -k examples/kustomize/install/default Throughout this tutorial, we will be building on the example provided in the examples/kustomize/ivory. When referring to a nested object within a YAML manifest, we will be using the . format similar to kubectl explain. For example, if we want to refer to the deepest element in this yaml file: spec: hippos: appetite: huge we would say spec.hippos.appetite. kubectl explain is your friend. You can use kubectl explain ivorycluster to introspect the ivorycluster.ivory-operator.ivorysql.org custom resource definition. Creating an Ivory cluster is pretty simple. Using the example in the examples/kustomize/ivory directory, all we have to do is run: kubectl apply -k examples/kustomize/ivory and IVYO will create a simple Ivory cluster named hippo in the ivory-operator namespace. You can track the status of your Ivory cluster using kubectl describe on the ivoryclusters.ivory-operator.ivorysql.org custom resource: kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo and you can track the state of the Ivory Pod using the following command: kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance IVYO created an Ivory cluster based on the information provided to it in the Kustomize manifests located in the examples/kustomize/ivory directory. Let’s better understand what happened by inspecting the examples/kustomize/ivory/ivory.yaml file: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi When we ran the kubectl apply command earlier, what we did was create a ivorycluster custom resource in Kubernetes. IVYO detected that we added a new ivorycluster resource and started to create all the objects needed to run Ivory in Kubernetes! What else happened? IVYO read the value from metadata.name to provide the Ivory cluster with the name hippo. Additionally, IVYO knew which containers to use for Ivory and pgBackRest by looking at the values in spec.image and spec.backups.pgbackrest.image respectively. The value in spec.postgresVersion is important as it will help IVYO track which major version of Ivory you are using. IVYO knows how many Ivory instances to create through the spec.instances section of the manifest. While name is optional, we opted to give it the name instance1. We could have also created multiple replicas and instances during cluster initialization, but we will cover that more when we discuss how to scale and create a HA Ivory cluster. A very important piece of your ivorycluster custom resource is the dataVolumeClaimSpec section. This describes the storage that your Ivory instance will use. It is modeled after the Persistent Volume Claim. If you do not provide a spec.instances.dataVolumeClaimSpec.storageClassName, then the default storage class in your Kubernetes environment is used. As part of creating an Ivory cluster, we also specify information about our backup archive. IVYO uses pgBackRest, an open source backup and restore tool designed to handle terabyte-scale backups. As part of initializing our cluster, we can specify where we want our backups and archives (write-ahead logs or WAL) stored. We will talk about this portion of the ivorycluster spec in greater depth in the disaster recovery section of this tutorial, and also see how we can store backups in Amazon S3, Google GCS, and Azure Blob Storage. The most common occurrence of this is due to PVCs not being bound. Ensure that you have set up your storage options correctly in any volumeClaimSpec. You can always update your settings and reapply your changes with kubectl apply. Also ensure that you have enough persistent volumes available: your Kubernetes administrator may need to provision more. If you are on OpenShift, you may need to set spec.openshift to true. We’re up and running — now let’s connect to our Ivory cluster! It’s one thing to create an Ivory cluster; it’s another thing to connect to it. Let’s explore how IVYO makes it possible to connect to an Ivory cluster! IVYO creates a series of Kubernetes Services to provide stable endpoints for connecting to your Ivory databases. These endpoints make it easy to provide a consistent way for your application to maintain connectivity to your data. To inspect what services are available, you can run the following command: kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo will yield something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 3h14m hippo-ha-config ClusterIP None <none> <none> 3h14m hippo-pods ClusterIP None <none> <none> 3h14m hippo-primary ClusterIP None <none> 5432/TCP 3h14m hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 3h14m You do not need to worry about most of these Services, as they are used to help manage the overall health of your Ivory cluster. For the purposes of connecting to your database, the Service of interest is called hippo-primary. Thanks to IVYO, you do not need to even worry about that, as that information is captured within a Secret! When your Ivory cluster is initialized, IVYO will bootstrap a database and Ivory user that your application can access. This information is stored in a Secret named with the pattern <clusterName>-pguser-<userName>. For our hippo cluster, this Secret is called hippo-pguser-hippo. This Secret contains the information you need to connect your application to your Ivory database: user: The name of the user account. password: The password for the user account. dbname: The name of the database that the user has access to by default. host: The name of the host of the database. This references the Service of the primary Ivory instance. port: The port that the database is listening on. uri: A PostgresSQL connection URI that provides all the information for logging into the Ivory database. jdbc-uri: A PostgresSQL JDBC connection URI that provides all the information for logging into the Ivory database via the JDBC driver. All connections are over TLS. IVYO provides its own certificate authority (CA) to allow you to securely connect your applications to your Ivory clusters. This allows you to use the verify-full \"SSL mode\" of Ivory, which provides eavesdropping protection and prevents MITM attacks. You can also choose to bring your own CA, which is described later in this tutorial in the Customize Cluster section. By default, IVYO deploys Services with the ClusterIP Service type. Based on how you want to expose your database, you may want to modify the Services to use a different Service type and NodePort value. You can modify the Services that IVYO manages from the following attributes: spec.service - this manages the Service for connecting to an Ivory primary. spec.userInterface.pgAdmin.service - this manages the Service for connecting to the pgAdmin management tool. For example, say you want to set the Ivory primary to use a NodePort service, a specific nodePort value, and set a specific annotation and label, you would add the following to your manifest: spec: service: metadata: annotations: my-annotation: value1 labels: my-label: value2 type: NodePort nodePort: 32000 For our hippo cluster, you would see the Service type and nodePort modification as well as the annotation and label. For example: kubectl -n ivory-operator get svc --selector=ivory-operator.ivorysql.org/cluster=hippo will yield something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha NodePort 10.105.57.191 <none> 5432:32000/TCP 48s hippo-ha-config ClusterIP None <none> <none> 48s hippo-pods ClusterIP None <none> <none> 48s hippo-primary ClusterIP None <none> 5432/TCP 48s hippo-replicas ClusterIP 10.106.18.99 <none> 5432/TCP 48s and the top of the output from running kubectl -n ivory-operator describe svc hippo-ha will show our custom annotation and label have been added: Name: hippo-ha Namespace: ivory-operator Labels: my-label=value2 ivory-operator.ivorysql.org/cluster=hippo ivory-operator.ivorysql.org/patroni=hippo-ha Annotations: my-annotation: value1 Note that setting the nodePort value is not allowed when using the (default) ClusterIP type, and it must be in-range and not otherwise in use or the operation will fail. Additionally, be aware that any annotations or labels provided here will win in case of conflicts with any annotations or labels a user configures elsewhere. Finally, if you are exposing your Services externally and are relying on TLS verification, you will need to use the custom TLS features of IVYO). For this tutorial, we are going to connect Keycloak, an open source identity management application. Keycloak can be deployed on Kubernetes and is backed by an Ivory database. We provide an example of deploying Keycloak andan ivorycluster, the manifest below deploys it using our hippo cluster that is already running: kubectl apply --filename=- <<EOF apiVersion: apps/v1 kind: Deployment metadata: name: keycloak namespace: ivory-operator labels: app.kubernetes.io/name: keycloak spec: selector: matchLabels: app.kubernetes.io/name: keycloak template: metadata: labels: app.kubernetes.io/name: keycloak spec: containers: - image: quay.io/keycloak/keycloak:latest args: [\"start-dev\"] name: keycloak env: - name: DB_VENDOR value: \"ivory\" - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } - name: KEYCLOAK_ADMIN value: \"admin\" - name: KEYCLOAK_ADMIN_PASSWORD value: \"admin\" - name: KC_PROXY value: \"edge\" ports: - name: http containerPort: 8080 - name: https containerPort: 8443 readinessProbe: httpGet: path: /realms/master port: 8080 restartPolicy: Always EOF Notice this part of the manifest: - name: DB_ADDR valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: port } } - name: DB_DATABASE valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: dbname } } - name: DB_USER valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: user } } - name: DB_PASSWORD valueFrom: { secretKeyRef: { name: hippo-pguser-hippo, key: password } } The above manifest shows how all of these values are derived from the hippo-pguser-hippo Secret. This means that we do not need to know any of the connection credentials or have to insecurely pass them around — they are made directly available to the application! Using this method, you can tie application directly into your GitOps pipeline that connect to Ivory without any prior knowledge of how IVYO will deploy Ivory: all of the information your application needs is propagated into the Secret! Now that we have seen how to connect an application to a cluster, let’s learn how to create a high availability Ivory cluster! Ivory is known for its reliability: it is very stable and typically \"just works.\" However, there are many things that can happen in a distributed environment like Kubernetes that can affect Ivory uptime, including: The database storage disk fails or some other hardware failure occurs The network on which the database resides becomes unreachable The host operating system becomes unstable and crashes A key database file becomes corrupted A data center is lost A Kubernetes component (e.g. a Service) is accidentally deleted There may also be downtime events that are due to the normal case of operations, such as performing a minor upgrade, security patching of operating system, hardware upgrade, or other maintenance. The good news: IVYO is prepared for this, and your Ivory cluster is protected from many of these scenarios. However, to maximize your high availability (HA), let’s first scale up your Ivory cluster. IVYO provides several ways to add replicas to make a HA cluster: Increase the spec.instances.replicas value Add an additional entry in spec.instances For the purposes of this tutorial, we will go with the first method and set spec.instances.replicas to 2. Your manifest should look similar to: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory Within moment, you should see a new Ivory instance initializing! You can see all of your Ivory Pods for the hippo cluster by running the following command: kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance-set Let’s test our high availability set up. An important part of building a resilient Ivory environment is testing its resiliency, so let’s run a few tests to see how IVYO performs under pressure! Let’s try removing the primary Service that our application is connected to. This test does not actually require a HA Ivory cluster, but it will demonstrate IVYO’s ability to react to environmental changes and heal things to ensure your applications can stay up. Recall in the connecting a Ivory cluster that we observed the Services that IVYO creates, e.g.: kubectl -n ivory-operator get svc \\ --selector=ivory-operator.ivorysql.org/cluster=hippo yields something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 4h8m hippo-ha-config ClusterIP None <none> <none> 4h8m hippo-pods ClusterIP None <none> <none> 4h8m hippo-primary ClusterIP None <none> 5432/TCP 4h8m hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 4h8m We also mentioned that the application is connected to the hippo-primary Service. What happens if we were to delete this Service? kubectl -n ivory-operator delete svc hippo-primary This would seem like it could create a downtime scenario, but run the above selector again: kubectl -n ivory-operator get svc \\ --selector=ivory-operator.ivorysql.org/cluster=hippo You should see something similar to: NAME TYPE CLUSTER-IP EXTERNAL-IP PORT(S) AGE hippo-ha ClusterIP 10.103.73.92 <none> 5432/TCP 4h8m hippo-ha-config ClusterIP None <none> <none> 4h8m hippo-pods ClusterIP None <none> <none> 4h8m hippo-primary ClusterIP None <none> 5432/TCP 3s hippo-replicas ClusterIP 10.98.110.215 <none> 5432/TCP 4h8m Wow — IVYO detected that the primary Service was deleted and it recreated it! Based on how your application connects to Ivory, it may not have even noticed that this event took place! Now let’s try a more extreme downtime event. StatefulSets are a Kubernetes object that provide helpful mechanisms for managing Pods that interface with stateful applications, such as databases. They provide a stable mechanism for managing Pods to help ensure data is retrievable in a predictable way. What happens if we remove the StatefulSet that is pointed to the Pod that represents the Ivory primary? First, let’s determine which Pod is the primary. We’ll store it in an environmental variable for convenience. PRIMARY_POD=$(kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[*].metadata.labels.ivory-operator\\.ivorysql\\.org/instance}') Inspect the environmental variable to see which Pod is the current primary: echo $PRIMARY_POD should yield something similar to: hippo-instance1-zj5s We can use the value above to delete the StatefulSet associated with the current Ivory primary instance: kubectl delete sts -n ivory-operator \"${PRIMARY_POD}\" Let’s see what happens. Try getting all of the StatefulSets for the Ivory instances in the hippo cluster: kubectl get sts -n ivory-operator \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance You should see something similar to: NAME READY AGE hippo-instance1-6kbw 1/1 15m hippo-instance1-zj5s 0/1 1s IVYO recreated the StatefulSet that was deleted! After this \"catastrophic\" event, IVYO proceeds to heal the Ivory instance so it can rejoin the cluster. We cover the high availability process in greater depth later in the documentation. What about the other instance? We can see that it became the new primary though the following command: kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/role=master \\ -o jsonpath='{.items[*].metadata.labels.ivory-operator\\.ivorysql\\.org/instance}' which should yield something similar to: hippo-instance1-6kbw You can test that the failover successfully occurred in a few ways. You can connect to the example Keycloak application that we deployed in the previous section. Based on Keycloak’s connection retry logic, you may need to wait a moment for it to reconnect, but you will see it connected and resume being able to read and write data. You can also connect to the Ivory instance directly and execute the following command: SELECT NOT pg_catalog.pg_is_in_recovery() is_primary; If it returns true (or t), then the Ivory instance is a primary! What if IVYO was down during the downtime event? Failover would still occur: the Ivory HA system works independently of IVYO and can maintain its own uptime. IVYO will still need to assist with some of the healing aspects, but your application will still maintain read/write connectivity to your Ivory cluster! IvorySQL supports synchronous replication, which is a replication mode designed to limit the risk of transaction loss. Synchronous replication waits for a transaction to be written to at least one additional server before it considers the transaction to be committed. For more information on synchronous replication, please read about IVYO’s high availability architecture To add synchronous replication to your Ivory cluster, you can add the following to your spec: spec: patroni: dynamicConfiguration: synchronous_mode: true While PostgreSQL defaults synchronous_commit to on, you may also want to explicitly set it, in which case the above block becomes: spec: patroni: dynamicConfiguration: synchronous_mode: true postgresql: parameters: synchronous_commit: \"on\" Note that Patroni, which manages many aspects of the cluster’s availability, will favor availability over synchronicity. This means that if a synchronous replica goes down, Patroni will allow for asynchronous replication to continue as well as writes to the primary. However, if you want to disable all writing if there are no synchronous replicas available, you would have to enable synchronous_mode_strict, i.e.: spec: patroni: dynamicConfiguration: synchronous_mode: true synchronous_mode_strict: true Kubernetes affinity rules, which include Pod anti-affinity and Node affinity, can help you to define where you want your workloads to reside. Pod anti-affinity is important for high availability: when used correctly, it ensures that your Ivory instances are distributed amongst different Nodes. Node affinity can be used to assign instances to specific Nodes, e.g. to utilize hardware that’s optimized for databases. IVYO sets up several labels for Ivory cluster management that can be used for Pod anti-affinity or affinity rules in general. These include: ivory-operator.ivorysql.org/cluster: This is assigned to all managed Pods in a Ivory cluster. The value of this label is the name of your Ivory cluster, in this case: hippo. ivory-operator.ivorysql.org/instance-set: This is assigned to all Ivory instances within a group of spec.instances. In the example above, the value of this label is instance1. If you do not assign a label, the value is automatically set by IVYO using a NN format, e.g. 00. ivory-operator.ivorysql.org/instance: This is a unique label assigned to each Ivory instance containing the name of the Ivory instance. Let’s look at how we can set up affinity rules for our Ivory cluster to help improve high availability. Kubernetes has two types of Pod anti-affinity: Preferred: With preferred (preferredDuringSchedulingIgnoredDuringExecution) Pod anti-affinity, Kubernetes will make a best effort to schedule Pods matching the anti-affinity rules to different Nodes. However, if it is not possible to do so, then Kubernetes may schedule one or more Pods to the same Node. Required: With required (requiredDuringSchedulingIgnoredDuringExecution) Pod anti-affinity, Kubernetes mandates that each Pod matching the anti-affinity rules must be scheduled to different Nodes. However, a Pod may not be scheduled if Kubernetes cannot find a Node that does not contain a Pod matching the rules. There is a trade-off with these two types of pod anti-affinity: while \"required\" anti-affinity will ensure that all the matching Pods are scheduled on different Nodes, if Kubernetes cannot find an available Node, your Ivory instance may not be scheduled. Likewise, while \"preferred\" anti-affinity will make a best effort to scheduled your Pods on different Nodes, Kubernetes may compromise and schedule more than one Ivory instance of the same cluster on the same Node. By understanding these trade-offs, the makeup of your Kubernetes cluster, and your requirements, you can choose the method that makes the most sense for your Ivory deployment. We’ll show examples of both methods below! First, let’s deploy our Ivory cluster with preferred Pod anti-affinity. Note that if you have a single-node Kubernetes cluster, you will not see your Ivory instances deployed to different nodes. However, your Ivory instances will be deployed. We can set up our HA Ivory cluster with preferred Pod anti-affinity like so: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Apply those changes in your Kubernetes cluster. Let’s take a closer look at this section: affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 spec.instances.affinity.podAntiAffinity follows the standard Kubernetes Pod anti-affinity spec. The values for the matchLabels are derived from what we described in the previous section: ivory-operator.ivorysql.org/cluster is set to our cluster name of hippo, and ivory-operator.ivorysql.org/instance-set is set to the instance set name of instance1. We choose a topologyKey of kubernetes.io/hostname, which is standard in Kubernetes clusters. Preferred Pod anti-affinity will perform a best effort to schedule your Ivory Pods to different nodes. Let’s see how you can require your Ivory Pods to be scheduled to different nodes. Required Pod anti-affinity forces Kubernetes to scheduled your Ivory Pods to different Nodes. Note that if Kubernetes is unable to schedule all Pods to different Nodes, some of your Ivory instances may become unavailable. Using the previous example, let’s indicate to Kubernetes that we want to use required Pod anti-affinity for our Ivory clusters: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi affinity: podAntiAffinity: requiredDuringSchedulingIgnoredDuringExecution: - topologyKey: kubernetes.io/hostname labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: hippo ivory-operator.ivorysql.org/instance-set: instance1 backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Apply those changes in your Kubernetes cluster. If you are in a single Node Kubernetes clusters, you will notice that not all of your Ivory instance Pods will be scheduled. This is due to the requiredDuringSchedulingIgnoredDuringExecution preference. However, if you have enough Nodes available, you will see the Ivory instance Pods scheduled to different Nodes: kubectl get pods -n ivory-operator -o wide \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance Node affinity can be used to assign your Ivory instances to Nodes with specific hardware or to guarantee a Ivory instance resides in a specific zone. Node affinity can be set within the spec.instances.affinity.nodeAffinity attribute, following the standard Kubernetes node affinity spec. Let’s see an example with required Node affinity. Let’s say we have a set of Nodes that are reserved for database usage that have a label workload-role=db. We can create a Ivory cluster with a required Node affinity rule to scheduled all of the databases to those Nodes using the following configuration: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: workload-role operator: In values: - db backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi In addition to affinity and anti-affinity settings, Kubernetes Pod Topology Spread Constraints can also help you to define where you want your workloads to reside. However, while PodAffinity allows any number of Pods to be added to a qualifying topology domain, and PodAntiAffinity allows only one Pod to be scheduled into a single topology domain, topology spread constraints allow you to distribute Pods across different topology domains with a finer level of control. The spread constraint API fields can be configured for instance, PgBouncer and pgBackRest repo host pods. The basic configuration is as follows: topologySpreadConstraints: - maxSkew: <integer> topologyKey: <string> whenUnsatisfiable: <string> labelSelector: <object> where \"maxSkew\" describes the maximum degree to which Pods can be unevenly distributed, \"topologyKey\" is the key that defines a topology in the Nodes' Labels, \"whenUnsatisfiable\" specifies what action should be taken when \"maxSkew\" can’t be satisfied, and \"labelSelector\" is used to find matching Pods. To help illustrate how you might use this with your cluster, we can review examples for configuring spread constraints on our Instance and pgBackRest repo host Pods. For this example, assume we have a three node Kubernetes cluster where the first node is labeled with my-node-label=one, the second node is labeled with my-node-label=two and the final node is labeled my-node-label=three. The label key my-node-label will function as our topologyKey. Note all three nodes in our examples will be schedulable, so a Pod could live on any of the three Nodes. To begin, we can set our topology spread constraints on our cluster Instance Pods. Given this configuration instances: - name: instance1 replicas: 5 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: ivory-operator.ivorysql.org/instance-set: instance1 we will expect 5 Instance pods to be created. Each of these Pods will have the standard ivory-operator.ivorysql.org/instance-set: instance1 Label set, so each Pod will be properly counted when determining the maxSkew. Since we have 3 nodes with a maxSkew of 1 and we’ve set whenUnsatisfiable to DoNotSchedule, we should see 2 Pods on 2 of the nodes and 1 Pod on the remaining Node, thus ensuring our Pods are distributed as evenly as possible. We can also set topology spread constraints on our cluster’s pgBackRest repo host pod. While we normally will only have a single pod per cluster, we could use a more generic label to add a preference that repo host Pods from different clusters are distributed among our Nodes. For example, by setting our matchLabel value to ivory-operator.ivorysql.org/pgbackrest: \"\" and our whenUnsatisfiable value to ScheduleAnyway, we will allow our repo host Pods to be scheduled no matter what Nodes may be available, but attempt to minimize skew as much as possible. repoHost: topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/pgbackrest: \"\" Now that each of our Pods has our desired Topology Spread Constraints defined, let’s put together a complete cluster definition: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 5 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: DoNotSchedule labelSelector: matchLabels: ivory-operator.ivorysql.org/instance-set: instance1 dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1G backups: pgbackrest: image: {{< param imagePGBackrest >}} repoHost: topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/pgbackrest: \"\" repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1G You can then apply those changes in your Kubernetes cluster. Once your cluster finishes deploying, you can check that your Pods are assigned to the correct Nodes: kubectl get pods -n ivory-operator -o wide --selector=ivory-operator.ivorysql.org/cluster=hippo We’ve now seen how IVYO helps your application stay \"always on\" with your Ivory database. Now let’s explore how IVYO can minimize or eliminate downtime for operations that would normally cause that, such as resizing your Ivory cluster. You did it — the application is a success! Traffic is booming, so much so that you need to add more resources to your Ivory cluster. However, you’re worried that any resize operation may cause downtime and create a poor experience for your end users. This is where IVYO comes in: IVYO will help orchestrate rolling out any potentially disruptive changes to your cluster to minimize or eliminate downtime for your application. To do so, we will assume that you have deployed a high availability Ivory cluster as described in the previous section. Let’s dive in. Memory and CPU resources are an important component for vertically scaling your Ivory cluster. Coupled with tweaks to your Ivory configuration file, allocating more memory and CPU to your cluster can help it to perform better under load. It’s important for instances in the same high availability set to have the same resources. IVYO lets you adjust CPU and memory within the resources sections of the ivoryclusters.ivory-operator.ivorysql.org custom resource. These include: spec.instances.resources section, which sets the resource values for the IvorySQL container, as well as any init containers in the associated pod and containers created by the pgDataVolume and pgWALVolume data migration jobs. spec.instances.sidecars.replicaCertCopy.resources section, which sets the resources for the replica-cert-copy sidecar container. spec.backups.pgbackrest.repoHost.resources section, which sets the resources for the pgBackRest repo host container, as well as any init containers in the associated pod and containers created by the pgBackRestVolume data migration job. spec.backups.pgbackrest.sidecars.pgbackrest.resources section, which sets the resources for the pgbackrest sidecar container. spec.backups.pgbackrest.sidecars.pgbackrestConfig.resources section, which sets the resources for the pgbackrest-config sidecar container. spec.backups.pgbackrest.jobs.resources section, which sets the resources for any pgBackRest backup job. spec.backups.pgbackrest.restore.resources section, which sets the resources for manual pgBackRest restore jobs. spec.dataSource.ivorycluster.resources section, which sets the resources for pgBackRest restore jobs created during the cloning process. The layout of these resources sections should be familiar: they follow the same pattern as the standard Kubernetes structure for setting container resources. Note that these settings also allow for the configuration of QoS classes. For example, using the spec.instances.resources section, let’s say we want to update our hippo Ivory cluster so that each instance has a limit of 2.0 CPUs and 4Gi of memory. We can make the following changes to the manifest: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi In particular, we added the following to spec.instances: resources: limits: cpu: 2.0 memory: 4Gi Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory Now, let’s watch how the rollout happens: watch \"kubectl -n ivory-operator get pods \\ --selector=ivory-operator.ivorysql.org/cluster=hippo,ivory-operator.ivorysql.org/instance \\ -o=jsonpath='{range .items[*]}{.metadata.name}{\\\"\\t\\\"}{.metadata.labels.ivory-operator\\.ivorysql\\.org/role}{\\\"\\t\\\"}{.status.phase}{\\\"\\t\\\"}{.spec.containers[].resources.limits}{\\\"\\n\\\"}{end}'\" Observe how each Pod is terminated one-at-a-time. This is part of a \"rolling update\". Because updating the resources of a Pod is a destructive action, IVYO first applies the CPU and memory changes to the replicas. IVYO ensures that the changes are successfully applied to a replica instance before moving on to the next replica. Once all of the changes are applied, IVYO will perform a \"controlled switchover\": it will promote a replica to become a primary, and apply the changes to the final Ivory instance. By rolling out the changes in this way, IVYO ensures there is minimal to zero disruption to your application: you are able to successfully roll out updates and your users may not even notice! Your application is a success! Your data continues to grow, and it’s becoming apparently that you need more disk. That’s great: you can resize your PVC directly on your ivoryclusters.ivory-operator.ivorysql.org custom resource with minimal to zero downtime. PVC resizing, also known as volume expansion, is a function of your storage class: it must support volume resizing. Additionally, PVCs can only be sized up: you cannot shrink the size of a PVC. You can adjust PVC sizes on all of the managed storage instances in a Ivory instance that are using Kubernetes storage. These include: spec.instances.dataVolumeClaimSpec.resources.requests.storage: The Ivory data directory (aka your database). spec.backups.pgbackrest.repos.volume.volumeClaimSpec.resources.requests.storage: The pgBackRest repository when using \"volume\" storage The above should be familiar: it follows the same pattern as the standard Kubernetes PVC structure. For example, let’s say we want to update our hippo Ivory cluster so that each instance now uses a 10Gi PVC and our backup repository uses a 20Gi PVC. We can do so with the following markup: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi In particular, we added the following to spec.instances: dataVolumeClaimSpec: resources: requests: storage: 10Gi and added the following to spec.backups.pgbackrest.repos.volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory Not all Kubernetes Storage Classes allow for volume expansion. However, with IVYO, you can still resize your Ivory cluster data volumes even if your storage class does not allow it! Let’s go back to the previous example: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi First, create a new instance that has the larger volume size. Call this instance instance2. The manifest would look like this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi - name: instance2 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi Take note of the block that contains instance2: - name: instance2 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi This creates a second set of two Ivory instances, both of which come up as replicas, that have a larger PVC. Once this new instance set is available and they are caught to the primary, you can then apply the following manifest: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance2 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 10Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 20Gi This will promote one of the instances with the larger PVC to be the new primary and remove the instances with the smaller PVCs! This method can also be used to shrink PVCs to use a smaller amount. There are many reasons why a IvorySQL Pod may not be scheduled: Resources are unavailable. Ensure that you have a Kubernetes Node with enough resources to satisfy your memory or CPU Request. PVC cannot be provisioned. Ensure that you request a PVC size that is available, or that your PVC storage class is set up correctly. Ensure that your storage class supports PVC resizing. You can check that by inspecting the allowVolumeExpansion attribute: kubectl get sc If the storage class does not support PVC resizing, you can use the technique described above to resize PVCs using a second instance set. You’ve now resized your Ivory cluster, but how can you configure Ivory to take advantage of the new resources? Let’s look at how we can customize the Ivory cluster configuration. Part of the trick of managing multiple instances in an Ivory cluster is ensuring all of the configuration changes are propagated to each of them. This is where IVYO helps: when you make an Ivory configuration change for a cluster, IVYO will apply it to all of the Ivory instances. For example, in our previous step we added CPU and memory limits of 2.0 and 4Gi respectively. Let’s tweak some of the Ivory settings to better use our new resources. We can do this in the spec.patroni.dynamicConfiguration section. Here is an example updated manifest that tweaks several settings: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - name: instance1 replicas: 2 resources: limits: cpu: 2.0 memory: 4Gi dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB In particular, we added the following to spec: patroni: dynamicConfiguration: postgresql: parameters: max_parallel_workers: 2 max_worker_processes: 2 shared_buffers: 1GB work_mem: 2MB Apply these updates to your Ivory cluster with the following command: kubectl apply -k examples/kustomize/ivory IVYO will go and apply these settings, restarting each Ivory instance when necessary. You can verify that the changes are present using the Ivory SHOW command, e.g. SHOW work_mem; should yield something similar to: work_mem ---------- 2MB All connections in IVYO use TLS to encrypt communication between components. IVYO sets up a PKI and certificate authority (CA) that allow you create verifiable endpoints. However, you may want to bring a different TLS infrastructure based upon your organizational requirements. The good news: IVYO lets you do this! There are a few different TLS endpoints that can be customized for IVYO, including those of the Ivory cluster and controlling how Ivory instances authenticate with each other. Let’s look at how we can customize TLS by defining a spec.customTLSSecret, used to both identify the cluster and encrypt communications; and a spec.customReplicationTLSSecret, used for replication authentication. To customize the TLS for an Ivory cluster, you will need to create two Secrets in the Namespace of your Ivory cluster. One of these Secrets will be the customTLSSecret and the other will be the customReplicationTLSSecret. Both secrets contain a TLS key (tls.key), TLS certificate (tls.crt) and CA certificate (ca.crt) to use. If spec.customTLSSecret is provided you must also provide spec.customReplicationTLSSecret and both must contain the same ca.crt. The custom TLS and custom replication TLS Secrets should contain the following fields (though see below for a workaround if you cannot control the field names of the Secret’s data): data: ca.crt: <value> tls.crt: <value> tls.key: <value> For example, if you have files named ca.crt, hippo.key, and hippo.crt stored on your local machine, you could run the following command to create a Secret from those files: kubectl create secret generic -n ivory-operator hippo-cluster.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=hippo.key \\ --from-file=tls.crt=hippo.crt After you create the Secrets, you can specify the custom TLS Secret in your ivorycluster.ivory-operator.ivorysql.org custom resource. For example, if you created a hippo-cluster.tls Secret and a hippo-replication.tls Secret, you would add them to your Ivory cluster: spec: customTLSSecret: name: hippo-cluster.tls customReplicationTLSSecret: name: hippo-replication.tls If you’re unable to control the key-value pairs in the Secret, you can create a mapping to tell the Ivory Operator what key holds the expected value. That would look similar to this: spec: customTLSSecret: name: hippo.tls items: - key: <tls.crt key in the referenced hippo.tls Secret> path: tls.crt - key: <tls.key key in the referenced hippo.tls Secret> path: tls.key - key: <ca.crt key in the referenced hippo.tls Secret> path: ca.crt For instance, if the hippo.tls Secret had the tls.crt in a key named hippo-tls.crt, the tls.key in a key named hippo-tls.key, and the ca.crt in a key named hippo-ca.crt, then your mapping would look like: spec: customTLSSecret: name: hippo.tls items: - key: hippo-tls.crt path: tls.crt - key: hippo-tls.key path: tls.key - key: hippo-ca.crt path: ca.crt Although the custom TLS and custom replication TLS Secrets share the same ca.crt, they do not share the same tls.crt: Your spec.customTLSSecret TLS certificate should have a Common Name (CN) setting that matches the primary Service name. This is the name of the cluster suffixed with -primary. For example, for our hippo cluster this would be hippo-primary. Your spec.customReplicationTLSSecret TLS certificate should have a Common Name (CN) setting that matches _ivoryrepl, which is the preset replication user. As with the other changes, you can roll out the TLS customizations with kubectl apply. There are several ways to add your own custom Kubernetes Labels to your Ivory cluster. Cluster: You can apply labels to any IVYO managed object in a cluster by editing the spec.metadata.labels section of the custom resource. Ivory: You can apply labels to an Ivory instance set and its objects by editing spec.instances.metadata.labels. pgBackRest: You can apply labels to pgBackRest and its objects by editing ivoryclusters.spec.backups.pgbackrest.metadata.labels. There are several ways to add your own custom Kubernetes Annotations to your Ivory cluster. Cluster: You can apply annotations to any IVYO managed object in a cluster by editing the spec.metadata.annotations section of the custom resource. Ivory: You can apply annotations to an Ivory instance set and its objects by editing spec.instances.metadata.annotations. pgBackRest: You can apply annotations to pgBackRest and its objects by editing spec.backups.pgbackrest.metadata.annotations. IVYO allows you to use pod priority classes to indicate the relative importance of a pod by setting a priorityClassName field on your Ivory cluster. This can be done as follows: Instances: Priority is defined per instance set and is applied to all Pods in that instance set by editing the spec.instances.priorityClassName section of the custom resource. Dedicated Repo Host: Priority defined under the repoHost section of the spec is applied to the dedicated repo host by editing the spec.backups.pgbackrest.repoHost.priorityClassName section of the custom resource. Backup (manual and scheduled): Priority is defined under the spec.backups.pgbackrest.jobs.priorityClassName section and applies that priority to all pgBackRest backup Jobs (manual and scheduled). Restore (data source or in-place): Priority is defined for either a \"data source\" restore or an in-place restore by editing the spec.dataSource.ivorycluster.priorityClassName section of the custom resource. Data Migration: The priority defined for the first instance set in the spec (array position 0) is used for the PGDATA and WAL migration Jobs. The pgBackRest repo migration Job will use the priority class applied to the repoHost. IvorySQL commits transactions by storing changes in its Write-Ahead Log (WAL). Because the way WAL files are accessed and utilized often differs from that of data files, and in high-performance situations, it can desirable to put WAL files on separate storage volume. With IVYO, this can be done by adding the walVolumeClaimSpec block to your desired instance in your ivorycluster spec, either when your cluster is created or anytime thereafter: spec: instances: - name: instance walVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi This volume can be removed later by removing the walVolumeClaimSpec section from the instance. Note that when changing the WAL directory, care is taken so as not to lose any WAL files. IVYO only deletes the PVC once there are no longer any WAL files on the previously configured volume. IVYO can run SQL for you as part of the cluster creation and initialization process. IVYO runs the SQL using the psql client so you can use meta-commands to connect to different databases, change error handling, or set and use variables. Its capabilities are described in the psql documentation. The Ivory cluster spec accepts a reference to a ConfigMap containing your init SQL file. Update your cluster spec to include the ConfigMap name, spec.databaseInitSQL.name, and the data key, spec.databaseInitSQL.key, for your SQL file. For example, if you create your ConfigMap with the following command: kubectl -n ivory-operator create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql You would add the following section to your ivorycluster spec: spec: databaseInitSQL: key: init.sql name: hippo-init-sql The ConfigMap must exist in the same namespace as your Ivory cluster. After you add the ConfigMap reference to your spec, apply the change with kubectl apply -k examples/kustomize/ivory. IVYO will create your hippo cluster and run your initialization SQL once the cluster has started. You can verify that your SQL has been run by checking the databaseInitSQL status on your Ivory cluster. While the status is set, your init SQL will not be run again. You can check cluster status with the kubectl describe command: kubectl -n ivory-operator describe ivoryclusters.ivory-operator.ivorysql.org hippo In some cases, due to how Kubernetes treats ivorycluster status, IVYO may run your SQL commands more than once. Please ensure that the commands defined in your init SQL are idempotent. Now that databaseInitSQL is defined in your cluster status, verify database objects have been created as expected. After verifying, we recommend removing the spec.databaseInitSQL field from your spec. Removing the field from the spec will also remove databaseInitSQL from the cluster status. IVYO uses the psql interactive terminal to execute SQL statements in your database. Statements are passed in using standard input and the filename flag (e.g. psql -f -). SQL statements are executed as superuser in the default maintenance database. This means you have full control to create database objects, extensions, or run any SQL statements that you might need. If you are creating users or databases, please see the User/Database Management documentation. Databases created through the user management section of the spec can be referenced in your initialization sql. For example, if a database zoo is defined: spec: users: - name: hippo databases: - \"zoo\" You can connect to zoo by adding the following psql meta-command to your SQL: \\c zoo create table t_zoo as select s, md5(random()::text) from generate_Series(1,5) s; By default, psql commits each SQL command as it completes. To combine multiple commands into a single transaction, use the BEGIN and COMMIT commands. BEGIN; create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; COMMIT; The exit code from psql will determine when the databaseInitSQL status is set. When psql returns 0 the status will be set and SQL will not be run again. When psql returns with an error exit code the status will not be set. IVYO will continue attempting to execute the SQL as part of its reconcile loop until psql returns normally. If psql exits with a failure, you will need to edit the file in your ConfigMap to ensure your SQL statements will lead to a successful psql return. The easiest way to make live changes to your ConfigMap is to use the following kubectl edit command: kubectl -n <cluster-namespace> edit configmap hippo-init-sql Be sure to transfer any changes back over to your local file. Another option is to make changes in your local file and use kubectl --dry-run to create a template and pipe the output into kubectl apply: kubectl create configmap hippo-init-sql --from-file=init.sql=/path/to/init.sql --dry-run=client -o yaml | kubectl apply -f - If you edit your ConfigMap and your changes aren’t showing up, you may be waiting for IVYO to reconcile your cluster. After some time, IVYO will automatically reconcile the cluster or you can trigger reconciliation by applying any change to your cluster (e.g. with kubectl apply -k examples/kustomize/ivory). To ensure that psql returns a failure exit code when your SQL commands fail, set the ON_ERROR_STOP variable as part of your SQL file: \\set ON_ERROR_STOP \\echo Any error will lead to exit code 3 create table t_random as select s, md5(random()::text) from generate_Series(1,5) s; If your Ivory configuration settings are not present, ensure that you are using the syntax that Ivory expects. You can see this in the Ivory configuration documentation. You’ve now seen how you can further customize your Ivory cluster, but what about managing users and databases? That’s a great question that is answered in the next section. IVYO comes with some out-of-the-box conveniences for managing users and databases in your Ivory cluster. However, you may have requirements where you need to create additional users, adjust user privileges or add additional databases to your cluster. For detailed information for how user and database management works in IVYO, please see the User Management section of the architecture guide. You can create a new user with the following snippet in the ivorycluster custom resource. Let’s add this to our hippo database: spec: users: - name: rhino You can now apply the changes and see that the new user is created. Note the following: The user would only be able to connect to the default ivory database. The user will not have any connection credentials populated into the hippo-pguser-rhino Secret. The user is unprivileged. Let’s create a new database named zoo that we will let the rhino user access: spec: users: - name: rhino databases: - zoo Inspect the hippo-pguser-rhino Secret. You should now see that the dbname and uri fields are now populated! We can set role privileges by using the standard role attributes that Ivory provides and adding them to the spec.users.options. Let’s say we want the rhino to become a superuser (be careful about doling out Ivory superuser privileges!). You can add the following to the spec: spec: users: - name: rhino databases: - zoo options: \"SUPERUSER\" There you have it: we have created a Ivory user named rhino with superuser privileges that has access to the rhino database (though a superuser has access to all databases!). Let’s say you want to revoke the superuser privilege from rhino. You can do so with the following: spec: users: - name: rhino databases: - zoo options: \"NOSUPERUSER\" If you want to add multiple privileges, you can add each privilege with a space between them in options, e.g.: spec: users: - name: rhino databases: - zoo options: \"CREATEDB CREATEROLE\" By default, IVYO does not give you access to the ivory user. However, you can get access to this account by doing the following: spec: users: - name: ivory This will create a Secret of the pattern <clusterName>-pguser-ivory that contains the credentials of the ivory account. For our hippo cluster, this would be hippo-pguser-ivory. IVYO does not delete users automatically: after you remove the user from the spec, it will still exist in your cluster. To remove a user and all of its objects, as a superuser you will need to run DROP OWNED in each database the user has objects in, and DROP ROLE in your Ivory cluster. For example, with the above rhino user, you would run the following: DROP OWNED BY rhino; DROP ROLE rhino; Note that you may need to run DROP OWNED BY rhino CASCADE; based upon your object ownership structure — be very careful with this command! IVYO does not delete databases automatically: after you remove all instances of the database from the spec, it will still exist in your cluster. To completely remove the database, you must run the DROP DATABASE command as a Ivory superuser. For example, to remove the zoo database, you would execute the following: DROP DATABASE zoo; Let’s look at how IVYO handles disaster recovery! Perhaps someone accidentally dropped the users table. Perhaps you want to clone your production database to a step-down environment. Perhaps you want to exercise your disaster recovery system (and it is important that you do!). Regardless of scenario, it’s important to know how you can perform a \"restore\" operation with IVYO to be able to recovery your data from a particular point in time, or clone a database for other purposes. Let’s look at how we can perform different types of restore operations. First, let’s understand the core restore properties on the custom resource. IVYO offers the ability to restore from an existing ivorycluster or a remote cloud-based data source, such as S3, GCS, etc. For more on that, see the Clone From Backups Stored in S3 / GCS / Azure Blob Storage section. Note that you cannot use both a local ivorycluster data source and a remote cloud-based data source at one time; if both the dataSource.ivorycluster and dataSource.pgbackrest fields are filled in, the local ivorycluster data source will take precedence. There are several attributes on the custom resource that are important to understand as part of the restore process. All of these attributes are grouped together in the spec.dataSource.ivorycluster section of the custom resource. Please review the table below to understand how each of these attributes work in the context of setting up a restore operation. spec.dataSource.ivorycluster.clusterName: The name of the cluster that you are restoring from. This corresponds to the metadata.name attribute on a different ivorycluster custom resource. spec.dataSource.ivorycluster.clusterNamespace: The namespace of the cluster that you are restoring from. Used when the cluster exists in a different namespace. spec.dataSource.ivorycluster.repoName: The name of the pgBackRest repository from the spec.dataSource.ivorycluster.clusterName to use for the restore. Can be one of repo1, repo2, repo3, or repo4. The repository must exist in the other cluster. spec.dataSource.ivorycluster.options: Any additional pgBackRest restore options or general options that IVYO allows. For example, you may want to set --process-max to help improve performance on larger databases; but you will not be able to set`--target-action`, since that option is currently disallowed. (IVYO always sets it to promote if a --target is present, and otherwise leaves it blank.) spec.dataSource.ivorycluster.resources: Setting resource limits and requests of the restore job can ensure that it runs efficiently. spec.dataSource.ivorycluster.affinity: Custom Kubernetes affinity rules constrain the restore job so that it only runs on certain nodes. spec.dataSource.ivorycluster.tolerations: Custom Kubernetes tolerations allow the restore job to run on tainted nodes. Let’s walk through some examples for how we can clone and restore our databases. Let’s create a clone of our hippo cluster that we created previously. We know that our cluster is named hippo (based on its metadata.name) and that we only have a single backup repository called repo1. Let’s call our new cluster elephant. We can create a clone of the hippo cluster using a manifest like this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Note this section of the spec: spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 This is the part that tells IVYO to create the elephant cluster as an independent copy of the hippo cluster. The above is all you need to do to clone a Ivory cluster! IVYO will work on creating a copy of your data on a new persistent volume claim (PVC) and work on initializing your cluster to spec. Easy! Did someone drop the user table? You may want to perform a point-in-time-recovery (PITR) to revert your database back to a state before a change occurred. Fortunately, IVYO can help you do that. You can set up a PITR using the restore command of pgBackRest, the backup management tool that powers the disaster recovery capabilities of IVYO. You will need to set a few options on spec.dataSource.ivorycluster.options to perform a PITR. These options include: --type=time: This tells pgBackRest to perform a PITR. --target: Where to perform the PITR to. An example recovery target is 2021-06-09 14:15:11-04. The timezone specified here as -04 for EDT. Please see the pgBackRest documentation for other timezone options. --set (optional): Choose which backup to start the PITR from. A few quick notes before we begin: To perform a PITR, you must have a backup that finished before your PITR time. In other words, you can’t perform a PITR back to a time where you do not have a backup! All relevant WAL files must be successfully pushed for the restore to complete correctly. Be sure to select the correct repository name containing the desired backup! With that in mind, let’s use the elephant example above. Let’s say we want to perform a point-in-time-recovery (PITR) to 2021-06-09 14:15:11-04, we can use the following manifest: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi The section to pay attention to is this: spec: dataSource: ivoryCluster: clusterName: hippo repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" Notice how we put in the options to specify where to make the PITR. Using the above manifest, IVYO will go ahead and create a new Ivory cluster that recovers its data up until 2021-06-09 14:15:11-04. At that point, the cluster is promoted and you can start accessing your database from that specific point in time! Similar to the PITR restore described above, you may want to perform a similar reversion back to a state before a change occurred, but without creating another IvorySQL cluster. Fortunately, IVYO can help you do this as well. You can set up a PITR using the restore command of pgBackRest, the backup management tool that powers the disaster recovery capabilities of IVYO. You will need to set a few options on spec.backups.pgbackrest.restore.options to perform a PITR. These options include: --type=time: This tells pgBackRest to perform a PITR. --target: Where to perform the PITR to. An example recovery target is 2021-06-09 14:15:11-04. --set (optional): Choose which backup to start the PITR from. A few quick notes before we begin: To perform a PITR, you must have a backup that finished before your PITR time. In other words, you can’t perform a PITR back to a time where you do not have a backup! All relevant WAL files must be successfully pushed for the restore to complete correctly. Be sure to select the correct repository name containing the desired backup! To perform an in-place restore, users will first fill out the restore section of the spec as follows: spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --type=time - --target=\"2021-06-09 14:15:11-04\" And to trigger the restore, you will then annotate the ivorycluster as follows: kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-restore=id1 And once the restore is complete, in-place restores can be disabled: spec: backups: pgbackrest: restore: enabled: false Notice how we put in the options to specify where to make the PITR. Using the above manifest, IVYO will go ahead and re-create your Ivory cluster to recover its data up until 2021-06-09 14:15:11-04. At that point, the cluster is promoted and you can start accessing your database from that specific point in time! You might need to restore specific databases from a cluster backup, for performance reasons or to move selected databases to a machine that does not have enough space to restore the entire cluster backup. pgBackRest supports this case, but it is important to make sure this is what you want. Restoring in this manner will restore the requested database from backup and make it accessible, but all of the other databases in the backup will NOT be accessible after restore. For example, if your backup includes databases test1, test2, and test3, and you request that test2 be restored, the test1 and test3 databases will NOT be accessible after restore is completed. Please review the pgBackRest documentation on the limitations on restoring individual databases. You can restore individual databases from a backup using a spec similar to the following: spec: backups: pgbackrest: restore: enabled: true repoName: repo1 options: - --db-include=hippo where --db-include=hippo would restore only the contents of the hippo database. Advanced high-availability and disaster recovery strategies involve spreading your database clusters across data centers to help maximize uptime. IVYO provides ways to deploy ivoryclusters that can span multiple Kubernetes clusters using an external storage system or IvorySQL streaming replication. The disaster recovery architecture documentation provides a high-level overview of standby clusters with IVYO can be found in the disaster recovery architecture documentation. This tutorial section will describe how to create three different types of standby clusters, one using an external storage system, one that is streaming data directly from the primary, and one that takes advantage of both external storage and streaming. These example clusters can be created in the same Kubernetes cluster, using a single IVYO instance, or spread across different Kubernetes clusters and IVYO instances with the correct storage and networking configurations. A repo-based standby will recover from WAL files a pgBackRest repo stored in external storage. The primary cluster should be created with a cloud-based backup configuration. The following manifest defines a ivorycluster with standby.enabled set to true and repoName configured to point to the s3 repo configured in the primary: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" standby: enabled: true repoName: repo1 A streaming standby relies on an authenticated connection to the primary over the network. The primary cluster should be accessible via the network and allow TLS authentication (TLS is enabled by default). In the following manifest, we have standby.enabled set to true and have provided both the host and port that point to the primary cluster. We have also defined customTLSSecret and customReplicationTLSSecret to provide certs that allow the standby to authenticate to the primary. For this type of standby, you must use custom TLS: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: repos: - name: repo1 volume: volumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true host: \"192.0.2.2\" port: 5432 Another option is to create a standby cluster using an external pgBackRest repo that streams from the primary. With this setup, the standby cluster will continue recovering from the pgBackRest repo if streaming replication falls behind. In this manifest, we have enabled the settings from both previous examples: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo-standby spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: { accessModes: [ReadWriteOnce], resources: { requests: { storage: 1Gi } } } backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" customTLSSecret: name: cluster-cert customReplicationTLSSecret: name: replication-cert standby: enabled: true repoName: repo1 host: \"192.0.2.2\" port: 5432 At some point, you will want to promote the standby to start accepting both reads and writes. This has the net effect of pushing WAL (transaction archives) to the pgBackRest repository, so we need to ensure we don’t accidentally create a split-brain scenario. Split-brain can happen if two primary instances attempt to write to the same repository. If the primary cluster is still active, make sure you shutdown the primary before trying to promote the standby cluster. Once the primary is inactive, we can promote the standby cluster by removing or disabling its spec.standby section: spec: standby: enabled: false This change triggers the promotion of the standby leader to a primary IvorySQL instance and the cluster begins accepting writes. You can clone a Ivory cluster from backups that are stored in AWS S3 (or a storage system that uses the S3 protocol), GCS, or Azure Blob Storage without needing an active Ivory cluster! The method to do so is similar to how you clone from an existing ivorycluster. This is useful if you want to have a data set for people to use but keep it compressed on cheaper storage. For the purposes of this example, let’s say that you created a Ivory cluster named hippo that has its backups stored in S3 that looks similar to this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: hippo spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 manual: repoName: repo1 options: - --type=full repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" Ensure that the credentials in ivyo-s3-creds match your S3 credentials. For more details on deploying a Ivory cluster using S3 for backups, please see the Backups section of the tutorial. For optimal performance when creating a new cluster from an active cluster, ensure that you take a recent full backup of the previous cluster. The above manifest is set up to take a full backup. Assuming hippo is created in the ivory-operator namespace, you can trigger a full backup with the following command: kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/pgbackrest-backup=\"$( date '+%F_%H:%M:%S' )\" Wait for the backup to complete. Once this is done, you can delete the Ivory cluster. Now, let’s clone the data from the hippo backup into a new cluster called elephant. You can use a manifest similar to this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: elephant spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/elephant/repo1 repos: - name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" There are a few things to note in this manifest. First, note that the spec.dataSource.pgbackrest object in our new ivorycluster is very similar but slightly different from the old ivorycluster’s spec.backups.pgbackrest object. The key differences are: No image is necessary when restoring from a cloud-based data source stanza is a required field when restoring from a cloud-based data source backups.pgbackrest has a repos field, which is an array dataSource.pgbackrest has a repo field, which is a single object Note also the similarities: We are reusing the secret for both (because the new restore pod needs to have the same credentials as the original backup pod) The repo object is the same The global object is the same This is because the new restore pod for the elephant ivorycluster will need to reuse the configuration and credentials that