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feat(agents)!: retrieve container tags hash from /info endpoint#1700

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feat(agents)!: retrieve container tags hash from /info endpoint#1700
dubloom wants to merge 5 commits intomainfrom
dubloom/container-back-propagation

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@dubloom dubloom commented Mar 10, 2026

What does this PR do?

A brief description of the change being made with this pull request.

Motivation

What inspired you to submit this pull request?

Additional Notes

Anything else we should know when reviewing?

How to test the change?

Describe here in detail how the change can be validated.

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📚 Documentation Check Results

⚠️ 798 documentation warning(s) found

📦 libdd-data-pipeline - 798 warning(s)

Updated: 2026-03-17 18:23:05 UTC | Commit: ad9a15b | missing-docs job results

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Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

  • Base Branch: origin/main
  • PR Branch: origin/dubloom/container-back-propagation

Summary by Rule

Rule Base Branch PR Branch Change

Annotation Counts by File

File Base Branch PR Branch Change

Annotation Stats by Crate

Crate Base Branch PR Branch Change
clippy-annotation-reporter 5 5 No change (0%)
datadog-ffe-ffi 1 1 No change (0%)
datadog-ipc 28 28 No change (0%)
datadog-live-debugger 6 6 No change (0%)
datadog-live-debugger-ffi 10 10 No change (0%)
datadog-profiling-replayer 4 4 No change (0%)
datadog-remote-config 3 3 No change (0%)
datadog-sidecar 59 59 No change (0%)
libdd-common 10 10 No change (0%)
libdd-common-ffi 12 12 No change (0%)
libdd-data-pipeline 5 5 No change (0%)
libdd-ddsketch 2 2 No change (0%)
libdd-dogstatsd-client 1 1 No change (0%)
libdd-profiling 13 13 No change (0%)
libdd-telemetry 19 19 No change (0%)
libdd-tinybytes 4 4 No change (0%)
libdd-trace-normalization 2 2 No change (0%)
libdd-trace-obfuscation 9 9 No change (0%)
libdd-trace-utils 15 15 No change (0%)
Total 208 208 No change (0%)

About This Report

This report tracks Clippy allow annotations for specific rules, showing how they've changed in this PR. Decreasing the number of these annotations generally improves code quality.

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🔒 Cargo Deny Results

⚠️ 1 issue(s) found, showing only errors (advisories, bans, sources)

📦 libdd-data-pipeline - 1 error(s)

Show output 
error[vulnerability]: Denial of Service via Stack Exhaustion
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:293:1
    │
293 │ time 0.3.41 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0009
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0009
    ├ ## Impact
      
      When user-provided input is provided to any type that parses with the RFC 2822 format, a denial of
      service attack via stack exhaustion is possible. The attack relies on formally deprecated and
      rarely-used features that are part of the RFC 2822 format used in a malicious manner. Ordinary,
      non-malicious input will never encounter this scenario.
      
      ## Patches
      
      A limit to the depth of recursion was added in v0.3.47. From this version, an error will be returned
      rather than exhausting the stack.
      
      ## Workarounds
      
      Limiting the length of user input is the simplest way to avoid stack exhaustion, as the amount of
      the stack consumed would be at most a factor of the length of the input.
    ├ Announcement: https://github.com/time-rs/time/blob/main/CHANGELOG.md#0347-2026-02-05
    ├ Solution: Upgrade to >=0.3.47 (try `cargo update -p time`)
    ├ time v0.3.41
      └── tracing-appender v0.2.3
          └── libdd-log v1.0.0
              └── (dev) libdd-data-pipeline v2.0.1

advisories FAILED, bans ok, sources ok

Updated: 2026-03-17 18:26:49 UTC | Commit: ad9a15b | dependency-check job results

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Benchmarks

Comparison

Benchmark execution time: 2026-03-17 16:48:22

Comparing candidate commit 7f861d8 in PR branch dubloom/container-back-propagation with baseline commit 5426a8b in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 59 metrics, 2 unstable metrics.

Explanation

This is an A/B test comparing a candidate commit's performance against that of a baseline commit. Performance changes are noted in the tables below as:

  • 🟩 = significantly better candidate vs. baseline
  • 🟥 = significantly worse candidate vs. baseline

We compute a confidence interval (CI) over the relative difference of means between metrics from the candidate and baseline commits, considering the baseline as the reference.

If the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD), the change is considered significant.

