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ci: dynamic pipeline#1642

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julio/rd-week-dynamic-pipeline
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ci: dynamic pipeline#1642
hoolioh wants to merge 24 commits intomainfrom
julio/rd-week-dynamic-pipeline

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@hoolioh
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@hoolioh hoolioh commented Mar 3, 2026

Create actions workspace to share and build code for repo actions:

  • Move code from clippy-annotation-reporter to ci-shared crate.
  • Port code from changed-crates bash script to changed-crates binary inside ci-crates.
  • Create affected-crates binary to compute dependency graph and get affected crates by the PR.

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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codecov-commenter commented Mar 3, 2026 •
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Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 71.25%. Comparing base (40898a4) to head (089947d).
⚠️ Report is 8 commits behind head on main.

Additional details and impacted files 
@@            Coverage Diff             @@
##             main    #1642      +/-   ##
==========================================
+ Coverage   71.19%   71.25%   +0.05%     
==========================================
  Files         423      425       +2     
  Lines       62453    62705     +252     
==========================================
+ Hits        44466    44678     +212     
- Misses      17987    18027      +40
Components Coverage Δ
libdd-crashtracker 63.04% <ø> (-0.23%) ⬇️
libdd-crashtracker-ffi 15.63% <ø> (-2.02%) ⬇️
libdd-alloc 98.77% <ø> (ø)
libdd-data-pipeline 87.38% <ø> (-0.33%) ⬇️
libdd-data-pipeline-ffi 72.83% <ø> (-1.09%) ⬇️
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 80.86% <ø> (ø)
libdd-profiling 81.60% <ø> (+0.04%) ⬆️
libdd-profiling-ffi 63.65% <ø> (ø)
datadog-sidecar 33.45% <ø> (-0.02%) ⬇️
datdog-sidecar-ffi 12.41% <ø> (-0.08%) ⬇️
spawn-worker 54.69% <ø> (ø)
libdd-tinybytes 93.16% <ø> (ø)
libdd-trace-normalization 81.71% <ø> (ø)
libdd-trace-obfuscation 94.67% <ø> (+0.46%) ⬆️
libdd-trace-protobuf 68.00% <ø> (ø)
libdd-trace-utils 88.97% <ø> (-0.21%) ⬇️
datadog-tracer-flare 90.45% <ø> (+1.49%) ⬆️
libdd-log 74.69% <ø> (ø)
🚀 New features to boost your workflow:
  • ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
  • 📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

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github-actions bot commented Mar 3, 2026

Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

  • Base Branch: origin/main
  • PR Branch: origin/julio/rd-week-dynamic-pipeline

Summary by Rule

Rule Base Branch PR Branch Change
unwrap_used 5 5 No change (0%)
Total 5 5 No change (0%)

Annotation Counts by File

File Base Branch PR Branch Change
.github/actions/clippy-annotation-reporter/src/analyzer/annotation.rs 5 5 No change (0%)

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 27 27 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-crashtracker 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 219 219 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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pr-commenter bot commented Mar 3, 2026 •
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Benchmarks

Comparison

Benchmark execution time: 2026-03-09 11:13:30

Comparing candidate commit cb5bf5f in PR branch julio/rd-week-dynamic-pipeline with baseline commit f8900a6 in branch main.

Found 0 performance improvements and 1 performance regressions! Performance is the same for 56 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 ----------------------------------'

scenario:benching serializing traces from their internal representation to msgpack

  • 🟥 execution_time [+812.926µs; +825.475µs] or [+5.808%; +5.898%]

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 cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 161.792µs 162.552µs ± 0.267µs 162.524µs ± 0.128µs 162.654µs 162.956µs 163.490µs 163.788µs 0.78% 1.400 4.181 0.16% 0.019µs 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 [162.515µs; 162.589µs] or [-0.023%; +0.023%] None None None

Group 2

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 14.768ms 14.816ms ± 0.030ms 14.811ms ± 0.013ms 14.824ms 14.872ms 14.926ms 14.992ms 1.22% 2.423 8.785 0.20% 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 [14.812ms; 14.820ms] or [-0.028%; +0.028%] None None None

Group 3

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 752.225µs 753.833µs ± 0.761µs 753.813µs ± 0.467µs 754.215µs 755.031µs 755.758µs 758.061µs 0.56% 1.071 4.070 0.10% 0.054µs 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 [753.727µs; 753.938µs] or [-0.014%; +0.014%] None None None

