diff --git a/docs/cn/ivorysql-doc/master/about_ivorysql.html b/docs/cn/ivorysql-doc/master/about_ivorysql.html index 234238b1..dcb10ce5 100644 --- a/docs/cn/ivorysql-doc/master/about_ivorysql.html +++ b/docs/cn/ivorysql-doc/master/about_ivorysql.html @@ -176,6 +176,9 @@

文档中心

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    文档中心

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    • @@ -507,7 +510,7 @@

    - wal2json + pg_stat_monitor 框架设计 diff --git a/docs/cn/ivorysql-doc/master/architecture/framework_design.html b/docs/cn/ivorysql-doc/master/architecture/framework_design.html index ed7ea2e5..2f4c0de5 100644 --- a/docs/cn/ivorysql-doc/master/architecture/framework_design.html +++ b/docs/cn/ivorysql-doc/master/architecture/framework_design.html @@ -176,6 +176,9 @@

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    • @@ -527,6 +530,13 @@

    IvorySQL生态插件

    AI驱动的自然语言转SQL扩展，支持多种大语言模型

    AI辅助查询、自然语言数据库交互

    + +

    12

    +

    pg_stat_monitor

    +

    2.3.1

    +

    收集性能统计数据，并通过统一视图和直方图形式直观展示查询性能指标。

    +

    性能监控

    +
    diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pg_ai_query.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pg_ai_query.html index 09980188..1fa68055 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pg_ai_query.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pg_ai_query.html @@ -176,6 +176,9 @@

    文档中心

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    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pg_cron.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pg_cron.html index 6dd93a3f..6d888266 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pg_cron.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pg_cron.html @@ -176,6 +176,9 @@

    文档中心

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    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html new file mode 100644 index 00000000..c572f6c4 --- /dev/null +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html @@ -0,0 +1,590 @@ + + + + + + pg_stat_monitor :: IvorySQL文档中心 + + + + + + + + +
    + +
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    +
    + +
    +
    + +
    + +
    +

    pg_stat_monitor

    +
    +

    1. 概述

    +
    +
    +

    pg_stat_monitor 是 PostgreSQL 的查询性能监控工具。它能收集性能统计数据，并通过统一视图和直方图形式直观展示查询性能指标。

    +
    +
    +

    该工具可帮助数据库用户全面掌握查询来源、执行情况、计划统计与详细信息，以及查询元数据。这极大地提升了可观测性，使用户能够有效调试和优化查询性能。

    +
    +
    +
    +
    +

    2. 安装

    +
    +
    +

    IvorySQL的安装包里已经集成了pg_stat_monitor插件，如果使用安装包安装的IvorySQL，通常不需要再手动安装pg_stat_monitor。如您使用源码方式安装了IvorySQL，可以继续通过源码方式继续安装pg_stat_monitor插件，IvorySQL社区为您提供了源码安装步骤：

    +
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    +

    要从源代码构建 pg_stat_monitor，您需要以下组件:

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    +
    +
      +
    • +

      git

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      make

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      gcc

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      pg_config

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      • +
    +
    +
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    您可以从 GitHub 上的发布页面 下载 pg_stat_monitor 指定版本的源代码，或者使用 git 命令：

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    +
    git clone https://github.com/percona/pg_stat_monitor.git
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    +
    +
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    编译并安装扩展程序，假如环境中已经安装了IvorySQL，安装路径为/usr/ivory-5

    +
    +
    +
    +
    cd pg_stat_monitor
+export PG_CONFIG=/usr/ivory-5/bin/pg_config
+make USE_PGXS=1
+make USE_PGXS=1 install
    +
    +
    +
    +
    +
    +

    3. 加载模块

    +
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    在启动时，将 pg_stat_monitor 添加到 shared_preload_libraries 配置参数中，以加载该库。这是因为 pg_stat_monitor 需要额外的共享内存。

    +
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    修改 shared_preload_libraries 参数：

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    shared_preload_libraries = 'pg_stat_monitor';
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    注意：若 shared_preload_libraries 参数中已存在其他模块（例如 pg_stat_statements），需用逗号分隔全部列出。pg_stat_monitor 必须排在 pg_stat_statements 之后，例如：

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    shared_preload_libraries = 'liboracle_parser, ivorysql_ora, pg_stat_statements, pg_stat_monitor';
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    +
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    配置完成后，重启 IvorySQL实例使配置生效。

    +
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    pg_ctl restart -D data
    +
    +
    +
    +

    将 pg_stat_monitor 添加至 shared_preload_libraries 后，该扩展会立即开始收集所有现有数据库的统计信息。要查看监控数据，需在每个待监控的数据库中创建视图。

    +
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    +
    +

    4. 创建扩展视图

    +
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    请使用具有超级用户或数据库所有者权限的账户执行以下操作。以超级用户身份连接至目标数据库，运行以下命令创建扩展：

    +
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    CREATE EXTENSION pg_stat_monitor;
    +
    +
    +
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    完成设置后，即可查看pg_stat_monitor收集的统计信息。

    +
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    +
    +
    +

    5. 使用

    +
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    +

    以获取查询执行时间信息为例，连接数据库执行以下SQL：

    +
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    +
    SELECT  userid,  total_exec_time, min_exec_time, max_exec_time, mean_exec_time, query FROM pg_stat_monitor;
    +
    +
    +
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    +
    userid | total_exec_time | min_exec_time | max_exec_time | mean_exec_time |                                            query
+--------+-----------------+---------------+---------------+----------------+----------------------------------------------------------------------------------------------
+     10 |        1.532168 |      0.749108 |       0.78306 |       0.766084 | SELECT userid,  datname, queryid, substr(query,$1, $2) AS query, calls FROM pg_stat_monitor
+     10 |        0.755857 |      0.755857 |      0.755857 |       0.755857 | SELECT application_name, client_ip, substr(query,$1,$2) as query FROM pg_stat_monitor
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_exec_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+(4 rows)
    +
    +
    +
    +

    更多关于pg_stat_monitor的使用，请参阅 pg_stat_monitor 官方文档

    +
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    + +
    +
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    +
    +
    + +
    + + + + + + + + diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html index bdbdc7a1..3032b706 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgaudit.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html index 4127a976..dfb31fb6 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgddl.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html index 1e7cb9fe..b4a8deac 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgroonga.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html index 6a593207..1d406a61 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgrouting.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html index 87ef7bae..eb033f0b 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgsql_http.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html b/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html index da0a1cf6..c55de330 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/pgvector.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html b/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html index 566d090c..9f20f9ba 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/plpgsql_check.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html b/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html index a700d8e1..f869edf3 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/postgis.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html b/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html index fcef0246..7d53501d 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/system_stats.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html b/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html index 750738fd..011b4bc1 100644 --- a/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html +++ b/docs/cn/ivorysql-doc/master/ecosystem_components/wal2json.html @@ -6,7 +6,7 @@ wal2json :: IvorySQL文档中心 - + @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • @@ -607,7 +610,7 @@

    3. 使用

    diff --git a/docs/cn/ivorysql-doc/master/gb18030.html b/docs/cn/ivorysql-doc/master/gb18030.html index 5f5ed3cf..2699e9e9 100644 --- a/docs/cn/ivorysql-doc/master/gb18030.html +++ b/docs/cn/ivorysql-doc/master/gb18030.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html b/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html index 2a0e5fdf..a14f101a 100644 --- a/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html +++ b/docs/cn/ivorysql-doc/master/getting-started/daily_maintenance.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html b/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html index a620438b..67ddc5f2 100644 --- a/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html +++ b/docs/cn/ivorysql-doc/master/getting-started/daily_monitoring.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/getting-started/quick_start.html b/docs/cn/ivorysql-doc/master/getting-started/quick_start.html index 2cc642d5..a857a02f 100644 --- a/docs/cn/ivorysql-doc/master/getting-started/quick_start.html +++ b/docs/cn/ivorysql-doc/master/getting-started/quick_start.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/installation_guide.html b/docs/cn/ivorysql-doc/master/installation_guide.html index 334e365e..813ec796 100644 --- a/docs/cn/ivorysql-doc/master/installation_guide.html +++ b/docs/cn/ivorysql-doc/master/installation_guide.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/migration_guide.html b/docs/cn/ivorysql-doc/master/migration_guide.html index ad4a07c6..c52c76e8 100644 --- a/docs/cn/ivorysql-doc/master/migration_guide.html +++ b/docs/cn/ivorysql-doc/master/migration_guide.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/operation_guide.html b/docs/cn/ivorysql-doc/master/operation_guide.html index b5802554..206a6d4c 100644 --- a/docs/cn/ivorysql-doc/master/operation_guide.html +++ b/docs/cn/ivorysql-doc/master/operation_guide.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • @@ -701,7 +704,211 @@

    -

    2. 管理IvorySQL版本

    +

    2. 使用 pg_upgrade 将 PostgreSQL 升级到 IvorySQL

    +
    +
    +

    pg_upgrade 支持将原生 PostgreSQL 集群升级到 IvorySQL。当源集群为原生 PostgreSQL ，而目标为 IvorySQL 集群时，必须使用 -g（--using-ora-pg）参数。

    +
    +
    +

    从 PostgreSQL 升级到 IvorySQL 属于“跨产品”迁移，建议在生产环境操作前完成完整的测试验证，并制定详细的回滚方案。

    +
    +
    +

    2.1. 参数说明

    + ++++ + + + + + + + + + + + + +
    参数说明

    -g / --using-ora-pg

    表示源集群为 PostgreSQL ，而目标为 IvorySQL 集群。启用此选项后，pg_upgrade 在连接两端集群时统一使用 PostgreSQL 标准端口，从而正确处理升级。

    +
    +
    +

    2.2. 升级步骤

    +
    +

    2.2.1. 第一步：初始化新 IvorySQL 集群

    +
    +
    +
    # 使用 IvorySQL 的 initdb 初始化新集群
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data
    +
    +
    +
    +
    +

    2.2.2. 第二步：停止源 PostgreSQL 集群

    +
    +
    +
    /usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop
    +
    +
    +
    +
    +

    2.2.3. 第三步：运行升级检查（可选）

    +
    +

    使用 -c 仅做兼容性检查，不修改任何数据：

    +
    +
    +
    +
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # 源 PG 可执行文件目录
+  -B /opt/ivorysql/bin \            # 目标 IvorySQL 可执行文件目录
+  -d /data/pg/data \                # 源 PG 数据目录
+  -D /data/ivorysql/data \          # 目标 IvorySQL 数据目录
+  -g \                              # 源为 PostgreSQL，目标为 IvorySQL
+  -c                                # 仅检查，不升级
    +
    +
    +
    +
    +

    2.2.4. 第四步：执行升级

    +
    +
    +
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g
    +
    +
    +
    +
    +

    2.2.5. 第五步：启动 IvorySQL 并验证

    +
    +
    +
    /opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# 验证数据库是否正常
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"
    +
    +
    +
    +
    +

    2.2.6. 第六步：清理旧集群

    +
    +

    升级完成后，pg_upgrade 会生成一个清理脚本：

    +
    +
    +
    +
    ./delete_old_cluster.sh
    +
    +
    +
    +
    +
    +

    2.3. 常用参数速查

    + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    参数长选项说明

    -b

    --old-bindir

    源集群可执行文件目录

    -B

    --new-bindir

    目标集群可执行文件目录

    -d

    --old-datadir

    源集群数据目录

    -D

    --new-datadir

    目标集群数据目录

    -g

    --using-ora-pg

    源为 PostgreSQL 升级到 IvorySQL

    -p

    --old-port

    源集群端口

    -P

    --new-port

    目标集群 PG 端口

    -q

    --old-oraport

    源集群 Oracle 端口

    -Q

    --new-oraport

    目标集群 Oracle 端口

    -j

    --jobs

    并行进程数

    -k

    --link

    使用硬链接替代文件复制

    -c

    --check

    仅检查兼容性，不执行升级

    -v

    --verbose

    输出详细日志

    +
    +
    +

    2.4. 注意事项

    +
    +
      +
    • +

      -g 参数仅在 源为纯 PostgreSQL 集群、目标为 IvorySQL 时使用。若源集群已是 IvorySQL，则无需此参数。

      +
      • +
    • +

      升级期间源集群和目标集群均须处于停止状态。

      +
      • +
    • +

      升级前务必对源集群做完整备份。

      +
      • +
    +
    +
    +
    + +
    +

    3. 管理IvorySQL版本

    IvorySQL基于PostgreSQL开发，版本更新频率与PostgreSQL版本更新频率保持一致，每年更新一个大版本，每季度更新一个小版本。IvorySQL目前发布的版本有1.0到5.0，分别基于PostgreSQL 14.0到18.0进行开发，最新版本为IvorySQL 5.0，基于PostgreSQL 18.0进行开发。IvorySQL 的所有版本全部都做到了向下兼容。相关版本特性可以查看 官网。

    @@ -709,7 +916,7 @@

    -

    3. 管理IvorySQL数据库访问

    +

    4. 管理IvorySQL数据库访问

    IvorySQL使用 角色 的概念管理数据库访问权限。一个角色可以看成是一个数据库用户或者是数据库用户组，这取决于角色被怎样设置。角色可以拥有数据库对象（例如，表和函数）并且能够把那些对象上的权限赋予给其他角色来控制谁能访问哪些对象。此外，还可以把一个角色中的 成员资格 授予给另一个角色，这样允许成员角色使用被赋予给另一个角色的权限。

    @@ -777,21 +984,21 @@

    -

    4. 定义数据对象

    +

    5. 定义数据对象

    IvorySQL基于PostgreSQL，具有完整的SQL，其定义数据对象可以参考 手册。在此基础之上，IvorySQL为兼容Oracle，还做了一些Oracle专有数据对象的兼容。

    -

    4.1. VARCHAR2

    +

    5.1. VARCHAR2

    -

    4.1.1. 概述

    +

    5.1.1. 概述

    具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。

    -

    4.1.2. 语法

    +

    5.1.2. 语法

    VARCHAR2(size)
    @@ -799,7 +1006,7 @@

    4.1.2. 语法

    -

    4.1.3. 用例

    +

    5.1.3. 用例

    create table test(a varchar2(5));
@@ -823,7 +1030,7 @@ 
    4.1.3. 用例
    -

    5. 查询数据

    +

    6. 查询数据

    IvorySQL基于PostgreSQL开发，具有完全的SQL，查询数据具体的操作可以参考 手册。

    @@ -831,7 +1038,7 @@

    5. 查询数据

    -

    6. 使用外部数据

    +

    7. 使用外部数据

    IvorySQL实现了部分的SQL/MED规定，允许我们使用普通SQL查询来访问位于IvorySQL之外的数据。这种数据被称为外部数据（注意这种用法不要和外键混淆，后者是数据库中的一种约束）。

    @@ -848,7 +1055,7 @@

    6.

    -

    7. 备份与恢复

    +

    8. 备份与恢复

    由于包含着有价值的数据，IvorySQL数据库应当被定期地备份。虽然过程相当简单，但清晰地理解其底层技术和假设是非常重要的。

    @@ -870,7 +1077,7 @@

    7. 备份

    -

    7.1. SQL转储

    +

    8.1. SQL转储

    SQL 转储方法的思想是创建一个由SQL命令组成的文件，当把这个文件回馈给服务器时，服务器将利用其中的SQL命令重建与转储时状态一样的数据库。 IvorySQL为此提供了工具pg_dump。这个工具的基本用法是：

    @@ -898,7 +1105,7 @@

    7.1. SQL转储

    ​ 由pg_dump创建的备份在内部是一致的， 也就是说，转储表现了pg_dump开始运行时刻的数据库快照，且在pg_dump运行过程中发生的更新将不会被转储。pg_dump工作的时候并不阻塞其他的对数据库的操作。（但是会阻塞那些需要排它锁的操作，比如大部分形式的ALTER TABLE）

    -

    7.1.1. 从转储中恢复

    +

    8.1.1. 从转储中恢复

    pg_dump生成的文本文件可以由psql程序读取。 从转储中恢复的常用命令是：

    @@ -940,7 +1147,7 @@

    7.1

    -

    7.1.2. 使用pg_dumpall

    +

    8.1.2. 使用pg_dumpall

    pg_dump每次只转储一个数据库，而且它不会转储关于角色或表空间（因为它们是集簇范围的）的信息。为了支持方便地转储一个数据库集簇的全部内容，提供了pg_dumpall程序。pg_dumpall备份一个给定集簇中的每一个数据库，并且也保留了集簇范围的数据，如角色和表空间定义。该命令的基本用法是：

    @@ -968,7 +1175,7 @@

    7.1.2.

    -

    7.1.3. 处理大型数据库

    +

    8.1.3. 处理大型数据库

    在一些具有最大文件尺寸限制的操作系统上创建大型的pg_dump输出文件可能会出现问题。幸运地是，pg_dump可以写出到标准输出，因此你可以使用标准Unix工具来处理这种潜在的问题。有几种可能的方法：

    @@ -1059,7 +1266,7 @@

    <

    -

    7.2. 文件系统级别备份

    +

    8.2. 文件系统级别备份

    另外一种备份策略是直接复制 IvorySQL用于存储数据库中数据的文件，你可以采用任何你喜欢的方式进行文件系统备份，例如：

    @@ -1098,7 +1305,7 @@

    -

    7.3. 连续归档和时间点恢复（PITR）

    +

    8.3. 连续归档和时间点恢复（PITR）

    在任何时间，IvorySQL在数据集簇目录的pg_wal/子目录下都保持有一个预写式日志（WAL）。这个日志存在的目的是为了保证崩溃后的安全：如果系统崩溃，可以“重放”从最后一次检查点以来的日志项来恢复数据库的一致性。该日志的存在也使得第三种备份数据库的策略变得可能：我们可以把一个文件系统级别的备份和WAL文件的备份结合起来。当需要恢复时，我们先恢复文件系统备份，然后从备份的WAL文件中重放来把系统带到一个当前状态。这种方法比之前的方法管理起来要更复杂，但是有其显著的优点：

    @@ -1131,7 +1338,7 @@

    -

    8. 装卸数据

    +

    9. 装卸数据

    copy 在 IvorySQL表和标准文件之间移动数据。COPY TO 把一个表的内容复制到一个文件，而COPY FROM 则从一个文件复制数据到一个表（把数据追加到表中原有数据）。COPY TO 也能复制一个SELECT查询的结果。

    @@ -1146,7 +1353,7 @@

    8. 装卸数据

    ​ 运行COPY的每个后端将在pg_stat_progress_copy视图中报告其进度。

    -

    8.1. 大纲

    +

    9.1. 大纲

    COPY table_name [ ( column_name [, ...] ) ]
@@ -1178,7 +1385,7 @@ 
    8.1. 大纲
    -

    8.2. 输出

    +

    9.2. 输出

    在成功完成时，一个COPY命令会返回一个形为

    @@ -1193,7 +1400,7 @@

    8.2. 输出

    -

    8.3. 注解

    +

    9.3. 注解

    COPY TO只能用于普通表，而不能用于视图，并且不能从子表或子分区复制行。 例如，COPY table TO 复制与SELECT * FROM ONLY table 相同的行。 语法COPY (SELECT * FROM table) TO …​ 可用于转储一个继承层次结构、分区表或视图中的所有行。

    @@ -1238,9 +1445,9 @@

    8.3. 注解

    -

    8.4. 文件格式

    +

    9.4. 文件格式

    -

    8.4.1. 文本格式

    +

    9.4.1. 文本格式

    在使用text格式时，读取或写入的是一个文本文件，其中每一行就是表中的一行。一行中的列被定界字符分隔。列值本身是由输出函数产生的或者是可被输入函数接受的属于每个属性数据类型的字符串。在为空值的列的位置使用指定的空值串。如果输入文件的任何行包含比预期更多或者更少的列，COPY FROM将会抛出一个错误。

    @@ -1317,7 +1524,7 @@

    8.4.1. 文本

    -

    8.4.2. CSV格式

    +

    9.4.2. CSV格式

    这种格式选项被用于导入和导出很多其他程序（例如电子表格）使用的逗号 分隔值（CSV）文件格式。不同于 IvorySQL标准文本格式使用的转义 规则，它产生并且识别一般的 CSV 转义机制。

    @@ -1356,7 +1563,7 @@

    8.4.2. CSV格式

    -

    8.4.3. 二进制格式

    +

    9.4.3. 二进制格式

    binary格式选项导致所有数据被以二进制格式 而不是文本格式存储/读取。它比文本和CSV格式要 快一些，但是二进制格式文件在不同的机器架构和 IvorySQL 版本之间的可移 植性要差些。还有，二进制格式与数据格式非常相关。例如不能从 一个smallint列中输出二进制数据并且把它读入到一个 integer列中，虽然这样做在文本格式中是可行的。

    @@ -1434,7 +1641,7 @@

    8.4.3.

    -

    8.5. 示例

    +

    9.5. 示例

    下面的例子使用竖线（|）作为域定界符把一个表复制到客户端：

    @@ -1509,13 +1716,13 @@

    8.5. 示例

    -

    9. 性能管理

    +

    10. 性能管理

    查询性能可能受多种因素影响。其中一些因素可以由用户控制，而其他的则属于系统下层设计的基本原理。

    -

    9.1. 使用EXPLAIN

    +

    10.1. 使用EXPLAIN

    IvorySQL为每个收到查询产生一个查询计划。 选择正确的计划来匹配查询结构和数据的属性对于好的性能来说绝对是最关键的，因此系统包含了一个复杂的规划器来尝试选择好的计划。 你可以使用EXPLAIN命令察看规划器为任何查询生成的查询计划。 阅读查询计划是一门艺术，它要求一些经验来掌握，但是本节只试图覆盖一些基础。

    @@ -1523,7 +1730,7 @@

    9.1. 使用EX

    ​ 这些例子使用EXPLAIN的默认“text”输出格式，这种格式紧凑并且便于阅读。如果你想把EXPLAIN的输出交给一个程序做进一步分析，你应该使用它的某种机器可读的输出格式（XML、JSON 或 YAML）。

    -

    9.1.1. EXPLAIN基础

    +

    10.1.1. EXPLAIN基础

    查询计划的结构是一个计划结点的树。最底层的结点是扫描结点：它们从表中返回未经处理的行。 不同的表访问模式有不同的扫描结点类型：顺序扫描、索引扫描、位图索引扫描。 也还有不是表的行来源，例如VALUES子句和FROM中返回集合的函数，它们有自己的结点类型。如果查询需要连接、聚集、排序、或者在未经处理的行上的其它操作，那么就会在扫描结点之上有其它额外的结点来执行这些操作。 并且，做这些操作通常都有多种方法，因此在这些位置也有可能出现不同的结点类型。 EXPLAIN给计划树中每个结点都输出一行，显示基本的结点类型和计划器为该计划结点的执行所做的开销估计。 第一行（最上层的结点）是对该计划的总执行开销的估计；计划器试图最小化的就是这个数字。

    @@ -1858,7 +2065,7 @@

    9.1.1. EXPLAI

    -

    9.1.2. EXPLAIN ANALYZE

    +

    10.1.2. EXPLAIN ANALYZE

    可以通过使用EXPLAIN的ANALYZE选项来检查规划器估计值的准确性。通过使用这个选项，EXPLAIN会实际执行该查询，然后显示真实的行计数和在每个计划结点中累计的真实运行时间，还会有一个普通EXPLAIN显示的估计值。例如，我们可能得到这样一个结果：

    @@ -2063,7 +2270,7 @@

    9.1.2. EX

    -

    9.1.3. 警告

    +

    10.1.3. 警告

    在两种有效的方法中EXPLAIN ANALYZE所度量的运行时间可能偏离同一个查询的正常执行。首先，由于不会有输出行被递交给客户端，网络传输开销和 I/O 转换开销没有被包括在内。其次，由EXPLAIN ANALYZE所增加的度量开销可能会很可观，特别是在操作系统调用gettimeofday()很慢的机器上。你可以使用pg_test_timing工具来度量在你的系统上的计时开销。

    @@ -2103,9 +2310,9 @@

    9.1.3. 警告

    -

    9.2. 规划器使用的统计信息

    +

    10.2. 规划器使用的统计信息

    -

    9.2.1. 单列统计信息

    +

    10.2.1. 单列统计信息

    如我们在上一节所见，查询规划器需要估计一个查询要检索的行数，这样才能对查询计划做出好的选择。 本节对系统用于这些估计的统计信息进行一个快速的介绍。

    @@ -2173,7 +2380,7 @@

    9.2

    -

    9.2.2. 扩展统计信息

    +

    10.2.2. 扩展统计信息

    常常可以看到由于查询子句中用到的多个列相互关联而运行着糟糕的执行计划的慢查询。规划器通常会假设多个条件是彼此独立的，这种假设在列值相互关联的情况下是不成立的。由于常规的统计信息天然的针对个体列的性质，它们无法捕捉到跨列关联的知识。不过，IvorySQL有能力计算多元统计信息，它能捕捉这类信息。

    @@ -2190,7 +2397,7 @@

    9.2

    ​ 下面的小节介绍当前支持的扩展统计信息类型。

    -
    9.2.2.1. 函数依赖
    +
    10.2.2.1. 函数依赖

    最简单的一类扩展统计信息跟踪函数依赖，这是在数据库范式定义中使用的概念。如果列a的值的知识足以决定列b的值，即不会有两个行具有相同的a值但是有不同的b值，我们就说列b函数依赖于列a。在一个完全规范化的数据库中，函数依赖应该仅存在于主键和超键上。不过，实际上很多数据集合会由于各种原因无法被完全规范化，常见的例子是为了性能而有意地反规范化。即使在一个完全规范化的数据库中，也会有某些列之间的部分关联，这些可以表达成部分函数依赖。

    @@ -2225,7 +2432,7 @@
    9.2.2.1. 函数

    在为涉及函数依赖列的查询计算选择度时，规划器会使用依赖系数来调整针对条件的选择度估计，这样就不会产生低估。

    -
    9.2.2.1.1. 函数依赖的限制
    +
    10.2.2.1.1. 函数依赖的限制

    当前只有在考虑简单等值条件（将列与常量值比较）和具有常量值的IN 子句时，函数依赖才适用。不会使用它们来改进比较两个列或者比较列和表达式的等值条件的估计， 也不会用它们来改进范围子句、LIKE或者任何其他类型的条件。

    @@ -2254,7 +2461,7 @@
    <
    -
    9.2.2.2. 多元可区分值计数
    +
    10.2.2.2. 多元可区分值计数

    单列统计信息存储每一列中可区分值的数量。在组合多个列（例如GROUP BY a, b）时，如果规划器只有单列统计数据，则对可区分值数量的估计常常会错误，导致选择不好的计划。

    @@ -2287,7 +2494,7 @@
    -
    9.2.2.3. 多元MCV列表
    +
    10.2.2.3. 多元MCV列表

    为每列存储的另一种统计信息是频繁值列表。 这样可以对单个列进行非常准确的估计，但是对于在多个列上具有条件的查询，可能会导致严重的错误估计。

    @@ -2332,7 +2539,7 @@
    9.2.2.3.
    -

    9.3. 用显示JOIN子句控制规划器

    +

    10.3. 用显示JOIN子句控制规划器

    我们可以在一定程度上用显式JOIN语法控制查询规划器。要明白为什么需要它，我们首先需要一些背景知识。

    @@ -2429,18 +2636,18 @@

    -

    9.4. 填充一个数据库

    +

    10.4. 填充一个数据库

    第一次填充数据库时可能需要插入大量的数据。本节包含一些如何让这个处理尽可能高效的建议。

    -

    9.4.1. 禁用自动提交

    +

    10.4.1. 禁用自动提交

    在使用多个INSERT时，关闭自动提交并且只在最后做一次提交（在普通 SQL 中，这意味着在开始发出BEGIN并且在结束时发出COMMIT。某些客户端库可能背着你就做了这些，在这种情况下你需要确定在你需要做这些时该库确实帮你做了）。如果你允许每一个插入都被独立地提交，IvorySQL要为每一个被增加的行做很多工作。在一个事务中做所有插入的一个额外好处是：如果一个行的插入失败则所有之前插入的行都会被回滚，这样你不会被卡在部分载入的数据中。

    -

    9.4.2. 使用COPY

    +

    10.4.2. 使用COPY

    使用COPY在一条命令中装载所有记录，而不是一系列INSERT命令。 COPY命令是为装载大量行而优化过的； 它没INSERT那么灵活，但是在大量数据装载时导致的负荷也更少。 因为COPY是单条命令，因此使用这种方法填充表时无须关闭自动提交。

    @@ -2455,7 +2662,7 @@

    9.4.2. 使用COPY

    -

    9.4.3. 移除索引

    +

    10.4.3. 移除索引

    如果你正在载入一个新创建的表，最快的方法是创建该表，用COPY批量载入该表的数据，然后创建表需要的任何索引。在已存在数据的表上创建索引要比在每一行被载入时增量地更新它更快。

    @@ -2464,7 +2671,7 @@

    9.4.3. 移除

    -

    9.4.4. 移除外键约束

    +

    10.4.4. 移除外键约束

    和索引一样，“成批地”检查外键约束比一行行检查效率更高。 因此，先删除外键约束、载入数据然后重建约束会很有用。 同样，载入数据和约束缺失期间错误检查的丢失之间也存在平衡。

    @@ -2473,19 +2680,19 @@

    9.4

    -

    9.4.5. 增加 maintenance_work_mem

    +

    10.4.5. 增加 maintenance_work_mem

    在载入大量数据时，临时增大maintenance_work_mem配置变量可以改进性能。这个参数也可以帮助加速CREATE INDEX命令和ALTER TABLE ADD FOREIGN KEY命令。 它不会对COPY本身起很大作用，所以这个建议只有在你使用上面的一个或两个技巧时才有用。

    -

    9.4.6. 增加max_wal_size

    +

    10.4.6. 增加max_wal_size

    临时增大max_wal_size配置变量也可以让大量数据载入更快。 这是因为向IvorySQL中载入大量的数据将导致检查点的发生比平常（由checkpoint_timeout配置变量指定）更频繁。无论何时发生一个检查点时，所有脏页都必须被刷写到磁盘上。 通过在批量数据载入时临时增加max_wal_size，所需的检查点数目可以被缩减。

    -

    9.4.7. 禁用WAL归档和流复制

    +

    10.4.7. 禁用WAL归档和流复制

    当使用 WAL 归档或流复制向一个安装中载入大量数据时，在录入结束后执行一次新的基础备份比处理大量的增量 WAL 数据更快。为了防止载入时记录增量 WAL，通过将wal_level设置为minimal、将archive_mode设置为off以及将max_wal_senders设置为零来禁用归档和流复制。 但需要注意的是，修改这些设置需要重启服务。

    @@ -2494,13 +2701,13 @@

    -

    9.4.8. 事后运行ANALYZE

    +

    10.4.8. 事后运行ANALYZE

    不管什么时候你显著地改变了表中的数据分布后，我们都强烈推荐运行ANALYZE。这包括向表中批量载入大量数据。运行ANALYZE（或者VACUUM ANALYZE）保证规划器有表的最新统计信息。 如果没有统计数据或者统计数据过时，那么规划器在查询规划时可能做出很差劲决定，导致在任意表上的性能低下。需要注意的是，如果启用了 autovacuum 守护进程，它可能会自动运行ANALYZE。

    -

    9.4.9. 关于pg_dump的一些注记

    +

    10.4.9. 关于pg_dump的一些注记

    pg_dump生成的转储脚本自动应用上面的若干个（但不是全部）技巧。 要尽可能快地载入pg_dump转储，你需要手工做一些额外的事情（请注意，这些要点适用于恢复一个转储，而不是创建它的时候。同样的要点也适用于使用psql载入一个文本转储或用pg_restore从一个pg_dump归档文件载入）。

    @@ -2537,7 +2744,7 @@

    -

    9.5. 非持久设置

    +

    10.5. 非持久设置

    持久性是数据库的一个保证已提交事务的记录的特性（即使是发生服务器崩溃或断电）。 然而，持久性会明显增加数据库的负荷，因此如果你的站点不需要这个保证，IvorySQL可以被配置成运行更快。在这种情况下，你可以调整下列配置来提高性能。除了下面列出的，在数据库软件崩溃的情况下也能保证持久性。当这些设置被使用时，只有突然的操作系统停止会产生数据丢失或损坏的风险。

    diff --git a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html index 398bb80c..9b71d98a 100644 --- a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html +++ b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/sys_context.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html index 2ec60dd1..a5d51bbe 100644 --- a/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html +++ b/docs/cn/ivorysql-doc/master/oracle_builtin_functions/userenv.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html index 88ea6bb0..2ab9c024 100644 --- a/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html +++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/anonymous_block.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html index 8383830b..4937dfe7 100644 --- a/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html +++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html index e12ecbc6..6ab625f4 100644 --- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html +++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_call_into.html @@ -176,6 +176,9 @@

    文档中心

  • wal2json
    • +
  • + pg_stat_monitor +
    • @@ -574,7 +577,15 @@

    3.4. 在参数或INTO子句中引用绑定变量

    -
    -- 设置输入绑定变量
+
    -- 创建独立函数 stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- 设置输入绑定变量
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- 使用绑定变量作为参数调用函数
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
index 94dbd492..dec3b68b 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
index 9fd8dad4..f36f1e39 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
index d76e3ce3..4b4a5981 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
index 10b113e4..8e32dd49 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
index 9b923587..e851c1d9 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
index c7d8b2fb..7fc90c55 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
index b4a9a34c..b42df5d6 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
index 55456ec2..7d191d40 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
index f8422af1..53acde24 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
@@ -176,6 +176,9 @@ 
    文档中心
    
   
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+  
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+  
    • 
 
   
 
diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
index 07b11542..33fcfafc 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
index a273afbd..dd1cd4af 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html
index 62b3bcda..3b04acf1 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/invisible_column.html
@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html
index ab0340c3..4f0b8122 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/package.html
@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html
index 0313aa44..713bdf5c 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/port_ip.html
@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/sequence.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/sequence.html
index d4599942..d875dfdc 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/sequence.html
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@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html b/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html
index 5282b332..fd18b331 100644
--- a/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html
+++ b/docs/cn/ivorysql-doc/master/oracle_compatibility/xml_functions.html
@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/pg_reference/pg_functions_reference.html b/docs/cn/ivorysql-doc/master/pg_reference/pg_functions_reference.html
index 281cea86..eaf99c6f 100644
--- a/docs/cn/ivorysql-doc/master/pg_reference/pg_functions_reference.html
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diff --git a/docs/cn/ivorysql-doc/master/pg_reference/pg_parameters_reference.html b/docs/cn/ivorysql-doc/master/pg_reference/pg_parameters_reference.html
index 9a4668f0..3269e9a6 100644
--- a/docs/cn/ivorysql-doc/master/pg_reference/pg_parameters_reference.html
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@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/problem_report_guide.html b/docs/cn/ivorysql-doc/master/problem_report_guide.html
index ed552c2d..abcb3fe2 100644
--- a/docs/cn/ivorysql-doc/master/problem_report_guide.html
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@@ -174,6 +174,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/release_notes.html b/docs/cn/ivorysql-doc/master/release_notes.html
index b29ef795..7b8a3865 100644
--- a/docs/cn/ivorysql-doc/master/release_notes.html
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@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/tools_reference.html b/docs/cn/ivorysql-doc/master/tools_reference.html
index dcf9e42d..6b17f403 100644
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@@ -176,6 +176,9 @@ 
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diff --git a/docs/cn/ivorysql-doc/master/welcome.html b/docs/cn/ivorysql-doc/master/welcome.html
index 4118714c..a9752dc8 100644
--- a/docs/cn/ivorysql-doc/master/welcome.html
+++ b/docs/cn/ivorysql-doc/master/welcome.html
@@ -175,6 +175,9 @@ 
    文档中心
    
   
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diff --git a/docs/cn/ivorysql-doc/v5.3/4.4.html b/docs/cn/ivorysql-doc/v5.3/4.4.html
index 29c5ccf6..409ac35d 100644
--- a/docs/cn/ivorysql-doc/v5.3/4.4.html
+++ b/docs/cn/ivorysql-doc/v5.3/4.4.html
@@ -715,7 +715,211 @@ 
    
-
    2. 管理IvorySQL版本
    
+
    2. 使用 pg_upgrade 将 PostgreSQL 升级到 IvorySQL
    
+
    
+
    
+
    pg_upgrade 支持将原生 PostgreSQL 集群升级到 IvorySQL。当源集群为原生 PostgreSQL ，而目标为 IvorySQL 集群时，必须使用 -g（--using-ora-pg）参数。
    
+
    
+
    
+
    从 PostgreSQL 升级到 IvorySQL 属于“跨产品”迁移，建议在生产环境操作前完成完整的测试验证，并制定详细的回滚方案。
    
+
    
+
    
+
    2.1. 参数说明
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
    



    参数说明
    -g / --using-ora-pg
    表示源集群为 PostgreSQL ，而目标为 IvorySQL 集群。启用此选项后，pg_upgrade 在连接两端集群时统一使用 PostgreSQL 标准端口，从而正确处理升级。
    
+
    
+
    
+
    2.2. 升级步骤
    
+
    
+
    2.2.1. 第一步：初始化新 IvorySQL 集群
    
+
    
+
    
+
    # 使用 IvorySQL 的 initdb 初始化新集群
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data
    
+
    
+
    
+
    
+
    
+
    2.2.2. 第二步：停止源 PostgreSQL 集群
    
+
    
+
    
+
    /usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop
    
+
    
+
    
+
    
+
    
+
    2.2.3. 第三步：运行升级检查（可选）
    
+
    
+
    使用 -c 仅做兼容性检查，不修改任何数据：
    
+
    
+
    
+
    
+
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # 源 PG 可执行文件目录
+  -B /opt/ivorysql/bin \            # 目标 IvorySQL 可执行文件目录
+  -d /data/pg/data \                # 源 PG 数据目录
+  -D /data/ivorysql/data \          # 目标 IvorySQL 数据目录
+  -g \                              # 源为 PostgreSQL，目标为 IvorySQL
+  -c                                # 仅检查，不升级
    
+
    
+
    
+
    
+
    
+
    2.2.4. 第四步：执行升级
    
+
    
+
    
+
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g
    
+
    
+
    
+
    
+
    
+
    2.2.5. 第五步：启动 IvorySQL 并验证
    
+
    
+
    
+
    /opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# 验证数据库是否正常
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"
    
+
    
+
    
+
    
+
    
+
    2.2.6. 第六步：清理旧集群
    
+
    
+
    升级完成后，pg_upgrade 会生成一个清理脚本：
    
+
    
+
    
+
    
+
    ./delete_old_cluster.sh
    
+
    
+
    
+
    
+
    
+
    
+
    2.3. 常用参数速查
    
+
+++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
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+
+
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+
    




    参数长选项说明
    -b
    --old-bindir
    源集群可执行文件目录
    -B
    --new-bindir
    目标集群可执行文件目录
    -d
    --old-datadir
    源集群数据目录
    -D
    --new-datadir
    目标集群数据目录
    -g
    --using-ora-pg
    源为 PostgreSQL 升级到 IvorySQL
    -p
    --old-port
    源集群端口
    -P
    --new-port
    目标集群 PG 端口
    -q
    --old-oraport
    源集群 Oracle 端口
    -Q
    --new-oraport
    目标集群 Oracle 端口
    -j
    --jobs
    并行进程数
    -k
    --link
    使用硬链接替代文件复制
    -c
    --check
    仅检查兼容性，不执行升级
    -v
    --verbose
    输出详细日志
    
+
    
+
    
+
    2.4. 注意事项
    
+
    
+
      
+
    • 
+
      -g 参数仅在 源为纯 PostgreSQL 集群、目标为 IvorySQL 时使用。若源集群已是 IvorySQL，则无需此参数。
      
+
      • 
+
    • 
+
      升级期间源集群和目标集群均须处于停止状态。
      
+
      • 
+
    • 
+
      升级前务必对源集群做完整备份。
      
+
      • 
+
    
+
    
+
    
+
    
+
    +
    +

    3. 管理IvorySQL版本

    IvorySQL基于PostgreSQL开发，版本更新频率与PostgreSQL版本更新频率保持一致，每年更新一个大版本，每季度更新一个小版本。IvorySQL目前发布的版本有1.0到5.3，分别基于PostgreSQL 14.0到18.3进行开发，最新版本为IvorySQL 5.3，基于PostgreSQL 18.3进行开发。IvorySQL 的所有版本全部都做到了向下兼容。相关版本特性可以查看 官网。

    @@ -723,7 +927,7 @@

    -

    3. 管理IvorySQL数据库访问

    +

    4. 管理IvorySQL数据库访问

    IvorySQL使用 角色 的概念管理数据库访问权限。一个角色可以看成是一个数据库用户或者是数据库用户组，这取决于角色被怎样设置。角色可以拥有数据库对象（例如，表和函数）并且能够把那些对象上的权限赋予给其他角色来控制谁能访问哪些对象。此外，还可以把一个角色中的 成员资格 授予给另一个角色，这样允许成员角色使用被赋予给另一个角色的权限。

    @@ -791,21 +995,21 @@

    -

    4. 定义数据对象

    +

    5. 定义数据对象

    IvorySQL基于PostgreSQL，具有完整的SQL，其定义数据对象可以参考 手册。在此基础之上，IvorySQL为兼容Oracle，还做了一些Oracle专有数据对象的兼容。

    -

    4.1. VARCHAR2

    +

    5.1. VARCHAR2

    -

    4.1.1. 概述

    +

    5.1.1. 概述

    具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。

    -

    4.1.2. 语法

    +

    5.1.2. 语法

    VARCHAR2(size)
    @@ -813,7 +1017,7 @@

    4.1.2. 语法

    -

    4.1.3. 用例

    +

    5.1.3. 用例

    create table test(a varchar2(5));
@@ -837,7 +1041,7 @@ 
    4.1.3. 用例
    -

    5. 查询数据

    +

    6. 查询数据

    IvorySQL基于PostgreSQL开发，具有完全的SQL，查询数据具体的操作可以参考 手册。

    @@ -845,7 +1049,7 @@

    5. 查询数据

    -

    6. 使用外部数据

    +

    7. 使用外部数据

    IvorySQL实现了部分的SQL/MED规定，允许我们使用普通SQL查询来访问位于IvorySQL之外的数据。这种数据被称为外部数据（注意这种用法不要和外键混淆，后者是数据库中的一种约束）。

    @@ -862,7 +1066,7 @@

    6.

    -

    7. 备份与恢复

    +

    8. 备份与恢复

    由于包含着有价值的数据，IvorySQL数据库应当被定期地备份。虽然过程相当简单，但清晰地理解其底层技术和假设是非常重要的。

    @@ -884,7 +1088,7 @@

    7. 备份

    -

    7.1. SQL转储

    +

    8.1. SQL转储

    SQL 转储方法的思想是创建一个由SQL命令组成的文件，当把这个文件回馈给服务器时，服务器将利用其中的SQL命令重建与转储时状态一样的数据库。 IvorySQL为此提供了工具pg_dump。这个工具的基本用法是：

    @@ -912,7 +1116,7 @@

    7.1. SQL转储

    ​ 由pg_dump创建的备份在内部是一致的， 也就是说，转储表现了pg_dump开始运行时刻的数据库快照，且在pg_dump运行过程中发生的更新将不会被转储。pg_dump工作的时候并不阻塞其他的对数据库的操作。（但是会阻塞那些需要排它锁的操作，比如大部分形式的ALTER TABLE）

    -

    7.1.1. 从转储中恢复

    +

    8.1.1. 从转储中恢复

    pg_dump生成的文本文件可以由psql程序读取。 从转储中恢复的常用命令是：

    @@ -954,7 +1158,7 @@

    7.1

    -

    7.1.2. 使用pg_dumpall

    +

    8.1.2. 使用pg_dumpall

    pg_dump每次只转储一个数据库，而且它不会转储关于角色或表空间（因为它们是集簇范围的）的信息。为了支持方便地转储一个数据库集簇的全部内容，提供了pg_dumpall程序。pg_dumpall备份一个给定集簇中的每一个数据库，并且也保留了集簇范围的数据，如角色和表空间定义。该命令的基本用法是：

    @@ -982,7 +1186,7 @@

    7.1.2.