Feel free to reach out to #apm-benchmarking-platform on Slack if you have any questions.

More details about the CI and significant changes

You can imagine this CI as a range of values that is likely to contain the true difference of means between the candidate and baseline commits.

CIs of the difference of means are often centered around 0%, because often changes are not that big:

---------------------------------(------|---^--------)-------------------------------->
                              -0.6%    0%  0.3%     +1.2%
                                 |          |        |
         lower bound of the CI --'          |        |
sample mean (center of the CI) -------------'        |
         upper bound of the CI ----------------------'

As described above, a change is considered significant if the CI is entirely outside the configured SIGNIFICANT_IMPACT_THRESHOLD (or the deprecated UNCONFIDENCE_THRESHOLD).

For instance, for an execution time metric, this confidence interval indicates a significantly worse performance:

----------------------------------------|---------|---(---------^---------)---------->
                                       0%        1%  1.3%      2.2%      3.1%
                                                  |   |         |         |
       significant impact threshold --------------'   |         |         |
                      lower bound of CI --------------'         |         |
       sample mean (center of the CI) --------------------------'         |
                      upper bound of CI ----------------------------------'

Candidate

Candidate benchmark details

Group 1

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
write only interface execution_time 1.314µs 3.247µs ± 1.481µs 3.029µs ± 0.028µs 3.053µs 3.687µs 14.069µs 15.749µs 419.90% 7.443 56.408 45.49% 0.105µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
write only interface execution_time [3.042µs; 3.452µs] or [-6.321%; +6.321%] None None None

Group 2

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
benching serializing traces from their internal representation to msgpack execution_time 14.170ms 14.231ms ± 0.026ms 14.228ms ± 0.012ms 14.241ms 14.269ms 14.337ms 14.394ms 1.17% 2.270 10.051 0.19% 0.002ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
benching serializing traces from their internal representation to msgpack execution_time [14.228ms; 14.235ms] or [-0.026%; +0.026%] None None None

Group 3

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
sdk_test_data/rules-based execution_time 145.048µs 146.605µs ± 1.577µs 146.332µs ± 0.442µs 146.864µs 148.017µs 153.418µs 161.412µs 10.31% 5.650 43.598 1.07% 0.112µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
sdk_test_data/rules-based execution_time [146.387µs; 146.824µs] or [-0.149%; +0.149%] None None None

Group 4

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
normalization/normalize_trace/test_trace execution_time 237.168ns 249.051ns ± 13.837ns 243.765ns ± 3.494ns 249.937ns 283.536ns 291.666ns 293.172ns 20.27% 1.895 2.485 5.54% 0.978ns 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
normalization/normalize_trace/test_trace execution_time [247.134ns; 250.969ns] or [-0.770%; +0.770%] None None None

Group 5

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
profile_add_sample2_frames_x1000 execution_time 738.134µs 739.346µs ± 0.590µs 739.296µs ± 0.433µs 739.731µs 740.420µs 740.649µs 741.613µs 0.31% 0.504 0.154 0.08% 0.042µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
profile_add_sample2_frames_x1000 execution_time [739.264µs; 739.427µs] or [-0.011%; +0.011%] None None None

Group 6

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
profile_add_sample_frames_x1000 execution_time 4.226ms 4.231ms ± 0.003ms 4.231ms ± 0.002ms 4.232ms 4.234ms 4.236ms 4.263ms 0.77% 5.514 54.123 0.07% 0.000ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
profile_add_sample_frames_x1000 execution_time [4.231ms; 4.231ms] or [-0.010%; +0.010%] None None None