Group 4

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 188.229ns 190.737ns ± 2.024ns 190.686ns ± 1.533ns 191.736ns 194.794ns 196.200ns 196.771ns 3.19% 0.790 0.037 1.06% 0.143ns 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 [190.456ns; 191.017ns] or [-0.147%; +0.147%] None None None

Group 5

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 34.101µs 34.625µs ± 0.699µs 34.331µs ± 0.130µs 34.502µs 36.001µs 36.046µs 38.572µs 12.35% 2.056 4.641 2.01% 0.049µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
redis/obfuscate_redis_string execution_time [34.528µs; 34.722µs] or [-0.280%; +0.280%] None None None

Group 6

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 88.699µs 88.947µs ± 0.262µs 88.921µs ± 0.080µs 89.005µs 89.123µs 89.253µs 92.229µs 3.72% 9.958 121.773 0.29% 0.019µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
sql/obfuscate_sql_string execution_time [88.911µs; 88.983µs] or [-0.041%; +0.041%] None None None

Group 7

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 17.620µs 25.025µs ± 9.252µs 17.902µs ± 0.144µs 33.089µs 41.975µs 42.685µs 69.679µs 289.23% 1.033 1.093 36.88% 0.654µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
two way interface execution_time [23.743µs; 26.307µs] or [-5.124%; +5.124%] None None None

Group 8

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 1.288µs 3.179µs ± 1.437µs 3.008µs ± 0.023µs 3.026µs 3.363µs 13.888µs 15.168µs 404.24% 7.528 57.158 45.10% 0.102µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
write only interface execution_time [2.980µs; 3.378µs] or [-6.267%; +6.267%] None None None

Group 9

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 47.824ms 48.314ms ± 1.265ms 48.173ms ± 0.084ms 48.229ms 48.415ms 51.339ms 62.408ms 29.55% 9.524 94.591 2.61% 0.089ms 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 [48.138ms; 48.489ms] or [-0.363%; +0.363%] None None None