were originally used in setting up the hippo ivorycluster. In this example, we are creating a new cluster which is also backing up to the same S3 bucket; only the spec.backups.pgbackrest.global field has changed to point to a different path. This will ensure that the new elephant cluster will be pre-populated with the data from `hippo’s backups, but will backup to its own folders, ensuring that the original backup repository is appropriately preserved. Deploy this manifest to create the elephant Ivory cluster. Observe that it comes up and running: kubectl -n ivory-operator describe ivorycluster elephant When it is ready, you will see that the number of expected instances matches the number of ready instances, e.g.: Instances: Name: 00 Ready Replicas: 1 Replicas: 1 Updated Replicas: 1 The previous example shows how to use an existing S3 repository to pre-populate a ivorycluster while using a new S3 repository for backing up. But ivoryclusters that use cloud-based data sources can also use local repositories. For example, assuming a ivorycluster called rhino that was meant to pre-populate from the original hippo ivorycluster, the manifest would look like this: apiVersion: ivory-operator.ivorysql.org/v1beta1 kind: IvoryCluster metadata: name: rhino spec: image: {{< param imageIvorySQL >}} postgresVersion: {{< param postgresVersion >}} dataSource: pgbackrest: stanza: db configuration: - secret: name: ivyo-s3-creds global: repo1-path: /pgbackrest/ivory-operator/hippo/repo1 repo: name: repo1 s3: bucket: \"my-bucket\" endpoint: \"s3.ca-central-1.amazonaws.com\" region: \"ca-central-1\" instances: - dataVolumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi backups: pgbackrest: image: {{< param imagePGBackrest >}} repos: - name: repo1 volume: volumeClaimSpec: accessModes: - \"ReadWriteOnce\" resources: requests: storage: 1Gi Now we’ve seen how to clone a cluster and perform a point-in-time-recovery, let’s see how we can monitor our Ivory cluster to detect and prevent issues from occurring. While having high availability and disaster recovery systems in place helps in the event of something going wrong with your IvorySQL cluster, monitoring helps you anticipate problems before they happen. Additionally, monitoring can help you diagnose and resolve issues that may cause degraded performance rather than downtime. Let’s look at how IVYO allows you to enable monitoring in your cluster. Let’s look at how we can add the IvorySQL Exporter sidecar to your cluster using the kustomize/ivory example in the Postgres Operator examples repository. Monitoring tools are added using the spec.monitoring section of the custom resource. Currently, the only monitoring tool supported is the IvorySQL Exporter configured with pgMonitor. In the kustomize/ivory/ivory.yaml file, add the following YAML to the spec: monitoring: pgmonitor: exporter: image: {{< param imagePostgresExporter >}} Save your changes and run: kubectl apply -k kustomize/ivory IVYO will detect the change and add the Exporter sidecar to all Ivory Pods that exist in your cluster. IVYO will also do the work to allow the Exporter to connect to the database and gather metrics that can be accessed using the IVYO Monitoring stack. IVYO allows you to configure the exporter sidecar to use TLS encryption. If you provide a custom TLS Secret via the exporter spec: monitoring: pgmonitor: exporter: customTLSSecret: name: hippo.tls Like other custom TLS Secrets that can be configured with IVYO, the Secret will need to be created in the same Namespace as your PostgresCluster. It should also contain the TLS key (tls.key) and TLS certificate (tls.crt) needed to enable encryption. data: tls.crt: <value> tls.key: <value> After you configure TLS for the exporter, you will need to update your Prometheus deployment to use TLS, and your connection to the exporter will be encrypted. Check out the Prometheus documentation for more information on configuring TLS for Prometheus. Once the IvorySQL Exporter has been enabled in your cluster, follow the steps outlined in IVYO Monitoring to install the monitoring stack. This will allow you to deploy a pgMonitor configuration of Prometheus, Grafana, and Alertmanager monitoring tools in Kubernetes. These tools will be set up by default to connect to the Exporter containers on your Ivory Pods. While the default Kustomize install should work in most Kubernetes environments, it may be necessary to further customize the project according to your specific needs. For instance, by default fsGroup is set to 26 for the securityContext defined for the various Deployments comprising the IVYO Monitoring stack: securityContext: fsGroup: 26 In most Kubernetes environments this setting is needed to ensure processes within the container have the permissions needed to write to any volumes mounted to each of the Pods comprising the IVYO Monitoring stack. However, when installing in an OpenShift environment (and more specifically when using the restricted Security Context Constraint), the fsGroup setting should be removed since OpenShift will automatically handle setting the proper fsGroup within the Pod’s securityContext. Additionally, within this same section it may also be necessary to modify the supplmentalGroups setting according to your specific storage configuration: securityContext: supplementalGroups : 65534 Therefore, the following files (located under kustomize/monitoring) should be modified and/or patched (e.g. using additional overlays) as needed to ensure the securityContext is properly defined for your Kubernetes environment: deploy-alertmanager.yaml deploy-grafana.yaml deploy-prometheus.yaml And to modify the configuration for the various storage resources (i.e. PersistentVolumeClaims) created by the IVYO Monitoring installer, the kustomize/monitoring/pvcs.yaml file can also be modified. Additionally, it is also possible to further customize the configuration for the various components comprising the IVYO Monitoring stack (Grafana, Prometheus and/or AlertManager) by modifying the following configuration resources: alertmanager-config.yaml alertmanager-rules-config.yaml grafana-datasources.yaml prometheus-config.yaml Finally, please note that the default username and password for Grafana can be updated by modifying the Grafana Secret in file kustomize/monitoring/grafana-secret.yaml. Once the Kustomize project has been modified according to your specific needs, IVYO Monitoring can then be installed using kubectl and Kustomize: kubectl apply -k kustomize/monitoring And similarly, once IVYO Monitoring has been installed, it can uninstalled using kubectl and Kustomize: kubectl delete -k kustomize/monitoring Now that we can monitor our cluster, let’s explore how connection pooling can be enabled using IVYO and how it is helpful. Connection pooling can be helpful for scaling and maintaining overall availability between your application and the database. IVYO helps facilitate this by supporting the PgBouncer connection pooler and state manager. Let’s look at how we can a connection pooler and connect it to our application! Let’s look at how we can add a connection pooler using the kustomize/keycloak example in the Ivory Operator repository examples folder. Connection poolers are added using the spec.proxy section of the custom resource. Currently, the only connection pooler supported is PgBouncer. The only required attribute for adding a PgBouncer connection pooler is to set the spec.proxy.pgBouncer.image attribute. In the kustomize/keycloak/ivory.yaml file, add the following YAML to the spec: proxy: pgBouncer: image: {{< param imageIvoryPGBouncer >}} (You can also find an example of this in the kustomize/examples/high-availability example). Save your changes and run: kubectl apply -k kustomize/keycloak IVYO will detect the change and create a new PgBouncer Deployment! That was fairly easy to set up, so now let’s look at how we can connect our application to the connection pooler. When a connection pooler is deployed to the cluster, IVYO adds additional information to the user Secrets to allow for applications to connect directly to the connection pooler. Recall that in this example, our user Secret is called keycloakdb-pguser-keycloakdb. Describe the user Secret: kubectl -n ivory-operator describe secrets keycloakdb-pguser-keycloakdb You should see that there are several new attributes included in this Secret that allow for you to connect to your Ivory instance via the connection pooler: pgbouncer-host: The name of the host of the PgBouncer connection pooler. This references the Service of the PgBouncer connection pooler. pgbouncer-port: The port that the PgBouncer connection pooler is listening on. pgbouncer-uri: A PostgreSQL connection URI that provides all the information for logging into the Ivory database via the PgBouncer connection pooler. pgbouncer-jdbc-uri: A PostgreSQL JDBC connection URI that provides all the information for logging into the Ivory database via the PgBouncer connection pooler using the JDBC driver. Note that by default, the connection string disable JDBC managing prepared transactions for optimal use with PgBouncer. Open up the file in kustomize/keycloak/keycloak.yaml. Update the DB_ADDR and DB_PORT values to be the following: - name: DB_ADDR valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-host } } - name: DB_PORT valueFrom: { secretKeyRef: { name: keycloakdb-pguser-keycloakdb, key: pgbouncer-port } } This changes Keycloak’s configuration so that it will now connect through the connection pooler. Apply the changes: kubectl apply -k kustomize/keycloak Kubernetes will detect the changes and begin to deploy a new Keycloak Pod. When it is completed, Keycloak will now be connected to Ivory via the PgBouncer connection pooler! IVYO deploys every cluster and component over TLS. This includes the PgBouncer connection pooler. If you are using your own custom TLS setup, you will need to provide a Secret reference for a TLS key / certificate pair for PgBouncer in spec.proxy.pgBouncer.customTLSSecret. Your TLS certificate for PgBouncer should have a Common Name (CN) setting that matches the PgBouncer Service name. This is the name of the cluster suffixed with -pgbouncer. For example, for our hippo cluster this would be hippo-pgbouncer. For the keycloakdb example, it would be keycloakdb-pgbouncer. To customize the TLS for PgBouncer, you will need to create a Secret in the Namespace of your Ivory cluster that contains the TLS key (tls.key), TLS certificate (tls.crt) and the CA certificate (ca.crt) to use. The Secret should contain the following values: data: ca.crt: <value> tls.crt: <value> tls.key: <value> For example, if you have files named ca.crt, keycloakdb-pgbouncer.key, and keycloakdb-pgbouncer.crt stored on your local machine, you could run the following command: kubectl create secret generic -n ivory-operator keycloakdb-pgbouncer.tls \\ --from-file=ca.crt=ca.crt \\ --from-file=tls.key=keycloakdb-pgbouncer.key \\ --from-file=tls.crt=keycloakdb-pgbouncer.crt You can specify the custom TLS Secret in the spec.proxy.pgBouncer.customTLSSecret.name field in your ivorycluster.ivory-operator.ivorysql.org custom resource, e.g.: spec: proxy: pgBouncer: customTLSSecret: name: keycloakdb-pgbouncer.tls The PgBouncer connection pooler is highly customizable, both from a configuration and Kubernetes deployment standpoint. Let’s explore some of the customizations that you can do! PgBouncer configuration can be customized through spec.proxy.pgBouncer.config. After making configuration changes, IVYO will roll them out to any PgBouncer instance and automatically issue a \"reload\". There are several ways you can customize the configuration: spec.proxy.pgBouncer.config.global: Accepts key-value pairs that apply changes globally to PgBouncer. spec.proxy.pgBouncer.config.databases: Accepts key-value pairs that represent PgBouncer database definitions. spec.proxy.pgBouncer.config.users: Accepts key-value pairs that represent connection settings applied to specific users. spec.proxy.pgBouncer.config.files: Accepts a list of files that are mounted in the /etc/pgbouncer directory and loaded before any other options are considered using PgBouncer’s include directive. For example, to set the connection pool mode to transaction, you would set the following configuration: spec: proxy: pgBouncer: config: global: pool_mode: transaction For a reference on PgBouncer configuration please see: https://www.pgbouncer.org/config.html IVYO deploys one PgBouncer instance by default. You may want to run multiple PgBouncer instances to have some level of redundancy, though you still want to be mindful of how many connections are going to your Ivory database! You can manage the number of PgBouncer instances that are deployed through the spec.proxy.pgBouncer.replicas attribute. You can manage the CPU and memory resources given to a PgBouncer instance through the spec.proxy.pgBouncer.resources attribute. The layout of spec.proxy.pgBouncer.resources should be familiar: it follows the same pattern as the standard Kubernetes structure for setting container resources. For example, let’s say we want to set some CPU and memory limits on our PgBouncer instances. We could add the following configuration: spec: proxy: pgBouncer: resources: limits: cpu: 200m memory: 128Mi As IVYO deploys the PgBouncer instances using a Deployment these changes are rolled out using a rolling update to minimize disruption between your application and Ivory instances! You can apply custom annotations and labels to your PgBouncer instances through the spec.proxy.pgBouncer.metadata.annotations and spec.proxy.pgBouncer.metadata.labels attributes respectively. Note that any changes to either of these two attributes take precedence over any other custom labels you have added. You can control the pod anti-affinity, pod affinity, and node affinity through the spec.proxy.pgBouncer.affinity attribute, specifically: spec.proxy.pgBouncer.affinity.nodeAffinity: controls node affinity for the PgBouncer instances. spec.proxy.pgBouncer.affinity.podAffinity: controls Pod affinity for the PgBouncer instances. spec.proxy.pgBouncer.affinity.podAntiAffinity: controls Pod anti-affinity for the PgBouncer instances. Each of the above follows the standard Kubernetes specification for setting affinity. For example, to set a preferred Pod anti-affinity rule for the kustomize/keycloak example, you would want to add the following to your configuration: spec: proxy: pgBouncer: affinity: podAntiAffinity: preferredDuringSchedulingIgnoredDuringExecution: - weight: 1 podAffinityTerm: labelSelector: matchLabels: ivory-operator.ivorysql.org/cluster: keycloakdb ivory-operator.ivorysql.org/role: pgbouncer topologyKey: kubernetes.io/hostname You can deploy PgBouncer instances to Nodes with Taints by setting Tolerations through spec.proxy.pgBouncer.tolerations. This attribute follows the Kubernetes standard tolerations layout. For example, if there were a set of Nodes with a Taint of role=connection-poolers:NoSchedule that you want to schedule your PgBouncer instances to, you could apply the following configuration: spec: proxy: pgBouncer: tolerations: - effect: NoSchedule key: role operator: Equal value: connection-poolers Note that setting a toleration does not necessarily mean that the PgBouncer instances will be assigned to Nodes with those taints. Tolerations act as a key: they allow for you to access Nodes. If you want to ensure that your PgBouncer instances are deployed to specific nodes, you need to combine setting tolerations with node affinity. Besides using affinity, anti-affinity and tolerations, you can also set Topology Spread Constraints through spec.proxy.pgBouncer.topologySpreadConstraints. This attribute follows the Kubernetes standard topology spread contraint layout. For example, since each of of our pgBouncer Pods will have the standard ivory-operator.ivorysql.org/role: pgbouncer Label set, we can use this Label when determining the maxSkew. In the example below, since we have 3 nodes with a maxSkew of 1 and we’ve set whenUnsatisfiable to ScheduleAnyway, we should ideally see 1 Pod on each of the nodes, but our Pods can be distributed less evenly if other constraints keep this from happening. proxy: pgBouncer: replicas: 3 topologySpreadConstraints: - maxSkew: 1 topologyKey: my-node-label whenUnsatisfiable: ScheduleAnyway labelSelector: matchLabels: ivory-operator.ivorysql.org/role: pgbouncer If you want to ensure that your PgBouncer instances are deployed more evenly (or not deployed at all), you need to update whenUnsatisfiable to DoNotSchedule. Now that we can enable connection pooling in a cluster, Let’s explore some administrative tasks such as manually restarting IvorySQL using IVYO. How do we do that? There are times when you might need to manually restart IvorySQL. This can be done by adding or updating a custom annotation to the cluster’s spec.metadata.annotations section. IVYO will notice the change and perform a rolling restart. For example, if you have a cluster named hippo in the namespace ivory-operator, all you need to do is patch the hippo ivorycluster with the following: kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"metadata\":{\"annotations\":{\"restarted\":\"'\"$(date)\"'\"}}}}' Watch your hippo cluster: you will see the rolling update has been triggered and the restart has begun. You can shut down an Ivory cluster by setting the spec.shutdown attribute to true. You can do this by editing the manifest, or, in the case of the hippo cluster, executing a command like the below: kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"shutdown\": true}}' The effect of this is that all the Kubernetes workloads for this cluster are scaled to 0. You can verify this with the following command: kubectl get deploy,sts,cronjob --selector=ivory-operator.ivorysql.org/cluster=hippo -n ivory-operator NAME READY AGE statefulset.apps/hippo-00-lwgx 0/0 1h NAME SCHEDULE SUSPEND ACTIVE cronjob.batch/hippo-repo1-full @daily True 0 To turn an Ivory cluster that is shut down back on, you can set spec.shutdown to false. You can pause the Ivory cluster reconciliation process by setting the spec.paused attribute to true. You can do this by editing the manifest, or, in the case of the hippo cluster, executing a command like the below: kubectl patch ivorycluster/hippo -n ivory-operator --type merge \\ --patch '{\"spec\":{\"paused\": true}}' Pausing a cluster will suspend any changes to the cluster’s current state until reconciliation is resumed. This allows you to fully control when changes to the ivorycluster spec are rolled out to the Ivory cluster. While paused, no statuses are updated other than the \"Progressing\" condition. To resume reconciliation of an Ivory cluster, you can either set spec.paused to false or remove the setting from your manifest. Credentials should be invalidated and replaced (rotated) as often as possible to minimize the risk of their misuse. Unlike passwords, every TLS certificate has an expiration, so replacing them is inevitable. In fact, IVYO automatically rotates the client certificates that it manages before the expiration date on the certificate. A new client certificate will be generated after 2/3rds of its working duration; so, for instance, a IVYO-created certificate with an expiration date 12 months in the future will be replaced by IVYO around the eight month mark. This is done so that you do not have to worry about running into problems or interruptions of service with an expired certificate. If you want to rotate a single client certificate, you can regenerate the certificate of an existing cluster by deleting the tls.key field from its certificate Secret. Is it time to rotate your IVYO root certificate? All you need to do is delete the ivyo-root-cacert secret. IVYO will regenerate it and roll it out seamlessly, ensuring your apps continue communicating with the Ivory cluster without having to update any configuration or deal with any downtime. kubectl delete secret ivyo-root-cacert IVYO only updates secrets containing the generated root certificate. It does not touch custom certificates. When you use your own TLS certificates with IVYO, you are responsible for replacing them appropriately. Here’s how. IVYO automatically detects and loads changes to the contents of IvorySQL server and replication Secrets without downtime. You or your certificate manager need only replace the values in the Secret referenced by spec.customTLSSecret. If instead you change spec.customTLSSecret to refer to a new Secret or new fields, IVYO will perform a rolling restart. When changing the IvorySQL certificate authority, make sure to update customReplicationTLSSecret as well. There may be times when you want to change the primary in your HA cluster. This can be done using the patroni.switchover section of the ivorycluster spec. It allows you to enable switchovers in your ivoryclusters, target a specific instance as the new primary, and run a failover if your ivorycluster has entered a bad state. Let’s go through the process of performing a switchover! First you need to update your spec to prepare your cluster to change the primary. Edit your spec to have the following fields: spec: patroni: switchover: enabled: true After you apply this change, IVYO will be looking for the trigger to perform a switchover in your cluster. You will trigger the switchover by adding the ivory-operator.ivorysql.org/trigger-switchover annotation to your custom resource. The best way to set this annotation is with a timestamp, so you know when you initiated the change. For example, for our hippo cluster, we can run the following command to trigger the switchover: kubectl annotate -n ivory-operator ivorycluster hippo \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" If you want to perform another switchover you can re-run the annotation command and add the --overwrite flag: kubectl annotate -n ivory-operator ivorycluster hippo --overwrite \\ ivory-operator.ivorysql.org/trigger-switchover=\"$(date)\" IVYO will detect this annotation and use the Patroni API to request a change to the current primary! The roles on your database instance Pods will start changing as Patroni works. The new primary will have the master role label, and the old primary will be updated to replica. The status of the switch will be tracked using the status.patroni.switchover field. This will be set to the value defined in your trigger annotation. If you use a timestamp as the annotation this is another way to determine when the switchover was requested. After the instance Pod labels have been updated and status.patroni.switchover has been set, the primary has been changed on your cluster! After changing the primary, we recommend that you disable switchovers by setting spec.patroni.switchover.enabled to false or remove the field from your spec entirely. If the field is removed the corresponding status will also be removed from the ivorycluster. Another option you have when switching the primary is providing a target instance as the new primary. This target instance will be used as the candidate when performing the switchover. The spec.patroni.switchover.targetInstance field takes the name of the instance that you are switching to. This name can be found in a couple different places; one is as the name of the StatefulSet and another is on the database Pod as the ivory-operator.ivorysql.org/instance label. The following commands can help you determine who is the current primary and what name to use as the targetInstance: $ kubectl get pods -l ivory-operator.ivorysql.org/cluster=hippo \\ -L ivory-operator.ivorysql.org/instance \\ -L ivory-operator.ivorysql.org/role -n ivory-operator NAME READY STATUS RESTARTS AGE INSTANCE ROLE hippo-instance1-jdb5-0 3/3 Running 0 2m47s hippo-instance1-jdb5 master hippo-instance1-wm5p-0 3/3 Running 0 2m47s hippo-instance1-wm5p replica In our example cluster hippo-instance1-jdb5 is currently the primary meaning we want to target hippo-instance1-wm5p in the switchover. Now that you know which instance is currently the primary and how to find your targetInstance, let’s update your cluster spec: spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p After applying this change you will once again need to trigger the switchover by annotating the ivorycluster (see above commands). You can verify the switchover has completed by checking the Pod role labels and status.patroni.switchover. Finally, we have the option to failover when your cluster has entered an unhealthy state. The only spec change necessary to accomplish this is updating the spec.patroni.switchover.type field to the Failover type. One note with this is that a targetInstance is required when performing a failover. Based on the example cluster above, assuming hippo-instance1-wm5p is still a replica, we can update the spec: spec: patroni: switchover: enabled: true targetInstance: hippo-instance1-wm5p type: Failover Apply this spec change and your ivorycluster will be prepared to perform the failover. Again you will need to trigger the switchover by annotating the ivorycluster (see above commands) and verify that the Pod role labels and status.patroni.switchover are updated accordingly. Errors encountered in the switchover process can leave your cluster in a bad state. If you encounter issues, found in the operator logs, you can update the spec to fix the issues and apply the change. Once the change has been applied, IVYO will attempt to perform the switchover again. We’ve covered a lot in terms of building, maintaining, scaling, customizing, restarting, and expanding our Ivory cluster. However, there may come a time where we need to delete our Ivory cluster. How do we do that? There comes a time when it is necessary to delete your cluster. If you have been following along with the example, you can delete your Ivory cluster by simply running: kubectl delete -k examples/kustomize/ivory IVYO will remove all of the objects associated with your cluster. With data retention, this is subject to the retention policy of your PVC. 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We are eager to get contributions from members of the wider IvorySQL community. If you want to see your changes to code or documents added to IvorySQL and appear in future versions, you need to understand the content of this section. ​IvorySQL community welcomes and appreciates all types of contributions and we are looking forward to your participation! Every member, contributor and leader should read our principles of conduct. We promise that everyone can participate in community and pay equal attention to everyone, no matter who. We are committed to acting and interacting in a way that contributes to the establishment of an open, enthusiastic, diverse, inclusive and healthy community. Our team is a continuously open team, focusing on parts of IvorySQL. In our team, there are reviewers, submitters and maintainers, and we have one or more repositories. The decision for the team is made by the maintainer. The typical promotion path for IvorySQL developers is from user to reviewer, then submitter and maintainer. But getting more roles doesn’t mean you have any privileges to other community members. Everyone in the IvorySQL community is equal and has the responsibility to cooperate constructively with other contributors to build a friendly community. These roles are natural rewards for your significant contributions to the development of IvorySQL, and provide you with more rights in the development workflow to improve your efficiency. At the same time，this requires you to undertake some additional duty: ​Team honor: now you are already one of the team reviewers/submitters/maintainers, it means that you represent the project and your team members. So, please be Mr.Nice Guy to defend the reputation of the team. ​Responsibility: submitters/maintainers have the right to merge pull requests, therefore, they take additional responsibility to deal with the consequences of accepting changes to the code base or documents. When a bug occurs, they should fix it. If they can not solve it, they should roll back the project. Also, they need to help the release manager solve any problems found in the test cycle. Before contributing, we need to know the current version of IvorySQL and the version of the document.At present, we maintain versions after version 5.0. Our version follows the update pace of PG. Please update to the latest version before contributing. After that, we need to read the format requirements carefully and be familiar with code format, code comment format, issue format, pull PR title format, document contribution format, and article contribution format. These can help you become a contributor of IvorySQL as soon as possible. IvorySQL is developed on GitHub. Anyone who wishes to contribute to it must have a Github account and be familiar with Git tools and workflow. It is also recommended that you follow the developer’s mailing list since some of the contributions may generate more detailed discussions there. Once you have your GitHub account, fork this repository so that you can have your private copy to start hacking on and to use as a source of pull requests. If the contribution you’re submitting is original work, you can assume that IvorySQL will release it as part of an overall IvorySQL release available to the downstream consumers under the Apache License, Version 2.0. If the contribution you’re submitting is NOT original work you have to indicate the name of the license and also make sure that it is similar in terms to the Apache License 2.0. Apache Software Foundation maintains a list of these licenses under Category A. In addition to that, you may be required to make proper attributions. Finally, keep in mind that it is NEVER a good idea to remove licensing headers from the work that is not your original one. Even if you are using parts of the file that originally had a licensing header at the top you should err on the side of preserving it. As always, if you are not quite sure about the licensing implications of your contributions, feel free to reach out to us on the developer mailing list. You can upload your modified bugs, new functions and other codes to your personal warehouse, and finally submit PR requests to merge them on the official website: https://github.com/IvorySQL/IvorySQL. The IvorySQL community provides Chinese and English documents. English documents are saved in …​ document repository, Chinese documents are saved in i18n document repository. You can contribute to one of them or both. GitHub: https://github.com/IvorySQL/IvorySQL Gitee:https://gitee.com/IvorySQL/ IvorySQL official website:https://github.com/IvorySQL/Ivory-www Mailing List website:https://lists.ivorysql.org/ You can submit your article to the blog in the IvorySQL-WWW code warehouse, or send it to the mailbox renjiao@highgo.com. Your chances of getting feedback and seeing your code merged into the project greatly depend on how granular your changes are. If you happen to have a bigger change in mind, we highly recommend engaging on the developer’s mailing list first and sharing your proposal with us before you spend a lot of time writing code. Even when your proposal gets validated by the community, we still recommend doing the actual work as a series of small, self-contained commits. This makes the reviewer’s job much easier and increases the timeliness of feedback. When it comes to C and C++ parts of IvorySQL, we follow PostgreSQL Coding Conventions. In addition to that: For C and Perl code, please run pgindent if necessary. We recommend using git diff --color when reviewing your changes so that you don’t have any spurious whitespace issues in the code that you submit. All new functionality that is contributed to IvorySQL should be covered by regression tests that are contributed alongside it. If you are uncertain about how to test or document your work, please raise the question on the ivorysql-hackers mailing list and the developer community will do its best to help you. At the very minimum, you should always be running make installcheck-world to make sure that you’re not breaking anything. If the change you’re working on touches functionality that is common between PostgreSQL and IvorySQL, you may be asked to forward-port it to PostgreSQL. This is not only so that we keep reducing the delta between the two projects, but also so that any change that is relevant to PostgreSQL can benefit from a much broader review of the upstream PostgreSQL community. In general, it is a good idea to keep both codebases handy so you can be sure whether your changes may need to be forward-ported. Once you are ready to share your work with the IvorySQL core team and the rest of the IvorySQL community, you should push all the commits to a branch in your own repository forked from the official IvorySQL and send us a pull request. A submitted pull request with passing validation checks is assumed to be available for peer review. Peer review is the process that ensures that contributions to IvorySQL are of high quality and align well with the road map and community expectations. Every member of the IvorySQL community is encouraged to review pull requests and provide feedback. Since you don’t have to be a core team member to be able to do that, we recommend following a stream of pull reviews to anybody who’s interested in becoming a long-term contributor to IvorySQL. One outcome of the peer review could be a consensus that you need to modify your pull request in certain ways. GitHub allows you to push additional commits into a branch from which a pull request was sent. Those additional commits will be then visible to all of the reviewers. A peer review converges when it receives at least one +1 and no -1s votes from the participants. At that point, you should expect one of the core team members to pull your changes into the project. At any time during the patch review, you may experience delays based on the availability of reviewers and core team members. Please be patient. That being said, don’t get discouraged either. If you’re not getting expected feedback for a few days add a comment asking for updates on the pull request itself or send an email to the mailing list. On occasion, you will see core team members committing directly to the repository without going through the pull request workflow. This is reserved for small changes only and the rule of thumb we use is this: if the change touches any functionality that may result in a test failure, then it has to go through a pull request workflow. If, on the other hand, the change is in the non-functional part of the codebase (such as fixing a typo inside of a comment block) core team members can decide to just commit to the repository directly. 1 Enter IvorySQL official website:https://github.com/IvorySQL/IvorySQL 2 Click New issue 1 bug report Title: ## Bug Report Describe the bug \\### IvorySQL Version The version of IvorySQL you are using \\### OS Version (uname -a) Operating system version(uname -a) \\### Configuration options ( config.status --config ) \\### Current Behavior \\### Expected behavior/code \\### Step to reproduce \\### Additional context that can be helpful for identifying the problem 2 Enhancement Title: ## Enhancement Describe the functions that you expect to be strengthened 3 Feature Request Title: ## Feature Request Describe the feature that you expect to be real Click submit new issue button. WELL DONE! 1 Open the ivorysql warehouse: https://github.com/IvorySQL/IvorySQL 2 Click the fork button in the upper right corner, Wait for the fork to finish cd $working_dir # $working_dir can be replaced by the directory where you want to place repo. For example, `cd ~/Documents/GitHub` git clone git@github.com:$user/IvorySQL.git # `$user` can be replaced by your GitHub ID. cd $working_dir/IvorySQL git checkout -b new-branch-name You can modify the code in new-branch-name. Git add <file> Git commit -m “commit-message” Git push -u origin new-branch-name 1 Open your warehouse: https://github.com/$user/IvorySQL ($user is your GitHub ID) . 2 Click Compare & pull request button and create a PR. A PR submission should contain only one function or one bug. Prohibit submitting multiple functions at one time. 1 Open your warehouse: https://github.com/$user/IvorySQL ($user is your GitHub ID) 。 2 Click Compare & pull request button. Fix test Describe the function leave a comment Give a detailed description of the submission function Click Create pull request button. WELL DONE! (1) Download Markdown or Typora document editor. (2) Check whether the source warehouse has updates. If there are updates, please update and synchronize to your own warehouse first. Refer to the following steps to update to the latest version: git remote git fetch upstream git merge upstream/main git push (3) Familiar with format [_pecification]. The IvorySQL community provides Chinese and English documents. English documents are saved in IvorySQL document repository, Chinese documents are saved in i18n document repository. You can contribute to one of them or both. You can start from any of following to help improve the IvorySQL documents on the IvorySQL website: ​ (1) Prepare complete documents. ​ (2) Fix incorrect spelling and formatting (Punctuation, space, indentation, code block, etc) . ​ (3) Improper or outdated instructions corrected or updated. ​ (4) Add missing content (sentences, paragraphs, or new documents) . ​ (5) Translate document from English to Chinese, or from Chinese to English. ​ (6) Submit, reply and resolve document issues or document-i18n issues. ​ (7) (Advanced) View pull requests created by others. The IvorySQL document is written in 'markdown'. To ensure the quality and consistency of the format, certain Markdown rules should be followed when modifying and updating the document. Markdown Specification ​ 1 Titles are used incrementally from the first level, and skipping is prohibited. For example: The third level title cannot be used directly under the first level title; The fourth level title cannot be used directly under the second level title. ​ 2 The title must use the ATX style uniformly. Indicate the title level by adding # before the title. ​ 3 The leading symbol # of the title must be followed by a blank space. ​ 4 The leading symbol \"#\" of the title can only be followed by one blank space and then the title content. There can be no more than one space. ​ 5 The title must appear at the beginning of a line, there must be no space before the # sign of the title. ​ 6 Only Chinese and English question marks, back quotes, Chinese and English single and double quotes and other symbols can appear at the end of the title. Other symbols such as colon, comma, period and exclamation point cannot be used at the end of the title. ​ 7 One line must be empty above the title. ​ 8 The same title cannot appear continuously in the document. If the first level title is # TiDB architecture, the next level title cannot be # # TiDB architecture. If it is not a continuous title, the title content can be repeated. ​ 9 Only one first level title in document. ​ 10 In general, except for TOC.md files, which can be indented by two spaces, other .md files must be indented by four spaces by default foe each level of indentation. ​ 11 Tab is not allowed in documents(including code blocks) . If indentation is required, spaces must be uniformly used instead. ​ 12 Continuous blank lines are prohibited. ​ 13 Multiple spaces are not allowed after the block reference symbol > . Only one space can be used, followed by the reference content. ​ 14 When using a ordered list, it must start from 1 and increase in order. ​ 15 When using a list, the identifier (+, -, * or number) of each list item can only be left blank, followed by the list content. ​ 16 The list (includeing ordered and unordered lists) must be empty before and after each line. ​ 17 There must be one blank line before and after the code block. ​ 18 Exposed URLs are prohibited in documents. If you want users to click and open the URL directly, wrap the URL with a pair of angle brackets (<URL>) . If the exposed URL must be used due to special circumstances, and the user does not need to open it by clicking, a pair of back quatation marks (URL) will be used to wrap the URL. ​ 19 When using bold, italic and other emphasis effects, redundant spaces are prohibited in the emphasis identifier, such as text . ​ 20 No extra space is allowed in the code block wrapped by a single backquote, such as ` text `. ​ 21 No extra spaces are allowed on both sides of the link text, such as [Link](https://www.example.com/) ​ 22 The link must have a link path. [Empty link]() and [empty link](#) are not allowed. 1 Titles are used incrementally from the first level, and skipping is prohibited. # Heading 1 ### Heading 3 We skipped out a 2nd level heading in this document 2 The title must use the ATX style uniformly. Indicate the title level by adding # before the title. # Heading 1 ## Heading 2 ### Heading 3 #### Heading 4 ## Another Heading 2 ### Another Heading 3 3 The leading symbol # of the title must be followed by a blank space. Multiple spaces after # are prohibited, and spaces before # are prohibited. Incorrect Example: # Heading 1 ## Heading 2 Correct Example: # Heading 1 ## Heading 2 4 Only Chinese and English question marks, back quotes, Chinese and English single and double quotes and other symbols can appear at the end of the title. Incorrect Example: # This is a heading. Correct Example: # This is a heading 5 One line must be empty above the title. Incorrect Example: # Heading 1 Some text Some more text## Heading 2 Correct Example: # Heading 1 Some text Some more text ## Heading 2 6 he same title cannot appear continuously in the document. If the first level title is # TiDB architecture, the next level title cannot be ## TiDB architecture. If it is not a continuous title, the title content can be repeated. Incorrect Example: # Some text ## Some text Correct Example: # Some text ## Some more text 7 Only one first level title in document. Incorrect Example: # Top level heading # Another top-level heading Correct Example: # Title ## Heading ## Another heading 8 In general, except for TOC.md files, which can be indented by two spaces, other .md files must be indented by four spaces by default foe each level of indentation. Incorrect Example: * List item * Nested list item indented by 3 spaces Correct Example: * List item * Nested list item indented by 4 spaces 9 Tab is not allowed in documents(including code blocks) . If indentation is required, spaces must be uniformly used instead. Incorrect Example: Some text * hard tab character used to indent the list item Correct Example: Some text * Spaces used to indent the list item instead 10 Continuous blank lines are prohibited. Incorrect Example: Some text here Some more text here Correct Example: Some text here Some more text here 11 Multiple spaces are not allowed after the block reference symbol > . Only one space can be used, followed by the reference content. Incorrect Example: > This is a blockquote with bad indentation> there should only be one. Correct Example: > This is a blockquote with correct> indentation. 12 When using a ordered list, it must start from 1 and increase in order. Incorrect Example: 1. Do this. 1. Do that. 1. Done. 0. Do this. 1. Do that. 2. Done. Correct Example: 1. Do this. 2. Do that. 3. Done. 13 When using a list, the identifier (+, -, * or number) of each list item can only be left blank, followed by the list content. Correct Example: * Foo * Bar * Baz 1. Foo * Bar 1. Baz 14 The list (includeing ordered and unordered lists) must be empty before and after each line. Incorrect Example: Some text* Some* List 1. Some2. List Some text Correct Example: Some text * Some * List 1. Some 2. List Some text 15 There must be one blank line before and after the code block. Incorrect Example: Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text Correct Example: Some text ​``` Code block ​``` ​``` Another code block ​``` Some more text 16 Exposed URLs are prohibited in documents. If you want users to click and open the URL directly, wrap the URL with a pair of angle brackets (<URL>) . If the exposed URL must be used due to special circumstances, and the user does not need to open it by clicking, a pair of back quatation marks (URL) will be used to wrap the URL. Incorrect Example: For more information, see https://www.example.com/. Correct Example: For more information, see <https://www.example.com/>. 17 When using bold, italic and other emphasis effects, redundant spaces are prohibited in the emphasis identifier, such as text . Incorrect Example: Here is some ** bold ** text. Here is some * italic * text. Here is some more __ bold __ text. Here is some more _ italic _ text. Correct Example: Here is some **bold** text. Here is some *italic* text. Here is some more __bold__ text. Here is some more _italic_ text. 18 No extra space is allowed in the code block wrapped by a single backquote, such as ` text `. Incorrect Example: some text some text Correct Example: some text 19 No extra spaces are allowed on both sides of the link text, such as [ Link ](https://www.example.com/) . Incorrect Example: [a link](https://www.example.com/) Correct Example: [a link](https://www.example.com/) 20 The link must have a link path. [Empty link]() and [empty link](#) are not allowed. Incorrect Example: [an empty link]() [an empty fragment](#) Correct Example: [a valid link](https://example.com/) [a valid fragment](#fragment) 21 Code blocks in the document are wrapped with three backquote, and the use of indented four-space code blocks is prohibited. Incorrect Example: Some text. # Indented code More text. Correct Example: ​```ruby # Fenced code ​``` More text. In order to test your modifications, you need to prepare the following environment. Node.js install Antora install Please refer to Antora docs。 After installation, the following display on the terminal indicates successful installation. Firstly, you need to know the location of the corresponding UI for the webpage, as shown in the following figure: The UI templates for both Chinese and English web pages are basically the same, so when making modifications, it is best to ensure that both templates are modified at the same time. After uploading the modified UI to your personal Github, you can consider generating your modified web page locally. IvorySQL Document Site is built by Antora. Before running Antora, remember to modify the corresponding playbook.yml file. After completing the above process, please run the command antora antora-playbook.yml --stacktrace on the terminal, and then patiently wait. After the successful operation is completed, you can view the webpage you have generated. You can start uploading to our ivorysql_web, the process of submitting PR is the same as before. Thank you for your contribution to the community _. We will consider whether to update the website after the review. 1 Download Markdown or Typora . 2 Check whether the source warehouse (https://github.com/IvorySQL/Ivory-www) has updates. If there are updates, please update and synchronize to your own warehouse first. Refer to the following steps to update to the latest version: # Download source code git clone https://github.com/IvorySQL/Ivory-www.git # Synchronize updates warehouse git pull 3 Familiar with format ([_specification_2]) The IvorySQL community provides Chinese and English documents. English documents are saved in IvorySQL document repository, Chinese documents are saved in i18n document repository. You can contribute to one of them or both. Let’s take a quick look at the information about the maintenance of the IvorySQL blog before contributing. It is helpful for you to submit blog and to be a contributor. (1) Clone code to local warehouse git clone https://github.com/IvorySQL/Ivory-www.git (2) Create a branch git checkout -b <branch-name> (3) Create a directory of your own articles in the blog directory, and please name your own directory according to the ([Specification](#7.4 Specification) ) . # Make English blog directory and files cd Ivory-www/blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md # Make Chinese blog directory and files cd Ivory-www/i18n/zh-CN/docusaurus-plugin-content-blog mkdir <YEAR-MONTH-DAY-title> cd <YEAR-MONTH-DAY-title> touch index.md (4) Write the blog to publish in index.md, put the required pictures in the blog in the same directory as index.md. (5) Submit Blog git add <file-path> git commit -m \"<message>\" git push origin <branch-name>:<branch-name> (1) Format of folder naming: year-month-day-foldername Example: 2022-1-28-ivorysql-arrived (2) File property is index.md (3) Picture property is .png, and put the pictures to be uploaded into the folder to be submitted in advance. Notice:The name of every picture is unique and cannot be repeated. Example: po-one.png Blogs are written in markdown or Typora, you can understand the design of blog by reading Blog | Docusaurus. (1) The header of blog includes the following information: --- slug: IvorySQL title: Welcome to IvorySQL community authors: [official] authorTwitter: IvorySql tags: [IvorySQL, Welcome, Database, Join Us] --- Prompt:You can copy the above template to your file and edit it. Notice:1) Add one space after slug, title, authors, tags. 2) The name of every slug is unique, the Chinese and English versions of the same blog can be the same. (2) Text format The text paragraph title is h2/\"Second level title\"; The body uses the default font size. (3) Naming format of inserted pictures [Hello](Hello-banner.png) (4) Naming format of inserted hyperlink [name](link) Github page Download source code and published packages. IvorySQL Document Site uses Antora to build. Also, IvorySQL Document Site is open source. It consists of three parts, such as ivorysql_docs, ivorysql_web and ivory-doc-builder. Welcome everyone who is willing to participate in open source work to join us, and remember to follow our code of conduct _. Due to the fact that IvorySQL Document Site is all hosted on Github, this allows any users to fork our document repository into their personal repository, make modifications to it, and then submit a PR. After being reviewed by our open source team, the modifications can be updated to our Document Site. In order to achieve the goat of correcting document errors more conveniently, you first need to establish a personal warehouse according to the size you want to update. As follows: If you want to modify the existing content or add a new page, you only need to fork ivorysql_docs to your personal repository. If you want to participate more deeply in the construction of IvorySQL Document Site, in addition to the ivorysql_docs, you also need to fork ivorysql_web and ivory-doc-builder to your personal repository. This section will introduce the process of modifying webpage content after discovering that it is inappropriate. In the upper right corner of a webpage with incorrect content, there is a button called edit this page, click on the button. As shown in the figure: After clicking, it will redirect to the editing page where we store the current page source .adoc file. Please modify the content in the Asciidoc format. As shown in the figure: After editing is completed. As shown in the figure: After confirming the update. As shown in the figure: Next, the relevant person of the open source team will be responsible for reviewing the content you submitted. After the review is completed, the updates you submitted will appear on the corresponding page. This section will introduce how to add new page components to a website, and the modifications involved in adding new pages mainly include the following: Add the .adoc file in the CN/modules/ROOT/pages/vX.X. Modify the CN/modules/ROOT/nav.adoc. If the modification involves modifying or adding images, please modify the images in images. Add the .adoc file in the EN/modules/ROOT/pages/vX.X. Modify the EN/modules/ROOT/nav.adoc. If the modification involves modifying or adding images, please modify the images in images. Firstly, you need to download the warehouse that you fork from IvorySQL to your personal computer. git clone https://github.com/$username$/ivorysql_docs.git Then, place the .adoc file to be added in the correct directory, remembering that both Chinese and English files should be prepared (Chinese and English files should have the same name), and each file should be placed in the correct directory. At the same time, modify the corresponding nav.adoc file (the modification method can refer to the existing content of the file). After the above modifications are completed, submit them to the personal warehouse first. git add . git commit -m \"$describe your change$\" git push Afterwards, submit the PR as follows If you are not satisfied with simply submitting on the webpage or only modifying the webpage content, or if you want to modify the webpage UI, this section will help you. Before reading this section, you need to confirm whether your Github personal repository has forked ivorysql_docs, ivorysql_web and ivory-doc-builder. The deployment of web pages is currently the responsibility of the open-source team. We value every submission and issue, so please do not worry about your contribution to the community being buried. If you don’t have much time, you can send an email to ivorysql-docs@ivorysql.org. We will have dedicated staff to handle every your letter, and we looking forward to hearing from you.","title":"IvorySQL Community Contribution Guide","component":"ivorysql-doc","version":"master","name":"community_contribution_guide","url":"/ivorysql-doc/master/contribution/community_contribution_guide.html","titles":[{"text":"1. Summary","hash":"summary","id":1},{"text":"1.1. Illustration","hash":"illustration","id":2},{"text":"1.2. Principles of Conduct","hash":"principles-of-conduct","id":3},{"text":"1.3. Description of Community Governance","hash":"description-of-community-governance","id":4},{"text":"2. Contributor’s Guide","hash":"contributors-guide","id":5},{"text":"2.1. Preparation before Contribution","hash":"preparation-before-contribution","id":6},{"text":"2.1.1. Getting started","hash":"getting-started","id":7},{"text":"2.1.2. Licensing of IvorySQL contributions","hash":"licensing-of-ivorysql-contributions","id":8},{"text":"2.2. What Contribution Can You Make","hash":"what-contribution-can-you-make","id":9},{"text":"2.2.1. Code Contribution","hash":"code-contribution","id":10},{"text":"2.2.2. Document Contribution(https://www.ivorysql.org/zh-CN/docs/intro)","hash":"document-contributionhttpswww-ivorysql-orgzh-cndocsintro","id":11},{"text":"2.2.3. Test IvorySQL and Report Bugs","hash":"test-ivorysql-and-report-bugs","id":12},{"text":"2.2.4. Participate in the Construction of IvorySQL Website","hash":"participate-in-the-construction-of-ivorysql-website","id":13},{"text":"2.2.5. Answer Questions on the Mailing List","hash":"answer-questions-on-the-mailing-list","id":14},{"text":"2.2.6. Contribute Article","hash":"contribute-article","id":15},{"text":"2.3. How to Contribute","hash":"how-to-contribute","id":16},{"text":"2.3.1. Coding Guidelines","hash":"coding-guidelines","id":17},{"text":"2.3.2. Changes applicable to upstream PostgreSQL","hash":"changes-applicable-to-upstream-postgresql","id":18},{"text":"2.3.3. Patch submission","hash":"patch-submission","id":19},{"text":"2.3.4. Patch review","hash":"patch-review","id":20},{"text":"2.3.5. Direct commits to the repository","hash":"direct-commits-to-the-repository","id":21},{"text":"3. Submit Issue","hash":"submit-issue","id":22},{"text":"3.1. First: Get into New issue page:","hash":"first-get-into-new-issue-page","id":23},{"text":"3.1.1. Second:Select the issue type","hash":"secondselect-the-issue-type","id":24},{"text":"3.1.2. Third: Submit","hash":"third-submit","id":25},{"text":"4. Contribute Code","hash":"contribute-code","id":26},{"text":"4.1. First: Fork ivorysql.org  warehouse","hash":"first-fork-ivorysql-org-warehouse","id":27},{"text":"4.2. Second: Clone the warehouse to local","hash":"second-clone-the-warehouse-to-local","id":28},{"text":"4.3. Third: Create a new Branch","hash":"third-create-a-new-branch","id":29},{"text":"4.4. Fourth: Edit Document or Modify Code","hash":"fourth-edit-document-or-modify-code","id":30},{"text":"4.5. Fifth: Generate commit","hash":"fifth-generate-commit","id":31},{"text":"4.6. Sixth: Push the modification to the remote end","hash":"sixth-push-the-modification-to-the-remote-end","id":32},{"text":"4.7. Seventh: Create a Pull Request","hash":"seventh-create-a-pull-request","id":33},{"text":"5. Submit PR","hash":"submit-pr","id":34},{"text":"5.1. First:Create a Pull Request","hash":"firstcreate-a-pull-request","id":35},{"text":"5.2. Second:Fill in PR information","hash":"secondfill-in-pr-information","id":36},{"text":"5.3. Third:Submit PR","hash":"thirdsubmit-pr","id":37},{"text":"6. Edit Documents","hash":"edit-documents","id":38},{"text":"6.1. Preparation","hash":"preparation","id":39},{"text":"6.2. Where to Contribute","hash":"where-to-contribute","id":40},{"text":"6.3. Specification","hash":"specification","id":41},{"text":"6.4. Example","hash":"example","id":42},{"text":"6.5. Environmental preparation","hash":"environmental-preparation","id":43},{"text":"6.6. Generate web pages","hash":"generate-web-pages","id":44},{"text":"7. Submit Blog","hash":"submit-blog","id":45},{"text":"7.1. Preparation","hash":"preparation-2","id":46},{"text":"7.2. Where to Congtribute","hash":"where-to-congtribute","id":47},{"text":"7.3. How to Contribute","hash":"how-to-contribute-2","id":48},{"text":"7.4. Specification","hash":"specification-2","id":49},{"text":"7.4.1. Submit specifications","hash":"submit-specifications","id":50},{"text":"7.4.2. Write blog","hash":"write-blog","id":51},{"text":"8. Website Contribution Guide","hash":"website-contribution-guide","id":52},{"text":"8.1. How to Contribute","hash":"how-to-contribute-3","id":53},{"text":"8.2. Modify Content","hash":"modify-content","id":54},{"text":"8.3. Add Page","hash":"add-page","id":55},{"text":"8.4. Test","hash":"test","id":56},{"text":"8.5. Deploy web pages","hash":"deploy-web-pages","id":57},{"text":"8.6. Tip","hash":"tip","id":58}]},"35":{"id":35,"text":"IvorySQL is maintained by a core team of developers with commit rights to the main IvorySQL repository on GitHub. At the same time, we are very eager to receive contributions from anybody in the wider IvorySQL community. This section covers all you need to know if you want to see your code or documentation changes be added to IvorySQL and appear in future releases. IvorySQL is developed on GitHub, and anybody wishing to contribute to it will have to have a GitHub account and be familiar with Git tools and workflow. It is also recommended that you follow the developer’s mailing list since some of the contributions may generate more detailed discussions there. Once you have your GitHub account, fork this repository so that you can have your private copy to start hacking on and to use as a source of pull requests. If the contribution you’re submitting is original work, you can assume that IvorySQL will release it as part of an overall IvorySQL release available to the downstream consumers under the Apache License, Version 2.0. If the contribution you’re submitting is NOT original work you have to indicate the name of the license and also make sure that it is similar in terms to the Apache License 2.0. Apache Software Foundation maintains a list of these licenses under Category A. In addition to that, you may be required to make proper attributions. Finally, keep in mind that it is NEVER a good idea to remove licensing headers from the work that is not your original one. Even if you are using parts of the file that originally had a licensing header at the top you should err on the side of preserving it. As always, if you are not quite sure about the licensing implications of your contributions, feel free to reach out to us on the developer mailing list. Your chances of getting feedback and seeing your code merged into the project greatly depend on how granular your changes are. If you happen to have a bigger change in mind, we highly recommend engaging on the developer’s mailing list first and sharing your proposal with us before you spend a lot of time writing code. Even when your proposal gets validated by the community, we still recommend doing the actual work as a series of small, self-contained commits. This makes the reviewer’s job much easier and increases the timeliness of feedback. When it comes to C and C++ parts of IvorySQL, we try to follow PostgreSQL Coding Conventions. In addition to that: For C and Perl code, please run pgindent if necessary. We recommend using git diff --color when reviewing your changes so that you don’t have any spurious whitespace issues in the code that you submit. All new functionality that is contributed to IvorySQL should be covered by regression tests that are contributed alongside it. If you are uncertain about how to test or document your work, please raise the question on the ivorysql-hackers mailing list and the developer community will do its best to help you. At the very minimum, you should always be running make installcheck-world to make sure that you’re not breaking anything. If the change you’re working on touches functionality that is common between PostgreSQL and IvorySQL, you may be asked to forward-port it to PostgreSQL. This is not only so that we keep reducing the delta between the two projects, but also so that any change that is relevant to PostgreSQL can benefit from a much broader review of the upstream PostgreSQL community. In general, it is a good idea to keep both codebases handy so you can be sure whether your changes may need to be forward-ported. Once you are ready to share your work with the IvorySQL core team and the rest of the IvorySQL community, you should push all the commits to a branch in your own repository forked from the official IvorySQL and send us a pull request. A submitted pull request with passing validation checks is assumed to be available for peer review. Peer review is the process that ensures that contributions to IvorySQL are of high quality and align well with the road map and community expectations. Every member of the IvorySQL community is encouraged to review pull requests and provide feedback. Since you don’t have to be a core team member to be able to do that, we recommend following a stream of pull reviews to anybody who’s interested in becoming a long-term contributor to IvorySQL. One outcome of the peer review could be a consensus that you need to modify your pull request in certain ways. GitHub allows you to push additional commits into a branch from which a pull request was sent. Those additional commits will be then visible to all of the reviewers. A peer review converges when it receives at least one +1 and no -1s votes from the participants. At that point, you should expect one of the core team members to pull your changes into the project. At any time during the patch review, you may experience delays based on the availability of reviewers and core team members. Please be patient. That being said, don’t get discouraged either. If you’re not getting expected feedback for a few days add a comment asking for updates on the pull request itself or send an email to the mailing list. On occasion, you will see core team members committing directly to the repository without going through the pull request workflow. This is reserved for small changes only and the rule of thumb we use is this: if the change touches any functionality that may result in a test failure, then it has to go through a pull request workflow. If, on the other hand, the change is in the non-functional part of the codebase (such as fixing a typo inside of a comment block) core team members can decide to just commit to the repository directly.","title":"FAQ","component":"ivorysql-doc","version":"master","name":"faq","url":"/ivorysql-doc/master/contribution/faq.html","titles":[{"text":"1. Contributing","hash":"contributing","id":1},{"text":"2. Getting started","hash":"getting-started","id":2},{"text":"3. Licensing of IvorySQL contributions","hash":"licensing-of-ivorysql-contributions","id":3},{"text":"4. Coding guidelines","hash":"coding-guidelines","id":4},{"text":"5. Changes applicable to upstream PostgreSQL","hash":"changes-applicable-to-upstream-postgresql","id":5},{"text":"6. Patch submission","hash":"patch-submission","id":6},{"text":"7. Patch review","hash":"patch-review","id":7},{"text":"8. Direct commits to the repository","hash":"direct-commits-to-the-repository","id":8}]},"36":{"id":36,"text":"IvorySQL adapts the following CPU architectures: Index Architecture Name Adapt to brands Multi-platform Media Packages 1 x86_64 Intel, AMD, ZHAOXIN, HYGON deb, rpm 2 aarch64 Phytium, Kunpeng deb, rpm 3 mips64el Loongson3000，Loongson4000 deb, rpm 4 loongarch64 Loongson5000 deb, rpm","title":"CPU Architecture Adaption","component":"ivorysql-doc","version":"master","name":"cpu_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html","titles":[]},"37":{"id":37,"text":"IvorySQL adapts following operating systems: Index OS name OS introduction Certification 1 Kylin Advanced Server Operating System V11 Kylin Advanced Server Operating System V11 is a new-generation server operating system tailor-made for enterprise-level critical businesses by Kylin Software, leveraging its years of accumulated technological R&D experience and rich application practice. With high reliability, high availability, high security, high performance, and high scalability as its core advantages, the product, on the basis of in-depth integration of AI technology, has further built a key technology system through independent innovation breakthroughs, including the high-speed network protocol MPTCP, hot patch management, intelligent fault diagnosis, scenario-based optimization, and secure container image repository. It serves as a solid, secure, and reliable supporting foundation for the informatization construction of Party and government organs, the digital transformation of key industries, and the implementation of major national projects. 