    -

    7.1.3. 处理大型数据库

    +

    8.1.3. 处理大型数据库

    在一些具有最大文件尺寸限制的操作系统上创建大型的pg_dump输出文件可能会出现问题。幸运地是，pg_dump可以写出到标准输出，因此你可以使用标准Unix工具来处理这种潜在的问题。有几种可能的方法：

    @@ -1073,7 +1277,7 @@

    <

    -

    7.2. 文件系统级别备份

    +

    8.2. 文件系统级别备份

    另外一种备份策略是直接复制 IvorySQL用于存储数据库中数据的文件，你可以采用任何你喜欢的方式进行文件系统备份，例如：

    @@ -1112,7 +1316,7 @@

    -

    7.3. 连续归档和时间点恢复（PITR）

    +

    8.3. 连续归档和时间点恢复（PITR）

    在任何时间，IvorySQL在数据集簇目录的pg_wal/子目录下都保持有一个预写式日志（WAL）。这个日志存在的目的是为了保证崩溃后的安全：如果系统崩溃，可以“重放”从最后一次检查点以来的日志项来恢复数据库的一致性。该日志的存在也使得第三种备份数据库的策略变得可能：我们可以把一个文件系统级别的备份和WAL文件的备份结合起来。当需要恢复时，我们先恢复文件系统备份，然后从备份的WAL文件中重放来把系统带到一个当前状态。这种方法比之前的方法管理起来要更复杂，但是有其显著的优点：

    @@ -1145,7 +1349,7 @@

    -

    8. 装卸数据

    +

    9. 装卸数据

    copy 在 IvorySQL表和标准文件之间移动数据。COPY TO 把一个表的内容复制到一个文件，而COPY FROM 则从一个文件复制数据到一个表（把数据追加到表中原有数据）。COPY TO 也能复制一个SELECT查询的结果。

    @@ -1160,7 +1364,7 @@

    8. 装卸数据

    ​ 运行COPY的每个后端将在pg_stat_progress_copy视图中报告其进度。

    -

    8.1. 大纲

    +

    9.1. 大纲

    COPY table_name [ ( column_name [, ...] ) ]
@@ -1192,7 +1396,7 @@ 
    8.1. 大纲
    -

    8.2. 输出

    +

    9.2. 输出

    在成功完成时，一个COPY命令会返回一个形为

    @@ -1207,7 +1411,7 @@

    8.2. 输出

    -

    8.3. 注解

    +

    9.3. 注解

    COPY TO只能用于普通表，而不能用于视图，并且不能从子表或子分区复制行。 例如，COPY table TO 复制与SELECT * FROM ONLY table 相同的行。 语法COPY (SELECT * FROM table) TO …​ 可用于转储一个继承层次结构、分区表或视图中的所有行。

    @@ -1252,9 +1456,9 @@

    8.3. 注解

    -

    8.4. 文件格式

    +

    9.4. 文件格式

    -

    8.4.1. 文本格式

    +

    9.4.1. 文本格式

    在使用text格式时，读取或写入的是一个文本文件，其中每一行就是表中的一行。一行中的列被定界字符分隔。列值本身是由输出函数产生的或者是可被输入函数接受的属于每个属性数据类型的字符串。在为空值的列的位置使用指定的空值串。如果输入文件的任何行包含比预期更多或者更少的列，COPY FROM将会抛出一个错误。

    @@ -1331,7 +1535,7 @@

    8.4.1. 文本

    -

    8.4.2. CSV格式

    +

    9.4.2. CSV格式

    这种格式选项被用于导入和导出很多其他程序（例如电子表格）使用的逗号 分隔值（CSV）文件格式。不同于 IvorySQL标准文本格式使用的转义 规则，它产生并且识别一般的 CSV 转义机制。

    @@ -1370,7 +1574,7 @@

    8.4.2. CSV格式

    -

    8.4.3. 二进制格式

    +

    9.4.3. 二进制格式

    binary格式选项导致所有数据被以二进制格式 而不是文本格式存储/读取。它比文本和CSV格式要 快一些，但是二进制格式文件在不同的机器架构和 IvorySQL 版本之间的可移 植性要差些。还有，二进制格式与数据格式非常相关。例如不能从 一个smallint列中输出二进制数据并且把它读入到一个 integer列中，虽然这样做在文本格式中是可行的。

    @@ -1448,7 +1652,7 @@

    8.4.3.

    -

    8.5. 示例

    +

    9.5. 示例

    下面的例子使用竖线（|）作为域定界符把一个表复制到客户端：

    @@ -1523,13 +1727,13 @@

    8.5. 示例

    -

    9. 性能管理

    +

    10. 性能管理

    查询性能可能受多种因素影响。其中一些因素可以由用户控制，而其他的则属于系统下层设计的基本原理。

    -

    9.1. 使用EXPLAIN

    +

    10.1. 使用EXPLAIN

    IvorySQL为每个收到查询产生一个查询计划。 选择正确的计划来匹配查询结构和数据的属性对于好的性能来说绝对是最关键的，因此系统包含了一个复杂的规划器来尝试选择好的计划。 你可以使用EXPLAIN命令察看规划器为任何查询生成的查询计划。 阅读查询计划是一门艺术，它要求一些经验来掌握，但是本节只试图覆盖一些基础。

    @@ -1537,7 +1741,7 @@

    9.1. 使用EX

    ​ 这些例子使用EXPLAIN的默认“text”输出格式，这种格式紧凑并且便于阅读。如果你想把EXPLAIN的输出交给一个程序做进一步分析，你应该使用它的某种机器可读的输出格式（XML、JSON 或 YAML）。

    -

    9.1.1. EXPLAIN基础

    +

    10.1.1. EXPLAIN基础

    查询计划的结构是一个计划结点的树。最底层的结点是扫描结点：它们从表中返回未经处理的行。 不同的表访问模式有不同的扫描结点类型：顺序扫描、索引扫描、位图索引扫描。 也还有不是表的行来源，例如VALUES子句和FROM中返回集合的函数，它们有自己的结点类型。如果查询需要连接、聚集、排序、或者在未经处理的行上的其它操作，那么就会在扫描结点之上有其它额外的结点来执行这些操作。 并且，做这些操作通常都有多种方法，因此在这些位置也有可能出现不同的结点类型。 EXPLAIN给计划树中每个结点都输出一行，显示基本的结点类型和计划器为该计划结点的执行所做的开销估计。 第一行（最上层的结点）是对该计划的总执行开销的估计；计划器试图最小化的就是这个数字。

    @@ -1872,7 +2076,7 @@

    9.1.1. EXPLAI

    -

    9.1.2. EXPLAIN ANALYZE

    +

    10.1.2. EXPLAIN ANALYZE

    可以通过使用EXPLAIN的ANALYZE选项来检查规划器估计值的准确性。通过使用这个选项，EXPLAIN会实际执行该查询，然后显示真实的行计数和在每个计划结点中累计的真实运行时间，还会有一个普通EXPLAIN显示的估计值。例如，我们可能得到这样一个结果：

    @@ -2077,7 +2281,7 @@

    9.1.2. EX

    -

    9.1.3. 警告

    +

    10.1.3. 警告

    在两种有效的方法中EXPLAIN ANALYZE所度量的运行时间可能偏离同一个查询的正常执行。首先，由于不会有输出行被递交给客户端，网络传输开销和 I/O 转换开销没有被包括在内。其次，由EXPLAIN ANALYZE所增加的度量开销可能会很可观，特别是在操作系统调用gettimeofday()很慢的机器上。你可以使用pg_test_timing工具来度量在你的系统上的计时开销。

    @@ -2117,9 +2321,9 @@

    9.1.3. 警告

    -

    9.2. 规划器使用的统计信息

    +

    10.2. 规划器使用的统计信息

    -

    9.2.1. 单列统计信息

    +

    10.2.1. 单列统计信息

    如我们在上一节所见，查询规划器需要估计一个查询要检索的行数，这样才能对查询计划做出好的选择。 本节对系统用于这些估计的统计信息进行一个快速的介绍。

    @@ -2187,7 +2391,7 @@

    9.2

    -

    9.2.2. 扩展统计信息

    +

    10.2.2. 扩展统计信息

    常常可以看到由于查询子句中用到的多个列相互关联而运行着糟糕的执行计划的慢查询。规划器通常会假设多个条件是彼此独立的，这种假设在列值相互关联的情况下是不成立的。由于常规的统计信息天然的针对个体列的性质，它们无法捕捉到跨列关联的知识。不过，IvorySQL有能力计算多元统计信息，它能捕捉这类信息。

    @@ -2204,7 +2408,7 @@

    9.2

    ​ 下面的小节介绍当前支持的扩展统计信息类型。

    -
    9.2.2.1. 函数依赖
    +
    10.2.2.1. 函数依赖

    最简单的一类扩展统计信息跟踪函数依赖，这是在数据库范式定义中使用的概念。如果列a的值的知识足以决定列b的值，即不会有两个行具有相同的a值但是有不同的b值，我们就说列b函数依赖于列a。在一个完全规范化的数据库中，函数依赖应该仅存在于主键和超键上。不过，实际上很多数据集合会由于各种原因无法被完全规范化，常见的例子是为了性能而有意地反规范化。即使在一个完全规范化的数据库中，也会有某些列之间的部分关联，这些可以表达成部分函数依赖。

    @@ -2239,7 +2443,7 @@
    9.2.2.1. 函数

    在为涉及函数依赖列的查询计算选择度时，规划器会使用依赖系数来调整针对条件的选择度估计，这样就不会产生低估。

    -
    9.2.2.1.1. 函数依赖的限制
    +
    10.2.2.1.1. 函数依赖的限制

    当前只有在考虑简单等值条件（将列与常量值比较）和具有常量值的IN 子句时，函数依赖才适用。不会使用它们来改进比较两个列或者比较列和表达式的等值条件的估计， 也不会用它们来改进范围子句、LIKE或者任何其他类型的条件。

    @@ -2268,7 +2472,7 @@
    <
    -
    9.2.2.2. 多元可区分值计数
    +
    10.2.2.2. 多元可区分值计数

    单列统计信息存储每一列中可区分值的数量。在组合多个列（例如GROUP BY a, b）时，如果规划器只有单列统计数据，则对可区分值数量的估计常常会错误，导致选择不好的计划。

    @@ -2301,7 +2505,7 @@
    -
    9.2.2.3. 多元MCV列表
    +
    10.2.2.3. 多元MCV列表

    为每列存储的另一种统计信息是频繁值列表。 这样可以对单个列进行非常准确的估计，但是对于在多个列上具有条件的查询，可能会导致严重的错误估计。

    @@ -2346,7 +2550,7 @@
    9.2.2.3.
    -

    9.3. 用显示JOIN子句控制规划器

    +

    10.3. 用显示JOIN子句控制规划器

    我们可以在一定程度上用显式JOIN语法控制查询规划器。要明白为什么需要它，我们首先需要一些背景知识。

    @@ -2443,18 +2647,18 @@

    -

    9.4. 填充一个数据库

    +

    10.4. 填充一个数据库

    第一次填充数据库时可能需要插入大量的数据。本节包含一些如何让这个处理尽可能高效的建议。

    -

    9.4.1. 禁用自动提交

    +

    10.4.1. 禁用自动提交

    在使用多个INSERT时，关闭自动提交并且只在最后做一次提交（在普通 SQL 中，这意味着在开始发出BEGIN并且在结束时发出COMMIT。某些客户端库可能背着你就做了这些，在这种情况下你需要确定在你需要做这些时该库确实帮你做了）。如果你允许每一个插入都被独立地提交，IvorySQL要为每一个被增加的行做很多工作。在一个事务中做所有插入的一个额外好处是：如果一个行的插入失败则所有之前插入的行都会被回滚，这样你不会被卡在部分载入的数据中。

    -

    9.4.2. 使用COPY

    +

    10.4.2. 使用COPY

    使用COPY在一条命令中装载所有记录，而不是一系列INSERT命令。 COPY命令是为装载大量行而优化过的； 它没INSERT那么灵活，但是在大量数据装载时导致的负荷也更少。 因为COPY是单条命令，因此使用这种方法填充表时无须关闭自动提交。

    @@ -2469,7 +2673,7 @@

    9.4.2. 使用COPY

    -

    9.4.3. 移除索引

    +

    10.4.3. 移除索引

    如果你正在载入一个新创建的表，最快的方法是创建该表，用COPY批量载入该表的数据，然后创建表需要的任何索引。在已存在数据的表上创建索引要比在每一行被载入时增量地更新它更快。

    @@ -2478,7 +2682,7 @@

    9.4.3. 移除

    -

    9.4.4. 移除外键约束

    +

    10.4.4. 移除外键约束

    和索引一样，“成批地”检查外键约束比一行行检查效率更高。 因此，先删除外键约束、载入数据然后重建约束会很有用。 同样，载入数据和约束缺失期间错误检查的丢失之间也存在平衡。

    @@ -2487,19 +2691,19 @@

    9.4

    -

    9.4.5. 增加 maintenance_work_mem

    +

    10.4.5. 增加 maintenance_work_mem

    在载入大量数据时，临时增大maintenance_work_mem配置变量可以改进性能。这个参数也可以帮助加速CREATE INDEX命令和ALTER TABLE ADD FOREIGN KEY命令。 它不会对COPY本身起很大作用，所以这个建议只有在你使用上面的一个或两个技巧时才有用。

    -

    9.4.6. 增加max_wal_size

    +

    10.4.6. 增加max_wal_size

    临时增大max_wal_size配置变量也可以让大量数据载入更快。 这是因为向IvorySQL中载入大量的数据将导致检查点的发生比平常（由checkpoint_timeout配置变量指定）更频繁。无论何时发生一个检查点时，所有脏页都必须被刷写到磁盘上。 通过在批量数据载入时临时增加max_wal_size，所需的检查点数目可以被缩减。

    -

    9.4.7. 禁用WAL归档和流复制

    +

    10.4.7. 禁用WAL归档和流复制

    当使用 WAL 归档或流复制向一个安装中载入大量数据时，在录入结束后执行一次新的基础备份比处理大量的增量 WAL 数据更快。为了防止载入时记录增量 WAL，通过将wal_level设置为minimal、将archive_mode设置为off以及将max_wal_senders设置为零来禁用归档和流复制。 但需要注意的是，修改这些设置需要重启服务。

    @@ -2508,13 +2712,13 @@

    -

    9.4.8. 事后运行ANALYZE

    +

    10.4.8. 事后运行ANALYZE

    不管什么时候你显著地改变了表中的数据分布后，我们都强烈推荐运行ANALYZE。这包括向表中批量载入大量数据。运行ANALYZE（或者VACUUM ANALYZE）保证规划器有表的最新统计信息。 如果没有统计数据或者统计数据过时，那么规划器在查询规划时可能做出很差劲决定，导致在任意表上的性能低下。需要注意的是，如果启用了 autovacuum 守护进程，它可能会自动运行ANALYZE。

    -

    9.4.9. 关于pg_dump的一些注记

    +

    10.4.9. 关于pg_dump的一些注记

    pg_dump生成的转储脚本自动应用上面的若干个（但不是全部）技巧。 要尽可能快地载入pg_dump转储，你需要手工做一些额外的事情（请注意，这些要点适用于恢复一个转储，而不是创建它的时候。同样的要点也适用于使用psql载入一个文本转储或用pg_restore从一个pg_dump归档文件载入）。

    @@ -2551,7 +2755,7 @@

    -

    9.5. 非持久设置

    +

    10.5. 非持久设置

    持久性是数据库的一个保证已提交事务的记录的特性（即使是发生服务器崩溃或断电）。 然而，持久性会明显增加数据库的负荷，因此如果你的站点不需要这个保证，IvorySQL可以被配置成运行更快。在这种情况下，你可以调整下列配置来提高性能。除了下面列出的，在数据库软件崩溃的情况下也能保证持久性。当这些设置被使用时，只有突然的操作系统停止会产生数据丢失或损坏的风险。

    diff --git a/docs/cn/ivorysql-doc/v5.3/7.22.html b/docs/cn/ivorysql-doc/v5.3/7.22.html index 0307d3ca..7b5bac4c 100644 --- a/docs/cn/ivorysql-doc/v5.3/7.22.html +++ b/docs/cn/ivorysql-doc/v5.3/7.22.html @@ -588,7 +588,15 @@