Group 7

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... execution_time 185.839µs 186.292µs ± 0.211µs 186.281µs ± 0.137µs 186.419µs 186.659µs 186.774µs 186.888µs 0.33% 0.288 -0.320 0.11% 0.015µs 1 200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput 5350801.918op/s 5367934.336op/s ± 6068.454op/s 5368220.684op/s ± 3932.188op/s 5372129.961op/s 5376933.964op/s 5379769.582op/s 5380988.805op/s 0.24% -0.282 -0.324 0.11% 429.104op/s 1 200
normalization/normalize_name/normalize_name/bad-name execution_time 17.820µs 17.897µs ± 0.049µs 17.892µs ± 0.022µs 17.916µs 17.956µs 17.977µs 18.415µs 2.92% 6.019 60.871 0.27% 0.003µs 1 200
normalization/normalize_name/normalize_name/bad-name throughput 54304473.776op/s 55875686.264op/s ± 150296.040op/s 55891255.355op/s ± 69979.979op/s 55959307.716op/s 56029701.015op/s 56061867.421op/s 56117940.104op/s 0.41% -5.805 57.867 0.27% 10627.535op/s 1 200
normalization/normalize_name/normalize_name/good execution_time 10.180µs 10.339µs ± 0.115µs 10.311µs ± 0.036µs 10.347µs 10.623µs 10.673µs 10.729µs 4.05% 1.800 2.461 1.11% 0.008µs 1 200
normalization/normalize_name/normalize_name/good throughput 93203291.206op/s 96734754.874op/s ± 1052168.169op/s 96980918.530op/s ± 335399.684op/s 97316300.278op/s 97787849.964op/s 97950867.237op/s 98234605.075op/s 1.29% -1.760 2.345 1.08% 74399.525op/s 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... execution_time [186.262µs; 186.321µs] or [-0.016%; +0.016%] None None None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput [5367093.307op/s; 5368775.365op/s] or [-0.016%; +0.016%] None None None
normalization/normalize_name/normalize_name/bad-name execution_time [17.890µs; 17.904µs] or [-0.038%; +0.038%] None None None
normalization/normalize_name/normalize_name/bad-name throughput [55854856.679op/s; 55896515.850op/s] or [-0.037%; +0.037%] None None None
normalization/normalize_name/normalize_name/good execution_time [10.323µs; 10.355µs] or [-0.154%; +0.154%] None None None
normalization/normalize_name/normalize_name/good throughput [96588934.485op/s; 96880575.263op/s] or [-0.151%; +0.151%] None None None

Group 8

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
tags/replace_trace_tags execution_time 2.389µs 2.430µs ± 0.019µs 2.424µs ± 0.009µs 2.436µs 2.470µs 2.479µs 2.488µs 2.64% 1.082 0.525 0.78% 0.001µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
tags/replace_trace_tags execution_time [2.427µs; 2.433µs] or [-0.109%; +0.109%] None None None

Group 9

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
redis/obfuscate_redis_string execution_time 33.744µs 34.126µs ± 0.632µs 33.844µs ± 0.064µs 33.963µs 35.377µs 35.410µs 37.719µs 11.45% 2.135 4.878 1.85% 0.045µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
redis/obfuscate_redis_string execution_time [34.038µs; 34.213µs] or [-0.257%; +0.257%] None None None

Group 10

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
two way interface execution_time 17.844µs 25.191µs ± 9.075µs 18.053µs ± 0.112µs 33.392µs 42.382µs 43.527µs 66.381µs 267.70% 0.983 0.709 35.94% 0.642µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
two way interface execution_time [23.933µs; 26.448µs] or [-4.993%; +4.993%] None None None

Group 11

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
receiver_entry_point/report/2598 execution_time 3.419ms 3.445ms ± 0.017ms 3.442ms ± 0.007ms 3.449ms 3.468ms 3.523ms 3.542ms 2.89% 2.699 10.277 0.50% 0.001ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
receiver_entry_point/report/2598 execution_time [3.442ms; 3.447ms] or [-0.070%; +0.070%] None None None

Group 12

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
concentrator/add_spans_to_concentrator execution_time 12.935ms 12.967ms ± 0.018ms 12.965ms ± 0.010ms 12.976ms 12.996ms 13.032ms 13.053ms 0.68% 1.226 3.455 0.14% 0.001ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
concentrator/add_spans_to_concentrator execution_time [12.965ms; 12.970ms] or [-0.019%; +0.019%] None None None