2 openKylin 2.0 SP1 OpenAtom openKylin is an open-source project incubated and operated by the OpenAtom Foundation. It was co-founded by basic software and hardware enterprises, non-profit organizations, associations, institutions of higher education, scientific research institutions, and individual developers. With the community vision of \"providing the world with an open-source operating system deeply integrated with artificial intelligence technology\", the project aims to jointly build a world-leading root community for intelligent desktop open-source operating systems on the basis of openness, voluntariness, equality, and collaboration, and promote the prosperity and development of Linux open-source technology and its software and hardware ecosystem. 3 OpenAnolis OS 23 Anolis OS 23, the Longxin Operating System, is an operating system developed by the OpenAnolis community based on the Open Source Ecosystem Development and Cooperation Initiative. It independently selects components from upstream native communities, undergoes continuous evolution, and ensures compatibility and stability. As an enterprise-level operating system built on Linux Kernel 6.6 LTS, Anolis OS 23 relies on in-depth optimization of the ANCK 6.6 kernel and fully supports domestic chips such as Haiguang, Phytium, Loongson (LoongArch), and Zhaoxin, as well as general-purpose x86_64/ARM64 architectures. It features specialized enhancements for virtualization, security features, and performance optimization. Through hierarchical architecture design and intelligent tuning tools, it maximizes the performance of hardware-software collaboration. Meanwhile, it natively supports AI ecosystem components and provides secure AI container images, accelerating model development and inference deployment. In terms of development toolchains, it integrates GCC 12.3+/LLVM 17, Python 3.11, Rust, and more, enabling efficient multi-platform development. For the desktop ecosystem, it is compatible with GNOME and DDE desktop environments, and expands the ecosystem to meet diverse scenario needs by integrating the Linglong package manager. Anolis OS 23 supports various common applications and domestic applications, helping enterprises achieve efficient, secure, and reliable digital transformation. 4 deppin 20 Deepin OS is a Linux distribution dedicated to providing global users with an elegant, user-friendly, secure, and reliable operating system. The official version of Deepin OS 20 (Build 1002) adopts a unified design style, with a redesigned desktop environment and applications, bringing a fresh visual experience. Its underlying repository has been upgraded to Debian 10.5, and the system installation adopts a dual-kernel mechanism (Kernel 5.4, Kernel 5.7), which comprehensively enhances system stability and compatibility. Additionally, it features a newly designed launcher menu, fingerprint recognition, and enhanced system security; some pre-installed applications in the system have been upgraded to the latest versions—all designed to deliver a better experience for you.","title":"Operating System Adaption","component":"ivorysql-doc","version":"master","name":"os_architecture_adaptation","url":"/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html","titles":[]},"38":{"id":38,"text":"IvorySQL, as an advanced open-source database compatible with Oracle and based on PostgreSQL, has powerful extension capabilities and supports a rich ecosystem of plugins. These plugins can help users enhance database functionality in different scenarios, including geospatial information processing, vector retrieval, full-text search, data definition extraction, and path planning. The following is a list of major plugins currently officially compatible with and supported by IvorySQL: + Index Plugin Name Version Function Description Use Cases 1 postgis 3.5.4 Provides geospatial data support for IvorySQL, including spatial indexes, spatial functions, and geographic object storage Geographic Information Systems (GIS), map services, location data analysis 2 pgvector 0.8.1 Supports vector similarity search, can be used to store and retrieve high-dimensional vector data AI applications, image retrieval, recommendation systems, semantic search 3 pgddl (DDL Extractor) 0.31 Extracts DDL (Data Definition Language) statements from databases, facilitating version management and migration Database version control, CI/CD integration, structure comparison and synchronization 4 pg_cron​ 1.6.0 Provides database-internal scheduled task scheduling functionality, supports regular SQL statement execution Data cleanup, regular statistics, automated maintenance tasks 5 pgsql-http​ 1.7.0 Allows HTTP requests to be initiated in SQL, interacting with external web services Data collection, API integration, microservice calls 6 plpgsql_check 2.8 Provides static analysis functionality for PL/pgSQL code, can detect potential errors during development Stored procedure development, code quality checking, debugging and optimization 7 pgroonga 4.0.4 Provides full-text search functionality for non-English languages, meeting the needs of high-performance applications Full-text search capabilities for languages like Chinese, Japanese, and Korean 8 pgaudit 18.0 Provides fine-grained auditing, recording database operation logs to support security auditing and compliance checks Database security auditing, compliance checks, audit report generation 9 pgrouting 3.8.0 Provides routing computation for geospatial data, supporting multiple algorithms and data formats Geospatial analysis, route planning, logistics optimization 10 system_stats 3.2 Provide functions for accessing system-level statistics. system monitor 11 pg_ai_query 0.1.1 AI-driven natural language to SQL extension supporting multiple LLMs AI-assisted querying, natural language database interaction 12 pg_stat_monitor 2.3.1 Collects performance statistics and provides query performance insights in a single view and graphically in histogram. Performance monitoring These plugins have all been tested and adapted by the IvorySQL team to ensure stable operation in the IvorySQL environment. Users can select appropriate plugins based on business needs to further enhance the capabilities and flexibility of the database system. We will continue to expand and enrich the IvorySQL plugin ecosystem. Community developers are welcome to submit new plugin adaptation suggestions or code contributions. For more detailed usage methods and the latest compatible versions of each plugin, please refer to the corresponding documentation chapters for each plugin.","title":"","component":"ivorysql-doc","version":"master","name":"ecosystem_overview","url":"/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html","titles":[{"text":"IvorySQL Ecosystem Plugin Compatibility List","hash":"ivorysql-ecosystem-plugin-compatibility-list","id":1}]},"39":{"id":39,"text":"pg_ai_query is an AI-powered natural language to SQL extension for IvorySQL/PostgreSQL. It leverages Large Language Models (LLMs) to convert user’s natural language descriptions directly into executable SQL query statements. It supports multiple AI models including OpenAI, Anthropic Claude, and Google Gemini. Project URL: https://github.com/benodiwal/pg_ai_query/tree/main License: Apache-2.0 Key features: Natural Language to SQL: Convert plain text descriptions into valid SQL queries Multi-model Support: Supports gpt-4o-mini, gpt-4o, gpt-5, claude-3-haiku-20240307, claude-sonnet-4-5-20250929, claude-4.5-opus and other large models System Table Protection: Blocks access to information_schema and pg_catalog Limited Scope: Only operates on user tables Configurable Limits: Built-in row limit enforcement API Key Security: Secure handling of API credentials The following demonstration uses v0.1.1 as an example. Prerequisites PostgreSQL 14+ with development headers CMake 3.16+ C++20 compatible compiler API key from OpenAI, Anthropic, or Google (Gemini) Install dependencies sudo apt-get install libcurl4-openssl-dev Compile and install IvorySQL If you need to compile IvorySQL from source, you can refer to the following configuration: ./configure \\ --prefix=$PWD/inst \\ --enable-cassert \\ --enable-debug \\ --enable-tap-tests \\ --enable-rpath \\ --enable-nls \\ --enable-injection-points \\ --with-tcl \\ --with-python \\ --with-gssapi \\ --with-pam \\ --with-ldap \\ --with-openssl \\ --with-libedit-preferred \\ --with-uuid=e2fs \\ --with-ossp-uuid \\ --with-libxml \\ --with-libxslt \\ --with-perl \\ --with-icu \\ --with-libnuma Compile and install pg_ai_query git clone --recurse-submodules https://github.com/benodiwal/pg_ai_query.git cd pg_ai_query mkdir build && cd build export PATH=\"$HOME/works/repo/ivorysql/IvorySQL/inst/bin:$PATH\" cmake .. -DCMAKE_INSTALL_PREFIX=$HOME/works/repo/ivorysql/IvorySQL/inst make && sudo make install Create extension CREATE EXTENSION pg_ai_query; Create a ~/.pg_ai.config configuration file in your home directory: [general] log_level = \"INFO\" enable_logging = false [query] enforce_limit = true default_limit = 1000 [response] show_explanation = true show_warnings = true show_suggested_visualization = false use_formatted_response = false [anthropic] # Your Anthropic API key (if using Claude) api_key = \"******\" # Default model to use (options: claude-sonnet-4-5-20250929) default_model = \"claude-sonnet-4-5-20250929\" # Custom API endpoint (optional) - for Anthropic-compatible APIs api_endpoint = \"https://open.bigmodel.cn/api/anthropic\" [prompts] # Use file paths to read custom prompts system_prompt = /home/highgo/.pg_ai.prompts explain_system_prompt = /home/highgo/.pg_ai.explain.prompts For more examples, please refer to: https://github.com/benodiwal/pg_ai_query/blob/main/docs/src/examples.md SELECT generate_query('Find all users'); Example output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135507cc16362d5d324ccd generate_query -------------------------------------------------------- SELECT * FROM public.users LIMIT 1000; + -- Explanation: -- Retrieves all columns and rows from the users table. + -- Warning: INFO: Applied LIMIT 1000 to prevent large result sets. Remove LIMIT if you need all data. + -- Note: Row limit was automatically applied to this query for safety (1 row) Execute the query: SELECT * FROM public.users LIMIT 1000; Output: id | name | email | age | created_at | city ----+---------------+-------------------+-----+----------------------------+--------------- 1 | Alice Johnson | alice@example.com | 28 | 2026-02-04 15:47:55.208111 | New York 2 | Bob Smith | bob@example.com | 35 | 2026-02-04 15:47:55.208111 | San Francisco 3 | Carol Davis | carol@example.com | 31 | 2026-02-04 15:47:55.208111 | Chicago 4 | David Wilson | david@example.com | 27 | 2026-02-04 15:47:55.208111 | Seattle 5 | Eva Brown | eva@example.com | 33 | 2026-02-04 15:47:55.208111 | Boston (5 rows) Generate test data SELECT generate_query('Generate 100 user records and insert into users table'); Output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_2026021114092101601c5650864a2d generate_query -------------------------------------------------------------------------------------------------------- INSERT INTO public.users (name, email, age, city, status) SELECT 'User_' || generate_series AS name, 'user' || generate_series || '@example.com' AS email, (18 + (generate_series % 50)) AS age, (ARRAY['Beijing','Shanghai','Guangzhou','Shenzhen','Hangzhou'])[1 + (generate_series % 5)] AS city, 'active' AS status FROM generate_series(1, 100); + -- Explanation: -- Generates 100 simulated user records and inserts them into the users table. Data includes auto-generated names, unique emails, random ages (18-67), random cities, and default status. + -- Warnings: -- 1. INFO: Relies on DEFAULT auto-increment setting for id column in users table, id is not manually inserted. -- 2. INFO: Uses generate_series function to generate sequence data, which is a PostgreSQL/IvorySQL feature. -- 3. WARN: Ensure users table is empty or id sequence does not conflict before running, otherwise duplicate inserts may occur. -- 4. WARN: Email format is simple simulation, actual environment may require more complex logic or duplicate checking. (1 row) Case-insensitive query SELECT generate_query('Show users from beijing, beijing is non-Case insensitive'); Output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211142845878f5f1a5a2f44a7 generate_query ----------------------------------------- SELECT id, name, email, age, created_at, city, status FROM public.users WHERE LOWER(city) = LOWER('beijing') LIMIT 100; + -- Explanation: -- Selects all user details for users located in Beijing, performing a case-insensitive match on the city column. + -- Warnings: -- 1. INFO: Using LOWER() on both sides ensures case-insensitive matching but may prevent the database from using a standard index on the city column if one exists. -- 2. INFO: Row limit of 100 applied to prevent large result sets. + -- Note: Row limit was automatically applied to this query for safety (1 row) SELECT explain_query('SELECT * FROM orders WHERE user_id = 12'); Output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260211175909d47a6871bcca4897 explain_query -------------------------------------------------------------------------------------------------------------- 1. Query Overview + - This query aims to retrieve all records from the orders table where user_id equals 12 (SELECT *). - This is a typical query that filters data based on a specific field (user_id). + 2. Performance Summary + - Total execution time: 0.021 ms - Planning time: 0.430 ms - Total cost: 18.12 - Rows returned: 0 rows (Actual Rows: 0) - Rows scanned: 0 rows (Rows Removed by Filter: 0) + 3. Execution Plan Analysis + - Key step: Sequential Scan - The database performed a full table scan on the orders table. - The planner estimated finding 3 rows, but actual execution returned 0 rows. - Filter condition: orders.user_id = 12, which means the database must read every row in the table to check this condition. + 4. Performance Issues + - Full table scan risk: Although the current table data volume is small (execution time is only 0.021ms), using Seq Scan (sequential scan) means the database is not using an index. If the orders table grows to millions of rows over time, this query approach will become extremely slow (high I/O consumption). - Missing index: The plan shows no index was used to locate rows where user_id = 12, indicating that a necessary B-Tree index may be missing on the user_id column. + 5. Optimization Recommendations + - Primary recommendation: Create an index on the user_id column to avoid full table scans. This will transform the query from O(N) (scanning all rows) to O(log N) (index lookup). - SQL optimization example: + CREATE INDEX idx_orders_user_id ON orders(user_id); + 6. Index Recommendations + - Recommended index: Create a B-Tree index on the user_id column of the orders table. - Reason: The query condition is based on equality comparison (=) of user_id. After creating the index, IvorySQL (PostgreSQL) will be able to use the index to quickly locate data, significantly reducing query time and resource consumption, especially when data volume is large. (1 row) Use English: While AI supports multiple languages, English works best Understand your database structure: The more you understand your database structure, the more accurate the generated queries will be Refine iteratively: Start broad, then add details step by step to improve results Be explicit: If you know specific tables or columns, mention them in your prompts - this helps AI generate more precise queries When tables referenced in the query do not exist, the system returns an error message: SELECT generate_query('List all products and prices'); Error output: [INFO] Text generation successful - model: claude-sonnet-4-5-20250929, response_id: msg_20260209135642777cbc5c82ca4a85 ERROR: Query generation failed: Cannot generate query. Referenced table(s) for 'products' or 'goods' do not exist in the database. Available tables: public.orders, public.student_scores, public.users, sys.dual In this case, the AI informs you of the available table list to help adjust your query.","title":"pg_ai_query","component":"ivorysql-doc","version":"master","name":"pg_ai_query","url":"/ivorysql-doc/master/ecosystem_components/pg_ai_query.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Quick Start","hash":"quick-start","id":2},{"text":"2.1. Installation","hash":"installation","id":3},{"text":"2.2. Configuration","hash":"configuration","id":4},{"text":"3. Usage Examples","hash":"usage-examples","id":5},{"text":"3.1. Basic Usage","hash":"basic-usage","id":6},{"text":"3.2. generate_query Examples","hash":"generate_query-examples","id":7},{"text":"3.3. explain_query Examples","hash":"explain_query-examples","id":8},{"text":"4. Best Practices","hash":"best-practices","id":9},{"text":"4.1. Prompt Writing Guidelines","hash":"prompt-writing-guidelines","id":10},{"text":"4.2. Error Handling Examples","hash":"error-handling-examples","id":11}]},"40":{"id":40,"text":"Running periodic tasks in PostgreSQL, such as executing VACUUM or deleting old data, is a common requirement. A simple way to achieve this is to configure cron or other external daemons to periodically connect to the database and run commands. However, as databases increasingly run as managed services or standalone containers, configuring and running a separate daemon often becomes impractical. Additionally, it’s difficult to make your cron jobs aware of failover or schedule tasks across cluster nodes. pg_cron is an open-source scheduled task extension for PostgreSQL that allows setting up cron-style task scheduling directly within the database for automating data maintenance tasks (cleanup, aggregation), database health checks, executing stored procedures and custom functions, and other operations. It stores cron jobs in tables, and periodic tasks automatically fail over with the PostgreSQL server. For more details, see pg_cron documentation. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 # Clone pg_cron source code git clone https://github.com/citusdata/pg_cron.git cd pg_cron # Set pg_config path to PATH environment variable, e.g.: export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make make install # Shared preload extensions shared_preload_libraries = 'pg_cron' # Specify task metadata storage database (default current database) cron.database_name = 'ivorysql' # Maximum number of concurrent tasks allowed cron.max_running_jobs = 5 pg_ctl restart -D ./data -l logfile Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension pg_cron; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'pg_cron'; name | default_version | installed_version | comment ---------+-----------------+-------------------+--------------------------- pg_cron | 1.6 | |Job scheduler for PostgreSQL (1 row) SELECT cron.schedule( 'nightly-data-cleanup', -- Task name (unique identifier) '0 3 * * *', -- Cron expression (daily at UTC 3:00) $$DELETE FROM logs WHERE created_at < now() - interval '30 days'$$ -- SQL to execute ); Cron expression quick reference: Example Meaning '0 * * * *' Execute every hour on the hour '*/15 * * * *' Execute every 15 minutes '0 9 * * 1-5' Execute at 9 AM on weekdays '0 1 1 * *' Execute at 1 AM on the 1st of every month pg_cron also allows using '$' to represent the last day of the month. # View all tasks SELECT * FROM cron.job; # View task execution history SELECT * FROM cron.job_run_details ORDER BY start_time DESC LIMIT 10; # Delete task SELECT cron.unschedule('nightly-data-cleanup'); # Pause task (update status) UPDATE cron.job SET active = false WHERE jobname = 'delete-job-run-details';","title":"pg_cron","component":"ivorysql-doc","version":"master","name":"pg_cron","url":"/ivorysql-doc/master/ecosystem_components/pg_cron.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation and Configuration","hash":"installation-and-configuration","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"2.2. Configuration File (ivorysql.conf)","hash":"configuration-file-ivorysql-conf","id":4},{"text":"2.3. Restart Service","hash":"restart-service","id":5},{"text":"2.4. Create Extension and Confirm pg_cron Version","hash":"create-extension-and-confirm-pg_cron-version","id":6},{"text":"3. Core Functionality Usage","hash":"core-functionality-usage","id":7},{"text":"3.1. Creating Scheduled Tasks","hash":"creating-scheduled-tasks","id":8},{"text":"3.2. Task Management","hash":"task-management","id":9}]},"41":{"id":41,"text":"pg_stat_monitor is a Query Performance Monitoring tool for PostgreSQL. pg_stat_monitor collects performance statistics and provides query performance insights in a single view and graphically in histogram. These insights allow database users to understand query origins, execution, planning statistics and details, query information, and metadata. This significantly improves observability, enabling users to debug and tune query performance. The IvorySQL installation package already includes the pg_stat_monitor plugin. If you installed IvorySQL using the installation package, you typically don’t need to manually install pg_stat_monitor. If you installed IvorySQL from source code, you can proceed to install the pg_stat_monitor plugin also from source. The IvorySQL community provides step-by-step instructions for source code installation： To build pg_stat_monitor from source code, you require the following: git make gcc pg_config You can download the source code of the latest release of pg_stat_monitor from the releases page on GitHub or using git: git clone https://github.com/percona/pg_stat_monitor.git Compile and install the extension. Assuming IvorySQL is already installed in the environment with the installation path at /usr/ivory-5. cd pg_stat_monitor export PG_CONFIG=/usr/ivory-5/bin/pg_config make USE_PGXS=1 make USE_PGXS=1 install Load pg_stat_monitor at the start time by adding it to the shared_preload_libraries configuration parameter. This is because pg_stat_monitor requires additional shared memory. modify the shared_preload_libraries parameter： shared_preload_libraries = 'pg_stat_monitor'; If you’ve added other modules to the shared_preload_libraries parameter (for example, pg_stat_statements), list all of them separated by commas for the ALTER SYSTEM command. shared_preload_libraries = 'liboracle_parser, ivorysql_ora, pg_stat_statements, pg_stat_monitor'; Start or restart the IvorySQL instance to apply the changes. pg_ctl restart -D data After you have added pg_stat_monitor to the shared_preload_libraries, it starts collecting statistics data for all existing databases. To access this data, you need to create the view on every database that you wish to monitor. Create the extension view with the user that has the privileges of a superuser or a database owner. Connect to psql as a superuser for a database and run the CREATE EXTENSION command: CREATE EXTENSION pg_stat_monitor; After the setup is complete, you can see the stats collected by pg_stat_monitor. To obtain query execution time information as an example, connect to the database and execute the following SQL： SELECT userid, total_exec_time, min_exec_time, max_exec_time, mean_exec_time, query FROM pg_stat_monitor; userid | total_exec_time | min_exec_time | max_exec_time | mean_exec_time | query --------+-----------------+---------------+---------------+----------------+---------------------------------------------------------------------------------------------- 10 | 1.532168 | 0.749108 | 0.78306 | 0.766084 | SELECT userid, datname, queryid, substr(query,$1, $2) AS query, calls FROM pg_stat_monitor 10 | 0.755857 | 0.755857 | 0.755857 | 0.755857 | SELECT application_name, client_ip, substr(query,$1,$2) as query FROM pg_stat_monitor 10 | 0 | 0 | 0 | 0 | SELECT userid, total_time, min_time, max_time, mean_time, query FROM pg_stat_monitor; 10 | 0 | 0 | 0 | 0 | SELECT userid, total_exec_time, min_time, max_time, mean_time, query FROM pg_stat_monitor; (4 rows) For more information on using pg_stat_monitor, please refer to pg_stat_monitor Official documentation","title":"pg_stat_monitor","component":"ivorysql-doc","version":"master","name":"pg_stat_monitor","url":"/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"3. Load the module","hash":"load-the-module","id":3},{"text":"4. Create the extension view","hash":"create-the-extension-view","id":4},{"text":"5. Usage","hash":"usage","id":5}]},"42":{"id":42,"text":"PgAudit is an auditing extension for IvorySQL that produces traceable log records for critical operations such as DDL, DML, and DCL. With the audit trail, database administrators can meet compliance requirements, quickly detect abnormal behavior, and identify accountability and impact scope when incidents occur. Comprehensive auditing: Captures SELECT, INSERT, UPDATE, DELETE, DDL commands, privilege changes, and more to build a complete activity timeline. Flexible scope control: Supports global, role-based, and object-level auditing, allowing fine-grained configuration by user, role, schema, or operation type. Seamless integration: Reuses PostgreSQL’s standard logging subsystem and works with tools like syslog and logrotate, aligning with existing log ingestion and analysis pipelines. Compliance ready: Generates structured audit logs suitable for meeting regulatory requirements in finance, government, and other regulated industries. Security enhancement: Records and inspects database activity to surface unauthorized access, anomalous DML, or potential data leakage risks in time. Operations insight: Helps replay operational actions, locate performance bottlenecks, and support SQL tuning and incident troubleshooting. A IvorySQL installation (recommended version aligned with the targeted PgAudit release). Build toolchain: gcc, make, tar, etc. Database superuser privileges to modify ivorysql.conf and restart the instance. Taking PgAudit 18.0 as an example: wget https://github.com/pgaudit/pgaudit/archive/refs/tags/18.0.tar.gz tar -xf 18.0.tar.gz cd pgaudit-18.0 make install USE_PGXS=1 PG_CONFIG=$PGHOME/bin/pg_config The commands above expect the environment variable PGHOME to point to the installed IvorySQL home directory. After installation, pgaudit.so will be placed in IvorySQL’s extension directory. Modify ivorysql.conf to load the plugin and configure common parameters: ---- shared_preload_libraries = 'pgaudit' # Requires an instance restart pgaudit.log = 'read, write, ddl' # Sample audit scope; adjust as needed ---- Restart or reload the database instance so the shared library configuration takes effect. CREATE EXTENSION IF NOT EXISTS pgaudit; SELECT name, default_version, installed_version, comment FROM pg_available_extensions WHERE name = 'pgaudit'; If the returned installed_version matches the expected release, the extension has been installed successfully. Execute the following SQL sample: [source,sql] ---- CREATE TABLE audit_demo(id serial PRIMARY KEY, info text); INSERT INTO audit_demo(info) VALUES ('pgaudit test'); SELECT * FROM audit_demo; UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1; DELETE FROM audit_demo WHERE id = 1; ---- Check the audit logs on the database server: tail -f $PGDATA/log/*.log | grep 'AUDIT:' 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE TABLE,TABLE,public.audit_demo,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,CREATE INDEX,INDEX,public.audit_demo_pkey,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.113 CST [11451] LOG: AUDIT: SESSION,1,1,DDL,ALTER SEQUENCE,SEQUENCE,public.audit_demo_id_seq,\"CREATE TABLE audit_demo(id serial PRIMARY KEY, info text)\",<not logged> 2025-10-31 15:56:32.117 CST [11451] LOG: AUDIT: SESSION,2,1,WRITE,INSERT,,,INSERT INTO audit_demo(info) VALUES ('pgaudit test'),<not logged> 2025-10-31 15:56:32.121 CST [11451] LOG: AUDIT: SESSION,3,1,READ,SELECT,,,SELECT * FROM audit_demo,<not logged> 2025-10-31 15:56:32.122 CST [11451] LOG: AUDIT: SESSION,4,1,WRITE,UPDATE,,,UPDATE audit_demo SET info = 'pgaudit update' WHERE id = 1,<not logged> 2025-10-31 15:56:32.127 CST [11451] LOG: AUDIT: SESSION,5,1,WRITE,DELETE,,,DELETE FROM audit_demo WHERE id = 1,<not logged> To record parameter values as well, enable pgaudit.log_parameter = 'on': ivorysql=# SHOW pgaudit.log_parameter; pgaudit.log_parameter ----------------------- on (1 row)","title":"PgAudit","component":"ivorysql-doc","version":"master","name":"pgaudit","url":"/ivorysql-doc/master/ecosystem_components/pgaudit.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Key Features","hash":"key-features","id":2},{"text":"3. Installation and Deployment","hash":"installation-and-deployment","id":3},{"text":"3.1. Prerequisites","hash":"prerequisites","id":4},{"text":"3.2. Compile and Install PgAudit","hash":"compile-and-install-pgaudit","id":5},{"text":"3.3. Baseline Configuration Before Registering the Extension","hash":"baseline-configuration-before-registering-the-extension","id":6},{"text":"3.4. Create the Extension and Verify","hash":"create-the-extension-and-verify","id":7},{"text":"4. Usage","hash":"usage","id":8}]},"43":{"id":43,"text":"pgddl is a SQL function extension specifically designed for PostgreSQL databases. It can generate clear, formatted SQL DDL (Data Definition Language) scripts directly from the database system catalog, such as CREATE TABLE or ALTER FUNCTION. It solves the problem that PostgreSQL natively lacks commands like SHOW CREATE TABLE, allowing users to easily obtain object creation statements in a pure SQL environment without relying on external tools (such as pg_dump). This extension provides a complete solution through a set of simple SQL functions, with advantages including: requiring only SQL queries to operate, supporting flexible object filtering through WHERE clauses, and intelligently handling dependencies between objects to generate complete scripts including Drop and Create steps. This makes it particularly suitable for scenarios such as database change management, upgrade script writing, and structural auditing. It should be noted that ddlx is still under development and may not yet cover all PostgreSQL object types and advanced options. Generated scripts should always be checked and tested in non-production environments first to ensure their correctness and safety. The IvorySQL installation package already integrates the pgddl plugin. If IvorySQL is installed using the installation package, pgddl can usually be used without manual installation. Other installation methods can refer to the source code installation steps below. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 Download pgddl v0.31 code from https://github.com/lacanoid/pgddl. cd pgddl # Set the PG_CONFIG environment variable to the pg_config path, e.g.: /usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/path/to/pg_config make PG_CONFIG=/path/to/pg_config install Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension ddlx; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'ddlx'; name | default_version | installed_version | comment ------+-----------------+-------------------+------------------------- ddlx | 0.31 | 0.31 | DDL eXtractor functions (1 row) For pgddl usage, please refer to the ddlx Official Documentation","title":"pgddl (DDL Extractor)","component":"ivorysql-doc","version":"master","name":"pgddl","url":"/ivorysql-doc/master/ecosystem_components/pgddl.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm ddlx Version","hash":"create-extension-and-confirm-ddlx-version","id":4},{"text":"4. Usage","hash":"usage","id":5}]},"44":{"id":44,"text":"PostgreSQL has built-in full-text search functionality, but when dealing with large-scale data, complex queries, and non-English languages (especially CJK languages like Chinese, Japanese, and Korean), its functionality and performance may not meet the requirements of high-performance applications. PGroonga was created to address this need. It is a PostgreSQL extension that deeply integrates Groonga, a high-performance full-featured full-text search engine, with the PostgreSQL database. Groonga itself is an excellent open-source search engine, renowned for its extreme speed and rich functionality, particularly excelling at handling multilingual text. PGroonga’s mission is to seamlessly bring Groonga’s powerful capabilities into the PostgreSQL world, providing users with an experience that far exceeds native full-text search. The PGroonga plugin is already included in the IvorySQL installation package. If you installed IvorySQL using the official released package, you typically do not need to manually install PGroonga and can skip the installation steps. You can choose their preferred installation method for PGroonga from the PGroonga package installation page. The IvorySQL community provides source code installation steps, demonstrated below using PGroonga v4.0.4 as an example. Setup Environment Operating System: Ubuntu 24.04 CPU Architecture: x86_64 IvorySQL: v5.0 When compile PGroonga, there is an option: HAVE_MSGPACK=1, which is used to support WAL. Enabling this option requires installing msgpack-c 1.4.1 or newer version. sudo apt install libmsgpack-dev Ensure Groonga >= 14.0.0 is installed. sudo apt install groonga libgroonga-dev Verify Groonga installation: highgo@ubuntu:~/work/IvorySQL/inst$ groonga --version Groonga 15.1.7 [Linux,x86_64,utf8,match-escalation-threshold=0,nfkc,mecab,message-pack,mruby,onigmo,zlib,lz4,zstandard,epoll,apache-arrow,xxhash,blosc,h3,simdjson,llama.cpp] wget https://packages.groonga.org/source/pgroonga/pgroonga-4.0.4.tar.gz tar xvf pgroonga-4.0.4.tar.gz cd pgroonga-4.0.4 Before running make , ensure that the pg_config command is in the PATH environment variable. For example, if IvorySQL is installed at ~/work/IvorySQL/inst , set the environment variables as follows: export PGHOME=~/work/IvorySQL/inst export PGDATA=$PGHOME/data export PATH=$PGHOME/bin:$PATH Then execute the following commands to compile and install: make HAVE_MSGPACK=1 make install Connect to the database with psql in pg mode and execute the following commands: postgres=# CREATE extension pgroonga; CREATE EXTENSION postgres=# SELECT * FROM pg_available_extensions WHERE name = 'pgroonga'; name | default_version | installed_version | comment ----------+-----------------+-------------------+-------------------------------------------------------------------------------- pgroonga | 4.0.4 | 4.0.4 | Super fast and all languages supported full text search index based on Groonga (1 row) postgres=# select version(); version -------------------------------------------------------------------------------- PostgreSQL (IvorySQL 5.0) 18.0 on x86_64-linux, compiled by gcc-13.3.0, 64-bit (1 row) For PGroonga usage, please refer to the PGroonga Official Documentation","title":"PGroonga","component":"ivorysql-doc","version":"master","name":"pgroonga","url":"/ivorysql-doc/master/ecosystem_components/pgroonga.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Dependencies","hash":"dependencies","id":3},{"text":"2.1.1. Install msgpack-c","hash":"install-msgpack-c","id":4},{"text":"2.1.2. Install Groonga","hash":"install-groonga","id":5},{"text":"2.2. Compile and Install PGroonga","hash":"compile-and-install-pgroonga","id":6},{"text":"2.2.1. Download and Extract the PGroonga Source Code","hash":"download-and-extract-the-pgroonga-source-code","id":7},{"text":"2.2.2. Compile","hash":"compile","id":8},{"text":"3. Create Extension PGroonga and Confirm the version","hash":"create-extension-pgroonga-and-confirm-the-version","id":9},{"text":"4. Usage","hash":"usage","id":10}]},"45":{"id":45,"text":"pgRouting is an open-source geospatial routing extension library built on PostgreSQL/PostGIS databases. It endows databases with powerful network analysis capabilities, enabling them to handle complex path planning and graph theory computation problems, such as calculating the shortest path between two points, performing Traveling Salesman Problem (TSP) analysis, or computing service area coverage. It embeds routing algorithms directly into the database, thereby avoiding complex data transfer and computation at the application layer. The core advantage of this extension lies in its ability to leverage PostgreSQL’s powerful data management capabilities and PostGIS’s rich spatial functions to perform efficient computation on spatial network data directly within the database. This not only simplifies application development processes but also significantly improves the performance of large-scale network analysis by reducing data movement. pgRouting is widely used in logistics and distribution, traffic navigation, network analysis, urban planning, and supply chain management, among other fields. Its open-source nature has attracted continuous contributions and improvements from developers worldwide, making it one of the preferred tools for path analysis and network solving in the spatial database domain. IvorySQL 5.0 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 Install dependencies It depends on perl, which is generally already installed when installing IvorySQL, so no need to install it here. CMake version requirement >= 3.12, Boost version >= 1.56 # Install dependencies sudo apt install cmake libboost-all-dev Compile and install wget https://github.com/pgRouting/pgrouting/releases/download/v3.8.0/pgrouting-3.8.0.tar.gz tar xvf pgrouting-3.8.0.tar.gz cd pgrouting-3.8.0 mkdir build cd build cmake .. -DPOSTGRESQL_PG_CONFIG=/path/to/pg_config # eg: /usr/local/ivorysql/ivorysql-5/bin/pg_config make sudo make install Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension pgrouting; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'pgrouting'; name | default_version | installed_version | comment -----------+-----------------+-------------------+--------------------- pgrouting | 3.8.0 | | pgRouting Extension (1 row) For pgRouting usage, please refer to the pgRouting Official Documentation","title":"pgRouting","component":"ivorysql-doc","version":"master","name":"pgrouting","url":"/ivorysql-doc/master/ecosystem_components/pgrouting.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm pgRouting Version","hash":"create-extension-and-confirm-pgrouting-version","id":4},{"text":"4. Usage","hash":"usage","id":5}]},"46":{"id":46,"text":"pgsql-http is an open-source extension designed for PostgreSQL databases that allows users to initiate HTTP requests directly within the database, acting as a built-in web client. The core purpose of this extension is to bridge the gap between databases and external web services, enabling interaction with external web services and API endpoints through simple SQL function calls without relying on external applications or middleware. With this extension, developers can directly retrieve network data (GET), submit data (POST/PUT), update (PATCH), or delete (DELETE) remote resources in SQL queries, triggers, or stored procedures. It provides rich functionality, including setting request headers, automatic URL encoding, sending JSON data, and parsing response status, headers, and content, greatly simplifying the process of integrating external data into database operations. Typical application scenarios include: real-time retrieval of external data (such as exchange rates, weather information) and storing it in tables; automatic notification of microservices through triggers when data changes; cleaning data in the database and directly submitting it to external APIs. It provides a powerful and flexible solution for building database-centric integrated applications. The pgsql-http plugin has been integrated into the IvorySQL installation package. If IvorySQL is installed using the installation package, pgsql-http can usually be used without manual installation. Other installation methods can refer to the source code installation steps below. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 Install Dependencies It depends on libcurl, and libcurl development files (such as libcurl4-openssl-dev) need to be installed in advance # Install dependencies sudo apt install libcurl4-openssl-dev Compile and Install Download the 1.7.0 source package pgsql-http-1.7.0.tar.gz from https://github.com/pramsey/pgsql-http/releases/tag/v1.7.0 tar xvf pgsql-http-1.7.0.tar.gz cd pgsql-http-1.7.0 # Ensure pg_config is accessible in PATH, e.g.: /usr/local/ivorysql/ivorysql-5/bin/pg_config make sudo make install Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension http; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'http'; name | default_version | installed_version | comment -----------+-----------------+-------------------+------------------------------------------------------------------------- http | 1.7 | 1.7 | HTTP client for PostgreSQL, allows web page retrieval inside the database. (1 row) For pgsql-http usage, please refer to pgsql-http official documentation","title":"pgsql-http","component":"ivorysql-doc","version":"master","name":"pgsql_http","url":"/ivorysql-doc/master/ecosystem_components/pgsql_http.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm http Version","hash":"create-extension-and-confirm-http-version","id":4},{"text":"4. Usage","hash":"usage","id":5}]},"47":{"id":47,"text":"The vector database is an important component of Generative Artificial Intelligence (GenAI). As a significant extension of PostgreSQL, pgvector not only supports vector calculations of up to 16000 dimensions but also provides powerful vector operations and indexing capabilities, enabling PostgreSQL to directly transform into an efficient vector database. IvorySQL, being developed based on PostgreSQL, inherits the seamless integration capability with pgvector extension, thereby offering users a wider range of data processing and analysis options. Additionally, in Oracle compatibility mode, the pgvector extension is also available, providing great convenience for Oracle users to use vector databases, allowing for easy migration and management of data and achieving more efficient business operations. PGVector has two indexing algorithms, IVFFLAT and HNSW. The working principle of IVFFLAT is to cluster similar vectors into regions and build an inverted index mapping each region to its vectors. This allows queries to focus on a subset of the data, enabling fast searches. By adjusting the parameters of lists and probes, IVFFLAT can balance the speed and accuracy of the dataset, enabling PostgreSQL to perform rapid semantic similarity searches on complex data. Through simple queries, applications can find the nearest neighbors to a query vector among millions of high-dimensional vectors. For tasks such as natural language processing and information retrieval, IVFFLAT provides an effective solution. When building an IVFFLAT index, you need to decide how many lists to include in the index. Each list represents a \"center\" which are computed using the k-means algorithm. Once all centers are determined, IVFFLAT determines which center each vector is closest to and adds it to the index. When querying vector data, you can decide how many centers to check, which is determined by the ivfflat.probes parameter. This results in a trade-off between ANN performance/recall: the more centers accessed, the more accurate the results, but at the expense of performance. HNSW (Hierarchical Navigating Small World) is a graph-based indexing algorithm consisting of multiple layers of neighborhood graphs, hence the name \"hierarchical\" NSW method. It constructs multiple layers of navigation graphs for a given graph according to certain rules, with the upper layers of the graph being sparser and the distances between nodes farther apart; and the lower layers of the graph being denser and the distances between nodes closer together. HNSW algorithm is a classic trade-off between space and time, as it achieves high search quality and speed, but at the cost of significant memory overhead. This is because it not only requires storing all vectors in memory but also maintaining the structure of the graph, which also needs to be stored. The IvorySQL 5(above version) has been installed in the environment, and the installation path is /usr/local/ivorysql/ivorysql-5 Setting PG_CONFIG export PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config Pull pg_vector source code git clone --branch v0.8.1 https://github.com/pgvector/pgvector.git Install pgvector cd pgvector sudo --preserve-env=PG_CONFIG make sudo --preserve-env=PG_CONFIG make install Create pgvector extension [ivorysql@localhost ivorysql-5]$ psql psql (18.0) Type \"help\" for help. ivorysql=# create extension vector; CREATE EXTENSION Now, pgvector is installed completely. For more usage cases, please refer to pgvector document In IvorySQL’s Oracle compatibility mode, the pgvector extension can also work correctly. We suggest users to test using port 1521, using the command: psql -p 1521. ivorysql=# CREATE TABLE items5 (id bigserial PRIMARY KEY, name varchar2(20), num number(20), embedding bit(3)); CREATE TABLE ivorysql=# INSERT INTO items5 (name, num, embedding) VALUES ('1st oracle data',0, '000'), ('2nd oracle data', 111, '111'); INSERT 0 2 ivorysql=# SELECT * FROM items5 ORDER BY bit_count(embedding # '101') LIMIT 5; id | name | num | embedding ----+-----------------+-----+----------- 2 | 2nd oracle data | 111 | 111 1 | 1st oracle data | 0 | 000 ivorysql=# declare i vector(3) := '[1,2,3]'; begin raise notice '%', i; end; ivorysql-# / NOTICE: [1,2,3] DO ivorysql=# CREATE OR REPLACE PROCEDURE ora_procedure() AS p vector(3) := '[4,5,6]'; begin raise notice '%', p; end; / CREATE PROCEDURE ivorysql=# call ora_procedure(); NOTICE: [4,5,6] CALL ivorysql=# CREATE OR REPLACE FUNCTION AddVector(a vector(3), b vector(3)) RETURN vector(3) IS BEGIN RETURN a + b; END; / CREATE FUNCTION ivorysql=# SELECT AddVector('[1,2,3]','[4,5,6]') FROM DUAL; addvector ---------------- [5,7,9] (1 row)","title":"pgvector","component":"ivorysql-doc","version":"master","name":"pgvector","url":"/ivorysql-doc/master/ecosystem_components/pgvector.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Principles","hash":"principles","id":2},{"text":"2.1. IVFFLAT","hash":"ivfflat","id":3},{"text":"2.2. HNSW","hash":"hnsw","id":4},{"text":"3. Installation","hash":"installation","id":5},{"text":"3.1. Source Code Installation","hash":"source-code-installation","id":6},{"text":"4. Oracle Compatible","hash":"oracle-compatible","id":7},{"text":"4.1. Data Type","hash":"data-type","id":8},{"text":"4.2. Anonymous Block","hash":"anonymous-block","id":9},{"text":"4.3. PROCEDURE","hash":"procedure","id":10},{"text":"4.4. FUNCTION","hash":"function","id":11}]},"48":{"id":48,"text":"During PostgreSQL database development, when writing stored procedures and functions, it is often difficult to discover potential issues such as syntax errors, type mismatches, undefined variables, etc., before runtime. Traditional approaches require waiting until the function is actually executed to discover these errors, which not only increases debugging costs but may also cause unexpected failures in production environments. plpgsql_check is a static code analysis tool (Linter) specifically designed for PostgreSQL’s PL/pgSQL language. It can perform deep checks on the source code of stored procedures and functions without actually executing them. This tool can proactively identify various code quality issues including syntax errors, type mismatches, unused variables, performance problems, security vulnerabilities, etc., helping developers ensure code correctness and robustness during the development phase. For more details, see plpgsql_check official documentation. The source installation environment is Ubuntu 24.04 (x86_64). IvorySQL 5 or higher version is already installed in the environment, with the installation path at /usr/local/ivorysql/ivorysql-5 # Download the 2.8.3 source package plpgsql_check-2.8.3.tar.gz from https://github.com/okbob/plpgsql_check/releases/tag/v2.8.3 tar xvf plpgsql_check-2.8.3.tar.gz cd plpgsql_check-2.8.3 # Set pg_config path to PATH environment variable, e.g.: export PATH=/usr/local/ivorysql/ivorysql-5/bin/:$PATH make USE_PGXS=1 clean make USE_PGXS=1 all sudo make USE_PGXS=1 install Connect to the database with psql and execute the following commands: ivorysql=# CREATE EXTENSION plpgsql_check; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'plpgsql_check'; name | default_version | installed_version | comment ---------------+-----------------+-------------------+--------------------------------------------------- plpgsql_check | 2.8 | 2.8 | extended check for plpgsql functions (1 row) -- Create a sample function CREATE OR REPLACE FUNCTION test_function(p_id integer) RETURNS text AS $$ DECLARE v_name text; v_unused integer; -- Unused variable BEGIN SELECT name INTO v_name FROM users WHERE id = p_id; RETURN v_naem; -- Spelling error END; $$ LANGUAGE plpgsql; -- Use plpgsql_check to check the function SELECT * FROM plpgsql_check_function('test_function(integer)'); Example check result: plpgsql_check_function -------------------------------------------------------------- error:42601:7:assignment:target variable \"v_naem\" is undefined warning:00000:4:DECLARE:unused variable \"v_unused\" (2 rows) For more detailed usage methods and advanced features, please refer to plpgsql_check official documentation.","title":"plpgsql_check","component":"ivorysql-doc","version":"master","name":"plpgsql_check","url":"/ivorysql-doc/master/ecosystem_components/plpgsql_check.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Installation","hash":"source-installation","id":3},{"text":"3. Create Extension and Confirm plpgsql_check Version","hash":"create-extension-and-confirm-plpgsql_check-version","id":4},{"text":"4. Usage","hash":"usage","id":5},{"text":"4.1. Check Single Function","hash":"check-single-function","id":6}]},"49":{"id":49,"text":"IvorySQL is fully compatible with PostgreSQL, allowing for seamless integration with PostGIS. Users can select the installation method for PostGIS that best suits their development environment from the PostGIS installation page. In addition to the installation methods provided by the PostGIS community, the IvorySQL community also offers a source code installation method, with Ubuntu 24.04 (x86_64) as the installation environment. Please ensure that IvorySQL 5.0 or above is installed in the environment. Install dependencies sudo apt install \\ docbook-xsl-ns \\ gettext \\ libgdal-dev \\ libgeos-dev \\ libjson-c-dev \\ libproj-dev \\ libprotobuf-c-dev \\ libsfcgal-dev \\ libxml2-dev \\ libxml2-utils \\ protobuf-c-compiler \\ xsltproc Install PostGIS $ wget https://download.osgeo.org/postgis/source/postgis-3.5.4.tar.gz $ tar xvf postgis-3.5.4.tar.gz $ cd postgis-3.5.4 $ ./configure --with-pgconfig=/path/to/pg_config eg: /usr/ivory-5/bin/pg_config, if ivorysql installation directory is /usr/ivory-5. $ make $ sudo make install If configure reports PGXS error, please change --with-pgconfig parameter value and confirm the parameter value based on the installation path of IvorySQL in the environment. Connect to the database with psql and execute the following commands: ivorysql=# CREATE extension postgis; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'postgis'; name | default_version | installed_version | comment ---------+-----------------+-------------------+------------------------------------------------------------ postgis | 3.5.4 | 3.5.4 | PostGIS geometry and geography spatial types and functions (1 row) For information about using PostGIS, please refer to the PostGIS 3.5 Official Documentation","title":"PostGIS","component":"ivorysql-doc","version":"master","name":"postgis","url":"/ivorysql-doc/master/ecosystem_components/postgis.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Code Installation","hash":"source-code-installation","id":3},{"text":"3. Create Extension and Verify PostGIS Version","hash":"create-extension-and-verify-postgis-version","id":4},{"text":"4. Using","hash":"using","id":5}]},"50":{"id":50,"text":"system_stats is a Postgres extension that provides functions to access system level statistics that can be used for monitoring. The source code installation environment is Ubuntu 24.04 (x86_64), in which IvorySQL 5 or a later version has been installed. The installation path is /usr/local/ivorysql/ivorysql-5. # download source code package from: https://github.com/EnterpriseDB/system_stats/releases/tag/v3.2 unzip v3.2.zip cd system_stats-3.2 # compile and install the extension make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config make PG_CONFIG=/usr/local/ivorysql/ivorysql-5/bin/pg_config install connect the database with psql in Oracle compatible mode and execute the following commands: ivorysql=# CREATE EXTENSION system_stats; CREATE EXTENSION ivorysql=# SELECT * FROM pg_available_extensions WHERE name = 'system_stats'; name | default_version | installed_version | comment --------------+-----------------+-------------------+----------------------------------------------- system_stats | 3.0 | 3.0 | EnterpriseDB system statistics for PostgreSQL (1 row) ivorysql=> select pg_sys_os_info() from dual; pg_sys_os_info -------------------------------------------------------------------------- (\"\"\"Ubuntu 24.04.1 LTS\"\" + \",\"Linux 6.14.0-29-generic\",Ubuntu,\"(none)\",5568,292,532,x86_64,,864122) (1 row) ivorysql=> select pg_sys_cpu_info() from dual; pg_sys_cpu_info ------------------------------------------------------------------------------------------------------------------------------------- (GenuineIntel,\"GenuineIntel model 154 family 6\",\"12th Gen Intel(R) Core(TM) i7-1260P\",,0,2,1,x86_64,2496000000,,,,48,32,1280,18432) (1 row) ivorysql=> select pg_sys_cpu_usage_info() from dual; pg_sys_cpu_usage_info ----------------------- (0,0,0,100,0,0,0,,,,) (1 row) ivorysql=> select pg_sys_memory_info() from dual; pg_sys_memory_info ----------------------------------------------------------------------------------- (4055482368,3911159808,144322560,4055887872,233725952,3822161920,2638602240,,,,,) (1 row) ivorysql=> select pg_sys_io_analysis_info() from dual; pg_sys_io_analysis_info --------------------------------------------------------------- (loop0,15,0,21504,0,1,0) (loop1,1362,0,27916288,0,680,0) (loop2,175,0,3354624,0,131,0) (loop3,141,0,1827840,0,83,0) (loop4,918,0,32752640,0,212,0) (loop5,156,0,2617344,0,67,0) (loop6,3707,0,131992576,0,828,0) (loop7,243,0,4321280,0,95,0) (fd0,0,0,0,0,0,0) (sda,639286,1580583,26547332096,240138948608,402702,2744653) (sda1,561,0,22966272,0,61,0) (sda2,636820,1580583,26491053056,240138948608,402316,2744653) (sr0,87,0,3219456,0,45,0) (sr1,0,0,0,0,0,0) (loop8,228,0,2491392,0,120,0) (loop10,2333,0,36571136,0,1628,0) (loop9,3730,0,66532352,0,3973,0) (loop11,87,0,709632,0,50,0) (loop12,156,0,1948672,0,52,0) (loop14,2579,0,97446912,0,2083,0) (loop13,30,0,102400,0,14,0) (loop15,82,0,1189888,0,117,0) (loop16,1357,0,28335104,0,500,0) (loop17,110,0,2187264,0,93,0) (loop18,129,0,2355200,0,28,0) (loop19,110,0,2202624,0,55,0) (26 rows) ivorysql=> select pg_sys_disk_info() from dual; pg_sys_disk_info ---------------------------------------------------------------------------------- (/,/dev/sda2,,,ext4,105086115840,50903396352,48797392896,6553600,619735,5933865) (1 row) ivorysql=> select pg_sys_load_avg_info() from dual; pg_sys_load_avg_info ---------------------- (0,0,0,) (1 row) ivorysql=> select pg_sys_process_info() from dual; pg_sys_process_info --------------------- (294,1,201,0,0) (1 row) ivorysql=> select pg_sys_network_info() from dual; pg_sys_network_info ----------------------------------------------------------------------------- (lo,127.0.0.1,1071575,5643,0,0,1071575,5643,0,0,0) (ens33,192.168.198.128,619877515,1490961,0,161,5540250106,4345449,0,0,1000) (2 rows) ivorysql=> select pg_sys_cpu_memory_by_process() from dual; pg_sys_cpu_memory_by_process ----------------------------------------------------------------------- (1,\"(systemd)\",864793,0,0.34,13840384) (2,\"(kthreadd)\",864793,0,0,0) (3,\"(pool_workqueue_release)\",864793,0,0,0) (4,\"(kworker/R-rcu_gp)\",864793,0,0,0) ......","title":"system_stats","component":"ivorysql-doc","version":"master","name":"system_stats","url":"/ivorysql-doc/master/ecosystem_components/system_stats.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Code Installation","hash":"source-code-installation","id":3},{"text":"3. Create extension and confirm the version","hash":"create-extension-and-confirm-the-version","id":4},{"text":"4. 