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WIP feat: agent-level HTTP client#1806

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yannham wants to merge 23 commits intomainfrom
yannham/apmsp-2722-agent-client-layer
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WIP feat: agent-level HTTP client#1806
yannham wants to merge 23 commits intomainfrom
yannham/apmsp-2722-agent-client-layer

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@yannham yannham commented Mar 26, 2026

WIP. Claude-generated API that I need to review, refine and clean first.

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.

@yannham yannham changed the base branch from main to ekump/APMSP-2516-implement-http-common-component March 26, 2026 10:42
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github-actions bot commented Mar 26, 2026 •
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📚 Documentation Check Results

✅ No documentation warnings found!

📦 libdd-agent-client - ✅ No warnings

Updated: 2026-03-30 12:18:36 UTC | Commit: 8e21604 | missing-docs job results

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github-actions bot commented Mar 26, 2026 •
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Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

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 20 20 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 55 55 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 8 8 No change (0%)
libdd-trace-utils 15 15 No change (0%)
Total 195 195 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

✅ No issues found!

📦 libdd-agent-client - ✅ No issues

Updated: 2026-03-30 12:21:24 UTC | Commit: 8e21604 | dependency-check job results

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Benchmarks

Comparison

Benchmark execution time: 2026-03-30 12:29:42

Comparing candidate commit 0f58a30 in PR branch yannham/apmsp-2722-agent-client-layer with baseline commit b49a17e in branch ekump/APMSP-2516-implement-http-common-component.

Found 3 performance improvements and 4 performance regressions! Performance is the same for 55 metrics, 0 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:credit_card/is_card_number/x371413321323331

  • 🟥 execution_time [+1.103µs; +1.105µs] or [+19.328%; +19.368%]
  • 🟥 throughput [-28443767.085op/s; -28384307.270op/s] or [-16.228%; -16.195%]

scenario:credit_card/is_card_number_no_luhn/x371413321323331

  • 🟥 execution_time [+1.108µs; +1.110µs] or [+19.412%; +19.453%]
  • 🟥 throughput [-28545525.991op/s; -28485069.244op/s] or [-16.288%; -16.254%]

scenario:normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて

  • 🟩 execution_time [-25.791µs; -25.624µs] or [-6.339%; -6.298%]
  • 🟩 throughput [+165266.718op/s; +166325.186op/s] or [+6.724%; +6.767%]

scenario:write only interface

  • 🟩 execution_time [-332.371ns; -314.490ns] or [-5.799%; -5.487%]

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 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 288.716µs 289.436µs ± 0.514µs 289.358µs ± 0.163µs 289.513µs 289.923µs 292.046µs 293.714µs 1.51% 4.683 29.719 0.18% 0.036µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
sql/obfuscate_sql_string execution_time [289.365µs; 289.507µs] or [-0.025%; +0.025%] None None None

Group 2

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
scenario metric min mean ± sd median ± mad p75 p95 p99 max peak_to_median_ratio skewness kurtosis cv sem runs sample_size
profile_serialize_compressed_pprof_timestamped_x1000 execution_time 912.491µs 916.544µs ± 3.018µs 915.934µs ± 1.272µs 917.397µs 920.824µs 930.573µs 936.748µs 2.27% 3.271 16.907 0.33% 0.213µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
profile_serialize_compressed_pprof_timestamped_x1000 execution_time [916.126µs; 916.962µs] or [-0.046%; +0.046%] None None None

Group 3

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 15.161µs 15.511µs ± 0.202µs 15.479µs ± 0.103µs 15.589µs 15.869µs 16.300µs 16.609µs 7.30% 1.678 5.524 1.30% 0.014µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
two way interface execution_time [15.483µs; 15.539µs] or [-0.181%; +0.181%] None None None

Group 4

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 145.151µs 146.966µs ± 1.691µs 146.689µs ± 0.622µs 147.377µs 148.654µs 153.408µs 163.048µs 11.15% 5.302 42.580 1.15% 0.120µs 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 [146.731µs; 147.200µs] or [-0.159%; +0.159%] None None None

Group 5

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 206.066µs 206.762µs ± 0.325µs 206.707µs ± 0.200µs 206.962µs 207.311µs 207.689µs 208.261µs 0.75% 0.977 1.865 0.16% 0.023µs 1 200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput 4801659.661op/s 4836491.853op/s ± 7591.671op/s 4837763.953op/s ± 4691.345op/s 4841861.848op/s 4846315.160op/s 4850817.941op/s 4852808.419op/s 0.31% -0.964 1.812 0.16% 536.812op/s 1 200
normalization/normalize_name/normalize_name/bad-name execution_time 18.546µs 18.662µs ± 0.062µs 18.665µs ± 0.045µs 18.701µs 18.759µs 18.816µs 18.887µs 1.19% 0.462 0.383 0.33% 0.004µs 1 200
normalization/normalize_name/normalize_name/bad-name throughput 52945378.317op/s 53585990.875op/s ± 177736.130op/s 53576990.590op/s ± 129236.781op/s 53735377.233op/s 53858312.518op/s 53873275.529op/s 53920032.023op/s 0.64% -0.440 0.328 0.33% 12567.842op/s 1 200
normalization/normalize_name/normalize_name/good execution_time 10.541µs 10.701µs ± 0.078µs 10.698µs ± 0.046µs 10.742µs 10.847µs 10.904µs 10.938µs 2.25% 0.512 0.110 0.73% 0.006µs 1 200
normalization/normalize_name/normalize_name/good throughput 91424264.419op/s 93458050.723op/s ± 680444.174op/s 93477184.632op/s ± 407113.454op/s 93911423.130op/s 94490545.150op/s 94653566.319op/s 94870407.747op/s 1.49% -0.472 0.053 0.73% 48114.669op/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 [206.717µs; 206.807µs] or [-0.022%; +0.022%] None None None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput [4835439.720op/s; 4837543.985op/s] or [-0.022%; +0.022%] None None None
normalization/normalize_name/normalize_name/bad-name execution_time [18.653µs; 18.670µs] or [-0.046%; +0.046%] None None None
normalization/normalize_name/normalize_name/bad-name throughput [53561358.357op/s; 53610623.393op/s] or [-0.046%; +0.046%] None None None
normalization/normalize_name/normalize_name/good execution_time [10.690µs; 10.711µs] or [-0.101%; +0.101%] None None None
normalization/normalize_name/normalize_name/good throughput [93363747.705op/s; 93552353.742op/s] or [-0.101%; +0.101%] None None None