    3.4. 在参数或INTO子句中引用绑定变量

    -
    -- 设置输入绑定变量
+
    -- 创建独立函数 stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- 设置输入绑定变量
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- 使用绑定变量作为参数调用函数
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/cn/ivorysql-doc/v5.3/_exports/index.pdf b/docs/cn/ivorysql-doc/v5.3/_exports/index.pdf
index b31b85bc..0eb3cbfd 100644
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diff --git a/docs/cn/ivorysql-doc/v5.3/ivorysql.pdf b/docs/cn/ivorysql-doc/v5.3/ivorysql.pdf
index b31b85bc..0eb3cbfd 100644
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diff --git a/docs/cn/search-index.js b/docs/cn/search-index.js
index 55a3b71f..e6b22c97 100644
--- a/docs/cn/search-index.js
+++ b/docs/cn/search-index.js
@@ -1 +1 @@
-antoraSearch.initSearch(lunr, 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pid | usesysid | usename | application_name | client_addr | client_hostname | client_port | backend_start | backend_ xmin | state | sent_lsn | write_lsn | flush_lsn | replay_lsn | write_lag | flush_lag | replay_lag | sync_priority | sync_state | reply_time -------+----------+----------+------------------+----------------+-----------------+-------------+-------------------------------+--------- -----+-----------+-----------+-----------+-----------+------------+-----------+-----------+------------+---------------+------------+------ ------------------------- 11176 | 10 | ivorysql | walreceiver | 192.168.31.102 | | 53416 | 2024-12-18 21:54:52.041847-05 | | streaming | 0/7000148 | 0/7000148 | 0/7000148 | 0/7000148 | | | | 0 | async | 2024- 12-18 22:52:07.325111-05 (1 row) 这里192.168.31.102为备节点的ip，async表示数据同步方式为异步流复制。 === 使用集群 集群中所有的写操作均在主节点执行，读操作则主备节点都可以执行。主节点的数据通过流复制同步到备节点。主节点写操作的结果在任何一个备节点都能够查询到。 例如，在主节点创建一个新的数据库test，并在主节点进行查询： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# create database test; CREATE DATABASE ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows) 在备节点查询： $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows)","title":"集群搭建","component":"ivorysql-doc","version":"master","name":"cluster_setup","url":"/ivorysql-doc/master/cluster_setup.html","titles":[{"text":"1. 主节点","hash":"主节点","id":1},{"text":"1.1. 安装并启动数据库","hash":"安装并启动数据库","id":2},{"text":"1.2. 关闭防火墙","hash":"关闭防火墙","id":3},{"text":"1.3. 配置文件","hash":"配置文件","id":4},{"text":"1.4. 重启主节点数据库服务","hash":"重启主节点数据库服务","id":5},{"text":"2. 备节点","hash":"备节点","id":6},{"text":"2.1. 安装数据库","hash":"安装数据库","id":7},{"text":"2.2. 关闭防火墙","hash":"关闭防火墙-2","id":8},{"text":"2.3. 搭建流复制","hash":"搭建流复制","id":9},{"text":"2.4. 配置环境变量","hash":"配置环境变量","id":10},{"text":"2.5. 启动备节点数据库服务","hash":"启动备节点数据库服务","id":11},{"text":"3. 集群的使用","hash":"集群的使用","id":12},{"text":"3.1. 查看集群状态","hash":"查看集群状态","id":13}]},"3":{"id":3,"text":"IvorySQL在开源PostgreSQL数据库的基础上提供独特的附加功能。 IvorySQL致力于通过创新和建立在开源数据库解决方案之上为其终端用户提供价值。我们的目标是为中小型企业提供一个具有高性能、可扩展性、可靠性和易于使用的解决方案。 IvorySQL提供的扩展功能将使用户能够建立高性能和可扩展的PostgreSQL数据库集群，具有更好的数据库兼容性和管理。这简化了从其他DBMS迁移到PostgreSQL的过程，增强了数据库管理经验。 在对原有的PostgreSQL改动最小的前提下，实现对Oracle的兼容。我们需要实现双parser、双端口、模式PL/pgSQL实现PL/iSQL的框架。实现流程图如下： 下图描述了对PostgreSQL现有系统表的变化以及所做的补充。 看看你能否访问数据库服务器的第一个例子就是试着创建一个数据库。一台运行着的IvorySQL服务器可以管理许多数据库。通常我们会为每个项目和每个用户单独使用一个数据库。 你的站点管理员可能已经为你创建了可以使用的数据库。如果这样你就可以省略这一步，并且跳到下一节。 要创建一个新的数据库，在我们这个例子里叫`mydb`，你可以使用下面的命令： $ createdb mydb 如果不产生任何响应则表示该步骤成功，你可以跳过本节的剩余部分。 如果你看到类似下面这样的信息： createdb: command not found 那么就是IvorySQL没有安装好。或者是根本没安装，或者是你的shell搜索路径没有设置正确。尝试用绝对路径调用该命令试试： $ /usr/local/pgsql/bin/createdb mydb 在你的站点上这个路径可能不一样。和你的站点管理员联系或者看看安装指导获取正确的位置。 另外一种响应可能是这样： createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory Is the server running locally and accepting connections on that socket? 这意味着该服务器没有启动，或者在`createdb`期望去连接它的时候没有在监听。同样，你也要查看安装指导或者咨询管理员。 另外一个响应可能是这样： createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: FATAL: role \"joe\" does not exist 在这里提到了你自己的登录名。如果管理员没有为你创建IvorySQL用户帐号，就会发生这些现象。（IvorySQL用户帐号和操作系统用户帐号是不同的。） 如果你是管理员，参阅 第 22 章 获取创建用户帐号的帮助。你需要变成安装IvorySQL的操作系统用户的身份（通常是 postgres）才能创建第一个用户帐号。也有可能是赋予你的IvorySQL用户名和你的操作系统用户名不同；这种情况下，你需要使用`-U`选项或者使用`PGUSER`环境变量指定你的IvorySQL用户名。 如果你有个数据库用户帐号，但是没有创建数据库所需要的权限，那么你会看到下面的信息： createdb: error: database creation failed: ERROR: permission denied to create database 并非所有用户都被许可创建新数据库。如果IvorySQL拒绝为你创建数据库，那么你需要让站点管理员赋予你创建数据库的权限。出现这种情况时请咨询你的站点管理员。如果你自己安装了IvorySQL，那么你应该以你启动数据库服务器的用户身份登录然后参考手册完成权限的赋予工作。http://www.postgresql.org/docs/17/tutorial-createdb.html#ftn.id-1.4.3.4.10.4[[1\\]] 你还可以用其它名字创建数据库。IvorySQL允许你在一个站点上创建任意数量的数据库。数据库名必须是以字母开头并且小于63个字符长。一个方便的做法是创建和你当前用户名同名的数据库。许多工具假设该数据库名为缺省数据库名，所以这样可以节省你的敲键。要创建这样的数据库，只需要键入： $ createdb 如果你再也不想使用你的数据库了，那么你可以删除它。比如，如果你是数据库`mydb`的所有人（创建人），那么你就可以用下面的命令删除它： $ dropdb mydb （对于这条命令而言，数据库名不是缺省的用户名，因此你就必须声明它）。这个动作将在物理上把所有与该数据库相关的文件都删除并且不可取消，因此做这中操作之前一定要考虑清楚。 更多关于 createdb 和 dropdb 的信息可以分别在 createdb和 dropdb中找到。 你可以通过指定表的名字和所有列的名字及其类型来创建表∶ CREATE TABLE weather ( city varchar(80), temp_lo int, -- 最低温度 temp_hi int, -- 最高温度 prcp real, -- 湿度 date date ); 你可以在 psql 输入这些命令以及换行符。psql 可以识别该命令直到分号才结束。 你可以在SQL命令中自由使用空白（即空格、制表符和换行符）。这就意味着你可以用和上面不同的对齐方式键入命令，或者将命令全部放在一行中。两个划线（“--”）引入注释。任何跟在它后面直到行尾的东西都会被忽略。SQL是对关键字和标识符大小写不敏感的语言，只有在标识符用双引号包围时才能保留它们的大小写（上例没有这么做）。 varchar(80) 指定了一个可以存储最长80个字符的任意字符串的数据类型。int 是普通的整数类型。real 是一种用于存储单精度浮点数的类型。date 类型应该可以自解释（没错，类型为`date`的列名字也是`date`。这么做可能比较方便或者容易让人混淆 — 你自己选择）。 IvorySQL支持标准的SQL类型`int`、smallint、real、double precision、char(N)、varchar(N)、date、time、timestamp`和`interval，还支持其他的通用功能的类型和丰富的几何类型。IvorySQL中可以定制任意数量的用户定义数据类型。因而类型名并不是语法关键字，除了SQL标准要求支持的特例外。 第二个例子将保存城市和它们相关的地理位置： CREATE TABLE cities ( name varchar(80), location point ); 类型`point`就是一种IvorySQL特有数据类型的例子。 最后，我们还要提到如果你不再需要某个表，或者你想以不同的形式重建它，那么你可以用下面的命令删除它： DROP TABLE tablename; 当一个表被创建后，它不包含任何数据。在数据库发挥作用之前，首先要做的是插入数据。一次插入一行数据。你也可以在一个命令中插入多行，但不能插入不完整的行。即使只知道其中一些列的值，也必须创建完整的行。 要创建一个新行，使用 INSERT命令。这条命令要求提供表的名字和其中列的值。例如，考虑 第 5 章中的产品表： CREATE TABLE products ( product_no integer, name text, price numeric ); CREATE TABLE new_products ( product_no int , name varchar(255), price DECIMAL(10, 2), release_date DATE ); 一个插入一行的命令将是： INSERT INTO products VALUES (1, 'Cheese', 9.99); 数据的值是按照这些列在表中出现的顺序列出的，并且用逗号分隔。通常，数据的值是文字（常量），但也允许使用标量表达式。 上面的语法的缺点是你必须知道表中列的顺序。要避免这个问题，你也可以显式地列出列。例如，下面的两条命令都有和上文那条 命令一样的效果： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99); INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1); 许多用户认为明确列出列的名字是个好习惯。 如果你没有获得所有列的值，那么你可以省略其中的一些。在这种情况下，这些列将被填充为它们的缺省值。例如： INSERT INTO products (product_no, name) VALUES (1, 'Cheese'); INSERT INTO products VALUES (1, 'Cheese'); 第二种形式是IvorySQL的一个扩展。它从使用给出的值从左开始填充列，有多少个给出的列值就填充多少个列，其他列的将使用缺省值。 为了保持清晰，你也可以显式地要求缺省值，用于单个的列或者用于整个行： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', DEFAULT); INSERT INTO products DEFAULT VALUES; 你可以在一个命令中插入多行： INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99), (2, 'Bread', 1.99), (3, 'Milk', 2.99); INSERT INTO new_products (product_no, name, price, release_date) VALUES (1, 'A', 100.00, '2025-05-29'), (2, 'B', 150.50, '2024-11-20'), (3, 'C', 200.75, '2025-05-29'); 也可以插入查询的结果（可能没有行、一行或多行）： INSERT INTO products (product_no, name, price) SELECT product_no, name, price FROM new_products WHERE release_date = '2025-05-29'; 这提供了用于计算要插入的行的SQL查询机制（ 第 7 章）的全部功能。 提示 在一次性插入大量数据时，考虑使用 COPY命令。它不如 INSERT命令那么灵活，但是更高效。 两个查询的结果可以用集合操作并、交、差进行组合。语法是: query1 UNION [ALL] query2 query1 INTERSECT [ALL] query2 query1 EXCEPT [ALL] query2 query1*和query2*都是可以使用以上所有特性的查询。集合操作也可以嵌套和级连，例如: query1 UNION query2 UNION query3 实际执行的是： (query1 UNION query2) UNION query3 UNION`有效地把*`query2的结果附加到query1*的结果上（不过我们不能保证这就是这些行实际被返回的顺序）。此外，它将删除结果中所有重复的行，就像`DISTINCT`做的那样，除非你使用了`UNION ALL`。 INTERSECT`返回那些同时存在于*`query1和query2*的结果中的行，除非声明了`INTERSECT ALL`，否则所有重复行都被消除。 EXCEPT`返回所有在*`query1的结果中但是不在query2的结果中的行（有时侯这叫做两个查询的*差）。同样的，除非声明了`EXCEPT ALL`，否则所有重复行都被消除。 为了计算两个查询的并、交、差，这两个查询必须是“并操作兼容的”，也就意味着它们都返回同样数量的列，并且对应的列有兼容的数据类型，如 第 10.5 节中描述的那样。 当优化器判断对于某一个特定的查询，并行查询是最快的执行策略时，优化器将创建一个查询计划。该计划包括一个 *Gather*或者*Gather Merge*节点。下面是一个简单的例子： EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%'; QUERY PLAN ------------------------------------------------------------------------------------- Gather (cost=1000.00..217018.43 rows=1 width=97) Workers Planned: 2 -> Parallel Seq Scan on pgbench_accounts (cost=0.00..216018.33 rows=1 width=97) Filter: (filler ~~ '%x%'::text) (4 rows) 在所有的情形下，`Gather`或*Gather Merge*节点都只有一个子计划，它是将被并行执行的计划的一部分。如果`Gather`或*Gather Merge*节点位于计划树的最顶层，那么整个查询将并行执行。如果它位于计划树的其他位置，那么只有查询中在它之下的那一部分会并行执行。在上面的例子中，查询只访问了一个表，因此除`Gather`节点本身之外只有一个计划节点。因为该计划节点是`Gather`节点的孩子节点，所以它会并行执行。 使用 EXPLAIN命令, 你能看到规划器选择的工作者数量。当查询执行期间到达`Gather`节点时，实现用户会话的进程将会请求和规划器选中的工作者数量一样多的 后台工作者进程。规划器将考虑使用的后台工作者的数量被限制为最多 max_parallel_workers_per_gather个。任何时候能够存在的后台工作者进程的总数由 max_worker_processes和 max_parallel_workers限制。因此，一个并行查询可能会使用比规划中少的工作者来运行，甚至有可能根本不使用工作者。最优的计划可能取决于可用的工作者的数量，因此这可能会导致不好的查询性能。如果这种情况经常发生，那么就应当考虑一下提高`max_worker_processes`和`max_parallel_workers`的值，这样更多的工作者可以同时运行；或者降低`max_parallel_workers_per_gather`，这样规划器会要求少一些的工作者。 为一个给定并行查询成功启动的后台工作者进程都将会执行计划的并行部分。这些工作者的领导者也将执行该计划，不过它还有一个额外的任务：它还必须读取所有由工作者产生的元组。当整个计划的并行部分只产生了少量元组时，领导者通常将表现为一个额外的加速查询执行的工作者。反过来，当计划的并行部分产生大量的元组时，领导者将几乎全用来读取由工作者产生的元组并且执行`Gather`或`Gather Merge`节点上层计划节点所要求的任何进一步处理。在这些情况下，领导者所作的执行并行部分的工作将会很少。 当计划的并行部分的顶层节点是`Gather Merge`而不是`Gather`时，它表示每个执行计划并行部分的进程会产生有序的元组，并且领导者执行一种保持顺序的合并。相反，`Gather`会以任何方便的顺序从工作者读取元组，这会破坏可能已经存在的排序顺序。 有几种设置会导致查询规划器在任何情况下都不生成并行查询计划。为了让并行查询计划能够被生成，必须配置好下列设置。 max_parallel_workers_per_gather必须被设置为大于零的值。这是一种特殊情况，更加普遍的原则是所用的工作者数量不能超过`max_parallel_workers_per_gather`所配置的数量。 此外，系统一定不能运行在单用户模式下。因为在单用户模式下，整个数据库系统运行在单个进程中，没有后台工作者进程可用。 如果下面的任一条件为真，即便对一个给定查询通常可以产生并行查询计划，规划器都不会为它产生并行查询计划： 查询要写任何数据或者锁定任何数据库行。如果一个查询在顶层或者CTE中包含了数据修改操作，那么不会为该查询产生并行计划。一种例外是，CREATE TABLE …​ AS、`SELECT INTO`以及`CREATE MATERIALIZED VIEW`这些创建新表并填充它的命令可以使用并行计划。 查询可能在执行过程中被暂停。只要在系统认为可能发生部分或者增量式执行，就不会产生并行计划。例如：用 DECLARE CURSOR创建的游标将永远不会使用并行计划。类似地，一个`FOR x IN query LOOP .. END LOOP`形式的 PL/pgSQL 循环也永远不会使用并行计划，因为当并行查询进行时，并行查询系统无法验证循环中的代码执行起来是安全的。 使用了任何被标记为`PARALLEL UNSAFE`的函数的查询。大多数系统定义的函数都被标记为`PARALLEL SAFE`，但是用户定义的函数默认被标记为`PARALLEL UNSAFE`。参见 第 15.4 节中的讨论。 该查询运行在另一个已经存在的并行查询内部。例如，如果一个被并行查询调用的函数自己发出一个 SQL 查询，那么该查询将不会使用并行计划。这是当前实现的一个限制，但是或许不值得移除这个限制，因为它会导致单个查询使用大量的进程。 即使对于一个特定的查询已经产生了并行查询计划，在一些情况下执行时也不会并行执行该计划。如果发生这种情况，那么领导者将会自己执行该计划在`Gather`节点之下的部分，就好像`Gather`节点不存在一样。上述情况将在满足下面的任一条件时发生： 因为后台工作者进程的总数不能超过 max_worker_processes，导致不能得到后台工作者进程。 由于为并行查询目的启动的后台工作者数量不能超过 max_parallel_workers这一限制而不能得到后台工作者。 客户端发送了一个执行消息，并且消息中要求取元组的数量不为零。执行消息可见 扩展查询协议中的讨论。因为 libpq当前没有提供方法来发送这种消息，所以这种情况只可能发生在不依赖 libpq 的客户端中。如果这种情况经常发生，那在它可能发生的会话中设置 max_parallel_workers_per_gather为零是一个很好的主意，这样可以避免产生连续运行时次优的查询计划。 因为每个工作者只执行完成计划的并行部分，所以不可能简单地产生一个普通查询计划并使用多个工作者运行它。每个工作者都会产生输出结果集的一个完全，因而查询并不会比普通查询运行得更快甚至还会产生不正确的结果。相反，计划的并行部分一定被查询优化器在内部当作一个*部分计划*，即它必须被构建出来，这样每一个执行该计划的进程将以无重复地方式产生输出行的一个子集，即保证每一个所需要的输出行正好只被一个合作进程生成。通常，这意味着该查询的驱动表上的扫描必须是一种可并行的扫描。 当前支持下列可并行的表扫描。 在一个*并行顺序扫描\\*中，表块将在合作进程之间被划分。一次会分发一个块，这样对表的访问还是保持顺序方式。 在一个*并行位图堆扫描\\*中，一个进程被选为领导者。这个进程执行对一个或者多个索引的扫描并且构建出一个位图指示需要访问哪些表块。这些表块接着会在合作进程之间划分（和并行顺序扫描中一样）。换句话说，堆扫描以并行方式进行但底层的索引扫描不是并行。 在一个*并行索引扫描\\*或者*并行只用索引的扫描\\*中，合作进程轮流从索引读取数据。当前，并行索引扫描仅有B-树索引支持。每一个进程将认领一个索引块并且扫描和返回该索引块引用的所有元组，其他进程可以同时地从一个不同的索引块返回元组。并行B-树扫描的结果会以每个工作者进程内的顺序返回。 其他扫描类型（例如非B-树索引的扫描）可能会在未来支持并行扫描。 正如在非并行计划中那样，驱动表可能被使用嵌套循环、哈希连接或者归并连接连接到一个或者多个其他表。连接的内侧可以是任何类型的被规划器支持的非并行计划，假设它能够安全地在并行工作者中运行。根据连接类型，内侧还可以是一种并行计划。 在一个*嵌套循环连接\\*中，内侧总是非并行的。尽管它会被完全执行，如果内侧是一个索引扫描也会很高效，因为外侧元组以及在索引中查找值的循环会被划分到多个合作进程。 在一个*归并连接\\*中，内侧总是一个非并行计划并且因此会被完全执行。这可能是不太高效的，特别是在排序必须被执行时，因为在每一个合作进程中工作数据和结果数据是重复的。 在一个哈希连接\\（没有“并行”前缀）中，每个合作进程都会完全执行内侧以构建哈希表的相同。如果哈希表很大或者该计划开销很大，这种方式就很低效。在一个并行哈希连接\\*中，内侧是一个*并行哈希\\，它把构建共享哈希表的工作划分到多个合作进程。 IvorySQL通过按两个阶段进行聚集来支持并行聚集。首先，每个参与到查询并行部分的进程执行一个聚集步骤，为该进程注意到的每个分组产生一个部分结果。这在计划中反映为一个`Partial Aggregate`节点。然后，部分结果通过`Gather`或者`Gather Merge`被传输到领导者。最后，领导者对来自所有工作者的结果进行重新聚集得到最终的结果。这在计划中反映为一个`Finalize Aggregate`节点。 因为`Finalize Aggregate`节点运行在领导者进程上，如果查询产生的分组数相对于其输入行数来说比较大，则查询规划器不会喜欢它。例如，在最坏的情况下，`Finalize Aggregate`节点看到的分组数可能与所有工作者进程在`Partial Aggregate`阶段看到的输入行数一样多。对于这类情况，使用并行聚集显然得不到性能收益。查询规划器会在规划过程中考虑这一点并且不太会在这种情况下选择并行聚集。 并行聚集并非在所有情况下都被支持。每一个聚集都必须是对并行 安全的并且必须有一个组合函数。如果该聚集有一个类型为`internal`的转移状态，它必须有序列化和反序列化函数。更多细节请参考 CREATE AGGREGATE。如果任何聚集函数调用包含`DISTINCT`或`ORDER BY`子句，则不支持并行聚集。对于有序集聚集或者当查询涉及`GROUPING SETS`时，也不支持并行聚集。只有在查询中涉及的所有连接也是该计划并行部分的组成部分时，才能使用并行聚集。 只要当IvorySQL需要从多个源中整合行到一个单一结果集时，它会使用`Append`或`MergeAppend`计划节点。在实现`UNION ALL`或扫描分区表时常常会发生这种情况。就像这些节点可以被用在任何其他计划中一样，它们可以被用在并行计划中。不过，在并行计划中，规划器使用的是`Parallel Append`节点。 当一个`Append`节点被用在并行计划中时，每个进程将按照子计划出现的顺序执行子计划，这样所有的参与进程会合作执行第一个子计划直到它被完成，然后同时移动到第二个计划。而在使用`Parallel Append`时，执行器将把它的子计划尽可能均匀地散布在参与进程中，这样多个子计划会被同时执行。这避免了竞争，也避免了子计划在那些不执行它的进程中产生启动代价。 此外，和常规的`Append`节点不同（在并行计划中使用时仅有部分子计划），Parallel Append`节点既可以有部分子计划也可以有非部分子计划。非部分子计划将仅被单个进程扫描，因为扫描它们不止一次会产生重复的结果。因此涉及到追加多个结果集的计划即使在没有有效的部分计划可用时，也能实现粗粒度的并行。例如，考虑一个针对分区表的查询，它只能通过使用一个不支持并行扫描的索引来实现。规划器可能会选择常规`Index Scan`计划的`Parallel Append。每个索引扫描必须被单一的进程执行完，但不同的扫描可以由不同的进程同时执行。 enable_parallel_append可以被用来禁用这种特性。 如果我们想要一个查询能产生并行计划但事实上又没有产生，可以尝试减小 parallel_setup_cost或者 parallel_tuple_cost。当然，这个计划可能比规划器优先产生的顺序计划还要慢，但也不总是如此。如果将这些设置为很小的值（例如把它们设置为零）也不能得到并行计划，那就可能是有某种原因导致查询规划器无法为你的查询产生并行计划。可能的原因可见 第 15.2 节和 第 15.4 节。 在执行一个并行计划时，可以用`EXPLAIN (ANALYZE,VERBOSE)`来显示每个计划节点在每个工作者上的统计信息。这些信息有助于确定是否所有的工作被均匀地分发到所有计划节点以及从总体上理解计划的性能特点。 ABORT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ABORT`回滚当前事务并且导致由该事务所作的所有更新被丢弃。这个命令的行为与标准SQL命令 `ROLLBACK的行为一样，并且只是为了历史原因存在。 WORK TRANSACTION 可选关键词。它们没有效果。 AND CHAIN 如果规定了`AND CHAIN` ，新事务立即启动，具有与刚刚完成的事务相同的事务特征(参见 http://www.postgresql.org/docs/17/sql-set-transaction.html[`SET TRANSACTION`])。否则，不会启动新事务。 使用 COMMIT成功地终止一个事务。 在一个事务块之外发出`ABORT`会发出一个警告消息并且不会产生效果。 中止所有更改： ABORT; 这个命令是一个因为历史原因而存在的IvorySQL扩展。`ROLLBACK`是等效的标准SQL命令。 BEGIN [ WORK | TRANSACTION ] [ transaction_mode [, ...] ] 其中 transaction_mode 是以下之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE BEGIN 开始一个事务块，也就是说所有 BEGIN 命令之后的所有语句将被在一个事务中执行，直到给出一个显式的 COMMIT或者 ROLLBACK。默认情况下（没有 BEGIN ），IvorySQL在“自动提交”模式中执行事务，也就是说每个语句都在自己的事务中执行并且在语句结束时隐式地执行一次提交（如果执行成功，否则会完成一次回滚）。 在一个事务块内的语句会执行得更快，因为事务的开始/提交也要求可观的CPU和磁盘活动。在进行多个相关更改时，在一个事务内执行多个语句也有助于保证一致性：在所有相关更新还没有完成之前，其他会话将不能看到中间状态。 如果指定了隔离级别、读/写模式或者延迟模式，新事务也会有那些特性，就像执行了 SET TRANSACTION一样。 WORK``TRANSACTION 可选的关键词。它们没有效果。 这个语句其他参数的含义请参考 SET TRANSACTION。 START TRANSACTION具有和`BEGIN` 相同的功能。 使用 COMMIT或者 ROLLBACK来终止一个事务块。 在已经在一个事务块中时发出`BEGIN`将惹出一个警告消息。事务状态不会被影响。要在一个事务块中嵌套事务，可以使用保存点（见 SAVEPOINT）。 由于向后兼容的原因，连续的 transaction_modes 之间的逗号可以被省略。 开始一个事务块： BEGIN; BEGIN 是一种IvorySQL语言扩展。它等效于SQL标准的命令 START TRANSACTION，它的参考页包含额外的兼容性信息。 DEFERRABLE transaction_mode 是一种IvorySQL语言扩展。 附带地，`BEGIN`关键词被用于嵌入式SQL中的一种不同目的。在移植数据库应用时，我们建议小心对待事务语义。 `COMMIT`提交当前事务。所有由该事务所作的更改会变得对他人可见并且被保证在崩溃发生时仍能持久。 WORK TRANSACTION 可选的关键词。它们没有效果。 AND CHAIN 如果指定了 AND CHAIN，则立即启动与刚刚完成的事务具有相同事务特征（参见 SET TRANSACTION）的新事务。否则，没有新事务被启动。 使用 ROLLBACK中止一个事务。 当不在一个事务内时发出 COMMIT 不会产生危害，但是它会产生一个警告消息。当`COMMIT AND CHAIN`不在事务内时是一个错误。 要提交当前事务并且让所有更改持久化： COMMIT; 命令 `COMMIT`符合SQL标准。表单`COMMIT TRANSACTION`为IvorySQL扩展。 COMMIT PREPARED transaction_id `COMMIT PREPARED`提交一个处于预备状态的事务。 transaction_id 要被提交的事务的事务标识符。 要提交一个预备的事务，你必须是原先执行该事务的同一用户或者超级用户。但是不需要处于执行该事务的同一会话中。 这个命令不能在一个事务块中执行。该预备事务将被立刻提交。 pg_prepared_xacts系统视图中列出了所有当前可用的预备事务。 提交由事务标识符`foobar`标识的事务： COMMIT PREPARED 'foobar'; COMMIT PREPARED 是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 END [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] END 提交当前事务。所有该事务做的更改便得对他人可见并且被保证发生崩溃时仍然是持久的。这个命令是一种IvorySQL扩展，它等效于 COMMIT。 WORK TRANSACTION 可选关键词，它们没有效果。 AND CHAIN 如果规定了`AND CHAIN`，则立即启动与刚完成事务具有相同事务特征(参见 SET TRANSACTION)的新事务。否则，没有新事务被启动。 使用 ROLLBACK可以中止一个事务。 当不在一个事务中时发出`END`没有危害，但是会产生一个警告消息。 要提交当前事务并且让所有更改持久化： END; END 是一种IvorySQL扩展，它提供和 COMMIT等效的功能，后者在SQL标准中指定。 PREPARE TRANSACTION transaction_id `PREPARE TRANSACTION`为两阶段提交准备当前事务。在这个命令之后，该事务不再与当前会话关联。相反，它的状态被完全存储在磁盘上，并且有很高的可能性它会被提交成功（即便在请求提交前发生数据库崩溃）。 一旦被准备好，事务稍后就可以分别用 COMMIT PREPARED或者 ROLLBACK PREPARED提交或者回滚。可以从任何会话而不仅仅是执行原始事务的会话中发出这些命令。 从发出命令的会话的角度来看，`PREPARE TRANSACTION`不像`ROLLBACK`命令：在执行它之后，就没有活跃的当前事务，并且该预备事务的效果也不再可见（如果该事务被提交，效果将重新变得可见）。 如果由于任何原因`PREPARE TRANSACTION`命令失败，它会变成一个`ROLLBACK`：当前事务会被取消。 transaction_id 一个任意的事务标识符，`COMMIT PREPARED`或者`ROLLBACK PREPARED`以后将用这个标识符来标识这个事务。该标识符必须写成一个字符串,并且长度必须小于200字节。它也不能与任何当前已经准备好的事务的标识符相同。 PREPARE TRANSACTION`并不是设计为在应用或者交互式会话中使用。它的目的是允许一个外部事务管理器在多个数据库或者其他事务性来源之间执行原子的全局事务。除非你在编写一个事务管理器，否则你可能不会用到`PREPARE TRANSACTION。 这个命令必须在一个事务块中使用。事务块用 BEGIN开始。 当前在已经执行过任何涉及到临时表或者会话的临时命名空间、创建带`WITH HOLD`的游标或者执行`LISTEN`、`UNLISTEN`或 `NOTIFY`的事务中，不允许`PREPARE`该事务。这些特性与当前会话绑定得太过紧密，所以对一个要被准备的事务来说没有什么用处。 如果用`SET`（不带`LOCAL`选项）修改过事务的任何运行时参数，这些效果会持续到`PREPARE TRANSACTION`之后，并且将不会被后续的任何`COMMIT PREPARED`或`ROLLBACK PREPARED`所影响。因此，在这一 方面`PREPARE TRANSACTION`的行为更像`COMMIT`而不是`ROLLBACK`。 所有当前可用的准备好事务被列在 pg_prepared_xacts系统视图中。 让一个事务处于准备好状态太久是不明智的。这将会干扰`VACUUM`回收存储的能力，并且在极限情况下可能导致数据库关闭以阻止事务ID回卷（见 第 25.1.5 节）。还要记住，该事务会继续持有它已经持有的锁。该特性的设计用法是，只要一个外部事务管理器已经验证其他数据库也准备好了要提交，一个准备好的事务将被正常地提交或者回滚。 如果没有建立一个外部事务管理器来跟踪准备好的事务并且确保它们被迅速地结束，最好禁用准备好事务特性（设置 max_prepared_transactions为零）。这将防止意外地创建准备好事务，不然该事务有可能被忘记并且最终导致问题。 为两阶段提交准备当前事务，使用`foobar`作为事务标识符： PREPARE TRANSACTION 'foobar'; `PREPARE TRANSACTION`是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 ROLLBACK [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] `ROLLBACK`回滚当前事务并且导致该事务所作的所有更新都被抛弃。 WORK TRANSACTION 可选关键词，没有效果。 AND CHAIN 如果指定了`AND CHAIN`，则立即启动与刚刚完成事务具有相同事务特征（参见 SET TRANSACTION）的新事务。否则，不会启动任何新事务。 使用 COMMIT可成功地终止一个事务。 在一个事务块之外发出`ROLLBACK`会发出一个警告并且不会有效果。事务块之外的`ROLLBACK AND CHAIN`是一个错误。 要中止所有更改： ROLLBACK; 命令`ROLLBACK`符合SQL标准。窗体`ROLLBACK TRANSACTION`是一个IvorySQL扩展。 ROLLBACK PREPARED transaction_id `ROLLBACK PREPARED`回滚一个处于准备好状态的事务。 transaction_id 要被回滚的事务的事务标识符。 要回滚一个准备好的事务，你必须是原先执行该事务的同一个用户或者是一个超级用户。但是你必须处在执行该事务的同一个会话中。 这个命令不能在一个事务块内被执行。准备好的事务会被立刻回滚。 pg_prepared_xacts系统视图中列出了当前可用的所有准备好的事务。 用事务标识符`foobar`回滚对应的事务： ROLLBACK PREPARED 'foobar'; `ROLLBACK PREPARED`是一种IvorySQL扩展。其意图是用于外部事务管理系统，其中有些已经被标准涵盖（例如 X/Open XA），但是那些系统的SQL方面未被标准化。 SAVEPOINT savepoint_name `SAVEPOINT`在当前事务中建立一个新保存点。 保存点是事务内的一种特殊标记，它允许所有在它被建立之后执行的命令被回滚，把该事务的状态恢复到它处于保存点时的样子。 savepoint_name 给新保存点的名字。 使用 ROLLBACK TO回滚到一个保存点。使用 RELEASE SAVEPOINT销毁一个保存点，但保持在它被建立之后执行的命令的效果。 保存点只能在一个事务块内建立。可以在一个事务内定义多个保存点。 要建立一个保存点并且后来撤销在它建立之后执行的所有命令的效果： BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); ROLLBACK TO SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); COMMIT; 上面的事务将插入值 1 和 3，但不会插入 2。 要建立并且稍后销毁一个保存点： BEGIN; INSERT INTO table1 VALUES (3); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (4); RELEASE SAVEPOINT my_savepoint; COMMIT; 上面的事务将插入 3 和 4。 当建立另一个同名保存点时，SQL要求之前的那个保存点自动被销毁。在IvorySQL中，旧的保存点会被保留，不过在进行回滚或释放时只能使用最近的那一个（用 `RELEASE SAVEPOINT`释放较新的保存点将会导致较旧的保存点再次变得可以被`ROLLBACK TO SAVEPOINT`和`RELEASE SAVEPOINT`访问）。在其他方面，`SAVEPOINT`完全符合SQL。 SET CONSTRAINTS { ALL | name [, ...] } { DEFERRED | IMMEDIATE } `SET CONSTRAINTS`设置当前事务内约束检查的行为。`IMMEDIATE`约束在每个语句结束时被检查。`DEFERRED`约束直到事务提交时才被检查。每个约束都有自己的`IMMEDIATE`或`DEFERRED`模式。 在创建时，一个约束会被给定三种特性之一：DEFERRABLE INITIALLY DEFERRED、DEFERRABLE INITIALLY IMMEDIATE`或者`NOT DEFERRABLE。第三类总是`IMMEDIATE`并且不会受到`SET CONSTRAINTS`命令的影响。前两类在每个事务开始时都处于指定的模式，但是它们的行为可以在一个事务内用`SET CONSTRAINTS`更改。 带有一个约束名称列表的`SET CONSTRAINTS`只更改那些约束（都必须是可延迟的）的模式。每一个约束名称都可以是模式限定的。如果没有指定模式名称，则当前的模式搜索路径将被用来寻找第一个匹配的名称。`SET CONSTRAINTS ALL`更改所有可延迟约束的模式。 当`SET CONSTRAINTS`把一个约束的模式从`DEFERRED`改成`IMMEDIATE`时，新模式会有追溯效果：任何还没有解决的数据修改（本来会在事务结束时被检查）会转而在`SET CONSTRAINTS`命令的执行期间被检查。如果任何这种约束被违背，SET CONSTRAINTS`将会失败（并且不会改变该约束模式）。这样，`SET CONSTRAINTS 可以被用来在一个事务中的特定点强制进行约束检查。 当前，只有`UNIQUE`、PRIMARY KEY、REFERENCES（外键）以及`EXCLUDE`约束受到这个设置的影响。`NOT NULL`以及`CHECK`约束总是在一行被插入或修改时立即检查（不是在语句结束时）。没有被声明为`DEFERRABLE`的唯一和排除约束也会被立刻检查。 被声明为“约束触发器”的触发器的引发也受到这个设置的控制 — 它们会在相关约束被检查的同时被引发。 因为IvorySQL并不要求约束名称在模式内唯一（但是在表内要求唯一），可能有多于一个约束匹配指定的约束名称。在这种情况下`SET CONSTRAINTS`将会在所有的匹配上操作。对于一个非模式限定的名称，一旦在搜索路径中的某个模式中发现一个或者多个匹配，路径中后面的模式将不会被搜索。 这个命令只修改当前事务内约束的行为。在事务块外部发出这个命令会产生一个警告并且也不会有任何效果。 这个命令符合SQL标准中定义的行为，但有一点限制：在 IvorySQL中，它不会应用在`NOT NULL`和`CHECK`约束上。还有，IvorySQL会立刻检查非可延迟的唯一约束，而不是按照标准建议的在语句结束时检查。 SET TRANSACTION transaction_mode [, ...] SET TRANSACTION SNAPSHOT snapshot_id SET SESSION CHARACTERISTICS AS TRANSACTION transaction_mode [, ...] 其中transaction_mode是下列之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE `SET TRANSACTION`命令设置当前会话的特性。`SET SESSION CHARACTERISTICS`设置一个会话后续事务的默认事务特性。在个体事务中可以用`SET TRANSACTION`覆盖这些默认值。 可用的事务特性是事务隔离级别、事务访问模式（读/写或只读）以及可延迟模式。此外，可以选择一个快照，不过只能用于当前事务而不能作为会话默认值。 一个事务的隔离级别决定当其他事务并行运行时该事务能看见什么数据： READ COMMITTED 一个语句只能看到在它开始前提交的行。这是默认值。 REPEATABLE READ 当前事务的所有语句只能看到这个事务中执行的第一个查询或者数据修改语句之前提交的行。 SERIALIZABLE 当前事务的所有语句只能看到这个事务中执行的第一个查询或者数据修改语句之前提交的行。如果并发的可序列化事务间的读写模式可能导致一种那些事务串行（一次一个）执行时不可能出现的情况，其中之一将会被回滚并且得到一个 `serialization_failure`错误。 SQL标准定义了一种额外的级别：READ UNCOMMITTED。在IvorySQL中`READ UNCOMMITTED`被视作`READ COMMITTED`。 一个事务执行了第一个查询或者数据修改语句（SELECT、INSERT、DELETE、UPDATE、FETCH`或`COPY）之后就无法更改事务隔离级别。更多有关事务隔离级别和并发控制的信息可见 第 13 章。 事务的访问模式决定该事务是否为读/写或者只读。读/写是默认值。当一个事务为只读时，如果SQL命令`INSERT`、UPDATE、DELETE`和`COPY FROM`要写的表不是一个临时表，则它们不被允许。不允许`CREATE、ALTER`以及`DROP`命令。不允许`COMMENT、GRANT、REVOKE、TRUNCATE。如果`EXPLAIN ANALYZE`和`EXECUTE`要执行的命令是上述命令之一，则它们也不被允许。这是一种高层的只读概念，它不能阻止所有对磁盘的写入。 只有事务也是`SERIALIZABLE`以及`READ ONLY`时，`DEFERRABLE`事务属性才会有效。当一个事务的所有这三个属性都被选择时，该事务在第一次获取其快照时可能会阻塞，在那之后它运行时就不会有`SERIALIZABLE`事务的开销并且不会有任何牺牲或者被一次序列化失败取消的风险。这种模式很适合于长时间运行的报表或者备份。 SET TRANSACTION SNAPSHOT`命令允许新的事务使用与一个现有事务相同的*快照*运行。已经存在的事务必须已经把它的快照用`pg_export_snapshot 函数（见 第 9.27.5 节）导出。该函数会返回一个快照标识符，SET TRANSACTION SNAPSHOT`需要被给定一个快照标识符来指定要导入的快照。在这个命令中该标识符必须被写成一个字符串，例如’000003A1-1'。SET TRANSACTION SNAPSHOT`只能在一个事务的开始执行，并且要在该事务的第一个查询或者数据修改语句（`SELECT、INSERT、DELETE、UPDATE、FETCH`或 `COPY）之前执行。此外，该事务必须已经被设置为`SERIALIZABLE`或者`REPEATABLE READ`隔离级别（否则，该快照将被立刻抛弃，因为`READ COMMITTED`模式会为每一个命令取一个新快照）。如果导入事务使用了`SERIALIZABLE`隔离级别，那么导入快照的事务必须也使用该隔离级别。还有，一个非只读可序列化事务不能导入来自只读事务的快照。 如果执行`SET TRANSACTION`之前没有`START TRANSACTION`或者`BEGIN`，它会发出一个警告并且不会有任何效果。 可以通过在`BEGIN`或者`START TRANSACTION`中指定想要的`transaction_modes来省掉`SET TRANSACTION。但是在`SET TRANSACTION SNAPSHOT`中该选项不可用。 会话默认的事务模式也可以通过配置参数 default_transaction_isolation、http://www.postgresql.org/docs/17/runtime-config-client.html#GUC-DEFAULT-TRANSACTION-READ-ONLY[default_transaction_read_only]和 default_transaction_deferrable来设置或检查（实际上`SET SESSION CHARACTERISTICS`只是用`SET`设置这些变量的等效体）。这意味着可以通过配置文件、ALTER DATABASE 等方式设置默认值。详见 第 20 章。 当前事务的模式可以类似的通过配置参数 transaction_isolation、 transaction_read_only、和 transaction_deferrable来设置或检查。设置这其中一个参数的作用与相应的`SET TRANSACTION`选项相同，在它何时可以完成方面，也有相同的限制。但是，这些参数不能在配置文件中设置，或者从活动SQL以外的任何来源来设置。 要用一个已经存在的事务的同一快照开始一个新事务，首先要从该现有事务导出快照。这将会返回快照标识符，例如： BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SELECT pg_export_snapshot(); pg_export_snapshot --------------------- 00000003-0000001B-1 (1 row) 然后在一个新开始的事务的开头把该快照标识符用在一个`SET TRANSACTION SNAPSHOT`命令中： BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SET TRANSACTION SNAPSHOT '00000003-0000001B-1'; SQL标准中定义了这些命令，不过`DEFERRABLE`事务模式和`SET TRANSACTION SNAPSHOT`形式除外，这两者是IvorySQL扩展。 SERIALIZABLE`是标准中默认的事务隔离级别。在IvorySQL中默认值是普通的`READ COMMITTED，但是你可以按上述的方式更改。 在SQL标准中，可以用这些命令设置一个其他的事务特性：诊断区域的尺寸。这个概念与嵌入式SQL有关，并且因此没有在IvorySQL服务器中实现。 SQL标准要求连续的 transaction_modes 之间有逗号，但是出于历史原因IvorySQL允许省略逗号。 START TRANSACTION [ transaction_mode [, ...] ] 其中 transaction_mode 是下列之一： ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE 这个命令开始一个新的事务块。如果指定了隔离级别、读写模式或者可延迟模式，新的事务将会具有这些特性，就像执行了 SET TRANSACTION一样。这和 BEGIN命令一样。 这些参数对于这个语句的含义可参考 SET TRANSACTION。 在标准中，没有必要发出`START TRANSACTION`来开始一个事务块：任何SQL命令会隐式地开始一个块。IvorySQL的行为可以被视作在每个命令之后隐式地发出一个没有跟随在`START TRANSACTION`（或者`BEGIN`）之后的`COMMIT`并且因此通常被称作 “自动提交”。为了方便，其他关系型数据库系统也可能会提供自动提交特性。 DEFERRABLE transaction_mode 是一种IvorySQL语言扩展。 SQL标准要求在连续的 transaction_modes 之间有逗号，但是由于历史原因IvorySQL允许省略逗号。 SQL输入由一个 命令 序列组成。一个命令由一个 记号 的序列构成，并由一个分号（“;”）终结。输入流的末端也会标志一个命令的结束。具体哪些记号是合法的与具体命令的语法有关。 一个记号可以是一个 关键词、一个 标识符、一个 带引号的标识符、一个 literal（或常量）或者一个特殊字符符号。记号通常以空白（空格、制表符、新行）来分隔，但在无歧义时并不强制要求如此（唯一的例子是一个特殊字符紧挨着其他记号）。 例如，下面是一个（语法上）合法的SQL输入： SELECT * FROM MY_TABLE; UPDATE MY_TABLE SET A = 5; INSERT INTO MY_TABLE VALUES (3, 'hi there'); 这是一个由三个命令组成的序列，每一行一个命令（尽管这不是必须地，在同一行中可以有超过一个命令，而且命令还可以被跨行分割）。 另外，注释 也可以出现在SQL输入中。它们不是记号，它们和空白完全一样。 根据标识命令、操作符、参数的记号不同，SQL的语法不很一致。最前面的一些记号通常是命令名，因此在上面的例子中我们通常会说一个“SELECT”、一个“UPDATE”和一个“INSERT”命令。但是例如`UPDATE`命令总是要求一个`SET`记号出现在一个特定位置，而`INSERT`则要求一个`VALUES`来完成命令。每个命令的精确语法规则在 第 VI 部分中介绍。 上例中的`SELECT`、`UPDATE`或`VALUES`记号是 关键词 的例子，即SQL语言中具有特定意义的词。记号`MY_TABLE`和`A`则是 标识符 的例子。它们标识表、列或者其他数据库对象的名字，取决于使用它们的命令。因此它们有时也被简称为“名字”。关键词和标识符具有相同的词法结构，这意味着我们无法在没有语言知识的前提下区分一个标识符和关键词。一个关键词的完整列表可以在 附录 C中找到。 SQL标识符和关键词必须以一个字母（a-z，也可以是带变音符的字母和非拉丁字母）或一个下划线（ _ ）开始。后续字符可以是字母、下划线（_）、数字（0-9）或美元符号（$）。注意根据SQL标准的字母规定，美元符号是不允许出现在标识符中的，因此它们的使用可能会降低应用的可移植性。SQL标准不会定义包含数字或者以下划线开头或结尾的关键词，因此这种形式的标识符不会与未来可能的标准扩展冲突。 系统中一个标识符的长度不能超过`NAMEDATALEN`-1字节，在命令中可以写超过此长度的标识符，但是它们会被截断。默认情况下，`NAMEDATALEN`的值为64，因此标识符的长度上限为63字节。如果这个限制有问题，可以在`src/include/pg_config_manual.h`中修改`NAMEDATALEN`常量。 关键词和不被引号修饰的标识符是大小写不敏感的。因此： UPDATE MY_TABLE SET A = 5; 可以等价地写成： uPDaTE my_TabLE SeT a = 5; 一个常见的习惯是将关键词写成大写，而名称写成小写，例如： UPDATE my_table SET a = 5; 这里还有第二种形式的标识符：受限标识符*或*被引号修饰的标识符。它是由双引号（\"）包围的一个任意字符序列。一个受限标识符总是一个标识符而不会是一个关键字。因此`\"select\"`可以用于引用一个名为“select”的列或者表，而一个没有引号修饰的`select`则会被当作一个关键词，从而在本应使用表或列名的地方引起解析错误。在上例中使用受限标识符的例子如下： UPDATE \"my_table\" SET \"a\" = 5; 受限标识符可以包含任何字符，除了代码为0的字符（如果要包含一个双引号，则写两个双引号）。这使得可以构建原本不被允许的表或列的名称，例如包含空格或花号的名字。但是长度限制依然有效。 引用标识符也使其区分大小写，而未引用的名称总是折叠成小写。例如，标识符`FOO`、foo`和`\"foo\"`在IvorySQL中被认为是相同的，但是`\"Foo\"`和`\"FOO\"`与这三个不同，并且彼此不同。（在IvorySQL中，将不带引号的名称折叠为小写与SQL标准不兼容，SQL标准规定不带引号的名称应折叠为大写。因此，根据标准，`foo`应等同于`\"FOO\"`而不是`\"foo\"。如果您想编写可移植应用程序，建议您始终引用某个特定的名称，或者永远不要引用它。） 一种受限标识符的变体允许包括转义的用代码点标识的Unicode字符。这种变体以`U&`（大写或小写U跟上一个花号）开始，后面紧跟双引号修饰的名称，两者之间没有任何空白，如`U&\"foo\"`（注意这里与操作符`&`似乎有一些混淆，但是在`&`操作符周围使用空白避免了这个问题）。在引号内，Unicode字符可以以转义的形式指定：反斜线接上4位16进制代码点号码或者反斜线和加号接上6位16进制代码点号码。例如，标识符`\"data\"`可以写成： U&\"d\\0061t\\+000061\" 下面的例子用斯拉夫语字母写出了俄语单词“slon”（大象）： U&\"\\0441\\043B\\043E\\043D\" 如果希望使用其他转义字符来代替反斜线，可以在字符串后使用`UESCAPE`子句，例如： U&\"d!0061t!+000061\" UESCAPE '!' 转义字符可以是除了16进制位、加号、单引号、双引号、空白字符之外的任意单个字符。请注意，转义字符在 UESCAPE 之后用单引号而不是双引号书写。 为了在标识符中包括转义字符本身，将其写两次即可。 4位或6位转义形式都可以被用来定义UTF-16代理对来组成代码点大于U+FFFF的字符，尽管6位形式的存在使得这种做法变得不必要（代理对并不被直接存储，而是绑定成一个单独的代码点）。 如果服务器编码不是UTF-8，则由其中一个转义序列标识的Unicode代码点转换为实际的服务器编码；如果不可能，则报告错误。 在IvorySQL中有三种 隐式类型常量：字符串、位串和数字。常量也可以被指定显式类型，这可以使得它被更精确地展示以及更有效地处理。这些选择将会在后续小节中讨论。 在SQL中，一个字符串常量是一个由单引号（ ' ）包围的任意字符序列，例如’This is a string'`。为了在一个字符串中包括一个单引号，可以写两个相连的单引号，例如’Dianne''s horse'`。注意这和一个双引号（\"）不同。 两个只由空白及至少一个新行分隔的字符串常量会被连接在一起，并且将作为一个写在一起的字符串常量来对待。例如： SELECT 'foo' 'bar'; 等同于： SELECT 'foobar'; 但是： SELECT 'foo' 'bar'; 则不是合法的语法（这种有些奇怪的行为是SQL指定的，IvorySQL遵循了该标准）。 IvorySQL也接受“转义”字符串常量，这也是SQL标准的一个扩展。一个转义字符串常量可以通过在开单引号前面写一个字母`E`（大写或小写形式）来指定，例如`E’foo'`（当一个转义字符串常量跨行时，只在第一个开引号之前写`E`）。在一个转义字符串内部，一个反斜线字符（\\）会开始一个C风格的 反斜线转义 序列，在其中反斜线和后续字符的组合表示一个特殊的字节值（如 表 4.1中所示）。 表 4.1. 反斜线转义序列 反斜线转义序列 解释 \\b 退格 \\f 换页 \\n 换行 \\r 回车 \\t 制表符 *`o*, `*`oo*, `*`ooo*` (o = 0–7) 八进制字节值 \\x*`h, `\\x`hh` (h* = 0–9, A–F) 十六进制字节值 \\u*`xxxx, `\\U`xxxxxxxx` (x* = 0–9, A–F) 16 或 32-位十六进制 Unicode 字符值 跟随在一个反斜线后面的任何其他字符被当做其字面意思。因此，要包括一个反斜线字符，请写两个反斜线（\\\\）。在一个转义字符串中包括一个单引号除了普通方法’'`之外，还可以写成`\\'`。 你要负责保证你创建的字节序列由服务器字符集编码中合法的字符组成，特别是在使用八进制或十六进制转义时。一个有用的替代方法是使用Unicode转义或替代的Unicode转义语法，如 第 4.1.2.3 节中所述；然后服务器将检查字符转换是否可行。 小心 如果配置参数 standard_conforming_strings为`off`，那么IvorySQL对常规字符串常量和转义字符串常量中的反斜线转义都识别。不过，在IvorySQL中该参数的默认值为`on`，意味着只在转义字符串常量中识别反斜线转义。这种行为更兼容标准，但是可能打断依赖于历史行为（反斜线转义总是会被识别）的应用。作为一种变通，你可以设置该参数为`off`，但是最好迁移到符合新的行为。如果你需要使用一个反斜线转义来表示一个特殊字符，为该字符串常量写上一个`E`。在`standard_conforming_strings`之外，配置参数 escape_string_warning和 backslash_quote也决定了如何对待字符串常量中的反斜线。代码零的字符不能出现在一个字符串常量中。 IvorySQL也支持另一种类型的字符串转义语法，它允许用代码点指定任意Unicode字符。一个Unicode转义字符串常量开始于`U&`（大写或小写形式的字母U，后跟花号），后面紧跟着开引号，之间没有任何空白，例如`U&'foo'`（注意这产生了与操作符`&`的混淆。在操作符周围使用空白来避免这个问题）。在引号内，Unicode字符可以通过写一个后跟4位十六进制代码点编号或者一个前面有加号的6位十六进制代码点编号的反斜线来指定。例如，字符串’data'`可以被写为 U&'d\\0061t\\+000061' 下面的例子用斯拉夫字母写出了俄语的单词“slon”（大象）： U&'\\0441\\043B\\043E\\043D' 如果想要一个不是反斜线的转义字符，可以在字符串之后使用`UESCAPE`子句来指定，例如： U&'d!0061t!+000061' UESCAPE '!' 转义字符可以是出一个十六进制位、加号、单引号、双引号或空白字符之外的任何单一字符。 要在一个字符串中包括一个表示其字面意思的转义字符，把它写两次。 4位或6位转义形式可用于指定UTF-16代理项对，以组成代码点大于U+FFFF的字符，尽管从技术上讲，6位形式的可用性使得这是不必要的（代理项对不是直接存储的，而是合并到单个代码点中。） 如果服务器编码不是UTF-8，则由这些转义序列之一标识的Unicode代码点将转换为实际的服务器编码；如果不可能，则会报告错误。 此外，字符串常量的Unicode转义语法仅在配置参数 standard_conforming_strings开启时才有效。这是因为否则这种语法可能会混淆解析SQL语句的客户端，可能导致SQL注入和类似的安全问题。如果该参数设置为off，则此语法将被拒绝并显示错误消息。 虽然用于指定字符串常量的标准语法通常都很方便，但是当字符串中包含了很多单引号或反斜线时很难理解它，因为每一个都需要被双写。要在这种情形下允许可读性更好的查询，IvorySQL提供了另一种被称为“美元引用”的方式来书写字符串常量。一个美元引用的字符串常量由一个美元符号（$）、一个可选的另个或更多字符的“标签”、另一个美元符号、一个构成字符串内容的任意字符序列、一个美元符号、开始这个美元引用的相同标签和一个美元符号组成。例如，这里有两种不同的方法使用美元引用指定字符串“Dianne’s horse”： $$Dianne's horse$$ $SomeTag$Dianne's horse$SomeTag$ 注意在美元引用字符串中，单引号可以在不被转义的情况下使用。事实上，在一个美元引用字符串中不需要对字符进行转义：字符串内容总是按其字面意思写出。反斜线不是特殊的，并且美元符号也不是特殊的，除非它们是匹配开标签的一个序列的一部分。 可以通过在每一个嵌套级别上选择不同的标签来嵌套美元引用字符串常量。这最常被用在编写函数定义上。例如： $function$ BEGIN RETURN ($1 ~ $q$[\\t\\r\\n\\v\\\\]$q$); END; $function$ 这里，序列`$q$[\\t\\r\\n\\v\\\\]$q$表示一个美元引用的文字串[\\t\\r\\n\\v\\\\]，当该函数体被IvorySQL执行时它将被识别。但是因为该序列不匹配外层的美元引用的定界符$function$`，它只是一些在外层字符串所关注的常量中的字符而已。 一个美元引用字符串的标签（如果有）遵循一个未被引用标识符的相同规则，除了它不能包含一个美元符号之外。标签是大小写敏感的，因此`$tag$String content$tag$是正确的，但是$TAG$String content$tag$`不正确。 一个跟着一个关键词或标识符的美元引用字符串必须用空白与之分隔开，否则美元引用定界符可能会被作为前面标识符的一部分。 美元引用不是SQL标准的一部分，但是在书写复杂字符串文字方面，它常常是一种比兼容标准的单引号语法更方便的方法。当要表示的字符串常量位于其他常量中时它特别有用，这种情况常常在过程函数定义中出现。如果用单引号语法，上一个例子中的每个反斜线将必须被写成四个反斜线，这在解析原始字符串常量时会被缩减到两个反斜线，并且接着在函数执行期间重新解析内层字符串常量时变成一个。 位串常量看起来像常规字符串常量在开引号之前（中间无空白）加了一个`B`（大写或小写形式），例如`B'1001'`。位串常量中允许的字符只有`0`和`1`。 作为一种选择，位串常量可以用十六进制记号法指定，使用一个前导`X`（大写或小写形式）,例如`X'1FF'`。这种记号法等价于一个用四个二进制位取代每个十六进制位的位串常量。 两种形式的位串常量可以以常规字符串常量相同的方式跨行继续。美元引用不能被用在位串常量中。 在这些一般形式中可以接受数字常量： digits digits.[digits][e[+-]digits] [digits].digits[e[+-]digits] digitse[+-]digits 其中 digits 是一个或多个十进制数字（0到9）。如果使用了小数点，在小数点前面或后面必须至少有一个数字。如果存在一个指数标记（e），在其后必须跟着至少一个数字。在该常量中不能嵌入任何空白或其他字符。注意任何前导的加号或减号并不实际被考虑为常量的一部分，它是一个应用到该常量的操作符。 这些是合法数字常量的例子： 42 3.5 4. .001 5e2 1.925e-3 如果一个不包含小数点和指数的数字常量的值适合类型`integer`（32 位），它首先被假定为类型`integer`。否则如果它的值适合类型`bigint`（64 位），它被假定为类型`bigint`。再否则它会被取做类型`numeric`。包含小数点和/或指数的常量总是首先被假定为类型`numeric`。 一个数字常量初始指派的数据类型只是类型转换算法的一个开始点。在大部分情况中，常量将被根据上下文自动被强制到最合适的类型。必要时，你可以通过造型它来强制一个数字值被解释为一种指定数据类型。例如，你可以这样强制一个数字值被当做类型`real`（float4）： REAL '1.23' -- string style 1.23::REAL -- IvorySQL (historical) style 这些实际上只是接下来要讨论的一般造型记号的特例。 一种任意类型的一个常量可以使用下列记号中的任意一种输入： type 'string' 'string'::type CAST ( 'string' AS type ) 字符串常量的文本被传递到名为 type 的类型的输入转换例程中。其结果是指定类型的一个常量。如果对该常量的类型没有歧义（例如，当它被直接指派给一个表列时），显式类型造型可以被忽略，在那种情况下它会被自动强制。 字符串常量可以使用常规SQL记号或美元引用书写。 也可以使用一个类似函数的语法来指定一个类型强制： typename ( 'string' ) 但是并非所有类型名都可以用在这种方法中，详见 第 4.2.9 节。 如 第 4.2.9 节中讨论的，::、CAST()`以及函数调用语法也可以被用来指定任意表达式的运行时类型转换。要避免语法歧义，type 'string'语法只能被用来指定简单文字常量的类型。type 'string'` 语法上的另一个限制是它无法对数组类型工作，指定一个数组常量的类型可使用`::`或`CAST()`。 CAST()`语法符合SQL。`type 'string'`语法是该标准的一般化：SQL指定这种语法只用于一些数据类型，但是IvorySQL允许它用于所有类型。带有::`的语法是IvorySQL的历史用法，就像函数调用语法一样。 一个操作符名是最多`NAMEDATALEN`-1（默认为63）的一个字符序列，其中的字符来自下面的列表： \\+ - * / < > = ~ ! @ # % ^ & | ` ? 不过，在操作符名上有一些限制： --`和/*`不能在一个操作符名的任何地方出现，因为它们将被作为一段注释的开始。 一个多字符操作符名不能以`+或-`结尾，除非该名称也至少包含这些字符中的一个： ~ ! @ # % ^ & | ` ? 例如，@-`是一个被允许的操作符名，但*-`不是。这些限制允许IvorySQL解析SQL兼容的查询而不需要在记号之间有空格。 当使用非SQL标准的操作符名时，你通常需要用空格分隔相邻的操作符来避免歧义。例如，如果你定义了一个名为`@的前缀操作符，你不能写`X*@Y，你必须写`X* @Y`来确保IvorySQL把它读作两个操作符名而不是一个。 一些不是数字字母的字符有一种不同于作为操作符的特殊含义。这些字符的详细用法可以在描述相应语法元素的地方找到。这一节只是为了告知它们的存在以及总结这些字符的目的。 跟随在一个美元符号（ $ ）后面的数字被用来表示在一个函数定义或一个预备语句中的位置参数。在其他上下文中该美元符号可以作为一个标识符或者一个美元引用字符串常量的一部分。 圆括号（ () ）具有它们通常的含义，用来分组表达式并且强制优先。在某些情况中，圆括号被要求作为一个特定 SQL 命令的固定语法的一部分。 方括号（ [] ）被用来选择一个数组中的元素。更多关于数组的信息见 第 8.15 节。 逗号（ , ）被用在某些语法结构中来分割一个列表的元素。 分号（ ; ）结束一个 SQL 命令。它不能出现在一个命令中间的任何位置，除了在一个字符串常量中或者一个被引用的标识符中。 冒号（ : ）被用来从数组中选择“切片”（见 第 8.15 节）。在某些 SQL 的“方言”（例如嵌入式 SQL）中，冒号被用来作为变量名的前缀。 星号（ * ）被用在某些上下文中标记一个表的所有域或者组合值。当它被用作一个聚集函数的参数时，它还有一种特殊的含义，即该聚集不要求任何显式参数。 句点（ . ）被用在数字常量中，并且被用来分割模式、表和列名。 一段注释是以双横杠开始并且延伸到行结尾的一个字符序列，例如： -- This is a standard SQL comment 另外，也可以使用 C 风格注释块： /* multiline comment * with nesting: /* nested block comment */ */ 这里该注释开始于 / 并且延伸到匹配出现的 /。这些注释块可按照SQL标准中指定的方式嵌套，但和C中不同。这样我们可以注释掉一大段可能包含注释块的代码。 在进一步的语法分析前，注释会被从输入流中被移除并且实际被替换为空白。 表 4.2显示了IvorySQL中操作符的优先级和结合性。大部分操作符具有相同的优先并且是左结合的。操作符的优先级和结合性被硬写在解析器中。如果您希望以不同于优先级规则所暗示的方式解析具有多个运算符的表达式，请添加括号。 表 4.2. 操作符优先级（从高到低） 操作符/元素 结合性 描述 . 左 表/列名分隔符 :: 左 IvorySQL-风格的类型转换 [ ] 左 数组元素选择 + - 右 一元加、一元减 ^ 左 指数 * / % 左 乘、除、模 + - 左 加、减 （任意其他操作符） 左 所有其他本地以及用户定义的操作符 BETWEEN IN LIKE ILIKE SIMILAR 范围包含、集合成员关系、字符串匹配 < > = ⇐ >= <> 比较操作符 IS ISNULL NOTNULL IS TRUE、IS FALSE、IS NULL、`IS DISTINCT FROM`等 NOT 右 逻辑否定 AND 左 逻辑合取 OR 左 逻辑析取 注意该操作符有限规则也适用于与上述内建操作符具有相同名称的用户定义的操作符。例如，如果你为某种自定义数据类型定义了一个“”操作符，它将具有和内建的“”操作符相同的优先级，不管你的操作符要做什么。 当一个模式限定的操作符名被用在`OPERATOR`语法中时，如下面的例子： SELECT 3 OPERATOR(pg_catalog.+) 4; `OPERATOR`结构被用来为“任意其他操作符”获得 表 4.2中默认的优先级。不管出现在`OPERATOR()`中的是哪个指定操作符，这都是真的。 注意 版本9.5之前的PostgreSQL使用的操作符优先级规则略有不同。特别是，⇐、>= 和 `<>`习惯于被当作普通操作符，`IS`测试习惯于具有较高的优先级。并且在一些认为`NOT`比`BETWEEN`优先级高的情况下，`NOT BETWEEN`和相关的结构的行为不一致。为了更好地兼容SQL标准并且减少对 逻辑上等价的结构不一致的处理，这些规则也得到了修改。在大部分情况下，这些变化不会导致行为上的变化，或者可能会产生“no such operator”错误，但可以通过增加圆括号解决。不过在一些极端情况中，查询可能在没有被报告解析错误的情况下发生行为的改变。 值表达式被用于各种各样的环境中，例如在`SELECT`命令的目标列表中、作为`INSERT`或`UPDATE`中的新列值或者若干命令中的搜索条件。为了区别于一个表表达式（是一个表）的结果，一个值表达式的结果有时候被称为一个 标量。值表达式因此也被称为 标量表达式（或者甚至简称为 表达式）。表达式语法允许使用算数、逻辑、集合和其他操作从原始部分计算值。 一个值表达式是下列之一： 一个常量或文字值 一个列引用 在一个函数定义体或预备语句中的一个位置参数引用 一个下标表达式 一个域选择表达式 一个操作符调用 一个函数调用 一个聚集表达式 一个窗口函数调用 一个类型转换 一个排序规则表达式 一个标量子查询 一个数组构造器 一个行构造器 另一个在圆括号（用来分组子表达式以及重载优先级）中的值表达式 在这个列表之外，还有一些结构可以被分类为一个表达式，但是它们不遵循任何一般语法规则。这些通常具有一个函数或操作符的语义并且在 第 9 章中的合适位置解释。一个例子是`IS NULL`子句。 我们已经在 第 4.1.2 节中讨论过常量。下面的小节会讨论剩下的选项。 一个列可以以下面的形式被引用： correlation.columnname correlation 是一个表（有可能以一个模式名限定）的名字，或者是在`FROM`子句中为一个表定义的别名。如果列名在当前索引所使用的表中都是唯一的，关联名称和分隔用的句点可以被忽略（另见 第 7 章）。 一个位置参数引用被用来指示一个由SQL语句外部提供的值。参数被用于SQL函数定义和预备查询中。某些客户端库还支持独立于SQL命令字符串来指定数据值，在这种情况中参数被用来引用那些线外数据值。一个参数引用的形式是： $number 例如，考虑一个函数`dept`的定义： CREATE FUNCTION dept(text) RETURNS dept AS $$ SELECT * FROM dept WHERE name = $1 $$ LANGUAGE SQL; 这里 $1 引用函数被调用时第一个函数参数的值。 如果一个表达式得到了一个数组类型的值，那么可以抽取出该数组值的一个特定元素： expression[subscript] 或者抽取出多个相邻元素（一个“数组切片”）： expression[lower_subscript:upper_subscript] （这里，方括号 [ ] 表示其字面意思）。每一个 下标 自身是一个表达式，它将四舍五入到最接近的整数值。 通常，数组 表达式 必须被加上括号，但是当要被加下标的表达式只是一个列引用或位置参数时，括号可以被忽略。还有，当原始数组是多维时，多个下标可以被连接起来。例如： mytable.arraycolumn[4] mytable.two_d_column[17][34] $1[10:42] (arrayfunction(a,b))[42] 最后一个例子中的圆括号是必需的。详见 第 8.15 节。 如果一个表达式得到一个组合类型（行类型）的值，那么可以抽取该行的指定域: expression.fieldname 通常行 表达式 必须被加上括号，但是当该表达式是仅从一个表引用或位置参数选择时，圆括号可以被忽略。例如： mytable.mycolumn $1.somecolumn (rowfunction(a,b)).col3 （因此，一个被限定的列引用实际上只是域选择语法的一种特例）。一种重要的特例是从一个组合类型的表列中抽取一个域： (compositecol).somefield (mytable.compositecol).somefield 这里需要圆括号来显示 compositecol 是一个列名而不是一个表名，在第二种情况中则是显示 mytable 是一个表名而不是一个模式名。 你可以通过书写 .* 来请求一个组合值的所有域： (compositecol).* 这种记法的行为根据上下文会有不同，详见 第 8.16.5 节。 对于一次操作符调用，有两种可能的语法： expression operator expression（二元中缀操作符） operator expression（一元前缀操作符） 其中 operator 记号遵循 第 4.1.3 节 的语法规则，或者是关键词`AND`、`OR`和`NOT`之一，或者是一个如下形式的受限定操作符名： OPERATOR(schema.operatorname) 哪个特定操作符存在以及它们是一元的还是二元的取决于由系统或用户定义的那些操作符。 第 9 章 描述了内建操作符。 一个函数调用的语法是一个函数的名称（可能受限于一个模式名）后面跟上封闭于圆括号中的参数列表： function_name ([expression [, expression ... ]] ) 例如，下面会计算 2 的平方根： sqrt(2) 当在一个某些用户不信任其他用户的数据库中发出查询时，在编写函数调用时应遵守 第 10.3 节 中的安全防范措施。 内建函数的列表在 第 9 章 中。