Group 10

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 3.890µs 3.913µs ± 0.003µs 3.912µs ± 0.001µs 3.914µs 3.919µs 3.921µs 3.923µs 0.28% -0.928 13.479 0.08% 0.000µs 1 200
credit_card/is_card_number/ throughput 254903807.956op/s 255585731.414op/s ± 208248.384op/s 255617218.364op/s ± 88377.153op/s 255698337.961op/s 255782777.711op/s 255931000.285op/s 257090215.339op/s 0.58% 0.964 13.722 0.08% 14725.384op/s 1 200
credit_card/is_card_number/ 3782-8224-6310-005 execution_time 76.522µs 77.748µs ± 0.572µs 77.695µs ± 0.394µs 78.118µs 78.757µs 79.282µs 79.682µs 2.56% 0.557 0.404 0.73% 0.040µs 1 200
credit_card/is_card_number/ 3782-8224-6310-005 throughput 12549939.517op/s 12862813.859op/s ± 94316.216op/s 12870843.043op/s ± 65553.510op/s 12927703.603op/s 12998449.869op/s 13052120.705op/s 13068211.404op/s 1.53% -0.511 0.321 0.73% 6669.164op/s 1 200
credit_card/is_card_number/ 378282246310005 execution_time 69.917µs 70.893µs ± 0.665µs 70.762µs ± 0.378µs 71.228µs 72.027µs 72.913µs 73.767µs 4.25% 1.119 1.715 0.94% 0.047µs 1 200
credit_card/is_card_number/ 378282246310005 throughput 13556208.863op/s 14106948.260op/s ± 131021.057op/s 14131882.916op/s ± 75875.231op/s 14200376.673op/s 14279605.236op/s 14298361.334op/s 14302720.710op/s 1.21% -1.052 1.465 0.93% 9264.588op/s 1 200
credit_card/is_card_number/37828224631 execution_time 3.893µs 3.912µs ± 0.003µs 3.912µs ± 0.001µs 3.913µs 3.917µs 3.921µs 3.925µs 0.34% 0.024 11.200 0.08% 0.000µs 1 200
credit_card/is_card_number/37828224631 throughput 254765317.005op/s 255623060.388op/s ± 192247.317op/s 255637087.715op/s ± 93335.670op/s 255734536.949op/s 255817156.583op/s 255922166.668op/s 256860237.694op/s 0.48% 0.005 11.304 0.08% 13593.938op/s 1 200
credit_card/is_card_number/378282246310005 execution_time 66.713µs 67.843µs ± 0.648µs 67.764µs ± 0.464µs 68.283µs 68.953µs 69.448µs 69.843µs 3.07% 0.462 -0.319 0.95% 0.046µs 1 200
credit_card/is_card_number/378282246310005 throughput 14317865.910op/s 14741293.502op/s ± 140225.748op/s 14757002.531op/s ± 101669.202op/s 14850254.958op/s 14946015.508op/s 14980769.372op/s 14989551.877op/s 1.58% -0.420 -0.384 0.95% 9915.458op/s 1 200
credit_card/is_card_number/37828224631000521389798 execution_time 52.137µs 52.213µs ± 0.031µs 52.211µs ± 0.017µs 52.228µs 52.268µs 52.297µs 52.336µs 0.24% 0.691 1.181 0.06% 0.002µs 1 200
credit_card/is_card_number/37828224631000521389798 throughput 19107190.643op/s 19152194.908op/s ± 11545.559op/s 19153002.363op/s ± 6063.335op/s 19158874.519op/s 19169223.434op/s 19174798.715op/s 19180175.371op/s 0.14% -0.686 1.171 0.06% 816.394op/s 1 200
credit_card/is_card_number/x371413321323331 execution_time 6.029µs 6.037µs ± 0.009µs 6.035µs ± 0.002µs 6.038µs 6.068µs 6.075µs 6.080µs 0.75% 3.235 10.296 0.15% 0.001µs 1 200
credit_card/is_card_number/x371413321323331 throughput 164460682.178op/s 165638187.404op/s ± 251006.000op/s 165693343.348op/s ± 61197.901op/s 165744761.477op/s 165833418.841op/s 165859434.516op/s 165876549.383op/s 0.11% -3.227 10.252 0.15% 17748.804op/s 1 200
credit_card/is_card_number_no_luhn/ execution_time 3.897µs 3.913µs ± 0.003µs 3.913µs ± 0.002µs 3.915µs 3.918µs 3.919µs 3.921µs 0.19% -0.666 4.058 0.07% 0.000µs 1 200
credit_card/is_card_number_no_luhn/ throughput 255065790.209op/s 255542005.062op/s ± 187888.478op/s 255539194.538op/s ± 120075.488op/s 255667413.108op/s 255816112.181op/s 255867497.722op/s 256612670.582op/s 0.42% 0.678 4.126 0.07% 13285.722op/s 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time 64.166µs 64.446µs ± 0.128µs 64.436µs ± 0.081µs 64.511µs 64.682µs 64.832µs 64.886µs 0.70% 0.755 1.023 0.20% 0.009µs 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput 15411596.317op/s 15516880.158op/s ± 30707.008op/s 15519159.153op/s ± 19491.936op/s 15539120.058op/s 15557346.532op/s 15580917.598op/s 15584695.829op/s 0.42% -0.741 0.991 0.20% 2171.313op/s 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time 58.166µs 58.390µs ± 0.156µs 58.358µs ± 0.078µs 58.448µs 58.713µs 58.891µs 59.001µs 1.10% 1.506 2.573 0.27% 0.011µs 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 throughput 16948935.420op/s 17126267.635op/s ± 45597.147op/s 17135682.964op/s ± 23022.750op/s 17157184.675op/s 17179354.886op/s 17186378.873op/s 17192033.212op/s 0.33% -1.488 2.502 0.27% 3224.205op/s 1 200
credit_card/is_card_number_no_luhn/37828224631 execution_time 3.893µs 3.912µs ± 0.002µs 3.912µs ± 0.001µs 3.913µs 3.916µs 3.917µs 3.918µs 0.16% -2.187 19.015 0.06% 0.000µs 1 200
credit_card/is_card_number_no_luhn/37828224631 throughput 255222886.336op/s 255630712.461op/s ± 151378.188op/s 255634864.237op/s ± 75734.968op/s 255716551.254op/s 255810902.980op/s 255900761.825op/s 256842564.147op/s 0.47% 2.215 19.279 0.06% 10704.054op/s 1 200