Group 13

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
credit_card/is_card_number/ execution_time 3.894µs 3.913µs ± 0.003µs 3.913µs ± 0.002µs 3.915µs 3.918µs 3.920µs 3.922µs 0.23% -1.064 10.246 0.07% 0.000µs 1 200
credit_card/is_card_number/ throughput 254994804.294op/s 255553251.144op/s ± 183124.401op/s 255578155.807op/s ± 107285.224op/s 255668824.520op/s 255776543.041op/s 255828027.814op/s 256814866.435op/s 0.48% 1.088 10.414 0.07% 12948.851op/s 1 200
credit_card/is_card_number/ 3782-8224-6310-005 execution_time 80.288µs 80.797µs ± 0.246µs 80.770µs ± 0.174µs 80.982µs 81.228µs 81.336µs 81.378µs 0.75% 0.196 -0.568 0.30% 0.017µs 1 200
credit_card/is_card_number/ 3782-8224-6310-005 throughput 12288339.545op/s 12376822.932op/s ± 37619.520op/s 12380857.957op/s ± 26654.776op/s 12405210.831op/s 12437149.549op/s 12454375.918op/s 12455158.917op/s 0.60% -0.184 -0.570 0.30% 2660.102op/s 1 200
credit_card/is_card_number/ 378282246310005 execution_time 72.975µs 73.642µs ± 0.362µs 73.606µs ± 0.259µs 73.900µs 74.256µs 74.642µs 74.808µs 1.63% 0.558 0.056 0.49% 0.026µs 1 200
credit_card/is_card_number/ 378282246310005 throughput 13367517.483op/s 13579580.270op/s ± 66528.401op/s 13585876.124op/s ± 47788.299op/s 13630883.501op/s 13673734.265op/s 13698887.793op/s 13703408.662op/s 0.87% -0.532 0.007 0.49% 4704.268op/s 1 200
credit_card/is_card_number/37828224631 execution_time 3.897µs 3.913µs ± 0.003µs 3.913µs ± 0.001µs 3.915µs 3.917µs 3.919µs 3.921µs 0.19% -1.013 7.976 0.06% 0.000µs 1 200
credit_card/is_card_number/37828224631 throughput 255066808.344op/s 255540882.616op/s ± 164635.684op/s 255548519.751op/s ± 86328.829op/s 255631576.894op/s 255772983.115op/s 255835254.588op/s 256615272.665op/s 0.42% 1.030 8.092 0.06% 11641.501op/s 1 200
credit_card/is_card_number/378282246310005 execution_time 69.976µs 70.668µs ± 0.306µs 70.646µs ± 0.207µs 70.868µs 71.201µs 71.451µs 71.609µs 1.36% 0.465 -0.097 0.43% 0.022µs 1 200
credit_card/is_card_number/378282246310005 throughput 13964697.638op/s 14150886.448op/s ± 61229.712op/s 14155160.023op/s ± 41514.566op/s 14195911.932op/s 14237569.426op/s 14267070.669op/s 14290557.836op/s 0.96% -0.443 -0.125 0.43% 4329.594op/s 1 200
credit_card/is_card_number/37828224631000521389798 execution_time 52.690µs 52.912µs ± 0.065µs 52.910µs ± 0.038µs 52.951µs 53.026µs 53.065µs 53.096µs 0.35% 0.036 0.517 0.12% 0.005µs 1 200
credit_card/is_card_number/37828224631000521389798 throughput 18833771.252op/s 18899311.646op/s ± 23164.663op/s 18900069.663op/s ± 13578.420op/s 18912880.182op/s 18933412.985op/s 18956864.707op/s 18979079.361op/s 0.42% -0.027 0.523 0.12% 1637.989op/s 1 200
credit_card/is_card_number/x371413321323331 execution_time 6.431µs 6.444µs ± 0.006µs 6.444µs ± 0.005µs 6.449µs 6.454µs 6.459µs 6.460µs 0.26% 0.220 -0.477 0.10% 0.000µs 1 200
credit_card/is_card_number/x371413321323331 throughput 154792188.746op/s 155188531.511op/s ± 153204.070op/s 155194444.499op/s ± 114891.033op/s 155302034.457op/s 155443041.020op/s 155474023.631op/s 155486474.929op/s 0.19% -0.216 -0.480 0.10% 10833.164op/s 1 200
credit_card/is_card_number_no_luhn/ execution_time 3.887µs 3.913µs ± 0.003µs 3.912µs ± 0.002µs 3.914µs 3.917µs 3.918µs 3.927µs 0.38% -2.062 22.615 0.08% 0.000µs 1 200
credit_card/is_card_number_no_luhn/ throughput 254617848.681op/s 255581794.020op/s ± 203791.331op/s 255595205.976op/s ± 107281.147op/s 255673774.263op/s 255835192.304op/s 255910728.925op/s 257248403.868op/s 0.65% 2.111 22.979 0.08% 14410.223op/s 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time 64.860µs 65.163µs ± 0.133µs 65.132µs ± 0.077µs 65.242µs 65.402µs 65.545µs 65.672µs 0.83% 0.907 1.011 0.20% 0.009µs 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput 15227206.232op/s 15346249.282op/s ± 31170.055op/s 15353374.106op/s ± 18264.241op/s 15368208.556op/s 15384565.589op/s 15398995.593op/s 15417775.153op/s 0.42% -0.894 0.975 0.20% 2204.056op/s 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time 58.189µs 58.376µs ± 0.117µs 58.359µs ± 0.065µs 58.425µs 58.602µs 58.819µs 58.935µs 0.99% 1.487 3.725 0.20% 0.008µs 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 throughput 16967728.761op/s 17130277.843op/s ± 34158.372op/s 17135192.133op/s ± 19076.303op/s 17154187.170op/s 17172217.330op/s 17179464.905op/s 17185406.153op/s 0.29% -1.466 3.624 0.20% 2415.362op/s 1 200
credit_card/is_card_number_no_luhn/37828224631 execution_time 3.893µs 3.913µs ± 0.003µs 3.913µs ± 0.001µs 3.914µs 3.919µs 3.921µs 3.933µs 0.52% 0.485 12.008 0.08% 0.000µs 1 200
credit_card/is_card_number_no_luhn/37828224631 throughput 254264073.193op/s 255551606.790op/s ± 216266.595op/s 255576033.871op/s ± 92720.290op/s 255667219.146op/s 255781974.066op/s 255833722.307op/s 256870558.552op/s 0.51% -0.450 12.036 0.08% 15292.358op/s 1 200