其他函数可以由用户增加。 参数可以有选择地被附加名称。详见 第 4.3 节。 注意 一个采用单一组合类型参数的函数可以被有选择地称为域选择语法，并且反过来域选择可以被写成函数的风格。也就是说，记号`col(table)`和`table.col`是可以互换的。这种行为是非 SQL 标准的但是在IvorySQL中被提供，因为它允许函数的使用来模拟“计算域”。 一个 聚集表达式 表示在由一个查询选择的行上应用一个聚集函数。一个聚集函数将多个输入减少到一个单一输出值，例如对输入的求和或平均。一个聚集表达式的语法是下列之一： aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ FILTER ( WHERE filter_clause ) ] 这里 aggregate_name 是一个之前定义的聚集（可能带有一个模式名限定），并且 expression 是任意自身不包含聚集表达式的值表达式或一个窗口函数调用。可选的 order_by_clause 和 filter_clause 描述如下。 第一种形式的聚集表达式为每一个输入行调用一次聚集。第二种形式和第一种相同，因为 ALL 是默认选项。第三种形式为输入行中表达式的每一个可区分值（或者对于多个表达式是值的可区分集合）调用一次聚集。第四种形式为每一个输入行调用一次聚集，因为没有特定的输入值被指定，它通常只对于 count() 聚集函数有用。最后一种形式被用于 *有序集 聚集函数，其描述如下。 大部分聚集函数忽略空输入，这样其中一个或多个表达式得到空值的行将被丢弃。除非另有说明，对于所有内建聚集都是这样。 例如，count(*) 得到输入行的总数。 count(f1) 得到输入行中 f1 为非空的数量，因为 count 忽略空值。而 count(distinct f1) 得到 f1 的非空可区分值的数量。 一般地，交给聚集函数的输入行是未排序的。在很多情况中这没有关系，例如不管接收到什么样的输入， min 总是产生相同的结果。但是，某些聚集函数（例如 array_agg 和 string_agg ）依据输入行的排序产生结果。当使用这类聚集时，可选的 order_by_clause 可以被用来指定想要的顺序。order_by_clause 与查询级别的 ORDER BY 子句（如 第 7.5 节 所述）具有相同的语法，除非它的表达式总是仅有表达式并且不能是输出列名称或编号。例如： SELECT array_agg(a ORDER BY b DESC) FROM table; 在处理多参数聚集函数时，注意 ORDER BY 出现在所有聚集参数之后。例如，要这样写： SELECT string_agg(a, ',' ORDER BY a) FROM table; 而不能这样写： SELECT string_agg(a ORDER BY a, ',') FROM table; -- 不正确 后者在语法上是合法的，但是它表示用两个`ORDER BY`键来调用一个单一参数聚集函数（第二个是无用的，因为它是一个常量）。 如果在 order_by_clause 之外指定了 DISTINCT ，那么所有的 ORDER BY 表达式必须匹配聚集的常规参数。也就是说，你不能在 DISTINCT 列表没有包括的表达式上排序。 注意 在一个聚集函数中指定 DISTINCT 以及 ORDER BY 的能力是一种IvorySQL扩展。按照到目前为止的描述，如果一般目的和统计性聚集中 排序是可选的，在要为它排序输入行时可以在该聚集的常规参数 列表中放置 ORDER BY 。有一个聚集函数的子集叫做 有序集聚集 ，它要求一个 order_by_clause，通常是因为该聚集的计算只对其输入行的特定顺序有意义。有序集聚集的典 型例子包括排名和百分位计算。按照上文的最后一种语法，对于 一个有序集聚集， order_by_clause 被写在 WITHIN GROUP (…​) 中。 order_by_clause 中的表达式 会像普通聚集参数一样对每一个输入行计算一次，按照每个 order_by_clause 的要求排序并 且交给该聚集函数作为输入参数（这和非 WITHIN GROUP order_by_clause 的情况不同，在其中表达 式的结果不会被作为聚集函数的参数）。如果有在 WITHIN GROUP 之前的参数表达式，会把它们称 为 直接参数 以便与列在 order_by_clause 中的 聚集参数 相区分。与普通聚集参数不同，针对 每次聚集调用只会计算一次直接参数，而不是为每一个输入行 计算一次。这意味着只有那些变量被 GROUP BY 分组时，它们才能包含这些变量。这个限制同样适用于根本不在 一个聚集表达式内部的直接参数。直接参数通常被用于百分数 之类的东西，它们只有作为每次聚集计算用一次的单一值才有意 义。直接参数列表可以为空，在这种情况下，写成 () 而不是 (*)（实际上 IvorySQL接受两种拼写，但是只有第一种符合 SQL 标准）。 有序集聚集的调用例子： SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_cont ----------------- 50489 这会从表 households 的 income 列得到第 50 个百分位或者中位的值。 这里`0.5`是一个直接参数，对于百分位部分是一个 在不同行之间变化的值的情况它没有意义。 如果指定了 FILTER ，那么只有对 filter_clause 计算为真的输入行会被交给该聚集函数，其他行会被丢弃。例如： SELECT count(*) AS unfiltered, count(*) FILTER (WHERE i < 5) AS filtered FROM generate_series(1,10) AS s(i); unfiltered | filtered ------------+---------- 10 | 4 (1 row) 预定义的聚集函数在 第 9.21 节 中描述。其他聚集函数可以由用户增加。 一个聚集表达式只能出现在 SELECT 命令的结果列表或是 HAVING 子句中。在其他子句（如 WHERE ）中禁止使用它，因为那些子句的计算在逻辑上是在聚集的结果被形成之前。 当一个聚集表达式出现在一个子查询中（见 第 4.2.11 节 和 第 9.23 节），聚集通常在该子查询的行上被计算。但是如果该聚集的参数（以及 filter_clause，如果有）只包含外层变量则会产生一个异常：该聚集则属于最近的那个外层，并且会在那个查询的行上被计算。该聚集表达式从整体上则是对其所出现于的子查询的一种外层引用，并且在那个子查询的任意一次计算中都作为一个常量。只出现在结果列表或 HAVING 子句的限制适用于该聚集所属的查询层次。 一个*窗口函数调用*表示在一个查询选择的行的某个部分上应用一个聚集类的函数。和非窗口聚集函数调用不同，这不会被约束为将被选择的行分组为一个单一的输出行 — 在查询输出中每一个行仍保持独立。不过，窗口函数能够根据窗口函数调用的分组声明（ PARTITION BY 列表）访问属于当前行所在分组中的所有行。一个窗口函数调用的语法是下列之一： function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) 其中 window_definition 的语法是 [ existing_window_name ] [ PARTITION BY expression [, ...] ] [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ] [ frame_clause ] 可选的 frame_clause 是下列之一 { RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ] { RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end [ frame_exclusion ] 其中 frame_start 和 frame_end 可以是下面形式中的一种 UNBOUNDED PRECEDING offset PRECEDING CURRENT ROW offset FOLLOWING UNBOUNDED FOLLOWING 而 frame_exclusion 可以是下列之一 EXCLUDE CURRENT ROW EXCLUDE GROUP EXCLUDE TIES EXCLUDE NO OTHERS 这里，expression 表示任何自身不含有窗口函数调用的值表达式。 window_name 是对定义在查询的 WINDOW 子句中的一个命名窗口声明的引用。还可以使用在 WINDOW 子句中定义命名窗口的相同语法在圆括号内给定一个完整的 window_definition，详见 SELECT 参考页。值得指出的是，OVER wname 并不严格地等价于 OVER (wname …​)，后者表示复制并修改窗口定义，并且在被引用窗口声明包括一个帧子句时会被拒绝。 PARTITION BY 选项将查询的行分组成为 分区，窗口函数会独立地处理它们。PARTITION BY 工作起来类似于一个查询级别的 GROUP BY 子句，不过它的表达式总是只是表达式并且不能是输出列的名称或编号。如果没有 PARTITION BY，该查询产生的所有行被当作一个单一分区来处理。ORDER BY 选项决定被窗口函数处理的一个分区中的行的顺序。它工作起来类似于一个查询级别的 ORDER BY 子句，但是同样不能使用输出列的名称或编号。如果没有 ORDER BY，行将被以未指定的顺序被处理。 frame_clause 指定构成 窗口帧 的行集合，它是当前分区的一个子集，窗口函数将作用在该帧而不是整个分区。帧中的行集合会随着哪一行是当前行而变化。在 RANGE、ROWS 或者 GROUPS 模式中可以指定帧，在每一种情况下，帧的范围都是从 frame_start 到 frame_end。如果 frame_end 被省略，则末尾默认为 CURRENT ROW。 UNBOUNDED PRECEDING 的一个 frame_start 表示该帧开始于分区的第一行，类似地 UNBOUNDED FOLLOWING 的一个 frame_end 表示该帧结束于分区的最后一行。 在 RANGE 或 GROUPS 模式中，CURRENT ROW 的一个 frame_start 表示帧开始于当前行的第一个 平级 行（被窗口的 ORDER BY 子句排序为与当前行等效的行），而 CURRENT ROW 的一个 frame_end 表示帧结束于当前行的最后一个平级行。在 ROWS 模式中，CURRENT ROW 就表示当前行。 在 offset PRECEDING 以及 offset FOLLOWING 帧选项中，offset 必须是一个不包含任何变量、聚集函数或者窗口函数的表达式。offset 的含义取决于帧模式： 在 ROWS 模式中， offset 必须得到一个非空、非负的整数，并且该选项表示帧开始于当前行之前或者之后指定数量的行。 在 GROUPS 模式中，offset 也必须得到一个非空、非负的整数，并且该选项表示帧开始于当前行的平级组之前或者之后指定数量的*平级组*，这里平级组是在 ORDER BY 顺序中等效的行集合（要使用 GROUPS 模式，在窗口定义中就必须有一个 ORDER BY 子句）。 在 RANGE 模式中，这些选项要求 ORDER BY 子句正好指定一列。offset 指定当前行中那一列的值与它在该帧中前面或后面的行中的列值的最大差值。offset 表达式的数据类型会随着排序列的数据类型而变化。对于数字的排序列，它通常是与排序列相同的类型，但对于日期时间排序列它是一个 interval。例如，如果排序列是类型 date 或者 timestamp，我们可以写 RANGE BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING。offset 仍然要求是非空且非负，不过“非负”的含义取决于它的数据类型。 在任何一种情况下，到帧末尾的距离都受限于到分区末尾的距离，因此对于离分区末尾比较近的行来说，帧可能会包含比较少的行。 注意在 ROWS 以及 GROUPS 模式中， 0 PRECEDING 和 0 FOLLOWING 与 CURRENT ROW 等效。通常在 RANGE 模式中，这个结论也成立（只要有一种合适的、与数据类型相关的“零”的含义）。 frame_exclusion 选项允许当前行周围的行被排除在帧之外，即便根据帧的开始和结束选项应该把它们包括在帧中。EXCLUDE CURRENT ROW 会把当前行排除在帧之外。EXCLUDE GROUP 会把当前行以及它在顺序上的平级行都排除在帧之外。EXCLUDE TIES 把当前行的任何平级行都从帧中排除，但不排除当前行本身。EXCLUDE NO OTHERS 只是明确地指定不排除当前行或其平级行的这种默认行为。 默认的帧选项是 RANGE UNBOUNDED PRECEDING，它和 RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW 相同。如果使用 ORDER BY，这会把该帧设置为从分区开始一直到当前行的最后一个 ORDER BY 平级行的所有行。如果不使用 ORDER BY，就意味着分区中所有的行都被包括在窗口帧中，因为所有行都成为了当前行的平级行。 限制是 frame_start 不能是 UNBOUNDED FOLLOWING 、frame_end 不能是 UNBOUNDED PRECEDING，并且在上述 frame_start 和 frame_end 选项的列表中 frame_end 选择不能早于 frame_start 选择出现 — 例如不允许 RANGE BETWEEN CURRENT ROW AND offset PRECEDING，但允许 ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING ，虽然它不会选择任何行。 如果指定了 FILTER ，那么只有对 filter_clause 计算为真的输入行会被交给该窗口函数，其他行会被丢弃。只有是聚集的窗口函数才接受 FILTER 。 内建的窗口函数在 表 9.60 中介绍。用户可以加入其他窗口函数。此外，任何内建的或者用户定义的通用聚集或者统计性聚集都可以被用作窗口函数（有序集和假想集聚集当前不能被用作窗口函数）。 使用 * 的语法被用来把参数较少的聚集函数当作窗口函数调用，例如 count(*) OVER (PARTITION BY x ORDER BY y)。星号（*）通常不被用于窗口相关的函数。窗口相关的函数不允许在函数参数列表中用 DISTINCT 或 ORDER BY。 只有在 SELECT 列表和查询的 ORDER BY 子句中才允许窗口函数调用。 更多关于窗口函数的信息可以在 第 3.5 节、 第 9.22 节 以及 第 7.2.5 节 中找到。 一个类型造型指定从一种数据类型到另一种数据类型的转换。IvorySQL接受两种等价的类型造型语法： CAST ( expression AS type ) expression::type CAST 语法遵从 SQL，而用 :: 的语法是IvorySQL的历史用法。 当一个造型被应用到一种未知类型的值表达式上时，它表示一种运行时类型转换。只有已经定义了一种合适的类型转换操作时，该造型才会成功。注意这和常量的造型（如 第 4.1.2.7 节 中所示）使用不同。应用于一个未修饰串文字的造型表示一种类型到一个文字常量值的初始赋值，并且因此它将对任意类型都成功（如果该串文字的内容对于该数据类型的输入语法是可接受的）。 如果一个值表达式必须产生的类型没有歧义（例如当它被指派给一个表列），通常可以省略显式类型造型，在这种情况下系统会自动应用一个类型造型。但是，只有对在系统目录中被标记为“OK to apply implicitly”的造型才会执行自动造型。其他造型必须使用显式造型语法调用。这种限制是为了防止出人意料的转换被无声无息地应用。 还可以用像函数的语法来指定一次类型造型： typename ( expression ) 不过，这只对那些名字也作为函数名可用的类型有效。例如，double precision 不能以这种方式使用，但是等效的 float8 可以。还有，如果名称 interval、time 和 timestamp 被用双引号引用，那么由于语法冲突的原因，它们只能以这种风格使用。因此，函数风格的造型语法的使用会导致不一致性并且应该尽可能被避免。 注意 函数风格的语法事实上只是一次函数调用。当两种标准造型语法之一被用来做一次运行时转换时，它将在内部调用一个已注册的函数来执行该转换。简而言之，这些转换函数具有和它们的输出类型相同的名字，并且因此“函数风格的语法”无非是对底层转换函数的一次直接调用。显然，一个可移植的应用不应当依赖于它。详见 CREATE CAST。 COLLATE 子句会重载一个表达式的排序规则。它被追加到它适用的表达式： expr COLLATE collation 这里 collation 可能是一个受模式限定的标识符。COLLATE 子句比操作符绑得更紧，需要时可以使用圆括号。 如果没有显式指定排序规则，数据库系统会从表达式所涉及的列中得到一个排序规则，如果该表达式没有涉及列，则会默认采用数据库的默认排序规则。 COLLATE 子句的两种常见使用是重载 ORDER BY 子句中的排序顺序，例如： SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE \"C\"; 以及重载具有区域敏感结果的函数或操作符调用的排序规则，例如： SELECT * FROM tbl WHERE a > 'foo' COLLATE \"C\"; 注意在后一种情况中，COLLATE 子句被附加到我们希望影响的操作符的一个输入参数上。COLLATE 子句被附加到该操作符或函数调用的哪个参数上无关紧要，因为被操作符或函数应用的排序规则是考虑所有参数得来的，并且一个显式的 COLLATE 子句将重载所有其他参数的排序规则（不过，附加非匹配 COLLATE 子句到多于一个参数是一种错误。详见 第 24.2 节）。因此，这会给出和前一个例子相同的结果： SELECT * FROM tbl WHERE a COLLATE \"C\" > 'foo'; 但是这是一个错误： SELECT * FROM tbl WHERE (a > 'foo') COLLATE \"C\"; 因为它尝试把一个排序规则应用到 > 操作符的结果，而它的数据类型是非可排序数据类型 boolean。 一个标量子查询是一种圆括号内的普通 SELECT 查询，它刚好返回一行一列（关于书写查询可见 第 7 章）。`SELECT`查询被执行并且该单一返回值被使用在周围的值表达式中。将一个返回超过一行或一列的查询作为一个标量子查询使用是一种错误（但是如果在一次特定执行期间该子查询没有返回行则不是错误，该标量结果被当作为空）。该子查询可以从周围的查询中引用变量，这些变量在该子查询的任何一次计算中都将作为常量。对于其他涉及子查询的表达式还可见 第 9.23 节。 例如，下列语句会寻找每个州中最大的城市人口： SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name) FROM states; 一个数组构造器是一个能构建一个数组值并且将值用于它的成员元素的表达式。一个简单的数组构造器由关键词 ARRAY、一个左方括号 [ 、一个用于数组元素值的表达式列表（用逗号分隔）以及最后的一个右方括号 ] 组成。例如： SELECT ARRAY[1,2,3+4]; array --------- {1,2,7} (1 row) 默认情况下，数组元素类型是成员表达式的公共类型，使用和 UNION 或 CASE 结构（见 第 10.5 节）相同的规则决定。你可以通过显式将数组构造器造型为想要的类型来重载，例如： SELECT ARRAY[1,2,22.7]::integer[]; array ---------- {1,2,23} (1 row) 这和把每一个表达式单独地造型为数组元素类型的效果相同。关于造型的更多信息请见 第 4.2.9 节。 多维数组值可以通过嵌套数组构造器来构建。在内层的构造器中，关键词 ARRAY 可以被忽略。例如，这些语句产生相同的结果： SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]]; array --------------- {{1,2},{3,4}} (1 row) SELECT ARRAY[[1,2],[3,4]]; array --------------- {{1,2},{3,4}} (1 row) 因为多维数组必须是矩形的，处于同一层次的内层构造器必须产生相同维度的子数组。任何被应用于外层 ARRAY 构造器的造型会自动传播到所有的内层构造器。 多维数组构造器元素可以是任何得到一个正确种类数组的任何东西，而不仅仅是一个子- ARRAY 结构。例如： CREATE TABLE arr(f1 int[], f2 int[]); INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]); SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr; array ------------------------------------------------ {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}} (1 row) 你可以构造一个空数组，但是因为无法得到一个无类型的数组，你必须显式地把你的空数组造型成想要的类型。例如： SELECT ARRAY[]::integer[]; array ------- {} (1 row) 也可以从一个子查询的结果构建一个数组。在这种形式中，数组构造器被写为关键词 ARRAY 后跟着一个加了圆括号（不是方括号）的子查询。例如： SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%'); array ------------------------------------------------------------------ {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412} (1 row) SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i)); array ---------------------------------- {{1,2},{2,4},{3,6},{4,8},{5,10}} (1 row) 子查询必须返回一个单一列。如果子查询的输出列是非数组类型， 结果的一维数组将为该子查询结果中的每一行有一个元素， 并且有一个与子查询的输出列匹配的元素类型。如果子查询的输出列 是一种数组类型，结果将是同类型的一个数组，但是要高一个维度。 在这种情况下，该子查询的所有行必须产生同样维度的数组，否则结果 就不会是矩形形式。 用 ARRAY 构建的一个数组值的下标总是从一开始。更多关于数组的信息，请见 第 8.15 节。 一个行构造器是能够构建一个行值（也称作一个组合类型）并用值作为其成员域的表达式。一个行构造器由关键词 ROW、一个左圆括号、用于行的域值的零个或多个表达式（用逗号分隔）以及最后的一个右圆括号组成。例如： SELECT ROW(1,2.5,'this is a test'); 当在列表中有超过一个表达式时，关键词 ROW 是可选的。 一个行构造器可以包括语法 rowvalue .*，它将被扩展为该行值的元素的一个列表，就像在一个顶层 SELECT 列表（见 第 8.16.5 节）中使用 .* 时发生的事情一样。例如，如果表 t 有列 f1 和 f2，那么这些是相同的： SELECT ROW(t.*, 42) FROM t; SELECT ROW(t.f1, t.f2, 42) FROM t; 注意 在PostgreSQL 8.2 以前，.* 语法不会在行构造器中被扩展，这样写 ROW(t.*, 42) 会创建一个有两个域的行，其第一个域是另一个行值。新的行为通常更有用。如果你需要嵌套行值的旧行为，写内层行值时不要用 .*，例如 ROW(t, 42)。 默认情况下，由一个 ROW 表达式创建的值是一种匿名记录类型。如果必要，它可以被造型为一种命名的组合类型 — 或者是一个表的行类型，或者是一种用 CREATE TYPE AS 创建的组合类型。为了避免歧义，可能需要一个显式造型。例如： CREATE TABLE mytable(f1 int, f2 float, f3 text); CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- 不需要造型因为只有一个 getf1() 存在 SELECT getf1(ROW(1,2.5,'this is a test')); getf1 ------- 1 (1 row) CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric); CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- 现在我们需要一个造型来指示要调用哪个函数： SELECT getf1(ROW(1,2.5,'this is a test')); ERROR: function getf1(record) is not unique SELECT getf1(ROW(1,2.5,'this is a test')::mytable); getf1 ------- 1 (1 row) SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype)); getf1 ------- 11 (1 row) 行构造器可以被用来构建存储在一个组合类型表列中的组合值，或者被传递给一个接受组合参数的函数。还有，可以比较两个行值，或者用 IS NULL 或 IS NOT NULL 测试一个行，例如： SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows 详见 第 9.24 节。如 第 9.23 节 中所讨论的，行构造器也可以被用来与子查询相连接。 子表达式的计算顺序没有被定义。特别地，一个操作符或函数的输入不必按照从左至右或其他任何固定顺序进行计算。 此外，如果一个表达式的结果可以通过只计算其一部分来决定，那么其他子表达式可能完全不需要被计算。例如，如果我们写： SELECT true OR somefunc(); 那么 somefunc() 将（可能）完全不被调用。如果我们写成下面这样也是一样： SELECT somefunc() OR true; 注意这和一些编程语言中布尔操作符从左至右的“短路”不同。 因此，在复杂表达式中使用带有副作用的函数是不明智的。在 WHERE 和 HAVING 子句中依赖副作用或计算顺序尤其危险，因为在建立一个执行计划时这些子句会被广泛地重新处理。这些子句中布尔表达式（ AND / OR / NOT 的组合）可能会以布尔代数定律所允许的任何方式被重组。 当有必要强制计算顺序时，可以使用一个 CASE 结构（见 第 9.18 节）。例如，在一个 WHERE 子句中使用下面的方法尝试避免除零是不可靠的： SELECT ... WHERE x > 0 AND y/x > 1.5; 但是这是安全的： SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END; 一个以这种风格使用的 CASE 结构将使得优化尝试失败，因此只有必要时才这样做（在这个特别的例子中，最好通过写 y > 1.5*x 来回避这个问题）。 不过，CASE 不是这类问题的万灵药。上述技术的一个限制是， 它无法阻止常量子表达式的提早计算。如 第 38.7 节 中所述，当查询被规划而不是被执行时，被标记成 IMMUTABLE 的函数和操作符可以被计算。因此 SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab; 很可能会导致一次除零失败，因为规划器尝试简化常量子表达式。即便是 表中的每一行都有 x > 0（这样运行时永远不会进入到 ELSE 分支）也是这样。 虽然这个特别的例子可能看起来愚蠢，没有明显涉及常量的情况可能会发生 在函数内执行的查询中，因为因为函数参数的值和本地变量可以作为常量 被插入到查询中用于规划目的。例如，在PL/pgSQL函数 中，使用一个 IF -THEN -ELSE 语句来 保护一种有风险的计算比把它嵌在一个 CASE 表达式中要安全得多。 另一个同类型的限制是，一个 CASE 无法阻止其所包含的聚集表达式 的计算，因为在考虑 SELECT 列表或 HAVING 子句中的 其他表达式之前，会先计算聚集表达式。例如，下面的查询会导致一个除零错误， 虽然看起来好像已经这种情况加以了保护： SELECT CASE WHEN min(employees) > 0 THEN avg(expenses / employees) END FROM departments; min() 和 avg() 聚集会在所有输入行上并行地计算， 因此如果任何行有 employees 等于零，在有机会测试 min() 的结果之前，就会发生除零错误。取而代之的是，可以使用 一个 WHERE 或 FILTER 子句来首先阻止有问题的输入行到达一个聚集函数。 IvorySQL允许带有命名参数的函数被使用 位置 或 命名 记号法调用。命名记号法对于有大量参数的函数特别有用，因为它让参数和实际参数之间的关联更明显和可靠。在位置记号法中，书写一个函数调用时，其参数值要按照它们在函数声明中被定义的顺序书写。在命名记号法中，参数根据名称匹配函数参数，并且可以以任何顺序书写。对于每一种记法，还要考虑函数参数类型的效果，这些在 第 10.3 节 有介绍。 在任意一种记号法中，在函数声明中给出了默认值的参数根本不需要在调用中写出。但是这在命名记号法中特别有用，因为任何参数的组合都可以被忽略。而在位置记号法中参数只能从右往左忽略。 IvorySQL也支持 混合 记号法，它组合了位置和命名记号法。在这种情况中，位置参数被首先写出并且命名参数出现在其后。 下列例子将展示所有三种记号法的用法： CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false) RETURNS text AS $$ SELECT CASE WHEN $3 THEN UPPER($1 || ' ' || $2) ELSE LOWER($1 || ' ' || $2) END; $$ LANGUAGE SQL IMMUTABLE STRICT; 函数 concat_lower_or_upper 有两个强制参数，a 和 b。此外，有一个可选的参数 uppercase，其默认值为 false。a 和 b 输入将被串接，并且根据 uppercase 参数被强制为大写或小写形式。这个函数的剩余细节对这里并不重要（详见 第 38 章）。 在IvorySQL中，位置记号法是给函数传递参数的传统机制。一个例子： SELECT concat_lower_or_upper('Hello', 'World', true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 所有参数被按照顺序指定。结果是大写形式，因为 uppercase 被指定为 true。另一个例子： SELECT concat_lower_or_upper('Hello', 'World'); concat_lower_or_upper ----------------------- hello world (1 row) 这里，uppercase 参数被忽略，因此它接收它的默认值 false，并导致小写形式的输出。在位置记号法中，参数可以按照从右往左被忽略并且因此而得到默认值。 在命名记号法中，每一个参数名都用 ⇒ 指定来把它与参数表达式分隔开。例如： SELECT concat_lower_or_upper(a => 'Hello', b => 'World'); concat_lower_or_upper ----------------------- hello world (1 row) 再次，参数 uppercase 被忽略，因此它被隐式地设置为 false。使用命名记号法的一个优点是参数可以用任何顺序指定，例如： SELECT concat_lower_or_upper(a => 'Hello', b => 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) SELECT concat_lower_or_upper(a => 'Hello', uppercase => true, b => 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 为了向后兼容性，基于 \":=\" 的旧语法仍被支持： SELECT concat_lower_or_upper(a := 'Hello', uppercase := true, b := 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 混合记号法组合了位置和命名记号法。不过，正如已经提到过的，命名参数不能超越位置参数。例如： SELECT concat_lower_or_upper('Hello', 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) 在上述查询中，参数 a 和 b 被以位置指定，而 uppercase 通过名字指定。在这个例子中，这只增加了一点文档。在一个具有大量带默认值参数的复杂函数中，命名的或混合的记号法可以节省大量的书写并且减少出错的机会。 注意 命名的和混合的调用记号法当前不能在调用聚集函数时使用（但是当聚集函数被用作窗口函数时它们可以被使用）。 ALTER TABLE [ IF EXISTS ] [ ONLY ] name [ * ] action; action: ADD ( add_coldef [, ... ] ) | MODIFY ( modify_coldef [, ... ] ) | DROP [ COLUMN ] ( column_name [, ... ] ) add_coldef: column_name data_type modify_coldef: column_name data_type alter_using alter_using: USING expression name 表名. column_name 列名. data_type 列类型. expression 值表达式. ADD keyword 增加表的列, 可以增加一个列或多个列. MODIFY keyword 修改表的列, 可以修改一个列或多个列. DROP keyword 删除表的列, 可以删除一个列或多个列. USING keyword 修改列的值. ADD: create table tb_test1(id int, flg char(10)); alter table tb_test1 add (name varchar); ALTER TABLE tb_test1 ADD adress varchar, ADD num int, ADD flg1 char; \\d tb_test1 Table \"public.tb_test1\" Column | Type | Collation | Nullable | Default --------+-----------------+-----------+----------+--------- id | pg_catalog.int4 | | | flg | char(10) | | | name | varchar2(4000) | | | adress | varchar2(4000) | | | num | pg_catalog.int4 | | | flg1 | char(1) | | | MODIFY: create table tb_test2(id int, flg char(10), num varchar); insert into tb_test2 values('1', 2, '3'); ALTER TABLE tb_test2 ALTER COLUMN id TYPE char; \\d tb_test2 Table \"public.tb_test2\" Column | Type | Collation | Nullable | Default --------+----------------+-----------+----------+--------- id | char(1) | | | flg | char(10) | | | num | varchar2(4000) | | | DROP: create table tb_test3(id int, flg1 char(10), flg2 char(11), flg3 char(12), flg4 char(13), flg5 char(14), flg6 char(15)); ALTER TABLE tb_test3 DROP id; \\d tb_test3 Table \"public.tb_test3\" Column | Type | Collation | Nullable | Default --------+----------+-----------+----------+--------- flg1 | char(10) | | | flg2 | char(11) | | | flg3 | char(12) | | | flg4 | char(13) | | | flg5 | char(14) | | | flg6 | char(15) | | | [ WITH [ RECURSIVE ] with_query [, ...] ] DELETE [ FROM ] [ ONLY ] table_name [ * ] [ [ AS ] alias ] [ USING using_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name 表名. alias 表别名. using_list 一个表表达式的列表，它允许在WHERE条件中出现 来自其他表的列. condition 一个返回boolean类型值的表达式. cursor_name 要在WHERE CURRENT OF情况中使用的游标的名称. output_expression 在每一行被删除后，会被DELETE计算并且返回的表达式. output_name 被返回列的名称. create table tb_test4(id int, flg char(10)); insert into tb_test4 values(1, '2'), (5, '6'); delete from tb_test4 where id = 1; table tb_test4; id | flg ----+------------ 5 | 6 (1 row) [ WITH [ RECURSIVE ] with_query [, ...] ] UPDATE [ ONLY ] table_name [ * ] [ [ AS ] alias ] SET { [ table_name | alias ] column_name = { expression | DEFAULT } | ( [ table_name | alias ] column_name [, ...] ) = [ ROW ] ( { expression | DEFAULT } [, ...] ) | ( [ table_name | alias ] column_name [, ...] ) = ( sub-SELECT ) } [, ...] [ FROM from_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name 表名. alias 表别名. column_name 列名. expression 值表达式. sub-SELECT select子句. from_list 表表达式. condition 一个返回boolean类型值的表达式. cursor_name 要在WHERE CURRENT OF情况中使用的游标 的名称. output_expression 在每一行被删除后，会被DELETE计算并且返回的表达式. output_name 被返回列的名称. create table tb_test5(id int, flg char(10)); insert into tb_test5 values(1, '2'), (3, '4'), (5, '6'); update tb_test5 a set a.id = 33 where a.id = 3; table tb_test5; Id | flg ----+------------ 1 | 2 5 | 6 33 | 4 (3 rows) set compatible_mode to oracle; create table students(student_id varchar(20) primary key , student_name varchar(40), student_pid int); select student_id,student_name from students group by student_id; ERROR: column \"students.student_name\" must appear in the GROUP BY clause or be used in an aggregate function SELECT 100 AS value FROM DUAL UNION SELECT 200 AS value FROM DUAL UNION SELECT 100 AS value FROM DUAL; value ------- 100 200 (2 rows) select_statement MINUS [ ALL | DISTINCT ] select_statement; select_statement 任何没有ORDER BY、LIMIT、 FOR NO KEY UPDATE、FOR UPDATE、 FOR SHARE和FOR KEY SHARE子句的 SELECT语句. ALL keyword 包含重复行结果. DISTINCT keyword 显示的消除重复行. select * from generate_series(1, 3) g(i) MINUS select * from generate_series(1, 3) g(i) where i = 1; i --- 2 3 (2 rows) 使用 q\\' 转义特殊字符。q\\' 转义字符通常在! [] {} () \\<> 和其他转义字符之后使用, 您也可以使用 \\, 也可以使用字母, 数字, \\=, +, -, *, \\&, \\$, \\%, #, 等, 不允许使用空格。 select q''' is goog ''; ?column? ------------ ' is goog (1 row) SELECT [ database {schema} | schema ] sequence {nextval | currval}; sequence 序列名. create sequence sq; select sq.nextval; nextval ----------- 1 (1 row) select sq.currval; nextval ----------- 1 (1 row) 将给定的不带时区的时间戳转换为指定的带时区的时间戳，如果指定时区或者时间戳为NULL，则返回NULL。 参数 描述 day 不带时区的时间戳 tz 指定的时区 select from_tz('2021-11-08 09:12:39','Asia/Shanghai') from dual; from_tz ----------------------------------- 2021-11-08 09:12:39 Asia/Shanghai (1 row) select from_tz('2021-11-08 09:12:39','SAST') from dual; from_tz -------------------------- 2021-11-08 09:12:39 SAST select from_tz(NULL,'SAST') from dual; from_tz --------- (1 row) select from_tz('2021-11-08 09:12:31',NULL) from dual; from_tz --------- (1 row) 获取当前数据库系统的时间戳。 select systimestamp(); systimestamp ------------------------------- 2021-12-02 14:38:59.879642+08 (1 row) select systimestamp; statement_timestamp ------------------------------- 2021-12-02 14:39:33.262828+08 将给定的带时区的时间戳转换为不带时区的UTC时间。 参数 描述 day 需要转化带时区的时间戳 select sys_extract_utc('2018-03-28 11:30:00.00 +09:00'::timestamptz) from dual; sys_extract_utc --------------------- 2018-03-28 02:30:00 (1 row) select sys_extract_utc(NULL) from dual; sys_extract_utc ----------------- (1 row) 获取当前会话的时区。 select sessiontimezone() from dual; sessiontimezone ----------------- PRC (1 row) set timezone to UTC; select sessiontimezone(); sessiontimezone ----------------- UTC (1 row) next_day 返回由格式名相同的第一个工作日的日期，该日期晚于当前日期。 无论日期的数据类型如何，返回类型始终为 DATE。 返回值具有与参数日期相同的小时、分钟和秒部分。 参数 描述 value 开始时间戳 weekday 星期几，可以是 \"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" 或者 0,1,2,3,4,5,6,0代表星期天 select next_day(to_timestamp('2020-02-29 14:40:50', 'YYYY-MM-DD HH24:MI:SS'), 'Tuesday') from dual; next_day --------------------- 2020-03-03 14:40:50 (1 row) select next_day('2020-07-01 19:43:51 +8'::timestamptz, 1) from dual; next_day --------------------- 2020-07-05 19:43:51 (1 row) last_day返回档期日期所在月份的最后一天。 参数 描述 value 指定的时间戳 select last_day(timestamp '2020-05-17 13:27:19') from dual; last_day --------------------- 2020-05-31 13:27:19 (1 row) select last_day('2020-11-29 19:20:40 +08'::timestamptz) from dual; last_day --------------------- 2020-11-30 19:20:40 (1 row) add_months 返回日期加上整数月份。 date 参数可以是日期时间值或任何可以隐式转换为 DATE 的值。 整数参数可以是整数或任何可以隐式转换为整数的值。 参数 描述 day oracle.date类型，需要被改变的时间戳 value 一个整形数据，需要增加的月数 select add_months(date '2020-02-15',7) from dual; add_months --------------------- 2020-09-15 00:00:00 (1 row) select add_months(timestamp '2018-12-15 19:12:09',12) from dual; add_months --------------------- 2019-12-15 19:12:09 (1 row) sysdate 返回数据库服务器的操作系统时间。 select sysdate; statement_sysdate --------------------- 2021-12-09 16:20:34 (1 row) select sysdate(); sysdate --------------------- 2021-12-09 16:21:39 (1 row) 转换第一个时区的时间到第二个时区的时间. 时区包括了 \"ast\", \"adt\", \"bst\", \"bdt\", \"cst\", \"cdt\", \"est\", \"edt\", \"gmt\", \"hst\", \"hdt\", \"mst\", \"mdt\", \"nst\", \"pst\", \"pdt\", \"yst\", \"ydt\". 参数 描述 day 需要被转换的时间戳 tz1 时间戳的时区 tz2 目标时区 select new_time(timestamp '2020-12-12 17:45:18', 'AST', 'ADT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'BST', 'BDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'CST', 'CDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) trunc函数返回一个日期, 按照指定格式被截断, fmt包括 \"Y\", \"YY\", \"YYY\", \"YYYY\",\"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". 参数 描述 value 指定的日期（oracle.date, timestamp, timestamptz） fmt 指定的格式, 如果被省略, 默认为 \"DDD\" select trunc(timestamp '2020-07-28 19:16:12', 'Q'); trunc --------------------- 2020-07-01 00:00:00 (1 row) select trunc(timestamptz '2020-09-27 18:30:21 + 08', 'MONTH'); trunc ------------------------ 2020-09-01 00:00:00+08 (1 row) round函数返回一个日期,按照指定的格式四舍五入, fmt 包括了 \"Y\", \"YY\", \"YYY\", \"YYYY\",\"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". 参数 描述 value 被转换的日期（oracle.date, timestamp, timestamptz） fmt 指定的格式, 如果被省略, 默认为 \"DDD\" select round(timestamp '2050-06-12 16:40:55', 'IYYY'); round --------------------- 2050-01-03 00:00:00 (1 row) TO_CHAR（str,[fmt]） 根据给定的格式将输入参数转换为 TEXT 数据类型的值。 如果省略 fmt，则数据将转换为系统默认格式的 TEXT 值。 如果 str 为 null，则该函数返回 null。 str 输入参数 （任意类型）。 fmt 输入格式参数,详见格式fmt。 select to_char('3 2:20:05'); to_char ----------------- 3 days 02:20:05 (1 row) select to_char('4.00'::numeric); to_char --------- 4 (1 row) select to_char(NULL); to_char --------- (1 row) select to_char(123,'xx'); to_char --------- 7b (1 row) TO_NUMBER(str,[fmt1]) 根据给定的格式将输入参数 str 转换为 NUMREIC 数据类型的值。 如果省略 fmt1，则数据将转换为系统默认格式的 NUMERIC 值。 如果 str 是 NUMERIC，则该函数返回 str。如果 str 计算结果为 null，则该函数返回 null。 如果它不能转换为 NUMERIC 数据类型，则该函数返回错误。 str 输入参数包括以下数据类型（double precision，numeric，text，integer等，但必须隐式转换为numeric）。 fmt1 输入格式参数，详见格式fmt1。 select to_number(1210.73::numeric, 9999.99::numeric); to_number ----------- 1210.73 (1 row) select to_number(NULL); to_number ----------- (1 row) select to_number('123'::text); to_number ----------- 123 (1 row) TO_DATE(str,[fmt]) 根据给定的格式将输入参数 str 转换为日期数据类型的值。 如果省略 fmt，则数据将转换为系统默认格式的日期值。 如果 str 为 null，则该函数返回 null。 如果 fmt 是 J，对于 Julian，则 char 必须是整数。 如果无法转换为 DATE，则该函数返回错误。 str 输入参数（integer，text，可以隐式转换为上述类型，符合日期格式的字符串）。 fmt 输入格式参数，详见格式fmt。 select to_date('50-11-28 ','RR-MM-dd '); to_date --------------------- 1950-11-28 00:00:00 (1 row) select to_date(2454336, 'J'); to_date --------------------- 2007-08-23 00:00:00 (1 row) select to_date('2019/11/22', 'yyyy-mm-dd'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('20-11-28 10:14:22','YY-MM-dd hh24:mi:ss'); to_date --------------------- 2020-11-28 10:14:22 (1 row) select to_date('2019/11/22'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('2019/11/27 10:14:22'); to_date --------------------- 2019-11-27 10:14:22 (1 row) select to_date('2020','RR'); to_date --------------------- 2020-01-01 00:00:00 (1 row) select to_date(NULL); to_date --------- (1 row) select to_date('-4712-07-23 14:31:23', 'syyyy-mm-dd hh24:mi:ss'); to_date ---------------------- -4712-07-23 14:31:23 (1 row) TO_TIMESTAMP(str,[fmt]) 根据给定的格式将输入参数 str 转换为不带时区的时间戳。 如果省略 fmt，则数据将转换为系统默认格式中不带时区值的时间戳。 如果 str 为 null，则该函数返回 null。 如果无法转换为不带时区的时间戳，则该函数返回错误。 str 输入参数（double precision,text，可以隐式转换为上述类型）。 fmt 输入格式参数，详见格式fmt。 select to_timestamp(1212121212.55::numeric); to_timestamp --------------------------- 2008-05-30 12:20:12.55 (1 row) select to_timestamp('2020/03/03 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp ------------------------ 2020-03-03 13:13:18 (1 row) select to_timestamp(NULL,NULL); to_timestamp -------------- (1 row) TO_YMINTERVAL(str) 将输入参数 str 时间间隔转换为年到月范围内的时间间隔。 只处理年月，其他部分省略。 如果输入参数为NULL，函数返回NULL，如果输入参数格式错误，函数返回错误。 str 输入参数（text，可以隐式转换为文本类型，必须是时间间隔格式。 兼容 SQL 标准的 SQL 间隔格式， ISO 持续时间格式与 ISO 8601:2004 标准兼容）。 select to_yminterval('P1Y-2M2D'); to_yminterval --------------- +000000000-10 (1 row) select to_yminterval('P1Y2M2D'); to_yminterval --------------- +000000001-02 (1 row) select to_yminterval('-01-02'); to_yminterval --------------- -000000001-02 (1 row) TO_DSINTERVAL(str) 将输入参数 str 的时间间隔转换为天到秒范围内的时间间隔。 输入参数包括：日、时、分、秒和微秒。 如果输入参数为NULL，函数返回NULL，如果输入参数包含年月或格式错误，函数返回错误。 str 输入参数（text，可以隐式转换为文本类型，必须是时间间隔格式。 兼容 SQL 标准的 SQL 间隔格式， ISO 持续时间格式与 ISO 8601:2004 标准兼容）。 select to_dsinterval('100 00 :02 :00'); to_dsinterval ------------------------------- +000000100 00:02:00.000000000 (1 row) select to_dsinterval('-100 00:02:00'); to_dsinterval ------------------------------- -000000100 00:02:00.000000000 (1 row) select to_dsinterval(NULL); to_dsinterval --------------- (1 row) TO_TIMESTAMP_TZ(str,[fmt]) 根据给定的格式将输入参数 str 转换为带时区的时间戳。 如果省略 fmt，则数据将转换为具有系统默认格式带时区值的时间戳。 如果 str 为 null，则该函数返回 null。 如果无法转换为带时区的时间戳，则该函数返回错误。 str 输入参数（text，可以隐式转换为文本类型）。 fmt 输入格式参数，详见格式fmt。 select to_timestamp_tz('2019','yyyy'); to_timestamp_tz ------------------------ 2019-01-01 00:00:00+08 (1 row) select to_timestamp_tz('2019-11','yyyy-mm'); to_timestamp_tz ------------------------ 2019-11-01 00:00:00+08 (1 row) select to_timestamp_tz('2003/12/13 10:13:18 +7:00'); to_timestamp_tz ------------------------ 2003-12-13 11:13:18+08 (1 row) select to_timestamp_tz('2019/12/13 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp_tz ------------------------ 2019-12-13 13:13:18+08 (1 row) select to_timestamp_tz(NULL); to_timestamp_tz ----------------- (1 row) GREATEST(expr1,expr2,…​) 获取一个或多个表达式的输入列表中的最大值。 如果任何 expr 的计算结果为 NULL，则该函数返回 NULL。 expr1` 输入参数（任意类型）。 `expr2` 输入参数（任意类型）。 `... select greatest('a','b','A','B'); greatest ---------- b (1 row) select greatest(',','.','/',';','!','@','?'); greatest ---------- @ (1 row) select greatest('瀚','高','数','据','库'); greatest ---------- 高 (1 row) SELECT greatest('HARRY', 'HARRIOT', 'HARRA'); greatest ---------- HARRY (1 row) SELECT greatest('HARRY', 'HARRIOT', NULL); greatest ---------- (1 row) SELECT greatest(1.1, 2.22, 3.33); greatest ---------- 3.33 (1 row) SELECT greatest('A', 6, 7, 5000, 'E', 'F','G') A; a --- G (1 row) LEAST(expr1,expr2,…​) 获取一个或多个表达式的输入列表中的最小值。 如果任何 expr 的计算结果为 NULL，则该函数返回 NULL。 expr1` 输入参数（任意类型）。 `expr2` 输入参数（任意类型）。 `... SELECT least(1,' 2', '3' ); least ------- 1 (1 row) SELECT least(NULL, NULL, NULL); least ------- (1 row) SELECT least('A', 6, 7, 5000, 'E', 'F','G') A; a ------ 5000 (1 row) select least(1,3,5,10); least ------- 1 (1 row) select least('a','A','b','B'); least ------- A (1 row) select least(',','.','/',';','!','@'); least ------- ! (1 row) select least('瀚','高','据','库'); least ------- 库 (1 row) SELECT least('HARRY', 'HARRIOT', NULL); least ------- (1 row) 模式 描述 HH 一天中的小时 （01-12） HH12 一天中的小时 （01-12） HH24 一天中的小时 （00-23） MI 分钟 （00-59）minute (00-59) SS 秒（00-59） MS 毫秒（000-999） US 微秒（000000-999999） SSSS 午夜后的秒（0-86399） AM, am, PM or pm 正午指示器（不带句号） A.M., a.m., P.M. or p.m. 正午指示器（带句号） Y,YYY 带逗号的年（4 位或者更多位） YYYY 年（4 位或者更多位） YYY 年的后三位 YY 年的后两位 Y 年的最后一位 IYYY ISO 8601 周编号方式的年（4 位或更多位） IYY ISO 8601 周编号方式的年的最后 3 位 IY ISO 8601 周编号方式的年的最后 2 位 I ISO 8601 周编号方式的年的最后一位 BC, bc, AD或者ad 纪元指示器（不带句号） B.C., b.c., A.D.或者a.d. 纪元指示器（带句号） MONTH 全大写形式的月名（空格补齐到 9 字符） Month 全首字母大写形式的月名（空格补齐到 9 字符） month 全小写形式的月名（空格补齐到 9 字符） MON 简写的大写形式的月名（英文 3 字符，本地化长度可变） Mon 简写的首字母大写形式的月名（英文 3 字符，本地化长度可变） mon 简写的小写形式的月名（英文 3 字符，本地化长度可变） MM 月编号（01-12） DAY 全大写形式的日名（空格补齐到 9 字符） Day 全首字母大写形式的日名（空格补齐到 9 字符） day 全小写形式的日名（空格补齐到 9 字符） DY 简写的大写形式的日名（英语 3 字符，本地化长度可变） Dy 简写的首字母大写形式的日名（英语 3 字符，本地化长度可变） dy 简写的小写形式的日名（英语 3 字符，本地化长度可变） DDD 一年中的日（001-366） IDDD ISO 8601 周编号方式的年中的日（001-371，年的第 1 日时第一个 ISO 周的周一） DD 月中的日（01-31） D 周中的日，周日（1）到周六（7） ID 周中的 ISO 8601 日，周一（1）到周日（7） W 月中的周（1-5）（第一周从该月的第一天开始） WW 年中的周数（1-53）（第一周从该年的第一天开始） IW ISO 8601 周编号方式的年中的周数（01 - 53；新的一年的第一个周四在第一周） CC 世纪（2 位数）（21 世纪开始于 2001-01-01） J 儒略日（从午夜 UTC 的公元前 4714 年 11 月 24 日开始的整数日数） Q 季度（to_date和to_timestamp会忽略） RM 大写形式的罗马计数法的月（I-XII；I 是 一月） rm 小写形式的罗马计数法的月（i-xii；i 是 一月） TZ 大写形式的时区名称 tz 小写形式的时区名称 OF 时区偏移量 模式 描述 9 带有指定位数的值 0 带前导零的值 . (period) 小数点 , (comma) 分组（千）分隔符 PR 尖括号内的负值 S 带符号的数字（使用区域） L 货币符号（使用区域） D 小数点（使用区域） G 分组分隔符（使用区域） MI 在指定位置的负号（如果数字 < 0） PL 在指定位置的正号（如果数字 > 0） SG 在指定位置的正/负号 RN 罗马数字（输入在 1 和 3999 之间） TH or th 序数后缀 V 移动指定位数（参阅注解） EEEE 科学记数的指数 NLS_LENGTH_SEMANTICS 使您能够使用字节或字符长度语义创建 CHAR 和 VARCHAR2 列。 现有列不受影响。 在这种情况下，默认语义是 BYTE。 SET NLS_LENGTH_SEMANTICS TO [NONE | BYTE | CHAR]; BYTE:数据以字节长度来存储。 CHAR:数据以字符长度来存储。 NONE:数据使用原生PostgreSQL存储方式。 create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 SET NLS_LENGTH_SEMANTICS TO BYTE; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- byte (1 row) insert into test values ('李老师您好'); 2021-12-14 15:28:11.906 HKT [6774] ERROR: value too long for type varchar2(5 byte) 2021-12-14 15:28:11.906 HKT [6774] STATEMENT: insert into test values ('李老师您好'); ERROR: value too long for type varchar2(5 byte) 具有最大长度大小字节或字符的可变长度字符串。 您必须为 VARCHAR2 指定大小。 最小大小为 1 个字节或 1 个字符。 VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('李老师您好'); INSERT 0 1 PL/iSQL 是 IvorySQL 的过程语言，用于为 IvorySQL 编写自定义函数、过程和包。 PL/iSQL 派生自 PostgreSQL 的 PL/pgsql，并增加了一些功能，但在语法上 PL/iSQL 更接近 Oracle 的 PL/SQL。 本文档描述了 PL/iSQL 程序的基本结构和构造。 iSQL 是一种程序化的块结构语言，支持四种不同的 程序类型，即 PACKAGES、PROCEDURES、FUNCTIONS 和 TRIGGERS。 iSQL 对每种类型的受支持程序使用相同的块结构。 一个块最多由三个部分组成：声明部分，可执行文件，和异常部分。 而声明和异常部分是可选的。 [DECLARE declarations] BEGIN statements [ EXCEPTION WHEN  THEN statements] END; 一个块至少可以由一个可执行部分组成 在 BEGIN 和 END 关键字中包含一个或多个 iSQL 语句。 CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS BEGIN NULL; END; / 所有关键字都不区分大小写。 标识符被隐式转换为小写，除非双引号， 就像它们在普通 SQL 命令中一样。 声明部分可用于声明变量和游标，并取决于使用块的上下文， 声明部分可以以关键字 DECLARE 开头。 CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS DECLARE quantity integer := 30; c_row pg_class%ROWTYPE; r_cursor refcursor; CURSOR c1 RETURN pg_proc%ROWTYPE; BEGIN NULL; end; / 可选的异常部分也可以包含在 BEGIN - END 块中。 异常部分以关键字 EXCEPTION 开始，一直持续到它出现的块的末尾。 如果块内的语句抛出异常，程序控制转到异常部分，根据异常和异常部分的内容，可能会或不会处理抛出的异常。 CREATE OR REPLACE FUNCTION reraise_test() RETURN void AS BEGIN BEGIN RAISE syntax_error; EXCEPTION WHEN syntax_error THEN BEGIN raise notice 'exception % thrown in inner block, reraising', sqlerrm; RAISE; EXCEPTION WHEN OTHERS THEN raise notice 'RIGHT - exception % caught in inner block', sqlerrm; END; END; EXCEPTION WHEN OTHERS THEN raise notice 'WRONG - exception % caught in outer block', sqlerrm; END; / 注意 与 PL/pgSQL 类似，PL/iSQL 使用 BEGIN/END 对语句进行分组， 并且不要将它们与用于事务控制的同名 SQL 命令混淆。 PL/iSQL 的 BEGIN/END 仅用于分组； 他们不开始或结束事务 要从 psql 客户端创建 PL/iSQL 程序，您可以使用类似于 PL/pgSQL 的$$语法 CREATE FUNCTION func() RETURNS void as $$ .. end$$ language plisql; 或者，您可以使用不带 $$ 的 Oracle 兼容语法的引用和语言规范， 并使用 /（正斜杠） 结束程序定义。 /（正斜杠）必须在换行符上 CREATE FUNCTION func() RETURN void AS … END; / CREATE [OR REPLACE] PROCEDURE procedure_name [(parameter_list)] is [DECLARE] -- variable declaration BEGIN -- stored procedure body END; / CREATE [OR REPLACE] FUNCTION function_name ([parameter_list]) RETURN return_type AS [DECLARE] -- variable declaration BEGIN -- function body return statement END; / CREATE [ OR REPLACE ] PACKAGE [schema.] *package_name* [invoker_rights_clause] [IS | AS] item_list[, item_list ...] END [*package_name*]; invoker_rights_clause: AUTHID [CURRENT_USER | DEFINER] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] function_declaration: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype; procedure_declaration: PROCEDURE procedure_name [(parameter_declaration[, ...])] type_definition: record_type_definition | ref_cursor_type_definition cursor_declaration: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype; item_declaration: cursor_declaration | cursor_variable_declaration | record_variable_declaration | variable_declaration | record_type_definition: TYPE record_type IS RECORD ( variable_declaration [, variable_declaration]... ) ; ref_cursor_type_definition: TYPE type IS REF CURSOR [ RETURN type%ROWTYPE ]; cursor_variable_declaration: curvar curtype; record_variable_declaration: recvar { record_type | rowtype_attribute | record_type%TYPE }; variable_declaration: varname datatype [ [ NOT NULL ] := expr ] parameter_declaration: parameter_name [IN] datatype [[:= | DEFAULT] expr] CREATE [ OR REPLACE ] PACKAGE BODY [schema.] package_name [IS | AS] [item_list[, item_list ...]] | item_list_2 [, item_list_2 ...] [initialize_section] END [package_name]; initialize_section: BEGIN statement[, ...] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] item_list_2: [ function_declaration function_definition procedure_declaration procedure_definition cursor_definition ] function_definition: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype [IS | AS] [declare_section] body; procedure_definition: PROCEDURE procedure_name [(parameter_declaration[, ...])] [IS | AS] [declare_section] body; cursor_definition: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype IS select_statement; body: BEGIN statement[, ...] END [name]; statement: [<
    diff --git a/docs/en/ivorysql-doc/master/architecture/framework_design.html b/docs/en/ivorysql-doc/master/architecture/framework_design.html index debdd2cb..83899846 100644 --- a/docs/en/ivorysql-doc/master/architecture/framework_design.html +++ b/docs/en/ivorysql-doc/master/architecture/framework_design.html @@ -172,6 +172,9 @@