credit_card/is_card_number_no_luhn/378282246310005 execution_time 54.567µs 54.870µs ± 0.230µs 54.804µs ± 0.154µs 55.025µs 55.367µs 55.472µs 55.565µs 1.39% 0.894 0.112 0.42% 0.016µs 1 200
credit_card/is_card_number_no_luhn/378282246310005 throughput 17996977.892op/s 18225197.780op/s ± 76250.565op/s 18246808.548op/s ± 51316.991op/s 18291757.400op/s 18308599.288op/s 18319517.904op/s 18326133.278op/s 0.43% -0.878 0.069 0.42% 5391.729op/s 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time 52.157µs 52.227µs ± 0.038µs 52.226µs ± 0.026µs 52.251µs 52.296µs 52.341µs 52.364µs 0.26% 0.607 0.538 0.07% 0.003µs 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput 19096983.234op/s 19147145.989op/s ± 13991.568op/s 19147539.571op/s ± 9705.978op/s 19157533.018op/s 19167572.016op/s 19171152.133op/s 19172902.936op/s 0.13% -0.602 0.526 0.07% 989.353op/s 1 200
credit_card/is_card_number_no_luhn/x371413321323331 execution_time 6.029µs 6.037µs ± 0.010µs 6.035µs ± 0.002µs 6.037µs 6.068µs 6.073µs 6.080µs 0.76% 2.780 7.070 0.16% 0.001µs 1 200
credit_card/is_card_number_no_luhn/x371413321323331 throughput 164467790.864op/s 165632858.981op/s ± 268625.159op/s 165713223.922op/s ± 55352.075op/s 165761538.978op/s 165819852.105op/s 165849306.835op/s 165854045.237op/s 0.08% -2.773 7.037 0.16% 18994.667op/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 [3.912µs; 3.913µs] or [-0.011%; +0.011%] None None None
credit_card/is_card_number/ throughput [255556870.191op/s; 255614592.637op/s] or [-0.011%; +0.011%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 execution_time [77.668µs; 77.827µs] or [-0.102%; +0.102%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 throughput [12849742.539op/s; 12875885.180op/s] or [-0.102%; +0.102%] None None None
credit_card/is_card_number/ 378282246310005 execution_time [70.801µs; 70.985µs] or [-0.130%; +0.130%] None None None
credit_card/is_card_number/ 378282246310005 throughput [14088790.002op/s; 14125106.518op/s] or [-0.129%; +0.129%] None None None
credit_card/is_card_number/37828224631 execution_time [3.912µs; 3.912µs] or [-0.010%; +0.010%] None None None
credit_card/is_card_number/37828224631 throughput [255596416.759op/s; 255649704.017op/s] or [-0.010%; +0.010%] None None None
credit_card/is_card_number/378282246310005 execution_time [67.753µs; 67.933µs] or [-0.132%; +0.132%] None None None
credit_card/is_card_number/378282246310005 throughput [14721859.562op/s; 14760727.442op/s] or [-0.132%; +0.132%] None None None
credit_card/is_card_number/37828224631000521389798 execution_time [52.209µs; 52.218µs] or [-0.008%; +0.008%] None None None
credit_card/is_card_number/37828224631000521389798 throughput [19150594.805op/s; 19153795.012op/s] or [-0.008%; +0.008%] None None None
credit_card/is_card_number/x371413321323331 execution_time [6.036µs; 6.039µs] or [-0.021%; +0.021%] None None None
credit_card/is_card_number/x371413321323331 throughput [165603400.386op/s; 165672974.421op/s] or [-0.021%; +0.021%] None None None
credit_card/is_card_number_no_luhn/ execution_time [3.913µs; 3.914µs] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/ throughput [255515965.526op/s; 255568044.598op/s] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time [64.428µs; 64.464µs] or [-0.027%; +0.027%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput [15512624.462op/s; 15521135.854op/s] or [-0.027%; +0.027%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time [58.369µs; 58.412µs] or [-0.037%; +0.037%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 throughput [17119948.309op/s; 17132586.961op/s] or [-0.037%; +0.037%] None None None
credit_card/is_card_number_no_luhn/37828224631 execution_time [3.912µs; 3.912µs] or [-0.008%; +0.008%] None None None
credit_card/is_card_number_no_luhn/37828224631 throughput [255609732.900op/s; 255651692.022op/s] or [-0.008%; +0.008%] None None None
credit_card/is_card_number_no_luhn/378282246310005 execution_time [54.838µs; 54.902µs] or [-0.058%; +0.058%] None None None
credit_card/is_card_number_no_luhn/378282246310005 throughput [18214630.185op/s; 18235765.375op/s] or [-0.058%; +0.058%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time [52.222µs; 52.232µs] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput [19145206.892op/s; 19149085.085op/s] or [-0.010%; +0.010%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 execution_time [6.036µs; 6.039µs] or [-0.023%; +0.023%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 throughput [165595630.117op/s; 165670087.844op/s] or [-0.022%; +0.022%] None None None