使用","hash":"使用","id":5}]},"51":{"id":51,"text":"wal2json is an output plugin for logical decoding. It generates JSON object for each transaction. The source code installation environment is Ubuntu 24.04 (x86_64), in which IvorySQL 5 or a later version has been installed. The installation path is /usr/ivory-5. # download source code package from: https://github.com/eulerto/wal2json/releases/tag/wal2json_2_6 unzip wal2json_2_6.zip cd wal2json_2_6 # compile and install the extension make PG_CONFIG=/usr/ivory-5/bin/pg_config make PG_CONFIG=/usr/ivory-5/bin/pg_config install If there is error \"xlocale.h: No such file or directory\" during compilation, user should remove the line of \"#define HAVE_XLOCALE_H 1\" from file /usr/ivory-5/include/postgresql/server/pg_config.h. Modify the postgresql.conf file to set wal_level as \"logical\", and set max_replication_slots/max_wal_senders. wal_level = logical max_replication_slots = 10 max_wal_senders = 10 Make sure the following content to be in pg_hba.conf. local replication all trust host replication all 127.0.0.1/32 trust host replication all ::1/128 trust Then restart the database. Open the first terminal and execute command: sudo -u ivorysql /usr/ivory-5/bin/pg_recvlogical -d postgres --slot wal2json_slot --create-slot -P wal2json Start monitoring, output the changes in JSON format and in real time. sudo -u ivorysql /usr/ivory-5/bin/pg_recvlogical -d postgres --slot wal2json_slot --start -o pretty-print=1 -f - Connect database in the second terminal: /usr/ivory-5/bin/psql -d postgres -p 1521 Execute the following SQL statement: CREATE TABLE test_cdc (id int primary key, name varchar(50)); INSERT INTO test_cdc VALUES (1, 'test1'); UPDATE test_cdc SET name = 'test1_update' WHERE id = 1; DELETE FROM test_cdc WHERE id = 1; DROP TABLE test_cdc; The following output will appear in the first terminal: { \"change\": [ ] } { \"change\": [ { \"kind\": \"insert\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1\"] } ] } { \"change\": [ { \"kind\": \"update\", \"schema\": \"public\", \"table\": \"test_cdc\", \"columnnames\": [\"id\", \"name\"], \"columntypes\": [\"integer\", \"sys.oravarcharbyte(50)\"], \"columnvalues\": [1, \"test1_update\"], \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ { \"kind\": \"delete\", \"schema\": \"public\", \"table\": \"test_cdc\", \"oldkeys\": { \"keynames\": [\"id\"], \"keytypes\": [\"integer\"], \"keyvalues\": [1] } } ] } { \"change\": [ ] }","title":"wal2json","component":"ivorysql-doc","version":"master","name":"wal2json","url":"/ivorysql-doc/master/ecosystem_components/wal2json.html","titles":[{"text":"1. Overview","hash":"overview","id":1},{"text":"2. Installation","hash":"installation","id":2},{"text":"2.1. Source Code Installation","hash":"source-code-installation","id":3},{"text":"2.2. Modify the configuration file","hash":"modify-the-configuration-file","id":4},{"text":"3. Use","hash":"use","id":5}]},"52":{"id":52,"text":"IvorySQL databases require periodic maintenance known as vacuuming. For many installations, it is sufficient to let vacuuming be performed by the autovacuum daemon. You might need to adjust the autovacuuming parameters described there to obtain best results for your situation. Some database administrators will want to supplement or replace the daemon’s activities with manually-managed VACUUM commands, which typically are executed according to a schedule by cron or Task Scheduler scripts. To set up manually-managed vacuuming properly, it is essential to understand the issues discussed in the next few subsections. Administrators who rely on autovacuuming may still wish to skim this material to help them understand and adjust autovacuuming. IvorySQL’s command has to process each table on a regular basis for several reasons: To recover or reuse disk space occupied by updated or deleted rows. To update data statistics used by the PostgreSQL query planner. To update the visibility map, which speeds up index-only scans. To protect against loss of very old data due to transaction ID wraparound or multixact ID wraparound. Each of these reasons dictates performing VACUUM operations of varying frequency and scope, as explained in the following subsections. There are two variants of VACUUM: standard VACUUM and VACUUM FULL. VACUUM FULL can reclaim more disk space but runs much more slowly. Also, the standard form of VACUUM can run in parallel with production database operations. (Commands such as SELECT, INSERT, UPDATE, and DELETE will continue to function normally, though you will not be able to modify the definition of a table with commands such as ALTER TABLE while it is being vacuumed.) VACUUM FULL requires an ACCESS EXCLUSIVE lock on the table it is working on, and therefore cannot be done in parallel with other use of the table. Generally, therefore, administrators should strive to use standard VACUUM and avoid VACUUM FULL. VACUUM creates a substantial amount of I/O traffic, which can cause poor performance for other active sessions. There are configuration parameters that can be adjusted to reduce the performance impact of background vacuuming. In IvorySQL，an UPDATE or DELETE of a row does not immediately remove the old version of the row. This approach is necessary to gain the benefits of multiversion concurrency control : the row version must not be deleted while it is still potentially visible to other transactions. But eventually, an outdated or deleted row version is no longer of interest to any transaction. The space it occupies must then be reclaimed for reuse by new rows, to avoid unbounded growth of disk space requirements. This is done by running VACUUM. The standard form of VACUUM removes dead row versions in tables and indexes and marks the space available for future reuse. However, it will not return the space to the operating system, except in the special case where one or more pages at the end of a table become entirely free and an exclusive table lock can be easily obtained. In contrast, VACUUM FULL actively compacts tables by writing a complete new version of the table file with no dead space. This minimizes the size of the table, but can take a long time. It also requires extra disk space for the new copy of the table, until the operation completes. The usual goal of routine vacuuming is to do standard VACUUM`s often enough to avoid needing `VACUUM FULL. The autovacuum daemon attempts to work this way, and in fact will never issue VACUUM FULL. In this approach, the idea is not to keep tables at their minimum size, but to maintain steady-state usage of disk space: each table occupies space equivalent to its minimum size plus however much space gets used up between vacuum runs. Although VACUUM FULL can be used to shrink a table back to its minimum size and return the disk space to the operating system, there is not much point in this if the table will just grow again in the future. Thus, moderately-frequent standard VACUUM runs are a better approach than infrequent VACUUM FULL runs for maintaining heavily-updated tables. Some administrators prefer to schedule vacuuming themselves, for example doing all the work at night when load is low. The difficulty with doing vacuuming according to a fixed schedule is that if a table has an unexpected spike in update activity, it may get bloated to the point that VACUUM FULL is really necessary to reclaim space. Using the autovacuum daemon alleviates this problem, since the daemon schedules vacuuming dynamically in response to update activity. It is unwise to disable the daemon completely unless you have an extremely predictable workload. One possible compromise is to set the daemon’s parameters so that it will only react to unusually heavy update activity, thus keeping things from getting out of hand, while scheduled `VACUUM`s are expected to do the bulk of the work when the load is typical. For those not using autovacuum, a typical approach is to schedule a database-wide VACUUM once a day during a low-usage period, supplemented by more frequent vacuuming of heavily-updated tables as necessary. (Some installations with extremely high update rates vacuum their busiest tables as often as once every few minutes.) If you have multiple databases in a cluster, don’t forget to VACUUM each one; the program vacuumdb might be helpful. Tip Plain VACUUM may not be satisfactory when a table contains large numbers of dead row versions as a result of massive update or delete activity. If you have such a table and you need to reclaim the excess disk space it occupies, you will need to use VACUUM FULL, or alternatively CLUSTER or one of the table-rewriting variants of ALTER TABLE. These commands rewrite an entire new copy of the table and build new indexes for it. All these options require an ACCESS EXCLUSIVE lock. Note that they also temporarily use extra disk space approximately equal to the size of the table, since the old copies of the table and indexes can’t be released until the new ones are complete. Tip If you have a table whose entire contents are deleted on a periodic basis, consider doing it with TRUNCATE rather than using DELETE followed by VACUUM. TRUNCATE removes the entire content of the table immediately, without requiring a subsequent VACUUM or VACUUM FULL to reclaim the now-unused disk space. The disadvantage is that strict MVCC semantics are violated. The IvorySQL query planner relies on statistical information about the contents of tables in order to generate good plans for queries. These statistics are gathered by the ANALYZE command, which can be invoked by itself or as an optional step in VACUUM. It is important to have reasonably accurate statistics, otherwise poor choices of plans might degrade database performance. The autovacuum daemon, if enabled, will automatically issue ANALYZE commands whenever the content of a table has changed sufficiently. However, administrators might prefer to rely on manually-scheduled ANALYZE operations, particularly if it is known that update activity on a table will not affect the statistics of “interesting” columns. The daemon schedules ANALYZE strictly as a function of the number of rows inserted or updated; it has no knowledge of whether that will lead to meaningful statistical changes. Tuples changed in partitions and inheritance children do not trigger analyze on the parent table. If the parent table is empty or rarely changed, it may never be processed by autovacuum, and the statistics for the inheritance tree as a whole won’t be collected. It is necessary to run ANALYZE on the parent table manually in order to keep the statistics up to date. As with vacuuming for space recovery, frequent updates of statistics are more useful for heavily-updated tables than for seldom-updated ones. But even for a heavily-updated table, there might be no need for statistics updates if the statistical distribution of the data is not changing much. A simple rule of thumb is to think about how much the minimum and maximum values of the columns in the table change. For example, a timestamp column that contains the time of row update will have a constantly-increasing maximum value as rows are added and updated; such a column will probably need more frequent statistics updates than, say, a column containing URLs for pages accessed on a website. The URL column might receive changes just as often, but the statistical distribution of its values probably changes relatively slowly. It is possible to run ANALYZE on specific tables and even just specific columns of a table, so the flexibility exists to update some statistics more frequently than others if your application requires it. In practice, however, it is usually best to just analyze the entire database, because it is a fast operation. ANALYZE uses a statistically random sampling of the rows of a table rather than reading every single row. Tip Although per-column tweaking of ANALYZE frequency might not be very productive, you might find it worthwhile to do per-column adjustment of the level of detail of the statistics collected by ANALYZE. Columns that are heavily used in WHERE clauses and have highly irregular data distributions might require a finer-grain data histogram than other columns. See ALTER TABLE SET STATISTICS, or change the database-wide default using the default_statistics_target configuration parameter. Tip The autovacuum daemon does not issue ANALYZE commands for foreign tables, since it has no means of determining how often that might be useful. If your queries require statistics on foreign tables for proper planning, it’s a good idea to run manually-managed ANALYZE commands on those tables on a suitable schedule. Tip The autovacuum daemon does not issue ANALYZE commands for partitioned tables. Inheritance parents will only be analyzed if the parent itself is changed - changes to child tables do not trigger autoanalyze on the parent table. If your queries require statistics on parent tables for proper planning, it is necessary to periodically run a manual ANALYZE on those tables to keep the statistics up to date. Vacuum maintains a visibility map for each table to keep track of which pages contain only tuples that are known to be visible to all active transactions (and all future transactions, until the page is again modified). This has two purposes. First, vacuum itself can skip such pages on the next run, since there is nothing to clean up. Second, it allows IvorySQL to answer some queries using only the index, without reference to the underlying table. Since PostgreSQL indexes don’t contain tuple visibility information, a normal index scan fetches the heap tuple for each matching index entry, to check whether it should be seen by the current transaction. An index-only scan, on the other hand, checks the visibility map first. If it’s known that all tuples on the page are visible, the heap fetch can be skipped. This is most useful on large data sets where the visibility map can prevent disk accesses. The visibility map is vastly smaller than the heap, so it can easily be cached even when the heap is very large. IvorySQL’s MVCC transaction semantics depend on being able to compare transaction ID (XID) numbers: a row version with an insertion XID greater than the current transaction’s XID is “in the future” and should not be visible to the current transaction. But since transaction IDs have limited size (32 bits) a cluster that runs for a long time (more than 4 billion transactions) would suffer transaction ID wraparound: the XID counter wraps around to zero, and all of a sudden transactions that were in the past appear to be in the future — which means their output become invisible. In short, catastrophic data loss. (Actually the data is still there, but that’s cold comfort if you cannot get at it.) To avoid this, it is necessary to vacuum every table in every database at least once every two billion transactions. The reason that periodic vacuuming solves the problem is that VACUUM will mark rows as frozen, indicating that they were inserted by a transaction that committed sufficiently far in the past that the effects of the inserting transaction are certain to be visible to all current and future transactions. Normal XIDs are compared using modulo-232 arithmetic. This means that for every normal XID, there are two billion XIDs that are “older” and two billion that are “newer”; another way to say it is that the normal XID space is circular with no endpoint. Therefore, once a row version has been created with a particular normal XID, the row version will appear to be “in the past” for the next two billion transactions, no matter which normal XID we are talking about. If the row version still exists after more than two billion transactions, it will suddenly appear to be in the future. To prevent this, IvorySQL reserves a special XID, FrozenTransactionId, which does not follow the normal XID comparison rules and is always considered older than every normal XID. Frozen row versions are treated as if the inserting XID were FrozenTransactionId, so that they will appear to be “in the past” to all normal transactions regardless of wraparound issues, and so such row versions will be valid until deleted, no matter how long that is. vacuum_freeze_min_age controls how old an XID value has to be before rows bearing that XID will be frozen. Increasing this setting may avoid unnecessary work if the rows that would otherwise be frozen will soon be modified again, but decreasing this setting increases the number of transactions that can elapse before the table must be vacuumed again. VACUUM uses the visibility map to determine which pages of a table must be scanned. Normally, it will skip pages that don’t have any dead row versions even if those pages might still have row versions with old XID values. Therefore, normal VACUUM`s won’t always freeze every old row version in the table. When that happens, `VACUUM will eventually need to perform an aggressive vacuum, which will freeze all eligible unfrozen XID and MXID values, including those from all-visible but not all-frozen pages. In practice most tables require periodic aggressive vacuuming. vacuum_freeze_table_age controls when VACUUM does that: all-visible but not all-frozen pages are scanned if the number of transactions that have passed since the last such scan is greater than vacuum_freeze_table_age minus vacuum_freeze_min_age. Setting vacuum_freeze_table_age to 0 forces VACUUM to always use its aggressive strategy. The maximum time that a table can go unvacuumed is two billion transactions minus the vacuum_freeze_min_age value at the time of the last aggressive vacuum. If it were to go unvacuumed for longer than that, data loss could result. To ensure that this does not happen, autovacuum is invoked on any table that might contain unfrozen rows with XIDs older than the age specified by the configuration parameter autovacuum_freeze_max_age. (This will happen even if autovacuum is disabled.) This implies that if a table is not otherwise vacuumed, autovacuum will be invoked on it approximately once every autovacuum_freeze_max_age minus vacuum_freeze_min_age transactions. For tables that are regularly vacuumed for space reclamation purposes, this is of little importance. However, for static tables (including tables that receive inserts, but no updates or deletes), there is no need to vacuum for space reclamation, so it can be useful to try to maximize the interval between forced autovacuums on very large static tables. Obviously one can do this either by increasing autovacuum_freeze_max_age or decreasing vacuum_freeze_min_age. The effective maximum for vacuum_freeze_table_age is 0.95 * autovacuum_freeze_max_age; a setting higher than that will be capped to the maximum. A value higher than autovacuum_freeze_max_age wouldn’t make sense because an anti-wraparound autovacuum would be triggered at that point anyway, and the 0.95 multiplier leaves some breathing room to run a manual VACUUM before that happens. As a rule of thumb, vacuum_freeze_table_age should be set to a value somewhat below autovacuum_freeze_max_age, leaving enough gap so that a regularly scheduled VACUUM or an autovacuum triggered by normal delete and update activity is run in that window. Setting it too close could lead to anti-wraparound autovacuums, even though the table was recently vacuumed to reclaim space, whereas lower values lead to more frequent aggressive vacuuming. The sole disadvantage of increasing autovacuum_freeze_max_age (and vacuum_freeze_table_age along with it) is that the pg_xact and pg_commit_ts subdirectories of the database cluster will take more space, because it must store the commit status and (if track_commit_timestamp is enabled) timestamp of all transactions back to the autovacuum_freeze_max_age horizon. The commit status uses two bits per transaction, so if autovacuum_freeze_max_age is set to its maximum allowed value of two billion, pg_xact can be expected to grow to about half a gigabyte and pg_commit_ts to about 20GB. If this is trivial compared to your total database size, setting autovacuum_freeze_max_age to its maximum allowed value is recommended. Otherwise, set it depending on what you are willing to allow for pg_xact and pg_commit_ts storage. (The default, 200 million transactions, translates to about 50MB of pg_xact storage and about 2GB of pg_commit_ts storage.) One disadvantage of decreasing vacuum_freeze_min_age is that it might cause VACUUM to do useless work: freezing a row version is a waste of time if the row is modified soon thereafter (causing it to acquire a new XID). So the setting should be large enough that rows are not frozen until they are unlikely to change any more. To track the age of the oldest unfrozen XIDs in a database, VACUUM stores XID statistics in the system tables pg_class and pg_database. In particular, the relfrozenxid column of a table’s pg_class row contains the oldest remaining unfrozen XID at the end of the most recent VACUUM that successfully advanced relfrozenxid (typically the most recent aggressive VACUUM). Similarly, the datfrozenxid column of a database’s pg_database row is a lower bound on the unfrozen XIDs appearing in that database — it is just the minimum of the per-table relfrozenxid values within the database. A convenient way to examine this information is to execute queries such as: SELECT c.oid::regclass as table_name, greatest(age(c.relfrozenxid),age(t.relfrozenxid)) as age FROM pg_class c LEFT JOIN pg_class t ON c.reltoastrelid = t.oid WHERE c.relkind IN ('r', 'm'); SELECT datname, age(datfrozenxid) FROM pg_database; The age column measures the number of transactions from the cutoff XID to the current transaction’s XID. VACUUM normally only scans pages that have been modified since the last vacuum, but relfrozenxid can only be advanced when every page of the table that might contain unfrozen XIDs is scanned. This happens when relfrozenxid is more than vacuum_freeze_table_age transactions old, when VACUUM’s `FREEZE option is used, or when all pages that are not already all-frozen happen to require vacuuming to remove dead row versions. When VACUUM scans every page in the table that is not already all-frozen, it should set age(relfrozenxid) to a value just a little more than the vacuum_freeze_min_age setting that was used (more by the number of transactions started since the VACUUM started). VACUUM will set relfrozenxid to the oldest XID that remains in the table, so it’s possible that the final value will be much more recent than strictly required. If no relfrozenxid-advancing VACUUM is issued on the table until autovacuum_freeze_max_age is reached, an autovacuum will soon be forced for the table. If for some reason autovacuum fails to clear old XIDs from a table, the system will begin to emit warning messages like this when the database’s oldest XIDs reach forty million transactions from the wraparound point: WARNING: database \"mydb\" must be vacuumed within 39985967 transactions HINT: To avoid a database shutdown, execute a database-wide VACUUM in that database. (A manual VACUUM should fix the problem, as suggested by the hint; but note that the VACUUM must be performed by a superuser, else it will fail to process system catalogs and thus not be able to advance the database’s datfrozenxid.) If these warnings are ignored, the system will shut down and refuse to start any new transactions once there are fewer than three million transactions left until wraparound: ERROR: database is not accepting commands to avoid wraparound data loss in database \"mydb\" HINT: Stop the postmaster and vacuum that database in single-user mode. The three-million-transaction safety margin exists to let the administrator recover without data loss, by manually executing the required VACUUM commands. However, since the system will not execute commands once it has gone into the safety shutdown mode, the only way to do this is to stop the server and start the server in single-user mode to execute VACUUM. The shutdown mode is not enforced in single-user mode. See the postgres reference page for details about using single-user mode. Multixact IDs are used to support row locking by multiple transactions. Since there is only limited space in a tuple header to store lock information, that information is encoded as a “multiple transaction ID”, or multixact ID for short, whenever there is more than one transaction concurrently locking a row. Information about which transaction IDs are included in any particular multixact ID is stored separately in the pg_multixact subdirectory, and only the multixact ID appears in the xmax field in the tuple header. Like transaction IDs, multixact IDs are implemented as a 32-bit counter and corresponding storage, all of which requires careful aging management, storage cleanup, and wraparound handling. There is a separate storage area which holds the list of members in each multixact, which also uses a 32-bit counter and which must also be managed. Whenever VACUUM scans any part of a table, it will replace any multixact ID it encounters which is older than vacuum_multixact_freeze_min_age by a different value, which can be the zero value, a single transaction ID, or a newer multixact ID. For each table, pg_class.relminmxid stores the oldest possible multixact ID still appearing in any tuple of that table. If this value is older than vacuum_multixact_freeze_table_age, an aggressive vacuum is forced. As discussed in the previous section, an aggressive vacuum means that only those pages which are known to be all-frozen will be skipped. mxid_age() can be used on pg_class.relminmxid to find its age. Aggressive VACUUM`s, regardless of what causes them, are guaranteed to be able to advance the table’s `relminmxid. Eventually, as all tables in all databases are scanned and their oldest multixact values are advanced, on-disk storage for older multixacts can be removed. As a safety device, an aggressive vacuum scan will occur for any table whose multixact-age is greater than autovacuum_multixact_freeze_max_age. Also, if the storage occupied by multixacts members exceeds 2GB, aggressive vacuum scans will occur more often for all tables, starting with those that have the oldest multixact-age. Both of these kinds of aggressive scans will occur even if autovacuum is nominally disabled. IvorySQL has an optional but highly recommended feature called autovacuum, whose purpose is to automate the execution of VACUUM and ANALYZE commands. When enabled, autovacuum checks for tables that have had a large number of inserted, updated or deleted tuples. These checks use the statistics collection facility; therefore, autovacuum cannot be used unless track_counts is set to true. In the default configuration, autovacuuming is enabled and the related configuration parameters are appropriately set. The “autovacuum daemon” actually consists of multiple processes. There is a persistent daemon process, called the autovacuum launcher, which is in charge of starting autovacuum worker processes for all databases. The launcher will distribute the work across time, attempting to start one worker within each database every autovacuum_naptime seconds. (Therefore, if the installation has N databases, a new worker will be launched every autovacuum_naptime/N seconds.) A maximum of autovacuum_max_workers worker processes are allowed to run at the same time. If there are more than autovacuum_max_workers databases to be processed, the next database will be processed as soon as the first worker finishes. Each worker process will check each table within its database and execute VACUUM and/or ANALYZE as needed. log_autovacuum_min_duration can be set to monitor autovacuum workers' activity. If several large tables all become eligible for vacuuming in a short amount of time, all autovacuum workers might become occupied with vacuuming those tables for a long period. This would result in other tables and databases not being vacuumed until a worker becomes available. There is no limit on how many workers might be in a single database, but workers do try to avoid repeating work that has already been done by other workers. Note that the number of running workers does not count towards max_connections or superuser_reserved_connections limits. Tables whose relfrozenxid value is more than autovacuum_freeze_max_age transactions old are always vacuumed (this also applies to those tables whose freeze max age has been modified via storage parameters; see below). Otherwise, if the number of tuples obsoleted since the last VACUUM exceeds the “vacuum threshold”, the table is vacuumed. The vacuum threshold is defined as: vacuum threshold = vacuum base threshold + vacuum scale factor * number of tuples where the vacuum base threshold is autovacuum_vacuum_threshold, the vacuum scale factor is autovacuum_vacuum_scale_factor, and the number of tuples is pg_class.reltuples. The table is also vacuumed if the number of tuples inserted since the last vacuum has exceeded the defined insert threshold, which is defined as: vacuum insert threshold = vacuum base insert threshold + vacuum insert scale factor * number of tuples where the vacuum insert base threshold is autovacuum_vacuum_insert_threshold, and vacuum insert scale factor is autovacuum_vacuum_insert_scale_factor. Such vacuums may allow portions of the table to be marked as all visible and also allow tuples to be frozen, which can reduce the work required in subsequent vacuums. For tables which receive INSERT operations but no or almost no UPDATE/DELETE operations, it may be beneficial to lower the table’s autovacuum_freeze_min_age as this may allow tuples to be frozen by earlier vacuums. The number of obsolete tuples and the number of inserted tuples are obtained from the cumulative statistics system; it is a semi-accurate count updated by each UPDATE, DELETE and INSERT operation. (It is only semi-accurate because some information might be lost under heavy load.) If the relfrozenxid value of the table is more than vacuum_freeze_table_age transactions old, an aggressive vacuum is performed to freeze old tuples and advance relfrozenxid; otherwise, only pages that have been modified since the last vacuum are scanned. For analyze, a similar condition is used: the threshold, defined as: analyze threshold = analyze base threshold + analyze scale factor * number of tuples is compared to the total number of tuples inserted, updated, or deleted since the last ANALYZE. Partitioned tables are not processed by autovacuum. Statistics should be collected by running a manual ANALYZE when it is first populated, and again whenever the distribution of data in its partitions changes significantly. Temporary tables cannot be accessed by autovacuum. Therefore, appropriate vacuum and analyze operations should be performed via session SQL commands. The default thresholds and scale factors are taken from postgresql.conf, but it is possible to override them (and many other autovacuum control parameters) on a per-table basis; see Storage Parameters for more information. If a setting has been changed via a table’s storage parameters, that value is used when processing that table; otherwise the global settings are used. See Section 20.10 for more details on the global settings. When multiple workers are running, the autovacuum cost delay parameters (see Section 20.4.4) are “balanced” among all the running workers, so that the total I/O impact on the system is the same regardless of the number of workers actually running. However, any workers processing tables whose per-table autovacuum_vacuum_cost_delay or autovacuum_vacuum_cost_limit storage parameters have been set are not considered in the balancing algorithm. Autovacuum workers generally don’t block other commands. If a process attempts to acquire a lock that conflicts with the SHARE UPDATE EXCLUSIVE lock held by autovacuum, lock acquisition will interrupt the autovacuum. For conflicting lock modes, see Table 13.3. However, if the autovacuum is running to prevent transaction ID wraparound (i.e., the autovacuum query name in the pg_stat_activity view ends with (to prevent wraparound)), the autovacuum is not automatically interrupted. Warning Regularly running commands that acquire locks conflicting with a SHARE UPDATE EXCLUSIVE lock (e.g., ANALYZE) can effectively prevent autovacuums from ever completing. In some situations it is worthwhile to rebuild indexes periodically with the REINDEX command or a series of individual rebuilding steps. B-tree index pages that have become completely empty are reclaimed for re-use. However, there is still a possibility of inefficient use of space: if all but a few index keys on a page have been deleted, the page remains allocated. Therefore, a usage pattern in which most, but not all, keys in each range are eventually deleted will see poor use of space. For such usage patterns, periodic reindexing is recommended. The potential for bloat in non-B-tree indexes has not been well researched. It is a good idea to periodically monitor the index’s physical size when using any non-B-tree index type. Also, for B-tree indexes, a freshly-constructed index is slightly faster to access than one that has been updated many times because logically adjacent pages are usually also physically adjacent in a newly built index. (This consideration does not apply to non-B-tree indexes.) It might be worthwhile to reindex periodically just to improve access speed. REINDEX can be used safely and easily in all cases. This command requires an ACCESS EXCLUSIVE lock by default, hence it is often preferable to execute it with its CONCURRENTLY option, which requires only a SHARE UPDATE EXCLUSIVE lock. It is a good idea to save the database server’s log output somewhere, rather than just discarding it via /dev/null. The log output is invaluable when diagnosing problems.Log output tends to be voluminous (especially at higher debug levels) so you won’t want to save it indefinitely. You need to rotate the log files so that new log files are started and old ones removed after a reasonable period of time. If you simply direct the stderr of postgres into a file, you will have log output, but the only way to truncate the log file is to stop and restart the server. This might be acceptable if you are using PostgreSQL in a development environment, but few production servers would find this behavior acceptable. A better approach is to send the server’s stderr output to some type of log rotation program. There is a built-in log rotation facility, which you can use by setting the configuration parameter logging_collector to true in postgresql.conf. You can also use this approach to capture the log data in machine readable CSV (comma-separated values) format. Alternatively, you might prefer to use an external log rotation program if you have one that you are already using with other server software. For example, the rotatelogs tool included in the Apache distribution can be used with PostgreSQL. One way to do this is to pipe the server’s stderr output to the desired program. If you start the server with pg_ctl, then stderr is already redirected to stdout, so you just need a pipe command, for example: pg_ctl start | rotatelogs /var/log/pgsql_log 86400 You can combine these approaches by setting up logrotate to collect log files produced by PostgreSQL built-in logging collector. In this case, the logging collector defines the names and location of the log files, while logrotate periodically archives these files. When initiating log rotation, logrotate must ensure that the application sends further output to the new file. This is commonly done with a postrotate script that sends a SIGHUP signal to the application, which then reopens the log file. In PostgreSQL, you can run pg_ctl with the logrotate option instead. When the server receives this command, the server either switches to a new log file or reopens the existing file, depending on the logging configuration. Note When using static log file names, the server might fail to reopen the log file if the max open file limit is reached or a file table overflow occurs. In this case, log messages are sent to the old log file until a successful log rotation. If logrotate is configured to compress the log file and delete it, the server may lose the messages logged in this time frame. To avoid this issue, you can configure the logging collector to dynamically assign log file names and use a prerotate script to ignore open log files. Another production-grade approach to managing log output is to send it to syslog and let syslog deal with file rotation. To do this, set the configuration parameter log_destination to syslog (to log to syslog only) in postgresql.conf. Then you can send a SIGHUP signal to the syslog daemon whenever you want to force it to start writing a new log file. If you want to automate log rotation, the logrotate program can be configured to work with log files from syslog. On many systems, however, syslog is not very reliable, particularly with large log messages; it might truncate or drop messages just when you need them the most. Also, on Linux, syslog will flush each message to disk, yielding poor performance. (You can use a “-” at the start of the file name in the syslog configuration file to disable syncing.) Note that all the solutions described above take care of starting new log files at configurable intervals, but they do not handle deletion of old, no-longer-useful log files. You will probably want to set up a batch job to periodically delete old log files. Another possibility is to configure the rotation program so that old log files are overwritten cyclically. pgBadger is an external project that does sophisticated log file analysis. check_postgres provides Nagios alerts when important messages appear in the log files, as well as detection of many other extraordinary conditions. Shared disk failover avoids synchronization overhead by having only one copy of the database. It uses a single disk array that is shared by multiple servers. If the main database server fails, the standby server is able to mount and start the database as though it were recovering from a database crash. This allows rapid failover with no data loss. Shared hardware functionality is common in network storage devices. Using a network file system is also possible, though care must be taken that the file system has full POSIX behavior . One significant limitation of this method is that if the shared disk array fails or becomes corrupt, the primary and standby servers are both nonfunctional. Another issue is that the standby server should never access the shared storage while the primary server is running. A modified version of shared hardware functionality is file system replication, where all changes to a file system are mirrored to a file system residing on another computer. The only restriction is that the mirroring must be done in a way that ensures the standby server has a consistent copy of the file system — specifically, writes to the standby must be done in the same order as those on the primary. DRBD is a popular file system replication solution for Linux. Warm and hot standby servers can be kept current by reading a stream of write-ahead log (WAL) records. If the main server fails, the standby contains almost all of the data of the main server, and can be quickly made the new primary database server. This can be synchronous or asynchronous and can only be done for the entire database server. A standby server can be implemented using file-based log shipping or streaming replication, or a combination of both. For information on hot standby Logical replication allows a database server to send a stream of data modifications to another server. IvorySQL logical replication constructs a stream of logical data modifications from the WAL. Logical replication allows replication of data changes on a per-table basis. In addition, a server that is publishing its own changes can also subscribe to changes from another server, allowing data to flow in multiple directions. For more information on logical replication. Through the logical decoding interface , third-party extensions can also provide similar functionality. A trigger-based replication setup typically funnels data modification queries to a designated primary server. Operating on a per-table basis, the primary server sends data changes (typically) asynchronously to the standby servers. Standby servers can answer queries while the primary is running, and may allow some local data changes or write activity. This form of replication is often used for offloading large analytical or data warehouse queries. Slony-I is an example of this type of replication, with per-table granularity, and support for multiple standby servers. Because it updates the standby server asynchronously (in batches), there is possible data loss during fail over. With SQL-based replication middleware, a program intercepts every SQL query and sends it to one or all servers. Each server operates independently. Read-write queries must be sent to all servers, so that every server receives any changes. But read-only queries can be sent to just one server, allowing the read workload to be distributed among them. If queries are simply broadcast unmodified, functions like random(), CURRENT_TIMESTAMP, and sequences can have different values on different servers. This is because each server operates independently, and because SQL queries are broadcast rather than actual data changes. If this is unacceptable, either the middleware or the application must determine such values from a single source and then use those values in write queries. Care must also be taken that all transactions either commit or abort on all servers, perhaps using two-phase commit (PREPARE TRANSACTION and COMMIT PREPARED). Pgpool-II and Continuent Tungsten are examples of this type of replication. For servers that are not regularly connected or have slow communication links, like laptops or remote servers, keeping data consistent among servers is a challenge. Using asynchronous multimaster replication, each server works independently, and periodically communicates with the other servers to identify conflicting transactions. The conflicts can be resolved by users or conflict resolution rules. Bucardo is an example of this type of replication. In synchronous multimaster replication, each server can accept write requests, and modified data is transmitted from the original server to every other server before each transaction commits. Heavy write activity can cause excessive locking and commit delays, leading to poor performance. Read requests can be sent to any server. Some implementations use shared disk to reduce the communication overhead. Synchronous multimaster replication is best for mostly read workloads, though its big advantage is that any server can accept write requests — there is no need to partition workloads between primary and standby servers, and because the data changes are sent from one server to another, there is no problem with non-deterministic functions like random(). IvorySQL does not offer this type of replication, though PostgreSQL two-phase commit (PREPARE TRANSACTION and COMMIT PREPARED) can be used to implement this in application code or middleware. The following table summarizes the capabilities of each of these scenarios. Feature Shared Disk File System Repl. Write-Ahead Log Shipping Logical Repl. Trigger-Based Repl. SQL Repl. Middle-ware Async. MM Repl. Sync. MM Repl. Popular examples NAS DRBD built-in streaming repl. built-in logical repl., pglogical Londiste, Slony pgpool-II Bucardo Comm. method shared disk disk blocks WAL logical decoding table rows SQL table rows table rows and row locks No special hardware required • • • • • • • Allows multiple primary servers • • • • No overhead on primary • • • • No waiting for multiple servers • with sync off with sync off • • Primary failure will never lose data • • with sync on with sync on • • Replicas accept read-only queries with hot standby • • • • • Per-table granularity • • • • No conflict resolution necessary • • • • • • There are a few solutions that do not fit into the above categories: Data Partitioning Data partitioning splits tables into data sets. Each set can be modified by only one server. For example, data can be partitioned by offices, e.g., London and Paris, with a server in each office. If queries combining London and Paris data are necessary, an application can query both servers, or primary/standby replication can be used to keep a read-only copy of the other office's data on each server. Multiple-Server Parallel Query Execution Many of the above solutions allow multiple servers to handle multiple queries, but none allow a single query to use multiple servers to complete faster. This solution allows multiple servers to work concurrently on a single query. It is usually accomplished by splitting the data among servers and having each server execute its part of the query and return results to a central server where they are combined and returned to the user. This can be implemented using the PL/Proxy tool set. It is usually wise to create the primary and standby servers so that they are as similar as possible, at least from the perspective of the database server. In particular, the path names associated with tablespaces will be passed across unmodified, so both primary and standby servers must have the same mount paths for tablespaces if that feature is used. Keep in mind that if CREATE TABLESPACE is executed on the primary, any new mount point needed for it must be created on the primary and all standby servers before the command is executed. Hardware need not be exactly the same, but experience shows that maintaining two identical systems is easier than maintaining two dissimilar ones over the lifetime of the application and system. In any case the hardware architecture must be the same — shipping from, say, a 32-bit to a 64-bit system will not work. In general, log shipping between servers running different major IvorySQL release levels is not possible. It is the policy of the IvorySQL Global Development Group not to make changes to disk formats during minor release upgrades, so it is likely that running different minor release levels on primary and standby servers will work successfully. However, no formal support for that is offered and you are advised to keep primary and standby servers at the same release level as much as possible. When updating to a new minor release, the safest policy is to update the standby servers first — a new minor release is more likely to be able to read WAL files from a previous minor release than vice versa. A server enters standby mode if a standby.signal file exists in the data directory when the server is started. In standby mode, the server continuously applies WAL received from the primary server. The standby server can read WAL from a WAL archive (see restore_command) or directly from the primary over a TCP connection (streaming replication). The standby server will also attempt to restore any WAL found in the standby cluster’s pg_wal directory. That typically happens after a server restart, when the standby replays again WAL that was streamed from the primary before the restart, but you can also manually copy files to pg_wal at any time to have them replayed. At startup, the standby begins by restoring all WAL available in the archive location, calling restore_command. Once it reaches the end of WAL available there and restore_command fails, it tries to restore any WAL available in the pg_wal directory. If that fails, and streaming replication has been configured, the standby tries to connect to the primary server and start streaming WAL from the last valid record found in archive or pg_wal. If that fails or streaming replication is not configured, or if the connection is later disconnected, the standby goes back to step 1 and tries to restore the file from the archive again. This loop of retries from the archive, pg_wal, and via streaming replication goes on until the server is stopped or failover is triggered by a trigger file. Standby mode is exited and the server switches to normal operation when pg_ctl promote is run, pg_promote() is called, or a trigger file is found (promote_trigger_file). Before failover, any WAL immediately available in the archive or in pg_wal will be restored, but no attempt is made to connect to the primary. Set up continuous archiving on the primary to an archive directory accessible from the standby.The archive location should be accessible from the standby even when the primary is down, i.e., it should reside on the standby server itself or another trusted server, not on the primary server. If you want to use streaming replication, set up authentication on the primary server to allow replication connections from the standby server(s); that is, create a role and provide a suitable entry or entries in pg_hba.conf with the database field set to replication. Also ensure max_wal_senders is set to a sufficiently large value in the configuration file of the primary server. If replication slots will be used, ensure that max_replication_slots is set sufficiently high as well. To set up the standby server, restore the base backup taken from primary server . Create a file standby.signal in the standby’s cluster data directory. Set restore_command to a simple command to copy files from the WAL archive. If you plan to have multiple standby servers for high availability purposes, make sure that recovery_target_timeline is set to latest (the default), to make the standby server follow the timeline change that occurs at failover to another standby. Note restore_command should return immediately if the file does not exist; the server will retry the command again if necessary. If you want to use streaming replication, fill in primary_conninfo with a libpq connection string, including the host name (or IP address) and any additional details needed to connect to the primary server. If the primary needs a password for authentication, the password needs to be specified in primary_conninfo as well. If you’re setting up the standby server for high availability purposes, set up WAL archiving, connections and authentication like the primary server, because the standby server will work as a primary server after failover. If you’re using a WAL archive, its size can be minimized using the archive_cleanup_command parameter to remove files that are no longer required by the standby server. The pg_archivecleanup utility is designed specifically to be used with archive_cleanup_command in typical single-standby configurations, see pg_archivecleanup. Note however, that if you’re using the archive for backup purposes, you need to retain files needed to recover from at least the latest base backup, even if they’re no longer needed by the standby. A simple example of configuration is: primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass options=''-c wal_sender_timeout=5000''' restore_command = 'cp /path/to/archive/%f %p' archive_cleanup_command = 'pg_archivecleanup /path/to/archive %r' You can have any number of standby servers, but if you use streaming replication, make sure you set max_wal_senders high enough in the primary to allow them to be connected simultaneously. Streaming replication allows a standby server to stay more up-to-date than is possible with file-based log shipping. The standby connects to the primary, which streams WAL records to the standby as they’re generated, without waiting for the WAL file to be filled. Streaming replication is asynchronous by default , in which case there is a small delay between committing a transaction in the primary and the changes becoming visible in the standby. This delay is however much smaller than with file-based log shipping, typically under one second assuming the standby is powerful enough to keep up with the load. With streaming replication, archive_timeout is not required to reduce the data loss window. If you use streaming replication without file-based continuous archiving, the server might recycle old WAL segments before the standby has received them. If this occurs, the standby will need to be reinitialized from a new base backup. You can avoid this by setting wal_keep_size to a value large enough to ensure that WAL segments are not recycled too early, or by configuring a replication slot for the standby. If you set up a WAL archive that’s accessible from the standby, these solutions are not required, since the standby can always use the archive to catch up provided it retains enough segments. To use streaming replication, set up a file-based log-shipping standby server. The step that turns a file-based log-shipping standby into streaming replication standby is setting the primary_conninfo setting to point to the primary server. Set listen_addresses and authentication options (see pg_hba.conf) on the primary so that the standby server can connect to the replication pseudo-database on the primary server. On systems that support the keepalive socket option, setting tcp_keepalives_idle, tcp_keepalives_interval and tcp_keepalives_count helps the primary promptly notice a broken connection. Set the maximum number of concurrent connections from the standby servers (see max_wal_senders for details). When the standby is started and primary_conninfo is set correctly, the standby will connect to the primary after replaying all WAL files available in the archive. If the connection is established successfully, you will see a walreceiver in the standby, and a corresponding walsender process in the primary. It is very important that the access privileges for replication be set up so that only trusted users can read the WAL stream, because it is easy to extract privileged information from it. Standby servers must authenticate to the primary as an account that has the REPLICATION privilege or a superuser. It is recommended to create a dedicated user account with REPLICATION and LOGIN privileges for replication. While REPLICATION privilege gives very high permissions, it does not allow the user to modify any data on the primary system, which the SUPERUSER privilege does. Client authentication for replication is controlled by a pg_hba.conf record specifying replication in the database field. For example, if the standby is running on host IP 192.168.1.100 and the account name for replication is foo, the administrator can add the following line to the pg_hba.conf file on the primary: # Allow the user \"foo\" from host 192.168.1.100 to connect to the primary # as a replication standby if the user's password is correctly supplied. # # TYPE DATABASE USER ADDRESS METHOD host replication foo 192.168.1.100/32 md5 The host name and port number of the primary, connection user name, and password are specified in the primary_conninfo. The password can also be set in the ~/.pgpass file on the standby (specify replication in the database field). For example, if the primary is running on host IP 192.168.1.50, port 5432, the account name for replication is foo, and the password is foopass, the administrator can add the following line to the postgresql.conf file on the standby: # The standby connects to the primary that is running on host 192.168.1.50 # and port 5432 as the user \"foo\" whose password is \"foopass\". primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' An important health indicator of streaming replication is the amount of WAL records generated in the primary, but not yet applied in the standby. You can calculate this lag by comparing the current WAL write location on the primary with the last WAL location received by the standby. These locations can be retrieved using pg_current_wal_lsn on the primary and pg_last_wal_receive_lsn on the standby, respectively . The last WAL receive location in the standby is also displayed in the process status of the WAL receiver process, displayed using the ps command . You can retrieve a list of WAL sender processes via the pg_stat_replication view. Large differences between pg_current_wal_lsn and the view’s sent_lsn field might indicate that the primary server is under heavy load, while differences between sent_lsn and pg_last_wal_receive_lsn on the standby might indicate network delay, or that the standby is under heavy load. On a hot standby, the status of the WAL receiver process can be retrieved via the pg_stat_wal_receiver view. A large difference between pg_last_wal_replay_lsn and the view’s flushed_lsn indicates that WAL is being received faster than it can be replayed. Replication slots provide an automated way to ensure that the primary does not remove WAL segments until they have been received by all standbys, and that the primary does not remove rows which could cause a recovery conflict even when the standby is disconnected. In lieu of using replication slots, it is possible to prevent the removal of old WAL segments using wal_keep_size, or by storing the segments in an archive using archive_command or archive_library. However, these methods often result in retaining more WAL segments than required, whereas replication slots retain only the number of segments known to be needed. On the other hand, replication slots can retain so many WAL segments that they fill up the space allocated for pg_wal; max_slot_wal_keep_size limits the size of WAL files retained by replication slots. Similarly, hot_standby_feedback and vacuum_defer_cleanup_age provide protection against relevant rows being removed by vacuum, but the former provides no protection during any time period when the standby is not connected, and the latter often needs to be set to a high value to provide adequate protection. Replication slots overcome these disadvantages. Each replication slot has a name, which can contain lower-case letters, numbers, and the underscore character. Existing replication slots and their state can be seen in the pg_replication_slots view. Slots can be created and dropped either via the streaming replication protocol or via SQL functions . You can create a replication slot like this: postgres=# SELECT * FROM pg_create_physical_replication_slot('node_a_slot'); slot_name | lsn -------------+----- node_a_slot | postgres=# SELECT slot_name, slot_type, active FROM pg_replication_slots; slot_name | slot_type | active -------------+-----------+-------- node_a_slot | physical | f (1 row) To configure the standby to use this slot, primary_slot_name should be configured on the standby. Here is a simple example: primary_conninfo = 'host=192.168.1.50 port=5432 user=foo password=foopass' primary_slot_name = 'node_a_slot' The cascading replication feature allows a standby server to accept replication connections and stream WAL records to other standbys, acting as a relay. This can be used to reduce the number of direct connections to the primary and also to minimize inter-site bandwidth overheads. A standby acting as both a receiver and a sender is known as a cascading standby. Standbys that are more directly connected to the primary are known as upstream servers, while those standby servers further away are downstream servers. Cascading replication does not place limits on the number or arrangement of downstream servers, though each standby connects to only one upstream server which eventually links to a single primary server. A cascading standby sends not only WAL records received from the primary but also those restored from the archive. So even if the replication connection in some upstream connection is terminated, streaming replication continues downstream for as long as new WAL records are available. Cascading replication is currently asynchronous. Synchronous replication settings have no effect on cascading replication at present. Hot standby feedback propagates upstream, whatever the cascaded arrangement. If an upstream standby server is promoted to become the new primary, downstream servers will continue to stream from the new primary if recovery_target_timeline is set to 'latest' (the default). To use cascading replication, set up the cascading standby so that it can accept replication connections (that is, set max_wal_senders and hot_standby, and configure host-based authentication). You will also need to set primary_conninfo in the downstream standby to point to the cascading standby. IvorySQL streaming replication is asynchronous by default. If the primary server crashes then some transactions that were committed may not have been replicated to the standby server, causing data loss. The amount of data loss is proportional to the replication delay at the time of failover. Synchronous replication offers the ability to confirm that all changes made by a transaction have been transferred to one or more synchronous standby servers. This extends that standard level of durability offered by a transaction commit. This level of protection is referred to as 2-safe replication in computer science theory, and group-1-safe (group-safe and 1-safe) when synchronous_commit is set to remote_write. When requesting synchronous replication, each commit of a write transaction will wait until confirmation is received that the commit has been written to the write-ahead log on disk of both the primary and standby server. The only possibility that data can be lost is if both the primary and the standby suffer crashes at the same time. This can provide a much higher level of durability, though only if the sysadmin is cautious about the placement and management of the two servers. Waiting for confirmation increases the user’s confidence that the changes will not be lost in the event of server crashes but it also necessarily increases the response time for the requesting transaction. The minimum wait time is the round-trip time between primary and standby. Read-only transactions and transaction rollbacks need not wait for replies from standby servers. Subtransaction commits do not wait for responses from standby servers, only top-level commits. Long running actions such as data loading or index building do not wait until the very final commit message. All two-phase commit actions require commit waits, including both prepare and commit. A synchronous standby can be a physical replication standby or a logical replication subscriber. It can also be any other physical or logical WAL replication stream consumer that knows how to send the appropriate feedback messages. Besides the built-in physical and logical replication systems, this includes special programs such as pg_receivewal and pg_recvlogical as well as some third-party replication systems and custom programs. Check the respective documentation for details on synchronous replication support. Once streaming replication has been configured, configuring synchronous replication requires only one additional configuration step: synchronous_standby_names must be set to a non-empty value. synchronous_commit must also be set to on, but since this is the default value, typically no change is required.This configuration will cause each commit to wait for confirmation that the standby has written the commit record to durable storage. synchronous_commit can be set by individual users, so it can be configured in the configuration file, for particular users or databases, or dynamically by applications, in order to control the durability guarantee on a per-transaction basis. After a commit record has been written to disk on the primary, the WAL record is then sent to the standby. The standby sends reply messages each time a new batch of WAL data is written to disk, unless wal_receiver_status_interval is set to zero on the standby. In the case that synchronous_commit is set to remote_apply, the standby sends reply messages when the commit record is replayed, making the transaction visible. If the standby is chosen as a synchronous standby, according to the setting of synchronous_standby_names on the primary, the reply messages from that standby will be considered along with those from other synchronous standbys to decide when to release transactions waiting for confirmation that the commit record has been received. These parameters allow the administrator to specify which standby servers should be synchronous standbys. Note that the configuration of synchronous replication is mainly on the primary. Named standbys must be directly connected to the primary; the primary knows nothing about downstream standby servers using cascaded replication. Setting synchronous_commit to remote_write will cause each commit to wait for confirmation that the standby has received the commit record and written it out to its own operating system, but not for the data to be flushed to disk on the standby. This setting provides a weaker guarantee of durability than on does: the standby could lose the data in the event of an operating system crash, though not a PostgreSQL crash. However, it’s a useful setting in practice because it can decrease the response time for the transaction. Data loss could only occur if both the primary and the standby crash and the database of the primary gets corrupted at the same time. Setting synchronous_commit to remote_apply will cause each commit to wait until the current synchronous standbys report that they have replayed the transaction, making it visible to user queries. In simple cases, this allows for load balancing with causal consistency. Users will stop waiting if a fast shutdown is requested. However, as when using asynchronous replication, the server will not fully shutdown until all outstanding WAL records are transferred to the currently connected standby servers. Synchronous replication supports one or more synchronous standby servers; transactions will wait until all the standby servers which are considered as synchronous confirm receipt of their data. The number of synchronous standbys that transactions must wait for replies from is specified in synchronous_standby_names. This parameter also specifies a list of standby names and the method (FIRST and ANY) to choose synchronous standbys from the listed ones. The method FIRST specifies a priority-based synchronous replication and makes transaction commits wait until their WAL records are replicated to the requested number of synchronous standbys chosen based on their priorities. The standbys whose names appear earlier in the list are given higher priority and will be considered as synchronous. Other standby servers appearing later in this list represent potential synchronous standbys. If any of the current synchronous standbys disconnects for whatever reason, it will be replaced immediately with the next-highest-priority standby. An example of synchronous_standby_names for a priority-based multiple synchronous standbys is: synchronous_standby_names = 'FIRST 2 (s1, s2, s3)' In this example, if four standby servers s1, s2, s3 and s4 are running, the two standbys s1 and s2 will be chosen as synchronous standbys because their names appear early in the list of standby names. s3 is a potential synchronous standby and will take over the role of synchronous standby when either of s1 or s2 fails. s4 is an asynchronous standby since its name is not in the list. The method ANY specifies a quorum-based synchronous replication and makes transaction commits wait until their WAL records are replicated to at least the requested number of synchronous standbys in the list. An example of synchronous_standby_names for a quorum-based multiple synchronous standbys is: synchronous_standby_names = 'ANY 2 (s1, s2, s3)' In this example, if four standby servers s1, s2, s3 and s4 are running, transaction commits will wait for replies from at least any two standbys of s1, s2 and s3. s4 is an asynchronous standby since its name is not in the list. The synchronous states of standby servers can be viewed using the pg_stat_replication view. Synchronous replication usually requires carefully planned and placed standby servers to ensure applications perform acceptably. Waiting doesn’t utilize system resources, but transaction locks continue to be held until the transfer is confirmed. As a result, incautious use of synchronous replication will reduce performance for database applications because of increased response times and higher contention. PostgreSQL allows the application developer to specify the durability level required via replication. This can be specified for the system overall, though it can also be specified for specific users or connections, or even individual transactions. For example, an application workload might consist of: 10% of changes are important customer details, while 90% of changes are less important data that the business can more easily survive if it is lost, such as chat messages between users. With synchronous replication options specified at the application level (on the primary) we can offer synchronous replication for the most important changes, without slowing down the bulk of the total workload. Application level options are an important and practical tool for allowing the benefits of synchronous replication for high performance applications. You should consider that the network bandwidth must be higher than the rate of generation of WAL data. synchronous_standby_names specifies the number and names of synchronous standbys that transaction commits made when synchronous_commit is set to on, remote_apply or remote_write will wait for responses from. Such transaction commits may never be completed if any one of synchronous standbys should crash. The best solution for high availability is to ensure you keep as many synchronous standbys as requested. This can be achieved by naming multiple potential synchronous standbys using synchronous_standby_names. In a priority-based synchronous replication, the standbys whose names appear earlier in the list will be used as synchronous standbys. Standbys listed after these will take over the role of synchronous standby if one of current ones should fail. In a quorum-based synchronous replication, all the standbys appearing in the list will be used as candidates for synchronous standbys. Even if one of them should fail, the other standbys will keep performing the role of candidates of synchronous standby. When a standby first attaches to the primary, it will not yet be properly synchronized. This is described as catchup mode. Once the lag between standby and primary reaches zero for the first time we move to real-time streaming state. The catch-up duration may be long immediately after the standby has been created. If the standby is shut down, then the catch-up period will increase according to the length of time the standby has been down. The standby is only able to become a synchronous standby once it has reached streaming state. This state can be viewed using the pg_stat_replication view. If primary restarts while commits are waiting for acknowledgment, those waiting transactions will be marked fully committed once the primary database recovers. There is no way to be certain that all standbys have received all outstanding WAL data at time of the crash of the primary. Some transactions may not show as committed on the standby, even though they show as committed on the primary. The guarantee we offer is that the application will not receive explicit acknowledgment of the successful commit of a transaction until the WAL data is known to be safely received by all the synchronous standbys. If you really cannot keep as many synchronous standbys as requested then you should decrease the number of synchronous standbys that transaction commits must wait for responses from in synchronous_standby_names (or disable it) and reload the configuration file on the primary server. If the primary is isolated from