credit_card/is_card_number_no_luhn/378282246310005 execution_time 55.239µs 55.555µs ± 0.176µs 55.530µs ± 0.105µs 55.640µs 55.902µs 56.095µs 56.286µs 1.36% 1.121 1.908 0.32% 0.012µs 1 200
credit_card/is_card_number_no_luhn/378282246310005 throughput 17766468.314op/s 18000453.890op/s ± 56873.966op/s 18008222.893op/s ± 34201.559op/s 18042168.085op/s 18074703.646op/s 18101762.478op/s 18103082.955op/s 0.53% -1.096 1.825 0.32% 4021.597op/s 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time 52.824µs 53.081µs ± 0.096µs 53.118µs ± 0.039µs 53.141µs 53.189µs 53.223µs 53.235µs 0.22% -0.977 -0.154 0.18% 0.007µs 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput 18784708.849op/s 18839023.681op/s ± 34113.300op/s 18825934.070op/s ± 13744.666op/s 18866042.119op/s 18907023.108op/s 18928297.580op/s 18930938.833op/s 0.56% 0.982 -0.144 0.18% 2412.175op/s 1 200
credit_card/is_card_number_no_luhn/x371413321323331 execution_time 6.429µs 6.440µs ± 0.006µs 6.440µs ± 0.003µs 6.443µs 6.451µs 6.459µs 6.465µs 0.38% 1.268 2.706 0.09% 0.000µs 1 200
credit_card/is_card_number_no_luhn/x371413321323331 throughput 154689055.575op/s 155272914.450op/s ± 138890.863op/s 155280426.903op/s ± 75014.255op/s 155364037.173op/s 155449586.029op/s 155496874.595op/s 155548987.264op/s 0.17% -1.260 2.674 0.09% 9821.067op/s 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
credit_card/is_card_number/ execution_time [3.913µs; 3.913µs] or [-0.010%; +0.010%] None None None
credit_card/is_card_number/ throughput [255527871.863op/s; 255578630.425op/s] or [-0.010%; +0.010%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 execution_time [80.763µs; 80.831µs] or [-0.042%; +0.042%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 throughput [12371609.229op/s; 12382036.636op/s] or [-0.042%; +0.042%] None None None
credit_card/is_card_number/ 378282246310005 execution_time [73.592µs; 73.692µs] or [-0.068%; +0.068%] None None None
credit_card/is_card_number/ 378282246310005 throughput [13570360.073op/s; 13588800.466op/s] or [-0.068%; +0.068%] None None None
credit_card/is_card_number/37828224631 execution_time [3.913µs; 3.914µs] or [-0.009%; +0.009%] None None None
credit_card/is_card_number/37828224631 throughput [255518065.694op/s; 255563699.539op/s] or [-0.009%; +0.009%] None None None
credit_card/is_card_number/378282246310005 execution_time [70.626µs; 70.711µs] or [-0.060%; +0.060%] None None None
credit_card/is_card_number/378282246310005 throughput [14142400.598op/s; 14159372.297op/s] or [-0.060%; +0.060%] None None None
credit_card/is_card_number/37828224631000521389798 execution_time [52.903µs; 52.921µs] or [-0.017%; +0.017%] None None None
credit_card/is_card_number/37828224631000521389798 throughput [18896101.246op/s; 18902522.045op/s] or [-0.017%; +0.017%] None None None
credit_card/is_card_number/x371413321323331 execution_time [6.443µs; 6.445µs] or [-0.014%; +0.014%] None None None
credit_card/is_card_number/x371413321323331 throughput [155167298.900op/s; 155209764.121op/s] or [-0.014%; +0.014%] None None None
credit_card/is_card_number_no_luhn/ execution_time [3.912µs; 3.913µs] or [-0.011%; +0.011%] None None None
credit_card/is_card_number_no_luhn/ throughput [255553550.502op/s; 255610037.539op/s] or [-0.011%; +0.011%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time [65.144µs; 65.181µs] or [-0.028%; +0.028%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput [15341929.412op/s; 15350569.152op/s] or [-0.028%; +0.028%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time [58.360µs; 58.393µs] or [-0.028%; +0.028%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 throughput [17125543.822op/s; 17135011.865op/s] or [-0.028%; +0.028%] None None None
credit_card/is_card_number_no_luhn/37828224631 execution_time [3.913µs; 3.914µs] or [-0.012%; +0.012%] None None None
credit_card/is_card_number_no_luhn/37828224631 throughput [255521634.320op/s; 255581579.260op/s] or [-0.012%; +0.012%] None None None
credit_card/is_card_number_no_luhn/378282246310005 execution_time [55.530µs; 55.579µs] or [-0.044%; +0.044%] None None None
credit_card/is_card_number_no_luhn/378282246310005 throughput [17992571.705op/s; 18008336.074op/s] or [-0.044%; +0.044%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time [53.068µs; 53.095µs] or [-0.025%; +0.025%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput [18834295.906op/s; 18843751.457op/s] or [-0.025%; +0.025%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 execution_time [6.439µs; 6.441µs] or [-0.012%; +0.012%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 throughput [155253665.512op/s; 155292163.388op/s] or [-0.012%; +0.012%] None None None