Group 11

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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/2597 execution_time 3.113ms 3.140ms ± 0.018ms 3.137ms ± 0.008ms 3.146ms 3.164ms 3.178ms 3.321ms 5.88% 5.373 49.712 0.57% 0.001ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
receiver_entry_point/report/2597 execution_time [3.137ms; 3.142ms] or [-0.079%; +0.079%] None None None

Group 12

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 533.829µs 534.847µs ± 0.538µs 534.828µs ± 0.279µs 535.096µs 535.414µs 537.054µs 539.457µs 0.87% 3.777 27.869 0.10% 0.038µs 1 200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput 1853714.249op/s 1869693.762op/s ± 1872.605op/s 1869759.998op/s ± 975.698op/s 1870840.218op/s 1871880.010op/s 1872471.189op/s 1873257.595op/s 0.19% -3.730 27.367 0.10% 132.413op/s 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time 380.140µs 380.784µs ± 0.283µs 380.773µs ± 0.201µs 380.959µs 381.240µs 381.579µs 381.612µs 0.22% 0.463 0.135 0.07% 0.020µs 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput 2620460.280op/s 2626162.825op/s ± 1951.831op/s 2626235.580op/s ± 1388.886op/s 2627675.767op/s 2628961.416op/s 2629829.852op/s 2630613.085op/s 0.17% -0.458 0.128 0.07% 138.015op/s 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time 189.713µs 190.098µs ± 0.164µs 190.088µs ± 0.112µs 190.204µs 190.393µs 190.502µs 190.612µs 0.28% 0.296 -0.106 0.09% 0.012µs 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput 5246258.006op/s 5260440.592op/s ± 4532.996op/s 5260717.447op/s ± 3100.268op/s 5263513.591op/s 5267429.418op/s 5269164.465op/s 5271126.472op/s 0.20% -0.291 -0.112 0.09% 320.531op/s 1 200
normalization/normalize_service/normalize_service/[empty string] execution_time 37.382µs 37.527µs ± 0.062µs 37.523µs ± 0.036µs 37.558µs 37.640µs 37.675µs 37.700µs 0.47% 0.269 -0.042 0.16% 0.004µs 1 200
normalization/normalize_service/normalize_service/[empty string] throughput 26524989.540op/s 26647604.293op/s ± 43964.812op/s 26650443.742op/s ± 25415.511op/s 26675802.071op/s 26717188.237op/s 26736157.493op/s 26751038.593op/s 0.38% -0.260 -0.047 0.16% 3108.782op/s 1 200
normalization/normalize_service/normalize_service/test_ASCII execution_time 45.783µs 45.885µs ± 0.057µs 45.875µs ± 0.035µs 45.918µs 45.989µs 46.051µs 46.086µs 0.46% 0.916 0.899 0.12% 0.004µs 1 200
normalization/normalize_service/normalize_service/test_ASCII throughput 21698359.532op/s 21793791.321op/s ± 26843.854op/s 21798143.383op/s ± 16839.293op/s 21812507.922op/s 21828143.934op/s 21839939.269op/s 21842200.296op/s 0.20% -0.908 0.880 0.12% 1898.147op/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 [534.773µs; 534.922µs] or [-0.014%; +0.014%] None None None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput [1869434.237op/s; 1869953.287op/s] or [-0.014%; +0.014%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time [380.745µs; 380.823µs] or [-0.010%; +0.010%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput [2625892.320op/s; 2626433.330op/s] or [-0.010%; +0.010%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time [190.076µs; 190.121µs] or [-0.012%; +0.012%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput [5259812.363op/s; 5261068.822op/s] or [-0.012%; +0.012%] None None None
normalization/normalize_service/normalize_service/[empty string] execution_time [37.518µs; 37.536µs] or [-0.023%; +0.023%] None None None
normalization/normalize_service/normalize_service/[empty string] throughput [26641511.193op/s; 26653697.393op/s] or [-0.023%; +0.023%] None None None
normalization/normalize_service/normalize_service/test_ASCII execution_time [45.877µs; 45.893µs] or [-0.017%; +0.017%] None None None
normalization/normalize_service/normalize_service/test_ASCII throughput [21790071.021op/s; 21797511.621op/s] or [-0.017%; +0.017%] None None None