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    • +
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    • diff --git a/docs/en/ivorysql-doc/master/containerization/docker_podman_deployment.html b/docs/en/ivorysql-doc/master/containerization/docker_podman_deployment.html index 1e3d8e5d..fffd488c 100644 --- a/docs/en/ivorysql-doc/master/containerization/docker_podman_deployment.html +++ b/docs/en/ivorysql-doc/master/containerization/docker_podman_deployment.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html b/docs/en/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html index f37a3fbe..dc12b628 100644 --- a/docs/en/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html +++ b/docs/en/ivorysql-doc/master/containerization/docker_swarm_compose_deployment.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/containerization/k8s_deployment.html b/docs/en/ivorysql-doc/master/containerization/k8s_deployment.html index ab215628..db0cad6f 100644 --- a/docs/en/ivorysql-doc/master/containerization/k8s_deployment.html +++ b/docs/en/ivorysql-doc/master/containerization/k8s_deployment.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/containerization/operator_deployment.html b/docs/en/ivorysql-doc/master/containerization/operator_deployment.html index ab272906..29f0fb02 100644 --- a/docs/en/ivorysql-doc/master/containerization/operator_deployment.html +++ b/docs/en/ivorysql-doc/master/containerization/operator_deployment.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/contribution/community_contribution_guide.html b/docs/en/ivorysql-doc/master/contribution/community_contribution_guide.html index fb0bbcd9..47f58cfd 100644 --- a/docs/en/ivorysql-doc/master/contribution/community_contribution_guide.html +++ b/docs/en/ivorysql-doc/master/contribution/community_contribution_guide.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/contribution/faq.html b/docs/en/ivorysql-doc/master/contribution/faq.html index 7522f72f..de856953 100644 --- a/docs/en/ivorysql-doc/master/contribution/faq.html +++ b/docs/en/ivorysql-doc/master/contribution/faq.html @@ -171,6 +171,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html b/docs/en/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html index 139b75da..22a5d944 100644 --- a/docs/en/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html +++ b/docs/en/ivorysql-doc/master/cpu_os_adaptation/cpu_architecture_adaptation.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html b/docs/en/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html index 244d9ef1..77469493 100644 --- a/docs/en/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html +++ b/docs/en/ivorysql-doc/master/cpu_os_adaptation/os_architecture_adaptation.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/developer_guide.html b/docs/en/ivorysql-doc/master/developer_guide.html index e7f7bdd2..2f733954 100644 --- a/docs/en/ivorysql-doc/master/developer_guide.html +++ b/docs/en/ivorysql-doc/master/developer_guide.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html b/docs/en/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html index 2c0e4428..a8656f35 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/ecosystem_overview.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • @@ -502,6 +505,13 @@