Group 14

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... execution_time 495.221µs 496.276µs ± 1.106µs 496.119µs ± 0.296µs 496.491µs 496.931µs 498.163µs 507.595µs 2.31% 7.782 71.100 0.22% 0.078µs 1 200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput 1970073.058op/s 2015016.553op/s ± 4415.499op/s 2015643.860op/s ± 1201.690op/s 2016662.789op/s 2018081.133op/s 2018695.174op/s 2019300.927op/s 0.18% -7.698 69.958 0.22% 312.223op/s 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time 369.940µs 370.451µs ± 0.265µs 370.427µs ± 0.193µs 370.635µs 370.887µs 371.166µs 371.236µs 0.22% 0.367 -0.168 0.07% 0.019µs 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput 2693701.120op/s 2699415.159op/s ± 1932.315op/s 2699588.342op/s ± 1403.267op/s 2700863.354op/s 2702393.134op/s 2702893.976op/s 2703143.909op/s 0.13% -0.364 -0.173 0.07% 136.635op/s 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time 168.744µs 169.120µs ± 0.131µs 169.109µs ± 0.078µs 169.195µs 169.355µs 169.442µs 169.650µs 0.32% 0.462 0.855 0.08% 0.009µs 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput 5894488.653op/s 5912981.230op/s ± 4589.340op/s 5913330.379op/s ± 2742.470op/s 5915832.629op/s 5919483.774op/s 5922438.460op/s 5926122.749op/s 0.22% -0.456 0.844 0.08% 324.515op/s 1 200
normalization/normalize_service/normalize_service/[empty string] execution_time 36.834µs 37.029µs ± 0.121µs 37.028µs ± 0.113µs 37.138µs 37.229µs 37.264µs 37.274µs 0.66% 0.121 -1.183 0.33% 0.009µs 1 200
normalization/normalize_service/normalize_service/[empty string] throughput 26828666.832op/s 27005831.770op/s ± 88171.785op/s 27006720.708op/s ± 82393.948op/s 27091779.497op/s 27128261.015op/s 27146686.096op/s 27149182.972op/s 0.53% -0.113 -1.188 0.33% 6234.687op/s 1 200
normalization/normalize_service/normalize_service/test_ASCII execution_time 46.197µs 46.301µs ± 0.055µs 46.291µs ± 0.033µs 46.331µs 46.417µs 46.449µs 46.472µs 0.39% 0.897 0.610 0.12% 0.004µs 1 200
normalization/normalize_service/normalize_service/test_ASCII throughput 21518202.762op/s 21597761.674op/s ± 25542.035op/s 21602527.120op/s ± 15448.537op/s 21616182.015op/s 21629914.193op/s 21637101.447op/s 21646417.071op/s 0.20% -0.890 0.597 0.12% 1806.095op/s 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... execution_time [496.123µs; 496.430µs] or [-0.031%; +0.031%] None None None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput [2014404.608op/s; 2015628.499op/s] or [-0.030%; +0.030%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time [370.414µs; 370.488µs] or [-0.010%; +0.010%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput [2699147.359op/s; 2699682.960op/s] or [-0.010%; +0.010%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time [169.101µs; 169.138µs] or [-0.011%; +0.011%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput [5912345.191op/s; 5913617.268op/s] or [-0.011%; +0.011%] None None None
normalization/normalize_service/normalize_service/[empty string] execution_time [37.013µs; 37.046µs] or [-0.045%; +0.045%] None None None
normalization/normalize_service/normalize_service/[empty string] throughput [26993612.008op/s; 27018051.531op/s] or [-0.045%; +0.045%] None None None
normalization/normalize_service/normalize_service/test_ASCII execution_time [46.294µs; 46.309µs] or [-0.016%; +0.016%] None None None
normalization/normalize_service/normalize_service/test_ASCII throughput [21594221.793op/s; 21601301.554op/s] or [-0.016%; +0.016%] None None None