remaining standby servers you should fail over to the best candidate of those other remaining standby servers. If you need to re-create a standby server while transactions are waiting, make sure that the commands pg_backup_start() and pg_backup_stop() are run in a session with synchronous_commit = off, otherwise those requests will wait forever for the standby to appear. When continuous WAL archiving is used in a standby, there are two different scenarios: the WAL archive can be shared between the primary and the standby, or the standby can have its own WAL archive. When the standby has its own WAL archive, set archive_mode to always, and the standby will call the archive command for every WAL segment it receives, whether it’s by restoring from the archive or by streaming replication. The shared archive can be handled similarly, but the archive_command or archive_library must test if the file being archived exists already, and if the existing file has identical contents. This requires more care in the archive_command or archive_library, as it must be careful to not overwrite an existing file with different contents, but return success if the exactly same file is archived twice. And all that must be done free of race conditions, if two servers attempt to archive the same file at the same time. If archive_mode is set to on, the archiver is not enabled during recovery or standby mode. If the standby server is promoted, it will start archiving after the promotion, but will not archive any WAL or timeline history files that it did not generate itself. To get a complete series of WAL files in the archive, you must ensure that all WAL is archived, before it reaches the standby. This is inherently true with file-based log shipping, as the standby can only restore files that are found in the archive, but not if streaming replication is enabled. When a server is not in recovery mode, there is no difference between on and always modes. If the primary server fails then the standby server should begin failover procedures. If the standby server fails then no failover need take place. If the standby server can be restarted, even some time later, then the recovery process can also be restarted immediately, taking advantage of restartable recovery. If the standby server cannot be restarted, then a full new standby server instance should be created. If the primary server fails and the standby server becomes the new primary, and then the old primary restarts, you must have a mechanism for informing the old primary that it is no longer the primary. This is sometimes known as STONITH (Shoot The Other Node In The Head), which is necessary to avoid situations where both systems think they are the primary, which will lead to confusion and ultimately data loss. Many failover systems use just two systems, the primary and the standby, connected by some kind of heartbeat mechanism to continually verify the connectivity between the two and the viability of the primary. It is also possible to use a third system (called a witness server) to prevent some cases of inappropriate failover, but the additional complexity might not be worthwhile unless it is set up with sufficient care and rigorous testing. PostgreSQL does not provide the system software required to identify a failure on the primary and notify the standby database server. Many such tools exist and are well integrated with the operating system facilities required for successful failover, such as IP address migration. Once failover to the standby occurs, there is only a single server in operation. This is known as a degenerate state. The former standby is now the primary, but the former primary is down and might stay down. To return to normal operation, a standby server must be recreated, either on the former primary system when it comes up, or on a third, possibly new, system. The pg_rewind utility can be used to speed up this process on large clusters. Once complete, the primary and standby can be considered to have switched roles. Some people choose to use a third server to provide backup for the new primary until the new standby server is recreated, though clearly this complicates the system configuration and operational processes. So, switching from primary to standby server can be fast but requires some time to re-prepare the failover cluster. Regular switching from primary to standby is useful, since it allows regular downtime on each system for maintenance. This also serves as a test of the failover mechanism to ensure that it will really work when you need it. Written administration procedures are advised. To trigger failover of a log-shipping standby server, run pg_ctl promote, call pg_promote(), or create a trigger file with the file name and path specified by the promote_trigger_file. If you’re planning to use pg_ctl promote or to call pg_promote() to fail over, promote_trigger_file is not required. If you’re setting up the reporting servers that are only used to offload read-only queries from the primary, not for high availability purposes, you don’t need to promote it. Hot standby is the term used to describe the ability to connect to the server and run read-only queries while the server is in archive recovery or standby mode. This is useful both for replication purposes and for restoring a backup to a desired state with great precision. The term hot standby also refers to the ability of the server to move from recovery through to normal operation while users continue running queries and/or keep their connections open. Running queries in hot standby mode is similar to normal query operation, though there are several usage and administrative differences explained below. When the hot_standby parameter is set to true on a standby server, it will begin accepting connections once the recovery has brought the system to a consistent state. All such connections are strictly read-only; not even temporary tables may be written. The data on the standby takes some time to arrive from the primary server so there will be a measurable delay between primary and standby. Running the same query nearly simultaneously on both primary and standby might therefore return differing results. We say that data on the standby is eventually consistent with the primary. Once the commit record for a transaction is replayed on the standby, the changes made by that transaction will be visible to any new snapshots taken on the standby. Snapshots may be taken at the start of each query or at the start of each transaction, depending on the current transaction isolation level. Transactions started during hot standby may issue the following commands: Query access: SELECT, COPY TO Cursor commands: DECLARE, FETCH, CLOSE Settings: SHOW, SET, RESET Transaction management commands: BEGIN, END, ABORT, START TRANSACTION SAVEPOINT, RELEASE, ROLLBACK TO SAVEPOINT EXCEPTION blocks and other internal subtransactions LOCK TABLE, though only when explicitly in one of these modes: ACCESS SHARE, ROW SHARE or ROW EXCLUSIVE. Plans and resources: PREPARE, EXECUTE, DEALLOCATE, DISCARD Plugins and extensions: LOAD UNLISTEN Transactions started during hot standby will never be assigned a transaction ID and cannot write to the system write-ahead log. Therefore, the following actions will produce error messages: Data Manipulation Language (DML): INSERT, UPDATE, DELETE, COPY FROM, TRUNCATE. Note that there are no allowed actions that result in a trigger being executed during recovery. This restriction applies even to temporary tables, because table rows cannot be read or written without assigning a transaction ID, which is currently not possible in a hot standby environment. Data Definition Language (DDL): CREATE, DROP, ALTER, COMMENT. This restriction applies even to temporary tables, because carrying out these operations would require updating the system catalog tables. SELECT …​ FOR SHARE | UPDATE, because row locks cannot be taken without updating the underlying data files. Rules on SELECT statements that generate DML commands. LOCK that explicitly requests a mode higher than ROW EXCLUSIVE MODE. LOCK in short default form, since it requests ACCESS EXCLUSIVE MODE. Transaction management commands that explicitly set non-read-only state: BEGIN READ WRITE, START TRANSACTION READ WRITE SET TRANSACTION READ WRITE, SET SESSION CHARACTERISTICS AS TRANSACTION READ WRITE SET transaction_read_only = off Two-phase commit commands: PREPARE TRANSACTION, COMMIT PREPARED, ROLLBACK PREPARED because even read-only transactions need to write WAL in the prepare phase (the first phase of two phase commit). Sequence updates: nextval(), setval() LISTEN, NOTIFY In normal operation, “read-only” transactions are allowed to use LISTEN and NOTIFY, so hot standby sessions operate under slightly tighter restrictions than ordinary read-only sessions. It is possible that some of these restrictions might be loosened in a future release. During hot standby, the parameter transaction_read_only is always true and may not be changed. But as long as no attempt is made to modify the database, connections during hot standby will act much like any other database connection. If failover or switchover occurs, the database will switch to normal processing mode. Sessions will remain connected while the server changes mode. Once hot standby finishes, it will be possible to initiate read-write transactions (even from a session begun during hot standby). Users can determine whether hot standby is currently active for their session by issuing SHOW in_hot_standby. (In server versions before 14, the in_hot_standby parameter did not exist; a workable substitute method for older servers is SHOW transaction_read_only.) In addition, a set of functions allow users to access information about the standby server. These allow you to write programs that are aware of the current state of the database. These can be used to monitor the progress of recovery, or to allow you to write complex programs that restore the database to particular states. The primary and standby servers are in many ways loosely connected. Actions on the primary will have an effect on the standby. As a result, there is potential for negative interactions or conflicts between them. The easiest conflict to understand is performance: if a huge data load is taking place on the primary then this will generate a similar stream of WAL records on the standby, so standby queries may contend for system resources, such as I/O. There are also additional types of conflict that can occur with hot standby. These conflicts are hard conflicts in the sense that queries might need to be canceled and, in some cases, sessions disconnected to resolve them. The user is provided with several ways to handle these conflicts. Conflict cases include: Access Exclusive locks taken on the primary server, including both explicit LOCK commands and various DDL actions, conflict with table accesses in standby queries. Dropping a tablespace on the primary conflicts with standby queries using that tablespace for temporary work files. Dropping a database on the primary conflicts with sessions connected to that database on the standby. Application of a vacuum cleanup record from WAL conflicts with standby transactions whose snapshots can still “see” any of the rows to be removed. Application of a vacuum cleanup record from WAL conflicts with queries accessing the target page on the standby, whether or not the data to be removed is visible. On the primary server, these cases simply result in waiting; and the user might choose to cancel either of the conflicting actions. However, on the standby there is no choice: the WAL-logged action already occurred on the primary so the standby must not fail to apply it. Furthermore, allowing WAL application to wait indefinitely may be very undesirable, because the standby’s state will become increasingly far behind the primary’s. Therefore, a mechanism is provided to forcibly cancel standby queries that conflict with to-be-applied WAL records. An example of the problem situation is an administrator on the primary server running DROP TABLE on a table that is currently being queried on the standby server. Clearly the standby query cannot continue if the DROP TABLE is applied on the standby. If this situation occurred on the primary, the DROP TABLE would wait until the other query had finished. But when DROP TABLE is run on the primary, the primary doesn’t have information about what queries are running on the standby, so it will not wait for any such standby queries. The WAL change records come through to the standby while the standby query is still running, causing a conflict. The standby server must either delay application of the WAL records (and everything after them, too) or else cancel the conflicting query so that the DROP TABLE can be applied. When a conflicting query is short, it’s typically desirable to allow it to complete by delaying WAL application for a little bit; but a long delay in WAL application is usually not desirable. So the cancel mechanism has parameters, max_standby_archive_delay and max_standby_streaming_delay, that define the maximum allowed delay in WAL application. Conflicting queries will be canceled once it has taken longer than the relevant delay setting to apply any newly-received WAL data. There are two parameters so that different delay values can be specified for the case of reading WAL data from an archive (i.e., initial recovery from a base backup or “catching up” a standby server that has fallen far behind) versus reading WAL data via streaming replication. In a standby server that exists primarily for high availability, it’s best to set the delay parameters relatively short, so that the server cannot fall far behind the primary due to delays caused by standby queries. However, if the standby server is meant for executing long-running queries, then a high or even infinite delay value may be preferable. Keep in mind however that a long-running query could cause other sessions on the standby server to not see recent changes on the primary, if it delays application of WAL records. Once the delay specified by max_standby_archive_delay or max_standby_streaming_delay has been exceeded, conflicting queries will be canceled. This usually results just in a cancellation error, although in the case of replaying a DROP DATABASE the entire conflicting session will be terminated. Also, if the conflict is over a lock held by an idle transaction, the conflicting session is terminated (this behavior might change in the future). Canceled queries may be retried immediately (after beginning a new transaction, of course). Since query cancellation depends on the nature of the WAL records being replayed, a query that was canceled may well succeed if it is executed again. Keep in mind that the delay parameters are compared to the elapsed time since the WAL data was received by the standby server. Thus, the grace period allowed to any one query on the standby is never more than the delay parameter, and could be considerably less if the standby has already fallen behind as a result of waiting for previous queries to complete, or as a result of being unable to keep up with a heavy update load. The most common reason for conflict between standby queries and WAL replay is “early cleanup”. Normally, PostgreSQL allows cleanup of old row versions when there are no transactions that need to see them to ensure correct visibility of data according to MVCC rules. However, this rule can only be applied for transactions executing on the primary. So it is possible that cleanup on the primary will remove row versions that are still visible to a transaction on the standby. Experienced users should note that both row version cleanup and row version freezing will potentially conflict with standby queries. Running a manual VACUUM FREEZE is likely to cause conflicts even on tables with no updated or deleted rows. Users should be clear that tables that are regularly and heavily updated on the primary server will quickly cause cancellation of longer running queries on the standby. In such cases the setting of a finite value for max_standby_archive_delay or max_standby_streaming_delay can be considered similar to setting statement_timeout. Remedial possibilities exist if the number of standby-query cancellations is found to be unacceptable. The first option is to set the parameter hot_standby_feedback, which prevents VACUUM from removing recently-dead rows and so cleanup conflicts do not occur. If you do this, you should note that this will delay cleanup of dead rows on the primary, which may result in undesirable table bloat. However, the cleanup situation will be no worse than if the standby queries were running directly on the primary server, and you are still getting the benefit of off-loading execution onto the standby. If standby servers connect and disconnect frequently, you might want to make adjustments to handle the period when hot_standby_feedback feedback is not being provided. For example, consider increasing max_standby_archive_delay so that queries are not rapidly canceled by conflicts in WAL archive files during disconnected periods. You should also consider increasing max_standby_streaming_delay to avoid rapid cancellations by newly-arrived streaming WAL entries after reconnection. Another option is to increase vacuum_defer_cleanup_age on the primary server, so that dead rows will not be cleaned up as quickly as they normally would be. This will allow more time for queries to execute before they are canceled on the standby, without having to set a high max_standby_streaming_delay. However it is difficult to guarantee any specific execution-time window with this approach, since vacuum_defer_cleanup_age is measured in transactions executed on the primary server. The number of query cancels and the reason for them can be viewed using the pg_stat_database_conflicts system view on the standby server. The pg_stat_database system view also contains summary information. Users can control whether a log message is produced when WAL replay is waiting longer than deadlock_timeout for conflicts. This is controlled by the log_recovery_conflict_waits parameter. If hot_standby is on in postgresql.conf (the default value) and there is a standby.signal file present, the server will run in hot standby mode. However, it may take some time for hot standby connections to be allowed, because the server will not accept connections until it has completed sufficient recovery to provide a consistent state against which queries can run. During this period, clients that attempt to connect will be refused with an error message. To confirm the server has come up, either loop trying to connect from the application, or look for these messages in the server logs: LOG: entering standby mode ... then some time later ... LOG: consistent recovery state reached LOG: database system is ready to accept read-only connections Consistency information is recorded once per checkpoint on the primary. It is not possible to enable hot standby when reading WAL written during a period when wal_level was not set to replica or logical on the primary. Reaching a consistent state can also be delayed in the presence of both of these conditions: A write transaction has more than 64 subtransactions Very long-lived write transactions If you are running file-based log shipping (\"warm standby\"), you might need to wait until the next WAL file arrives, which could be as long as the archive_timeout setting on the primary. The settings of some parameters determine the size of shared memory for tracking transaction IDs, locks, and prepared transactions. These shared memory structures must be no smaller on a standby than on the primary in order to ensure that the standby does not run out of shared memory during recovery. For example, if the primary had used a prepared transaction but the standby had not allocated any shared memory for tracking prepared transactions, then recovery could not continue until the standby’s configuration is changed. The parameters affected are: max_connections max_prepared_transactions max_locks_per_transaction max_wal_senders max_worker_processes The easiest way to ensure this does not become a problem is to have these parameters set on the standbys to values equal to or greater than on the primary. Therefore, if you want to increase these values, you should do so on all standby servers first, before applying the changes to the primary server. Conversely, if you want to decrease these values, you should do so on the primary server first, before applying the changes to all standby servers. Keep in mind that when a standby is promoted, it becomes the new reference for the required parameter settings for the standbys that follow it. Therefore, to avoid this becoming a problem during a switchover or failover, it is recommended to keep these settings the same on all standby servers. The WAL tracks changes to these parameters on the primary. If a hot standby processes WAL that indicates that the current value on the primary is higher than its own value, it will log a warning and pause recovery, for example: WARNING: hot standby is not possible because of insufficient parameter settings DETAIL: max_connections = 80 is a lower setting than on the primary server, where its value was 100. LOG: recovery has paused DETAIL: If recovery is unpaused, the server will shut down. HINT: You can then restart the server after making the necessary configuration changes. At that point, the settings on the standby need to be updated and the instance restarted before recovery can continue. If the standby is not a hot standby, then when it encounters the incompatible parameter change, it will shut down immediately without pausing, since there is then no value in keeping it up. It is important that the administrator select appropriate settings for max_standby_archive_delay and max_standby_streaming_delay. The best choices vary depending on business priorities. For example if the server is primarily tasked as a High Availability server, then you will want low delay settings, perhaps even zero, though that is a very aggressive setting. If the standby server is tasked as an additional server for decision support queries then it might be acceptable to set the maximum delay values to many hours, or even -1 which means wait forever for queries to complete. Transaction status \"hint bits\" written on the primary are not WAL-logged, so data on the standby will likely re-write the hints again on the standby. Thus, the standby server will still perform disk writes even though all users are read-only; no changes occur to the data values themselves. Users will still write large sort temporary files and re-generate relcache info files, so no part of the database is truly read-only during hot standby mode. Note also that writes to remote databases using dblink module, and other operations outside the database using PL functions will still be possible, even though the transaction is read-only locally. The following types of administration commands are not accepted during recovery mode: Data Definition Language (DDL): e.g., CREATE INDEX Privilege and Ownership: GRANT, REVOKE, REASSIGN Maintenance commands: ANALYZE, VACUUM, CLUSTER, REINDEX Again, note that some of these commands are actually allowed during \"read only\" mode transactions on the primary. As a result, you cannot create additional indexes that exist solely on the standby, nor statistics that exist solely on the standby. If these administration commands are needed, they should be executed on the primary, and eventually those changes will propagate to the standby. pg_cancel_backend() and pg_terminate_backend() will work on user backends, but not the startup process, which performs recovery. pg_stat_activity does not show recovering transactions as active. As a result, pg_prepared_xacts is always empty during recovery. If you wish to resolve in-doubt prepared transactions, view pg_prepared_xacts on the primary and issue commands to resolve transactions there or resolve them after the end of recovery. pg_locks will show locks held by backends, as normal. pg_locks also shows a virtual transaction managed by the startup process that owns all AccessExclusiveLocks held by transactions being replayed by recovery. Note that the startup process does not acquire locks to make database changes, and thus locks other than AccessExclusiveLocks do not show in pg_locks for the Startup process; they are just presumed to exist. The Nagios plugin check_pgsql will work, because the simple information it checks for exists. The check_postgres monitoring script will also work, though some reported values could give different or confusing results. For example, last vacuum time will not be maintained, since no vacuum occurs on the standby. Vacuums running on the primary do still send their changes to the standby. WAL file control commands will not work during recovery, e.g., pg_backup_start, pg_switch_wal etc. Dynamically loadable modules work, including pg_stat_statements. Advisory locks work normally in recovery, including deadlock detection. Note that advisory locks are never WAL logged, so it is impossible for an advisory lock on either the primary or the standby to conflict with WAL replay. Nor is it possible to acquire an advisory lock on the primary and have it initiate a similar advisory lock on the standby. Advisory locks relate only to the server on which they are acquired. Trigger-based replication systems such as Slony, Londiste and Bucardo won’t run on the standby at all, though they will run happily on the primary server as long as the changes are not sent to standby servers to be applied. WAL replay is not trigger-based so you cannot relay from the standby to any system that requires additional database writes or relies on the use of triggers. New OIDs cannot be assigned, though some UUID generators may still work as long as they do not rely on writing new status to the database. Currently, temporary table creation is not allowed during read-only transactions, so in some cases existing scripts will not run correctly. This restriction might be relaxed in a later release. This is both an SQL standard compliance issue and a technical issue. DROP TABLESPACE can only succeed if the tablespace is empty. Some standby users may be actively using the tablespace via their temp_tablespaces parameter. If there are temporary files in the tablespace, all active queries are canceled to ensure that temporary files are removed, so the tablespace can be removed and WAL replay can continue. Running DROP DATABASE or ALTER DATABASE …​ SET TABLESPACE on the primary will generate a WAL entry that will cause all users connected to that database on the standby to be forcibly disconnected. This action occurs immediately, whatever the setting of max_standby_streaming_delay. Note that ALTER DATABASE …​ RENAME does not disconnect users, which in most cases will go unnoticed, though might in some cases cause a program confusion if it depends in some way upon database name. In normal (non-recovery) mode, if you issue DROP USER or DROP ROLE for a role with login capability while that user is still connected then nothing happens to the connected user — they remain connected. The user cannot reconnect however. This behavior applies in recovery also, so a DROP USER on the primary does not disconnect that user on the standby. The cumulative statistics system is active during recovery. All scans, reads, blocks, index usage, etc., will be recorded normally on the standby. However, WAL replay will not increment relation and database specific counters. I.e. replay will not increment pg_stat_all_tables columns (like n_tup_ins), nor will reads or writes performed by the startup process be tracked in the pg_statio views, nor will associated pg_stat_database columns be incremented. Autovacuum is not active during recovery. It will start normally at the end of recovery. The checkpointer process and the background writer process are active during recovery. The checkpointer process will perform restartpoints (similar to checkpoints on the primary) and the background writer process will perform normal block cleaning activities. This can include updates of the hint bit information stored on the standby server. The CHECKPOINT command is accepted during recovery, though it performs a restartpoint rather than a new checkpoint. On the primary, parameters wal_level and vacuum_defer_cleanup_age can be used. max_standby_archive_delay and max_standby_streaming_delay have no effect if set on the primary. On the standby, parameters hot_standby, max_standby_archive_delay and max_standby_streaming_delay can be used. vacuum_defer_cleanup_age has no effect as long as the server remains in standby mode, though it will become relevant if the standby becomes primary. There are several limitations of hot standby. These can and probably will be fixed in future releases: Full knowledge of running transactions is required before snapshots can be taken. Transactions that use large numbers of subtransactions (currently greater than 64) will delay the start of read-only connections until the completion of the longest running write transaction. If this situation occurs, explanatory messages will be sent to the server log. Valid starting points for standby queries are generated at each checkpoint on the primary. If the standby is shut down while the primary is in a shutdown state, it might not be possible to re-enter hot standby until the primary is started up, so that it generates further starting points in the WAL logs. This situation isn’t a problem in the most common situations where it might happen. Generally, if the primary is shut down and not available anymore, that’s likely due to a serious failure that requires the standby being converted to operate as the new primary anyway. And in situations where the primary is being intentionally taken down, coordinating to make sure the standby becomes the new primary smoothly is also standard procedure. At the end of recovery, AccessExclusiveLocks held by prepared transactions will require twice the normal number of lock table entries. If you plan on running either a large number of concurrent prepared transactions that normally take AccessExclusiveLocks, or you plan on having one large transaction that takes many AccessExclusiveLocks, you are advised to select a larger value of max_locks_per_transaction, perhaps as much as twice the value of the parameter on the primary server. You need not consider this at all if your setting of max_prepared_transactions is 0. The Serializable transaction isolation level is not yet available in hot standby. An attempt to set a transaction to the serializable isolation level in hot standby mode will generate an error.","title":"Maintenance","component":"ivorysql-doc","version":"master","name":"daily_maintenance","url":"/ivorysql-doc/master/getting-started/daily_maintenance.html","titles":[{"text":"1. Routine Vacuuming","hash":"routine-vacuuming","id":1},{"text":"1.1. Vacuuming Basics","hash":"vacuuming-basics","id":2},{"text":"1.2. Recovering Disk Space","hash":"recovering-disk-space","id":3},{"text":"1.3. Updating Planner Statistics","hash":"updating-planner-statistics","id":4},{"text":"1.4. Updating the Visibility Map","hash":"updating-the-visibility-map","id":5},{"text":"1.5. Preventing Transaction ID Wraparound Failures","hash":"preventing-transaction-id-wraparound-failures","id":6},{"text":"1.6. The Autovacuum Daemon","hash":"the-autovacuum-daemon","id":7},{"text":"2. Routine Reindexing","hash":"routine-reindexing","id":8},{"text":"3. Log File Maintenance","hash":"log-file-maintenance","id":9},{"text":"4. High Availability, Load Balancing, and Replication","hash":"high-availability-load-balancing-and-replication","id":10},{"text":"4.1. Comparison of Different Solutions","hash":"comparison-of-different-solutions","id":11},{"text":"4.1.1. Shared Disk Failover","hash":"shared-disk-failover","id":12},{"text":"4.1.2. File System (Block Device) Replication","hash":"file-system-block-device-replication","id":13},{"text":"4.1.3. Write-Ahead Log Shipping","hash":"write-ahead-log-shipping","id":14},{"text":"4.1.4. Logical Replication","hash":"logical-replication","id":15},{"text":"4.1.5. Trigger-Based Primary-Standby Replication","hash":"trigger-based-primary-standby-replication","id":16},{"text":"4.1.6. SQL-Based Replication Middleware","hash":"sql-based-replication-middleware","id":17},{"text":"4.1.7. Asynchronous Multimaster Replication","hash":"asynchronous-multimaster-replication","id":18},{"text":"4.1.8. Synchronous Multimaster Replication","hash":"synchronous-multimaster-replication","id":19},{"text":"4.2. Log-Shipping Standby Servers","hash":"log-shipping-standby-servers","id":20},{"text":"4.2.1. Planning","hash":"planning","id":21},{"text":"4.2.2. Standby Server Operation","hash":"standby-server-operation","id":22},{"text":"4.2.3. Preparing the Primary for Standby Servers","hash":"preparing-the-primary-for-standby-servers","id":23},{"text":"4.2.4. Setting Up a Standby Server","hash":"setting-up-a-standby-server","id":24},{"text":"4.2.5. Streaming Replication","hash":"streaming-replication","id":25},{"text":"4.2.5.1. Authentication","hash":"authentication","id":26},{"text":"4.2.5.2. Monitoring","hash":"monitoring","id":27},{"text":"4.2.6. Replication Slots","hash":"replication-slots","id":28},{"text":"4.2.6.1. Querying And Manipulating Replication Slots","hash":"querying-and-manipulating-replication-slots","id":29},{"text":"4.2.6.2. Configuration Example","hash":"configuration-example","id":30},{"text":"4.2.7. Cascading Replication","hash":"cascading-replication","id":31},{"text":"4.2.8. Synchronous Replication","hash":"synchronous-replication","id":32},{"text":"4.2.8.1. Basic Configuration","hash":"basic-configuration","id":33},{"text":"4.2.8.2. Multiple Synchronous Standbys","hash":"multiple-synchronous-standbys","id":34},{"text":"4.2.8.3. Planning For Performance","hash":"planning-for-performance","id":35},{"text":"4.2.8.4. Planning For High Availability","hash":"planning-for-high-availability","id":36},{"text":"4.2.9. Continuous Archiving in Standby","hash":"continuous-archiving-in-standby","id":37},{"text":"4.3. Failover","hash":"failover","id":38},{"text":"4.4. Hot Standby","hash":"hot-standby","id":39},{"text":"4.4.1. User’s Overview","hash":"users-overview","id":40},{"text":"4.4.2. Handling Query Conflicts","hash":"handling-query-conflicts","id":41},{"text":"4.4.3. Administrator’s Overview","hash":"administrators-overview","id":42},{"text":"4.4.4. Hot Standby Parameter Reference","hash":"hot-standby-parameter-reference","id":43},{"text":"4.4.5. Caveats","hash":"caveats","id":44}]},"53":{"id":53,"text":"On most Unix platforms, IvorySQL modifies its command title as reported by ps, so that individual server processes can readily be identified. A sample display is $ ps auxww | grep ^postgres postgres 15551 0.0 0.1 57536 7132 pts/0 S 18:02 0:00 postgres -i postgres 15554 0.0 0.0 57536 1184 ? Ss 18:02 0:00 postgres: background writer postgres 15555 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: checkpointer postgres 15556 0.0 0.0 57536 916 ? Ss 18:02 0:00 postgres: walwriter postgres 15557 0.0 0.0 58504 2244 ? Ss 18:02 0:00 postgres: autovacuum launcher postgres 15558 0.0 0.0 17512 1068 ? Ss 18:02 0:00 postgres: stats collector postgres 15582 0.0 0.0 58772 3080 ? Ss 18:04 0:00 postgres: joe runbug 127.0.0.1 idle postgres 15606 0.0 0.0 58772 3052 ? Ss 18:07 0:00 postgres: tgl regression [local] SELECT waiting postgres 15610 0.0 0.0 58772 3056 ? Ss 18:07 0:00 postgres: tgl regression [local] idle in transaction (The appropriate invocation of ps varies across different platforms, as do the details of what is shown. This example is from a recent Linux system.) The first process listed here is the primary server process. The command arguments shown for it are the same ones used when it was launched. The next four processes are background worker processes automatically launched by the primary process. (The “autovacuum launcher” process will not be present if you have set the system not to run autovacuum.) Each of the remaining processes is a server process handling one client connection. Each such process sets its command line display in the form postgres: user database host activity The user, database, and (client) host items remain the same for the life of the client connection, but the activity indicator changes. The activity can be idle (i.e., waiting for a client command), idle in transaction (waiting for client inside a BEGIN block), or a command type name such as SELECT. Also, waiting is appended if the server process is presently waiting on a lock held by another session. In the above example we can infer that process 15606 is waiting for process 15610 to complete its transaction and thereby release some lock. (Process 15610 must be the blocker, because there is no other active session. In more complicated cases it would be necessary to look into the pg_locks system view to determine who is blocking whom.) If cluster_name has been configured the cluster name will also be shown in ps output: $ psql -c 'SHOW cluster_name' cluster_name -------------- server1 (1 row) $ ps aux|grep server1 postgres 27093 0.0 0.0 30096 2752 ? Ss 11:34 0:00 postgres: server1: background writer ... If you have turned off update_process_title then the activity indicator is not updated; the process title is set only once when a new process is launched. On some platforms this saves a measurable amount of per-command overhead; on others it’s insignificant. Tip Solaris requires special handling. You must use /usr/ucb/ps, rather than /bin/ps. You also must use two w flags, not just one. In addition, your original invocation of the postgres command must have a shorter ps status display than that provided by each server process. If you fail to do all three things, the ps output for each server process will be the original postgres command line. IvorySQL’s cumulative statistics system supports collection and reporting of information about server activity. Presently, accesses to tables and indexes in both disk-block and individual-row terms are counted. The total number of rows in each table, and information about vacuum and analyze actions for each table are also counted. If enabled, calls to user-defined functions and the total time spent in each one are counted as well. IvorySQL also supports reporting dynamic information about exactly what is going on in the system right now, such as the exact command currently being executed by other server processes, and which other connections exist in the system. This facility is independent of the cumulative statistics system. Since collection of statistics adds some overhead to query execution, the system can be configured to collect or not collect information. This is controlled by configuration parameters that are normally set in postgresql.conf. The parameter track_activities enables monitoring of the current command being executed by any server process. The parameter track_counts controls whether cumulative statistics are collected about table and index accesses. The parameter track_functions enables tracking of usage of user-defined functions. The parameter track_io_timing enables monitoring of block read and write times. The parameter track_wal_io_timing enables monitoring of WAL write times. Normally these parameters are set in postgresql.conf so that they apply to all server processes, but it is possible to turn them on or off in individual sessions using the SET command. (To prevent ordinary users from hiding their activity from the administrator, only superusers are allowed to change these parameters with SET.) Cumulative statistics are collected in shared memory. Every IvorySQL process collects statistics locally, then updates the shared data at appropriate intervals. When a server, including a physical replica, shuts down cleanly, a permanent copy of the statistics data is stored in the pg_stat subdirectory, so that statistics can be retained across server restarts. In contrast, when starting from an unclean shutdown (e.g., after an immediate shutdown, a server crash, starting from a base backup, and point-in-time recovery), all statistics counters are reset. Several predefined views, listed in Table 1 , are available to show the current state of the system. There are also several other views, listed in Table 2 , available to show the accumulated statistics. Alternatively, one can build custom views using the underlying cumulative statistics functions. When using the cumulative statistics views and functions to monitor collected data, it is important to realize that the information does not update instantaneously. Each individual server process flushes out accumulated statistics to shared memory just before going idle, but not more frequently than once per PGSTAT_MIN_INTERVAL milliseconds (1 second unless altered while building the server); so a query or transaction still in progress does not affect the displayed totals and the displayed information lags behind actual activity. However, current-query information collected by track_activities is always up-to-date. Another important point is that when a server process is asked to display any of the accumulated statistics, accessed values are cached until the end of its current transaction in the default configuration. So the statistics will show static information as long as you continue the current transaction. Similarly, information about the current queries of all sessions is collected when any such information is first requested within a transaction, and the same information will be displayed throughout the transaction. This is a feature, not a bug, because it allows you to perform several queries on the statistics and correlate the results without worrying that the numbers are changing underneath you. When analyzing statistics interactively, or with expensive queries, the time delta between accesses to individual statistics can lead to significant skew in the cached statistics. To minimize skew, stats_fetch_consistency can be set to snapshot, at the price of increased memory usage for caching not-needed statistics data. Conversely, if it’s known that statistics are only accessed once, caching accessed statistics is unnecessary and can be avoided by setting stats_fetch_consistency to none. You can invoke pg_stat_clear_snapshot() to discard the current transaction’s statistics snapshot or cached values (if any). The next use of statistical information will (when in snapshot mode) cause a new snapshot to be built or (when in cache mode) accessed statistics to be cached. A transaction can also see its own statistics (not yet flushed out to the shared memory statistics) in the views pg_stat_xact_all_tables, pg_stat_xact_sys_tables, pg_stat_xact_user_tables, and pg_stat_xact_user_functions. These numbers do not act as stated above; instead they update continuously throughout the transaction. Some of the information in the dynamic statistics views shown in Table 1 is security restricted. Ordinary users can only see all the information about their own sessions (sessions belonging to a role that they are a member of). In rows about other sessions, many columns will be null. Note, however, that the existence of a session and its general properties such as its sessions user and database are visible to all users. Superusers and roles with privileges of built-in role pg_read_all_stats can see all the information about all sessions. Dynamic Statics Views View Name Description pg_stat_activity One row per server process, showing information related to the current activity of that process, such as state and current query. pg_stat_replication One row per WAL sender process, showing statistics about replication to that sender’s connected standby server. pg_stat_wal_receiver Only one row, showing statistics about the WAL receiver from that receiver’s connected server. pg_stat_recovery_prefetch Only one row, showing statistics about blocks prefetched during recovery. pg_stat_subscription At least one row per subscription, showing information about the subscription workers. pg_stat_ssl One row per connection (regular and replication), showing information about SSL used on this connection. pg_stat_gssapi One row per connection (regular and replication), showing information about GSSAPI authentication and encryption used on this connection. pg_stat_progress_analyze One row for each backend (including autovacuum worker processes) running ANALYZE, showing current progress. pg_stat_progress_create_index One row for each backend running CREATE INDEX or REINDEX, showing current progress. pg_stat_progress_vacuum One row for each backend (including autovacuum worker processes) running VACUUM, showing current progress. pg_stat_progress_cluster One row for each backend running CLUSTER or VACUUM FULL, showing current progress. pg_stat_progress_basebackup One row for each WAL sender process streaming a base backup, showing current progress. pg_stat_progress_copy One row for each backend running COPY, showing current progress. Collected Statistics Views View Name Description pg_stat_archiver One row only, showing statistics about the WAL archiver process’s activity. See pg_stat_archiver for details. pg_stat_bgwriter One row only, showing statistics about the background writer process’s activity. See pg_stat_bgwriter for details. pg_stat_wal One row only, showing statistics about WAL activity. See pg_stat_wal for details. pg_stat_database One row per database, showing database-wide statistics. See pg_stat_database for details. pg_stat_database_conflicts One row per database, showing database-wide statistics about query cancels due to conflict with recovery on standby servers. See pg_stat_database_conflicts for details. pg_stat_all_tables One row for each table in the current database, showing statistics about accesses to that specific table. See pg_stat_all_tables for details. pg_stat_sys_tables Same as pg_stat_all_tables, except that only system tables are shown. pg_stat_user_tables Same as pg_stat_all_tables, except that only user tables are shown. pg_stat_xact_all_tables Similar to pg_stat_all_tables, but counts actions taken so far within the current transaction (which are not yet included in pg_stat_all_tables and related views). The columns for numbers of live and dead rows and vacuum and analyze actions are not present in this view. pg_stat_xact_sys_tables Same as pg_stat_xact_all_tables, except that only system tables are shown. pg_stat_xact_user_tables Same as pg_stat_xact_all_tables, except that only user tables are shown. pg_stat_all_indexes One row for each index in the current database, showing statistics about accesses to that specific index. See pg_stat_all_indexes for details. pg_stat_sys_indexes Same as pg_stat_all_indexes, except that only indexes on system tables are shown. pg_stat_user_indexes Same as pg_stat_all_indexes, except that only indexes on user tables are shown. pg_statio_all_tables One row for each table in the current database, showing statistics about I/O on that specific table. See pg_statio_all_tables for details. pg_statio_sys_tables Same as pg_statio_all_tables, except that only system tables are shown. pg_statio_user_tables Same as pg_statio_all_tables, except that only user tables are shown. pg_statio_all_indexes One row for each index in the current database, showing statistics about I/O on that specific index. See pg_statio_all_indexes for details. pg_statio_sys_indexes Same as pg_statio_all_indexes, except that only indexes on system tables are shown. pg_statio_user_indexes Same as pg_statio_all_indexes, except that only indexes on user tables are shown. pg_statio_all_sequences One row for each sequence in the current database, showing statistics about I/O on that specific sequence. See pg_statio_all_sequences for details. pg_statio_sys_sequences Same as pg_statio_all_sequences, except that only system sequences are shown. (Presently, no system sequences are defined, so this view is always empty.) pg_statio_user_sequences Same as pg_statio_all_sequences, except that only user sequences are shown. pg_stat_user_functions One row for each tracked function, showing statistics about executions of that function. See pg_stat_user_functions for details. pg_stat_xact_user_functions Similar to pg_stat_user_functions, but counts only calls during the current transaction (which are not yet included in pg_stat_user_functions). pg_stat_slru One row per SLRU, showing statistics of operations. See pg_stat_slru for details. pg_stat_replication_slots One row per replication slot, showing statistics about the replication slot’s usage. See pg_stat_replication_slots for details. pg_stat_subscription_stats One row per subscription, showing statistics about errors. See pg_stat_subscription_stats for details. The per-index statistics are particularly useful to determine which indexes are being used and how effective they are. The pg_statio_ views are primarily useful to determine the effectiveness of the buffer cache. When the number of actual disk reads is much smaller than the number of buffer hits, then the cache is satisfying most read requests without invoking a kernel call. However, these statistics do not give the entire story: due to the way in which IvorySQL handles disk I/O, data that is not in the IvorySQL buffer cache might still reside in the kernel’s I/O cache, and might therefore still be fetched without requiring a physical read. Users interested in obtaining more detailed information on IvorySQL I/O behavior are advised to use the IvorySQL statistics views in combination with operating system utilities that allow insight into the kernel’s handling of I/O. The pg_stat_activity view will have one row per server process, showing information related to the current activity of that process. pg_stat_activity View Column Type Description datid oid OID of the database this backend is connected to datname name Name of the database this backend is connected to pid integer Process ID of this backend leader_pid integer Process ID of the parallel group leader, if this process is a parallel query worker. NULL if this process is a parallel group leader or does not participate in parallel query. usesysid oid OID of the user logged into this backend usename name Name of the user logged into this backend application_name text Name of the application that is connected to this backend client_addr inet IP address of the client connected to this backend. If this field is null, it indicates either that the client is connected via a Unix socket on the server machine or that this is an internal process such as autovacuum. client_hostname text Host name of the connected client, as reported by a reverse DNS lookup of client_addr. This field will only be non-null for IP connections, and only when log_hostname is enabled. client_port integer TCP port number that the client is using for communication with this backend, or -1 if a Unix socket is used. If this field is null, it indicates that this is an internal server process. backend_start timestamp with time zone Time when this process was started. For client backends, this is the time the client connected to the server. xact_start timestamp with time zone Time when this process' current transaction was started, or null if no transaction is active. If the current query is the first of its transaction, this column is equal to the query_start column. query_start timestamp with time zone Time when the currently active query was started, or if state is not active, when the last query was started state_change timestamp with time zone Time when the state was last changed wait_event_type text The type of event for which the backend is waiting, if any; otherwise NULL. wait_event text Wait event name if backend is currently waiting, otherwise NULL. state text Current overall state of this backend. Possible values are: active: The backend is executing a query. idle: The backend is waiting for a new client command. idle in transaction: The backend is in a transaction, but is not currently executing a query. idle in transaction (aborted): This state is similar to idle in transaction, except one of the statements in the transaction caused an error. fastpath function call: The backend is executing a fast-path function. disabled: This state is reported if track_activities is disabled in this backend. backend_xid xid Top-level transaction identifier of this backend, if any. backend_xmin xid The current backend’s xmin horizon. query_id bigint Identifier of this backend’s most recent query. If state is active this field shows the identifier of the currently executing query. In all other states, it shows the identifier of last query that was executed. Query identifiers are not computed by default so this field will be null unless compute_query_id parameter is enabled or a third-party module that computes query identifiers is configured. query text Text of this backend’s most recent query. If state is active this field shows the currently executing query. In all other states, it shows the last query that was executed. By default the query text is truncated at 1024 bytes; this value can be changed via the parameter track_activity_query_size. backend_type text Type of current backend. Possible types are autovacuum launcher, autovacuum worker, logical replication launcher, logical replication worker, parallel worker, background writer, client backend, checkpointer, archiver, startup, walreceiver, walsender and walwriter. In addition, background workers registered by extensions may have additional types. Note The wait_event and state columns are independent. If a backend is in the active state, it may or may not be waiting on some event. If the state is active and wait_event is non-null, it means that a query is being executed, but is being blocked somewhere in the system. Wait Event Types Wait Event Type Description Activity The server process is idle. This event type indicates a process waiting for activity in its main processing loop. wait_event will identify the specific wait point BufferPin The server process is waiting for exclusive access to a data buffer. Buffer pin waits can be protracted if another process holds an open cursor that last read data from the buffer in question. Client The server process is waiting for activity on a socket connected to a user application. Thus, the server expects something to happen that is independent of its internal processes. wait_event will identify the specific wait point. Extension The server process is waiting for some condition defined by an extension module. IO The server process is waiting for an I/O operation to complete. wait_event will identify the specific wait point. IPC The server process is waiting for some interaction with another server process. wait_event will identify the specific wait point. Lock The server process is waiting for a heavyweight lock. Heavyweight locks, also known as lock manager locks or simply locks, primarily protect SQL-visible objects such as tables. However, they are also used to ensure mutual exclusion for certain internal operations such as relation extension. wait_event will identify the type of lock awaited. LWLock The server process is waiting for a lightweight lock. Most such locks protect a particular data structure in shared memory. wait_event will contain a name identifying the purpose of the lightweight lock. (Some locks have specific names; others are part of a group of locks each with a similar purpose.) . Timeout The server process is waiting for a timeout to expire. wait_event will identify the specific wait point. Wait Events of Type Activity Activity Wait Event Description ArchiverMain Waiting in main loop of archiver process. AutoVacuumMain Waiting in main loop of autovacuum launcher process. BgWriterHibernate Waiting in background writer process, hibernating. BgWriterMain Waiting in main loop of background writer process. CheckpointerMain Waiting in main loop of checkpointer process. LogicalApplyMain Waiting in main loop of logical replication apply process. LogicalLauncherMain Waiting in main loop of logical replication launcher process. RecoveryWalStream Waiting in main loop of startup process for WAL to arrive, during streaming recovery. SysLoggerMain Waiting in main loop of syslogger process. WalReceiverMain Waiting in main loop of WAL receiver process. WalSenderMain Waiting in main loop of WAL sender process. WalWriterMain Waiting in main loop of WAL writer process. Wait Events of Type BufferPin BufferPin Wait Event Description BufferPin Waiting to acquire an exclusive pin on a buffer. Wait Events of Type Client Client Wait Event Description ClientRead Waiting to read data from the client. ClientWrite Waiting to write data to the client. GSSOpenServer Waiting to read data from the client while establishing a GSSAPI session. LibPQWalReceiverConnect Waiting in WAL receiver to establish connection to remote server. LibPQWalReceiverReceive Waiting in WAL receiver to receive data from remote server. SSLOpenServer Waiting for SSL while attempting connection. WalSenderWaitForWAL Waiting for WAL to be flushed in WAL sender process. WalSenderWriteData Waiting for any activity when processing replies from WAL receiver in WAL sender process. Wait Events of Type Extension Extension Wait Event Description Extension Waiting in an extension. Wait Events of Type IO IO Wait Event Description BaseBackupRead Waiting for base backup to read from a file. BufFileRead Waiting for a read from a buffered file. BufFileWrite Waiting for a write to a buffered file. BufFileTruncate Waiting for a buffered file to be truncated. ControlFileRead Waiting for a read from the pg_control file. ControlFileSync Waiting for the pg_control file to reach durable storage. ControlFileSyncUpdate Waiting for an update to the pg_control file to reach durable storage. ControlFileWrite Waiting for a write to the pg_control file. ControlFileWriteUpdate Waiting for a write to update the pg_control file. CopyFileRead Waiting for a read during a file copy operation. CopyFileWrite Waiting for a write during a file copy operation. DSMFillZeroWrite Waiting to fill a dynamic shared memory backing file with zeroes. DataFileExtend Waiting for a relation data file to be extended. DataFileFlush Waiting for a relation data file to reach durable storage. DataFileImmediateSync Waiting for an immediate synchronization of a relation data file to durable storage. DataFilePrefetch Waiting for an asynchronous prefetch from a relation data file. DataFileRead Waiting for a read from a relation data file. DataFileSync Waiting for changes to a relation data file to reach durable storage. DataFileTruncate Waiting for a relation data file to be truncated. DataFileWrite Waiting for a write to a relation data file. LockFileAddToDataDirRead Waiting for a read while adding a line to the data directory lock file. LockFileAddToDataDirSync Waiting for data to reach durable storage while adding a line to the data directory lock file. LockFileAddToDataDirWrite Waiting for a write while adding a line to the data directory lock file. LockFileCreateRead Waiting to read while creating the data directory lock file. LockFileCreateSync Waiting for data to reach durable storage while creating the data directory lock file. LockFileCreateWrite Waiting for a write while creating the data directory lock file. LockFileReCheckDataDirRead Waiting for a read during recheck of the data directory lock file. LogicalRewriteCheckpointSync Waiting for logical rewrite mappings to reach durable storage during a checkpoint. LogicalRewriteMappingSync Waiting for mapping data to reach durable storage during a logical rewrite. LogicalRewriteMappingWrite Waiting for a write of mapping data during a logical rewrite. LogicalRewriteSync Waiting for logical rewrite mappings to reach durable storage. LogicalRewriteTruncate Waiting for truncate of mapping data during a logical rewrite. LogicalRewriteWrite Waiting for a write of logical rewrite mappings. RelationMapRead Waiting for a read of the relation map file. RelationMapSync Waiting for the relation map file to reach durable storage. RelationMapWrite Waiting for a write to the relation map file. ReorderBufferRead Waiting for a read during reorder buffer management. ReorderBufferWrite Waiting for a write during reorder buffer management. ReorderLogicalMappingRead Waiting for a read of a logical mapping during reorder buffer management. ReplicationSlotRead Waiting for a read from a replication slot control file. ReplicationSlotRestoreSync Waiting for a replication slot control file to reach durable storage while restoring it to memory. ReplicationSlotSync Waiting for a replication slot control file to reach durable storage. ReplicationSlotWrite Waiting for a write to a replication slot control file. SLRUFlushSync Waiting for SLRU data to reach durable storage during a checkpoint or database shutdown. SLRURead Waiting for a read of an SLRU page. SLRUSync Waiting for SLRU data to reach durable storage following a page write. SLRUWrite Waiting for a write of an SLRU page. SnapbuildRead Waiting for a read of a serialized historical catalog snapshot. SnapbuildSync Waiting for a serialized historical catalog snapshot to reach durable storage. SnapbuildWrite Waiting for a write of a serialized historical catalog snapshot. TimelineHistoryFileSync Waiting for a timeline history file received via streaming replication to reach durable storage. TimelineHistoryFileWrite Waiting for a write of a timeline history file received via streaming replication. TimelineHistoryRead Waiting for a read of a timeline history file. TimelineHistorySync Waiting for a newly created timeline history file to reach durable storage. TimelineHistoryWrite Waiting for a write of a newly created timeline history file. TwophaseFileRead Waiting for a read of a two phase state file. TwophaseFileSync Waiting for a two phase state file to reach durable storage. TwophaseFileWrite Waiting for a write of a two phase state file. VersionFileWrite Waiting for the version file to be written while creating a database. WALBootstrapSync Waiting for WAL to reach durable storage during bootstrapping. WALBootstrapWrite Waiting for a write of a WAL page during bootstrapping. WALCopyRead Waiting for a read when creating a new WAL segment by copying an existing one. WALCopySync Waiting for a new WAL segment created by copying an existing one to reach durable storage. WALCopyWrite Waiting for a write when creating a new WAL segment by copying an existing one. WALInitSync Waiting for a newly initialized WAL file to reach durable storage. WALInitWrite Waiting for a write while initializing a new WAL file. WALRead Waiting for a read from a WAL file. WALSenderTimelineHistoryRead Waiting for a read from a timeline history file during a walsender timeline command. WALSync Waiting for a WAL file to reach durable storage. WALSyncMethodAssign Waiting for data to reach durable storage while assigning a new WAL sync method. WALWrite Waiting for a write to a WAL file. Wait Events of Type IPC IPC Wait Event Description AppendReady Waiting for subplan nodes of an Append plan node to be ready. ArchiveCleanupCommand Waiting for archive_cleanup_command to complete. ArchiveCommand Waiting for archive_command to complete. BackendTermination Waiting for the termination of another backend. BackupWaitWalArchive Waiting for WAL files required for a backup to be successfully archived. BgWorkerShutdown Waiting for background worker to shut down. BgWorkerStartup Waiting for background worker to start up. BtreePage Waiting for the page number needed to continue a parallel B-tree scan to become available. BufferIO Waiting for buffer I/O to complete. CheckpointDone Waiting for a checkpoint to complete. CheckpointStart Waiting for a checkpoint to start. ExecuteGather Waiting for activity from a child process while executing a Gather plan node. HashBatchAllocate Waiting for an elected Parallel Hash participant to allocate a hash table. HashBatchElect Waiting to elect a Parallel Hash participant to allocate a hash table. HashBatchLoad Waiting for other Parallel Hash participants to finish loading a hash table. HashBuildAllocate Waiting for an elected Parallel Hash participant to allocate the initial hash table. HashBuildElect Waiting to elect a Parallel Hash participant to allocate the initial hash table. HashBuildHashInner Waiting for other Parallel Hash participants to finish hashing the inner relation. HashBuildHashOuter Waiting for other Parallel Hash participants to finish partitioning the outer relation. HashGrowBatchesAllocate Waiting for an elected