Group 13

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 2.341µs 2.403µs ± 0.018µs 2.403µs ± 0.005µs 2.410µs 2.430µs 2.441µs 2.445µs 1.76% -1.297 3.396 0.76% 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 [2.401µs; 2.406µs] or [-0.106%; +0.106%] None None None

Group 14

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 144.731µs 146.883µs ± 1.774µs 146.570µs ± 0.495µs 147.142µs 148.690µs 154.168µs 164.005µs 11.89% 5.747 46.625 1.20% 0.125µs 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 [146.637µs; 147.129µs] or [-0.167%; +0.167%] None None None

Group 15

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 241.134ns 251.019ns ± 13.037ns 245.404ns ± 2.629ns 251.539ns 283.200ns 294.321ns 299.679ns 22.12% 2.048 3.436 5.18% 0.922ns 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 [249.213ns; 252.826ns] or [-0.720%; +0.720%] None None None

Group 16

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 10.620ms 10.651ms ± 0.013ms 10.649ms ± 0.008ms 10.658ms 10.677ms 10.683ms 10.709ms 0.56% 0.918 1.605 0.12% 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 [10.649ms; 10.653ms] or [-0.017%; +0.017%] None None None

Group 17

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 4.956µs 5.022µs ± 0.034µs 5.031µs ± 0.026µs 5.048µs 5.063µs 5.067µs 5.067µs 0.73% -0.474 -1.161 0.68% 0.002µs 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 [5.017µs; 5.026µs] or [-0.094%; +0.094%] None None None

Group 18

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 185.275µs 185.777µs ± 0.253µs 185.787µs ± 0.197µs 185.959µs 186.197µs 186.310µs 186.491µs 0.38% 0.219 -0.673 0.14% 0.018µs 1 200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput 5362184.028op/s 5382820.319op/s ± 7341.639op/s 5382506.839op/s ± 5704.106op/s 5388719.672op/s 5393656.201op/s 5396191.248op/s 5397391.009op/s 0.28% -0.214 -0.678 0.14% 519.132op/s 1 200
normalization/normalize_name/normalize_name/bad-name execution_time 17.929µs 17.998µs ± 0.055µs 17.993µs ± 0.022µs 18.015µs 18.053µs 18.080µs 18.624µs 3.51% 7.566 83.387 0.30% 0.004µs 1 200
normalization/normalize_name/normalize_name/bad-name throughput 53692946.732op/s 55563203.225op/s ± 165613.099op/s 55578298.045op/s ± 69424.780op/s 55648523.926op/s 55715826.999op/s 55741921.723op/s 55775402.298op/s 0.35% -7.313 79.514 0.30% 11710.615op/s 1 200
normalization/normalize_name/normalize_name/good execution_time 10.273µs 10.359µs ± 0.044µs 10.358µs ± 0.036µs 10.388µs 10.428µs 10.464µs 10.530µs 1.66% 0.401 0.068 0.43% 0.003µs 1 200
normalization/normalize_name/normalize_name/good throughput 94965600.150op/s 96535714.282op/s ± 412521.608op/s 96540592.265op/s ± 331801.537op/s 96915997.389op/s 97127952.216op/s 97283281.816op/s 97340394.937op/s 0.83% -0.377 0.006 0.43% 29169.683op/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 [185.741µs; 185.812µs] or [-0.019%; +0.019%] None None None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput [5381802.839op/s; 5383837.800op/s] or [-0.019%; +0.019%] None None None
normalization/normalize_name/normalize_name/bad-name execution_time [17.990µs; 18.005µs] or [-0.042%; +0.042%] None None None
normalization/normalize_name/normalize_name/bad-name throughput [55540250.842op/s; 55586155.607op/s] or [-0.041%; +0.041%] None None None
normalization/normalize_name/normalize_name/good execution_time [10.353µs; 10.365µs] or [-0.059%; +0.059%] None None None
normalization/normalize_name/normalize_name/good throughput [96478542.755op/s; 96592885.810op/s] or [-0.059%; +0.059%] None None None