    IvorySQL

    AI-driven natural language to SQL extension supporting multiple LLMs

    AI-assisted querying, natural language database interaction

    + +

    12

    +

    pg_stat_monitor

    +

    2.3.1

    +

    Collects performance statistics and provides query performance insights in a single view and graphically in histogram.

    +

    Performance monitoring

    +
    diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pg_ai_query.html b/docs/en/ivorysql-doc/master/ecosystem_components/pg_ai_query.html index f5758de9..68dba658 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pg_ai_query.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pg_ai_query.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pg_cron.html b/docs/en/ivorysql-doc/master/ecosystem_components/pg_cron.html index da9a5cf6..a20461f4 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pg_cron.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pg_cron.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html b/docs/en/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html new file mode 100644 index 00000000..49f62845 --- /dev/null +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pg_stat_monitor.html @@ -0,0 +1,570 @@ + + + + + + pg_stat_monitor :: IvorySQL Document Site + + + + + + + + +
    + +
    +
    +
    + +
    +
    + +
    + +
    +

    pg_stat_monitor

    +
    +

    1. Overview

    +
    +
    +

    pg_stat_monitor is a Query Performance Monitoring tool for PostgreSQL. pg_stat_monitor collects performance statistics and provides query performance insights in a single view and graphically in histogram.

    +
    +
    +

    These insights allow database users to understand query origins, execution, planning statistics and details, query information, and metadata. This significantly improves observability, enabling users to debug and tune query performance.

    +
    +
    +
    +
    +

    2. Installation

    +
    +
    +

    The IvorySQL installation package already includes the pg_stat_monitor plugin. If you installed IvorySQL using the installation package, you typically don’t need to manually install pg_stat_monitor. If you installed IvorySQL from source code, you can proceed to install the pg_stat_monitor plugin also from source. The IvorySQL community provides step-by-step instructions for source code installation：

    +
    +
    +

    To build pg_stat_monitor from source code, you require the following:

    +
    +
    +
      +
    • +

      git

      +
      • +
    • +

      make

      +
      • +
    • +

      gcc

      +
      • +
    • +

      pg_config

      +
      • +
    +
    +
    +

    You can download the source code of the latest release of pg_stat_monitor from the releases page on GitHub or using git:

    +
    +
    +
    +
    git clone https://github.com/percona/pg_stat_monitor.git
    +
    +
    +
    +

    Compile and install the extension. Assuming IvorySQL is already installed in the environment with the installation path at /usr/ivory-5.

    +
    +
    +
    +
    cd pg_stat_monitor
+export PG_CONFIG=/usr/ivory-5/bin/pg_config
+make USE_PGXS=1
+make USE_PGXS=1 install
    +
    +
    +
    +
    +
    +

    3. Load the module

    +
    +
    +

    Load pg_stat_monitor at the start time by adding it to the shared_preload_libraries configuration parameter. This is because pg_stat_monitor requires additional shared memory.

    +
    +
    +

    modify the shared_preload_libraries parameter：

    +
    +
    +
    +
    shared_preload_libraries = 'pg_stat_monitor';
    +
    +
    +
    + + + + + +
    + + +If you’ve added other modules to the shared_preload_libraries parameter (for example, pg_stat_statements), list all of them separated by commas for the ALTER SYSTEM command. +
    +
    +
    +
    +
    shared_preload_libraries = 'liboracle_parser, ivorysql_ora, pg_stat_statements, pg_stat_monitor';
    +
    +
    +
    +

    Start or restart the IvorySQL instance to apply the changes.

    +
    +
    +
    +
    pg_ctl restart -D data
    +
    +
    +
    +

    After you have added pg_stat_monitor to the shared_preload_libraries, it starts collecting statistics data for all existing databases. To access this data, you need to create the view on every database that you wish to monitor.

    +
    +
    +
    +
    +

    4. Create the extension view

    +
    +
    +

    Create the extension view with the user that has the privileges of a superuser or a database owner. Connect to psql as a superuser for a database and run the CREATE EXTENSION command:

    +
    +
    +
    +
    CREATE EXTENSION pg_stat_monitor;
    +
    +
    +
    +

    After the setup is complete, you can see the stats collected by pg_stat_monitor.

    +
    +
    +
    +
    +

    5. Usage

    +
    +
    +

    To obtain query execution time information as an example, connect to the database and execute the following SQL：

    +
    +
    +
    +
    SELECT  userid,  total_exec_time, min_exec_time, max_exec_time, mean_exec_time, query FROM pg_stat_monitor;
    +
    +
    +
    +
    +
    userid | total_exec_time | min_exec_time | max_exec_time | mean_exec_time |                                            query
+--------+-----------------+---------------+---------------+----------------+----------------------------------------------------------------------------------------------
+     10 |        1.532168 |      0.749108 |       0.78306 |       0.766084 | SELECT userid,  datname, queryid, substr(query,$1, $2) AS query, calls FROM pg_stat_monitor
+     10 |        0.755857 |      0.755857 |      0.755857 |       0.755857 | SELECT application_name, client_ip, substr(query,$1,$2) as query FROM pg_stat_monitor
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+     10 |               0 |             0 |             0 |              0 | SELECT  userid,  total_exec_time, min_time, max_time, mean_time, query FROM pg_stat_monitor;
+(4 rows)
    +
    +
    +
    +

    For more information on using pg_stat_monitor, please refer to pg_stat_monitor Official documentation

    +
    +
    +
    + +
    +
    +
    +
    +
    + +
    + + + + + + + diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html index bf642253..898b65e5 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgaudit.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html index f08d6699..ceca9516 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgddl.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html index e76706a6..655c6731 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgroonga.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html index aa8d4c88..e1f9f97e 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgrouting.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html index 1aa0d5b9..723cc5ae 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgsql_http.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html b/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html index ecdc0f20..b24dfe14 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/pgvector.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html b/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html index bd5581a0..20f2e6a1 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/plpgsql_check.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html b/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html index 7661d120..5d418f15 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/postgis.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html b/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html index 828e1fc9..fdf13462 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/system_stats.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html b/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html index 65220866..23f4f93b 100644 --- a/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html +++ b/docs/en/ivorysql-doc/master/ecosystem_components/wal2json.html @@ -6,7 +6,7 @@ wal2json :: IvorySQL Document Site - + @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • @@ -578,7 +581,7 @@

    3. Use

    diff --git a/docs/en/ivorysql-doc/master/gb18030.html b/docs/en/ivorysql-doc/master/gb18030.html index 7add9944..9267aa5c 100644 --- a/docs/en/ivorysql-doc/master/gb18030.html +++ b/docs/en/ivorysql-doc/master/gb18030.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html b/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html index 8b0e3582..87bcf4f7 100644 --- a/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html +++ b/docs/en/ivorysql-doc/master/getting-started/daily_maintenance.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html b/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html index 3179bf6c..b7ad2585 100644 --- a/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html +++ b/docs/en/ivorysql-doc/master/getting-started/daily_monitoring.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/getting-started/quick_start.html b/docs/en/ivorysql-doc/master/getting-started/quick_start.html index b14e0e14..acd36b64 100644 --- a/docs/en/ivorysql-doc/master/getting-started/quick_start.html +++ b/docs/en/ivorysql-doc/master/getting-started/quick_start.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/installation_guide.html b/docs/en/ivorysql-doc/master/installation_guide.html index 6f79ac5c..6466a44c 100644 --- a/docs/en/ivorysql-doc/master/installation_guide.html +++ b/docs/en/ivorysql-doc/master/installation_guide.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/migration_guide.html b/docs/en/ivorysql-doc/master/migration_guide.html index e8083920..14ef774b 100644 --- a/docs/en/ivorysql-doc/master/migration_guide.html +++ b/docs/en/ivorysql-doc/master/migration_guide.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/operation_guide.html b/docs/en/ivorysql-doc/master/operation_guide.html index c33563d4..e781fe0d 100644 --- a/docs/en/ivorysql-doc/master/operation_guide.html +++ b/docs/en/ivorysql-doc/master/operation_guide.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • @@ -671,7 +674,211 @@

    -

    2. Managing IvorySQL Versions

    +

    2. Use pg_upgrade to upgrade PostgreSQL to IvorySQL

    +
    +
    +

    pg_upgrade supports upgrading native PostgreSQL clusters to IvorySQL. When the source cluster is native PostgreSQL and the target is an IvorySQL cluster, the -g（--using-ora-pg）parameter must be used.

    +
    +
    +

    Upgrading from PostgreSQL to IvorySQL is considered a "cross-product" migration. It is recommended to complete comprehensive testing and validation before performing the operation in a production environment, and to develop a detailed rollback plan.

    +
    +
    +

    2.1. Parameter description

    + ++++ + + + + + + + + + + + + +
    parameterdescription

    -g / --using-ora-pg

    Indicates that the source cluster is PostgreSQL and the target is an IvorySQL cluster. When this option is enabled, pg_upgrade uniformly uses the PostgreSQL standard port when connecting to both clusters, ensuring proper handling of the upgrade.

    +
    +
    +

    2.2. Upgrade steps

    +
    +

    2.2.1. Step 1: Initialize the new IvorySQL cluster

    +
    +
    +
    # Use IvorySQL's initdb to initialize the new cluster
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data
    +
    +
    +
    +
    +

    2.2.2. Step 2: Stop the source PostgreSQL cluster

    +
    +
    +
    /usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop
    +
    +
    +
    +
    +

    2.2.3. Step 3: Run the upgrade check (optional)

    +
    +

    Use -c to perform compatibility checks only without modifying any data:

    +
    +
    +
    +
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # Source PG executable directory
+  -B /opt/ivorysql/bin \            # Target IvorySQL executable directory
+  -d /data/pg/data \                # Source PG data directory
+  -D /data/ivorysql/data \          # Target IvorySQL data directory
+  -g \                              # Source is PostgreSQL, target is IvorySQL
+  -c                                # Check only, do not upgrade
    +
    +
    +
    +
    +

    2.2.4. Step 4: Perform the upgrade

    +
    +
    +
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g
    +
    +
    +
    +
    +

    2.2.5. Step 5: Start IvorySQL and verify

    +
    +
    +
    /opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# Verify if the database is functioning properly
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"
    +
    +
    +
    +
    +

    2.2.6. Step 6: Clean up the old cluster

    +
    +

    After the upgrade is completed,pg_upgrade will generate a cleanup script:

    +
    +
    +
    +
    ./delete_old_cluster.sh
    +
    +
    +
    +
    +
    +

    2.3. Quick reference for common parameters

    + +++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ParametersLong optionsDescription

    -b

    --old-bindir

    Source cluster executable directory

    -B

    --new-bindir

    Target cluster executable directory

    -d

    --old-datadir

    Source cluster data directory

    -D

    --new-datadir

    Target cluster data directory

    -g

    --using-ora-pg

    Upgrade from PostgreSQL to IvorySQL

    -p

    --old-port

    Source cluster port

    -P

    --new-port

    Target cluster PG port

    -q

    --old-oraport

    Source cluster Oracle port

    -Q

    --new-oraport

    Target cluster Oracle port

    -j

    --jobs

    Number of parallel processes

    -k

    --link

    Use hard links instead of file copying

    -c

    --check

    Check compatibility only, do not perform the upgrade

    -v

    --verbose

    Output detailed logs

    +
    +
    +

    2.4. Precautions

    +
    +
      +
    • +

      The -g parameter is only used when the source is a pure PostgreSQL cluster、and the target is IvorySQL . If the source cluster is already IvorySQL, this parameter is not needed.

      +
      • +
    • +

      Both the source and target clusters must be in a stopped state during the upgrade.

      +
      • +
    • +

      Be sure to perform a complete backup of the source cluster before upgrading.

      +
      • +
    +
    +
    +
    +
    +
    +

    3. Managing IvorySQL Versions

    IvorySQL is based on PostgreSQL and is updated at the same frequency as PostgreSQL, with one major release per year and one minor release per quarter. IvorySQL 5.0 is based on PostgreSQL 18.0, and all versions of IvorySQL are backward compatible.The relevant version features can be viewed by looking at Official Website。

    @@ -679,7 +886,7 @@

    -

    3. Managing IvorySQL database access

    +

    4. Managing IvorySQL database access

    IvorySQL uses the concept of Roles to manage database access rights. A role can be thought of as a database user or a group of database users, depending on how the role is set. Roles can own database objects (for example, tables and functions) and can delegate permissions on those objects to other roles to control who can access which objects. In addition, membership in one role can be granted to another role, allowing member roles to use the permissions granted to another role.

    @@ -747,21 +954,21 @@

    -

    4. Defining Data Objects

    +

    5. Defining Data Objects

    IvorySQL is based on PostgreSQL and has a full SQL with defined data objects that can be referred to doc.On top of that, IvorySQL has done some Oracle proprietary data object compatibility for Oracle compatibility.

    -

    4.1. VARCHAR2

    +

    5.1. VARCHAR2

    -

    4.1.1. Overview

    +

    5.1.1. Overview

    Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character.

    -

    4.1.2. Grammar

    +

    5.1.2. Grammar

    VARCHAR2(size)
    @@ -769,7 +976,7 @@

    4.1.2. Grammar

    -

    4.1.3. Use Cases

    +

    5.1.3. Use Cases

    create table test(a varchar2(5));
@@ -793,7 +1000,7 @@ 
    4.1.3. Use Cases
    -

    5. Search Data

    +

    6. Search Data

    IvorySQL is developed based on PostgreSQL, with full SQL, query data specific operations can be referred to doc.

    @@ -801,7 +1008,7 @@

    5. Search Data

    -

    6. Use of foreign data

    +

    7. Use of foreign data

    IvorySQL implements portions of the SQL/MED specification, allowing you to access data that resides outside IvorySQL using regular SQL queries. Such data is referred to as foreign data. (Note that this usage is not to be confused with foreign keys, which are a type of constraint within the database.)

    @@ -818,7 +1025,7 @@

    6

    -

    7. Backup and Restore

    +

    8. Backup and Restore

    As with everything that contains valuable data, IvorySQL databases should be backed up regularly. While the procedure is essentially simple, it is important to have a clear understanding of the underlying techniques and assumptions.

    @@ -840,7 +1047,7 @@

    7.

    -

    7.1. SQL Dump

    +

    8.1. SQL Dump

    The idea behind this dump method is to generate a file with SQL commands that, when fed back to the server, will recreate the database in the same state as it was at the time of the dump. IvorySQL provides the utility program pg_dumpfor this purpose. The basic usage of this command is:

    @@ -868,7 +1075,7 @@

    7.1. SQL Dump

    ​ Dumps created by pg_dump are internally consistent, meaning, the dump represents a snapshot of the database at the time pg_dump began running. pg_dump does not block other operations on the database while it is working. (Exceptions are those operations that need to operate with an exclusive lock, such as most forms of ALTER TABLE.)

    -

    7.1.1. Restoring the Dump

    +

    8.1.1. Restoring the Dump

    Text files created by pg_dump are intended to be read in by the psql program. The general command form to restore a dump is

    @@ -910,7 +1117,7 @@

    7.1

    -

    7.1.2. Using pg_dumpall

    +

    8.1.2. Using pg_dumpall

    pg_dump dumps only a single database at a time, and it does not dump information about roles or tablespaces (because those are cluster-wide rather than per-database). To support convenient dumping of the entire contents of a database cluster, the pg_dumpall program is provided. pg_dumpall backs up each database in a given cluster, and also preserves cluster-wide data such as role and tablespace definitions. The basic usage of this command is:

    @@ -938,7 +1145,7 @@

    7.1.2.

    -

    7.1.3. Handling Large Databases

    +

    8.1.3. Handling Large Databases

    Some operating systems have maximum file size limits that cause problems when creating large pg_dump output files. Fortunately, pg_dump can write to the standard output, so you can use standard Unix tools to work around this potential problem. There are several possible methods:

    @@ -1029,7 +1236,7 @@

    -

    7.2. File System Level Backup

    +

    8.2. File System Level Backup

    An alternative backup strategy is to directly copy the files that IvorySQL uses to store the data in the database，You can use whatever method you prefer for doing file system backups; for example:
    @@ -1070,7 +1277,7 @@

    -

    7.3. Continuous Archiving and Point-in-Time Recovery (PITR)

    +

    8.3. Continuous Archiving and Point-in-Time Recovery (PITR)

    At all times,IvorySQLmaintains a write ahead log (WAL) in the pg_wal/ subdirectory of the cluster’s data directory. The log records every change made to the database’s data files. This log exists primarily for crash-safety purposes: if the system crashes, the database can be restored to consistency by “replaying” the log entries made since the last checkpoint. However, the existence of the log makes it possible to use a third strategy for backing up databases: we can combine a file-system-level backup with backup of the WAL files. If recovery is needed, we restore the file system backup and then replay from the backed-up WAL files to bring the system to a current state. This approach is more complex to administer than either of the previous approaches, but it has some significant benefits:

    @@ -1103,7 +1310,7 @@

    -

    8. Loading and unloading data

    +

    9. Loading and unloading data

    COPY moves data between IvorySQL tables and standard file-system files. COPY TO copies the contents of a table to a file, while COPY FROM copies data from a file to a table (appending the data to whatever is in the table already). COPY TO can also copy the results of a SELECT query.

    @@ -1118,7 +1325,7 @@

    -

    8.1. Synopsis

    +

    9.1. Synopsis

    COPY table_name [ ( column_name [, ...] ) ]
@@ -1150,7 +1357,7 @@ 
    8.1. Synopsis
    -

    8.2. Outputs

    +

    9.2. Outputs

    On successful completion, a COPY command returns a command tag of the form

    @@ -1167,7 +1374,7 @@

    8.2. Outputs

    -

    8.3. Notes

    +

    9.3. Notes

    COPY TO can be used only with plain tables, not views, and does not copy rows from child tables or child partitions. For example, COPY `table TO` copies the same rows as SELECT * FROM ONLY `table. The syntax `COPY (SELECT * FROM `table) TO …​` can be used to dump all of the rows in an inheritance hierarchy, partitioned table, or view.

    @@ -1212,9 +1419,9 @@

    8.3. Notes

    -

    8.4. File Formats

    +

    9.4. File Formats

    -

    8.4.1. Text Format

    +

    9.4.1. Text Format

    When the text format is used, the data read or written is a text file with one line per table row. Columns in a row are separated by the delimiter character. The column values themselves are strings generated by the output function, or acceptable to the input function, of each attribute’s data type. The specified null string is used in place of columns that are null. COPY FROM will raise an error if any line of the input file contains more or fewer columns than are expected.

    @@ -1291,7 +1498,7 @@

    8.4.1. Text Forma

    -

    8.4.2. CSV Format

    +

    9.4.2. CSV Format

    This format option is used for importing and exporting the Comma Separated Value (CSV) file format used by many other programs, such as spreadsheets. Instead of the escaping rules used by IvorySQL’s standard text format, it produces and recognizes the common CSV escaping mechanism.

    @@ -1330,7 +1537,7 @@

    8.4.2. CSV Format

    -

    8.4.3. Binary Format

    +

    9.4.3. Binary Format

    The binary format option causes all data to be stored/read as binary format rather than as text. It is somewhat faster than the text and CSV formats, but a binary-format file is less portable across machine architectures and IvorySQL versions. Also, the binary format is very data type specific; for example it will not work to output binary data from a smallint column and read it into an integer column, even though that would work fine in text format.

    @@ -1396,7 +1603,7 @@

    8.4.3. Binary

    -

    8.4.4. File Trailer

    +

    9.4.4. File Trailer

    The file trailer consists of a 16-bit integer word containing -1. This is easily distinguished from a tuple’s field-count word.

    @@ -1406,7 +1613,7 @@

    8.4.4. File Tra

    -

    8.5. Examples

    +

    9.5. Examples

    The following example copies a table to the client using the vertical bar (|) as the field delimiter:

    @@ -1483,13 +1690,13 @@

    8.5. Examples

    -

    9. Performance Tips

    +

    10. Performance Tips

    Query performance can be affected by a variety of factors. Some of these factors can be controlled by the user, while others are fundamentals of the system’s lower-level design.

    -

    9.1. Using EXPLAIN

    +

    10.1. Using EXPLAIN

    IvorySQL devises a query plan for each query it receives. Choosing the right plan to match the query structure and the properties of the data is absolutely critical for good performance, so the system includes a complex planner that tries to choose good plans. You can use the EXPLAIN command to see what query plan the planner creates for any query. Plan-reading is an art that requires some experience to master, but this section attempts to cover the basics.

    @@ -1497,7 +1704,7 @@

    9.1. Using EX

    ​ The examples use `EXPLAIN’s default “text” output format, which is compact and convenient for humans to read. If you want to feed `EXPLAIN’s output to a program for further analysis, you should use one of its machine-readable output formats (XML, JSON, or YAML) instead.

    -

    9.1.1. EXPLAIN Basics

    +

    10.1.1. EXPLAIN Basics

    The structure of a query plan is a tree of plan nodes. Nodes at the bottom level of the tree are scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. There are also non-table row sources, such as VALUES clauses and set-returning functions in FROM, which have their own scan node types. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes above the scan nodes to perform these operations. Again, there is usually more than one possible way to do these operations, so different node types can appear here too. The output of EXPLAIN has one line for each node in the plan tree, showing the basic node type plus the cost estimates that the planner made for the execution of that plan node. Additional lines might appear, indented from the node’s summary line, to show additional properties of the node. The very first line (the summary line for the topmost node) has the estimated total execution cost for the plan; it is this number that the planner seeks to minimize.

    @@ -1834,7 +2041,7 @@

    9.1.1. EXPL

    -

    9.1.2. EXPLAIN ANALYZE

    +

    10.1.2. EXPLAIN ANALYZE

    It is possible to check the accuracy of the planner’s estimates by using EXPLAIN’s `ANALYZE option. With this option, EXPLAIN actually executes the query, and then displays the true row counts and true run time accumulated within each plan node, along with the same estimates that a plain EXPLAIN shows. For example, we might get a result like this:

    @@ -2044,7 +2251,7 @@

    9.1.2. EX

    -

    9.1.3. Caveats

    +

    10.1.3. Caveats

    There are two significant ways in which run times measured by EXPLAIN ANALYZE can deviate from normal execution of the same query. First, since no output rows are delivered to the client, network transmission costs and I/O conversion costs are not included. Second, the measurement overhead added by EXPLAIN ANALYZE can be significant, especially on machines with slow gettimeofday() operating-system calls. You can use the pg_test_timing tool to measure the overhead of timing on your system.

    @@ -2084,9 +2291,9 @@

    9.1.3. Caveats

    -

    9.2. Statistics Used by the Planner

    +

    10.2. Statistics Used by the Planner

    -

    9.2.1. Single-Column Statistics

    +

    10.2.1. Single-Column Statistics

    As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates.

    @@ -2154,7 +2361,7 @@

    -

    9.2.2. Extended Statistics

    +

    10.2.2. Extended Statistics

    It is common to see slow queries running bad execution plans because multiple columns used in the query clauses are correlated. The planner normally assumes that multiple conditions are independent of each other, an assumption that does not hold when column values are correlated. Regular statistics, because of their per-individual-column nature, cannot capture any knowledge about cross-column correlation. However, IvorySQL has the ability to compute multivariate statistics, which can capture such information.

    @@ -2171,7 +2378,7 @@

    9

    The following subsections describe the kinds of extended statistics that are currently supported.

    -
    9.2.2.1. Functional Dependencies
    +
    10.2.2.1. Functional Dependencies

    The simplest kind of extended statistics tracks functional dependencies, a concept used in definitions of database normal forms. We say that column b is functionally dependent on column a if knowledge of the value of a is sufficient to determine the value of b, that is there are no two rows having the same value of a but different values of b. In a fully normalized database, functional dependencies should exist only on primary keys and superkeys. However, in practice many data sets are not fully normalized for various reasons; intentional denormalization for performance reasons is a common example. Even in a fully normalized database, there may be partial correlation between some columns, which can be expressed as partial functional dependency.

    @@ -2206,7 +2413,7 @@
    -
    9.2.2.1.1. Limitations of Functional Dependencies
    +
    10.2.2.1.1. Limitations of Functional Dependencies

    Functional dependencies are currently only applied when considering simple equality conditions that compare columns to constant values, and IN clauses with constant values. They are not used to improve estimates for equality conditions comparing two columns or comparing a column to an expression, nor for range clauses, LIKE or any other type of condition.

    @@ -2235,7 +2442,7 @@
    -
    9.2.2.2. Multivariate N-Distinct Counts
    +
    10.2.2.2. Multivariate N-Distinct Counts

    Single-column statistics store the number of distinct values in each column. Estimates of the number of distinct values when combining more than one column (for example, for GROUP BY a, b) are frequently wrong when the planner only has single-column statistical data, causing it to select bad plans.

    @@ -2268,7 +2475,7 @@
    -
    9.2.2.3. Multivariate MCV Lists
    +
    10.2.2.3. Multivariate MCV Lists

    Another type of statistic stored for each column are most-common value lists. This allows very accurate estimates for individual columns, but may result in significant misestimates for queries with conditions on multiple columns.

    @@ -2313,7 +2520,7 @@
    -

    9.3. Controlling the Planner with Explicit JOIN Clauses

    +

    10.3. Controlling the Planner with Explicit JOIN Clauses

    It is possible to control the query planner to some extent by using the explicit JOIN syntax. To see why this matters, we first need some background.

    @@ -2410,18 +2617,18 @@

    -

    9.4. Populating a Database

    +

    10.4. Populating a Database

    One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible.

    -

    9.4.1. Disable Autocommit

    +

    10.4.1. Disable Autocommit

    When using multiple INSERT`s, turn off autocommit and just do one commit at the end. (In plain SQL, this means issuing `BEGIN at the start and COMMIT at the end. Some client libraries might do this behind your back, in which case you need to make sure the library does it when you want it done.) If you allow each insertion to be committed separately, IvorySQL is doing a lot of work for each row that is added. An additional benefit of doing all insertions in one transaction is that if the insertion of one row were to fail then the insertion of all rows inserted up to that point would be rolled back, so you won’t be stuck with partially loaded data.

    -

    9.4.2. Use COPY

    +

    10.4.2. Use COPY

    Use COPY to load all the rows in one command, instead of using a series of INSERT commands. The COPY command is optimized for loading large numbers of rows; it is less flexible than INSERT, but incurs significantly less overhead for large data loads. Since COPY is a single command, there is no need to disable autocommit if you use this method to populate a table.

    @@ -2436,7 +2643,7 @@

    9.4.2. Use COPY

    -

    9.4.3. Remove Indexes

    +

    10.4.3. Remove Indexes

    If you are loading a freshly created table, the fastest method is to create the table, bulk load the table’s data using COPY, then create any indexes needed for the table. Creating an index on pre-existing data is quicker than updating it incrementally as each row is loaded.

    @@ -2445,7 +2652,7 @@

    9.4.3. Remo

    -

    9.4.4. Remove Foreign Key Constraints

    +

    10.4.4. Remove Foreign Key Constraints

    Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing.

    @@ -2454,19 +2661,19 @@

    -

    9.4.5. Increase maintenance_work_mem

    +

    10.4.5. Increase maintenance_work_mem

    Temporarily increasing the maintenance_work_mem configuration variable when loading large amounts of data can lead to improved performance. This will help to speed up CREATE INDEX commands and ALTER TABLE ADD FOREIGN KEY commands. It won’t do much for COPY itself, so this advice is only useful when you are using one or both of the above techniques.

    -

    9.4.6. Increase max_wal_size

    +

    10.4.6. Increase max_wal_size

    Temporarily increasing the max_wal_size configuration variable can also make large data loads faster. This is because loading a large amount of data into IvorySQL will cause checkpoints to occur more often than the normal checkpoint frequency (specified by the checkpoint_timeout configuration variable). Whenever a checkpoint occurs, all dirty pages must be flushed to disk. By increasing max_wal_size temporarily during bulk data loads, the number of checkpoints that are required can be reduced.

    -

    9.4.7. Disable WAL Archival and Streaming Replication

    +

    10.4.7. Disable WAL Archival and Streaming Replication

    When loading large amounts of data into an installation that uses WAL archiving or streaming replication, it might be faster to take a new base backup after the load has completed than to process a large amount of incremental WAL data. To prevent incremental WAL logging while loading, disable archiving and streaming replication, by setting wal_level to minimal, archive_mode to off, and max_wal_senders to zero. But note that changing these settings requires a server restart, and makes any base backups taken before unavailable for archive recovery and standby server, which may lead to data loss.

    @@ -2475,13 +2682,13 @@

    -

    9.4.8. Run ANALYZE Afterwards

    +

    10.4.8. Run ANALYZE Afterwards

    Whenever you have significantly altered the distribution of data within a table, running ANALYZE is strongly recommended. This includes bulk loading large amounts of data into the table. Running ANALYZE (or VACUUM ANALYZE) ensures that the planner has up-to-date statistics about the table. With no statistics or obsolete statistics, the planner might make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics. Note that if the autovacuum daemon is enabled, it might run ANALYZE automatically.

    -

    9.4.9. Some Notes about pg_dump

    +

    10.4.9. Some Notes about pg_dump

    Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To restore a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while restoring a dump, not while creating it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.)

    @@ -2513,7 +2720,7 @@

    -

    9.5. Non-Durable Settings

    +

    10.5. Non-Durable Settings

    Durability is a database feature that guarantees the recording of committed transactions even if the server crashes or loses power. However, durability adds significant database overhead, so if your site does not require such a guarantee, IvorySQL can be configured to run much faster. The following are configuration changes you can make to improve performance in such cases. Except as noted below, durability is still guaranteed in case of a crash of the database software; only an abrupt operating system crash creates a risk of data loss or corruption when these settings are used.

    diff --git a/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html b/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html index a86caa32..74f14129 100644 --- a/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html +++ b/docs/en/ivorysql-doc/master/oracle_builtin_functions/sys_context.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html b/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html index 413d7f85..e61799f2 100644 --- a/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html +++ b/docs/en/ivorysql-doc/master/oracle_builtin_functions/userenv.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html b/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html index d6c7d61f..f9ea4bd7 100644 --- a/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html +++ b/docs/en/ivorysql-doc/master/oracle_compatibility/anonymous_block.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html b/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html index 04ed4b06..fe80cadd 100644 --- a/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html +++ b/docs/en/ivorysql-doc/master/oracle_compatibility/builtin_types_functions.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html index 888bfec8..17f84e19 100644 --- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html +++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_call_into.html @@ -172,6 +172,9 @@

    IvorySQL

  • wal2json
    • +
  • + pg_stat_monitor +
    • @@ -546,7 +549,15 @@

    3.4. Reference bind variables in parameters or INTO clauses

    -
    -- Set input bind variables
+
    -- Create functions stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- Set input bind variables
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- Call a function using bind variables as parameters
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
index ba4ba6c9..f38d4bc6 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_case_conversion.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
index 99d6be0a..e25b68e9 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_empty_string_to_null.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
index 734d5db8..db5791ed 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_force_view.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
index ec01add9..9baeba4a 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_function_procedure.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
index 751c7d9c..c68df1ec 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_like_operator.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
index 0db00e4c..ca46a1e6 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nested_function.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
index 4b8f9bfb..2a4ef517 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_nls_parameter.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
index c73c9e05..e2b6042c 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_out_parameter.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
index 399a2fa4..58b6fb4a 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_rowid.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
index 4711d472..e30afd50 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_sys_guid.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
index 30bf2a18..caa0ac8f 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/compat_type_rowtype.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html b/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html
index 924f0d9a..17a971af 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/invisible_column.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/package.html b/docs/en/ivorysql-doc/master/oracle_compatibility/package.html
index 0b1edf33..bbcbc915 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/package.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/package.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html b/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html
index 60ea33ec..fa1d1a9b 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/port_ip.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html b/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html
index eb3e2e9b..a7089239 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/sequence.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html b/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html
index 92e135c6..ce20d368 100644
--- a/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html
+++ b/docs/en/ivorysql-doc/master/oracle_compatibility/xml_functions.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/release_notes.html b/docs/en/ivorysql-doc/master/release_notes.html
index c03b7f94..469e1d57 100644
--- a/docs/en/ivorysql-doc/master/release_notes.html
+++ b/docs/en/ivorysql-doc/master/release_notes.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/tools_reference.html b/docs/en/ivorysql-doc/master/tools_reference.html
index f6cce37d..371ebde6 100644
--- a/docs/en/ivorysql-doc/master/tools_reference.html
+++ b/docs/en/ivorysql-doc/master/tools_reference.html
@@ -172,6 +172,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/master/welcome.html b/docs/en/ivorysql-doc/master/welcome.html
index 2914bfdf..e94cf2e2 100644
--- a/docs/en/ivorysql-doc/master/welcome.html
+++ b/docs/en/ivorysql-doc/master/welcome.html
@@ -171,6 +171,9 @@ 
    IvorySQL
    
   
  • 
     wal2json
   
    • 
+  
  • 
+    pg_stat_monitor
+  
    • 
 
   
 
diff --git a/docs/en/ivorysql-doc/v5.3/4.4.html b/docs/en/ivorysql-doc/v5.3/4.4.html
index 83f83191..0599b2a4 100644
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+++ b/docs/en/ivorysql-doc/v5.3/4.4.html
@@ -698,7 +698,211 @@ 
    
-
    2. Managing IvorySQL Versions
    
+
    2. Use pg_upgrade to upgrade PostgreSQL to IvorySQL
    
+
    
+
    
+
    pg_upgrade supports upgrading native PostgreSQL clusters to IvorySQL. When the source cluster is native PostgreSQL and the target is an IvorySQL cluster, the -g（--using-ora-pg）parameter must be used.
    
+
    
+
    
+
    Upgrading from PostgreSQL to IvorySQL is considered a "cross-product" migration. It is recommended to complete comprehensive testing and validation before performing the operation in a production environment, and to develop a detailed rollback plan.
    
+
    
+
    
+
    2.1. Parameter description
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
    



    parameterdescription
    -g / --using-ora-pg
    Indicates that the source cluster is PostgreSQL and the target is an IvorySQL cluster. When this option is enabled, pg_upgrade uniformly uses the PostgreSQL standard port when connecting to both clusters, ensuring proper handling of the upgrade.
    
+
    
+
    
+
    2.2. Upgrade steps
    
+
    
+
    2.2.1. Step 1: Initialize the new IvorySQL cluster
    
+
    
+
    
+
    # Use IvorySQL's initdb to initialize the new cluster
+/opt/ivorysql/bin/initdb -D /data/ivorysql/data
    
+
    
+
    
+
    
+
    
+
    2.2.2. Step 2: Stop the source PostgreSQL cluster
    
+
    
+
    
+
    /usr/lib/postgresql/16/bin/pg_ctl -D /data/pg/data stop
    
+
    
+
    
+
    
+
    
+
    2.2.3. Step 3: Run the upgrade check (optional)
    
+
    
+
    Use -c to perform compatibility checks only without modifying any data:
    
+
    
+
    
+
    
+
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \   # Source PG executable directory
+  -B /opt/ivorysql/bin \            # Target IvorySQL executable directory
+  -d /data/pg/data \                # Source PG data directory
+  -D /data/ivorysql/data \          # Target IvorySQL data directory
+  -g \                              # Source is PostgreSQL, target is IvorySQL
+  -c                                # Check only, do not upgrade
    
+
    
+
    
+
    
+
    
+
    2.2.4. Step 4: Perform the upgrade
    
+
    
+
    
+
    /opt/ivorysql/bin/pg_upgrade \
+  -b /usr/lib/postgresql/16/bin \
+  -B /opt/ivorysql/bin \
+  -d /data/pg/data \
+  -D /data/ivorysql/data \
+  -g
    
+
    
+
    
+
    
+
    
+
    2.2.5. Step 5: Start IvorySQL and verify
    
+
    
+
    
+
    /opt/ivorysql/bin/pg_ctl -D /data/ivorysql/data start
+
+# Verify if the database is functioning properly
+/opt/ivorysql/bin/psql -p 5432 -c "SELECT version();"
    
+
    
+
    
+
    
+
    
+
    2.2.6. Step 6: Clean up the old cluster
    
+
    
+
    After the upgrade is completed,pg_upgrade will generate a cleanup script:
    
+
    
+
    
+
    
+
    ./delete_old_cluster.sh
    
+
    
+
    
+
    
+
    
+
    
+
    2.3. Quick reference for common parameters
    
+
+++++
+
+
+
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    ParametersLong optionsDescription
    -b
    --old-bindir
    Source cluster executable directory
    -B
    --new-bindir
    Target cluster executable directory
    -d
    --old-datadir
    Source cluster data directory
    -D
    --new-datadir
    Target cluster data directory
    -g
    --using-ora-pg
    Upgrade from PostgreSQL to IvorySQL
    -p
    --old-port
    Source cluster port
    -P
    --new-port
    Target cluster PG port
    -q
    --old-oraport
    Source cluster Oracle port
    -Q
    --new-oraport
    Target cluster Oracle port
    -j
    --jobs
    Number of parallel processes
    -k
    --link
    Use hard links instead of file copying
    -c
    --check
    Check compatibility only, do not perform the upgrade
    -v
    --verbose
    Output detailed logs
    
+
    
+
    
+
    2.4. Precautions
    
+
    
+
      
+
    • 
+
      The -g parameter is only used when the source is a pure PostgreSQL cluster、and the target is IvorySQL . If the source cluster is already IvorySQL, this parameter is not needed.
      