Parallel Hash participant to allocate more batches. HashGrowBatchesDecide Waiting to elect a Parallel Hash participant to decide on future batch growth. HashGrowBatchesElect Waiting to elect a Parallel Hash participant to allocate more batches. HashGrowBatchesFinish Waiting for an elected Parallel Hash participant to decide on future batch growth. HashGrowBatchesRepartition Waiting for other Parallel Hash participants to finish repartitioning. HashGrowBucketsAllocate Waiting for an elected Parallel Hash participant to finish allocating more buckets. HashGrowBucketsElect Waiting to elect a Parallel Hash participant to allocate more buckets. HashGrowBucketsReinsert Waiting for other Parallel Hash participants to finish inserting tuples into new buckets. LogicalSyncData Waiting for a logical replication remote server to send data for initial table synchronization. LogicalSyncStateChange Waiting for a logical replication remote server to change state. MessageQueueInternal Waiting for another process to be attached to a shared message queue. MessageQueuePutMessage Waiting to write a protocol message to a shared message queue. MessageQueueReceive Waiting to receive bytes from a shared message queue. MessageQueueSend Waiting to send bytes to a shared message queue. ParallelBitmapScan Waiting for parallel bitmap scan to become initialized. ParallelCreateIndexScan Waiting for parallel CREATE INDEX workers to finish heap scan. ParallelFinish Waiting for parallel workers to finish computing. ProcArrayGroupUpdate Waiting for the group leader to clear the transaction ID at end of a parallel operation. ProcSignalBarrier Waiting for a barrier event to be processed by all backends. Promote Waiting for standby promotion. RecoveryConflictSnapshot Waiting for recovery conflict resolution for a vacuum cleanup. RecoveryConflictTablespace Waiting for recovery conflict resolution for dropping a tablespace. RecoveryEndCommand Waiting for recovery_end_command to complete. RecoveryPause Waiting for recovery to be resumed. ReplicationOriginDrop Waiting for a replication origin to become inactive so it can be dropped. ReplicationSlotDrop Waiting for a replication slot to become inactive so it can be dropped. RestoreCommand Waiting for restore_command to complete. SafeSnapshot Waiting to obtain a valid snapshot for a READ ONLY DEFERRABLE transaction. SyncRep Waiting for confirmation from a remote server during synchronous replication. WalReceiverExit Waiting for the WAL receiver to exit. WalReceiverWaitStart Waiting for startup process to send initial data for streaming replication. XactGroupUpdate Waiting for the group leader to update transaction status at end of a parallel operation. Wait Events of Type Lock Lock Wait Event Description advisory Waiting to acquire an advisory user lock. extend Waiting to extend a relation. frozenid Waiting to update pg_database.datfrozenxid and pg_database.datminmxid. object Waiting to acquire a lock on a non-relation database object. page Waiting to acquire a lock on a page of a relation. relation Waiting to acquire a lock on a relation. spectoken Waiting to acquire a speculative insertion lock. transactionid Waiting for a transaction to finish. tuple Waiting to acquire a lock on a tuple. userlock Waiting to acquire a user lock. virtualxid Waiting to acquire a virtual transaction ID lock. Wait Events of Type LWLock LWLock Wait Event Description AddinShmemInit Waiting to manage an extension’s space allocation in shared memory. AutoFile Waiting to update the postgresql.auto.conf file. Autovacuum Waiting to read or update the current state of autovacuum workers. AutovacuumSchedule Waiting to ensure that a table selected for autovacuum still needs vacuuming. BackgroundWorker Waiting to read or update background worker state. BtreeVacuum Waiting to read or update vacuum-related information for a B-tree index. BufferContent Waiting to access a data page in memory. BufferMapping Waiting to associate a data block with a buffer in the buffer pool. CheckpointerComm Waiting to manage fsync requests. CommitTs Waiting to read or update the last value set for a transaction commit timestamp. CommitTsBuffer Waiting for I/O on a commit timestamp SLRU buffer. CommitTsSLRU Waiting to access the commit timestamp SLRU cache. ControlFile Waiting to read or update the pg_control file or create a new WAL file. DynamicSharedMemoryControl Waiting to read or update dynamic shared memory allocation information. LockFastPath Waiting to read or update a process' fast-path lock information. LockManager Waiting to read or update information about “heavyweight” locks. LogicalRepWorker Waiting to read or update the state of logical replication workers. MultiXactGen Waiting to read or update shared multixact state. MultiXactMemberBuffer Waiting for I/O on a multixact member SLRU buffer. MultiXactMemberSLRU Waiting to access the multixact member SLRU cache. MultiXactOffsetBuffer Waiting for I/O on a multixact offset SLRU buffer. MultiXactOffsetSLRU Waiting to access the multixact offset SLRU cache. MultiXactTruncation Waiting to read or truncate multixact information. NotifyBuffer Waiting for I/O on a NOTIFY message SLRU buffer. NotifyQueue Waiting to read or update NOTIFY messages. NotifyQueueTail Waiting to update limit on NOTIFY message storage. NotifySLRU Waiting to access the NOTIFY message SLRU cache. OidGen Waiting to allocate a new OID. OldSnapshotTimeMap Waiting to read or update old snapshot control information. ParallelAppend Waiting to choose the next subplan during Parallel Append plan execution. ParallelHashJoin Waiting to synchronize workers during Parallel Hash Join plan execution. ParallelQueryDSA Waiting for parallel query dynamic shared memory allocation. PerSessionDSA Waiting for parallel query dynamic shared memory allocation. PerSessionRecordType Waiting to access a parallel query’s information about composite types. PerSessionRecordTypmod Waiting to access a parallel query’s information about type modifiers that identify anonymous record types. PerXactPredicateList Waiting to access the list of predicate locks held by the current serializable transaction during a parallel query. PredicateLockManager Waiting to access predicate lock information used by serializable transactions. ProcArray Waiting to access the shared per-process data structures (typically, to get a snapshot or report a session’s transaction ID). RelationMapping Waiting to read or update a pg_filenode.map file (used to track the filenode assignments of certain system catalogs). RelCacheInit Waiting to read or update a pg_internal.init relation cache initialization file. ReplicationOrigin Waiting to create, drop or use a replication origin. ReplicationOriginState Waiting to read or update the progress of one replication origin. ReplicationSlotAllocation Waiting to allocate or free a replication slot. ReplicationSlotControl Waiting to read or update replication slot state. ReplicationSlotIO Waiting for I/O on a replication slot. SerialBuffer Waiting for I/O on a serializable transaction conflict SLRU buffer. SerializableFinishedList Waiting to access the list of finished serializable transactions. SerializablePredicateList Waiting to access the list of predicate locks held by serializable transactions. PgStatsDSA Waiting for stats dynamic shared memory allocator access PgStatsHash Waiting for stats shared memory hash table access PgStatsData Waiting for shared memory stats data access SerializableXactHash Waiting to read or update information about serializable transactions. SerialSLRU Waiting to access the serializable transaction conflict SLRU cache. SharedTidBitmap Waiting to access a shared TID bitmap during a parallel bitmap index scan. SharedTupleStore Waiting to access a shared tuple store during parallel query. ShmemIndex Waiting to find or allocate space in shared memory. SInvalRead Waiting to retrieve messages from the shared catalog invalidation queue. SInvalWrite Waiting to add a message to the shared catalog invalidation queue. SubtransBuffer Waiting for I/O on a sub-transaction SLRU buffer. SubtransSLRU Waiting to access the sub-transaction SLRU cache. SyncRep Waiting to read or update information about the state of synchronous replication. SyncScan Waiting to select the starting location of a synchronized table scan. TablespaceCreate Waiting to create or drop a tablespace. TwoPhaseState Waiting to read or update the state of prepared transactions. WALBufMapping Waiting to replace a page in WAL buffers. WALInsert Waiting to insert WAL data into a memory buffer. WALWrite Waiting for WAL buffers to be written to disk. WrapLimitsVacuum Waiting to update limits on transaction id and multixact consumption. XactBuffer Waiting for I/O on a transaction status SLRU buffer. XactSLRU Waiting to access the transaction status SLRU cache. XactTruncation Waiting to execute pg_xact_status or update the oldest transaction ID available to it. XidGen Waiting to allocate a new transaction ID. Note Extensions can add LWLock types to the list shown in Table 12. In some cases, the name assigned by an extension will not be available in all server processes; so an LWLock wait event might be reported as just “extension” rather than the extension-assigned name. Wait Events of Type Timeout Timeout Wait Event Description BaseBackupThrottle Waiting during base backup when throttling activity. CheckpointWriteDelay Waiting between writes while performing a checkpoint. PgSleep Waiting due to a call to pg_sleep or a sibling function. RecoveryApplyDelay Waiting to apply WAL during recovery because of a delay setting. RecoveryRetrieveRetryInterval Waiting during recovery when WAL data is not available from any source (pg_wal, archive or stream). RegisterSyncRequest Waiting while sending synchronization requests to the checkpointer, because the request queue is full. VacuumDelay Waiting in a cost-based vacuum delay point. VacuumTruncate Waiting to acquire an exclusive lock to truncate off any empty pages at the end of a table vacuumed. Here is an example of how wait events can be viewed: SELECT pid, wait_event_type, wait_event FROM pg_stat_activity WHERE wait_event is NOT NULL; pid | wait_event_type | wait_event ------+-----------------+------------ 2540 | Lock | relation 6644 | LWLock | ProcArray (2 rows) The pg_stat_replication view will contain one row per WAL sender process, showing statistics about replication to that sender’s connected standby server. Only directly connected standbys are listed; no information is available about downstream standby servers. pg_stat_replication View Column Type Description pid integer Process ID of a WAL sender process usesysid oid OID of the user logged into this WAL sender process usename name Name of the user logged into this WAL sender process application_name text Name of the application that is connected to this WAL sender client_addr inet IP address of the client connected to this WAL sender. If this field is null, it indicates that the client is connected via a Unix socket on the server machine. client_hostname text Host name of the connected client, as reported by a reverse DNS lookup of client_addr. This field will only be non-null for IP connections, and only when log_hostname is enabled. client_port integer TCP port number that the client is using for communication with this WAL sender, or -1 if a Unix socket is used backend_start timestamp with time zone Time when this process was started, i.e., when the client connected to this WAL sender backend_xmin xid This standby’s xmin horizon reported by hot_standby_feedback. state text Current WAL sender state. Possible values are: startup: This WAL sender is starting up. catchup: This WAL sender’s connected standby is catching up with the primary. streaming: This WAL sender is streaming changes after its connected standby server has caught up with the primary. backup: This WAL sender is sending a backup. stopping: This WAL sender is stopping. sent_lsn pg_lsn Last write-ahead log location sent on this connection write_lsn pg_lsn Last write-ahead log location written to disk by this standby server flush_lsn pg_lsn Last write-ahead log location flushed to disk by this standby server replay_lsn pg_lsn Last write-ahead log location replayed into the database on this standby server write_lag interval Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written it (but not yet flushed it or applied it). This can be used to gauge the delay that synchronous_commit level remote_write incurred while committing if this server was configured as a synchronous standby. flush_lag interval Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written and flushed it (but not yet applied it). This can be used to gauge the delay that synchronous_commit level on incurred while committing if this server was configured as a synchronous standby. replay_lag interval Time elapsed between flushing recent WAL locally and receiving notification that this standby server has written, flushed and applied it. This can be used to gauge the delay that synchronous_commit level remote_apply incurred while committing if this server was configured as a synchronous standby. sync_priority integer Priority of this standby server for being chosen as the synchronous standby in a priority-based synchronous replication. This has no effect in a quorum-based synchronous replication. sync_state text Synchronous state of this standby server. Possible values are: async: This standby server is asynchronous. potential: This standby server is now asynchronous, but can potentially become synchronous if one of current synchronous ones fails. sync: This standby server is synchronous. quorum: This standby server is considered as a candidate for quorum standbys. reply_time timestamp with time zone Send time of last reply message received from standby server The lag times reported in the pg_stat_replication view are measurements of the time taken for recent WAL to be written, flushed and replayed and for the sender to know about it. These times represent the commit delay that was (or would have been) introduced by each synchronous commit level, if the remote server was configured as a synchronous standby. For an asynchronous standby, the replay_lag column approximates the delay before recent transactions became visible to queries. If the standby server has entirely caught up with the sending server and there is no more WAL activity, the most recently measured lag times will continue to be displayed for a short time and then show NULL. Lag times work automatically for physical replication. Logical decoding plugins may optionally emit tracking messages; if they do not, the tracking mechanism will simply display NULL lag. Note The reported lag times are not predictions of how long it will take for the standby to catch up with the sending server assuming the current rate of replay. Such a system would show similar times while new WAL is being generated, but would differ when the sender becomes idle. In particular, when the standby has caught up completely, pg_stat_replication shows the time taken to write, flush and replay the most recent reported WAL location rather than zero as some users might expect. This is consistent with the goal of measuring synchronous commit and transaction visibility delays for recent write transactions. To reduce confusion for users expecting a different model of lag, the lag columns revert to NULL after a short time on a fully replayed idle system. Monitoring systems should choose whether to represent this as missing data, zero or continue to display the last known value. The pg_stat_replication_slots view will contain one row per logical replication slot, showing statistics about its usage. pg_stat_replication_slots View Column Type Description slot_name text A unique, cluster-wide identifier for the replication slot spill_txns bigint Number of transactions spilled to disk once the memory used by logical decoding to decode changes from WAL has exceeded logical_decoding_work_mem. The counter gets incremented for both top-level transactions and subtransactions. spill_count bigint Number of times transactions were spilled to disk while decoding changes from WAL for this slot. This counter is incremented each time a transaction is spilled, and the same transaction may be spilled multiple times. spill_bytes bigint Amount of decoded transaction data spilled to disk while performing decoding of changes from WAL for this slot. This and other spill counters can be used to gauge the I/O which occurred during logical decoding and allow tuning logical_decoding_work_mem. stream_txns bigint Number of in-progress transactions streamed to the decoding output plugin after the memory used by logical decoding to decode changes from WAL for this slot has exceeded logical_decoding_work_mem. Streaming only works with top-level transactions (subtransactions can’t be streamed independently), so the counter is not incremented for subtransactions. stream_count bigint Number of times in-progress transactions were streamed to the decoding output plugin while decoding changes from WAL for this slot. This counter is incremented each time a transaction is streamed, and the same transaction may be streamed multiple times. stream_bytes bigint Amount of transaction data decoded for streaming in-progress transactions to the decoding output plugin while decoding changes from WAL for this slot. This and other streaming counters for this slot can be used to tune logical_decoding_work_mem. total_txns bigint Number of decoded transactions sent to the decoding output plugin for this slot. This counts top-level transactions only, and is not incremented for subtransactions. Note that this includes the transactions that are streamed and/or spilled. total_bytes bigint Amount of transaction data decoded for sending transactions to the decoding output plugin while decoding changes from WAL for this slot. Note that this includes data that is streamed and/or spilled. stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_wal_receiver view will contain only one row, showing statistics about the WAL receiver from that receiver’s connected server. pg_stat_wal_receiver View Column Type Description pid integer Process ID of the WAL receiver process status text Activity status of the WAL receiver process receive_start_lsn pg_lsn First write-ahead log location used when WAL receiver is started receive_start_tli integer First timeline number used when WAL receiver is started written_lsn pg_lsn Last write-ahead log location already received and written to disk, but not flushed. This should not be used for data integrity checks. flushed_lsn pg_lsn Last write-ahead log location already received and flushed to disk, the initial value of this field being the first log location used when WAL receiver is started received_tli integer Timeline number of last write-ahead log location received and flushed to disk, the initial value of this field being the timeline number of the first log location used when WAL receiver is started last_msg_send_time timestamp with time zone Send time of last message received from origin WAL sender last_msg_receipt_time timestamp with time zone Receipt time of last message received from origin WAL sender latest_end_lsn pg_lsn Last write-ahead log location reported to origin WAL sender latest_end_time timestamp with time zone Time of last write-ahead log location reported to origin WAL sender slot_name text Replication slot name used by this WAL receiver sender_host text Host of the IvorySQL instance this WAL receiver is connected to. This can be a host name, an IP address, or a directory path if the connection is via Unix socket. (The path case can be distinguished because it will always be an absolute path, beginning with /.) sender_port integer Port number of the IvorySQL instance this WAL receiver is connected to. conninfo text Connection string used by this WAL receiver, with security-sensitive fields obfuscated. The pg_stat_recovery_prefetch view will contain only one row. The columns wal_distance, block_distance and io_depth show current values, and the other columns show cumulative counters that can be reset with the pg_stat_reset_shared function. pg_stat_recovery_prefetch View Column Type Description stats_reset timestamp with time zone Time at which these statistics were last reset prefetch bigint Number of blocks prefetched because they were not in the buffer pool hit bigint Number of blocks not prefetched because they were already in the buffer pool skip_init bigint Number of blocks not prefetched because they would be zero-initialized skip_new bigint Number of blocks not prefetched because they didn’t exist yet skip_fpw bigint Number of blocks not prefetched because a full page image was included in the WAL skip_rep bigint Number of blocks not prefetched because they were already recently prefetched wal_distance int How many bytes ahead the prefetcher is looking block_distance int How many blocks ahead the prefetcher is looking io_depth int How many prefetches have been initiated but are not yet known to have completed pg_stat_subscription View Column Type Description subid oid OID of the subscription subname name Name of the subscription pid integer Process ID of the subscription worker process relid oid OID of the relation that the worker is synchronizing; null for the main apply worker received_lsn pg_lsn Last write-ahead log location received, the initial value of this field being 0 last_msg_send_time timestamp with time zone Send time of last message received from origin WAL sender last_msg_receipt_time timestamp with time zone Receipt time of last message received from origin WAL sender latest_end_lsn pg_lsn Last write-ahead log location reported to origin WAL sender latest_end_time timestamp with time zone Time of last write-ahead log location reported to origin WAL sender The pg_stat_subscription_stats view will contain one row per subscription. pg_stat_subscription_stats View Column Type Description subid oid OID of the subscription subname name Name of the subscription apply_error_count bigint Number of times an error occurred while applying changes sync_error_count bigint Number of times an error occurred during the initial table synchronization stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_ssl view will contain one row per backend or WAL sender process, showing statistics about SSL usage on this connection. It can be joined to pg_stat_activity or pg_stat_replication on the pid column to get more details about the connection. pg_stat_ssl View Column Type Description pid integer Process ID of a backend or WAL sender process ssl boolean True if SSL is used on this connection version text Version of SSL in use, or NULL if SSL is not in use on this connection cipher text Name of SSL cipher in use, or NULL if SSL is not in use on this connection bits integer Number of bits in the encryption algorithm used, or NULL if SSL is not used on this connection client_dn text Distinguished Name (DN) field from the client certificate used, or NULL if no client certificate was supplied or if SSL is not in use on this connection. This field is truncated if the DN field is longer than NAMEDATALEN (64 characters in a standard build) client_serial numeric Serial number of the client certificate, or NULL if no client certificate was supplied or if SSL is not in use on this connection. The combination of certificate serial number and certificate issuer uniquely identifies a certificate (unless the issuer erroneously reuses serial numbers) issuer_dn text DN of the issuer of the client certificate, or NULL if no client certificate was supplied or if SSL is not in use on this connection. This field is truncated like client_dn The pg_stat_gssapi view will contain one row per backend, showing information about GSSAPI usage on this connection. It can be joined to pg_stat_activity or pg_stat_replication on the pid column to get more details about the connection. pg_stat_gssapi View Column Type Description pid integer Process ID of a backend gss_authenticated boolean True if GSSAPI authentication was used for this connection principal text Principal used to authenticate this connection, or NULL if GSSAPI was not used. Truncated to NAMEDATALEN (64 characters in a standard build) if longer. encrypted boolean True if GSSAPI encryption is in use on this connection The pg_stat_archiver view will always have a single row, containing data about the archiver process of the cluster. pg_stat_archiver View Column Type Description archived_count bigint Number of WAL files that have been successfully archived last_archived_wal text Name of the WAL file most recently successfully archived last_archived_time timestamp with time zone Time of the most recent successful archive operation failed_count bigint Number of failed attempts for archiving WAL files last_failed_wal text Name of the WAL file of the most recent failed archival operation last_failed_time timestamp with time zone Time of the most recent failed archival operation stats_reset timestamp with time zone Time at which these statistics were last reset Normally, WAL files are archived in order, oldest to newest, but that is not guaranteed, and does not hold under special circumstances like when promoting a standby or after crash recovery. Therefore it is not safe to assume that all files older than last_archived_wal have also been successfully archived. The pg_stat_bgwriter view will always have a single row, containing global data for the cluster. pg_stat_bgwriter View Column Type Description checkpoints_timed bigint Number of scheduled checkpoints that have been performed checkpoints_req bigint Number of requested checkpoints that have been performed checkpoint_write_time double precision Total amount of time that has been spent in the portion of checkpoint processing where files are written to disk, in milliseconds checkpoint_sync_time double precision Total amount of time that has been spent in the portion of checkpoint processing where files are synchronized to disk, in milliseconds buffers_checkpoint bigint Number of buffers written during checkpoints buffers_clean bigint Number of buffers written by the background writer maxwritten_clean bigint Number of times the background writer stopped a cleaning scan because it had written too many buffers buffers_backend bigint Number of buffers written directly by a backend buffers_backend_fsync bigint Number of times a backend had to execute its own fsync call (normally the background writer handles those even when the backend does its own write) buffers_alloc bigint Number of buffers allocated stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_wal view will always have a single row, containing data about WAL activity of the cluster. pg_stat_wal View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table on which the index is being created. index_relid oid OID of the index being created or reindexed. During a non-concurrent CREATE INDEX, this is 0. command text The command that is running: CREATE INDEX, CREATE INDEX CONCURRENTLY, REINDEX, or REINDEX CONCURRENTLY. phase text Current processing phase of index creation. See [Table 1.39](https://www.postgresql.org/docs/current/progress-reporting.html#CREATE-INDEX-PHASES). lockers_total bigint Total number of lockers to wait for, when applicable. lockers_done bigint Number of lockers already waited for. current_locker_pid bigint Process ID of the locker currently being waited for. blocks_total bigint Total number of blocks to be processed in the current phase. blocks_done bigint Number of blocks already processed in the current phase. tuples_total bigint Total number of tuples to be processed in the current phase. tuples_done bigint Number of tuples already processed in the current phase. partitions_total bigint When creating an index on a partitioned table, this column is set to the total number of partitions on which the index is to be created. This field is 0 during a REINDEX. partitions_done bigint When creating an index on a partitioned table, this column is set to the number of partitions on which the index has been created. This field is 0 during a REINDEX. The pg_stat_database view will contain one row for each database in the cluster, plus one for shared objects, showing database-wide statistics. pg_stat_database View Column Type Description datid oid OID of this database, or 0 for objects belonging to a shared relation datname name Name of this database, or NULL for shared objects. numbackends integer Number of backends currently connected to this database, or NULL for shared objects. This is the only column in this view that returns a value reflecting current stateall other columns return the accumulated values since the last reset. xact_commit bigint Number of transactions in this database that have been committed xact_rollback bigint Number of transactions in this database that have been rolled back blks_read bigint Number of disk blocks read in this database blks_hit bigint Number of times disk blocks were found already in the buffer cache, so that a read was not necessary (this only includes hits in the IvorySQL buffer cache, not the operatinsystem’s file system cache) tup_returned bigint Number of live rows fetched by sequential scans and index entries returned by index scans in this database tup_fetched bigint Number of live rows fetched by index scans in this database tup_inserted bigint Number of rows inserted by queries in this database tup_updated bigint Number of rows updated by queries in this database tup_deleted bigint Number of rows deleted by queries in this database conflicts bigint Number of queries canceled due to conflicts with recovery in this database. (Conflicts occur only on standby servers; seepg_stat_database_conflicts](https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-DATABASE-CONFLICTS-VIEW) for details.) temp_files bigint Number of temporary files created by queries in this database. All temporary files are counted, regardless of why the temporary file was created (e.g., sorting or hashing)and regardless of the [log_temp_files](https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-TEMP-FILES) setting. temp_bytes bigint Total amount of data written to temporary files by queries in this database. All temporary files are counted, regardless of why the temporary file was created, and regardlesof the [log_temp_files](https://www.postgresql.org/docs/current/runtime-config-logging.html#GUC-LOG-TEMP-FILES) setting. deadlocks bigint Number of deadlocks detected in this database checksum_failures bigint Number of data page checksum failures detected in this database (or on a shared object), or NULL if data checksums are not enabled. checksum_last_failure timestamp with time zone Time at which the last data page checksum failure was detected in this database (or on a shared object), or NULL if data checksums are noenabled. blk_read_time double precision Time spent reading data file blocks by backends in this database, in milliseconds (if [track_io_timing](https://www.postgresql.org/docs/currenruntime-config-statistics.html#GUC-TRACK-IO-TIMING) is enabled, otherwise zero) blk_write_time double precision Time spent writing data file blocks by backends in this database, in milliseconds (if [track_io_timing](https://www.postgresql.org/docs/currenruntime-config-statistics.html#GUC-TRACK-IO-TIMING) is enabled, otherwise zero) session_time double precision Time spent by database sessions in this database, in milliseconds (note that statistics are only updated when the state of a session changes, so if sessions havbeen idle for a long time, this idle time won’t be included) active_time double precision Time spent executing SQL statements in this database, in milliseconds (this corresponds to the states active and fastpath function call inpg_stat_activity](https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-ACTIVITY-VIEW)) idle_in_transaction_time double precision Time spent idling while in a transaction in this database, in milliseconds (this corresponds to the states idle in transaction and idle itransaction (aborted) in [pg_stat_activity](https://www.postgresql.org/docs/current/monitoring-stats.html#MONITORING-PG-STAT-ACTIVITY-VIEW)) sessions bigint Total number of sessions established to this database sessions_abandoned bigint Number of database sessions to this database that were terminated because connection to the client was lost sessions_fatal bigint Number of database sessions to this database that were terminated by fatal errors sessions_killed bigint Number of database sessions to this database that were terminated by operator intervention stats_reset timestamp with time zone Time at which these statistics were last reset The pg_stat_database_conflicts view will contain one row per database, showing database-wide statistics about query cancels occurring due to conflicts with recovery on standby servers. This view will only contain information on standby servers, since conflicts do not occur on primary servers. pg_stat_database_conflicts View Column Type Description datid oid OID of a database datname name Name of this database confl_tablespace bigint Number of queries in this database that have been canceled due to dropped tablespaces confl_lock bigint Number of queries in this database that have been canceled due to lock timeouts confl_snapshot bigint Number of queries in this database that have been canceled due to old snapshots confl_bufferpin bigint Number of queries in this database that have been canceled due to pinned buffers confl_deadlock bigint Number of queries in this database that have been canceled due to deadlocks The pg_stat_all_tables view will contain one row for each table in the current database (including TOAST tables), showing statistics about accesses to that specific table. The pg_stat_user_tables and pg_stat_sys_tables views contain the same information, but filtered to only show user and system tables respectively. pg_stat_all_tables View Column Type Description relid oid OID of a table schemaname name Name of the schema that this table is in relname name Name of this table seq_scan bigint Number of sequential scans initiated on this table seq_tup_read bigint Number of live rows fetched by sequential scans idx_scan bigint Number of index scans initiated on this table idx_tup_fetch bigint Number of live rows fetched by index scans n_tup_ins bigint Number of rows inserted n_tup_upd bigint Number of rows updated (includes HOT updated rows) n_tup_del bigint Number of rows deleted n_tup_hot_upd bigint Number of rows HOT updated (i.e., with no separate index update required) n_live_tup bigint Estimated number of live rows n_dead_tup bigint Estimated number of dead rows n_mod_since_analyze bigint Estimated number of rows modified since this table was last analyzed n_ins_since_vacuum bigint Estimated number of rows inserted since this table was last vacuumed last_vacuum timestamp with time zone Last time at which this table was manually vacuumed (not counting VACUUM FULL) last_autovacuum timestamp with time zone Last time at which this table was vacuumed by the autovacuum daemon last_analyze timestamp with time zone Last time at which this table was manually analyzed last_autoanalyze timestamp with time zone Last time at which this table was analyzed by the autovacuum daemon vacuum_count bigint Number of times this table has been manually vacuumed (not counting VACUUM FULL) autovacuum_count bigint Number of times this table has been vacuumed by the autovacuum daemon analyze_count bigint Number of times this table has been manually analyzed autoanalyze_count bigint Number of times this table has been analyzed by the autovacuum daemon The pg_stat_all_indexes view will contain one row for each index in the current database, showing statistics about accesses to that specific index. The pg_stat_user_indexes and pg_stat_sys_indexes views contain the same information, but filtered to only show user and system indexes respectively. pg_stat_all_indexes View Column Type Description relid oid OID of the table for this index indexrelid oid OID of this index schemaname name Name of the schema this index is in relname name Name of the table for this index indexrelname name Name of this index idx_scan bigint Number of index scans initiated on this index idx_tup_read bigint Number of index entries returned by scans on this index idx_tup_fetch bigint Number of live table rows fetched by simple index scans using this index Indexes can be used by simple index scans, “bitmap” index scans, and the optimizer. In a bitmap scan the output of several indexes can be combined via AND or OR rules, so it is difficult to associate individual heap row fetches with specific indexes when a bitmap scan is used. Therefore, a bitmap scan increments the pg_stat_all_indexes.idx_tup_read count(s) for the index(es) it uses, and it increments the pg_stat_all_tables.idx_tup_fetch count for the table, but it does not affect pg_stat_all_indexes.idx_tup_fetch. The optimizer also accesses indexes to check for supplied constants whose values are outside the recorded range of the optimizer statistics because the optimizer statistics might be stale. Note The idx_tup_read and idx_tup_fetch counts can be different even without any use of bitmap scans, because idx_tup_read counts index entries retrieved from the index while idx_tup_fetch counts live rows fetched from the table. The latter will be less if any dead or not-yet-committed rows are fetched using the index, or if any heap fetches are avoided by means of an index-only scan. The pg_statio_all_tables view will contain one row for each table in the current database (including TOAST tables), showing statistics about I/O on that specific table. The pg_statio_user_tables and pg_statio_sys_tables views contain the same information, but filtered to only show user and system tables respectively. pg_statio_all_tables View Column Type Description relid oid OID of a table schemaname name Name of the schema that this table is in relname name Name of this table heap_blks_read bigint Number of disk blocks read from this table heap_blks_hit bigint Number of buffer hits in this table idx_blks_read bigint Number of disk blocks read from all indexes on this table idx_blks_hit bigint Number of buffer hits in all indexes on this table toast_blks_read bigint Number of disk blocks read from this table’s TOAST table (if any) toast_blks_hit bigint Number of buffer hits in this table’s TOAST table (if any) tidx_blks_read bigint Number of disk blocks read from this table’s TOAST table indexes (if any) tidx_blks_hit bigint Number of buffer hits in this table’s TOAST table indexes (if any) The pg_statio_all_indexes view will contain one row for each index in the current database, showing statistics about I/O on that specific index. The pg_statio_user_indexes and pg_statio_sys_indexes views contain the same information, but filtered to only show user and system indexes respectively. pg_statio_all_indexes View Column Type Description relid oid OID of the table for this index indexrelid oid OID of this index schemaname name Name of the schema this index is in relname name Name of the table for this index indexrelname name Name of this index idx_blks_read bigint Number of disk blocks read from this index idx_blks_hit bigint Number of buffer hits in this index The pg_statio_all_sequences view will contain one row for each sequence in the current database, showing statistics about I/O on that specific sequence. pg_statio_all_sequences View Column Type Description relid oid OID of a sequence schemaname name Name of the schema this sequence is in relname name Name of this sequence blks_read bigint Number of disk blocks read from this sequence blks_hit bigint Number of buffer hits in this sequence The pg_stat_user_functions view will contain one row for each tracked function, showing statistics about executions of that function. The track_functions parameter controls exactly which functions are tracked. pg_stat_user_functions View Column Type Description funcid oid OID of a function schemaname name Name of the schema this function is in funcname name Name of this function calls bigint Number of times this function has been called total_time double precision Total time spent in this function and all other functions called by it, in milliseconds self_time double precision Total time spent in this function itself, not including other functions called by it, in milliseconds IvorySQL accesses certain on-disk information via SLRU (simple least-recently-used) caches. The pg_stat_slru view will contain one row for each tracked SLRU cache, showing statistics about access to cached pages. pg_stat_slru View Column Type Description name text Name of the SLRU blks_zeroed bigint Number of blocks zeroed during initializations blks_hit bigint Number of times disk blocks were found already in the SLRU, so that a read was not necessary (this only includes hits in the SLRU, not the operating system’s file system cache) blks_read bigint Number of disk blocks read for this SLRU blks_written bigint Number of disk blocks written for this SLRU blks_exists bigint Number of blocks checked for existence for this SLRU flushes bigint Number of flushes of dirty data for this SLRU truncates bigint Number of truncates for this SLRU stats_reset timestamp with time zone Time at which these statistics were last reset Other ways of looking at the statistics can be set up by writing queries that use the same underlying statistics access functions used by the standard views shown above. For details such as the functions' names, consult the definitions of the standard views. (For example, in psql you could issue \\d+ pg_stat_activity.) The access functions for per-database statistics take a database OID as an argument to identify which database to report on. The per-table and per-index functions take a table or index OID. The functions for per-function statistics take a function OID. Note that only tables, indexes, and functions in the current database can be seen with these functions. Additional Statistics Functions Function Description pg_backend_pid () → integer Returns the process ID of the server process attached to the current session. pg_stat_get_activity ( integer ) → setof record Returns a record of information about the backend with the specified process ID, or one record for each active backend in the system if NULL is specified. The fields returned are a subset of those in the pg_stat_activity view. pg_stat_get_snapshot_timestamp () → timestamp with time zone Returns the timestamp of the current statistics snapshot, or NULL if no statistics snapshot has been taken. A snapshot is taken the first time cumulative statistics are accessed in a transaction if stats_fetch_consistency is set to snapshot. pg_stat_clear_snapshot () → void Discards the current statistics snapshot or cached information. pg_stat_reset () → void Resets all statistics counters for the current database to zero. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_shared ( text ) → void Resets some cluster-wide statistics counters to zero, depending on the argument. The argument can be bgwriter, archiver, wal, or recovery_prefetch to reset the counters shown in the respective views. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_single_table_counters ( oid ) → void Resets statistics for a single table or index in the current database or shared across all databases in the cluster to zero. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_single_function_counters ( oid ) → void Resets statistics for a single function in the current database to zero. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_slru ( text ) → void Resets statistics to zero for a single SLRU cache, or for all SLRUs in the cluster. If the argument is NULL, all counters shown in the pg_stat_slru view for all SLRU caches are reset. The argument can be one of CommitTs, MultiXactMember, MultiXactOffset, Notify, Serial, Subtrans, or Xact. If the argument is other (or any unrecognized name), then the counters for all other SLRU caches, such as extension-defined caches, are reset. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_replication_slot ( text ) → void Resets statistics of the replication slot defined by the argument. If the argument is NULL, resets statistics for all the replication slots. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. pg_stat_reset_subscription_stats ( oid ) → void Resets statistics for a single subscription shown in the pg_stat_subscription_stats view to zero. If the argument is NULL, reset statistics for all subscriptions. This function is restricted to superusers by default, but other users can be granted EXECUTE to run the function. Warning Using pg_stat_reset() also resets counters that autovacuum uses to determine when to trigger a vacuum or an analyze. Resetting these counters can cause autovacuum to not perform necessary work, which can cause problems such as table bloat or out-dated table statistics. A database-wide ANALYZE is recommended after the statistics have been reset. pg_stat_get_activity, the underlying function of the pg_stat_activity view, returns a set of records containing all the available information about each backend process. Sometimes it may be more convenient to obtain just a subset of this information. In such cases, an older set of per-backend statistics access functions can be used; These access functions use a backend ID number, which ranges from one to the number of currently active backends. The function pg_stat_get_backend_idset provides a convenient way to generate one row for each active backend for invoking these functions. For example, to show the PIDs and current queries of all backends: SELECT pg_stat_get_backend_pid(s.backendid) AS pid, pg_stat_get_backend_activity(s.backendid) AS query FROM (SELECT pg_stat_get_backend_idset() AS backendid) AS s; Per-Backend Statistics Functions Function Description pg_stat_get_backend_idset () → setof integer Returns the set of currently active backend ID numbers (from 1 to the number of active backends). pg_stat_get_backend_activity ( integer ) → text Returns the text of this backend’s most recent query. pg_stat_get_backend_activity_start ( integer ) → timestamp with time zone Returns the time when the backend’s most recent query was started. pg_stat_get_backend_client_addr ( integer ) → inet Returns the IP address of the client connected to this backend. pg_stat_get_backend_client_port ( integer ) → integer Returns the TCP port number that the client is using for communication. pg_stat_get_backend_dbid ( integer ) → oid Returns the OID of the database this backend is connected to. pg_stat_get_backend_pid ( integer ) → integer Returns the process ID of this backend. pg_stat_get_backend_start ( integer ) → timestamp with time zone Returns the time when this process was started. pg_stat_get_backend_userid ( integer ) → oid Returns the OID of the user logged into this backend. pg_stat_get_backend_wait_event_type ( integer ) → text Returns the wait event type name if this backend is currently waiting, otherwise NULL. pg_stat_get_backend_wait_event ( integer ) → text Returns the wait event name if this backend is currently waiting, otherwise NULL. pg_stat_get_backend_xact_start ( integer ) → timestamp with time zone Returns the time when the backend’s current transaction was started. Another useful tool for monitoring database activity is the pg_locks system table. It allows the database administrator to view information about the outstanding locks in the lock manager. For example, this capability can be used to: View all the locks currently outstanding, all the locks on relations in a particular database, all the locks on a particular relation, or all the locks held by a particular IvorySQL session. Determine the relation in the current database with the most ungranted locks (which might be a source of contention among database clients). Determine the effect of lock contention on overall database performance, as well as the extent to which contention varies with overall database traffic. IvorySQL has the ability to report the progress of certain commands during command execution. Currently, the only commands which support progress reporting are ANALYZE, CLUSTER, CREATE INDEX, VACUUM, COPY, and BASE_BACKUP (i.e., replication command that pg_basebackup issues to take a base backup). This may be expanded in the future. Whenever ANALYZE is running, the pg_stat_progress_analyze view will contain a row for each backend that is currently running that command. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_analyze View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table being analyzed. phase text Current processing phase. See Table 1.37. sample_blks_total bigint Total number of heap blocks that will be sampled. sample_blks_scanned bigint Number of heap blocks scanned. ext_stats_total bigint Number of extended statistics. ext_stats_computed bigint Number of extended statistics computed. This counter only advances when the phase is computing extended statistics. child_tables_total bigint Number of child tables. child_tables_done bigint Number of child tables scanned. This counter only advances when the phase is acquiring inherited sample rows. current_child_table_relid oid OID of the child table currently being scanned. This field is only valid when the phase is acquiring inherited sample rows. ANALYZE Phases Phase Description initializing The command is preparing to begin scanning the heap. This phase is expected to be very brief. acquiring sample rows The command is currently scanning the table given by relid to obtain sample rows. acquiring inherited sample rows The command is currently scanning child tables to obtain sample rows. Columns child_tables_total, child_tables_done, and current_child_table_relid contain the progress information for this phase. computing statistics The command is computing statistics from the sample rows obtained during the table scan. computing extended statistics The command is computing extended statistics from the sample rows obtained during the table scan. finalizing analyze The command is updating pg_class. When this phase is completed, ANALYZE will end. Note Note that when ANALYZE is run on a partitioned table, all of its partitions are also recursively analyzed. In that case, ANALYZE progress is reported first for the parent table, whereby its inheritance statistics are collected, followed by that for each partition. Whenever CREATE INDEX or REINDEX is running, the pg_stat_progress_create_index view will contain one row for each backend that is currently creating indexes. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_create_index View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table on which the index is being created. index_relid oid OID of the index being created or reindexed. During a non-concurrent CREATE INDEX, this is 0. command text The command that is running: CREATE INDEX, CREATE INDEX CONCURRENTLY, REINDEX, or REINDEX CONCURRENTLY. phase text Current processing phase of index creation. See Table 1.39. lockers_total bigint Total number of lockers to wait for, when applicable. lockers_done bigint Number of lockers already waited for. current_locker_pid bigint Process ID of the locker currently being waited for. blocks_total bigint Total number of blocks to be processed in the current phase. blocks_done bigint Number of blocks already processed in the current phase. tuples_total bigint Total number of tuples to be processed in the current phase. tuples_done bigint Number of tuples already processed in the current phase. partitions_total bigint When creating an index on a partitioned table, this column is set to the total number of partitions on which the index is to be created. This field is 0 during a REINDEX. partitions_done bigint When creating an index on a partitioned table, this column is set to the number of partitions on which the index has been created. This field is 0 during a REINDEX. CREATE INDEX Phases Phase Description initializing CREATE INDEX or REINDEX is preparing to create the index. This phase is expected to be very brief. waiting for writers before build CREATE INDEX CONCURRENTLY or REINDEX CONCURRENTLY is waiting for transactions with write locks that can potentially see the table to finish. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. building index The index is being built by the access method-specific code. In this phase, access methods that support progress reporting fill in their own progress data, and the subphase is indicated in this column. Typically, blocks_total and blocks_done will contain progress data, as well as potentially tuples_total and tuples_done. waiting for writers before validation CREATE INDEX CONCURRENTLY or REINDEX CONCURRENTLY is waiting for transactions with write locks that can potentially write into the table to finish. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. index validation: scanning index CREATE INDEX CONCURRENTLY is scanning the index searching for tuples that need to be validated. This phase is skipped when not in concurrent mode. Columns blocks_total (set to the total size of the index) and blocks_done contain the progress information for this phase. index validation: sorting tuples CREATE INDEX CONCURRENTLY is sorting the output of the index scanning phase. index validation: scanning table CREATE INDEX CONCURRENTLY is scanning the table to validate the index tuples collected in the previous two phases. This phase is skipped when not in concurrent mode. Columns blocks_total (set to the total size of the table) and blocks_done contain the progress information for this phase. waiting for old snapshots CREATE INDEX CONCURRENTLY or REINDEX CONCURRENTLY is waiting for transactions that can potentially see the table to release their snapshots. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. waiting for readers before marking dead REINDEX CONCURRENTLY is waiting for transactions with read locks on the table to finish, before marking the old index dead. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. waiting for readers before dropping REINDEX CONCURRENTLY is waiting for transactions with read locks on the table to finish, before dropping the old index. This phase is skipped when not in concurrent mode. Columns lockers_total, lockers_done and current_locker_pid contain the progress information for this phase. Whenever VACUUM is running, the pg_stat_progress_vacuum view will contain one row for each backend (including autovacuum worker processes) that is currently vacuuming. The tables below describe the information that will be reported and provide information about how to interpret it. Progress for VACUUM FULL commands is reported via pg_stat_progress_cluster because both VACUUM FULL and CLUSTER rewrite the table, while regular VACUUM only modifies it in place. pg_stat_progress_vacuum View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table being vacuumed. phase text Current processing phase of vacuum. heap_blks_total bigint Total number of heap blocks in the table. This number is reported as of the beginning of the scan; blocks added later will not be (and need not be) visited by this VACUUM. heap_blks_scanned bigint Number of heap blocks scanned. Because the visibility map is used to optimize scans, some blocks will be skipped without inspection; skipped blocks are included in this total, so that this number will eventually become equal to heap_blks_total when the vacuum is complete. This counter only advances when the phase is scanning heap. heap_blks_vacuumed bigint Number of heap blocks vacuumed. Unless the table has no indexes, this counter only advances when the phase is vacuuming heap. Blocks that contain no dead tuples are skipped, so the counter may sometimes skip forward in large increments. index_vacuum_count bigint Number of completed index vacuum cycles. max_dead_tuples bigint Number of dead tuples that we can store before needing to perform an index vacuum cycle, based on maintenance_work_mem. num_dead_tuples bigint Number of dead tuples collected since the last index vacuum cycle. VACUUM Phases Phase Description initializing VACUUM is preparing to begin scanning the heap. This phase is expected to be very brief. scanning heap VACUUM is currently scanning the heap. It will prune and defragment each page if required, and possibly perform freezing activity. The heap_blks_scanned column can be used to monitor the progress of the scan. vacuuming indexes VACUUM is currently vacuuming the indexes. If a table has any indexes, this will happen at least once per vacuum, after the heap has been completely scanned. It may happen multiple times per vacuum if maintenance_work_mem (or, in the case of autovacuum, autovacuum_work_mem if set) is insufficient to store the number of dead tuples found. vacuuming heap VACUUM is currently vacuuming the heap. Vacuuming the heap is distinct from scanning the heap, and occurs after each instance of vacuuming indexes. If heap_blks_scanned is less than heap_blks_total, the system will return to scanning the heap after this phase is completed; otherwise, it will begin cleaning up indexes after this phase is completed. cleaning up indexes VACUUM is currently cleaning up indexes. This occurs after the heap has been completely scanned and all vacuuming of the indexes and the heap has been completed. truncating heap VACUUM is currently truncating the heap so as to return empty pages at the end of the relation to the operating system. This occurs after cleaning up indexes. performing final cleanup VACUUM is performing final cleanup. During this phase, VACUUM will vacuum the free space map, update statistics in pg_class, and report statistics to the cumulative statistics system. When this phase is completed, VACUUM will end. Whenever CLUSTER or VACUUM FULL is running, the pg_stat_progress_cluster view will contain a row for each backend that is currently running either command. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_cluster View Column Type Description pid integer Process ID of backend. datid oid OID of the database to which this backend is connected. datname name Name of the database to which this backend is connected. relid oid OID of the table being clustered. command text The command that is running. Either CLUSTER or VACUUM FULL. phase text Current processing phase. See Table 1.43. cluster_index_relid oid If the table is being scanned using an index, this is the OID of the index being used; otherwise, it is zero. heap_tuples_scanned bigint Number of heap tuples scanned. This counter only advances when the phase is seq scanning heap, index scanning heap or writing new heap. heap_tuples_written bigint Number of heap tuples written. This counter only advances when the phase is seq scanning heap, index scanning heap or writing new heap. heap_blks_total bigint Total number of heap blocks in the table. This number is reported as of the beginning of seq scanning heap. heap_blks_scanned bigint Number of heap blocks scanned. This counter only advances when the phase is seq scanning heap. index_rebuild_count bigint Number of indexes rebuilt. This counter only advances when the phase is rebuilding index. CLUSTER and VACUUM FULL Phases Phase Description initializing The command is preparing to begin scanning the heap. This phase is expected to be very brief. seq scanning heap The command is currently scanning the table using a sequential scan. index scanning heap CLUSTER is currently scanning the table using an index scan. sorting tuples CLUSTER is currently sorting tuples. writing new heap CLUSTER is currently writing the new heap. swapping relation files The command is currently swapping newly-built files into place. rebuilding index The command is currently rebuilding an index. performing final cleanup The command is performing final cleanup. When this phase is completed, CLUSTER or VACUUM FULL will end. Whenever an application like pg_basebackup is taking a base backup, the pg_stat_progress_basebackup view will contain a row for each WAL sender process that is currently running the BASE_BACKUP replication command and streaming the backup. The tables below describe the information that will be reported and provide information about how to interpret it. pg_stat_progress_basebackup View Column TypeDescription pid `integer`Process ID of a WAL sender process. phase `text`Current processing phase. backup_total bigint`Total amount of data that will be streamed. This is estimated and reported as of the beginning of `streaming database files phase. Note that this is only an approximation since the database may change during streaming database files phase and WAL log may be included in the backup later. This is always the same value as backup_streamed once the amount of data streamed exceeds the estimated total size. If the estimation is disabled in pg_basebackup (i.e., --no-estimate-size option is specified), this is NULL. backup_streamed bigint`Amount of data streamed. This counter only advances when the phase is `streaming database files or transferring wal files. tablespaces_total `bigint`Total number of tablespaces that will be streamed. tablespaces_streamed bigint`Number of tablespaces streamed. This counter only advances when the phase is `streaming database files. Base Backup Phases Phase Description initializing The WAL sender process is preparing to begin the backup. This phase is expected to be very brief. waiting for checkpoint to finish The WAL sender process is currently performing pg_backup_start to prepare to take a base backup, and waiting for the start-of-backup checkpoint to finish. estimating backup size The WAL sender process is currently estimating the total amount of database files that will be streamed as a base backup. streaming database files The WAL sender process is currently streaming database files as a base backup. waiting for wal archiving to finish The WAL sender process is currently performing pg_backup_stop to finish the backup, and waiting for all the WAL files required for the base backup to be successfully archived. If either --wal-method=none or --wal-method=stream is specified in pg_basebackup, the backup will end when this phase is completed. transferring wal files The WAL sender process is currently transferring all WAL logs generated during the backup. This phase occurs after waiting for wal archiving to finish phase if --wal-method=fetch is specified in pg_basebackup. The backup will end when this phase is completed. Whenever COPY is running, the pg_stat_progress_copy view will contain one row for each backend that is currently running a COPY command. The table below describes the information that will be reported and provides information about how to interpret it. pg_stat_progress_copy View Column TypeDescription pid `integer`Process ID of backend. datid `oid`OID of the database to which this backend is connected. datname `name`Name of the database to which this backend is connected. relid oid`OID of the table on which the `COPY command is executed. It is set to 0 if copying from a SELECT query. command text`The command that is running: `COPY FROM, or COPY TO. type text`The io type that the data is read from or written to: `FILE, PROGRAM, PIPE (for COPY FROM STDIN and COPY TO STDOUT), or CALLBACK (used for example during the initial table synchronization in logical replication). bytes_processed bigint`Number of bytes already processed by `COPY command. bytes_total bigint`Size of source file for `COPY FROM command in bytes. It is set to 0 if not available. tuples_processed bigint`Number of tuples already processed by `COPY command. tuples_excluded bigint`Number of tuples not processed because they were excluded by the `WHERE clause of the COPY command. IvorySQL provides facilities to support dynamic tracing of the database server. This allows an external utility to be called at specific points in the code and thereby trace execution. A number of probes or trace points are already inserted into the source code. These probes are intended to be used by database developers and administrators. By default the probes are not compiled into IvorySQL; the user needs to explicitly tell the configure script to make the probes available. Currently, the DTrace utility is supported, which, at the time of this writing, is available on Solaris, macOS, FreeBSD, NetBSD, and Oracle Linux. The SystemTap project for Linux provides a DTrace equivalent and can also be used. Supporting other dynamic tracing utilities is theoretically possible by changing the definitions for the macros in src/include/utils/probes.h. By default, probes are not available, so you will need to explicitly tell the configure script to make the probes available in IvorySQL. To include DTrace support specify --enable-dtrace to configure. A number of standard probes are provided in the source code, More probes can certainly be added to enhance IvorySQL’s observability. Built-in DTrace Probes Name Parameters Description transaction-start (LocalTransactionId) Probe that fires at the start of a new transaction. arg0 is the transaction ID. transaction-commit (LocalTransactionId) Probe that fires when a transaction completes successfully. arg0 is the transaction ID. transaction-abort (LocalTransactionId) Probe that fires when a transaction completes unsuccessfully. arg0 is the transaction ID. query-start (const char *) Probe that fires when the processing of a query is started. arg0 is the query string. query-done (const char *) Probe that fires when the processing of a query is complete. arg0 is the query string. query-parse-start (const char *) Probe that fires when the parsing of a query is started. arg0 is the query string. query-parse-done (const char *) Probe that fires when the parsing of a query is complete. arg0 is the query string. query-rewrite-start (const char *) Probe that fires when the rewriting of a query is started. arg0 is the query string. query-rewrite-done (const char *) Probe that fires when the rewriting of a query is complete. arg0 is the query string. query-plan-start () Probe that fires when the planning of a query is started. query-plan-done () Probe that fires when the planning of a query is complete. query-execute-start () Probe that fires when the execution of a query is started. query-execute-done () Probe that fires when the execution of a query is complete. statement-status (const char *) Probe that fires anytime the server process updates its pg_stat_activity.status. arg0 is the new status string. checkpoint-start (int) Probe that fires when a checkpoint is started. arg0 holds the bitwise flags used to distinguish different checkpoint types, such as shutdown, immediate or force. checkpoint-done (int, int, int, int, int) Probe that fires when a checkpoint is complete. (The probes listed next fire in sequence during checkpoint processing.) arg0 is the number of buffers written. arg1 is the total number of buffers. arg2, arg3 and arg4 contain the number of WAL files added, removed and recycled respectively. clog-checkpoint-start (bool) Probe that fires when the CLOG portion of a checkpoint is started. arg0 is true for normal checkpoint, false for shutdown checkpoint. clog-checkpoint-done (bool) Probe that fires when the CLOG portion of a checkpoint is complete. arg0 has the same meaning as for clog-checkpoint-start. subtrans-checkpoint-start (bool) Probe that fires when the SUBTRANS portion of a checkpoint is started. arg0 is true for normal checkpoint, false for shutdown checkpoint. subtrans-checkpoint-done (bool) Probe that fires when the SUBTRANS portion of a checkpoint is complete. arg0 has the same meaning as for subtrans-checkpoint-start. multixact-checkpoint-start (bool) Probe that fires when the MultiXact portion of a checkpoint is started. arg0 is true for normal checkpoint, false for shutdown checkpoint. multixact-checkpoint-done (bool) Probe that fires when the MultiXact portion of a checkpoint is complete. arg0 has the same meaning as for multixact-checkpoint-start. buffer-checkpoint-start (int) Probe that fires when the buffer-writing portion of a checkpoint is started. arg0 holds the bitwise flags used to distinguish different checkpoint types, such as shutdown, immediate or force. buffer-sync-start (int, int) Probe that fires when we begin to write dirty buffers during checkpoint (after identifying which buffers must be written). arg0 is the total number of buffers. arg1 is the number that are currently dirty and need to be written. buffer-sync-written (int) Probe that fires after each buffer is written during checkpoint. arg0 is the ID number of the buffer. buffer-sync-done (int, int, int) Probe that fires when all dirty buffers have been written. arg0 is the total number of buffers. arg1 is the number of buffers actually written by the checkpoint process. arg2 is the number that were expected to be written (arg1 of buffer-sync-start); any difference reflects other processes flushing buffers during the checkpoint. buffer-checkpoint-sync-start () Probe that fires after dirty buffers have been written to the kernel, and before starting to issue fsync requests. buffer-checkpoint-done () Probe that fires when syncing of buffers to disk is complete. twophase-checkpoint-start () Probe that fires when the two-phase portion of a checkpoint is started. twophase-checkpoint-done () Probe that fires when the two-phase portion of a checkpoint is complete. buffer-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool) Probe that fires when a buffer read is started. arg0 and arg1 contain the fork and block numbers of the page (but arg1 will be -1 if this is a relation extension request). arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is true for a relation extension request, false for normal read. buffer-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, bool, bool) Probe that fires when a buffer read is complete. arg0 and arg1 contain the fork and block numbers of the page (if this is a relation extension request, arg1 now contains the block number of the newly added block). arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is true for a relation extension request, false for normal read. arg7 is true if the buffer was found in the pool, false if not. buffer-flush-start (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires before issuing any write request for a shared buffer. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. buffer-flush-done (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires when a write request is complete. (Note that this just reflects the time to pass the data to the kernel; it’s typically not actually been written to disk yet.) The arguments are the same as for buffer-flush-start. buffer-write-dirty-start (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires when a server process begins to write a dirty buffer. (If this happens often, it implies that shared_buffers is too small or the background writer control parameters need adjustment.) arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. buffer-write-dirty-done (ForkNumber, BlockNumber, Oid, Oid, Oid) Probe that fires when a dirty-buffer write is complete. The arguments are the same as for buffer-write-dirty-start. wal-buffer-write-dirty-start () Probe that fires when a server process begins to write a dirty WAL buffer because no more WAL buffer space is available. (If this happens often, it implies that wal_buffers is too small.) wal-buffer-write-dirty-done () Probe that fires when a dirty WAL buffer write is complete. wal-insert (unsigned char, unsigned char) Probe that fires when a WAL record is inserted. arg0 is the resource manager (rmid) for the record. arg1 contains the info flags. wal-switch () Probe that fires when a WAL segment switch is requested. smgr-md-read-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) Probe that fires when beginning to read a block from a relation. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. smgr-md-read-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) Probe that fires when a block read is complete. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is the number of bytes actually read, while arg7 is the number requested (if these are different it indicates trouble). smgr-md-write-start (ForkNumber, BlockNumber, Oid, Oid, Oid, int) Probe that fires when beginning to write a block to a relation. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. smgr-md-write-done (ForkNumber, BlockNumber, Oid, Oid, Oid, int, int, int) Probe that fires when a block write is complete. arg0 and arg1 contain the fork and block numbers of the page. arg2, arg3, and arg4 contain the tablespace, database, and relation OIDs identifying the relation. arg5 is the ID of the backend which created the temporary relation for a local buffer, or InvalidBackendId (-1) for a shared buffer. arg6 is the number of bytes actually written, while arg7 is the number requested (if these are different it indicates trouble). sort-start (int, bool, int, int, bool, int) Probe that fires when a sort operation is started. arg0 indicates heap, index or datum sort. arg1 is true for unique-value enforcement. arg2 is the number of key columns. arg3 is the number of kilobytes of work memory allowed. arg4 is true if random access to the sort result is required. arg5 indicates serial when 0, parallel worker when 1, or parallel leader when 2. sort-done (bool, long) Probe that fires when a sort is complete. arg0 is true for external sort, false for internal sort. arg1 is the number of disk blocks used for an external sort, or kilobytes of memory used for an internal sort. lwlock-acquire (char *, LWLockMode) Probe that fires when an LWLock has been acquired. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-release (char *) Probe that fires when an LWLock has been released (but note that any released waiters have not yet been awakened). arg0 is the LWLock’s tranche. lwlock-wait-start (char *, LWLockMode) Probe that fires when an LWLock was not immediately available and a server process has begun to wait for the lock to become available. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-wait-done (char *, LWLockMode) Probe that fires when a server process has been released from its wait for an LWLock (it does not actually have the lock yet). arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-condacquire (char *, LWLockMode) Probe that fires when an LWLock was successfully acquired when the caller specified no waiting. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lwlock-condacquire-fail (char *, LWLockMode) Probe that fires when an LWLock was not successfully acquired when the caller specified no waiting. arg0 is the LWLock’s tranche. arg1 is the requested lock mode, either exclusive or shared. lock-wait-start (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) Probe that fires when a request for a heavyweight lock (lmgr lock) has begun to wait because the lock is not available. arg0 through arg3 are the tag fields identifying the object being locked. arg4 indicates the type of object being locked. arg5 indicates the lock type being requested. lock-wait-done (unsigned int, unsigned int, unsigned int, unsigned int, unsigned int, LOCKMODE) Probe that fires when a request for a heavyweight lock (lmgr lock) has finished waiting (i.e., has acquired the lock). The arguments are the same as for lock-wait-start. deadlock-found () Probe that fires when a deadlock is found by the deadlock detector. Defined Types Used in Probe Parameters Type Definition LocalTransactionId unsigned int LWLockMode int LOCKMODE int BlockNumber unsigned int Oid unsigned int ForkNumber int bool unsigned char The example below shows a DTrace script for analyzing transaction counts in the system, as an alternative to snapshotting pg_stat_database before and after a performance test: #!/usr/sbin/dtrace -qs postgresql$1:::transaction-start { @start[\"Start\"] = count(); self->ts = timestamp; } postgresql$1:::transaction-abort { @abort[\"Abort\"] = count(); } postgresql$1:::transaction-commit /self->ts/ { @commit[\"Commit\"] = count(); @time[\"Total time (ns)\"] = sum(timestamp - self->ts); self->ts=0; } When executed, the example D script gives output such as: # ./txn_count.d `pgrep -n postgres` or ./txn_count.d <PID> ^C Start 71 Commit 70 Total time (ns) 2312105013 Note SystemTap uses a different notation for trace scripts than DTrace does, even though the underlying trace points are compatible. One point worth noting is that at this writing, SystemTap scripts must reference probe names using double underscores in place of hyphens. This is expected to be fixed in future SystemTap releases. New probes can be defined within the code wherever the developer desires, though this will require a recompilation. Below are the steps for inserting new probes: Decide on probe names and data to be made available through the probes Add the probe definitions to src/backend/utils/probes.d Include pg_trace.h if it is not already present in the module(s) containing the probe points, and insert TRACE_POSTGRESQL probe macros at the desired locations in the source code Recompile and verify that the new probes are available Example: Here is an example of how you would add a probe to trace all new transactions by transaction ID. Decide that the probe will be named transaction-start and requires a parameter of type LocalTransactionId Add the probe definition to src/backend/utils/probes.d: ``` probe transaction__start(LocalTransactionId); ``` Note the use of the double underline in the probe name. In a DTrace script using the probe, the double underline needs to be replaced with a hyphen, so `transaction-start` is the name to document for users. At compile time, transaction__start is converted to a macro called TRACE_POSTGRESQL_TRANSACTION_START (notice the underscores are single here), which is available by including pg_trace.h. Add the macro call to the appropriate location in the source code. In this case, it looks like the following: ``` TRACE_POSTGRESQL_TRANSACTION_START(vxid.localTransactionId); ``` After recompiling and running the new binary, check that your newly added probe is available by executing the following DTrace command. You should see similar output: ``` # dtrace -ln transaction-start ID PROVIDER MODULE FUNCTION NAME 18705 postgresql49878 postgres StartTransactionCommand transaction-start 18755 postgresql49877 postgres StartTransactionCommand transaction-start 18805 postgresql49876 postgres StartTransactionCommand transaction-start 18855 postgresql49875 postgres StartTransactionCommand transaction-start 18986 postgresql49873 postgres StartTransactionCommand transaction-start ``` There are a few things to be careful about when adding trace macros to the C code: You should take care that the data types specified for a probe’s parameters match the data types of the variables used in the macro. Otherwise, you will get compilation errors. On most platforms, if IvorySQL is built with --enable-dtrace, the arguments to a trace macro will be evaluated whenever control passes through the macro, even if no tracing is being done. This is usually not worth worrying about if you are just reporting the values of a few local variables. But beware of putting expensive function calls into the arguments. If you need to do that, consider protecting the macro with a check to see if the trace is actually enabled: ``` if (TRACE_POSTGRESQL_TRANSACTION_START_ENABLED()) TRACE_POSTGRESQL_TRANSACTION_START(some_function(...)); ``` Each trace macro has a corresponding ENABLED macro. Each table has a primary heap disk file where most of the data is stored. If the table has any columns with potentially-wide values, there also might be a TOAST file associated with the table, which is used to store values too wide to fit comfortably in the main table . There will be one valid index on the TOAST table, if present. There also might be indexes associated with the base table. Each table and index is stored in a separate disk file — possibly more than one file, if the file would exceed one gigabyte. You can monitor disk space in three ways: using the SQL functions, using the oid2name module, or using manual inspection of the system catalogs. The SQL functions are the easiest to use and are generally recommended. The remainder of this section shows how to do it by inspection of the system catalogs. Using psql on a recently vacuumed or analyzed database, you can issue queries to see the disk usage of any table: SELECT pg_relation_filepath(oid), relpages FROM pg_class WHERE relname = 'customer'; pg_relation_filepath | relpages ----------------------+---------- base/16384/16806 | 60 (1 row) Each page is typically 8 kilobytes. (Remember, relpages is only updated by VACUUM, ANALYZE, and a few DDL commands such as CREATE INDEX.) The file path name is of interest if you want to examine the table’s disk file directly. To show the space used by TOAST tables, use a query like the following: SELECT relname, relpages FROM pg_class, (SELECT reltoastrelid FROM pg_class WHERE relname = 'customer') AS ss WHERE oid = ss.reltoastrelid OR oid = (SELECT indexrelid FROM pg_index WHERE indrelid = ss.reltoastrelid) ORDER BY relname; relname | relpages ----------------------+---------- pg_toast_16806 | 0 pg_toast_16806_index | 1 You can easily display index sizes, too: SELECT c2.relname, c2.relpages FROM pg_class c, pg_class c2, pg_index i WHERE c.relname = 'customer' AND c.oid = i.indrelid AND c2.oid = i.indexrelid ORDER BY c2.relname; relname | relpages -------------------+---------- customer_id_index | 26 It is easy to find your largest tables and indexes using this information: SELECT relname, relpages FROM pg_class ORDER BY relpages DESC; relname | relpages ----------------------+---------- bigtable | 3290 customer | 3144 The most important disk monitoring task of a database administrator is to make sure the disk doesn’t become full. A filled data disk will not result in data corruption, but it might prevent useful activity from occurring. If the disk holding the WAL files grows full, database server panic and consequent shutdown might occur. If you cannot free up additional space on the disk by deleting other things, you can move some of the database files to other file systems by making use of tablespaces. Tip Some file systems perform badly when they are almost full, so do not wait until the disk is completely full to take action. If your system supports per-user disk quotas, then the database will naturally be subject to whatever quota is placed on the user the server runs as. Exceeding the quota will have the same bad effects as running out of disk space entirely.","title":"Monitoring","component":"ivorysql-doc","version":"master","name":"daily_monitoring","url":"/ivorysql-doc/master/getting-started/daily_monitoring.html","titles":[{"text":"1. Monitoring Database Activity","hash":"monitoring-database-activity","id":1},{"text":"1.1. Standard Unix Tools","hash":"standard-unix-tools","id":2},{"text":"1.2. The Cumulative Statistics System","hash":"the-cumulative-statistics-system","id":3},{"text":"1.2.1. Statistics Collection Configuration","hash":"statistics-collection-configuration","id":4},{"text":"1.2.2. Viewing Statistics","hash":"viewing-statistics","id":5},{"text":"1.2.3. pg_stat_activity","hash":"pg_stat_activity","id":6},{"text":"1.2.4. pg_stat_replication","hash":"pg_stat_replication","id":7},{"text":"1.2.5. pg_stat_replication_slots","hash":"pg_stat_replication_slots","id":8},{"text":"1.2.6. pg_stat_wal_receiver","hash":"pg_stat_wal_receiver","id":9},{"text":"1.2.7. pg_stat_recovery_prefetch","hash":"pg_stat_recovery_prefetch","id":10},{"text":"1.2.8. pg_stat_subscription","hash":"pg_stat_subscription","id":11},{"text":"1.2.9. pg_stat_subscription_stats","hash":"pg_stat_subscription_stats","id":12},{"text":"1.2.10. pg_stat_ssl","hash":"pg_stat_ssl","id":13},{"text":"1.2.11. pg_stat_gssapi","hash":"pg_stat_gssapi","id":14},{"text":"1.2.12. pg_stat_archiver","hash":"pg_stat_archiver","id":15},{"text":"1.2.13. pg_stat_bgwriter","hash":"pg_stat_bgwriter","id":16},{"text":"1.2.14. pg_stat_wal","hash":"pg_stat_wal","id":17},{"text":"1.2.15. pg_stat_database","hash":"pg_stat_database","id":18},{"text":"1.2.16. pg_stat_database_conflicts","hash":"pg_stat_database_conflicts","id":19},{"text":"1.2.17. pg_stat_all_tables","hash":"pg_stat_all_tables","id":20},{"text":"1.2.18. pg_stat_all_indexes","hash":"pg_stat_all_indexes","id":21},{"text":"1.2.19. pg_statio_all_tables","hash":"pg_statio_all_tables","id":22},{"text":"1.2.20. pg_statio_all_indexes","hash":"pg_statio_all_indexes","id":23},{"text":"1.2.21. pg_statio_all_sequences","hash":"pg_statio_all_sequences","id":24},{"text":"1.2.22. pg_stat_user_functions","hash":"pg_stat_user_functions","id":25},{"text":"1.2.23. pg_stat_slru","hash":"pg_stat_slru","id":26},{"text":"1.2.24. Statistics Functions","hash":"statistics-functions","id":27},{"text":"1.3. View Locks","hash":"view-locks","id":28},{"text":"1.4. Progress Reporting","hash":"progress-reporting","id":29},{"text":"1.4.1. ANALYZE Progress Reporting","hash":"analyze-progress-reporting","id":30},{"text":"1.4.2. CREATE INDEX Progress Reporting","hash":"create-index-progress-reporting","id":31},{"text":"1.4.3. VACUUM Progress Reporting","hash":"vacuum-progress-reporting","id":32},{"text":"1.4.4. CLUSTER Progress Reporting","hash":"cluster-progress-reporting","id":33},{"text":"1.4.5. Base Backup Progress Reporting","hash":"base-backup-progress-reporting","id":34},{"text":"1.4.6. COPY Progress Reporting","hash":"copy-progress-reporting","id":35},{"text":"1.5. Dynamic Tracing","hash":"dynamic-tracing","id":36},{"text":"1.5.1. Compiling for Dynamic Tracing","hash":"compiling-for-dynamic-tracing","id":37},{"text":"1.5.2. Built-in Probes","hash":"built-in-probes","id":38},{"text":"1.5.3. Using Probes","hash":"using-probes","id":39},{"text":"1.5.4. Defining New Probes","hash":"defining-new-probes","id":40},{"text":"2. Monitoring Disk Usage","hash":"monitoring-disk-usage","id":41},{"text":"2.1. Determining Disk Usage","hash":"determining-disk-usage","id":42},{"text":"2.2. Disk Full Failure","hash":"disk-full-failure","id":43}]},"54":{"id":54,"text":"Hardware Parameter Minimum Recommended CPU 4 cores 16 cores RAM 4GB 64GB Storage 800MB,HDD 5GB+,SSD or NvMe Network Gigabit network 10G network Software Currently, IvorySQL supports but is not limited to linux(CentOS 8.X/CentOS Stream 9/Ubuntu). The operating system used for the quick start is CentOS Stream 9. Pre-requirements Before getting started, please create an user and grant it root privileges. All the installation steps will be performed by this user. Here we just name it 'ivorysql'. How to create a sudo user installation Create or edit IvorySQL yum repository configuration /etc/yum.repos.d/ivorysql.repo vim /etc/yum.repos.d/ivorysql.repo [ivorysql5] name=IvorySQL Server 5 $releasever - $basearch baseurl=https://yum.highgo.com/dists/ivorysql-rpms/5/redhat/rhel-$releasever-$basearch enabled=1 gpgcheck=0 After saving and exiting, you can install IvorySQL 4 with the following steps $ sudo dnf install -y ivorysql5-5.0 Setting environment variables Add below contents in ~/.bash_profile file and source to make it effective: PATH=/usr/ivory-5/bin:$PATH export PATH PGDATA=/usr/ivory-5/data export PGDATA $ source ~/.bash_profile Initializing database $ initdb -D /usr/ivory-5/data The -D option specifies the directory where the database cluster should be stored. This is the only information required by initdb, but you can avoid writing it by setting the PGDATA environment variable, which can be convenient since the database server can find the database directory later by the same variable. For more options, refer to initdb --help. Starting IvorySQL service $ pg_ctl -D /usr/ivory-5/data -l ivory.log start The -D option specifies the file system location of the database configuration files. If this option is omitted, the environment variable PGDATA in <<setting-environment-variables>> is used. -l option appends the server log output to filename. If the file does not exist, it is created. For more options, refer to pg_ctl --help. Confirm it’s successfully started: $ ps -ef | grep postgres ivorysql 3214 1 0 20:35 ? 00:00:00 /usr/ivory-5/bin/postgres -D /usr/ivory-5/data ivorysql 3215 3214 0 20:35 ? 00:00:00 postgres: checkpointer ivorysql 3216 3214 0 20:35 ? 00:00:00 postgres: background writer ivorysql 3218 3214 0 20:35 ? 00:00:00 postgres: walwriter ivorysql 3219 3214 0 20:35 ? 00:00:00 postgres: autovacuum launcher ivorysql 3220 3214 0 20:35 ? 00:00:00 postgres: logical replication launcher ivorysql 3238 1551 0 20:35 pts/0 00:00:00 grep --color=auto postgres Get IvorySQL image from Docker Hub $ docker pull ivorysql/ivorysql:5.0-ubi8 Running IvorySQL $ docker run --name ivorysql -p 5434:5432 -e IVORYSQL_PASSWORD=your_password -d ivorysql/ivorysql:5.0-ubi8 Check if the IvorySQL container is running successfully $ docker ps | grep ivorysql CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES 6faa2d0ed705 ivorysql:5.0-ubi8 \"docker-entrypoint.s…\" 50 seconds ago Up 49 seconds 5866/tcp, 0.0.0.0:5434->5432/tcp ivorysql Connect to IovrySQL via psql: $ psql -d <database> psql (18.0) Type \"help\" for help. ivorysql=# The -d option specifies the name of the database to connect to. ivorysql is the default database of IvorySQL. However,IvorySQL of lower versions need the users themselves to connect to postgres database at the first connection and then create the ivorysql database.The latest IvorySQL can do all these for users. For more options, refer to psql --help. When running IvorySQL in Docker, additional parameters need to be added, like: psql -d ivorysql -U ivorysql -h 127.0.0.1 -p 5434 Now you can start your journey of IvorySQL! Enjoy! To explore additional installation methods, please refer to the Installation.","title":"Quick Start","component":"ivorysql-doc","version":"master","name":"quick_start","url":"/ivorysql-doc/master/getting-started/quick_start.html","titles":[{"text":"1. Environmental requirements","hash":"environmental-requirements","id":1},{"text":"2. Quick installation","hash":"quick-installation","id":2},{"text":"2.1. yum installation","hash":"yum-installation","id":3},{"text":"2.2. Running IvorySQL in docker","hash":"running-ivorysql-in-docker","id":4},{"text":"3. Connecting to IvorySQL","hash":"connecting-to-ivorysql","id":5}]},"55":{"id":55,"text":"IvorySQL provides compatibility with the Oracle built-in function SYS_CONTEXT('namespace', 'parameter' [, length ]), which returns the value of the parameter associated with the given context at the current moment. It can be used in both SQL and PL/SQL languages. IvorySQL provides the following built-in namespaces: USERENV - Describes the current session. SYS_SESSION_ROLES - Indicates whether a specified role is currently enabled for the session. The implementation principle of SYS_CONTEXT involves dynamically querying system tables and PostgreSQL’s built-in functions to ensure real-time results. It uses SECURITY INVOKER to ensure the function executes with the caller’s permissions, avoiding privilege escalation issues. The implementation logic is as follows: CREATE OR REPLACE FUNCTION sys.sys_context(a varchar2, b varchar2) RETURNS varchar2 AS $$ DECLARE res varchar2; BEGIN IF upper(a) = 'USERENV' THEN CASE upper(b) WHEN 'CURRENT_SCHEMA' THEN SELECT current_schema() INTO res; WHEN 'LANG' THEN SELECT sys.get_lang() INTO res; ... ELSE RAISE EXCEPTION 'invalid USERENV parameter: %', b; END CASE; ELSIF upper(a) = 'SYS_SESSION_ROLES' THEN CASE upper(b) WHEN 'LOGIN' THEN SELECT CASE WHEN rolcanlogin = 't' THEN 'TRUE' ELSE 'FALSE' END INTO res FROM pg_roles WHERE oid = current_user::regrole::oid; ... ELSE RAISE EXCEPTION 'invalid SYS_SESSION_ROLES parameter: %', b; END CASE; ELSE SELECT current_setting(a||'.'||b, true) INTO res; END IF; RETURN res; END; $$ LANGUAGE plisql SECURITY INVOKER; Parameter Name Return Value CURRENT_SCHEMA The name of the currently active default schema. This value may change during the duration of a session through use of an ALTER SESSION SET CURRENT_SCHEMA statement. This may also change during the duration of a session to reflect the owner of any active definer’s rights object. When used directly in the body of a view definition, this returns the default schema used when executing the cursor that is using the view; it does not respect views used in the cursor as being definer’s rights. CURRENT_SCHEMAID Identifier of the currently active default schema. SESSION_USER The name of the session user (the user who logged on). This may change during the duration of a database session as Real Application Security sessions are attached or detached. For enterprise users, returns the schema. For other users, returns the database user name. If a Real Application Security session is currently attached to the database session, returns user XS$NULL. SESSION_USERID The identifier of the session user (the user who logged on). PROXY_USER Name of the database user who opened the current session on behalf of SESSION_USER. PROXY_USERID Identifier of the database user who opened the current session on behalf of SESSION_USER. CURRENT_USER The name of the database user whose privileges are currently active. This may change during the duration of a database session as Real Application Security sessions are attached or detached, or to reflect the owner of any active definer’s rights object. When no definer’s rights object is active, CURRENT_USER returns the same value as SESSION_USER. When used directly in the body of a view definition, this returns the user that is executing the cursor that is using the view; it does not respect views used in the cursor as being definer’s rights. For enterprise users, returns schema. If a Real Application Security user is currently active, returns user XS$NULL. CURRENT_USERID The identifier of the database user whose privileges are currently active. CURRENT_EDITION_NAME The name of the current edition. CLIENT_PROGRAM_NAME The name of the program used for the database session. Parameter Name Return Value IP_ADDRESS The IP address of the client. HOST Name of the host machine from which the client has connected. ISDBA Returns TRUE if the user has been authenticated as having DBA privileges either through the operating system or through a password file. LANG The abbreviated name for the language, a shorter form than the existing 'LANGUAGE' parameter. LANGUAGE The language and territory currently used by your session, along with the database character set, in this form:language_territory.characterset NLS_DATE_FORMAT The date format for the session. PLATFORM_SLASH The slash character that is used as the file path delimiter for your platform. DB_NAME Name of the database as specified in the DB_NAME initialization parameter. SID The session ID. SESSIONID The auditing session identifier. You cannot use this attribute in distributed SQL statements. CLIENT_INFO Returns up to 64 bytes of user session information that can be stored by an application using the DBMS_APPLICATION_INFO package. ENTRYID The current audit entry number. The audit entryid sequence is shared between fine-grained audit records and regular audit records. You cannot use this attribute in distributed SQL statements. The correct auditing entry identifier can be seen only through an audit handler for standard or fine-grained audit. TERMINAL The operating system identifier for the client of the current session. In distributed SQL statements, this attribute returns the identifier for your local session. In a distributed environment, this is supported only for remote SELECT statements, not for remote INSERT, UPDATE, or DELETE operations. (The return length of this parameter may vary by operating system.) Parameter Name Return Value DBA Returns TRUE if the current user is a database administrator. LOGIN Returns TRUE if the current user is a login role. CREATEROLE Returns TRUE if the current session’s user has the privilege to create roles. CREATEDB Returns TRUE if the current session’s user has the privilege to create databases.","title":"Feature Overview","component":"ivorysql-doc","version":"master","name":"sys_context","url":"/ivorysql-doc/master/oracle_builtin_functions/sys_context.html","titles":[{"text":"1. Implementation Principle","hash":"implementation-principle","id":1},{"text":"2. Parameters supported by namespace USERENV","hash":"parameters-supported-by-namespace-userenv","id":2},{"text":"3. Parameters supported by namespace SYS_SESSION_ROLES","hash":"parameters-supported-by-namespace-sys_session_roles","id":3}]},"56":{"id":56,"text":"IvorySQL provides compatibility with Oracle’s built-in function USERENV('parameter'), which is used to return information about the current session. This is a legacy function, and IvorySQL recommends taht you can use the SYS_CONTEXT function with its built-in USERENV namespace for current functionality. USERENV parses function calls through Bison rules, checks parameter validity, and maps them to corresponding SQL functions. The parsing rules are implemented in ora_gram.y, with the following logic: USERENV '(' Sconst ')' { char *normalized_param = downcase_identifier($3, strlen($3), true, true); #define CHECK_AND_CALL(param, func_name) \\ if (strcmp(normalized_param, param) == 0) \\ $$ = (Node *) makeFuncCall(OracleSystemFuncName(func_name), NIL, COERCE_EXPLICIT_CALL, @1); CHECK_AND_CALL(\"client_info\", \"get_client_info\") else CHECK_AND_CALL(\"entryid\", \"get_entryid\") else CHECK_AND_CALL(\"terminal\", \"get_terminal\") else CHECK_AND_CALL(\"isdba\", \"get_isdba\") else CHECK_AND_CALL(\"lang\", \"get_lang\") else CHECK_AND_CALL(\"language\", \"get_language\") else CHECK_AND_CALL(\"sessionid\", \"get_sessionid\") else CHECK_AND_CALL(\"sid\", \"get_sid\") else ereport(ERROR, (errcode(ERRCODE_FEATURE_NOT_SUPPORTED), errmsg(\"invalid USERENV parameter: \\\"%s\\\".\", $3))); #undef CHECK_AND_CALL } The specific functionality is implemented in builtin_functions—​1.0.sql. For example: CREATE OR REPLACE FUNCTION sys.get_language() RETURNS varchar2 AS $$ SELECT (regexp_split_to_array(current_setting('lc_monetary'), '\\\\.'))[1]||'.'||pg_client_encoding(); $$ LANGUAGE sql STRICT; Parameter Name Return Value sid The user ID of the current session. sessionid Returns the audit session identifier. language Returns the language, territory, and character set of the current database session. lang Returns the ISO abbreviation of the language name. isdba Returns \"TRUE\" if the user has been authenticated as an administrator or has administrator privileges through the operating system or password file; otherwise, returns \"FALSE\".","title":"Feature Overview","component":"ivorysql-doc","version":"master","name":"userenv","url":"/ivorysql-doc/master/oracle_builtin_functions/userenv.html","titles":[{"text":"1. Implementation Principle","hash":"implementation-principle","id":1},{"text":"2. Supported parameters","hash":"supported-parameters","id":2}]},"57":{"id":57,"text":"This document is a design document for the PLSQL anonymous block compatible Oracle syntax function, in order to be compatible with Oracle’s anonymous block statements in IvorySQL. Anonymous blocks are PLSQL structures that dynamically create and execute procedural code without the need to persistently store the code as database objects in the system directory. In this implementation, IvorySQL is mainly compatible with the syntax format of PLSQL anonymous blocks, and the parts we mainly deal with include client tool psql, master server and PSQL side support. declare i integer := 10; begin raise notice '%', i; end; / NOTICE: 10 DECLARE grade CHAR(1); BEGIN grade := 'B'; CASE grade WHEN 'A' THEN raise notice 'Excellent'; WHEN 'B' THEN raise notice 'Very Good'; END CASE; EXCEPTION WHEN CASE_NOT_FOUND THEN raise notice 'No such grade'; END; / NOTICE: Very Good","title":"Compatible with Oracle anonymous block","component":"ivorysql-doc","version":"master","name":"anonymous_block","url":"/ivorysql-doc/master/oracle_compatibility/anonymous_block.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function description","hash":"function-description","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"58":{"id":58,"text":"char varchar varchar2 number binary_float binary_double date timestamp timestamp with time zone timestamp with local time zone interval year to month interval day to second raw long sysdate systimestamp add_months last_day next_day months_between current_date current_timestamp new_time tz_offset trunc instr instrb substr substrb trim ltrim rtrim length lengthb rawtohex replace regexp_replace regexp_substr regexp_instr regexp_like to_number to_char to_date to_timestamp to_timestamp_tz to_yminterval to_dsinterval numtodsinterval numtoyminterval localtimestamp from_tz sys_extract_utc sessiontimezone hextoraw uid USERENV asciistr to_multi_byte to_single_byte compose decompose sys_context function: view the corresponding date and time, the test cases are as follows: Query the date of the current system: select sysdate() from dual; sysdate ------------ 2023-07-06 (1 row) Check the date pushed forward by 1 day: select sysdate()-1 from dual; ?column? ------------ 2023-07-05 (1 row) function: return the current system date and time (including microseconds and time zone) on the local database, the test cases are as follows: Date and time to query the current date: select systimestamp() from dual; systimestamp ----------------------------------- 2023-07-06 10:18:31.674322 +08:00 (1 row) function: the function adds a date to the number of months (n), and returns the same day that is n months apart, supporting parameters: date, number; The test cases are as follows: Check the same day of the following month on the current date (July 6): select add_months(sysdate(),1) from dual; add_months ------------ 2023-08-06 (1 row) Query the same day of the previous month for the current date: select add_months(sysdate(),-1) from dual; add_months ------------ 2023-06-06 (1 row) function: return the last day of the month where the specified date is located, support parameters: date, the test cases are as follows: Check the last day of the month in which the day is located: select last_day(sysdate())from dual; last_day ------------ 2023-07-31 (1 row) Query the last day of the month on which a day falls: select last_day(to_date('2019-09-01'))from dual; last_day ------------ 2019-09-30 (1 row) function: return the next date of the specified date. Supported parameters: date, integer /date, text, Note: When the second parameter in the function passes the number of weeks more hours than the existing week, the date of the next week will be returned; When the date passed by the second parameter in the function is greater than the existing number of weeks, the corresponding day of the week of the week is returned. The test cases are as follows: Query the next day of the current date: select next_day(sysdate(),1) from dual; next_day ------------ 2023-07-07 (1 row) Next Friday for the current date: select next_day(sysdate(),'FRIDAY') from dual; next_day ------------ 2023-07-07 (1 row) function: return the month of difference between date1 and date2 of date type, support parameters: date, date, description: if date1 is later than date2, return a positive number; If date1 is earlier than date2, a negative number is returned; If date1 and date2 are the same day of a month, the return result is an integer; If not the same day, results with decimal parts are returned on a monthly basis of 31 days. The test cases are as follows: To find the month that differs between the same day in different months: select months_between(to_date('2023-07-06'),to_date('2023-08-06')) from dual; months_between ---------------- -1 (1 row) Query the month that differs between different days of different months: select months_between(to_date('2023-07-06'),to_date('2023-08-05')) from dual; months_between -------------------- -0.967741935483871 (1 row) function: return the current date of the current time zone, the test cases are as follows: To query the current date in the current time zone: select current_date from dual; current_date -------------- 2023-07-06 (1 row) function: return the current date and current time of the current time zone, including the current time zone information. Support parameters: integer, Note: The returned time can be adjusted with precision. The test cases are as follows: To query the current date and time in the current time zone: select current_timestamp from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:01.440600 +08:00 (1 row) Query the current date and time in the current time zone (the precision is adjusted to the first three decimal places): select current_timestamp(3) from dual; current_timestamp ----------------------------------- 2023-07-06 10:27:14.182000 +08:00 (1 row) function: return the date in another time zone corresponding to a certain time zone, support parameters: date, text, text, the test case is as follows: Returns the date for the current date in another time zone: select sysdate() bj_time,new_time(sysdate(),'PDT','GMT')los_angles from dual; bj_time | los_angles ------------+------------ 2023-07-06 | 2023-07-06 (1 row) function: return the offset of the given time zone and the standard time zone, support parameters: text, the test case is as follows: Returns the offset of a given time zone from the standard time zone: select tz_offset('US/Eastern') from dual; tz_offset ----------- -04:00 (1 row) function: you can intercept the date to get the desired value, such as year, month, day, hour, minute, support parameters: date/date, text, the test case is as follows: Intercept the current date: select trunc(sysdate()) from dual; trunc ------------ 2023-07-06 (1 row) Truncating the year, only the year is correct, and the month and day are not accurate values: select trunc(sysdate(),'yyyy') from dual; trunc ------------ 2023-01-01 (1 row) Intercept the month, the return value only the month is correct, the year and day are not accurate values: select trunc(sysdate(),'mm') from dual; trunc ------------ 2023-07-01 (1 row) function: string search that checks whether the source string contains the target string and returns the match position. Supported parameter forms are as follows: instr(string, str) instr(string, str, start_position, nth_appearance) The following are test cases: Returns the first match by default: SELECT INSTR('database administration', 'data') FROM DUAL; instr ------- 1 (1 row) Specify the starting position and match sequence: SELECT INSTR('database administration', 'i', 1, 2) FROM DUAL; instr ------- 15 (1 row) Supports reverse search from the end of the string: SELECT INSTR('mississippi river', 's', -5, 2) FROM DUAL; instr ------- 6 (1 row) Returns 0 when no match is found: SELECT INSTR('database administration', 'z') FROM DUAL; instr ------- 0 (1 row) instr can also be used for like-style fuzzy matching: select * from tableName where instr(name,'helloworld')>0; Implementation notes: Searches for one string inside another with a classic brute-force algorithm. Reuses the text_instring helper function. Uses the isByte flag to detect multibyte encodings and branch between single-byte and multibyte handling. The sign of position decides forward or backward search and sets loop bounds and step. Iterates byte by byte through the source string, comparing with the pattern until a match is found. function: string lookup function, return the position of the string, support parameters: varchar2, text, number DEFAULT 1, number DEFAULT 1, the following are test cases: RETURNS THE POSITION OF THE STRING IN CORPORATE FLOOR WHEN THE FIRST OR OCCURS BY DEFAULT: SELECT INSTRB('CORPORATE FLOOR','OR') \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 2 (1 row) Returns the position of the string in the corporate floor where the query starts with the fifth character and the second occurrence of or: SELECT INSTRB('CORPORATE FLOOR','OR',5,2) \"Instring in bytes\" FROM DUAL; Instring in bytes ------------------- 14 (1 row) function: intercept string function, truncated in characters, support parameters: text, integer, test cases are as follows: Intercept the string from the fifth character in 'It is nice today', followed by: SELECT SUBSTR('It is nice today',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- s nice today (1 row) function: intercept string function, intercept in bytes, support parameters: varchar2, number/varchar2, number, number, the test cases are as follows: Intercept the string starting with the fifth byte in 'It’s nice today' and then onwards: SELECT SUBSTRB('It is nice today',5) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- s nice today (1 row) Intercept the string in 'It is nice today' starting with the fifth byte and ending with the eighth byte: SELECT SUBSTRB('It is nice today',5,8) \"Substring with bytes\" FROM DUAL; Substring with bytes ---------------------- s nice t (1 row) function: remove the left and right spaces or corresponding data of the specified string, support parameters: varchar2 / varchar2, varchar2, the test cases are as follows: Remove the left and right spaces of ' aaa bbb ccc ': select trim(' aaa bbb ccc ')trim from dual; trim ------------- aaa bbb ccc (1 row) Remove aaa from 'aaa bbb ccc': select trim('aaa bbb ccc','aaa')trim from dual; trim ---------- bbb ccc (1 row) function: remove the left space or corresponding data of the specified string, support parameters: varchar2 / varchar2, varchar2, the test cases are as follows: Remove the space to the left of ' abcdefg ': select ltrim(' abcdefg ')ltrim from dual; ltrim ------------ abcdefg (1 row) Traverse from the left side of 'abcdefg', remove it as soon as a character appears in 'fegab', and return the result if it is absent: select ltrim('abcdefg','fegab')ltrim from dual; ltrim ------- cdefg (1 row) function: remove the space on the right side of the specified string, the test case is as follows: Remove the space to the right of ' abcdefg ': select rtrim(' abcdefg ')rtrim from dual; rtrim ---------------- abcdefg (1 row) Traverse from the right side of 'abcdefg', remove it as soon as a character appears in 'fegab', and return the result if it is absent: select rtrim('abcdefg','fegab')rtrim from dual; rtrim ------- abcd (1 row) function: find the length of the specified string character, support parameters: char/integer/varchar2 The test cases are as follows: Query the character length of 223: select length(223) from dual; length -------- 3 (1 row) Query the character length of '223': select length('223') from dual; length -------- 3 (1 row) To query the character length of 'ivorysql database' : select length('ivorysql database') from dual; length -------- 17 (1 row) function: find the length of the specified string byte, support parameters: char/bytea/varchar2 test cases are as follows: Query the byte lengthb of 'ivorysql': select lengthb('ivorysq'::char) from dual; lengthb --------- 1 (1 row) Query the byte lengthb of '0x2C': select lengthb('0x2C'::bytea) from dual; lengthb --------- 4 (1 row) Query the byte lengthb of the 'ivorysql database': select lengthb('ivorysql database') from dual; lengthb --------- 17 (1 row) function: replace the character in the specified string or delete the character, support parameters: text, text, text/varchar2, varchar2, varchar2 DEFAULT NULL::varchar2, test for example: Replace 'j' in 'jack and jue' with 'bl' : select replace('jack and jue','j','bl') from dual; replace ---------------- black and blue (1 row) Remove the 'j' in 'jack and jue' : select replace('jack and jue','j') from dual; replace ------------ ack and ue (1 row) which is an extension of the replace function. Function: Used to perform matching and replacement through regular expressions. Supported parameters: text, text, text /text, text, text, integer/varchar2, varchar2/varchar2, varchar2 varchar2, varchar2 varchar2, for example: Replace the matched number with *#: select regexp_replace('01234abcd56789','[0-9]','*#')from dual; regexp_replace -------------------------- *#*#*#*#*#abcd*#*#*#*#*# (1 row) Start with the second number by replacing the matched number with *#: select regexp_replace('01234abcd56789','[0-9]','*#',2)from dual; regexp_replace ------------------------- 0*#*#*#*#abcd*#*#*#*#*# Delete '01' from '01234abcd56789': select regexp_replace('01234abcd56789','01')from dual; regexp_replace ---------------- 234abcd56789 (1 row) Replace 01234abcd56789' with 'xxx': select regexp_replace('01234abcd56789','012','xxx')from dual; regexp_replace ---------------- xxx34abcd56789 (1 row) function: pick up the character substring described by the regular expression, support parameters: text, text, integer /text, text, integer, integer / text, text, integer, integer, text /varchar2, varchar2, the test cases are as follows: Query the 012 string starting with the first number in '012ab34': select regexp_substr('012ab34', '012',1) from dual; regexp_substr --------------- 012 (1 row) Query the 012 string in '012ab34' starting from the first number of the first group: select regexp_substr('012ab34', '012',1,1) from dual; regexp_substr --------------- 012 (1 row) Query '012a012Ab34' for case-insensitive 012 strings starting from the first number of the first group: select regexp_substr('012a012Ab34', '012A',1,1,'i') from dual; regexp_substr --------------- 012a (1 row) Query '012a012Ab34' for case-sensitive 012 strings starting from the first group of numbers: select regexp_substr('012a012Ab34', '012A',1,1,'c') from dual; regexp_substr --------------- 012A (1 row) Query the 'Database' substring in 'Data': select regexp_substr('Database' , 'Data') from dual; regexp_substr --------------- Data (1 row)s function: used to calibrate the start position of the character substring that conforms to the regular expression, support parameters: text, text, integer /text, text, integer, integer / text, text, integer, integer, text, integer / varchar2, varchar2, the test case is as follows: Query 'abcaBcabc' for the position of the abc substring starting from the first character: SELECT regexp_instr('abcaBcabc', 'abc', 1); regexp_instr -------------- 1 (1 row) Query 'abcaBcabc' starting from the first character, where the abc substring appears for the third time: SELECT regexp_instr('abcaBcabc', 'abc', 1, 3); regexp_instr -------------- 7 (1 row) Query 'abcabcabc' starting from the first character and occurring after the second occurrence of the abc substring: SELECT regexp_instr('abcaBcabc', 'abc', 1, 2,1); regexp_instr -------------- 7 (1 row) Query 'abcaBcabc' from the first character, where it occurs after the first occurrence of the abc substring (case sensitive): SELECT regexp_instr('abcaBcabc', 'abc',1,2,1,'c'); regexp_instr -------------- 7 (1 row) Query the 'Database' substring in 'Data': SELECT regexp_instr('Database', 'Data'); regexp_instr -------------- 1 (1 row) function: similar to like, used for fuzzy queries. Supported parameters: varchar2, varchar2 /varchar2, varchar2 varchar2, First create a regexp_like table for the test case query: create table t_regexp_like ( id varchar(4), value varchar(10) ); insert into t_regexp_like values ('1','1234560'); insert into t_regexp_like values ('2','1234560'); insert into t_regexp_like values ('3','1b3b560'); insert into t_regexp_like values ('4','abc'); insert into t_regexp_like values ('5','abcde'); insert into t_regexp_like values ('6','ADREasx'); insert into t_regexp_like values ('7','123 45'); insert into t_regexp_like values ('8','adc de'); insert into t_regexp_like values ('9','adc,.de'); insert into t_regexp_like values ('10','abcbvbnb'); insert into t_regexp_like values ('11','11114560'); The test cases are as follows: Query t_regexp_like columns with abc in the table: select * from t_regexp_like where regexp_like(value,'abc'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) Query t_regexp_like columns with ABC in the table (not case sensitive): select * from t_regexp_like where regexp_like(value,'ABC','i'); id | value ----+---------- 4 | abc 5 | abcde 10 | abcbvbnb (3 rows) function: is to change some processed strings arranged in a certain format back to a numeric format, support parameters: text/text, text test cases are as follows: Convert the string '-34,338,492' to numeric format: SELECT to_number('-34,338,492', '99,999,999') from dual; to_number ----------- -34338492 (1 row) Convert the string '5.01-' to numeric format: SELECT to_number('5.01-', '9.99S'); to_number ----------- -5.01 (1 row) function: convert numbers or dates to character types, support parameters: date/date, text/timestamp/timestamp, text test cases are as follows: To convert the current system date to character format: select to_char(sysdate()) from dual; to_char ------------ 2023-07-10 (1 row) Convert current system date to month/day/year character format: select to_char(sysdate(),'mm/dd/yyyy') from dual; to_char ------------ 07/10/2023 (1 row) Converts the timestamp format of the current date to character format SELECT to_char(sysdate()::timestamp); to_char ---------------------------- 2023-07-10 09:46:44.000000 Convert timestamp format of current date to month/date/year character format: SELECT to_char(sysdate()::timestamp,'MM-YYYY-DD'); to_char ------------ 07-2023-10 (1 row) function: convert character type to date type, support parameters: text/text, text test cases are as follows: Convert '2023/07/06' to date type: select to_date('20230706') from dual; to_date ------------ 2023-07-06 (1 row) Convert '-44-02-01' to date type: SELECT to_date('-44,0201','YYYY-MM-DD'); to_date ------------ 0044-02-01 (1 row) function: can store year, month, day, hour, minute, second, and can also store fractional parts of seconds. Supported parameters: text/text, text test cases are as follows: Query '2018-11-02 12:34:56.025' output as a date: SELECT to_timestamp('20181102.12.34.56.025'); to_timestamp ---------------------------- 2018-11-02 12:34:56.025000 (1 row) Query '2011,12,18 11:38' output as a date: SELECT to_timestamp('2011,12,18 11:38 ', 'YYYY-MM-DD HH24:MI:SS'); to_timestamp ---------------------------- 2011-12-18 11:38:00.000000 (1 row) function: according to the time query, the time string has T, Z and milliseconds, time zone. The test cases are as follows: Query '2016-10-9 14:10:10.123000' output as a date: SELECT to_timestamp_tz('2016-10-9 14:10:10.123000') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-10-09 14:10:10.123000 +08:00 (1 row) Query '10-9-2016 14:10:10.123000 +8:30' output as a date: SELECT to_timestamp_tz('10-9-2016 14:10:10.123000 +8:30', 'DD-MM-YYYY HH24:MI:SS.FF TZH:TZM') FROM DUAL; to_timestamp_tz ----------------------------------- 2016-09-10 13:40:10.123000 +08:00 (1 row) function: convert a string type to a year and month time difference type, support parameters: text, The test cases are as follows: Query the date after two years and eight months after '20110101': select to_date('20110101','yyyymmdd')+to_yminterval('02-08') from dual; ?column? ------------ 2013-09-01 (1 row) function: add a date plus a certain hour or number of days into another date, support parameters: text, test cases are as follows: Query the current system time plus the date in 9 and a half hours (currently 2023-07-06, 18:00): select sysdate()+to_dsinterval('0 09:30:00')as newdate from dual; newdate ------------ 2023-07-07 (1 row) function: convert numbers into time interval type data. The supporting parameters: double precision, text test cases are as follows: Convert 100.00 hours to interval type data: SELECT NUMTODSINTERVAL(100.00, 'hour'); numtodsinterval ------------------------------- +000000004 04:00:00.000000000 (1 row) Convert 100 minutes to interval type data: SELECT NUMTODSINTERVAL(100, 'minute'); numtodsinterval ------------------------------- +000000000 01:40:00.000000000 (1 row) function: convert numbers into date interval type data. Convert 1, year to date interval: double precision, text, the test case is as follows: SELECT NUMTOYMINTERVAL(1.00,'year'); numtoyminterval ----------------- +000000001-00 (1 row) Convert 1, mouth to date interval: SELECT NUMTOYMINTERVAL(1,'month'); numtoyminterval ----------------- +000000000-01 (1 row) function: return the date and time in the session, support parameters: integer, add parameters to the function as precision, the test cases are as follows: To return the date and time in the current session: select localtimestamp from dual; localtimestamp ---------------------------- 2023-07-07 09:18:15.896472 (1 row) Returns the date and time in the current session with a precision of 1: select localtimestamp(1) from dual; localtimestamp ---------------------------- 2023-07-07 09:18:16.100000 (1 row) function: convert time from one time zone to another, support parameters; timestamp, text, the test case is as follows: Convert '2000-03-28 08:00:00', '3:00' to the current time zone: SELECT FROM_TZ(TIMESTAMP '2000-03-28 08:00:00', '3:00') FROM DUAL; from_tz ----------------------------------- 2000-03-28 13:00:00.000000 +08:00 (1 row) function: convert a timestamptz to UTC time zone time. Supported parameters: timestamp with time zone The test cases are as follows: Query conversion timestamp '2000-03-28 11:30:00.00 -8:00' to the time after UTC time zone: select sys_extract_utc(timestamp '2000-03-28 11:30:00.00 -8:00') from dual; sys_extract_utc ---------------------------- 2000-03-28 19:30:00.000000 (1 row) function: view time zone details, test cases are as follows: To view the details of the current time zone: select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Shanghai (1 row) After modifying the timezone, check the time zone belief information: set timezone = 'Asia/Hong_Kong'; SET select sessiontimezone() from dual; sessiontimezone ----------------- Asia/Hong_Kong (1 row) function: convert the binary value represented by the string into a RAW value. Support parameters: text, the test cases are as follows: Convert the string 'abcdef' to a raw value: select hextoraw('abcdef')from dual; hextoraw ---------- \\xabcdef (1 row) function: get the instance name of the database. The test cases are as follows: Get the instance name of the current database: select uid() from dual; uid ----- 10 (1 row) function: return the information of the current user environment, the test cases are as follows: Check whether the current user is DBA, and if so, return true: select userenv('isdba')from dual; get_isdba ----------- TRUE (1 row) To view the session flag: select userenv('sessionid')from dual; get_sessionid --------------- 1 (1 row) function: input string, return ASCII characters, the test cases are as follows: string with only ascii chars: select asciistr('Hello, World!') from dual; asciistr --------------- Hello, World! (1 row) string with non-ascii chars: select asciistr('你好') from dual; asciistr ------------ \\4F60\\597D string with mixed ascii and non-ascii: select asciistr('ABÄCDE') from dual; asciistr ------------ AB\\00C4CDE (1 row) function: Convert half-width characters in a string to full-width characters: input half-width characters, Convert to full-width characters: select to_multi_byte('1.2'::text) ; to_multi_byte --------------- １．２ function: Convert full-width characters in a string to half-width characters: input full-width characters, Convert to half-width characters: select to_single_byte('１．２'); to_single_byte ---------------- 1.2 function: Combine base characters and combining marks into a composite Unicode character: input base character 'a' with a combining mark '768', return à： select compose('a'||chr(768)) from dual; compose --------- à (1 row) function: Decompose composite Unicode characters (like those with accents or special symbols) into their base characters and combining marks. input é, return a base character 'e' with a combining mark '301': select asciistr(decompose('é')) from dual; asciistr ---------- e\\0301 which returns the value of the parameter associated with the given context at the current moment. It can be used in both SQL and PL/SQL languages. ivorysql=# select sys_context('USERENV','DB_NAME'); sys_context ------------- ivorysql (1 row)","title":"Built-in data types and built-in functions","component":"ivorysql-doc","version":"master","name":"builtin_types_functions","url":"/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html","titles":[{"text":"1. Built-in data types","hash":"built-in-data-types","id":1},{"text":"2. Built-in functions","hash":"built-in-functions","id":2},{"text":"3. Built-in function descriptions","hash":"built-in-function-descriptions","id":3},{"text":"3.1. sysdate function","hash":"sysdate-function","id":4},{"text":"3.2. systimestamp function","hash":"systimestamp-function","id":5},{"text":"3.3. add_months function","hash":"add_months-function","id":6},{"text":"3.4. last_day function","hash":"last_day-function","id":7},{"text":"3.5. next_day function","hash":"next_day-function","id":8},{"text":"3.6. months_between function","hash":"months_between-function","id":9},{"text":"3.7. current_date function","hash":"current_date-function","id":10},{"text":"3.8. current_timestamp function","hash":"current_timestamp-function","id":11},{"text":"3.9. new_time function","hash":"new_time-function","id":12},{"text":"3.10. tz_offset function","hash":"tz_offset-function","id":13},{"text":"3.11. trunc function","hash":"trunc-function","id":14},{"text":"3.12. instr function","hash":"instr-function","id":15},{"text":"3.13. instrb function","hash":"instrb-function","id":16},{"text":"3.14. substr function","hash":"substr-function","id":17},{"text":"3.15. substrb function","hash":"substrb-function","id":18},{"text":"3.16. trim function","hash":"trim-function","id":19},{"text":"3.17. ltrim function","hash":"ltrim-function","id":20},{"text":"3.18. rtrim function","hash":"rtrim-function","id":21},{"text":"3.19. length function","hash":"length-function","id":22},{"text":"3.20. lengthb function","hash":"lengthb-function","id":23},{"text":"3.21. replace function","hash":"replace-function","id":24},{"text":"3.22. regexp_replace function","hash":"regexp_replace-function","id":25},{"text":"3.23. regexp_substr function","hash":"regexp_substr-function","id":26},{"text":"3.24. regexp_instr function","hash":"regexp_instr-function","id":27},{"text":"3.25. regexp_like function","hash":"regexp_like-function","id":28},{"text":"3.26. to_number function","hash":"to_number-function","id":29},{"text":"3.27. to_char function","hash":"to_char-function","id":30},{"text":"3.28. to_date function","hash":"to_date-function","id":31},{"text":"3.29. to_timestamp function","hash":"to_timestamp-function","id":32},{"text":"3.30. to_timestamp_tz function","hash":"to_timestamp_tz-function","id":33},{"text":"3.31. to_yminterval function","hash":"to_yminterval-function","id":34},{"text":"3.32. to_dsinterval function","hash":"to_dsinterval-function","id":35},{"text":"3.33. numtodsinterval function","hash":"numtodsinterval-function","id":36},{"text":"3.34. numtoyminterval function","hash":"numtoyminterval-function","id":37},{"text":"3.35. localtimestamp function","hash":"localtimestamp-function","id":38},{"text":"3.36. from_tz function","hash":"from_tz-function","id":39},{"text":"3.37. sys_extract_utc function","hash":"sys_extract_utc-function","id":40},{"text":"3.38. sessiontimezone function","hash":"sessiontimezone-function","id":41},{"text":"3.39. hextoraw function","hash":"hextoraw-function","id":42},{"text":"3.40. uid function","hash":"uid-function","id":43},{"text":"3.41. USERENV function","hash":"userenv-function","id":44},{"text":"3.42. ASCIISTR function","hash":"asciistr-function","id":45},{"text":"3.43. TO_MULTI_BYTE function","hash":"to_multi_byte-function","id":46},{"text":"3.44. TO_SINGLE_BYTE function","hash":"to_single_byte-function","id":47},{"text":"3.45. COMPOSE function","hash":"compose-function","id":48},{"text":"3.46. DECOMPOSE function","hash":"decompose-function","id":49},{"text":"3.47. SYS_CONTEXT function","hash":"sys_context-function","id":50}]},"59":{"id":59,"text":"In IvorySQL, the CALL statement supports invoking standalone function stored procedures, as well as functions and stored procedures within packages or object types. The CALL syntax for invoking functions adds an INTO clause, where the insertion target is a host variable. CALL supports invoking standalone functions and stored procedures, as well as those defined within packages. When using CALL to invoke functions, the INTO clause syntax is added, with the insertion target being a host variable. When calling functions/stored procedures with no parameters or all default values, empty parentheses cannot be omitted. CALL supports referencing bind variables in parameters or the INTO clause when invoking functions and stored procedures. The binding variables corresponding to OUT parameters in the CALL statement support validation of precision and data types. Output binding variables are not allowed to be repeatedly bound. -- Create function ivorysql=# create or replace function f_defs(a number default 1314) ivorysql-# return number ivorysql-# is ivorysql-# begin ivorysql-# raise notice '%', a; ivorysql-# return a; ivorysql-# end; ivorysql-# / CREATE FUNCTION -- Declare bind variable ivorysql=# variable x number -- Call the function and retrieve the return value ivorysql=# call f_defs() into :x; NOTICE: 1314 Call completed. ivorysql=# print x X ------ 1314 ivorysql=# create table tb1(c1 int); CREATE TABLE -- Create package specification ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# procedure test_p ; ivorysql-# end; ivorysql-# / CREATE PACKAGE -- Create package body ivorysql=# create or replace package body pkg is ivorysql-# procedure test_p is ivorysql-# begin ivorysql-# insert into tb1 values(1); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 2; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY -- Call the procedure in the package ivorysql=# call pkg.test_p(); CALL ivorysql=# select * from tb1; c1 ----- 1 (1 row) -- Create a function with default parameters ivorysql=# CREATE OR REPLACE FUNCTION default_arg_func(p_num NUMBER DEFAULT 100) RETURN NUMBER AS ivorysql-# BEGIN ivorysql-# RETURN p_num + 5; ivorysql-# END; ivorysql-# / CREATE FUNCTION -- Correctly call a function with default parameters (must include parentheses) ivorysql=# VARIABLE default_result NUMBER; ivorysql=# CALL default_arg_func() INTO :default_result; -- Use the default value of 100 Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 105 -- Call with parameters ivorysql=# CALL default_arg_func(200) INTO :default_result; Call completed. ivorysql=# PRINT default_result; DEFAULT_RESULT ---------------- 205 -- Create functions stand_alone_func CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER) RETURN NUMBER IS BEGIN RETURN p_input * 2; END; / -- Set input bind variables ivorysql=# VARIABLE input_num NUMBER = 7; -- Call a function using bind variables as parameters ivorysql=# VARIABLE func_result NUMBER; ivorysql=# CALL stand_alone_func(:input_num) INTO :func_result; Call completed. ivorysql=# PRINT func_result; FUNC_RESULT ------------- 14 -- Create a procedure with OUT parameters ivorysql=# CREATE OR REPLACE PROCEDURE out_param_proc( ivorysql(# p_in IN VARCHAR2, ivorysql(# p_out OUT VARCHAR2, ivorysql(# p_num_out OUT NUMBER ivorysql(# ) AS ivorysql-# BEGIN ivorysql-# p_out := p_in || ' processed'; ivorysql-# p_num_out := LENGTH(p_in); ivorysql-# END; ivorysql-# / CREATE PROCEDURE -- Test OUT parameter type matching ivorysql=# VARIABLE out_var VARCHAR2(50); ivorysql=# VARIABLE num_var NUMBER; ivorysql=# CALL out_param_proc('Test input', :out_var, :num_var); Call completed. ivorysql=# PRINT out_var; OUT_VAR ---------------------- Test input processed ivorysql=# PRINT num_var; NUM_VAR --------- 10 -- Test insufficient OUT parameter precision (will be truncated) ivorysql=# VARIABLE short_out VARCHAR2(5); ivorysql=# CALL out_param_proc('Long input string', :short_out, :num_var); Call completed. ivorysql=# PRINT short_out; SHORT_OUT ----------- Long -- Test type mismatch (will throw an error) ivorysql=# VARIABLE wrong_type NUMBER; ivorysql=# CALL out_param_proc('Test', :wrong_type, :num_var); ERROR: invalid input syntax for type numeric: \"Test processed\" -- Prepare binding variables ivorysql=# VARIABLE dup_var VARCHAR2(100); -- Attempting duplicate binding (will throw an error) ivorysql=# CALL out_param_func('Test', :dup_var) INTO :dup_var; ERROR: output parameter cannot be a duplicate bind -- Correct approach: Use different binding variables ivorysql=# VARIABLE out1 VARCHAR2(100); ivorysql=# CALL out_param_proc('Correct usage', :out1, :num_var); Call completed. ivorysql=# PRINT out1; OUT1 ------------------------- Correct usage processed","title":"Compatible with Oracle’s CALL INTO","component":"ivorysql-doc","version":"master","name":"compat_call_into","url":"/ivorysql-doc/master/oracle_compatibility/compat_call_into.