Group 19

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz cb5bf5f 1773053791 julio/rd-week-dynamic-pipeline
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 4.202ms 4.207ms ± 0.008ms 4.206ms ± 0.001ms 4.207ms 4.210ms 4.218ms 4.318ms 2.67% 12.263 161.999 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_frames_x1000 execution_time [4.205ms; 4.208ms] or [-0.027%; +0.027%] None None None

Baseline

Omitted due to size.

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📚 Documentation Check Results

✅ No documentation warnings found!

📦 clippy-annotation-reporter - ✅ No warnings

Updated: 2026-03-04 16:31:29 UTC | Commit: dc5f70a | missing-docs job results

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🔒 Cargo Deny Results

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

📦 clippy-annotation-reporter - 1 error(s)

Show output 
error[vulnerability]: `idna` accepts Punycode labels that do not produce any non-ASCII when decoded
    ┌─ /home/runner/work/libdatadog/libdatadog/.github/actions/Cargo.lock:107:1
    │
107 │ idna 0.5.0 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2024-0421
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2024-0421
    ├ `idna` 0.5.0 and earlier accepts Punycode labels that do not produce any non-ASCII output, which means that either ASCII labels or the empty root label can be masked such that they appear unequal without IDNA processing or when processed with a different implementation and equal when processed with `idna` 0.5.0 or earlier.
      
      Concretely, `example.org` and `xn--example-.org` become equal after processing by `idna` 0.5.0 or earlier. Also, `example.org.xn--` and `example.org.` become equal after processing by `idna` 0.5.0 or earlier.
      
      In applications using `idna` (but not in `idna` itself) this may be able to lead to privilege escalation when host name comparison is part of a privilege check and the behavior is combined with a client that resolves domains with such labels instead of treating them as errors that preclude DNS resolution / URL fetching and with the attacker managing to introduce a DNS entry (and TLS certificate) for an `xn--`-masked name that turns into the name of the target when processed by `idna` 0.5.0 or earlier.
      
      ## Remedy
      
      Upgrade to `idna` 1.0.3 or later, if depending on `idna` directly, or to `url` 2.5.4 or later, if depending on `idna` via `url`. (This issue was fixed in `idna` 1.0.0, but versions earlier than 1.0.3 are not recommended for other reasons.)
      
      When upgrading, please take a moment to read about [alternative Unicode back ends for `idna`](https://docs.rs/crate/idna_adapter/latest).
      
      If you are using Rust earlier than 1.81 in combination with SQLx 0.8.2 or earlier, please also read an [issue](https://github.com/servo/rust-url/issues/992) about combining them with `url` 2.5.4 and `idna` 1.0.3.
      
      ## Additional information
      
      This issue resulted from `idna` 0.5.0 and earlier implementing the UTS 46 specification literally on this point and the specification having this bug. The specification bug has been fixed in [revision 33 of UTS 46](https://www.unicode.org/reports/tr46/tr46-33.html#Modifications).
      
      ## Acknowledgements
      
      Thanks to kageshiron for recognizing the security implications of this behavior.
    ├ Announcement: https://bugzilla.mozilla.org/show_bug.cgi?id=1887898
    ├ Solution: Upgrade to >=1.0.0 (try `cargo update -p idna`)
    ├ idna v0.5.0
      └── url v2.5.2
          ├── clippy-annotation-reporter v0.1.0
          ├── httpmock v0.6.8
          │   └── (dev) clippy-annotation-reporter v0.1.0 (*)
          ├── isahc v1.7.2
          │   └── httpmock v0.6.8 (*)
          └── octocrab v0.44.1
              └── clippy-annotation-reporter v0.1.0 (*)

advisories FAILED, bans ok, sources ok

Updated: 2026-03-04 16:35:19 UTC | Commit: dc5f70a | dependency-check job results