+
      • 
+
    • 
+
      Both the source and target clusters must be in a stopped state during the upgrade.
      
+
      • 
+
    • 
+
      Be sure to perform a complete backup of the source cluster before upgrading.
      
+
      • 
+
    
+
    
+
    
+
    
+
    +
    +

    3. Managing IvorySQL Versions

    IvorySQL is based on PostgreSQL and is updated at the same frequency as PostgreSQL, with one major release per year and one minor release per quarter. IvorySQL 5.3 is based on PostgreSQL 18.3, and all versions of IvorySQL are backward compatible.The relevant version features can be viewed by looking at Official Website。

    @@ -706,7 +910,7 @@

    -

    3. Managing IvorySQL database access

    +

    4. Managing IvorySQL database access

    IvorySQL uses the concept of Roles to manage database access rights. A role can be thought of as a database user or a group of database users, depending on how the role is set. Roles can own database objects (for example, tables and functions) and can delegate permissions on those objects to other roles to control who can access which objects. In addition, membership in one role can be granted to another role, allowing member roles to use the permissions granted to another role.

    @@ -774,21 +978,21 @@

    -

    4. Defining Data Objects

    +

    5. Defining Data Objects

    IvorySQL is based on PostgreSQL and has a full SQL with defined data objects that can be referred to doc.On top of that, IvorySQL has done some Oracle proprietary data object compatibility for Oracle compatibility.

    -

    4.1. VARCHAR2

    +

    5.1. VARCHAR2

    -

    4.1.1. Overview

    +

    5.1.1. Overview

    Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character.

    -

    4.1.2. Grammar

    +

    5.1.2. Grammar

    VARCHAR2(size)
    @@ -796,7 +1000,7 @@

    4.1.2. Grammar

    -

    4.1.3. Use Cases

    +

    5.1.3. Use Cases

    create table test(a varchar2(5));
@@ -820,7 +1024,7 @@ 
    4.1.3. Use Cases
    -

    5. Search Data

    +

    6. Search Data

    IvorySQL is developed based on PostgreSQL, with full SQL, query data specific operations can be referred to doc.

    @@ -828,7 +1032,7 @@

    5. Search Data

    -

    6. Use of foreign data

    +

    7. Use of foreign data

    IvorySQL implements portions of the SQL/MED specification, allowing you to access data that resides outside IvorySQL using regular SQL queries. Such data is referred to as foreign data. (Note that this usage is not to be confused with foreign keys, which are a type of constraint within the database.)

    @@ -845,7 +1049,7 @@

    6

    -

    7. Backup and Restore

    +

    8. Backup and Restore

    As with everything that contains valuable data, IvorySQL databases should be backed up regularly. While the procedure is essentially simple, it is important to have a clear understanding of the underlying techniques and assumptions.

    @@ -867,7 +1071,7 @@

    7.

    -

    7.1. SQL Dump

    +

    8.1. SQL Dump

    The idea behind this dump method is to generate a file with SQL commands that, when fed back to the server, will recreate the database in the same state as it was at the time of the dump. IvorySQL provides the utility program pg_dumpfor this purpose. The basic usage of this command is:

    @@ -895,7 +1099,7 @@

    7.1. SQL Dump

    ​ Dumps created by pg_dump are internally consistent, meaning, the dump represents a snapshot of the database at the time pg_dump began running. pg_dump does not block other operations on the database while it is working. (Exceptions are those operations that need to operate with an exclusive lock, such as most forms of ALTER TABLE.)

    -

    7.1.1. Restoring the Dump

    +

    8.1.1. Restoring the Dump

    Text files created by pg_dump are intended to be read in by the psql program. The general command form to restore a dump is

    @@ -937,7 +1141,7 @@

    7.1

    -

    7.1.2. Using pg_dumpall

    +

    8.1.2. Using pg_dumpall

    pg_dump dumps only a single database at a time, and it does not dump information about roles or tablespaces (because those are cluster-wide rather than per-database). To support convenient dumping of the entire contents of a database cluster, the pg_dumpall program is provided. pg_dumpall backs up each database in a given cluster, and also preserves cluster-wide data such as role and tablespace definitions. The basic usage of this command is:

    @@ -965,7 +1169,7 @@

    7.1.2.

    -

    7.1.3. Handling Large Databases

    +

    8.1.3. Handling Large Databases

    Some operating systems have maximum file size limits that cause problems when creating large pg_dump output files. Fortunately, pg_dump can write to the standard output, so you can use standard Unix tools to work around this potential problem. There are several possible methods:

    @@ -1056,7 +1260,7 @@

    -

    7.2. File System Level Backup

    +

    8.2. File System Level Backup

    An alternative backup strategy is to directly copy the files that IvorySQL uses to store the data in the database，You can use whatever method you prefer for doing file system backups; for example:
    @@ -1097,7 +1301,7 @@

    -

    7.3. Continuous Archiving and Point-in-Time Recovery (PITR)

    +

    8.3. Continuous Archiving and Point-in-Time Recovery (PITR)

    At all times,IvorySQLmaintains a write ahead log (WAL) in the pg_wal/ subdirectory of the cluster’s data directory. The log records every change made to the database’s data files. This log exists primarily for crash-safety purposes: if the system crashes, the database can be restored to consistency by “replaying” the log entries made since the last checkpoint. However, the existence of the log makes it possible to use a third strategy for backing up databases: we can combine a file-system-level backup with backup of the WAL files. If recovery is needed, we restore the file system backup and then replay from the backed-up WAL files to bring the system to a current state. This approach is more complex to administer than either of the previous approaches, but it has some significant benefits:

    @@ -1130,7 +1334,7 @@

    -

    8. Loading and unloading data

    +

    9. Loading and unloading data

    COPY moves data between IvorySQL tables and standard file-system files. COPY TO copies the contents of a table to a file, while COPY FROM copies data from a file to a table (appending the data to whatever is in the table already). COPY TO can also copy the results of a SELECT query.

    @@ -1145,7 +1349,7 @@

    -

    8.1. Synopsis

    +

    9.1. Synopsis

    COPY table_name [ ( column_name [, ...] ) ]
@@ -1177,7 +1381,7 @@ 
    8.1. Synopsis
    -

    8.2. Outputs

    +

    9.2. Outputs

    On successful completion, a COPY command returns a command tag of the form

    @@ -1194,7 +1398,7 @@

    8.2. Outputs

    -

    8.3. Notes

    +

    9.3. Notes

    COPY TO can be used only with plain tables, not views, and does not copy rows from child tables or child partitions. For example, COPY `table TO` copies the same rows as SELECT * FROM ONLY `table. The syntax `COPY (SELECT * FROM `table) TO …​` can be used to dump all of the rows in an inheritance hierarchy, partitioned table, or view.

    @@ -1239,9 +1443,9 @@

    8.3. Notes

    -

    8.4. File Formats

    +

    9.4. File Formats

    -

    8.4.1. Text Format

    +

    9.4.1. Text Format

    When the text format is used, the data read or written is a text file with one line per table row. Columns in a row are separated by the delimiter character. The column values themselves are strings generated by the output function, or acceptable to the input function, of each attribute’s data type. The specified null string is used in place of columns that are null. COPY FROM will raise an error if any line of the input file contains more or fewer columns than are expected.

    @@ -1318,7 +1522,7 @@

    8.4.1. Text Forma

    -

    8.4.2. CSV Format

    +

    9.4.2. CSV Format

    This format option is used for importing and exporting the Comma Separated Value (CSV) file format used by many other programs, such as spreadsheets. Instead of the escaping rules used by IvorySQL’s standard text format, it produces and recognizes the common CSV escaping mechanism.

    @@ -1357,7 +1561,7 @@

    8.4.2. CSV Format

    -

    8.4.3. Binary Format

    +

    9.4.3. Binary Format

    The binary format option causes all data to be stored/read as binary format rather than as text. It is somewhat faster than the text and CSV formats, but a binary-format file is less portable across machine architectures and IvorySQL versions. Also, the binary format is very data type specific; for example it will not work to output binary data from a smallint column and read it into an integer column, even though that would work fine in text format.

    @@ -1423,7 +1627,7 @@

    8.4.3. Binary

    -

    8.4.4. File Trailer

    +

    9.4.4. File Trailer

    The file trailer consists of a 16-bit integer word containing -1. This is easily distinguished from a tuple’s field-count word.

    @@ -1433,7 +1637,7 @@

    8.4.4. File Tra

    -

    8.5. Examples

    +

    9.5. Examples

    The following example copies a table to the client using the vertical bar (|) as the field delimiter:

    @@ -1510,13 +1714,13 @@

    8.5. Examples

    -

    9. Performance Tips

    +

    10. Performance Tips

    Query performance can be affected by a variety of factors. Some of these factors can be controlled by the user, while others are fundamentals of the system’s lower-level design.

    -

    9.1. Using EXPLAIN

    +

    10.1. Using EXPLAIN

    IvorySQL devises a query plan for each query it receives. Choosing the right plan to match the query structure and the properties of the data is absolutely critical for good performance, so the system includes a complex planner that tries to choose good plans. You can use the EXPLAIN command to see what query plan the planner creates for any query. Plan-reading is an art that requires some experience to master, but this section attempts to cover the basics.

    @@ -1524,7 +1728,7 @@

    9.1. Using EX

    ​ The examples use `EXPLAIN’s default “text” output format, which is compact and convenient for humans to read. If you want to feed `EXPLAIN’s output to a program for further analysis, you should use one of its machine-readable output formats (XML, JSON, or YAML) instead.

    -

    9.1.1. EXPLAIN Basics

    +

    10.1.1. EXPLAIN Basics

    The structure of a query plan is a tree of plan nodes. Nodes at the bottom level of the tree are scan nodes: they return raw rows from a table. There are different types of scan nodes for different table access methods: sequential scans, index scans, and bitmap index scans. There are also non-table row sources, such as VALUES clauses and set-returning functions in FROM, which have their own scan node types. If the query requires joining, aggregation, sorting, or other operations on the raw rows, then there will be additional nodes above the scan nodes to perform these operations. Again, there is usually more than one possible way to do these operations, so different node types can appear here too. The output of EXPLAIN has one line for each node in the plan tree, showing the basic node type plus the cost estimates that the planner made for the execution of that plan node. Additional lines might appear, indented from the node’s summary line, to show additional properties of the node. The very first line (the summary line for the topmost node) has the estimated total execution cost for the plan; it is this number that the planner seeks to minimize.

    @@ -1861,7 +2065,7 @@

    9.1.1. EXPL

    -

    9.1.2. EXPLAIN ANALYZE

    +

    10.1.2. EXPLAIN ANALYZE

    It is possible to check the accuracy of the planner’s estimates by using EXPLAIN’s `ANALYZE option. With this option, EXPLAIN actually executes the query, and then displays the true row counts and true run time accumulated within each plan node, along with the same estimates that a plain EXPLAIN shows. For example, we might get a result like this:

    @@ -2071,7 +2275,7 @@

    9.1.2. EX

    -

    9.1.3. Caveats

    +

    10.1.3. Caveats

    There are two significant ways in which run times measured by EXPLAIN ANALYZE can deviate from normal execution of the same query. First, since no output rows are delivered to the client, network transmission costs and I/O conversion costs are not included. Second, the measurement overhead added by EXPLAIN ANALYZE can be significant, especially on machines with slow gettimeofday() operating-system calls. You can use the pg_test_timing tool to measure the overhead of timing on your system.

    @@ -2111,9 +2315,9 @@

    9.1.3. Caveats

    -

    9.2. Statistics Used by the Planner

    +

    10.2. Statistics Used by the Planner

    -

    9.2.1. Single-Column Statistics

    +

    10.2.1. Single-Column Statistics

    As we saw in the previous section, the query planner needs to estimate the number of rows retrieved by a query in order to make good choices of query plans. This section provides a quick look at the statistics that the system uses for these estimates.

    @@ -2181,7 +2385,7 @@

    -

    9.2.2. Extended Statistics

    +

    10.2.2. Extended Statistics

    It is common to see slow queries running bad execution plans because multiple columns used in the query clauses are correlated. The planner normally assumes that multiple conditions are independent of each other, an assumption that does not hold when column values are correlated. Regular statistics, because of their per-individual-column nature, cannot capture any knowledge about cross-column correlation. However, IvorySQL has the ability to compute multivariate statistics, which can capture such information.

    @@ -2198,7 +2402,7 @@

    9

    The following subsections describe the kinds of extended statistics that are currently supported.

    -
    9.2.2.1. Functional Dependencies
    +
    10.2.2.1. Functional Dependencies

    The simplest kind of extended statistics tracks functional dependencies, a concept used in definitions of database normal forms. We say that column b is functionally dependent on column a if knowledge of the value of a is sufficient to determine the value of b, that is there are no two rows having the same value of a but different values of b. In a fully normalized database, functional dependencies should exist only on primary keys and superkeys. However, in practice many data sets are not fully normalized for various reasons; intentional denormalization for performance reasons is a common example. Even in a fully normalized database, there may be partial correlation between some columns, which can be expressed as partial functional dependency.

    @@ -2233,7 +2437,7 @@
    -
    9.2.2.1.1. Limitations of Functional Dependencies
    +
    10.2.2.1.1. Limitations of Functional Dependencies

    Functional dependencies are currently only applied when considering simple equality conditions that compare columns to constant values, and IN clauses with constant values. They are not used to improve estimates for equality conditions comparing two columns or comparing a column to an expression, nor for range clauses, LIKE or any other type of condition.

    @@ -2262,7 +2466,7 @@
    -
    9.2.2.2. Multivariate N-Distinct Counts
    +
    10.2.2.2. Multivariate N-Distinct Counts

    Single-column statistics store the number of distinct values in each column. Estimates of the number of distinct values when combining more than one column (for example, for GROUP BY a, b) are frequently wrong when the planner only has single-column statistical data, causing it to select bad plans.

    @@ -2295,7 +2499,7 @@
    -
    9.2.2.3. Multivariate MCV Lists
    +
    10.2.2.3. Multivariate MCV Lists

    Another type of statistic stored for each column are most-common value lists. This allows very accurate estimates for individual columns, but may result in significant misestimates for queries with conditions on multiple columns.

    @@ -2340,7 +2544,7 @@
    -

    9.3. Controlling the Planner with Explicit JOIN Clauses

    +

    10.3. Controlling the Planner with Explicit JOIN Clauses

    It is possible to control the query planner to some extent by using the explicit JOIN syntax. To see why this matters, we first need some background.

    @@ -2437,18 +2641,18 @@

    -

    9.4. Populating a Database

    +

    10.4. Populating a Database

    One might need to insert a large amount of data when first populating a database. This section contains some suggestions on how to make this process as efficient as possible.

    -

    9.4.1. Disable Autocommit

    +

    10.4.1. Disable Autocommit

    When using multiple INSERT`s, turn off autocommit and just do one commit at the end. (In plain SQL, this means issuing `BEGIN at the start and COMMIT at the end. Some client libraries might do this behind your back, in which case you need to make sure the library does it when you want it done.) If you allow each insertion to be committed separately, IvorySQL is doing a lot of work for each row that is added. An additional benefit of doing all insertions in one transaction is that if the insertion of one row were to fail then the insertion of all rows inserted up to that point would be rolled back, so you won’t be stuck with partially loaded data.

    -

    9.4.2. Use COPY

    +

    10.4.2. Use COPY

    Use COPY to load all the rows in one command, instead of using a series of INSERT commands. The COPY command is optimized for loading large numbers of rows; it is less flexible than INSERT, but incurs significantly less overhead for large data loads. Since COPY is a single command, there is no need to disable autocommit if you use this method to populate a table.

    @@ -2463,7 +2667,7 @@

    9.4.2. Use COPY

    -

    9.4.3. Remove Indexes

    +

    10.4.3. Remove Indexes

    If you are loading a freshly created table, the fastest method is to create the table, bulk load the table’s data using COPY, then create any indexes needed for the table. Creating an index on pre-existing data is quicker than updating it incrementally as each row is loaded.

    @@ -2472,7 +2676,7 @@

    9.4.3. Remo

    -

    9.4.4. Remove Foreign Key Constraints

    +

    10.4.4. Remove Foreign Key Constraints

    Just as with indexes, a foreign key constraint can be checked “in bulk” more efficiently than row-by-row. So it might be useful to drop foreign key constraints, load data, and re-create the constraints. Again, there is a trade-off between data load speed and loss of error checking while the constraint is missing.

    @@ -2481,19 +2685,19 @@

    -

    9.4.5. Increase maintenance_work_mem

    +

    10.4.5. Increase maintenance_work_mem

    Temporarily increasing the maintenance_work_mem configuration variable when loading large amounts of data can lead to improved performance. This will help to speed up CREATE INDEX commands and ALTER TABLE ADD FOREIGN KEY commands. It won’t do much for COPY itself, so this advice is only useful when you are using one or both of the above techniques.

    -

    9.4.6. Increase max_wal_size

    +

    10.4.6. Increase max_wal_size

    Temporarily increasing the max_wal_size configuration variable can also make large data loads faster. This is because loading a large amount of data into IvorySQL will cause checkpoints to occur more often than the normal checkpoint frequency (specified by the checkpoint_timeout configuration variable). Whenever a checkpoint occurs, all dirty pages must be flushed to disk. By increasing max_wal_size temporarily during bulk data loads, the number of checkpoints that are required can be reduced.

    -

    9.4.7. Disable WAL Archival and Streaming Replication

    +

    10.4.7. Disable WAL Archival and Streaming Replication

    When loading large amounts of data into an installation that uses WAL archiving or streaming replication, it might be faster to take a new base backup after the load has completed than to process a large amount of incremental WAL data. To prevent incremental WAL logging while loading, disable archiving and streaming replication, by setting wal_level to minimal, archive_mode to off, and max_wal_senders to zero. But note that changing these settings requires a server restart, and makes any base backups taken before unavailable for archive recovery and standby server, which may lead to data loss.

    @@ -2502,13 +2706,13 @@

    -

    9.4.8. Run ANALYZE Afterwards

    +

    10.4.8. Run ANALYZE Afterwards

    Whenever you have significantly altered the distribution of data within a table, running ANALYZE is strongly recommended. This includes bulk loading large amounts of data into the table. Running ANALYZE (or VACUUM ANALYZE) ensures that the planner has up-to-date statistics about the table. With no statistics or obsolete statistics, the planner might make poor decisions during query planning, leading to poor performance on any tables with inaccurate or nonexistent statistics. Note that if the autovacuum daemon is enabled, it might run ANALYZE automatically.

    -

    9.4.9. Some Notes about pg_dump

    +

    10.4.9. Some Notes about pg_dump

    Dump scripts generated by pg_dump automatically apply several, but not all, of the above guidelines. To restore a pg_dump dump as quickly as possible, you need to do a few extra things manually. (Note that these points apply while restoring a dump, not while creating it. The same points apply whether loading a text dump with psql or using pg_restore to load from a pg_dump archive file.)

    @@ -2540,7 +2744,7 @@

    -

    9.5. Non-Durable Settings

    +

    10.5. Non-Durable Settings

    Durability is a database feature that guarantees the recording of committed transactions even if the server crashes or loses power. However, durability adds significant database overhead, so if your site does not require such a guarantee, IvorySQL can be configured to run much faster. The following are configuration changes you can make to improve performance in such cases. Except as noted below, durability is still guaranteed in case of a crash of the database software; only an abrupt operating system crash creates a risk of data loss or corruption when these settings are used.

    diff --git a/docs/en/ivorysql-doc/v5.3/7.22.html b/docs/en/ivorysql-doc/v5.3/7.22.html index a937f6d9..12141aec 100644 --- a/docs/en/ivorysql-doc/v5.3/7.22.html +++ b/docs/en/ivorysql-doc/v5.3/7.22.html @@ -573,7 +573,15 @@