Group 15

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
sql/obfuscate_sql_string execution_time 87.025µs 87.266µs ± 0.322µs 87.217µs ± 0.066µs 87.294µs 87.544µs 87.668µs 91.400µs 4.80% 10.750 134.799 0.37% 0.023µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
sql/obfuscate_sql_string execution_time [87.221µs; 87.310µs] or [-0.051%; +0.051%] None None None

Group 16

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
benching string interning on wordpress profile execution_time 160.136µs 161.012µs ± 0.394µs 160.948µs ± 0.146µs 161.116µs 161.455µs 162.580µs 163.977µs 1.88% 3.802 23.732 0.24% 0.028µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
benching string interning on wordpress profile execution_time [160.957µs; 161.067µs] or [-0.034%; +0.034%] None None None

Group 17

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
profile_add_sample_timestamped_x1000 execution_time 4.184ms 4.190ms ± 0.008ms 4.189ms ± 0.001ms 4.190ms 4.193ms 4.197ms 4.301ms 2.69% 12.173 159.891 0.20% 0.001ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
profile_add_sample_timestamped_x1000 execution_time [4.188ms; 4.191ms] or [-0.028%; +0.028%] None None None

Group 18

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
benching deserializing traces from msgpack to their internal representation execution_time 50.782ms 51.177ms ± 1.292ms 51.016ms ± 0.082ms 51.093ms 51.314ms 58.971ms 64.821ms 27.06% 8.566 76.810 2.52% 0.091ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
benching deserializing traces from msgpack to their internal representation execution_time [50.998ms; 51.356ms] or [-0.350%; +0.350%] None None None

Group 19

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
ip_address/quantize_peer_ip_address_benchmark execution_time 4.987µs 5.070µs ± 0.048µs 5.081µs ± 0.033µs 5.107µs 5.152µs 5.158µs 5.165µs 1.66% 0.114 -1.120 0.94% 0.003µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
ip_address/quantize_peer_ip_address_benchmark execution_time [5.064µs; 5.077µs] or [-0.131%; +0.131%] None None None

Group 20

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 7f861d8 1773765055 dubloom/container-back-propagation
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
single_flag_killswitch/rules-based execution_time 190.243ns 193.012ns ± 2.039ns 192.872ns ± 1.531ns 194.147ns 197.339ns 198.482ns 200.069ns 3.73% 0.888 0.601 1.05% 0.144ns 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
single_flag_killswitch/rules-based execution_time [192.730ns; 193.295ns] or [-0.146%; +0.146%] None None None

Baseline

Omitted due to size.