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3},{"text":"3.1. Call INTO invokes a function and inserts the result into a host variable.","hash":"call-into-invokes-a-function-and-inserts-the-result-into-a-host-variable","id":4},{"text":"3.2. Call the stored procedure in the package","hash":"call-the-stored-procedure-in-the-package","id":5},{"text":"3.3. Call with no parameters or default parameters","hash":"call-with-no-parameters-or-default-parameters","id":6},{"text":"3.4. Reference bind variables in parameters or INTO clauses","hash":"reference-bind-variables-in-parameters-or-into-clauses","id":7},{"text":"3.5. The OUT parameters in CALL statements support precision and data type validation","hash":"the-out-parameters-in-call-statements-support-precision-and-data-type-validation","id":8},{"text":"3.6. Output binding variables do not allow duplicate binding.","hash":"output-binding-variables-do-not-allow-duplicate-binding","id":9}]},"60":{"id":60,"text":"In order to meet the case compatibility of PG and Oracle’s reference identifiers, ivorysql has designed three case conversion modes for reference identifiers. Select the conversion mode via the GUC parameter \"identifier_case_switch\"; If the value of the guc parameter \"identifier_case_switch\" is \"normal\": 1). The letters in the identifier referenced by the double quotation mark are left unchanged. If the value of the guc parameter \"identifier_case_switch\" is \"interchange\": 1). If the letters in the identifier referenced by the double quotation mark are all uppercase, uppercase is converted to lowercase. 2). If the letters in the identifier referenced by the double quotation mark are all lowercase, lowercase is converted to uppercase. 3). If the letters in the identifier enclosed in double quotation marks are mixed-case, the identifier is left unchanged. If the value of the guc parameter \"identifier_case_switch\" is \"lowercase\": 1). If the letters in the identifier referenced by the double quotation mark are all uppercase, uppercase is converted to lowercase. 2). If the letters in the identifier enclosed in double quotation marks are mixed-case, the identifier is left unchanged. Add the -C option to the initdb program to set the case conversion mode, and the corresponding value of -C is: \"normal\" ------ \"0\"synonymy \"interchange\" ------ \"1\"synonymy \"lowercase\" ------ \"2\"synonymy During initialization of the database cluster, the case conversion pattern is saved to the global/pg_control file in the data directory; normal ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = normal; SET ivorysql=# CREATE TABLE \"NORMAL_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Normal_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"normal_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"NORMAL_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Normal_1\"; ERROR: relation \"Normal_1\" does not exist LINE 1: select * from \"Normal_1\"; ivorysql=# select * from \"normal_1\"; ERROR: relation \"normal\" does not exist LINE 1: select * from \"normal\"; ivorysql=# select * from NORMAL_1; ERROR: relation \"normal_1\" does not exist LINE 1: select * from NORMAL_1; ivorysql=# select * from \"Normal_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_2\"; ERROR: relation \"NORMAL_2\" does not exist LINE 1: select * from \"NORMAL_2\"; ivorysql=# select * from \"normal_2\"; ERROR: relation \"normal_2\" does not exist LINE 1: select * from \"normal_2\"; ivorysql=# select * from Normal_2; ERROR: relation \"normal_2\" does not exist LINE 1: select * from Normal_2; ivorysql=# select * from \"normal_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"NORMAL_3\"; ERROR: relation \"NORMAL_3\" does not exist LINE 1: select * from \"NORMAL_3\"; ivorysql=# select * from \"Normal_3\"; ERROR: relation \"Normal_3\" does not exist LINE 1: select * from \"Normal_3\"; ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE interchange ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = interchange; SET ivorysql=# CREATE TABLE \"INTER_CHANGE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Inter_Change_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"inter_change_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"INTER_CHANGE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_1\"; ERROR: relation \"Inter_Change_1\" does not exist LINE 1: select * from \"Inter_Change_1\"; ivorysql=# select * from \"inter_change_1\"; ERROR: relation \"INTER_CHANGE_1\" does not exist LINE 1: select * from \"inter_change_1\"; ivorysql=# select * from INTER_CHANGE_1; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Inter_Change_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_2\"; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from \"INTER_CHANGE_2\"; ivorysql=# select * from \"inter_change_2\"; ERROR: relation \"INTER_CHANGE_2\" does not exist LINE 1: select * from \"inter_change_2\"; ivorysql=# select * from Inter_Change_2; ERROR: relation \"inter_change_2\" does not exist LINE 1: select * from Inter_Change_2; ivorysql=# select * from \"inter_change_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"INTER_CHANGE_3\"; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# select * from \"Inter_Change_3\"; ERROR: relation \"Inter_Change_3\" does not exist LINE 1: select * from \"Inter_Change_3\"; ivorysql=# select * from inter_change_3; ERROR: relation \"inter_change_3\" does not exist LINE 1: select * from \"INTER_CHANGE_3\"; ivorysql=# drop table \"INTER_CHANGE_1\"; DROP TABLE ivorysql=# drop table \"Inter_Change_2\"; DROP TABLE ivorysql=# drop table \"inter_change_3\"; DROP TABLE lowercase ivorysql=# SET ivorysql.compatible_mode to oracle; SET ivorysql=# SET ivorysql.enable_case_switch = true; SET ivorysql=# SET ivorysql.identifier_case_switch = lowercase; SET ivorysql=# CREATE TABLE \"LOWER_CASE_1\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"Lower_Case_2\"(c1 int, c2 int); CREATE TABLE ivorysql=# CREATE TABLE \"lower_case_3\"(c1 int, c2 int); CREATE TABLE ivorysql=# select * from \"LOWER_CASE_1\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_1\"; ERROR: relation \"Lower_Case_1\" does not exist LINE 1: select * from \"Lower_Case_1\"; ivorysql=# select * from \"lower_case_1\"; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from LOWER_CASE_1; c1 | c2 ----+---- (0 行记录) ivorysql=# select * from \"Lower_Case_2\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"LOWER_CASE_2\"; ivorysql=# select * from \"lower_case_2\"; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from \"lower_case_2\"; ivorysql=# select * from Lower_Case_2; ERROR: relation \"lower_case_2\" does not exist LINE 1: select * from Lower_Case_2; ivorysql=# select * from \"lower_case_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"LOWER_CASE_3\"; c1 | c2 ----+---- (0 rows) ivorysql=# select * from \"Lower_Case_3\"; ERROR: relation \"Lower_Case_3\" does not exist LINE 1: select * from \"Lower_Case_3\"; ivorysql=# select * from LOWER_CASE_3; c1 | c2 ----+---- (0 行记录) ivorysql=# drop table \"NORMAL_1\"; DROP TABLE ivorysql=# drop table \"Normal_2\"; DROP TABLE ivorysql=# drop table \"normal_3\"; DROP TABLE","title":"Reference identifier case conversion design","component":"ivorysql-doc","version":"master","name":"compat_case_conversion","url":"/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function","hash":"function","id":2},{"text":"2.1. Three modes of case conversion (interchange by default)","hash":"three-modes-of-case-conversion-interchange-by-default","id":3},{"text":"2.2. When the database cluster is initialized","hash":"when-the-database-cluster-is-initialized","id":4},{"text":"2.3. Use Cases","hash":"use-cases","id":5}]},"61":{"id":61,"text":"In IvorySQL’s Oracle compatibility mode, support converting empty strings to NULL for storage, providing behavior consistent with Oracle Database. Oracle compatibility mode supports converting empty strings to NULL for storage. The feature is controlled by parameter ivorysql.enable_emptystring_to_null, with default value on. When this parameter is enabled, inserting an empty string will automatically convert it to NULL value for storage. NULL values after conversion can be queried using the IS NULL condition. -- Create test table ivorysql=# create table abc (id int); CREATE TABLE -- Check empty string to NULL parameter status ivorysql=# show ivorysql.enable_emptystring_to_null; ivorysql.enable_emptystring_to_NULL ------------------------------------- on (1 row) -- Insert empty string ivorysql=# insert into abc values(''); INSERT 0 1 -- Query table data, displays as NULL ivorysql=# select * from abc; id \\---- (1 row) -- Query using IS NULL condition ivorysql=# select * from abc where id is null; id \\---- (1 row)","title":"Oracle Compatible Empty String to NULL","component":"ivorysql-doc","version":"master","name":"compat_empty_string_to_null","url":"/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3}]},"62":{"id":62,"text":"This document explains the purpose of Force View in IvorySQL and shows how it enables users to create placeholder views when dependencies are not yet ready while keeping the behavior aligned with Oracle. Force View supports migrating views even when dependent tables are not yet ready, and can swiftly revert to regular views through automatic or explicit compilation once dependencies are satisfied. CREATE [OR REPLACE] FORCE VIEW: Creates a view object even when the query references missing tables or functions. The system retains the SQL definition and returns the message WARNING: View created with compilation errors. Automatic compilation: When a Force view is accessed, IvorySQL attempts recompilation. If it succeeds, the view becomes a normal view; if it fails, it raises view \"<schema>.<name>\" has errors. Dependency fallback: When a normal view is invalidated because dependencies are removed or altered, IvorySQL automatically converts it into a Force view, preserving the latest valid definition so it can be restored quickly once dependencies are rebuilt. -- The base table does not exist, but the Force view placeholder is created successfully. CREATE FORCE VIEW fv_customer AS SELECT c_id, c_name FROM missing_customer; -- Expected output: WARNING: View created with compilation errors -- Provide the missing dependency. CREATE TABLE missing_customer( c_id int primary key, c_name text ); INSERT INTO missing_customer VALUES (1, 'Alice'); -- Accessing the Force view triggers automatic compilation. SELECT * FROM fv_customer; -- On success, the view becomes a normal view and returns data. -- Recreate the view definition as a Force view to make it invalid again. CREATE OR REPLACE FORCE VIEW fv_customer AS SELECT c_id, upper(c_name) AS c_name FROM missing_customer_v2; -- Explicit compilation fails because dependencies are still missing. ALTER VIEW fv_customer COMPILE; -- Expected output: WARNING: View altered with compilation errors -- Supply the required dependency and compile again. CREATE TABLE missing_customer_v2( c_id int, c_name text ); ALTER VIEW fv_customer COMPILE; -- Expected output: ALTER VIEW succeeds and the view returns to normal.","title":"Force View","component":"ivorysql-doc","version":"master","name":"compat_force_view","url":"/ivorysql-doc/master/oracle_compatibility/compat_force_view.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3},{"text":"3.1. Create a Force View with Missing Dependencies","hash":"create-a-force-view-with-missing-dependencies","id":4},{"text":"3.2. Automatic Compilation and Recovery","hash":"automatic-compilation-and-recovery","id":5},{"text":"3.3. Explicit Compilation and Failure Fallback","hash":"explicit-compilation-and-failure-fallback","id":6}]},"63":{"id":63,"text":"PostgreSQL supports functions and stored procedures, but there are syntax differences between PostgreSQL and Oracle. To enable Oracle’s PL/SQL statements to run on IvorySQL—i.e., to achieve \"syntax compatibility\"—IvorySQL adopts the following solution: If an Oracle clause has a counterpart clause with the same function in IvorySQL, it is directly mapped to the corresponding IvorySQL clause; otherwise, only the syntax of the Oracle clause is implemented, while its function is not. The specific Oracle compatibility support includes the following aspects: CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer AS BEGIN RETURN 1; END; / CREATE or replace NONEDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / configure --help --with-max-funarg=MAXFUNARG CREATE or replace EDITIONABLE FUNCTION ora_func RETURN integer IS BEGIN RETURN 1; END; / CREATE OR REPLACE FUNCTION ora_func (num1 IN int, num2 IN int) RETURN int AS num3 int :=10; num4 int :=10; num5 int; BEGIN num3 := num1 + num2; num4 := num1 * num2; num5 := num3 * num4; RETURN num5; END; / CREATE FUNCTION select ora_func(5,9)from dual; ora_func ---------- 630 (1 row) CREATE OR REPLACE FUNCTION test_nocopy(a IN int, b OUT NOCOPY int, c IN OUT NOCOPY int) RETURN int IS BEGIN b := a; c := a; return 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = METADATA IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID CURRENT_USER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON () IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING pg_catalog.int4 IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED IS BEGIN RETURN 1; END; / CREATE or replace FUNCTION ora_func RETURN integer SHARING = NONE AUTHID DEFINER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) DEFAULT COLLATION USING_NLS_COMP DETERMINISTIC PARALLEL_ENABLE ( PARTITION A BY RANGE ( B, C ) CLUSTER A BY ( E,F ) ) RESULT_CACHE RELIES_ON ( data_source1, data_source2) AGGREGATE USING int PIPELINED TABLE POLYMORPHIC USING pg_catalog.int4 SQL_MACRO IS BEGIN RETURN 1; END; / alter function public.test_func noneditionable; alter function test_func compile; alter function test_func compile debug; alter function test_func compile debug sd = mv; alter function test_func compile debug reuse settings; CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE NONEDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE EDITIONABLE PROCEDURE ora_procedure IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = NONE IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID DEFINER IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D ) IS p integer := 20; begin raise notice '%', p; end; / CREATE OR REPLACE PROCEDURE ora_procedure SHARING = METADATA DEFAULT COLLATION USING_NLS_COMP AUTHID CURRENT_USER ACCESSIBLE BY ( FUNCTION A.B, PROCEDURE C.D, PACKAGE E, TRIGGER F, TYPE G ) IS p integer := 20; begin raise notice '%', p; end; / alter procedure test_proc editionable; alter procedure public.test_proc noneditionable; alter procedure test_proc compile; alter procedure test_proc compile debug; alter procedure test_proc compile debug sd = mv; alter procedure test_proc compile debug reuse settings; create or replace function f_noparentheses return int is begin return 11; end; / select f_noparentheses from dual; create or replace procedure protest as begin raise notice 'protest'; end; / CALL protest(); They can be found in file contrib/ivorysql_ora/src/sysview/sysview--1.0.sql. Including DBA_PROCEDURES, ALL_PROCEDURES, USER_PROCEDURES, DBA_SOURCE, ALL_SOURCE, USER_SOURCE, DBA_ARGUMENTS, ALL_ARGUMENTS, USER_ARGUMENTS etc.","title":"PL/iSQL function and stored procedure","component":"ivorysql-doc","version":"master","name":"compat_function_procedure","url":"/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Compatibility support","hash":"compatibility-support","id":2},{"text":"2.1. EDITIONABLE/NONEDITIONABLE is supported in CREATE FUNCTION syntax","hash":"editionablenoneditionable-is-supported-in-create-function-syntax","id":3},{"text":"2.2. Keyword 'RETURN' and 'IS' are supported in CREATE FUNCTION syntax","hash":"keyword-return-and-is-are-supported-in-create-function-syntax","id":4},{"text":"2.3. In the CREATE FUNCTION syntax, a function can have no parameters, and no parentheses () after the function name","hash":"in-the-create-function-syntax-a-function-can-have-no-parameters-and-no-parentheses-after-the-function-name","id":5},{"text":"2.4. Increase the number of parameters in CREATE FUNCTION syntax, the max value can be specified by configure command","hash":"increase-the-number-of-parameters-in-create-function-syntax-the-max-value-can-be-specified-by-configure-command","id":6},{"text":"2.5. Psql uses slash (/) as terminator of statement in CREATE FUNCTION syntax","hash":"psql-uses-slash-as-terminator-of-statement-in-create-function-syntax","id":7},{"text":"2.6. No DECLARE keyword before variable in CREATE FUNCTION syntax","hash":"no-declare-keyword-before-variable-in-create-function-syntax","id":8},{"text":"2.7. Support NOCOPY for OUT parameter in CREATE FUNCTION syntax","hash":"support-nocopy-for-out-parameter-in-create-function-syntax","id":9},{"text":"2.8. Support sharing clause in CREATE FUNCTION syntax","hash":"support-sharing-clause-in-create-function-syntax","id":10},{"text":"2.9. Support invoker_rights (AUTHID) in CREATE FUNCTION syntax，and change default permission to be DR（DEFINER）","hash":"support-invoker_rights-authid-in-create-function-syntaxand-change-default-permission-to-be-drdefiner","id":11},{"text":"2.10. Support ACCESSIBLE BY in CREATE FUNCTION syntax","hash":"support-accessible-by-in-create-function-syntax","id":12},{"text":"2.11. Support DEFAULT COLLATION in CREATE FUNCTION syntax","hash":"support-default-collation-in-create-function-syntax","id":13},{"text":"2.12. Support deterministic clause in CREATE FUNCTION syntax","hash":"support-deterministic-clause-in-create-function-syntax","id":14},{"text":"2.13. Support parallel_enable clause in CREATE FUNCTION syntax","hash":"support-parallel_enable-clause-in-create-function-syntax","id":15},{"text":"2.14. Support result_cache clause in CREATE FUNCTION syntax","hash":"support-result_cache-clause-in-create-function-syntax","id":16},{"text":"2.15. Support aggregate clause in CREATE FUNCTION syntax","hash":"support-aggregate-clause-in-create-function-syntax","id":17},{"text":"2.16. Support pipelined clause in CREATE FUNCTION syntax","hash":"support-pipelined-clause-in-create-function-syntax","id":18},{"text":"2.17. Support sql_macro clause in CREATE FUNCTION syntax","hash":"support-sql_macro-clause-in-create-function-syntax","id":19},{"text":"2.18. Compatibility with ALTER FUNCTION syntax","hash":"compatibility-with-alter-function-syntax","id":20},{"text":"2.19. Support EDITIONABLE/NONEDITIONABLE in CREATE PROCEDURE syntax","hash":"support-editionablenoneditionable-in-create-procedure-syntax","id":21},{"text":"2.20. In the CREATE PROCEDURE syntax, a procedure can have no parameters, and no parentheses () after the procedure name","hash":"in-the-create-procedure-syntax-a-procedure-can-have-no-parameters-and-no-parentheses-after-the-procedure-name","id":22},{"text":"2.21. Psql uses slash (/) as terminator of statement in CREATE PROCEDURE syntax","hash":"psql-uses-slash-as-terminator-of-statement-in-create-procedure-syntax","id":23},{"text":"2.22. Support sharing clause in CREATE PROCEDURE syntax","hash":"support-sharing-clause-in-create-procedure-syntax","id":24},{"text":"2.23. Support DEFAULT COLLATION clause in CREATE PROCEDURE syntax","hash":"support-default-collation-clause-in-create-procedure-syntax","id":25},{"text":"2.24. Support invoker_rights clause (AUTHID) in CREATE PROCEDURE syntax","hash":"support-invoker_rights-clause-authid-in-create-procedure-syntax","id":26},{"text":"2.25. Support ACCESSIBLE BY clause in CREATE PROCEDURE syntax","hash":"support-accessible-by-clause-in-create-procedure-syntax","id":27},{"text":"2.26. Compatibility with ALTER PROCEDURE syntax","hash":"compatibility-with-alter-procedure-syntax","id":28},{"text":"2.27. Function and procedure can have no parameter","hash":"function-and-procedure-can-have-no-parameter","id":29},{"text":"2.28. Views related with function and stored procedure","hash":"views-related-with-function-and-stored-procedure","id":30},{"text":"2.29. Support (--) and (/* */)","hash":"support-and","id":31},{"text":"2.30. pg_dump adds one slash (/) at the end of definition of function/procedure when backup SQL file","hash":"pg_dump-adds-one-slash-at-the-end-of-definition-of-functionprocedure-when-backup-sql-file","id":32}]},"64":{"id":64,"text":"This document is intended to provide people using like fuzzy queries with an in-depth understanding of Oracle-compatible fuzzy query like implementations. Database name Like fuzzy queries oracle oracle’s string type is varchar2, which supports fuzzy queries using the Like keyword with wildcards for columns of number, date, and string field types IvorySQL The basic type of IvorySQL’s string is text, so like is based on text, and other IvorySQL types can be implicitly converted to text, so that they can be automatically converted without creating operator create table t_ora_like (id int ,str1 varchar(8), date1 timestamp with time zone, date2 time with time zone, num int, str2 varchar(8)); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123456,'test1','2022-09-26 16:39:20','2022-09-26 16:39:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123457,'test2','2022-09-26 16:40:20','2022-09-26 16:40:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (223456,'test3','2022-09-26 16:41:20','2022-09-26 16:41:20'); insert into t_ora_like (id ,str1 ,date1 ,date2) values (123458,'test4','2022-09-26 16:42:20','2022-09-26 16:42:20'); select * from t_ora_like where str1 like 'test%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date1 like '2022%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where date2 like '16%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 223456 | test3 | 2022-09-26 16:41:20.000000 +08:00 | 16:41:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (4 rows) select * from t_ora_like where id like '123%'; id | str1 | date1 | date2 | num | str2 --------+-------+-----------------------------------+-------------+-----+------ 123456 | test1 | 2022-09-26 16:39:20.000000 +08:00 | 16:39:20+08 | | 123457 | test2 | 2022-09-26 16:40:20.000000 +08:00 | 16:40:20+08 | | 123458 | test4 | 2022-09-26 16:42:20.000000 +08:00 | 16:42:20+08 | | (3 rows) select * from t_ora_like where id like null; id | str1 | date1 | date2 | num | str2 ----+------+-------+-------+-----+------ (0 rows)","title":"Compatible with Oracle Like","component":"ivorysql-doc","version":"master","name":"compat_like_operator","url":"/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function description","hash":"function-description","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"65":{"id":65,"text":"Using Oracle-style nested subfunctions in IvorySQL. Allows declaring and invoking subfunctions or subprocedures inside anonymous blocks, functions, or procedures, with scope limited to the parent block. Nested subfunctions can read and update variables defined in the parent scope while introducing their own local variables; the parent scope cannot directly access the subfunction’s internal state. Supports overloading resolution that distinguishes homonymous subfunctions by argument count, data type, or named parameters. DO $$ DECLARE v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; RETURN p_value * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; END; $$ LANGUAGE plisql; DO $$ DECLARE v_base_multiplier integer := 20; v_audit_counter integer := 0; v_result integer; FUNCTION inner_square(p_value integer) RETURN integer IS BEGIN RAISE NOTICE 'inner_square called'; v_audit_counter := v_audit_counter + 1; RETURN v_base_multiplier * p_value; END; BEGIN v_result := inner_square(10); RAISE NOTICE 'result=%', v_result; RAISE NOTICE 'v_audit_counter=%', v_audit_counter; END; $$ LANGUAGE plisql; -- Polymorphic nested function specializing on argument type DO $$ DECLARE v_last_notice text := 'none'; FUNCTION describe_value(p_input anyelement) RETURN text IS BEGIN v_last_notice := format('polymorphic dispatch with %s', pg_typeof(p_input)); RETURN v_last_notice; END; FUNCTION describe_value(p_input anyarray, p_element anyelement) RETURN text IS BEGIN v_last_notice := format('array dispatch with %s', pg_typeof(p_input)::text); RETURN v_last_notice; END; BEGIN RAISE NOTICE '%', describe_value(100); RAISE NOTICE '%', describe_value('IvorySQL'::text); -- explicit cast avoids ambiguous literal RAISE NOTICE '%', describe_value(ARRAY[1,2,3], NULL::int); -- extra arg guides anyarray resolution RAISE NOTICE 'last notice=%', v_last_notice; END; $$ LANGUAGE plisql;","title":"Oracle-Compatible Nested Subfunctions","component":"ivorysql-doc","version":"master","name":"compat_nested_function","url":"/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Feature Description","hash":"feature-description","id":2},{"text":"3. Test Cases","hash":"test-cases","id":3}]},"66":{"id":66,"text":"National Language Support(NLS), which refers to the localization support functionality provided by Oracle. IvorySQL offers Oracle-compatible NLS parameter functionality. The following parameters are included: Parameter Name Description ivorysql.datetime_ignore_nls_mask Indicates whether the date format ignores the influence of NLS parameters. Default is 0 . nls_length_semantics Oracle-compatible parameter that specifies whether the size unit for CHAR , VARCHAR , and VARCHAR2 type modifiers is bytes or characters. nls_date_format Specifies the default date format, which can be viewed using the SHOW command. Default is 'YYYY-MM-DD' . nls_timestamp_format Oracle-compatible parameter that controls the format of timestamps. nls_timestamp_tz_format Oracle-compatible parameter that controls the format of timestamps with time zones. nls_territory Oracle-compatible parameter that specifies the default region for the database. nls_iso_currency Oracle-compatible parameter that assigns a unique currency symbol to a specified country or region. nls_currency Oracle-compatible parameter that specifies the symbol for the local currency, corresponding to the placeholder L in numeric string formats. Example: ivorysql=# set ivorysql.datetime_ignore_nls_mask = 0; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ERROR: datetime format picture ends before converting entire input string LINE 1: select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; ^ ivorysql=# set ivorysql.datetime_ignore_nls_mask = 2; SET ivorysql=# select '2025-10-15 11:00:00.102030 CST'::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) Example: ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; oratimestamp ---------------------------- 2025-10-15 11:00:00.102030 (1 row) ivorysql=# set nls_timestamp_format=\"pg\"; SET ivorysql=# select '2025-10-15 11:00:00.102030 '::oratimestamp ; ERROR: date format not recognized LINE 1: select '2025-10-15 11:00:00.102030 '::oratimestamp ; ^ IvorySQL uses the value of the nls_length_semantics parameter to determine the length type, which can be either BYTE or CHAR (default is BYTE )。 Example: ivorysql=# alter session set nls_length_semantics = char; SET ivorysql=# create table character_tb(char_c char(6), char_b varchar2(6 byte), char_v varchar(6)); CREATE TABLE ivorysql=# ivorysql=# insert into character_tb values('abcdef', '123456', 'OK'); INSERT 0 1 ivorysql=# select * from character_tb ; char_c | char_b | char_v --------+--------+-------- abcdef | 123456 | OK (1 row) ivorysql=# select length(char_b), length(char_c), length(char_v) from character_tb; length | length | length --------+--------+-------- 6 | 6 | 2 (1 row) Example: ivorysql=# show ivorysql.identifier_case_switch; ivorysql.identifier_case_switch --------------------------------- interchange (1 row) ivorysql=# set nls_currency to \"CHINA\"; SET ivorysql=# show nls_currency; nls_currency -------------- CHINA (1 row) ivorysql=# set nls_currency to \"China\"; SET ivorysql=# show nls_currency; nls_currency -------------- China (1 row) The behavior of nls_currency and nls_iso_currency is impacted by the Oracle ivorysql.identifier_case_switch compatibility feature.","title":"NLS Parameters","component":"ivorysql-doc","version":"master","name":"compat_nls_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Description","hash":"description","id":2},{"text":"2.1. NLS Date Mask Settings","hash":"nls-date-mask-settings","id":3},{"text":"2.2. Disabling NLS Date or Timestamp Format","hash":"disabling-nls-date-or-timestamp-format","id":4},{"text":"2.3. Parameter nls_length_semantics Settings","hash":"parameter-nls_length_semantics-settings","id":5},{"text":"2.4. NLS Currency Symbols Settings","hash":"nls-currency-symbols-settings","id":6}]},"67":{"id":67,"text":"IvorySQL provides Oracle-compatible OUT parameter functionality, including functions and procedures with OUT parameters, support for OUT parameters in anonymous blocks, and libpq support for OUT parameters. This document aims to introduce the functionality of OUT parameters to users. IvorySQL provides Oracle-compatible OUT parameter functionality, including the following features. Syntax： CREATE [ OR REPLACE ] FUNCTION name ( [ [ argmode ] [ argname ] argtype [ { DEFAULT | = } default_expr ] [, ...] ] ) [ RETURNS rettype | RETURNS TABLE ( column_name column_type [, ...] ) ] { LANGUAGE lang_name | TRANSFORM { FOR TYPE type_name } [, ... ] | WINDOW | { IMMUTABLE | STABLE | VOLATILE } | [ NOT ] LEAKPROOF | { CALLED ON NULL INPUT | RETURNS NULL ON NULL INPUT | STRICT } | { [ EXTERNAL ] SECURITY INVOKER | [ EXTERNAL ] SECURITY DEFINER } | PARALLEL { UNSAFE | RESTRICTED | SAFE } | COST execution_cost | ROWS result_rows | SUPPORT support_function | SET configuration_parameter { TO value | = value | FROM CURRENT } | AS 'definition' | AS 'obj_file', 'link_symbol' | sql_body } ... The argmode can be IN, OUT, INOUT (IN OUT), or VARIADIC. If not specified, the default is IN. The argmode can also be written after the argname. Unlike native PostgreSQL, Oracle-compatible OUT parameters are not associated with the return value data type. Neither IN OUT nor OUT modes can have default values. If there are OUT parameters and the function’s return type is not void, the function body must include a RETURN statement. Supports binding variables in the form of colon placeholders, such as :1, :name. Added new DO+USING syntax： DO [ LANGUAGE lang_name ] code [USING IN | OUT | IN OUT, …​] Supports binding variables in libpq by position and by parameter name, providing the system function get_parameter_description, which returns the relationship between variable names and their positions based on the SQL statement. The libpq interface provides functions for preparing, binding, and executing statements, which are similar to the corresponding OCI functions. The usage process is as follows: Use IvyHandleAlloc to allocate statement and error handles. Call IvyStmtPrepare to prepare the statement. Call IvyBindByPos or IvyBindByName to bind parameters. Call IvyStmtExecute to execute, which can be repeated. Call IvyFreeHandle to release the statement and error handles. Additionally, a series of interface functions have been implemented, including Ivyconnectdb, Ivystatus, Ivyexec, IvyresultStatus, IvyCreatePreparedStatement, IvybindOutParameterByPos, IvyexecPreparedStatement, IvyexecPreparedStatement2, Ivynfields, Ivyntuples, and Ivyclear. Example programs can be found in the src/interfaces/libpq/ivytest directory of the source code. OUT parameters are not associated with the return value data type ivorysql=# create or replace function test_return_out(id integer,price out integer,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000; ivorysql-# name := 'test a char out'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / CREATE FUNCTION Neither IN OUT nor OUT modes can have default values ivorysql=# create or replace function test_return_inout(id integer,price in out integer default 100,name out varchar) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 20000 + price; ivorysql-# name := 'this is a test'; ivorysql-# return 'welcome to QingDao'; ivorysql-# end; ivorysql-# / ERROR: IN OUT formal parameters may have no default expressions If there are OUT parameters and the function’s return type is not void, the function body must include a RETURN statement. --if function's return type is non-void, the function body must has RETURN statement --if there is no RETURN statement, the function can be created, but when it is called, --an error is raised ivorysql=# create or replace function f2(id integer,price out integer) return varchar ivorysql-# as ivorysql-# begin ivorysql-# price := 2; ivorysql-# end; ivorysql-# / CREATE FUNCTION ivorysql=# declare ivorysql-# a varchar(20); ivorysql-# b int; ivorysql-# begin ivorysql-# a := f2(1, b); ivorysql-# end; ivorysql-# / ERROR: Function returned without value CONTEXT: PL/iSQL function f2(pg_catalog.int4,pg_catalog.int4) line 0 at RETURN PL/iSQL function inline_code_block line 5 at assignment Supports binding variables in the form of colon placeholders and new DO+USING syntax ivorysql=# do $$ ivorysql$# declare ivorysql$# a int; ivorysql$# begin ivorysql$# :x := 1; ivorysql$# :y := 2; ivorysql$# end; $$ using out, out; $1 | $2 ----+---- 1 | 2 (1 row) system function get_parameter_descr() ivorysql=# select * from get_parameter_description('insert into t values(:x,:y)'); name | position -------+---------- false | 0 :x | 1 :y | 2 (3 rows)","title":"out parameter","component":"ivorysql-doc","version":"master","name":"compat_out_parameter","url":"/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"2.1. functions with OUT parameters","hash":"functions-with-out-parameters","id":3},{"text":"2.2. support for OUT parameters in anonymous blocks","hash":"support-for-out-parameters-in-anonymous-blocks","id":4},{"text":"2.3. calling functions with OUT parameters in libpq","hash":"calling-functions-with-out-parameters-in-libpq","id":5},{"text":"3. test examples","hash":"test-examples","id":6},{"text":"3.1. functions with OUT parameters","hash":"functions-with-out-parameters-2","id":7},{"text":"3.2. support OUT parameters in anonymous block","hash":"support-out-parameters-in-anonymous-block","id":8}]},"68":{"id":68,"text":"IvorySQL provides Oracle-compatible RowID functionality. RowID is a pseudo-column automatically generated by the database when a table is created, returning the address of each row in the database. RowID should have the following characteristics: 1. Logically identifies each row with a unique value 2. Allows quick querying and modification of other columns in the table via ROWID, but cannot be inserted or modified itself 3. Users can control whether this feature is enabled IvorySQL provides multiple ways to enable the RowID functionality. In IvorySQL’s Oracle-compatible mode, the RowID functionality can be enabled by setting ivorysql.default_with_rowids to on. The default value for this parameter is off. Once enabled, tables created will automatically include a RowID column, which can be viewed using \\d+ table_name. ivorysql=# show ivorysql.default_with_rowids; ivorysql.default_with_rowids ------------------------------ off (1 row) ivorysql=# create table t(a int); CREATE TABLE ivorysql=# \\d+ t Table \"public.t\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap Users can choose to include this option for tables that require it; without the WITH ROWID option, a regular table will be created. ivorysql=# create table t2(a int) with rowid; CREATE TABLE ivorysql=# \\d+ t2 Table \"public.t2\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Indexes: \"t2_16432_rowid_idx\" btree (rowid) Access method: heap Has ROWID: yes This approach allows a regular table to add a ROWID column using the ALTER command when ROWID functionality is needed. The ROWID can also be removed using the ALTER TABLE … SET WITHOUT ROWID command. ivorysql=# create table t3(a int); CREATE TABLE ivorysql=# alter table t3 set with rowid; ALTER TABLE ivorysql=# \\d+ t3; Table \"public.t3\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | Access method: heap Has ROWID: yes","title":"RowID","component":"ivorysql-doc","version":"master","name":"compat_rowid","url":"/ivorysql-doc/master/oracle_compatibility/compat_rowid.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Enable the functionality","hash":"enable-the-functionality","id":2},{"text":"2.1. Enable the functionality through GUC parameters","hash":"enable-the-functionality-through-guc-parameters","id":3},{"text":"2.2. Enable the functionality by adding the WITH ROWID option in the table creation statement.","hash":"enable-the-functionality-by-adding-the-with-rowid-option-in-the-table-creation-statement","id":4},{"text":"2.3. Enable the functionality by executing the command ALTER TABLE … SET WITH ROWID on an existing table.","hash":"enable-the-functionality-by-executing-the-command-alter-table-set-with-rowid-on-an-existing-table","id":5}]},"69":{"id":69,"text":"IvorySQL’s sys_guid() is a powerful random number generation function that generates and returns a 16-byte database-level unique identifier (raw value). ivorysql=# select sys_guid() from dual; sys_guid ------------------------------------ \\x3ed9426c8a093442a38bea09a74f44a1 (1 row) create table student ( student_id raw(16) default sys_guid() primary key, student_name varchar2(100) not null ); insert into student(student_name) values ('Steven');","title":"sys_guid() function","component":"ivorysql-doc","version":"master","name":"compat_sys_guid","url":"/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Usage example","hash":"usage-example","id":2},{"text":"3. The sys_guid function can generate default values for primary keys when creating a table","hash":"the-sys_guid-function-can-generate-default-values-for-primary-keys-when-creating-a-table","id":3},{"text":"4. The primary key is automatically populated when new data is added","hash":"the-primary-key-is-automatically-populated-when-new-data-is-added","id":4}]},"70":{"id":70,"text":"IvorySQL provides Oracle-compatible PL/SQL data type functionality, including %TYPE and %ROWTYPE. This document aims to introduce users to the functionality of %TYPE and %ROWTYPE. IvorySQL provides Oracle-compatible %TYPE and %ROWTYPE functionality, including the following content. Create table and function. CREATE TABLE t1(id int, name varchar(20)); --function's parameter datatype is tablename.columnname%TYPE CREATE OR REPLACE FUNCTION fun1(v t1.id%TYPE) RETURN varchar AS BEGIN RETURN v; END; / The state of function is valid and function can be executed successfully. SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) SELECT fun1(1) FROM dual; fun1 ------ 1 (1 row) Modifying the previously defined declaration of a reference causes the function’s status to become invalid, but the function can still execute successfully. ALTER TABLE t1 ALTER COLUMN id TYPE varchar(20); SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --n prostatus ----------- n (1 row) --after changing the column id type from int to varchar, call the function again SELECT fun1('a') FROM dual; --successfully fun1 ------ a (1 row) re-compile the function, its state become valid. ALTER FUNCTION fun1 COMPILE; SELECT prostatus FROM pg_proc WHERE proname like 'fun1'; --v prostatus ----------- v (1 row) Example： --the following testcase will fail DECLARE name VARCHAR(25) NOT NULL := 'Niu'; surname name%TYPE ; BEGIN raise notice 'name=%' ,name; raise notice 'surname=%' ,surname; END; / ERROR: variable \"surname\" must have a default value, since it's declared NOT NULL LINE 3: surname name%TYPE ; ^ Example： CREATE TABLE employees(first_name varchar(20) not null, last_name varchar(20) not null, phone_number varchar(50)); INSERT INTO employees VALUES ('Steven','Niu','1-650-555-1234'); CREATE OR REPLACE PROCEDURE p0(v employees%ROWTYPE) AS BEGIN raise notice 'v.first_name = %, v.last_name = %, v.phone_number = %', v.first_name, v.last_name, v.phone_number; END; / DECLARE a employees%ROWTYPE; BEGIN select * into a from employees ; raise notice 'a=%', a; call p0(a); END; / NOTICE: a=(Steven,Niu,1-650-555-1234) NOTICE: v.first_name = Steven, v.last_name = Niu, v.phone_number = 1-650-555-1234 \\df p0 List of functions Schema | Name | Result data type | Argument data types | Type --------+------+------------------+------------------------+------ public | p0 | | IN v employees%ROWTYPE | proc (1 row) Support inserting a variables declared with %TYPE or %ROWTYPE into table. grammar: INSERT INTO table_name VALUES row_variable ; Example: CREATE TABLE t1(id int, name varchar(20)); DECLARE v1 t1%ROWTYPE; BEGIN FOR i IN 1 .. 5 LOOP v1.id := i; v1.name := 'a' || i; INSERT INTO t1 VALUES v1; END LOOP; END; / SELECT * FROM t1; id | name ----+------ 1 | a1 2 | a2 3 | a3 4 | a4 5 | a5 (5 rows) example: CREATE TABLE t1(id int, name varchar(20)); DELETE FROM t1; DECLARE v1 t1%ROWTYPE; v2 t1%ROWTYPE; BEGIN v1.id := 11; v1.name := 'abc'; INSERT INTO t1 VALUES v1; v2.id := 22; v2.name := 'new'; UPDATE t1 SET ROW = v2; END; / SELECT * FROM t1; id | name ----+------ 22 | new (1 row)","title":"%ROWTYPE、%TYPE","component":"ivorysql-doc","version":"master","name":"compat_type_rowtype","url":"/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html","titles":[{"text":"1. Purpose","hash":"purpose","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"2.1. If the reference changes, variables declared with %TYPE or %ROWTYPE will change accordingly.","hash":"if-the-reference-changes-variables-declared-with-type-or-rowtype-will-change-accordingly","id":3},{"text":"2.2. Variables declared with %TYPE inherit the constraints of the referenced variable.","hash":"variables-declared-with-type-inherit-the-constraints-of-the-referenced-variable","id":4},{"text":"2.3. Use table_name%ROWTYPE or view_name%ROWTYPE as the parameter type of a function / stored procedure or the return type of a function","hash":"use-table_namerowtype-or-view_namerowtype-as-the-parameter-type-of-a-function-stored-procedure-or-the-return-type-of-a-function","id":5},{"text":"2.4. Enhancement to INSERT statement","hash":"enhancement-to-insert-statement","id":6},{"text":"2.5. Enhancement to UPDATE statement","hash":"enhancement-to-update-statement","id":7}]},"71":{"id":71,"text":"The introduction of this feature is to accommodate Oracle’s invisible column functionality, making database design simpler and enhancing the flexibility of data management. Users will have better control over the visibility of columns. Invisible columns are also useful during application migration. Making new columns invisible means that they will not be visible to any existing application, but they can still be referenced by any new applications, thus simplifying the online migration of applications. Invisible columns allow users to conceal specific columns during operations like SELECT *. You can use invisible column to make changes to a table without disrupting applications that use the table. Any generic access of a table does not show the invisible columns in the table. Invisible columns must be explicitly referenced by column names in order to be accessed in queries and other operations. Support for invisible columns in psql extended describe (\\d+) is added, in oracle mode only. ivorysql=# CREATE TABLE mytable (a INT, b INT INVISIBLE, c INT); CREATE TABLE You can insert a value into an invisible column only if you explicitly specify the invisible column in the column list for the INSERT statement. Omitting the column list in the INSERT statement is not allowed. Errors will be reported. ivorysql=# INSERT INTO mytable (a, b, c) VALUES (1,2,3); INSERT 0 1 ivorysql=# INSERT INTO mytable VALUES (4,5,6); ERROR: INSERT has more expressions than target columns LINE 1: INSERT INTO mytable VALUES (4,5,6); You can use a SELECT statement to display output for an invisible column only if you explicitly specify the invisible column in the column list. ivorysql=# select * from mytable; a | c ---+--- 1 | 3 (1 row) ivorysql=# select a,b,c from mytable; a | b | c ---+---+--- 1 | 2 | 3 (1 row) ivorysql=# \\d+ mytable Table \"public.mytable\" Column | Type | Collation | Nullable | Default | Invisible | Storage | Compression | Stats target | Description --------+-----------------+-----------+----------+---------+-----------+---------+-------------+--------------+------------- a | pg_catalog.int4 | | | | | plain | | | b | pg_catalog.int4 | | | | invisible | plain | | | c | pg_catalog.int4 | | | | | plain | | | Access method: heap Modify column is not supported currently. Will be enhanced in later version; In Oracle, there are special considerations for invisible columns and column ordering. When a table contains one or more invisible columns, the invisible columns are not included in the column order for the table. We have not yet addressed this part.","title":"Invisible Columns","component":"ivorysql-doc","version":"master","name":"invisible_column","url":"/ivorysql-doc/master/oracle_compatibility/invisible_column.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"3. Test cases","hash":"test-cases","id":3},{"text":"3.1. Create a table with invisible columns","hash":"create-a-table-with-invisible-columns","id":4},{"text":"3.2. Insert values into invisible columns","hash":"insert-values-into-invisible-columns","id":5},{"text":"3.3. Display output for invisible columns","hash":"display-output-for-invisible-columns","id":6},{"text":"3.4. Support for invisible columns in psql extended describe (\\d+) in Oracle mode","hash":"support-for-invisible-columns-in-psql-extended-describe-d-in-oracle-mode","id":7},{"text":"4. Limitations","hash":"limitations","id":8}]},"72":{"id":72,"text":"IvorySQL provides compatibility for Oracle packages. A package is an encapsulated collection of related program objects stored together in the database. Program objects are procedures, functions, variables, constants, cursors, and exceptions. This document aims to provide a comprehensive understanding of the process of implementing custom packages. IvorySQL provides compatibility for Oracle custom packages, including creation, alteration, and deletion of packages and package bodies. We also add support for package-related commands in the PostgreSQL interactive terminal (psql) with the new \\dk command. The CREATE OR REPLACE PACKAGE statement is used to create or replace the specification of a package. A package is a collection of related stored procedures, functions, and other program objects, stored as a single unit in the database. The package specification declares these objects, and the package body defines them. To create or replace the specification of a package in your own schema, you must have the CREATE PROCEDURE system privilege. If you are creating or replacing a package in another user’s schema, you need the CREATE ANY PROCEDURE system privilege. The CREATE OR REPLACE PACKAGE BODY statement is used to create or replace a package body. To create a package body requires the same privileges as creating the specification of a package, and it is required that the package body and package specification be in the same schema, with the package specification already existing. This statement defines the objects declared in the package specification. When the package specification contains cursors or subprograms, a package body must be present to define them. Otherwise, the package body is optional. The ALTER PACKAGE statement is used to modify the properties of a package.The privileges required to execute the ALTER PACKAGE statement are: you must be the owner of the package or have the ALTER ANY PROCEDURE privilege to modify packages owned by other users. The DROP PACKAGE statement deletes a package in the database. This statement will remove both the package body and the package specification.The DROP PACKAGE BODY statement only deletes the package body. It is not possible to delete a single object within the package using this statement. The privileges required:the package must be in the user’s schema, or the user must have the DROP ANY PROCEDURE system privilege. The DISCARD PACKAGE functionality is implemented for compatibility with PostgreSQL’s DISCARD feature. In psql, \\dk[+] is used to view the definition information of packages and package bodies. Command Descriptions \\dk[+] List the package information currently visible. \\dk[+] xxx List the package specification and package body content of the xxx package. ivorysql=# create or replace package pkg is ivorysql-# var1 integer; ivorysql-# var2 integer; ivorysql-# function test_f(id integer) return integer; ivorysql-# procedure test_p(id integer); ivorysql-# end; ivorysql-# / CREATE PACKAGE ivorysql=# create or replace package body pkg is ivorysql-# var3 integer; ivorysql-# function test_f(id integer) return integer is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg test_f'); ivorysql-# return id; ivorysql-# end; ivorysql-# procedure test_p(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('pkg proc'); ivorysql-# end; ivorysql-# --privite function ivorysql-# function test_piv1(id integer) return integer is ivorysql-# begin ivorysql-# return id; ivorysql-# end; ivorysql-# --privite procedure ivorysql-# procedure test_piv2(id integer) is ivorysql-# begin ivorysql-# dbms_output.put_line('privite proc'); ivorysql-# end; ivorysql-# begin ivorysql-# var1 := 1; ivorysql-# var2 := 2; ivorysql-# var3 := 4; ivorysql-# end; ivorysql-# / CREATE PACKAGE BODY ivorysql=# alter package pkg noneditionable; ALTER PACKAGE ivorysql=# Drop package pkg; DROP PACKAGE ivorysql=# Drop package body pkg; DROP PACKAGE BODY ivorysql=# discard package; DISCARD PACKAGES ivorysql=# \\dk List of packages Schema | Name | Owner --------+----------+---------- public | pkg | ivorysql public | test_pkg | ivorysql (2 rows) ivorysql=# \\dk pkg List of packages Schema | Name | Owner --------+------+---------- public | pkg | ivorysql (1 row) ivorysql=# \\dk pkg1 Did not find any package named \"pkg1\". ivorysql=# \\dk+ List of packages Schema | Name | Owner | Security | Editionable | Use Collation | Specification | Package Body --------+----------+----------+----------+-------------+---------------+-----------------------------------------------+----------------------------------------------------- --------- public | pkg | ivorysql | definer | Editionable | default | var1 integer; +| | | | | | | var2 integer; +| | | | | | | function test_f(id integer) return integer; +| | | | | | | procedure test_p(id integer); +| | | | | | | end | public | test_pkg | ivorysql | definer | Editionable | default | var1 integer; +| FUNCTION test_f(id integer) RETURN integer IS + | | | | | | FUNCTION test_f(id integer) RETURN integer;+| BEGIN + | | | | | | end | dbms_output.put_line('invoke function test_pkg.t est_f');+ | | | | | | | RETURN 23; + | | | | | | | end; + | | | | | | | BEGIN + | | | | | | | var1 := 23; + | | | | | | | end (2 rows)","title":"Package","component":"ivorysql-doc","version":"master","name":"package","url":"/ivorysql-doc/master/oracle_compatibility/package.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function descriptions","hash":"function-descriptions","id":2},{"text":"2.1. Create the specification of a package","hash":"create-the-specification-of-a-package","id":3},{"text":"2.2. Create package body","hash":"create-package-body","id":4},{"text":"2.3. Alter package","hash":"alter-package","id":5},{"text":"2.4. Drop package and package body","hash":"drop-package-and-package-body","id":6},{"text":"2.5. DISCARD PACKAGE","hash":"discard-package","id":7},{"text":"2.6. Use \\dk[+] in psql for package and package body information","hash":"use-dk-in-psql-for-package-and-package-body-information","id":8},{"text":"3. Test cases","hash":"test-cases","id":9},{"text":"3.1. Create the specification of a package","hash":"create-the-specification-of-a-package-2","id":10},{"text":"3.2. Create package body","hash":"create-package-body-2","id":11},{"text":"3.3. Alter package","hash":"alter-package-2","id":12},{"text":"3.4. Drop package and package body","hash":"drop-package-and-package-body-2","id":13},{"text":"3.5. DISCARD PACKAGE","hash":"discard-package-2","id":14},{"text":"3.6. Use \\dk[+] in psql for package and package body information","hash":"use-dk-in-psql-for-package-and-package-body-information-2","id":15}]},"73":{"id":73,"text":"In order to distinguish the Oracle port, IP and PG port IP. THERE IS NOW A NEED TO INCREASE THE PROCESSING OF ORAPORT AND ORAHOST; Add ivoryhost: You need to add the parameter ivoryhost when connecting, and its function is similar to host; New ivoryport: Compared to host, the function of port is relatively complex. It involves specifying ports in the configure phase and connection phase; ./configure --with-oraport=5555 ./initdb .... ./pg_ctl -D ../data start ./pg_ctl -o \"-p 5433 -o 1522\" -D ../data","title":"Added Oracle compatibility mode ports and IP","component":"ivorysql-doc","version":"master","name":"port_ip","url":"/ivorysql-doc/master/oracle_compatibility/port_ip.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function","hash":"function","id":2},{"text":"3. Test method:","hash":"test-method","id":3}]},"74":{"id":74,"text":"This document is designed for compatibility with Oracle sequence functions, in order to use Oracle’s sequences in IvorySQL. Sequence is a database object, similar to tables and views, that represents an integer sequence that can be used by any table and view in the global database namespace. Sequence values can be accessed using NEXTVAL and CURRVAL. Sequences can be in ascending or descending order. ivorysql=# CREATE sequence seq; CREATE SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 1 (1 row) ivorysql=# ALTER sequence seq restart start with 10; ALTER SEQUENCE ivorysql=# SELECT seq.NEXTVAL FROM DUAL; nextval --------- 10 (1 row) ivorysql=# DROP SEQUENCE seq; DROP SEQUENCE","title":"Compatible with Oracle sequence","component":"ivorysql-doc","version":"master","name":"sequence","url":"/ivorysql-doc/master/oracle_compatibility/sequence.html","titles":[{"text":"1. Objective","hash":"objective","id":1},{"text":"2. Function description","hash":"function-description","id":2},{"text":"3. Test cases","hash":"test-cases","id":3}]},"75":{"id":75,"text":"In Oracle, SQL code often contains XML functions. To ensure consistency in data format and structure when migrating from Oracle to IvorySQL, IvorySQL achieves high compatibility with Oracle XML functions, building upon the foundation of PostgreSQL. This compatibility means that users do not need to make extensive modifications to their existing XML processing logic, thereby ensuring the integrity and accuracy of the data. Furthermore, IvorySQL’s cross-platform compatibility reduces the additional user maintenance and upgrade costs caused by format differences, making data processing and management more efficient, reliable, and flexible. XML (eXtended Markup Language) is a text-based format language used to structure any document that can be tagged. It is a lightweight, extensible, standard, and easy-to-understand language for storing data. IvorySQL achieves compatibility with commonly used XML SQL functions. It maintains consistency with PostgreSQL by utilizing the same underlying processing functions, which are provided by the libxml2 library interface. These XML functions are provided as a sub-plugin of the ivorysql_ora plugin, ensuring compatibility and consistency with PostgreSQL databases in terms of XML processing. Due to Oracle’s XML functions requiring certain parameter types to be XMLType, such as the existsnode() function below: Prototype: EXISTSNODE(XMLType_instance, XPath_string [, namespace_string ]) Demo: SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a') from dual; Therefore, for compatibility purposes, an XMLType data type has been added. Its function is to convert the string provided by the user into an internal XMLType type, allowing SQL statements to be migrated from Oracle to IvorySQL without modification. Additionally, to avoid confusion with the existing keyword \"extract\" in PostgreSQL, IvorySQL has renamed the original keyword to \"PGEXTRACT\" to ensure clarity and accuracy in function calls. When implementing these Oracle-compatible XML functions, IvorySQL adopted two different approaches. Among them, besides the UPDATEXML function, the other functions are implemented using SQL functions. Since the number of parameters for the UPDATEXML function is uncertain, an expression-based approach was used for its implementation. This required writing specialized syntax parsing and executor code to ensure the correctness and flexibility of its functionality. Num Function Name Function Introduction 1 extract(XML) This function is used to return the corresponding content under the XML node path. The parameter XMLType_instance is used to specify the XMLType instance, while Xpath_string is used to specify the XML node path. 2 extractvalue This function provides retrieval functionality for XML content. The extractvalue function can only return one value from one node. 3 existsnode This function is used to check whether the XML content matches the specified path expression. 4 deletexml This function is used to delete XML nodes at specified paths. 5 appendchildxml This function is used to insert child nodes into an XML object. It takes an XML object and an XML fragment as parameters, and inserts the XML fragment as a child node into the XML object. 6 updatexml This function is used to update the content of a specific XML node path. 7 insertxmlbefore This function is used to insert child nodes before a specified path in XML. 8 insertxmlafter This function is used to insert child nodes after a specified path in XML. 9 insertchildxml This function is used to insert child nodes into a specified XML path. 10 insertchildxmlbefore This function is used to insert child nodes before a specified XML path. 11 insertchildxmlafter This function is used to insert child nodes after a specified XML path. 12 xmlisvalid This functions is used to check the xml content is valid or not ivorysql=# set ivorysql.compatible_mode to oracle; SET ivorysql=# create table inaf(a int, b xmltype); CREATE TABLE ivorysql=# insert into inaf values(1,xmltype('<a><b>100</b></a>')); INSERT 0 1 ivorysql=# insert into inaf values(2, ''); INSERT 0 1 ivorysql=# select * from inaf; a | b ---+------------------- 1 | <a><b>100</b></a> 2 | (2 rows) ivorysql=# create table xmltest(id int, data XMLType); CREATE TABLE ivorysql=# insert into xmltest values(1, '<value>one</value>'); INSERT 0 1 ivorysql=# insert into xmltest values(2, '<value>two</value>'); INSERT 0 1 ivorysql=# select * from xmltest; id | data ----+-------------------- 1 | <value>one</value> 2 | <value>two</value> (2 rows) ivorysql=# create table xmlnstest(id int, data xmltype); CREATE TABLE ivorysql=# INSERT INTO xmlnstest VALUES(1, xmltype('<soapenv:Envelope xmlns:soapenv=\"http://schemas.xmlsoap.org/soap/envelope/\" xmlns:typ=\"http://www.def.com\" xmlns:web=\"http://www.abc.com\"><soapenv:Body><web:BBB><typ:EEE>41</typ:EEE><typ:FFF>42</typ:FFF></web:BBB></soapenv:Body></soapenv:Envelope>')); INSERT 0 1 ivorysql# SELECT extract(XMLType('<AA><ID>1</ID></AA>'), '/AA/ID') from dual; extract ------------ <ID>1</ID> (1 row) ivorysql# SELECT extractvalue(XMLType('<a><b>100</b></a>'),'/a/b') from dual; extractvalue -------------- 100 (1 row) ivorysql=# SELECT existsnode(XMLType('<a><b>d</b></a>'), '/a/b') from dual; existsnode ------------ 1 (1 row) ivorysql=# SELECT deletexml(XMLType('<test><value>oldnode</value><value>oldnode</value></test>'), '/test/value') from dual; deletexml ----------- <test/> (1 row) ivorysql=# SELECT appendchildxml(XMLType('<test><value></value><value></value></test>'), '/test/value', XMLTYPE('<name>newnode</name>')) from dual; appendchildxml -------------------------- <test> + <value> + <name>newnode</name>+ </value> + <value> + <name>newnode</name>+ </value> + </test> (1 row) ivorysql=# SELECT updatexml(xmltype('<value>one</value>'), '/value', xmltype('<newvalue>1111</newvalue>')) FROM dual; updatexml --------------------------- <newvalue>1111</newvalue> (1 row) ivorysql=# SELECT insertxmlbefore(XMLType('<a>222<b>100</b><b>200</b></a>'), '/a/b', XMLTYPE('<c>88</c>')) from dual; insertxmlbefore -------------------------------------------------- <a>222<c>88</c><b>100</b><c>88</c><b>200</b></a> (1 row) ivorysql=# SELECT insertxmlafter(XMLType('<a><b>100</b></a>'),'/a/b',XMLType('<c>88</c>')) from dual; insertxmlafter ---------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT insertchildxml(XMLType('<a>one<b></b>three<b></b></a>'), '//b', 'name', XMLTYPE('<name>newnode</name>')) from dual; insertchildxml ----------------------------------------------------------------------- <a>one<b><name>newnode</name></b>three<b><name>newnode</name></b></a> (1 row) ivorysql=# SELECT insertchildxmlbefore(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlbefore ---------------------- <a> + <c>88</c> + <b>100</b> + </a> (1 row) ivorysql=# SELECT insertchildxmlafter(XMLType('<a><b>100</b></a>'), '/a', 'b', XMLType('<c>88</c>')) from dual; insertchildxmlafter --------------------- <a> + <b>100</b> + <c>88</c> + </a> (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a>')); xmlisvalid ------------ f (1 row) ivorysql=# SELECT xmlisvalid(XMLTYPE('<a/>')); xmlisvalid ------------ t (1 row)","title":"XML","component":"ivorysql-doc","version":"master","name":"xml_functions","url":"/ivorysql-doc/master/oracle_compatibility/xml_functions.html","titles":[{"text":"1. 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index 31b1d87c..a25f6a31 100644
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