    3.4. Reference bind variables in parameters or INTO clauses

    -
    -- Set input bind variables
+
    -- Create functions stand_alone_func
+CREATE OR REPLACE FUNCTION stand_alone_func(p_input NUMBER)
+RETURN NUMBER
+IS
+BEGIN
+    RETURN p_input * 2;
+END;
+/
+-- Set input bind variables
 ivorysql=# VARIABLE input_num NUMBER = 7;
 -- Call a function using bind variables as parameters
 ivorysql=# VARIABLE func_result NUMBER;
diff --git a/docs/en/ivorysql-doc/v5.3/_exports/index.pdf b/docs/en/ivorysql-doc/v5.3/_exports/index.pdf
index 646ce65f..286e3fbc 100644
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diff --git a/docs/en/ivorysql-doc/v5.3/ivorysql.pdf b/docs/en/ivorysql-doc/v5.3/ivorysql.pdf
index 646ce65f..286e3fbc 100644
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diff --git a/docs/en/search-index.js b/docs/en/search-index.js
index f455e25c..17b71a60 100644
--- a/docs/en/search-index.js
+++ b/docs/en/search-index.js
@@ -1 +1 @@
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-p  -U ivorysql -D /usr/local/ivorysql/ivorysql-5/data Specifies the host name of the machine on which the server is running; Specifies the TCP port or local Unix domain socket file extension on which the server is listening for connections. Defaults is 5432; User name to connect as; Directory to write the output to. pg_basebackup will create the directory and any parent directories if necessary. The directory may already exist, but it is an error if the directory already exists and is not empty. For more options, refer to pg_basebackup --help. Add below contents in ~/.bash_profile file: PATH=/usr/local/ivorysql/ivorysql-5/bin:$PATH export PATH PGDATA=/usr/local/ivorysql/ivorysql-5/data export PGDATA Source to make it effective: $ source ~/.bash_profile $ sudo pg_ctl -D /usr/local/ivorysql/ivorysql-5/data start Run below command on the primary node, you will see walsender: $ ps -ef |grep postgres ... ivorysql 11176 8067 0 21:54 ? 00:00:00 postgres: walsender ivorysql 192.168.31.102(53416) streaming 0/7000060... while it is walreceiver on standby: $ ps -ef | grep postgres ... ivorysql 6567 6139 0 21:54 ? 00:00:00 postgres: walreceiver streaming 0/7000060 ... On the primary node, connect to IvorySQL and show the status: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# select * from pg_stat_replication; pid | usesysid | usename | application_name | client_addr | client_hostname | client_port | backend_start | backend_ xmin | state | sent_lsn | write_lsn | flush_lsn | replay_lsn | write_lag | flush_lag | replay_lag | sync_priority | sync_state | reply_time -------+----------+----------+------------------+----------------+-----------------+-------------+-------------------------------+--------- -----+-----------+-----------+-----------+-----------+------------+-----------+-----------+------------+---------------+------------+------ ------------------------- 11176 | 10 | ivorysql | walreceiver | 192.168.31.102 | | 53416 | 2024-12-18 21:54:52.041847-05 | | streaming | 0/7000148 | 0/7000148 | 0/7000148 | 0/7000148 | | | | 0 | async | 2024- 12-18 22:52:07.325111-05 (1 row) Here 192.168.31.102 is the ip address of the standby node, and async means the data synchronization method is asynchronous. All writing operations are performed on the primary node, while reading can be on both primary and standby. The data on primary is synchronized to standby through streaming replication. The writing result can be queried on all the nodes in the cluster. Below is an example. Create a new database test on primary and query: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# create database test; CREATE DATABASE ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows) Query on the standby node: $ psql -d ivorysql psql (18.0) Type \"help\" for help. ivorysql=# \\l List of databases Name | Owner | Encoding | Locale Provider | Collate | Ctype | ICU Locale | ICU Rules | Access privileges -----------+----------+----------+-----------------+-------------+-------------+------------+-----------+----------------------- ivorysql | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | template0 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql template1 | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | =c/ivorysql + | | | | | | | | ivorysql=CTc/ivorysql test | ivorysql | UTF8 | libc | en_US.UTF-8 | en_US.UTF-8 | | | (4 rows)","title":"Building an IvorySQL cluster","component":"ivorysql-doc","version":"master","name":"cluster_setup","url":"/ivorysql-doc/master/cluster_setup.html","titles":[{"text":"1. Primary node","hash":"primary-node","id":1},{"text":"1.1. Installing and start database","hash":"installing-and-start-database","id":2},{"text":"1.2. Stopping firewall","hash":"stopping-firewall","id":3},{"text":"1.3. Setting environment variables","hash":"setting-environment-variables","id":4},{"text":"1.4. Restarting IvorySQL sevice","hash":"restarting-ivorysql-sevice","id":5},{"text":"2. Standby node","hash":"standby-node","id":6},{"text":"2.1. Installing database","hash":"installing-database","id":7},{"text":"2.2. Stopping firewall","hash":"stopping-firewall-2","id":8},{"text":"2.3. Building streaming replication","hash":"building-streaming-replication","id":9},{"text":"2.4. Setting environment variables","hash":"setting-environment-variables-2","id":10},{"text":"2.5. Starting IvorySQL sevice","hash":"starting-ivorysql-sevice","id":11},{"text":"3. Experience the IvorySQL cluster","hash":"experience-the-ivorysql-cluster","id":12},{"text":"3.1. Checking cluster status","hash":"checking-cluster-status","id":13},{"text":"3.2. Using the cluster","hash":"using-the-cluster","id":14}]},"3":{"id":3,"text":"lvorySQL provides unique additional functionality on top of the open source PostgreSQL database. IvorySQL is committed to delivering value to its end-users through innovation and building on top of open source based database solutions. Our goal is to deliver a solution with high performance，scalability，reliability，and ease of use for small medium and large-scale enterprises. The extended functionality provided by IvorySQL will enable users to build highly performant and scalable PostgreSQL database clusters with better database compatibility and administration.This simplifies the process of migration to PostgreSQLfrom other DBMS with enhanced database administration experiences. The IvorySQL follows the same general architecture of PostgreSQL with some additions,but it does not deviate from its core philosophy.Thediagram below depicts essentially how IvorySQL operates. The yellow color in the diagram shows the new modules added by IvorySQL on top of existing PostgreSQL while IvorySQL has also made changes to existing modules and logical structures as well. The most noteworthy of those modules that received updates for supporting oracle compatibility are backend parser and system catalogs. The following diagram depicts the changes made to PostgreSQL’s existing directories and the additions that have been made. The first test to see whether you can access the database server is to try to create a database. A running IvorySQL server can manage many databases. Typically, a separate database is used for each project or for each user. Possibly, your site administrator has already created a database for your use. In that case you can omit this step and skip ahead to the next section. To create a new database, in this example named mydb, you use the following command: $ createdb mydb If this produces no response then this step was successful and you can skip over the remainder of this section. If you see a message similar to: createdb: command not found then IvorySQL was not installed properly. Either it was not installed at all or your shell’s search path was not set to include it. Try calling the command with an absolute path instead: $ /usr/local/pgsql/bin/createdb mydb The path at your site might be different. Contact your site administrator or check the installation instructions to correct the situation. Another response could be this: createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: No such file or directory Is the server running locally and accepting connections on that socket? This means that the server was not started, or it is not listening where createdb expects to contact it. Again, check the installation instructions or consult the administrator. Another response could be this: createdb: error: connection to server on socket \"/tmp/.s.PGSQL.5432\" failed: FATAL: role \"joe\" does not exist where your own login name is mentioned. This will happen if the administrator has not created a IvorySQL user account for you. (IvorySQL user accounts are distinct from operating system user accounts.) If you are the administrator, You will need to become the operating system user under which IvorySQL was installed (usually postgres) to create the first user account. It could also be that you were assigned a IvorySQL user name that is different from your operating system user name; in that case you need to use the -U switch or set the PGUSER environment variable to specify your IvorySQL user name. If you have a user account but it does not have the privileges required to create a database, you will see the following: createdb: error: database creation failed: ERROR: permission denied to create database Not every user has authorization to create new databases. If IvorySQL refuses to create databases for you then the site administrator needs to grant you permission to create databases. Consult your site administrator if this occurs. If you installed IvorySQL yourself then you should log in for the purposes of this tutorial under the user account that you started the server as. [1] You can also create databases with other names. IvorySQL allows you to create any number of databases at a given site. Database names must have an alphabetic first character and are limited to 63 bytes in length. A convenient choice is to create a database with the same name as your current user name. Many tools assume that database name as the default, so it can save you some typing. To create that database, simply type: $ createdb If you do not want to use your database anymore you can remove it. For example, if you are the owner (creator) of the database mydb, you can destroy it using the following command: $ dropdb mydb (For this command, the database name does not default to the user account name. You always need to specify it.) This action physically removes all files associated with the database and cannot be undone, so this should only be done with a great deal of forethought. More about createdb and dropdb can be found in createdb and dropdb respectively. You can create a new table by specifying the table name, along with all column names and their types: CREATE TABLE weather ( city varchar(80), temp_lo int, -- low temperature temp_hi int, -- high temperature prcp real, -- precipitation date date ); You can enter this into psql with the line breaks. psql will recognize that the command is not terminated until the semicolon. White space (i.e., spaces, tabs, and newlines) can be used freely in SQL commands. That means you can type the command aligned differently than above, or even all on one line. Two dashes (“--”) introduce comments. Whatever follows them is ignored up to the end of the line. SQL is case insensitive about key words and identifiers, except when identifiers are double-quoted to preserve the case (not done above). varchar(80) specifies a data type that can store arbitrary character strings up to 80 characters in length. int is the normal integer type. real is a type for storing single precision floating-point numbers. date should be self-explanatory. (Yes, the column of type date is also named date. This might be convenient or confusing — you choose.) IvorySQL supports the standard SQL types int, smallint, real, double precision, char(`N), `varchar(`N), `date, time, timestamp, and interval, as well as other types of general utility and a rich set of geometric types. IvorySQL can be customized with an arbitrary number of user-defined data types. Consequently, type names are not key words in the syntax, except where required to support special cases in the SQL standard. The second example will store cities and their associated geographical location: CREATE TABLE cities ( name varchar(80), location point ); The point type is an example of a IvorySQL-specific data type. Finally, it should be mentioned that if you don’t need a table any longer or want to recreate it differently you can remove it using the following command: DROP TABLE tablename; When a table is created, it contains no data. The first thing to do before a database can be of much use is to insert data. Data is inserted one row at a time. You can also insert more than one row in a single command, but it is not possible to insert something that is not a complete row. Even if you know only some column values, a complete row must be created. To create a new row, use the INSERT command. The command requires the table name and column values. CREATE TABLE products ( product_no integer, name text, price numeric ); CREATE TABLE new_products ( product_no int , name varchar(255), price DECIMAL(10, 2), release_date DATE ); An example command to insert a row would be: INSERT INTO products VALUES (1, 'Cheese', 9.99); The data values are listed in the order in which the columns appear in the table, separated by commas. Usually, the data values will be literals (constants), but scalar expressions are also allowed. The above syntax has the drawback that you need to know the order of the columns in the table. To avoid this you can also list the columns explicitly. For example, both of the following commands have the same effect as the one above: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99); INSERT INTO products (name, price, product_no) VALUES ('Cheese', 9.99, 1); Many users consider it good practice to always list the column names. If you don’t have values for all the columns, you can omit some of them. In that case, the columns will be filled with their default values. For example: INSERT INTO products (product_no, name) VALUES (1, 'Cheese'); INSERT INTO products VALUES (1, 'Cheese'); The second form is a IvorySQL extension. It fills the columns from the left with as many values as are given, and the rest will be defaulted. For clarity, you can also request default values explicitly, for individual columns or for the entire row: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', DEFAULT); INSERT INTO products DEFAULT VALUES; You can insert multiple rows in a single command: INSERT INTO products (product_no, name, price) VALUES (1, 'Cheese', 9.99), (2, 'Bread', 1.99), (3, 'Milk', 2.99); INSERT INTO new_products (product_no, name, price, release_date) VALUES (1, 'A', 100.00, '2025-05-29'), (2, 'B', 150.50, '2024-11-20'), (3, 'C', 200.75, '2025-05-29'); It is also possible to insert the result of a query (which might be no rows, one row, or many rows): INSERT INTO products (product_no, name, price) SELECT product_no, name, price FROM new_products WHERE release_date = '2025-05-29'; This provides the full power of the SQL query mechanism for computing the rows to be inserted. Tip When inserting a lot of data at the same time, consider using the COPY command. It is not as flexible as the INSERT command, but is more efficient. The results of two queries can be combined using the set operations union, intersection, and difference. The syntax is query1 UNION [ALL] query2 query1 INTERSECT [ALL] query2 query1 EXCEPT [ALL] query2 where query1 and query2 are queries that can use any of the features discussed up to this point. UNION effectively appends the result of query2 to the result of query1 (although there is no guarantee that this is the order in which the rows are actually returned). Furthermore, it eliminates duplicate rows from its result, in the same way as DISTINCT, unless UNION ALL is used. INTERSECT returns all rows that are both in the result of query1 and in the result of query2. Duplicate rows are eliminated unless INTERSECT ALL is used. EXCEPT returns all rows that are in the result of query1 but not in the result of query2. (This is sometimes called the difference between two queries.) Again, duplicates are eliminated unless EXCEPT ALL is used. In order to calculate the union, intersection, or difference of two queries, the two queries must be “union compatible”, which means that they return the same number of columns and the corresponding columns have compatible data types. Set operations can be combined, for example query1 UNION query2 EXCEPT query3 which is equivalent to (query1 UNION query2) EXCEPT query3 As shown here, you can use parentheses to control the order of evaluation. Without parentheses, UNION and EXCEPT associate left-to-right, but INTERSECT binds more tightly than those two operators. Thus query1 UNION query2 INTERSECT query3 means query1 UNION (query2 INTERSECT query3) You can also surround an individual query with parentheses. This is important if the query needs to use any of the clauses discussed in following sections, such as LIMIT. Without parentheses, you’ll get a syntax error, or else the clause will be understood as applying to the output of the set operation rather than one of its inputs. For example, SELECT a FROM b UNION SELECT x FROM y LIMIT 10 is accepted, but it means (SELECT a FROM b UNION SELECT x FROM y) LIMIT 10 not SELECT a FROM b UNION (SELECT x FROM y LIMIT 10) When the optimizer determines that parallel query is the fastest execution strategy for a particular query, it will create a query plan that includes a Gather or Gather Merge node. Here is a simple example: EXPLAIN SELECT * FROM pgbench_accounts WHERE filler LIKE '%x%'; QUERY PLAN ------------------------------------------------------------------------------------- Gather (cost=1000.00..217018.43 rows=1 width=97) Workers Planned: 2 -> Parallel Seq Scan on pgbench_accounts (cost=0.00..216018.33 rows=1 width=97) Filter: (filler ~~ '%x%'::text) (4 rows) In all cases, the Gather or Gather Merge node will have exactly one child plan, which is the portion of the plan that will be executed in parallel. If the Gather or Gather Merge node is at the very top of the plan tree, then the entire query will execute in parallel. If it is somewhere else in the plan tree, then only the portion of the plan below it will run in parallel. In the example above, the query accesses only one table, so there is only one plan node other than the Gather node itself; since that plan node is a child of the Gather node, it will run in parallel. Using EXPLAIN, you can see the number of workers chosen by the planner. When the Gather node is reached during query execution, the process that is implementing the user’s session will request a number of background worker processes equal to the number of workers chosen by the planner. The number of background workers that the planner will consider using is limited to at most max_parallel_workers_per_gather. The total number of background workers that can exist at any one time is limited by both max_worker_processes and max_parallel_workers. Therefore, it is possible for a parallel query to run with fewer workers than planned, or even with no workers at all. The optimal plan may depend on the number of workers that are available, so this can result in poor query performance. If this occurrence is frequent, consider increasing max_worker_processes and max_parallel_workers so that more workers can be run simultaneously or alternatively reducing max_parallel_workers_per_gather so that the planner requests fewer workers. Every background worker process that is successfully started for a given parallel query will execute the parallel portion of the plan. The leader will also execute that portion of the plan, but it has an additional responsibility: it must also read all of the tuples generated by the workers. When the parallel portion of the plan generates only a small number of tuples, the leader will often behave very much like an additional worker, speeding up query execution. Conversely, when the parallel portion of the plan generates a large number of tuples, the leader may be almost entirely occupied with reading the tuples generated by the workers and performing any further processing steps that are required by plan nodes above the level of the Gather node or Gather Merge node. In such cases, the leader will do very little of the work of executing the parallel portion of the plan. When the node at the top of the parallel portion of the plan is Gather Merge rather than Gather, it indicates that each process executing the parallel portion of the plan is producing tuples in sorted order, and that the leader is performing an order-preserving merge. In contrast, Gather reads tuples from the workers in whatever order is convenient, destroying any sort order that may have existed. There are several settings that can cause the query planner not to generate a parallel query plan under any circumstances. In order for any parallel query plans whatsoever to be generated, the following settings must be configured as indicated. max_parallel_workers_per_gather must be set to a value that is greater than zero. This is a special case of the more general principle that no more workers should be used than the number configured via max_parallel_workers_per_gather. In addition, the system must not be running in single-user mode. Since the entire database system is running as a single process in this situation, no background workers will be available. Even when it is in general possible for parallel query plans to be generated, the planner will not generate them for a given query if any of the following are true: The query writes any data or locks any database rows. If a query contains a data-modifying operation either at the top level or within a CTE, no parallel plans for that query will be generated. As an exception, the following commands, which create a new table and populate it, can use a parallel plan for the underlying SELECT part of the query: CREATE TABLE …​ AS SELECT INTO CREATE MATERIALIZED VIEW REFRESH MATERIALIZED VIEW The query might be suspended during execution. In any situation in which the system thinks that partial or incremental execution might occur, no parallel plan is generated. For example, a cursor created using DECLARE CURSOR will never use a parallel plan. Similarly, a PL/pgSQL loop of the form FOR x IN query LOOP .. END LOOP will never use a parallel plan, because the parallel query system is unable to verify that the code in the loop is safe to execute while parallel query is active. The query uses any function marked PARALLEL UNSAFE. Most system-defined functions are PARALLEL SAFE, but user-defined functions are marked PARALLEL UNSAFE by default. The query is running inside of another query that is already parallel. For example, if a function called by a parallel query issues an SQL query itself, that query will never use a parallel plan. This is a limitation of the current implementation, but it may not be desirable to remove this limitation, since it could result in a single query using a very large number of processes. Even when parallel query plan is generated for a particular query, there are several circumstances under which it will be impossible to execute that plan in parallel at execution time. If this occurs, the leader will execute the portion of the plan below the Gather node entirely by itself, almost as if the Gather node were not present. This will happen if any of the following conditions are met: No background workers can be obtained because of the limitation that the total number of background workers cannot exceed max_worker_processes. No background workers can be obtained because of the limitation that the total number of background workers launched for purposes of parallel query cannot exceed max_parallel_workers. The client sends an Execute message with a non-zero fetch count. See the discussion of the extended query protocol. Since libpq currently provides no way to send such a message, this can only occur when using a client that does not rely on libpq. If this is a frequent occurrence, it may be a good idea to set max_parallel_workers_per_gather to zero in sessions where it is likely, so as to avoid generating query plans that may be suboptimal when run serially. Because each worker executes the parallel portion of the plan to completion, it is not possible to simply take an ordinary query plan and run it using multiple workers. Each worker would produce a full copy of the output result set, so the query would not run any faster than normal but would produce incorrect results. Instead, the parallel portion of the plan must be what is known internally to the query optimizer as a partial plan; that is, it must be constructed so that each process that executes the plan will generate only a subset of the output rows in such a way that each required output row is guaranteed to be generated by exactly one of the cooperating processes. Generally, this means that the scan on the driving table of the query must be a parallel-aware scan. The following types of parallel-aware table scans are currently supported. In a parallel sequential scan, the table’s blocks will be divided into ranges and shared among the cooperating processes. Each worker process will complete the scanning of its given range of blocks before requesting an additional range of blocks. In a parallel bitmap heap scan, one process is chosen as the leader. That process performs a scan of one or more indexes and builds a bitmap indicating which table blocks need to be visited. These blocks are then divided among the cooperating processes as in a parallel sequential scan. In other words, the heap scan is performed in parallel, but the underlying index scan is not. In a parallel index scan or parallel index-only scan, the cooperating processes take turns reading data from the index. Currently, parallel index scans are supported only for btree indexes. Each process will claim a single index block and will scan and return all tuples referenced by that block; other processes can at the same time be returning tuples from a different index block. The results of a parallel btree scan are returned in sorted order within each worker process. Other scan types, such as scans of non-btree indexes, may support parallel scans in the future. Just as in a non-parallel plan, the driving table may be joined to one or more other tables using a nested loop, hash join, or merge join. The inner side of the join may be any kind of non-parallel plan that is otherwise supported by the planner provided that it is safe to run within a parallel worker. Depending on the join type, the inner side may also be a parallel plan. In a nested loop join, the inner side is always non-parallel. Although it is executed in full, this is efficient if the inner side is an index scan, because the outer tuples and thus the loops that look up values in the index are divided over the cooperating processes. In a merge join, the inner side is always a non-parallel plan and therefore executed in full. This may be inefficient, especially if a sort must be performed, because the work and resulting data are duplicated in every cooperating process. In a hash join (without the \"parallel\" prefix), the inner side is executed in full by every cooperating process to build identical copies of the hash table. This may be inefficient if the hash table is large or the plan is expensive. In a parallel hash join, the inner side is a parallel hash that divides the work of building a shared hash table over the cooperating processes. IvorySQL supports parallel aggregation by aggregating in two stages. First, each process participating in the parallel portion of the query performs an aggregation step, producing a partial result for each group of which that process is aware. This is reflected in the plan as a Partial Aggregate node. Second, the partial results are transferred to the leader via Gather or Gather Merge. Finally, the leader re-aggregates the results across all workers in order to produce the final result. This is reflected in the plan as a Finalize Aggregate node. Because the Finalize Aggregate node runs on the leader process, queries that produce a relatively large number of groups in comparison to the number of input rows will appear less favorable to the query planner. For example, in the worst-case scenario the number of groups seen by the Finalize Aggregate node could be as many as the number of input rows that were seen by all worker processes in the Partial Aggregate stage. For such cases, there is clearly going to be no performance benefit to using parallel aggregation. The query planner takes this into account during the planning process and is unlikely to choose parallel aggregate in this scenario. Parallel aggregation is not supported in all situations. Each aggregate must be safe for parallelism and must have a combine function. If the aggregate has a transition state of type internal, it must have serialization and deserialization functions. See CREATE AGGREGATE for more details. Parallel aggregation is not supported if any aggregate function call contains DISTINCT or ORDER BY clause and is also not supported for ordered set aggregates or when the query involves GROUPING SETS. It can only be used when all joins involved in the query are also part of the parallel portion of the plan. Whenever IvorySQL needs to combine rows from multiple sources into a single result set, it uses an Append or MergeAppend plan node. This commonly happens when implementing UNION ALL or when scanning a partitioned table. Such nodes can be used in parallel plans just as they can in any other plan. However, in a parallel plan, the planner may instead use a Parallel Append node. When an Append node is used in a parallel plan, each process will execute the child plans in the order in which they appear, so that all participating processes cooperate to execute the first child plan until it is complete and then move to the second plan at around the same time. When a Parallel Append is used instead, the executor will instead spread out the participating processes as evenly as possible across its child plans, so that multiple child plans are executed simultaneously. This avoids contention, and also avoids paying the startup cost of a child plan in those processes that never execute it. Also, unlike a regular Append node, which can only have partial children when used within a parallel plan, a Parallel Append node can have both partial and non-partial child plans. Non-partial children will be scanned by only a single process, since scanning them more than once would produce duplicate results. Plans that involve appending multiple results sets can therefore achieve coarse-grained parallelism even when efficient partial plans are not available. For example, consider a query against a partitioned table that can only be implemented efficiently by using an index that does not support parallel scans. The planner might choose a Parallel Append of regular Index Scan plans; each individual index scan would have to be executed to completion by a single process, but different scans could be performed at the same time by different processes. enable_parallel_append can be used to disable this feature. If a query that is expected to do so does not produce a parallel plan, you can try reducing parallel_setup_cost or parallel_tuple_cost. Of course, this plan may turn out to be slower than the serial plan that the planner preferred, but this will not always be the case. If you don’t get a parallel plan even with very small values of these settings (e.g., after setting them both to zero), there may be some reason why the query planner is unable to generate a parallel plan for your query. When executing a parallel plan, you can use EXPLAIN (ANALYZE, VERBOSE) to display per-worker statistics for each plan node. This may be useful in determining whether the work is being evenly distributed between all plan nodes and more generally in understanding the performance characteristics of the plan. ABORT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ABORT rolls back the current transaction and causes all the updates made by the transaction to be discarded. This command is identical in behavior to the standard SQL command ROLLBACK, and is present only for historical reasons. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use COMMIT to successfully terminate a transaction. Issuing ABORT outside of a transaction block emits a warning and otherwise has no effect. To abort all changes: ABORT; This command is a IvorySQL extension present for historical reasons. ROLLBACK is the equivalent standard SQL command. BEGIN [ WORK | TRANSACTION ] [ transaction_mode [, ...] ] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE BEGIN initiates a transaction block, that is, all statements after a BEGIN command will be executed in a single transaction until an explicit COMMIT or ROLLBACK is given. By default (without BEGIN), IvorySQL executes transactions in “autocommit” mode, that is, each statement is executed in its own transaction and a commit is implicitly performed at the end of the statement (if execution was successful, otherwise a rollback is done). Statements are executed more quickly in a transaction block, because transaction start/commit requires significant CPU and disk activity. Execution of multiple statements inside a transaction is also useful to ensure consistency when making several related changes: other sessions will be unable to see the intermediate states wherein not all the related updates have been done. If the isolation level, read/write mode, or deferrable mode is specified, the new transaction has those characteristics, as if SET TRANSACTION was executed. WORK TRANSACTION Optional key words. They have no effect. Refer to SET TRANSACTION for information on the meaning of the other parameters to this statement. START TRANSACTION has the same functionality as BEGIN. Use COMMIT or ROLLBACK to terminate a transaction block. Issuing BEGIN when already inside a transaction block will provoke a warning message. The state of the transaction is not affected. To nest transactions within a transaction block, use savepoints (see SAVEPOINT). For reasons of backwards compatibility, the commas between successive transaction_modes can be omitted. To begin a transaction block: BEGIN; BEGIN is a IvorySQL language extension. It is equivalent to the SQL-standard command START TRANSACTION, whose reference page contains additional compatibility information. The DEFERRABLE transaction_mode is a IvorySQL language extension. Incidentally, the BEGIN key word is used for a different purpose in embedded SQL. You are advised to be careful about the transaction semantics when porting database applications. COMMIT [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] COMMIT commits the current transaction. All changes made by the transaction become visible to others and are guaranteed to be durable if a crash occurs. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use ROLLBACK to abort a transaction. Issuing COMMIT when not inside a transaction does no harm, but it will provoke a warning message. COMMIT AND CHAIN when not inside a transaction is an error. To commit the current transaction and make all changes permanent: COMMIT; The command COMMIT conforms to the SQL standard. The form COMMIT TRANSACTION is a IvorySQL extension. COMMIT PREPARED transaction_id COMMIT PREPARED commits a transaction that is in prepared state. transaction_id The transaction identifier of the transaction that is to be committed. To commit a prepared transaction, you must be either the same user that executed the transaction originally, or a superuser. But you do not have to be in the same session that executed the transaction. This command cannot be executed inside a transaction block. The prepared transaction is committed immediately. All currently available prepared transactions are listed in the pg_prepared_xacts system view. Commit the transaction identified by the transaction identifier foobar: COMMIT PREPARED 'foobar'; COMMIT PREPARED is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. END [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] END commits the current transaction. All changes made by the transaction become visible to others and are guaranteed to be durable if a crash occurs. This command is a IvorySQL extension that is equivalent to COMMIT. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use ROLLBACK to abort a transaction. Issuing END when not inside a transaction does no harm, but it will provoke a warning message. To commit the current transaction and make all changes permanent: END; END is a IvorySQL extension that provides functionality equivalent to COMMIT, which is specified in the SQL standard. PREPARE TRANSACTION transaction_id PREPARE TRANSACTION prepares the current transaction for two-phase commit. After this command, the transaction is no longer associated with the current session; instead, its state is fully stored on disk, and there is a very high probability that it can be committed successfully, even if a database crash occurs before the commit is requested. Once prepared, a transaction can later be committed or rolled back with COMMIT PREPARED or ROLLBACK PREPARED, respectively. Those commands can be issued from any session, not only the one that executed the original transaction. From the point of view of the issuing session, PREPARE TRANSACTION is not unlike a ROLLBACK command: after executing it, there is no active current transaction, and the effects of the prepared transaction are no longer visible. (The effects will become visible again if the transaction is committed.) If the PREPARE TRANSACTION command fails for any reason, it becomes a ROLLBACK: the current transaction is canceled. transaction_id An arbitrary identifier that later identifies this transaction for COMMIT PREPARED or ROLLBACK PREPARED. The identifier must be written as a string literal, and must be less than 200 bytes long. It must not be the same as the identifier used for any currently prepared transaction. PREPARE TRANSACTION is not intended for use in applications or interactive sessions. Its purpose is to allow an external transaction manager to perform atomic global transactions across multiple databases or other transactional resources. Unless you’re writing a transaction manager, you probably shouldn’t be using PREPARE TRANSACTION. This command must be used inside a transaction block. Use BEGIN to start one. It is not currently allowed to PREPARE a transaction that has executed any operations involving temporary tables or the session’s temporary namespace, created any cursors WITH HOLD, or executed LISTEN, UNLISTEN, or NOTIFY. Those features are too tightly tied to the current session to be useful in a transaction to be prepared. If the transaction modified any run-time parameters with SET (without the LOCAL option), those effects persist after PREPARE TRANSACTION, and will not be affected by any later COMMIT PREPARED or ROLLBACK PREPARED. Thus, in this one respect PREPARE TRANSACTION acts more like COMMIT than ROLLBACK. All currently available prepared transactions are listed in the pg_prepared_xacts system view. It is unwise to leave transactions in the prepared state for a long time. This will interfere with the ability of VACUUM to reclaim storage, and in extreme cases could cause the database to shut down to prevent transaction ID wraparound (see Section 25.1.5). Keep in mind also that the transaction continues to hold whatever locks it held. The intended usage of the feature is that a prepared transaction will normally be committed or rolled back as soon as an external transaction manager has verified that other databases are also prepared to commit. If you have not set up an external transaction manager to track prepared transactions and ensure they get closed out promptly, it is best to keep the prepared-transaction feature disabled by setting max_prepared_transactions to zero. This will prevent accidental creation of prepared transactions that might then be forgotten and eventually cause problems. Prepare the current transaction for two-phase commit, using foobar as the transaction identifier: PREPARE TRANSACTION 'foobar'; PREPARE TRANSACTION is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. ROLLBACK [ WORK | TRANSACTION ] [ AND [ NO ] CHAIN ] ROLLBACK rolls back the current transaction and causes all the updates made by the transaction to be discarded. WORK TRANSACTION Optional key words. They have no effect. AND CHAIN If AND CHAIN is specified, a new transaction is immediately started with the same transaction characteristics (see SET TRANSACTION) as the just finished one. Otherwise, no new transaction is started. Use COMMIT to successfully terminate a transaction. Issuing ROLLBACK outside of a transaction block emits a warning and otherwise has no effect. ROLLBACK AND CHAIN outside of a transaction block is an error. To abort all changes: ROLLBACK; The command ROLLBACK conforms to the SQL standard. The form ROLLBACK TRANSACTION is a IvorySQL extension. ROLLBACK PREPARED transaction_id ROLLBACK PREPARED rolls back a transaction that is in prepared state. transaction_id The transaction identifier of the transaction that is to be rolled back. To roll back a prepared transaction, you must be either the same user that executed the transaction originally, or a superuser. But you do not have to be in the same session that executed the transaction. This command cannot be executed inside a transaction block. The prepared transaction is rolled back immediately. All currently available prepared transactions are listed in the pg_prepared_xacts system view. Roll back the transaction identified by the transaction identifier foobar: ROLLBACK PREPARED 'foobar'; ROLLBACK PREPARED is a IvorySQL extension. It is intended for use by external transaction management systems, some of which are covered by standards (such as X/Open XA), but the SQL side of those systems is not standardized. SAVEPOINT savepoint_name SAVEPOINT establishes a new savepoint within the current transaction. A savepoint is a special mark inside a transaction that allows all commands that are executed after it was established to be rolled back, restoring the transaction state to what it was at the time of the savepoint. savepoint_name The name to give to the new savepoint. If savepoints with the same name already exist, they will be inaccessible until newer identically-named savepoints are released. Use ROLLBACK TO to rollback to a savepoint. Use RELEASE SAVEPOINT to destroy a savepoint, keeping the effects of commands executed after it was established. Savepoints can only be established when inside a transaction block. There can be multiple savepoints defined within a transaction. To establish a savepoint and later undo the effects of all commands executed after it was established: BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); ROLLBACK TO SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); COMMIT; The above transaction will insert the values 1 and 3, but not 2. To establish and later destroy a savepoint: BEGIN; INSERT INTO table1 VALUES (3); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (4); RELEASE SAVEPOINT my_savepoint; COMMIT; The above transaction will insert both 3 and 4. To use a single savepoint name: BEGIN; INSERT INTO table1 VALUES (1); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (2); SAVEPOINT my_savepoint; INSERT INTO table1 VALUES (3); -- rollback to the second savepoint ROLLBACK TO SAVEPOINT my_savepoint; SELECT * FROM table1; -- shows rows 1 and 2 -- release the second savepoint RELEASE SAVEPOINT my_savepoint; -- rollback to the first savepoint ROLLBACK TO SAVEPOINT my_savepoint; SELECT * FROM table1; -- shows only row 1 COMMIT; The above transaction shows row 3 being rolled back first, then row 2. SQL requires a savepoint to be destroyed automatically when another savepoint with the same name is established. In IvorySQL, the old savepoint is kept, though only the more recent one will be used when rolling back or releasing. (Releasing the newer savepoint with RELEASE SAVEPOINT will cause the older one to again become accessible to ROLLBACK TO SAVEPOINT and RELEASE SAVEPOINT.) Otherwise, SAVEPOINT is fully SQL conforming. SET CONSTRAINTS { ALL | name [, ...] } { DEFERRED | IMMEDIATE } SET CONSTRAINTS sets the behavior of constraint checking within the current transaction. IMMEDIATE constraints are checked at the end of each statement. DEFERRED constraints are not checked until transaction commit. Each constraint has its own IMMEDIATE or DEFERRED mode. Upon creation, a constraint is given one of three characteristics: DEFERRABLE INITIALLY DEFERRED, DEFERRABLE INITIALLY IMMEDIATE, or NOT DEFERRABLE. The third class is always IMMEDIATE and is not affected by the SET CONSTRAINTS command. The first two classes start every transaction in the indicated mode, but their behavior can be changed within a transaction by SET CONSTRAINTS. SET CONSTRAINTS with a list of constraint names changes the mode of just those constraints (which must all be deferrable). Each constraint name can be schema-qualified. The current schema search path is used to find the first matching name if no schema name is specified. SET CONSTRAINTS ALL changes the mode of all deferrable constraints. When SET CONSTRAINTS changes the mode of a constraint from DEFERRED to IMMEDIATE, the new mode takes effect retroactively: any outstanding data modifications that would have been checked at the end of the transaction are instead checked during the execution of the SET CONSTRAINTS command. If any such constraint is violated, the SET CONSTRAINTS fails (and does not change the constraint mode). Thus, SET CONSTRAINTS can be used to force checking of constraints to occur at a specific point in a transaction. Currently, only UNIQUE, PRIMARY KEY, REFERENCES (foreign key), and EXCLUDE constraints are affected by this setting. NOT NULL and CHECK constraints are always checked immediately when a row is inserted or modified (not at the end of the statement). Uniqueness and exclusion constraints that have not been declared DEFERRABLE are also checked immediately. The firing of triggers that are declared as “constraint triggers” is also controlled by this setting — they fire at the same time that the associated constraint should be checked. Because IvorySQL does not require constraint names to be unique within a schema (but only per-table), it is possible that there is more than one match for a specified constraint name. In this case SET CONSTRAINTS will act on all matches. For a non-schema-qualified name, once a match or matches have been found in some schema in the search path, schemas appearing later in the path are not searched. This command only alters the behavior of constraints within the current transaction. Issuing this outside of a transaction block emits a warning and otherwise has no effect. This command complies with the behavior defined in the SQL standard, except for the limitation that, in IvorySQL, it does not apply to NOT NULL and CHECK constraints. Also, IvorySQL checks non-deferrable uniqueness constraints immediately, not at end of statement as the standard would suggest. SET TRANSACTION transaction_mode [, ...] SET TRANSACTION SNAPSHOT snapshot_id SET SESSION CHARACTERISTICS AS TRANSACTION transaction_mode [, ...] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE The SET TRANSACTION command sets the characteristics of the current transaction. It has no effect on any subsequent transactions. SET SESSION CHARACTERISTICS sets the default transaction characteristics for subsequent transactions of a session. These defaults can be overridden by SET TRANSACTION for an individual transaction. The available transaction characteristics are the transaction isolation level, the transaction access mode (read/write or read-only), and the deferrable mode. In addition, a snapshot can be selected, though only for the current transaction, not as a session default. The isolation level of a transaction determines what data the transaction can see when other transactions are running concurrently: READ COMMITTED A statement can only see rows committed before it began. This is the default. REPEATABLE READ All statements of the current transaction can only see rows committed before the first query or data-modification statement was executed in this transaction. SERIALIZABLE All statements of the current transaction can only see rows committed before the first query or data-modification statement was executed in this transaction. If a pattern of reads and writes among concurrent serializable transactions would create a situation which could not have occurred for any serial (one-at-a-time) execution of those transactions, one of them will be rolled back with a serialization_failure error. The SQL standard defines one additional level, READ UNCOMMITTED. In IvorySQL READ UNCOMMITTED is treated as READ COMMITTED. The transaction isolation level cannot be changed after the first query or data-modification statement (SELECT, INSERT, DELETE, UPDATE, FETCH, or COPY) of a transaction has been executed. See Chapter 13 for more information about transaction isolation and concurrency control. The transaction access mode determines whether the transaction is read/write or read-only. Read/write is the default. When a transaction is read-only, the following SQL commands are disallowed: INSERT, UPDATE, DELETE, and COPY FROM if the table they would write to is not a temporary table; all CREATE, ALTER, and DROP commands; COMMENT, GRANT, REVOKE, TRUNCATE; and EXPLAIN ANALYZE and EXECUTE if the command they would execute is among those listed. This is a high-level notion of read-only that does not prevent all writes to disk. The DEFERRABLE transaction property has no effect unless the transaction is also SERIALIZABLE and READ ONLY. When all three of these properties are selected for a transaction, the transaction may block when first acquiring its snapshot, after which it is able to run without the normal overhead of a SERIALIZABLE transaction and without any risk of contributing to or being canceled by a serialization failure. This mode is well suited for long-running reports or backups. The SET TRANSACTION SNAPSHOT command allows a new transaction to run with the same snapshot as an existing transaction. The pre-existing transaction must have exported its snapshot with the pg_export_snapshot function. That function returns a snapshot identifier, which must be given to SET TRANSACTION SNAPSHOT to specify which snapshot is to be imported. The identifier must be written as a string literal in this command, for example '00000003-0000001B-1'. SET TRANSACTION SNAPSHOT can only be executed at the start of a transaction, before the first query or data-modification statement (SELECT, INSERT, DELETE, UPDATE, FETCH, or COPY) of the transaction. Furthermore, the transaction must already be set to SERIALIZABLE or REPEATABLE READ isolation level (otherwise, the snapshot would be discarded immediately, since READ COMMITTED mode takes a new snapshot for each command). If the importing transaction uses SERIALIZABLE isolation level, then the transaction that exported the snapshot must also use that isolation level. Also, a non-read-only serializable transaction cannot import a snapshot from a read-only transaction. If SET TRANSACTION is executed without a prior START TRANSACTION or BEGIN, it emits a warning and otherwise has no effect. It is possible to dispense with SET TRANSACTION by instead specifying the desired transaction_modes in BEGIN or START TRANSACTION. But that option is not available for SET TRANSACTION SNAPSHOT. The session default transaction modes can also be set or examined via the configuration parameters default_transaction_isolation, default_transaction_read_only, and default_transaction_deferrable. (In fact SET SESSION CHARACTERISTICS is just a verbose equivalent for setting these variables with SET.) This means the defaults can be set in the configuration file, via ALTER DATABASE, etc. Consult Chapter 20 for more information. The current transaction’s modes can similarly be set or examined via the configuration parameters transaction_isolation, transaction_read_only, and transaction_deferrable. Setting one of these parameters acts the same as the corresponding SET TRANSACTION option, with the same restrictions on when it can be done. However, these parameters cannot be set in the configuration file, or from any source other than live SQL. To begin a new transaction with the same snapshot as an already existing transaction, first export the snapshot from the existing transaction. That will return the snapshot identifier, for example: BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SELECT pg_export_snapshot(); pg_export_snapshot --------------------- 00000003-0000001B-1 (1 row) Then give the snapshot identifier in a SET TRANSACTION SNAPSHOT command at the beginning of the newly opened transaction: BEGIN TRANSACTION ISOLATION LEVEL REPEATABLE READ; SET TRANSACTION SNAPSHOT '00000003-0000001B-1'; These commands are defined in the SQL standard, except for the DEFERRABLE transaction mode and the SET TRANSACTION SNAPSHOT form, which are IvorySQL extensions. SERIALIZABLE is the default transaction isolation level in the standard. In IvorySQL the default is ordinarily READ COMMITTED, but you can change it as mentioned above. In the SQL standard, there is one other transaction characteristic that can be set with these commands: the size of the diagnostics area. This concept is specific to embedded SQL, and therefore is not implemented in the IvorySQL server. The SQL standard requires commas between successive transaction_modes, but for historical reasons IvorySQL allows the commas to be omitted. START TRANSACTION [ transaction_mode [, ...] ] where transaction_mode is one of: ISOLATION LEVEL { SERIALIZABLE | REPEATABLE READ | READ COMMITTED | READ UNCOMMITTED } READ WRITE | READ ONLY [ NOT ] DEFERRABLE This command begins a new transaction block. If the isolation level, read/write mode, or deferrable mode is specified, the new transaction has those characteristics, as if SET TRANSACTION was executed. This is the same as the BEGIN command. Refer to SET TRANSACTION for information on the meaning of the parameters to this statement. In the standard, it is not necessary to issue START TRANSACTION to start a transaction block: any SQL command implicitly begins a block. IvorySQL’s behavior can be seen as implicitly issuing a COMMIT after each command that does not follow START TRANSACTION (or BEGIN), and it is therefore often called “autocommit”. Other relational database systems might offer an autocommit feature as a convenience. The DEFERRABLE transaction_mode is a IvorySQL language extension. The SQL standard requires commas between successive transaction_modes, but for historical reasons IvorySQL allows the commas to be omitted. See also the compatibility section of SET TRANSACTION. SQL input consists of a sequence of commands. A command is composed of a sequence of tokens, terminated by a semicolon (“;”). The end of the input stream also terminates a command. Which tokens are valid depends on the syntax of the particular command. A token can be a key word, an identifier, a quoted identifier, a literal (or constant), or a special character symbol. Tokens are normally separated by whitespace (space, tab, newline), but need not be if there is no ambiguity (which is generally only the case if a special character is adjacent to some other token type). For example, the following is (syntactically) valid SQL input: SELECT * FROM MY_TABLE; UPDATE MY_TABLE SET A = 5; INSERT INTO MY_TABLE VALUES (3, 'hi there'); This is a sequence of three commands, one per line (although this is not required; more than one command can be on a line, and commands can usefully be split across lines). Additionally, comments can occur in SQL input. They are not tokens, they are effectively equivalent to whitespace. The SQL syntax is not very consistent regarding what tokens identify commands and which are operands or parameters. The first few tokens are generally the command name, so in the above example we would usually speak of a “SELECT”, an “UPDATE”, and an “INSERT” command. But for instance the UPDATE command always requires a SET token to appear in a certain position, and this particular variation of INSERT also requires a VALUES in order to be complete. Tokens such as SELECT, UPDATE, or VALUES in the example above are examples of key words, that is, words that have a fixed meaning in the SQL language. The tokens MY_TABLE and A are examples of identifiers. They identify names of tables, columns, or other database objects, depending on the command they are used in. Therefore they are sometimes simply called “names”. Key words and identifiers have the same lexical structure, meaning that one cannot know whether a token is an identifier or a key word without knowing the language. A complete list of key words can be found in Appendix C. SQL identifiers and key words must begin with a letter (a-z, but also letters with diacritical marks and non-Latin letters) or an underscore (_). Subsequent characters in an identifier or key word can be letters, underscores, digits (0-9), or dollar signs ($). Note that dollar signs are not allowed in identifiers according to the letter of the SQL standard, so their use might render applications less portable. The SQL standard will not define a key word that contains digits or starts or ends with an underscore, so identifiers of this form are safe against possible conflict with future extensions of the standard. The system uses no more than NAMEDATALEN-1 bytes of an identifier; longer names can be written in commands, but they will be truncated. By default, NAMEDATALEN is 64 so the maximum identifier length is 63 bytes. If this limit is problematic, it can be raised by changing the NAMEDATALEN constant in src/include/pg_config_manual.h. Key words and unquoted identifiers are case insensitive. Therefore: UPDATE MY_TABLE SET A = 5; can equivalently be written as: uPDaTE my_TabLE SeT a = 5; A convention often used is to write key words in upper case and names in lower case, e.g.: UPDATE my_table SET a = 5; There is a second kind of identifier: the delimited identifier or quoted identifier. It is formed by enclosing an arbitrary sequence of characters in double-quotes (\"). A delimited identifier is always an identifier, never a key word. So \"select\" could be used to refer to a column or table named “select”, whereas an unquoted select would be taken as a key word and would therefore provoke a parse error when used where a table or column name is expected. The example can be written with quoted identifiers like this: UPDATE \"my_table\" SET \"a\" = 5; Quoted identifiers can contain any character, except the character with code zero. (To include a double quote, write two double quotes.) This allows constructing table or column names that would otherwise not be possible, such as ones containing spaces or ampersands. The length limitation still applies. Quoting an identifier also makes it case-sensitive, whereas unquoted names are always folded to lower case. For example, the identifiers FOO, foo, and \"foo\" are considered the same by IvorySQL, but \"Foo\" and \"FOO\" are different from these three and each other. (The folding of unquoted names to lower case in IvorySQL is incompatible with the SQL standard, which says that unquoted names should be folded to upper case. Thus, foo should be equivalent to \"FOO\" not \"foo\" according to the standard. If you want to write portable applications you are advised to always quote a particular name or never quote it.) A variant of quoted identifiers allows including escaped Unicode characters identified by their code points. This variant starts with U& (upper or lower case U followed by ampersand) immediately before the opening double quote, without any spaces in between, for example U&\"foo\". (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the identifier \"data\" could be written as U&\"d\\0061t\\+000061\" The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters: U&\"\\0441\\043B\\043E\\043D\" If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example: U&\"d!0061t!+000061\" UESCAPE '!' The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character. Note that the escape character is written in single quotes, not double quotes, after UESCAPE. To include the escape character in the identifier literally, write it twice. Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.) If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that’s not possible. There are three kinds of implicitly-typed constants in IvorySQL: strings, bit strings, and numbers. Constants can also be specified with explicit types, which can enable more accurate representation and more efficient handling by the system. These alternatives are discussed in the following subsections. A string constant in SQL is an arbitrary sequence of characters bounded by single quotes ('), for example 'This is a string'. To include a single-quote character within a string constant, write two adjacent single quotes, e.g., 'Dianne''s horse'. Note that this is not the same as a double-quote character (\"). Two string constants that are only separated by whitespace with at least one newline are concatenated and effectively treated as if the string had been written as one constant. For example: SELECT 'foo' 'bar'; is equivalent to: SELECT 'foobar'; but: SELECT 'foo' 'bar'; is not valid syntax. (This slightly bizarre behavior is specified by SQL; IvorySQL is following the standard.) IvorySQL also accepts “escape” string constants, which are an extension to the SQL standard. An escape string constant is specified by writing the letter E (upper or lower case) just before the opening single quote, e.g., E’foo'. (When continuing an escape string constant across lines, write E only before the first opening quote.) Within an escape string, a backslash character (\\) begins a C-like backslash escape sequence, in which the combination of backslash and following character(s) represent a special byte value. Table 5.1. Backslash Escape Sequences Backslash Escape Sequence Interpretation \\b backspace \\f form feed \\n newline \\r carriage return \\t tab *`o*, `*`oo*, `*`ooo*` (o = 0–7) octal byte value \\x*`h, `\\x`hh` (h* = 0–9, A–F) hexadecimal byte value \\u*`xxxx, `\\U`xxxxxxxx` (x* = 0–9, A–F) 16 or 32-bit hexadecimal Unicode character value Any other character following a backslash is taken literally. Thus, to include a backslash character, write two backslashes (\\\\). Also, a single quote can be included in an escape string by writing \\', in addition to the normal way of ''. It is your responsibility that the byte sequences you create, especially when using the octal or hexadecimal escapes, compose valid characters in the server character set encoding. A useful alternative is to use Unicode escapes or the alternative Unicode escape syntax, ; then the server will check that the character conversion is possible. Caution If the configuration parameter standard_conforming_strings is off, then IvorySQL recognizes backslash escapes in both regular and escape string constants. However, as of IvorySQL, the default is on, meaning that backslash escapes are recognized only in escape string constants. This behavior is more standards-compliant, but might break applications which rely on the historical behavior, where backslash escapes were always recognized. As a workaround, you can set this parameter to off, but it is better to migrate away from using backslash escapes. If you need to use a backslash escape to represent a special character, write the string constant with an E.In addition to standard_conforming_strings, the configuration parameters escape_string_warning and backslash_quote govern treatment of backslashes in string constants.The character with the code zero cannot be in a string constant. IvorySQL also supports another type of escape syntax for strings that allows specifying arbitrary Unicode characters by code point. A Unicode escape string constant starts with U& (upper or lower case letter U followed by ampersand) immediately before the opening quote, without any spaces in between, for example U&'foo'. (Note that this creates an ambiguity with the operator &. Use spaces around the operator to avoid this problem.) Inside the quotes, Unicode characters can be specified in escaped form by writing a backslash followed by the four-digit hexadecimal code point number or alternatively a backslash followed by a plus sign followed by a six-digit hexadecimal code point number. For example, the string 'data' could be written as U&'d\\0061t\\+000061' The following less trivial example writes the Russian word “slon” (elephant) in Cyrillic letters: U&'\\0441\\043B\\043E\\043D' If a different escape character than backslash is desired, it can be specified using the UESCAPE clause after the string, for example: U&'d!0061t!+000061' UESCAPE '!' The escape character can be any single character other than a hexadecimal digit, the plus sign, a single quote, a double quote, or a whitespace character. To include the escape character in the string literally, write it twice. Either the 4-digit or the 6-digit escape form can be used to specify UTF-16 surrogate pairs to compose characters with code points larger than U+FFFF, although the availability of the 6-digit form technically makes this unnecessary. (Surrogate pairs are not stored directly, but are combined into a single code point.) If the server encoding is not UTF-8, the Unicode code point identified by one of these escape sequences is converted to the actual server encoding; an error is reported if that’s not possible. Also, the Unicode escape syntax for string constants only works when the configuration parameter standard_conforming_strings is turned on. This is because otherwise this syntax could confuse clients that parse the SQL statements to the point that it could lead to SQL injections and similar security issues. If the parameter is set to off, this syntax will be rejected with an error message. While the standard syntax for specifying string constants is usually convenient, it can be difficult to understand when the desired string contains many single quotes or backslashes, since each of those must be doubled. To allow more readable queries in such situations, IvorySQL provides another way, called “dollar quoting”, to write string constants. A dollar-quoted string constant consists of a dollar sign ($), an optional “tag” of zero or more characters, another dollar sign, an arbitrary sequence of characters that makes up the string content, a dollar sign, the same tag that began this dollar quote, and a dollar sign. For example, here are two different ways to specify the string “Dianne’s horse” using dollar quoting: $$Dianne's horse$$ $SomeTag$Dianne's horse$SomeTag$ Notice that inside the dollar-quoted string, single quotes can be used without needing to be escaped. Indeed, no characters inside a dollar-quoted string are ever escaped: the string content is always written literally. Backslashes are not special, and neither are dollar signs, unless they are part of a sequence matching the opening tag. It is possible to nest dollar-quoted string constants by choosing different tags at each nesting level. This is most commonly used in writing function definitions. For example: $function$ BEGIN RETURN ($1 ~ $q$[\\t\\r\\n\\v\\\\]$q$); END; $function$ Here, the sequence $q$[\\t\\r\\n\\v\\\\]$q$ represents a dollar-quoted literal string [\\t\\r\\n\\v\\\\], which will be recognized when the function body is executed by IvorySQL. But since the sequence does not match the outer dollar quoting delimiter $function$, it is just some more characters within the constant so far as the outer string is concerned. The tag, if any, of a dollar-quoted string follows the same rules as an unquoted identifier, except that it cannot contain a dollar sign. Tags are case sensitive, so $tag$String content$tag$ is correct, but $TAG$String content$tag$ is not. A dollar-quoted string that follows a keyword or identifier must be separated from it by whitespace; otherwise the dollar quoting delimiter would be taken as part of the preceding identifier. Dollar quoting is not part of the SQL standard, but it is often a more convenient way to write complicated string literals than the standard-compliant single quote syntax. It is particularly useful when representing string constants inside other constants, as is often needed in procedural function definitions. With single-quote syntax, each backslash in the above example would have to be written as four backslashes, which would be reduced to two backslashes in parsing the original string constant, and then to one when the inner string constant is re-parsed during function execution. Bit-string constants look like regular string constants with a B (upper or lower case) immediately before the opening quote (no intervening whitespace), e.g., B'1001'. The only characters allowed within bit-string constants are 0 and 1. Alternatively, bit-string constants can be specified in hexadecimal notation, using a leading X (upper or lower case), e.g., X'1FF'. This notation is equivalent to a bit-string constant with four binary digits for each hexadecimal digit. Both forms of bit-string constant can be continued across lines in the same way as regular string constants. Dollar quoting cannot be used in a bit-string constant. Numeric constants are accepted in these general forms: digits digits.[digits][e[+-]digits] [digits].digits[e[+-]digits] digitse[+-]digits where digits is one or more decimal digits (0 through 9). At least one digit must be before or after the decimal point, if one is used. At least one digit must follow the exponent marker (e), if one is present. There cannot be any spaces or other characters embedded in the constant. Note that any leading plus or minus sign is not actually considered part of the constant; it is an operator applied to the constant. These are some examples of valid numeric constants: 42 3.5 4. .001 5e2 1.925e-3 A numeric constant that contains neither a decimal point nor an exponent is initially presumed to be type integer if its value fits in type integer (32 bits); otherwise it is presumed to be type bigint if its value fits in type bigint (64 bits); otherwise it is taken to be type numeric. Constants that contain decimal points and/or exponents are always initially presumed to be type numeric. The initially assigned data type of a numeric constant is just a starting point for the type resolution algorithms. In most cases the constant will be automatically coerced to the most appropriate type depending on context. When necessary, you can force a numeric value to be interpreted as a specific data type by casting it. For example, you can force a numeric value to be treated as type real (float4) by writing: REAL '1.23' -- string style 1.23::REAL -- IvorySQL (historical) style These are actually just special cases of the general casting notations discussed next. A constant of an arbitrary type can be entered using any one of the following notations: type 'string' 'string'::type CAST ( 'string' AS type ) The string constant’s text is passed to the input conversion routine for the type called type. The result is a constant of the indicated type. The explicit type cast can be omitted if there is no ambiguity as to the type the constant must be (for example, when it is assigned directly to a table column), in which case it is automatically coerced. The string constant can be written using either regular SQL notation or dollar-quoting. It is also possible to specify a type coercion using a function-like syntax: typename ( 'string' ) but not all type names can be used in this way. The ::, CAST(), and function-call syntaxes can also be used to specify run-time type conversions of arbitrary expressions. To avoid syntactic ambiguity, the `type 'string'` syntax can only be used to specify the type of a simple literal constant. Another restriction on the `type 'string'` syntax is that it does not work for array types; use :: or CAST() to specify the type of an array constant. The CAST() syntax conforms to SQL. The `type 'string'` syntax is a generalization of the standard: SQL specifies this syntax only for a few data types, but IvorySQL allows it for all types. The syntax with :: is historical IvorySQL usage, as is the function-call syntax. An operator name is a sequence of up to NAMEDATALEN-1 (63 by default) characters from the following list: \\+ - * / < > = ~ ! @ # % ^ & | ` ? There are a few restrictions on operator names, however: -- and /* cannot appear anywhere in an operator name, since they will be taken as the start of a comment. A multiple-character operator name cannot end in + or -, unless the name also contains at least one of these characters: ~ ! @ # % ^ & | ` ? For example, @- is an allowed operator name, but *- is not. This restriction allows IvorySQL to parse SQL-compliant queries without requiring spaces between tokens. When working with non-SQL-standard operator names, you will usually need to separate adjacent operators with spaces to avoid ambiguity. For example, if you have defined a prefix operator named @, you cannot write X*@Y; you must write X* @Y to ensure that IvorySQL reads it as two operator names not one. Some characters that are not alphanumeric have a special meaning that is different from being an operator. Details on the usage can be found at the location where the respective syntax element is described. This section only exists to advise the existence and summarize the purposes of these characters. A dollar sign ($) followed by digits is used to represent a positional parameter in the body of a function definition or a prepared statement. In other contexts the dollar sign can be part of an identifier or a dollar-quoted string constant. Parentheses (()) have their usual meaning to group expressions and enforce precedence. In some cases parentheses are required as part of the fixed syntax of a particular SQL command. Brackets ([]) are used to select the elements of an array. Commas (,) are used in some syntactical constructs to separate the elements of a list. The semicolon (;) terminates an SQL command. It cannot appear anywhere within a command, except within a string constant or quoted identifier. The colon (:) is used to select “slices” from arrays. In certain SQL dialects (such as Embedded SQL), the colon is used to prefix variable names. The asterisk (*) is used in some contexts to denote all the fields of a table row or composite value. It also has a special meaning when used as the argument of an aggregate function, namely that the aggregate does not require any explicit parameter. The period (.) is used in numeric constants, and to separate schema, table, and column names. A comment is a sequence of characters beginning with double dashes and extending to the end of the line, e.g.: -- This is a standard SQL comment Alternatively, C-style block comments can be used: /* multiline comment * with nesting: /* nested block comment */ */ where the comment begins with / and extends to the matching occurrence of /. These block comments nest, as specified in the SQL standard but unlike C, so that one can comment out larger blocks of code that might contain existing block comments. A comment is removed from the input stream before further syntax analysis and is effectively replaced by whitespace. Table 5.2 shows the precedence and associativity of the operators in IvorySQL. Most operators have the same precedence and are left-associative. The precedence and associativity of the operators is hard-wired into the parser. Add parentheses if you want an expression with multiple operators to be parsed in some other way than what the precedence rules imply. Table 5.2. Operator Precedence (highest to lowest) Operator/Element Associativity Description . left table/column name separator :: left IvorySQL-style typecast [ ] left array element selection + - right unary plus, unary minus ^ left exponentiation * / % left multiplication, division, modulo + - left addition, subtraction (any other operator) left all other native and user-defined operators BETWEEN IN LIKE ILIKE SIMILAR range containment, set membership, string matching < > = ⇐ >= <> comparison operators IS ISNULL NOTNULL IS TRUE, IS FALSE, IS NULL, IS DISTINCT FROM, etc. NOT right logical negation AND left logical conjunction OR left logical disjunction Note that the operator precedence rules also apply to user-defined operators that have the same names as the built-in operators mentioned above. For example, if you define a “” operator for some custom data type it will have the same precedence as the built-in “” operator, no matter what yours does. When a schema-qualified operator name is used in the OPERATOR syntax, as for example in: SELECT 3 OPERATOR(pg_catalog.+) 4; the OPERATOR construct is taken to have the default precedence shown in Table 5.2 for “any other operator”. This is true no matter which specific operator appears inside OPERATOR(). Note In particular, ⇐ >= and <> used to be treated as generic operators; IS tests used to have higher priority; and NOT BETWEEN and related constructs acted inconsistently, being taken in some cases as having the precedence of NOT rather than BETWEEN. These rules were changed for better compliance with the SQL standard and to reduce confusion from inconsistent treatment of logically equivalent constructs. In most cases, these changes will result in no behavioral change, or perhaps in “no such operator” failures which can be resolved by adding parentheses. However there are corner cases in which a query might change behavior without any parsing error being reported. Value expressions are used in a variety of contexts, such as in the target list of the SELECT command, as new column values in INSERT or UPDATE, or in search conditions in a number of commands. The result of a value expression is sometimes called a scalar, to distinguish it from the result of a table expression (which is a table). Value expressions are therefore also called scalar expressions (or even simply expressions). The expression syntax allows the calculation of values from primitive parts using arithmetic, logical, set, and other operations. A value expression is one of the following: A constant or literal value A column reference A positional parameter reference, in the body of a function definition or prepared statement A subscripted expression A field selection expression An operator invocation A function call An aggregate expression A window function call A type cast A collation expression A scalar subquery An array constructor A row constructor Another value expression in parentheses (used to group subexpressions and override precedence) In addition to this list, there are a number of constructs that can be classified as an expression but do not follow any general syntax rules. These generally have the semantics of a function or operator . An example is the IS NULL clause. A column can be referenced in the form: correlation.columnname correlation is the name of a table (possibly qualified with a schema name), or an alias for a table defined by means of a FROM clause. The correlation name and separating dot can be omitted if the column name is unique across all the tables being used in the current query. A positional parameter reference is used to indicate a value that is supplied externally to an SQL statement. Parameters are used in SQL function definitions and in prepared queries. Some client libraries also support specifying data values separately from the SQL command string, in which case parameters are used to refer to the out-of-line data values. The form of a parameter reference is: $number For example, consider the definition of a function, dept, as: CREATE FUNCTION dept(text) RETURNS dept AS $$ SELECT * FROM dept WHERE name = $1 $$ LANGUAGE SQL; Here the $1 references the value of the first function argument whenever the function is invoked. If an expression yields a value of an array type, then a specific element of the array value can be extracted by writing expression[subscript] or multiple adjacent elements (an “array slice”) can be extracted by writing expression[lower_subscript:upper_subscript] (Here, the brackets [ ] are meant to appear literally.) Each subscript is itself an expression, which will be rounded to the nearest integer value. In general the array expression must be parenthesized, but the parentheses can be omitted when the expression to be subscripted is just a column reference or positional parameter. Also, multiple subscripts can be concatenated when the original array is multidimensional. For example: mytable.arraycolumn[4] mytable.two_d_column[17][34] $1[10:42] (arrayfunction(a,b))[42] The parentheses in the last example are required. If an expression yields a value of a composite type (row type), then a specific field of the row can be extracted by writing expression.fieldname In general the row expression must be parenthesized, but the parentheses can be omitted when the expression to be selected from is just a table reference or positional parameter. For example: mytable.mycolumn $1.somecolumn (rowfunction(a,b)).col3 (Thus, a qualified column reference is actually just a special case of the field selection syntax.) An important special case is extracting a field from a table column that is of a composite type: (compositecol).somefield (mytable.compositecol).somefield The parentheses are required here to show that compositecol is a column name not a table name, or that mytable is a table name not a schema name in the second case. You can ask for all fields of a composite value by writing .*: (compositecol).* This notation behaves differently depending on context. There are two possible syntaxes for an operator invocation: expression operator expression (binary infix operator) operator expression (unary prefix operator) where the operator token follows the syntax rules , or is one of the key words AND, OR, and NOT, or is a qualified operator name in the form: OPERATOR(schema.operatorname) Which particular operators exist and whether they are unary or binary depends on what operators have been defined by the system or the user. The syntax for a function call is the name of a function (possibly qualified with a schema name), followed by its argument list enclosed in parentheses: function_name ([expression [, expression ... ]] ) For example, the following computes the square root of 2: sqrt(2) Other functions can be added by the user. When issuing queries in a database where some users mistrust other users, The arguments can optionally have names attached. Note A function that takes a single argument of composite type can optionally be called using field-selection syntax, and conversely field selection can be written in functional style. That is, the notations col(table) and table.col are interchangeable. This behavior is not SQL-standard but is provided in IvorySQL because it allows use of functions to emulate “computed fields”. An aggregate expression represents the application of an aggregate function across the rows selected by a query. An aggregate function reduces multiple inputs to a single output value, such as the sum or average of the inputs. The syntax of an aggregate expression is one of the following: aggregate_name (expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (ALL expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name (DISTINCT expression [ , ... ] [ order_by_clause ] ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( * ) [ FILTER ( WHERE filter_clause ) ] aggregate_name ( [ expression [ , ... ] ] ) WITHIN GROUP ( order_by_clause ) [ FILTER ( WHERE filter_clause ) ] where aggregate_name is a previously defined aggregate (possibly qualified with a schema name) and expression is any value expression that does not itself contain an aggregate expression or a window function call. The optional order_by_clause and filter_clause are described below. The first form of aggregate expression invokes the aggregate once for each input row. The second form is the same as the first, since ALL is the default. The third form invokes the aggregate once for each distinct value of the expression (or distinct set of values, for multiple expressions) found in the input rows. The fourth form invokes the aggregate once for each input row; since no particular input value is specified, it is generally only useful for the count() aggregate function. The last form is used with *ordered-set aggregate functions, which are described below. Most aggregate functions ignore null inputs, so that rows in which one or more of the expression(s) yield null are discarded. This can be assumed to be true, unless otherwise specified, for all built-in aggregates. For example, count(*) yields the total number of input rows; count(f1) yields the number of input rows in which f1 is non-null, since count ignores nulls; and count(distinct f1) yields the number of distinct non-null values of f1. Ordinarily, the input rows are fed to the aggregate function in an unspecified order. In many cases this does not matter; for example, min produces the same result no matter what order it receives the inputs in. However, some aggregate functions (such as array_agg and string_agg) produce results that depend on the ordering of the input rows. When using such an aggregate, the optional order_by_clause can be used to specify the desired ordering. The order_by_clause has the same syntax as for a query-level ORDER BY clause, except that its expressions are always just expressions and cannot be output-column names or numbers. For example: SELECT array_agg(a ORDER BY b DESC) FROM table; When dealing with multiple-argument aggregate functions, note that the ORDER BY clause goes after all the aggregate arguments. For example, write this: SELECT string_agg(a, ',' ORDER BY a) FROM table; not this: SELECT string_agg(a ORDER BY a, ',') FROM table; -- incorrect The latter is syntactically valid, but it represents a call of a single-argument aggregate function with two ORDER BY keys (the second one being rather useless since it’s a constant). If DISTINCT is specified in addition to an order_by_clause, then all the ORDER BY expressions must match regular arguments of the aggregate; that is, you cannot sort on an expression that is not included in the DISTINCT list. Note The ability to specify both DISTINCT and ORDER BY in an aggregate function is a IvorySQL extension. Placing ORDER BY within the aggregate’s regular argument list, as described so far, is used when ordering the input rows for general-purpose and statistical aggregates, for which ordering is optional. There is a subclass of aggregate functions called ordered-set aggregates for which an order_by_clause is required, usually because the aggregate’s computation is only sensible in terms of a specific ordering of its input rows. Typical examples of ordered-set aggregates include rank and percentile calculations. For an ordered-set aggregate, the order_by_clause is written inside WITHIN GROUP (…​), as shown in the final syntax alternative above. The expressions in the order_by_clause are evaluated once per input row just like regular aggregate arguments, sorted as per the order_by_clause's requirements, and fed to the aggregate function as input arguments. (This is unlike the case for a non-WITHIN GROUP order_by_clause, which is not treated as argument(s) to the aggregate function.) The argument expressions preceding WITHIN GROUP, if any, are called direct arguments to distinguish them from the aggregated arguments listed in the order_by_clause. Unlike regular aggregate arguments, direct arguments are evaluated only once per aggregate call, not once per input row. This means that they can contain variables only if those variables are grouped by GROUP BY; this restriction is the same as if the direct arguments were not inside an aggregate expression at all. Direct arguments are typically used for things like percentile fractions, which only make sense as a single value per aggregation calculation. The direct argument list can be empty; in this case, write just () not (*). (IvorySQL will actually accept either spelling, but only the first way conforms to the SQL standard.) An example of an ordered-set aggregate call is: SELECT percentile_cont(0.5) WITHIN GROUP (ORDER BY income) FROM households; percentile_cont ----------------- 50489 which obtains the 50th percentile, or median, value of the income column from table households. Here, 0.5 is a direct argument; it would make no sense for the percentile fraction to be a value varying across rows. If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the aggregate function; other rows are discarded. For example: SELECT count(*) AS unfiltered, count(*) FILTER (WHERE i < 5) AS filtered FROM generate_series(1,10) AS s(i); unfiltered | filtered ------------+---------- 10 | 4 (1 row) Other aggregate functions can be added by the user. An aggregate expression can only appear in the result list or HAVING clause of a SELECT command. It is forbidden in other clauses, such as WHERE, because those clauses are logically evaluated before the results of aggregates are formed. When an aggregate expression appears in a subquery,the aggregate is normally evaluated over the rows of the subquery. But an exception occurs if the aggregate’s arguments (and filter_clause if any) contain only outer-level variables: the aggregate then belongs to the nearest such outer level, and is evaluated over the rows of that query. The aggregate expression as a whole is then an outer reference for the subquery it appears in, and acts as a constant over any one evaluation of that subquery. The restriction about appearing only in the result list or HAVING clause applies with respect to the query level that the aggregate belongs to. A window function call represents the application of an aggregate-like function over some portion of the rows selected by a query. Unlike non-window aggregate calls, this is not tied to grouping of the selected rows into a single output row — each row remains separate in the query output. However the window function has access to all the rows that would be part of the current row’s group according to the grouping specification (PARTITION BY list) of the window function call. The syntax of a window function call is one of the following: function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ([expression [, expression ... ]]) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER window_name function_name ( * ) [ FILTER ( WHERE filter_clause ) ] OVER ( window_definition ) where window_definition has the syntax [ existing_window_name ] [ PARTITION BY expression [, ...] ] [ ORDER BY expression [ ASC | DESC | USING operator ] [ NULLS { FIRST | LAST } ] [, ...] ] [ frame_clause ] The optional frame_clause can be one of { RANGE | ROWS | GROUPS } frame_start [ frame_exclusion ] { RANGE | ROWS | GROUPS } BETWEEN frame_start AND frame_end [ frame_exclusion ] where frame_start and frame_end can be one of UNBOUNDED PRECEDING offset PRECEDING CURRENT ROW offset FOLLOWING UNBOUNDED FOLLOWING and frame_exclusion can be one of EXCLUDE CURRENT ROW EXCLUDE GROUP EXCLUDE TIES EXCLUDE NO OTHERS Here, expression represents any value expression that does not itself contain window function calls. window_name is a reference to a named window specification defined in the query’s WINDOW clause. Alternatively, a full window_definition can be given within parentheses, using the same syntax as for defining a named window in the WINDOW clause; see the SELECT reference page for details. It’s worth pointing out that OVER wname is not exactly equivalent to OVER (wname …​); the latter implies copying and modifying the window definition, and will be rejected if the referenced window specification includes a frame clause. The PARTITION BY clause groups the rows of the query into partitions, which are processed separately by the window function. PARTITION BY works similarly to a query-level GROUP BY clause, except that its expressions are always just expressions and cannot be output-column names or numbers. Without PARTITION BY, all rows produced by the query are treated as a single partition. The ORDER BY clause determines the order in which the rows of a partition are processed by the window function. It works similarly to a query-level ORDER BY clause, but likewise cannot use output-column names or numbers. Without ORDER BY, rows are processed in an unspecified order. The frame_clause specifies the set of rows constituting the window frame, which is a subset of the current partition, for those window functions that act on the frame instead of the whole partition. The set of rows in the frame can vary depending on which row is the current row. The frame can be specified in RANGE, ROWS or GROUPS mode; in each case, it runs from the frame_start to the frame_end. If frame_end is omitted, the end defaults to CURRENT ROW. A frame_start of UNBOUNDED PRECEDING means that the frame starts with the first row of the partition, and similarly a frame_end of UNBOUNDED FOLLOWING means that the frame ends with the last row of the partition. In RANGE or GROUPS mode, a frame_start of CURRENT ROW means the frame starts with the current row’s first peer row (a row that the window’s ORDER BY clause sorts as equivalent to the current row), while a frame_end of CURRENT ROW means the frame ends with the current row’s last peer row. In ROWS mode, CURRENT ROW simply means the current row. In the offset PRECEDING and offset FOLLOWING frame options, the offset must be an expression not containing any variables, aggregate functions, or window functions. The meaning of the offset depends on the frame mode: In ROWS mode, the offset must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of rows before or after the current row. In GROUPS mode, the offset again must yield a non-null, non-negative integer, and the option means that the frame starts or ends the specified number of peer groups before or after the current row’s peer group, where a peer group is a set of rows that are equivalent in the ORDER BY ordering. (There must be an ORDER BY clause in the window definition to use GROUPS mode.) In RANGE mode, these options require that the ORDER BY clause specify exactly one column. The offset specifies the maximum difference between the value of that column in the current row and its value in preceding or following rows of the frame. The data type of the offset expression varies depending on the data type of the ordering column. For numeric ordering columns it is typically of the same type as the ordering column, but for datetime ordering columns it is an interval. For example, if the ordering column is of type date or timestamp, one could write RANGE BETWEEN '1 day' PRECEDING AND '10 days' FOLLOWING. The offset is still required to be non-null and non-negative, though the meaning of “non-negative” depends on its data type. In any case, the distance to the end of the frame is limited by the distance to the end of the partition, so that for rows near the partition ends the frame might contain fewer rows than elsewhere. Notice that in both ROWS and GROUPS mode, 0 PRECEDING and 0 FOLLOWING are equivalent to CURRENT ROW. This normally holds in RANGE mode as well, for an appropriate data-type-specific meaning of “zero”. The frame_exclusion option allows rows around the current row to be excluded from the frame, even if they would be included according to the frame start and frame end options. EXCLUDE CURRENT ROW excludes the current row from the frame. EXCLUDE GROUP excludes the current row and its ordering peers from the frame. EXCLUDE TIES excludes any peers of the current row from the frame, but not the current row itself. EXCLUDE NO OTHERS simply specifies explicitly the default behavior of not excluding the current row or its peers. The default framing option is RANGE UNBOUNDED PRECEDING, which is the same as RANGE BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW. With ORDER BY, this sets the frame to be all rows from the partition start up through the current row’s last ORDER BY peer. Without ORDER BY, this means all rows of the partition are included in the window frame, since all rows become peers of the current row. Restrictions are that frame_start cannot be UNBOUNDED FOLLOWING, frame_end cannot be UNBOUNDED PRECEDING, and the frame_end choice cannot appear earlier in the above list of frame_start and frame_end options than the frame_start choice does — for example RANGE BETWEEN CURRENT ROW AND `offset PRECEDING` is not allowed. But, for example, ROWS BETWEEN 7 PRECEDING AND 8 PRECEDING is allowed, even though it would never select any rows. If FILTER is specified, then only the input rows for which the filter_clause evaluates to true are fed to the window function; other rows are discarded. Only window functions that are aggregates accept a FILTER clause. Other window functions can be added by the user. Also, any built-in or user-defined general-purpose or statistical aggregate can be used as a window function. (Ordered-set and hypothetical-set aggregates cannot presently be used as window functions.) The syntaxes using are used for calling parameter-less aggregate functions as window functions, for example count() OVER (PARTITION BY x ORDER BY y). The asterisk (*) is customarily not used for window-specific functions. Window-specific functions do not allow DISTINCT or ORDER BY to be used within the function argument list. Window function calls are permitted only in the SELECT list and the ORDER BY clause of the query. A type cast specifies a conversion from one data type to another. IvorySQL accepts two equivalent syntaxes for type casts: CAST ( expression AS type ) expression::type The CAST syntax conforms to SQL; the syntax with :: is historical IvorySQL usage. When a cast is applied to a value expression of a known type, it represents a run-time type conversion. The cast will succeed only if a suitable type conversion operation has been defined. Notice that this is subtly different from the use of casts with constants. A cast applied to an unadorned string literal represents the initial assignment of a type to a literal constant value, and so it will succeed for any type (if the contents of the string literal are acceptable input syntax for the data type). An explicit type cast can usually be omitted if there is no ambiguity as to the type that a value expression must produce (for example, when it is assigned to a table column); the system will automatically apply a type cast in such cases. However, automatic casting is only done for casts that are marked “OK to apply implicitly” in the system catalogs. Other casts must be invoked with explicit casting syntax. This restriction is intended to prevent surprising conversions from being applied silently. It is also possible to specify a type cast using a function-like syntax: typename ( expression ) However, this only works for types whose names are also valid as function names. For example, double precision cannot be used this way, but the equivalent float8 can. Also, the names interval, time, and timestamp can only be used in this fashion if they are double-quoted, because of syntactic conflicts. Therefore, the use of the function-like cast syntax leads to inconsistencies and should probably be avoided. Note The function-like syntax is in fact just a function call. When one of the two standard cast syntaxes is used to do a run-time conversion, it will internally invoke a registered function to perform the conversion. By convention, these conversion functions have the same name as their output type, and thus the “function-like syntax” is nothing more than a direct invocation of the underlying conversion function. Obviously, this is not something that a portable application should rely on. For further details see CREATE CAST. The COLLATE clause overrides the collation of an expression. It is appended to the expression it applies to: expr COLLATE collation where collation is a possibly schema-qualified identifier. The COLLATE clause binds tighter than operators; parentheses can be used when necessary. If no collation is explicitly specified, the database system either derives a collation from the columns involved in the expression, or it defaults to the default collation of the database if no column is involved in the expression. The two common uses of the COLLATE clause are overriding the sort order in an ORDER BY clause, for example: SELECT a, b, c FROM tbl WHERE ... ORDER BY a COLLATE \"C\"; and overriding the collation of a function or operator call that has locale-sensitive results, for example: SELECT * FROM tbl WHERE a > 'foo' COLLATE \"C\"; Note that in the latter case the COLLATE clause is attached to an input argument of the operator we wish to affect. It doesn’t matter which argument of the operator or function call the COLLATE clause is attached to, because the collation that is applied by the operator or function is derived by considering all arguments, and an explicit COLLATE clause will override the collations of all other arguments. (Attaching non-matching COLLATE clauses to more than one argument, however, is an error.) Thus, this gives the same result as the previous example: SELECT * FROM tbl WHERE a COLLATE \"C\" > 'foo'; But this is an error: SELECT * FROM tbl WHERE (a > 'foo') COLLATE \"C\"; because it attempts to apply a collation to the result of the > operator, which is of the non-collatable data type boolean. A scalar subquery is an ordinary SELECT query in parentheses that returns exactly one row with one column. The SELECT query is executed and the single returned value is used in the surrounding value expression. It is an error to use a query that returns more than one row or more than one column as a scalar subquery. (But if, during a particular execution, the subquery returns no rows, there is no error; the scalar result is taken to be null.) The subquery can refer to variables from the surrounding query, which will act as constants during any one evaluation of the subquery. For example, the following finds the largest city population in each state: SELECT name, (SELECT max(pop) FROM cities WHERE cities.state = states.name) FROM states; An array constructor is an expression that builds an array value using values for its member elements. A simple array constructor consists of the key word ARRAY, a left square bracket [, a list of expressions (separated by commas) for the array element values, and finally a right square bracket ]. For example: SELECT ARRAY[1,2,3+4]; array --------- {1,2,7} (1 row) By default, the array element type is the common type of the member expressions, determined using the same rules as for UNION or CASE constructs. You can override this by explicitly casting the array constructor to the desired type, for example: SELECT ARRAY[1,2,22.7]::integer[]; array ---------- {1,2,23} (1 row) This has the same effect as casting each expression to the array element type individually. Multidimensional array values can be built by nesting array constructors. In the inner constructors, the key word ARRAY can be omitted. For example, these produce the same result: SELECT ARRAY[ARRAY[1,2], ARRAY[3,4]]; array --------------- {{1,2},{3,4}} (1 row) SELECT ARRAY[[1,2],[3,4]]; array --------------- {{1,2},{3,4}} (1 row) Since multidimensional arrays must be rectangular, inner constructors at the same level must produce sub-arrays of identical dimensions. Any cast applied to the outer ARRAY constructor propagates automatically to all the inner constructors. Multidimensional array constructor elements can be anything yielding an array of the proper kind, not only a sub-ARRAY construct. For example: CREATE TABLE arr(f1 int[], f2 int[]); INSERT INTO arr VALUES (ARRAY[[1,2],[3,4]], ARRAY[[5,6],[7,8]]); SELECT ARRAY[f1, f2, '{{9,10},{11,12}}'::int[]] FROM arr; array ------------------------------------------------ {{{1,2},{3,4}},{{5,6},{7,8}},{{9,10},{11,12}}} (1 row) You can construct an empty array, but since it’s impossible to have an array with no type, you must explicitly cast your empty array to the desired type. For example: SELECT ARRAY[]::integer[]; array ------- {} (1 row) It is also possible to construct an array from the results of a subquery. In this form, the array constructor is written with the key word ARRAY followed by a parenthesized (not bracketed) subquery. For example: SELECT ARRAY(SELECT oid FROM pg_proc WHERE proname LIKE 'bytea%'); array ------------------------------------------------------------------ {2011,1954,1948,1952,1951,1244,1950,2005,1949,1953,2006,31,2412} (1 row) SELECT ARRAY(SELECT ARRAY[i, i*2] FROM generate_series(1,5) AS a(i)); array ---------------------------------- {{1,2},{2,4},{3,6},{4,8},{5,10}} (1 row) The subquery must return a single column. If the subquery’s output column is of a non-array type, the resulting one-dimensional array will have an element for each row in the subquery result, with an element type matching that of the subquery’s output column. If the subquery’s output column is of an array type, the result will be an array of the same type but one higher dimension; in this case all the subquery rows must yield arrays of identical dimensionality, else the result would not be rectangular. The subscripts of an array value built with ARRAY always begin with one. A row constructor is an expression that builds a row value (also called a composite value) using values for its member fields. A row constructor consists of the key word ROW, a left parenthesis, zero or more expressions (separated by commas) for the row field values, and finally a right parenthesis. For example: SELECT ROW(1,2.5,'this is a test'); The key word ROW is optional when there is more than one expression in the list. A row constructor can include the syntax rowvalue., which will be expanded to a list of the elements of the row value, just as occurs when the . syntax is used at the top level of a SELECT list .For example, if table t has columns f1 and f2, these are the same: SELECT ROW(t.*, 42) FROM t; SELECT ROW(t.f1, t.f2, 42) FROM t; Note Before PostgreSQL 8.2, the . syntax was not expanded in row constructors, so that writing ROW(t., 42) created a two-field row whose first field was another row value. The new behavior is usually more useful. If you need the old behavior of nested row values, write the inner row value without .*, for instance ROW(t, 42). By default, the value created by a ROW expression is of an anonymous record type. If necessary, it can be cast to a named composite type — either the row type of a table, or a composite type created with CREATE TYPE AS. An explicit cast might be needed to avoid ambiguity. For example: CREATE TABLE mytable(f1 int, f2 float, f3 text); CREATE FUNCTION getf1(mytable) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- No cast needed since only one getf1() exists SELECT getf1(ROW(1,2.5,'this is a test')); getf1 ------- 1 (1 row) CREATE TYPE myrowtype AS (f1 int, f2 text, f3 numeric); CREATE FUNCTION getf1(myrowtype) RETURNS int AS 'SELECT $1.f1' LANGUAGE SQL; -- Now we need a cast to indicate which function to call: SELECT getf1(ROW(1,2.5,'this is a test')); ERROR: function getf1(record) is not unique SELECT getf1(ROW(1,2.5,'this is a test')::mytable); getf1 ------- 1 (1 row) SELECT getf1(CAST(ROW(11,'this is a test',2.5) AS myrowtype)); getf1 ------- 11 (1 row) Row constructors can be used to build composite values to be stored in a composite-type table column, or to be passed to a function that accepts a composite parameter. Also, it is possible to compare two row values or test a row with IS NULL or IS NOT NULL, for example: SELECT ROW(1,2.5,'this is a test') = ROW(1, 3, 'not the same'); SELECT ROW(table.*) IS NULL FROM table; -- detect all-null rows The order of evaluation of subexpressions is not defined. In particular, the inputs of an operator or function are not necessarily evaluated left-to-right or in any other fixed order. Furthermore, if the result of an expression can be determined by evaluating only some parts of it, then other subexpressions might not be evaluated at all. For instance, if one wrote: SELECT true OR somefunc(); then somefunc() would (probably) not be called at all. The same would be the case if one wrote: SELECT somefunc() OR true; Note that this is not the same as the left-to-right “short-circuiting” of Boolean operators that is found in some programming languages. As a consequence, it is unwise to use functions with side effects as part of complex expressions. It is particularly dangerous to rely on side effects or evaluation order in WHERE and HAVING clauses, since those clauses are extensively reprocessed as part of developing an execution plan. Boolean expressions (AND/OR/NOT combinations) in those clauses can be reorganized in any manner allowed by the laws of Boolean algebra. When it is essential to force evaluation order, a CASE construct can be used. For example, this is an untrustworthy way of trying to avoid division by zero in a WHERE clause: SELECT ... WHERE x > 0 AND y/x > 1.5; But this is safe: SELECT ... WHERE CASE WHEN x > 0 THEN y/x > 1.5 ELSE false END; A CASE construct used in this fashion will defeat optimization attempts, so it should only be done when necessary. (In this particular example, it would be better to sidestep the problem by writing y > 1.5*x instead.) CASE is not a cure-all for such issues, however. One limitation of the technique illustrated above is that it does not prevent early evaluation of constant subexpressions. As described in Section 38.7, functions and operators marked IMMUTABLE can be evaluated when the query is planned rather than when it is executed. Thus for example SELECT CASE WHEN x > 0 THEN x ELSE 1/0 END FROM tab; is likely to result in a division-by-zero failure due to the planner trying to simplify the constant subexpression, even if every row in the table has x > 0 so that the ELSE arm would never be entered at run time. While that particular example might seem silly, related cases that don’t obviously involve constants can occur in queries executed within functions, since the values of function arguments and local variables can be inserted into queries as constants for planning purposes. Within PL/pgSQL functions, for example, using an IF-THEN-ELSE statement to protect a risky computation is much safer than just nesting it in a CASE expression. Another limitation of the same kind is that a CASE cannot prevent evaluation of an aggregate expression contained within it, because aggregate expressions are computed before other expressions in a SELECT list or HAVING clause are considered. For example, the following query can cause a division-by-zero error despite seemingly having protected against it: SELECT CASE WHEN min(employees) > 0 THEN avg(expenses / employees) END FROM departments; The min() and avg() aggregates are computed concurrently over all the input rows, so if any row has employees equal to zero, the division-by-zero error will occur before there is any opportunity to test the result of min(). Instead, use a WHERE or FILTER clause to prevent problematic input rows from reaching an aggregate function in the first place. IvorySQL allows functions that have named parameters to be called using either positional or named notation. Named notation is especially useful for functions that have a large number of parameters, since it makes the associations between parameters and actual arguments more explicit and reliable. In positional notation, a function call is written with its argument values in the same order as they are defined in the function declaration. In named notation, the arguments are matched to the function parameters by name and can be written in any order. In either notation, parameters that have default values given in the function declaration need not be written in the call at all. But this is particularly useful in named notation, since any combination of parameters can be omitted; while in positional notation parameters can only be omitted from right to left. IvorySQL also supports mixed notation, which combines positional and named notation. In this case, positional parameters are written first and named parameters appear after them. The following examples will illustrate the usage of all three notations, using the following function definition: CREATE FUNCTION concat_lower_or_upper(a text, b text, uppercase boolean DEFAULT false) RETURNS text AS $$ SELECT CASE WHEN $3 THEN UPPER($1 || ' ' || $2) ELSE LOWER($1 || ' ' || $2) END; $$ LANGUAGE SQL IMMUTABLE STRICT; Function concat_lower_or_upper has two mandatory parameters, a and b. Additionally there is one optional parameter uppercase which defaults to false. The a and b inputs will be concatenated, and forced to either upper or lower case depending on the uppercase parameter. The remaining details of this function definition are not important here . Positional notation is the traditional mechanism for passing arguments to functions in IvorySQL. An example is: SELECT concat_lower_or_upper('Hello', 'World', true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) All arguments are specified in order. The result is upper case since uppercase is specified as true. Another example is: SELECT concat_lower_or_upper('Hello', 'World'); concat_lower_or_upper ----------------------- hello world (1 row) Here, the uppercase parameter is omitted, so it receives its default value of false, resulting in lower case output. In positional notation, arguments can be omitted from right to left so long as they have defaults. In named notation, each argument’s name is specified using ⇒ to separate it from the argument expression. For example: SELECT concat_lower_or_upper(a => 'Hello', b => 'World'); concat_lower_or_upper ----------------------- hello world (1 row) Again, the argument uppercase was omitted so it is set to false implicitly. One advantage of using named notation is that the arguments may be specified in any order, for example: SELECT concat_lower_or_upper(a => 'Hello', b => 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) SELECT concat_lower_or_upper(a => 'Hello', uppercase => true, b => 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) An older syntax based on \":=\" is supported for backward compatibility: SELECT concat_lower_or_upper(a := 'Hello', uppercase := true, b := 'World'); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) The mixed notation combines positional and named notation. However, as already mentioned, named arguments cannot precede positional arguments. For example: SELECT concat_lower_or_upper('Hello', 'World', uppercase => true); concat_lower_or_upper ----------------------- HELLO WORLD (1 row) In the above query, the arguments a and b are specified positionally, while uppercase is specified by name. In this example, that adds little except documentation. With a more complex function having numerous parameters that have default values, named or mixed notation can save a great deal of writing and reduce chances for error. Note Named and mixed call notations currently cannot be used when calling an aggregate function (but they do work when an aggregate function is used as a window function). ALTER TABLE [ IF EXISTS ] [ ONLY ] name [ * ] action; action: ADD ( add_coldef [, ... ] ) | MODIFY ( modify_coldef [, ... ] ) | DROP [ COLUMN ] ( column_name [, ... ] ) add_coldef: column_name data_type modify_coldef: column_name data_type alter_using alter_using: USING expression name Table name. column_name Column name. data_type Column type. expression The value expression. ADD keyword Adds a column to the table, either one or more columns. MODIFY keyword Modify a column of the table, you can modify one or more columns. DROP keyword Deletes a column of a table, you can delete one or more columns. USING keyword Modifies the value of a column. ADD: create table tb_test1(id int, flg char(10)); alter table tb_test1 add (name varchar); ALTER TABLE tb_test1 ADD adress varchar, ADD num int, ADD flg1 char; \\d tb_test1 Table \"public.tb_test1\" Column | Type | Collation | Nullable | Default --------+-----------------+-----------+----------+--------- id | pg_catalog.int4 | | | flg | char(10) | | | name | varchar2(4000) | | | adress | varchar2(4000) | | | num | pg_catalog.int4 | | | flg1 | char(1) | | | MODIFY: create table tb_test2(id int, flg char(10), num varchar); insert into tb_test2 values('1', 2, '3'); ALTER TABLE tb_test2 ALTER COLUMN id TYPE char; \\d tb_test2 Table \"public.tb_test2\" Column | Type | Collation | Nullable | Default --------+----------------+-----------+----------+--------- id | char(1) | | | flg | char(10) | | | num | varchar2(4000) | | | DROP: create table tb_test3(id int, flg1 char(10), flg2 char(11), flg3 char(12), flg4 char(13), flg5 char(14), flg6 char(15)); ALTER TABLE tb_test3 DROP id; \\d tb_test3 Table \"public.tb_test3\" Column | Type | Collation | Nullable | Default --------+----------+-----------+----------+--------- flg1 | char(10) | | | flg2 | char(11) | | | flg3 | char(12) | | | flg4 | char(13) | | | flg5 | char(14) | | | flg6 | char(15) | | | [ WITH [ RECURSIVE ] with_query [, ...] ] DELETE [ FROM ] [ ONLY ] table_name [ * ] [ [ AS ] alias ] [ USING using_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name The name of the table. alias The table alias. using_list A list of table expressions that allow columns from other tables to appear in the WHERE condition. condition An expression that returns a boolean type value. cursor_name The name of the cursor to be used in the WHERE CURRENT OF case. output_expression An expression that is calculated by DELETE and returned after each row is deleted. output_name The name of the returned column. create table tb_test4(id int, flg char(10)); insert into tb_test4 values(1, '2'), (5, '6'); delete from tb_test4 where id = 1; table tb_test4; id | flg ----+------------ 5 | 6 (1 row) [ WITH [ RECURSIVE ] with_query [, ...] ] UPDATE [ ONLY ] table_name [ * ] [ [ AS ] alias ] SET { [ table_name | alias ] column_name = { expression | DEFAULT } | ( [ table_name | alias ] column_name [, ...] ) = [ ROW ] ( { expression | DEFAULT } [, ...] ) | ( [ table_name | alias ] column_name [, ...] ) = ( sub-SELECT ) } [, ...] [ FROM from_list ] [ WHERE condition | WHERE CURRENT OF cursor_name ] [ RETURNING * | output_expression [ [ AS ] output_name ] [, ...] ] table_name Table name. alias Table alias. column_name Column name. expression Value expression. sub-SELECT select clause. from_list Table expression. condition An expression that returns a value of type boolean. cursor_name The name of the cursor to be used in the WHERE CURRENT OF case. output_expression An expression that is computed by DELETE and returned after each row is deleted. output_name The name of the column being returned. create table tb_test5(id int, flg char(10)); insert into tb_test5 values(1, '2'), (3, '4'), (5, '6'); update tb_test5 a set a.id = 33 where a.id = 3; table tb_test5; Id | flg ----+------------ 1 | 2 5 | 6 33 | 4 (3 rows) set ivorysql.compatible_mode to oracle; create table students(student_id varchar(20) primary key , student_name varchar(40), student_pid int); select student_id,student_name from students group by student_id; ERROR: column \"students.student_name\" must appear in the GROUP BY clause or be used in an aggregate function SELECT 100 AS value FROM DUAL UNION SELECT 200 AS value FROM DUAL UNION SELECT 100 AS value FROM DUAL; value ------- 100 200 (2 rows) select_statement MINUS [ ALL | DISTINCT ] select_statement; select_statement Any SELECT statement without the ORDER BY, LIMIT, FOR NO KEY UPDATE, FOR UPDATE, FOR SHARE, and FOR KEY SHARE clauses. ALL keyword contains duplicate row results. DISTINCT keyword shows the elimination of duplicate rows. select * from generate_series(1, 3) g(i) MINUS select * from generate_series(1, 3) g(i) where i = 1; i --- 2 3 (2 rows) Use q\\' to escape special characters. q\\' escaped characters are usually used after ! [] {} () \\<> and other escaping characters, you can also use \\, letters, numbers, \\=, +, -, *, \\&, \\$, \\%, #, etc., no spaces are allowed. select q''' is goog ''; ?column? ------------ ' is goog (1 row) SELECT [ database {schema} | schema ] sequence {nextval | currval}; `sequence`Sequence Name. create sequence sq; select sq.nextval; nextval ----------- 1 (1 row) select sq.currval; nextval ----------- 1 (1 row) Convert the given timestamp without time zone to the specified timestamp with time zone, or return NULL if the specified time zone or timestamp is NULL. Parameters Description day Timestamp without time zone tz Specified time zone select from_tz('2021-11-08 09:12:39','Asia/Shanghai') from dual; from_tz ----------------------------------- 2021-11-08 09:12:39 Asia/Shanghai (1 row) select from_tz('2021-11-08 09:12:39','SAST') from dual; from_tz -------------------------- 2021-11-08 09:12:39 SAST select from_tz(NULL,'SAST') from dual; from_tz --------- (1 row) select from_tz('2021-11-08 09:12:31',NULL) from dual; from_tz --------- (1 row) Get the timestamp of the current database system. select oracle.systimestamp(); systimestamp ------------------------------- 2021-12-02 14:38:59.879642+08 (1 row) select systimestamp; statement_timestamp ------------------------------- 2021-12-02 14:39:33.262828+08 Converts the given timestamp with time zone to UTC time without time zone. Parameters Description day Need to convert time stamp with time zone select sys_extract_utc('2018-03-28 11:30:00.00 +09:00'::timestamptz) from dual; sys_extract_utc --------------------- 2018-03-28 02:30:00 (1 row) select oracle.sys_extract_utc(NULL) from dual; sys_extract_utc ----------------- (1 row) Gets the time zone of the current session. select sessiontimezone() from dual; sessiontimezone ----------------- PRC (1 row) set timezone to UTC; select oracle.sessiontimezone(); sessiontimezone ----------------- UTC (1 row) next_day returns the date of the first weekday with the same format name, which is later than the current date. The return type is always DATE, regardless of the date's data type. The return value has the same hour, minute, and second parts as the Parameters date. Parameters Description value Start Timestamp weekday The day of the week, can be \"Sunday\", \"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\" or 0,1,2,3,4,5,6,0 for Sunday select next_day(to_timestamp('2020-02-29 14:40:50', 'YYYY-MM-DD HH24:MI:SS'), 'Tuesday') from dual; next_day --------------------- 2020-03-03 14:40:50 (1 row) select next_day('2020-07-01 19:43:51 +8'::timestamptz, 1) from dual; next_day --------------------- 2020-07-05 19:43:51 (1 row) last_day returns the last day of the month in which the slot date falls. Parameters Description value Specified timestamp select last_day(timestamp '2020-05-17 13:27:19') from dual; last_day --------------------- 2020-05-31 13:27:19 (1 row) select last_day('2020-11-29 19:20:40 +08'::timestamptz) from dual; last_day --------------------- 2020-11-30 19:20:40 (1 row) add_months returns the date plus an integer month. date Parameters can be date-time values or any value that can be implicitly converted to DATE. integer Parameters can be an integer or any value that can be implicitly converted to an integer. Parameters Description day oracle.date type, the timestamp that needs to be changed value A shaping data, the number of months to be added select add_months(date '2020-02-15',7) from dual; add_months --------------------- 2020-09-15 00:00:00 (1 row) select add_months(timestamp '2018-12-15 19:12:09',12) from dual; add_months --------------------- 2019-12-15 19:12:09 (1 row) sysdate returns the operating system time of the database server. select sysdate; statement_sysdate --------------------- 2021-12-09 16:20:34 (1 row) select sysdate(); sysdate --------------------- 2021-12-09 16:21:39 (1 row) Convert the time of the first time zone to the time of the second time zone. The time zones include \"ast\", \"adt\", \"bst\", \"bdt\", \"cst\", \"cdt\", \"est\", \"edt\", \"gmt\", \"hst\", \"hdt\", \"mst\", \"mdt\", \"nst\", \"pst\", \"pdt\", \"yst\", \"ydt\". Parameters Description day Timestamp to be converted tz1 Timestamped time zones tz2 Target time zone select new_time(timestamp '2020-12-12 17:45:18', 'AST', 'ADT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'BST', 'BDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) select new_time(timestamp '2020-12-12 17:45:18', 'CST', 'CDT') from dual; new_time --------------------- 2020-12-12 18:45:18 (1 row) The trunc function returns a date, truncated in the specified format. fmt includes \"Y\", \"YY\", \"YYYY\", \"YYYY\", \"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D\", \" MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". Parameters Description value The specified date (oracle.date, timestamp, timestamptz) fmt The specified format, if omitted, defaults to \"DDD\" select trunc(timestamp '2020-07-28 19:16:12', 'Q'); trunc --------------------- 2020-07-01 00:00:00 (1 row) select trunc(timestamptz '2020-09-27 18:30:21 + 08', 'MONTH'); trunc ------------------------ 2020-09-01 00:00:00+08 (1 row) The round function returns a date, rounded to the specified format. fmt includes \"Y\", \"YY\", \"YYYY\", \"YYYY\", \"YEAR\", \"SYYYY\", \"SYEAR\", \"I\", \"IY\", \"IYY\", \"IYYY\", \"Q\", \"WW\", \"Iw\", \"W\", \"DAY\", \"DY\", \"D \", \"MONTH\", \"MONn\", \"MM\", \"RM\", \"CC\", \"SCC\", \"DDD\", \"DD\", \"J\", \"HH\", \"HH12\", \"HH24\", \"MI\". Parameters Description value The date being converted (oracle.date, timestamp, timestamptz) fmt The specified format, if omitted, defaults to \"DDD\" select round(timestamp '2050-06-12 16:40:55', 'IYYY'); round --------------------- 2050-01-03 00:00:00 (1 row) TO_CHAR (str,[fmt]) Converts the input Parameters to a TEXT data type value according to the given format. If fmt is omitted, the data will be converted to a TEXT value in the system default format. If str is null, the function returns null. str Input Parameters (any type). fmt Input format Parameters, see format fmt for details. select to_char('3 2:20:05' ); to_char ----------------- 3 days 02:20:05 (1 row) select to_char('4.00'::numeric); to_char --------- 4 (1 row) select to_char(NULL); to_char --------- (1 row) select to_char(123,'xx'); to_char --------- 7b (1 row) TO_NUMBER(str,[fmt1]) Converts the input Parameters str to a value of the NUMREIC data type according to the given format. If fmt1 is omitted, the data will be converted to a NUMERIC value in the system default format. If str is NUMERIC, the function returns str. If str calculates to null, the function returns null. If it cannot be converted to the NUMERIC data type, the function returns an error. str input Parameters include the following data types (double precision, numeric, text, integer, etc., but must be implicitly converted to numeric). fmt1 Input format Parameters, see format fmt1 for details. select to_number(1210.73::numeric, 9999.99::numeric); to_number ----------- 1210.73 (1 row) select to_number(NULL); to_number ----------- (1 row) select to_number('123'::text); to_number ----------- 123 (1 row) TO_DATE(str,[fmt]) Converts the input Parameters str to a date data type value according to the given format. If fmt is omitted, the data will be converted to a date value in the system default format. If str is null, the function returns null. If fmt is J, for Julian, then char must be an integer. The function returns an error if it cannot be converted to DATE. str input Parameters (integer, text, can be implicitly converted to the above type, string that matches the date format). fmt input format Parameters, see format fmt for details. select to_date('50-11-28 ','RR-MM-dd '); to_date --------------------- 1950-11-28 00:00:00 (1 row) select to_date(2454336, 'J'); to_date --------------------- 2007-08-23 00:00:00 (1 row) select to_date('2019/11/22', 'yyyy-mm-dd'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('20-11-28 10:14:22','YY-MM-dd hh24:mi:ss'); to_date --------------------- 2020-11-28 10:14:22 (1 row) select to_date('2019/11/22'); to_date --------------------- 2019-11-22 00:00:00 (1 row) select to_date('2019/11/27 10:14:22'); to_date --------------------- 2019-11-27 10:14:22 (1 row) select to_date('2020','RR'); to_date --------------------- 2020-01-01 00:00:00 (1 row) select to_date(NULL); to_date --------- (1 row) select to_date('-4712-07-23 14:31:23', 'syyyy-mm-dd hh24:mi:ss'); to_date ---------------------- -4712-07-23 14:31:23 (1 row) TO_TIMESTAMP(str,[fmt]) Converts the input Parameters str to a timestamp without a time zone according to the given format. If fmt is omitted, the data is converted to a timestamp with no time zone value in the system default format. If str is null, the function returns null. If it cannot be converted to a timestamp without a time zone, the function returns an error. str input Parameters (double precision,text, which can be implicitly converted to the above type). fmt Input format Parameters, see format fmt for details. select to_timestamp(1212121212.55::numeric); to_timestamp --------------------------- 2008-05-30 12:20:12.55 (1 row) select to_timestamp('2020/03/03 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp ------------------------ 2020-03-03 13:13:18 (1 row) select to_timestamp(NULL,NULL); to_timestamp -------------- (1 row) TO_YMINTERVAL(str) Converts the input Parameters str time interval to a time interval in the year-to-month range. Only the year and month are processed, other parts are omitted. If the input Parameters is NULL, the function returns NULL, and if the input Parameters is in the wrong format, the function returns an error. str Input Parameters (text, can be implicitly converted to text type, must be in time interval format. (SQL interval format compatible with SQL standard, ISO duration format compatible with ISO 8601:2004 standard). select to_yminterval('P1Y-2M2D'); to_yminterval --------------- +000000000-10 (1 row) select to_yminterval('P1Y2M2D'); to_yminterval --------------- +000000001-02 (1 row) select to_yminterval('-01-02'); to_yminterval --------------- -000000001-02 (1 row) TO_DSINTERVAL(str) converts the time interval of the input Parameters str to a time interval in the range of days to seconds. Input Parameters include: day, hour, minute, second and microsecond. If the input Parameters is NULL, the function returns NULL, and if the input Parameters contains the year and month or is in the wrong format, the function returns an error. str Input Parameters (text, can be implicitly converted to text type, must be in time interval format. (SQL interval format compatible with SQL standard, ISO duration format compatible with ISO 8601:2004 standard). select to_dsinterval('100 00 :02 :00'); to_dsinterval ------------------------------- +000000100 00:02:00.000000000 (1 row) select to_dsinterval('-100 00:02:00'); to_dsinterval ------------------------------- -000000100 00:02:00.000000000 (1 row) select to_dsinterval(NULL); to_dsinterval --------------- (1 row) TO_TIMESTAMP_TZ(str,[fmt]) Converts the input Parameters str to a timestamp with a time zone according to the given format. If fmt is omitted, the data will be converted to a timestamp with a time zone value in the system default format. If str is null, the function returns null. If it cannot be converted to a timestamp with a time zone, the function returns an error. str input Parameters (text, which can be implicitly converted to a text type). fmt Enter format Parameters, see format fmt for details. select to_timestamp_tz('2019','yyyy'); to_timestamp_tz ------------------------ 2019-01-01 00:00:00+08 (1 row) select to_timestamp_tz('2019-11','yyyy-mm'); to_timestamp_tz ------------------------ 2019-11-01 00:00:00+08 (1 row) select to_timestamp_tz('2003/12/13 10:13:18 +7:00'); to_timestamp_tz ------------------------ 2003-12-13 11:13:18+08 (1 row) select to_timestamp_tz('2019/12/13 10:13:18 +5:00', 'YYYY/MM/DD HH:MI:SS TZH:TZM'); to_timestamp_tz ------------------------ 2019-12-13 13:13:18+08 (1 row) select to_timestamp_tz(NULL); to_timestamp_tz ----------------- (1 row) GREATEST(expr1,expr2,…​) Gets the maximum value in the input list of one or more expressions. If the result of any expr calculation is NULL, the function returns NULL. expr1` Enter Parameters (of any type). `expr2` Enter Parameters (of any type). `... select greatest('a','b','A','B'); greatest ---------- b (1 row) select greatest(',','.','/',';','!','@','?'); greatest ---------- @ (1 row) select greatest('瀚','高','数','据','库'); greatest ---------- 高 (1 row) SELECT greatest('HARRY', 'HARRIOT', 'HARRA'); greatest ---------- HARRY (1 row) SELECT greatest('HARRY', 'HARRIOT', NULL); greatest ---------- (1 row) SELECT greatest(1.1, 2.22, 3.33); greatest ---------- 3.33 (1 row) SELECT greatest('A', 6, 7, 5000, 'E', 'F','G') A; a --- G (1 row) LEAST(expr1,expr2,…​) Gets the smallest value in the input list of one or more expressions. If the result of any expr calculation is NULL, the function returns NULL. expr1` Enter Parameters (of any type). `expr2` Enter Parameters (of any type). `... SELECT least(1,' 2', '3' ); least ------- 1 (1 row) SELECT least(NULL, NULL, NULL); least ------- (1 row) SELECT least('A', 6, 7, 5000, 'E', 'F','G') A; a ------ 5000 (1 row) select least(1,3,5,10); least ------- 1 (1 row) select least('a','A','b','B'); least ------- A (1 row) select least(',','.','/',';','!','@'); least ------- ! (1 row) select least('瀚','高','据','库'); least ------- 库 (1 row) SELECT least('HARRY', 'HARRIOT', NULL); least ------- (1 row) NLS_LENGTH_SEMANTICS enables you to create CHAR and VARCHAR2 columns using byte or character length semantics. Existing columns are not affected. In this case, the default semantics is BYTE. SET NLS_LENGTH_SEMANTICS TO [NONE | BYTE | CHAR]; BYTE: The data is stored in byte length. CHAR:Data is stored in character length. NONE:Data is stored using native IvorySQL storage. create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 SET NLS_LENGTH_SEMANTICS TO BYTE; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- byte (1 row) insert into test values ('Hello,Mr.li'); 2021-12-14 15:28:11.906 HKT [6774] ERROR: value too long for type varchar2(5 byte) 2021-12-14 15:28:11.906 HKT [6774] STATEMENT: insert into test values ('Hello,Mr.li'); ERROR: value too long for type varchar2(5 byte) Variable length strings with maximum length bytes or characters. You must specify the size for VARCHAR2. The minimum size is 1 byte or 1 character. VARCHAR2(size) create table test(a varchar2(5)); CREATE TABLE SET NLS_LENGTH_SEMANTICS TO CHAR; SET SHOW NLS_LENGTH_SEMANTICS; nls_length_semantics ---------------------- char (1 row) insert into test values ('Hello,Mr.li'); INSERT 0 1 PL/iSQL is IvorySQL’s procedural language for writing custom functions, procedures and packages for IvorySQL. PL/iSQL is derived from IvorySQL’s PL/pgsql with some added features, but syntactically PL/iSQL is closer to Oracle’s PL/SQL. This document Describes the basic structure and construction of PL/iSQL programs. iSQL is a procedural block structure language that supports four different program types, PACKAGES, PROCEDURES, FUNCTIONS, and TRIGGERS. iSQL supports four different program types, PACKAGES, PROCEDURES, FUNCTIONS, and TRIGGERS. iSQL uses the same block structure for each type of supported program. A block consists of up to three parts: a declaration part, an executable, and an exception part. The declaration and exception sections are optional. [DECLARE declarations] BEGIN statements [ EXCEPTION WHEN  THEN statements] END; A block can consist of at least one executable section Contains one or more iSQL statements in the BEGIN and END keywords. CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS BEGIN NULL; END; / All keywords are case-insensitive. Identifiers are implicitly converted to lowercase unless double-quoted, just as they are in normal SQL commands. The declaration section can be used to declare variables and cursors, and depending on the context in which the block is used, the declaration section can begin with the keyword DECLARE. CREATE OR REPLACE FUNCTION null_func() RETURN VOID AS DECLARE quantity integer := 30; c_row pg_class%ROWTYPE; r_cursor refcursor; CURSOR c1 RETURN pg_proc%ROWTYPE; BEGIN NULL; end; / An optional exception section can also be included in a BEGIN - END block. The exception section begins with the keyword EXCEPTION and continues until the end of the block in which it appears. If a statement within the block throws an exception, program control goes to the exception section, which may or may not handle the thrown exception, depending on the contents of the exception and exception sections. CREATE OR REPLACE FUNCTION reraise_test() RETURN void AS BEGIN BEGIN RAISE syntax_error; EXCEPTION WHEN syntax_error THEN BEGIN raise notice 'exception % thrown in inner block, reraising', sqlerrm; RAISE; EXCEPTION WHEN OTHERS THEN raise notice 'RIGHT - exception % caught in inner block', sqlerrm; END; END; EXCEPTION WHEN OTHERS THEN raise notice 'WRONG - exception % caught in outer block', sqlerrm; END; / Note Like PL/pgSQL, PL/iSQL uses BEGIN/END to group statements, and do not confuse them with the SQL commands of the same name used for transaction control. PL/iSQL’s BEGIN/END are used only for grouping; they do not start or end transactions To create a PL/iSQL program from a psql client, you can use a syntax similar to PL/pgSQL’s $$ CREATE FUNCTION func() RETURNS void as $$ .. end$$ language plisql; Alternatively, you can use the Oracle-compliant syntax of references and language specifications without $$ and end the program definition with / (forward slash). The */ (forward slash) must be on the newline character CREATE FUNCTION func() RETURN void AS … END; / CREATE [OR REPLACE] PROCEDURE procedure_name [(parameter_list)] is [DECLARE] -- variable declaration BEGIN -- stored procedure body END; / CREATE [OR REPLACE] FUNCTION function_name ([parameter_list]) RETURN return_type AS [DECLARE] -- variable declaration BEGIN -- function body return statement END; / CREATE [ OR REPLACE ] PACKAGE [schema.] *package_name* [invoker_rights_clause] [IS | AS] item_list[, item_list ...] END [*package_name*]; invoker_rights_clause: AUTHID [CURRENT_USER | DEFINER] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] function_declaration: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype; procedure_declaration: PROCEDURE procedure_name [(parameter_declaration[, ...])] type_definition: record_type_definition | ref_cursor_type_definition cursor_declaration: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype; item_declaration: cursor_declaration | cursor_variable_declaration | record_variable_declaration | variable_declaration | record_type_definition: TYPE record_type IS RECORD ( variable_declaration [, variable_declaration]... ) ; ref_cursor_type_definition: TYPE type IS REF CURSOR [ RETURN type%ROWTYPE ]; cursor_variable_declaration: curvar curtype; record_variable_declaration: recvar { record_type | rowtype_attribute | record_type%TYPE }; variable_declaration: varname datatype [ [ NOT NULL ] := expr ] parameter_declaration: parameter_name [IN] datatype [[:= | DEFAULT] expr] CREATE [ OR REPLACE ] PACKAGE BODY [schema.] package_name [IS | AS] [item_list[, item_list ...]] | item_list_2 [, item_list_2 ...] [initialize_section] END [package_name]; initialize_section: BEGIN statement[, ...] item_list: [ function_declaration | procedure_declaration | type_definition | cursor_declaration | item_declaration ] item_list_2: [ function_declaration function_definition procedure_declaration procedure_definition cursor_definition ] function_definition: FUNCTION function_name [(parameter_declaration[, ...])] RETURN datatype [IS | AS] [declare_section] body; procedure_definition: PROCEDURE procedure_name [(parameter_declaration[, ...])] [IS | AS] [declare_section] body; cursor_definition: CURSOR name [(cur_param_decl[, ...])] RETURN rowtype IS select_statement; body: BEGIN statement[, ...] END [name]; statement: [<