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codecov-commenter commented Mar 12, 2026 •
edited
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Codecov Report

❌ Patch coverage is 85.13514% with 11 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.49%. Comparing base (5426a8b) to head (7f861d8).

Additional details and impacted files 
@@           Coverage Diff           @@
##             main    #1700   +/-   ##
=======================================
  Coverage   71.48%   71.49%           
=======================================
  Files         430      430           
  Lines       64041    64116   +75     
=======================================
+ Hits        45782    45837   +55     
- Misses      18259    18279   +20
Components Coverage Δ
libdd-crashtracker 63.95% <ø> (-0.02%) ⬇️
libdd-crashtracker-ffi 18.19% <ø> (ø)
libdd-alloc 98.77% <ø> (ø)
libdd-data-pipeline 87.96% <100.00%> (+0.01%) ⬆️
libdd-data-pipeline-ffi 74.85% <ø> (ø)
libdd-common 79.73% <ø> (ø)
libdd-common-ffi 73.40% <ø> (ø)
libdd-telemetry 62.48% <ø> (ø)
libdd-telemetry-ffi 16.75% <ø> (ø)
libdd-dogstatsd-client 82.64% <ø> (ø)
datadog-ipc 80.35% <ø> (ø)
libdd-profiling 81.59% <ø> (-0.02%) ⬇️
libdd-profiling-ffi 63.65% <ø> (ø)
datadog-sidecar 33.04% <0.00%> (-0.07%) ⬇️
datdog-sidecar-ffi 10.70% <0.00%> (-0.10%) ⬇️
spawn-worker 54.69% <ø> (ø)
libdd-tinybytes 93.16% <ø> (ø)
libdd-trace-normalization 81.71% <ø> (ø)
libdd-trace-obfuscation 91.80% <ø> (ø)
libdd-trace-protobuf 68.25% <ø> (ø)
libdd-trace-utils 88.98% <ø> (ø)
datadog-tracer-flare 90.46% <ø> (ø)
libdd-log 74.69% <ø> (ø)
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dd-octo-sts bot commented Mar 12, 2026 •
edited
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Artifact Size Benchmark Report

aarch64-alpine-linux-musl
Artifact Baseline Commit Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so 8.70 MB 8.70 MB 0% (0 B) 👌
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a 100.42 MB 100.43 MB +0% (+8.05 KB) 👌
aarch64-unknown-linux-gnu
Artifact Baseline Commit Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a 117.12 MB 117.14 MB +.01% (+13.50 KB) 🔍
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.28 MB 11.28 MB +0% (+160 B) 👌
libdatadog-x64-windows
Artifact Baseline Commit Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll 27.19 MB 27.19 MB +.01% (+4.00 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib 76.61 KB 76.61 KB 0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb 186.17 MB 186.19 MB +.01% (+24.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib 917.13 MB 917.19 MB +0% (+62.24 KB) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll 9.94 MB 9.95 MB +.02% (+3.00 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib 76.61 KB 76.61 KB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb 24.80 MB 24.80 MB +.03% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib 51.48 MB 51.49 MB +.01% (+8.50 KB) 🔍
libdatadog-x86-windows
Artifact Baseline Commit Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll 22.99 MB 23.00 MB +.01% (+4.00 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib 77.80 KB 77.80 KB 0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb 190.40 MB 190.42 MB +.01% (+24.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib 900.81 MB 900.87 MB +0% (+61.33 KB) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll 7.54 MB 7.54 MB +.02% (+2.00 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib 77.80 KB 77.80 KB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb 26.54 MB 26.54 MB 0% (0 B) 👌
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib 47.10 MB 47.11 MB +.01% (+7.44 KB) 🔍
x86_64-alpine-linux-musl
Artifact Baseline Commit Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a 87.62 MB 87.63 MB +0% (+8.74 KB) 👌
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so 10.22 MB 10.22 MB 0% (0 B) 👌
x86_64-unknown-linux-gnu
Artifact Baseline Commit Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a 109.99 MB 110.00 MB +.01% (+14.01 KB) 🔍
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.97 MB 11.97 MB +0% (+88 B) 👌

@dubloom dubloom changed the title chore(agents): retrieve container tags hash from /info endpoint feat(agents)!: retrieve container tags hash from /info endpoint Mar 17, 2026
@dubloom dubloom mentioned this pull request Mar 18, 2026
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