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Artifact Size Benchmark Report

aarch64-alpine-linux-musl
Artifact Baseline Commit Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a 97.27 MB 97.28 MB +.01% (+14.55 KB) 🔍
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so 8.51 MB 8.51 MB 0% (0 B) 👌
aarch64-unknown-linux-gnu
Artifact Baseline Commit Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.12 MB 11.12 MB 0% (0 B) 👌
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a 112.87 MB 112.87 MB 0% (0 B) 👌
libdatadog-x64-windows
Artifact Baseline Commit Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll 27.15 MB 27.15 MB 0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib 76.26 KB 76.26 KB 0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb 185.89 MB 185.89 MB 0% (0 B) 👌
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib 914.84 MB 914.84 MB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll 9.93 MB 9.93 MB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib 76.26 KB 76.26 KB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb 24.76 MB 24.76 MB 0% (0 B) 👌
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib 51.40 MB 51.40 MB 0% (0 B) 👌
libdatadog-x86-windows
Artifact Baseline Commit Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll 22.96 MB 22.96 MB 0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib 77.44 KB 77.44 KB 0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb 190.10 MB 190.11 MB +0% (+8.00 KB) 👌
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib 898.43 MB 898.43 MB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll 7.53 MB 7.53 MB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib 77.44 KB 77.44 KB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb 26.51 MB 26.51 MB 0% (0 B) 👌
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib 47.03 MB 47.03 MB 0% (0 B) 👌
x86_64-alpine-linux-musl
Artifact Baseline Commit Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a 85.23 MB 85.23 MB 0% (0 B) 👌
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so 10.04 MB 10.04 MB 0% (0 B) 👌
x86_64-unknown-linux-gnu
Artifact Baseline Commit Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a 105.85 MB 105.85 MB 0% (0 B) 👌
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.78 MB 11.78 MB 0% (0 B) 👌

@hoolioh
ci: create workspace for actions
089947d 
Create actions workspace to share and build code for repo actions:
* Move code from clippy-annotation-reporter to ci-shared crate.
* Port code from changed-crates bash script to changed-crates binary
  inside ci-crates.
* Create affected-crates binary to compute dependency graph and get
  affected crates by the PR.
* Modify lint workflow to manage affected crates.
@hoolioh hoolioh force-pushed the julio/rd-week-dynamic-pipeline branch from d33ce6c to 089947d Compare March 4, 2026 16:19
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iunanua
iunanua reviewed Mar 5, 2026
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@hoolioh hoolioh changed the title ci: create workspace for actions ci: dynamic pipeline Mar 5, 2026
@hoolioh hoolioh force-pushed the julio/rd-week-dynamic-pipeline branch from 7e53a10 to 34635e3 Compare March 5, 2026 13:52
hoolioh and others added 15 commits March 5, 2026 15:11
@hoolioh
Fix call to action
c9785c8
@hoolioh
compile action
8fd143e
@hoolioh
Restore workflows.
344aa18
@hoolioh
Modify fuzz to just run the tests if the crates just changed
32182b8
@hoolioh
Modify lint workflow
a20def1
@hoolioh
restore id
76262ad
@iunanua
remove if
b0ec864
@iunanua
triggers and log
847325b
@iunanua
call fetch_base
150a5bf
@iunanua
Change workflow triggers
b0048f8
@hoolioh
Fix base reference name
e784a9b
@hoolioh
split test and make ffi dynamic
97e6af2
@hoolioh
make unit test workflow dynamic
8610e98
@iunanua
chore(ci): setup miri crates before running (#1679)
026698c 
# 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.
@hoolioh
Merge branch 'julio/rd-week-unit-test-workflow' into julio/rd-week-dy…
16dfffc 
…namic-pipeline
hoolioh and others added 6 commits March 7, 2026 08:27
@hoolioh
fix spellcheck
c8cd45f
@hoolioh
make clippy and rustfmt dynamic
63c0349
@hoolioh
fix shellcheck
36fe200
@hoolioh
fix: search crates by exact name rather than substring
ada2860
@hoolioh
fix: only run tests on crates containing tracing integration tests
9c957d3
@iunanua
ci: changed-crates refactor (#1691)
cb5bf5f 
# What does this PR do?

Remove the `github.event_name == 'pull_request'` checks 
 
# 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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@hoolioh @codecov-commenter @iunanua