Group 6

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 14.868ms 14.899ms ± 0.017ms 14.898ms ± 0.011ms 14.908ms 14.926ms 14.949ms 14.981ms 0.55% 1.051 3.006 0.11% 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 [14.896ms; 14.901ms] or [-0.016%; +0.016%] None None None

Group 7

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 3.896µs 3.917µs ± 0.004µs 3.917µs ± 0.002µs 3.919µs 3.925µs 3.928µs 3.929µs 0.31% -0.057 4.237 0.10% 0.000µs 1 200
credit_card/is_card_number/ throughput 254541989.861op/s 255286493.150op/s ± 256663.037op/s 255325291.820op/s ± 151133.453op/s 255465501.356op/s 255583456.533op/s 255660798.830op/s 256703205.162op/s 0.54% 0.076 4.317 0.10% 18148.817op/s 1 200
credit_card/is_card_number/ 3782-8224-6310-005 execution_time 76.048µs 77.607µs ± 0.612µs 77.637µs ± 0.438µs 78.042µs 78.569µs 78.965µs 79.309µs 2.15% 0.008 -0.300 0.79% 0.043µs 1 200
credit_card/is_card_number/ 3782-8224-6310-005 throughput 12608930.407op/s 12886248.414op/s ± 101679.680op/s 12880434.622op/s ± 72886.812op/s 12960153.906op/s 13059462.714op/s 13111866.128op/s 13149666.136op/s 2.09% 0.032 -0.303 0.79% 7189.839op/s 1 200
credit_card/is_card_number/ 378282246310005 execution_time 67.506µs 67.659µs ± 0.098µs 67.643µs ± 0.057µs 67.705µs 67.823µs 67.888µs 68.356µs 1.05% 2.176 11.635 0.14% 0.007µs 1 200
credit_card/is_card_number/ 378282246310005 throughput 14629322.619op/s 14780134.711op/s ± 21277.180op/s 14783565.111op/s ± 12459.712op/s 14794838.822op/s 14804998.464op/s 14810432.914op/s 14813515.852op/s 0.20% -2.138 11.285 0.14% 1504.524op/s 1 200
credit_card/is_card_number/37828224631 execution_time 3.901µs 3.917µs ± 0.004µs 3.917µs ± 0.002µs 3.919µs 3.925µs 3.931µs 3.934µs 0.43% 0.739 2.528 0.11% 0.000µs 1 200
credit_card/is_card_number/37828224631 throughput 254226161.544op/s 255276131.212op/s ± 275573.245op/s 255315005.680op/s ± 159401.247op/s 255460742.178op/s 255594713.375op/s 255777624.114op/s 256367839.869op/s 0.41% -0.726 2.522 0.11% 19485.971op/s 1 200
credit_card/is_card_number/378282246310005 execution_time 64.493µs 64.733µs ± 0.095µs 64.734µs ± 0.057µs 64.793µs 64.876µs 64.996µs 65.008µs 0.42% 0.005 0.308 0.15% 0.007µs 1 200
credit_card/is_card_number/378282246310005 throughput 15382738.919op/s 15448216.358op/s ± 22786.045op/s 15447865.000op/s ± 13685.988op/s 15460597.508op/s 15486478.994op/s 15501533.629op/s 15505676.628op/s 0.37% 0.005 0.303 0.15% 1611.217op/s 1 200
credit_card/is_card_number/37828224631000521389798 execution_time 44.718µs 44.949µs ± 0.081µs 44.943µs ± 0.058µs 45.003µs 45.092µs 45.148µs 45.179µs 0.53% 0.166 0.001 0.18% 0.006µs 1 200
credit_card/is_card_number/37828224631000521389798 throughput 22133940.114op/s 22247592.361op/s ± 40238.735op/s 22250377.209op/s ± 28771.888op/s 22275196.016op/s 22306925.313op/s 22336140.196op/s 22362413.496op/s 0.50% -0.156 -0.001 0.18% 2845.308op/s 1 200
credit_card/is_card_number/x371413321323331 execution_time 6.799µs 6.809µs ± 0.006µs 6.808µs ± 0.004µs 6.813µs 6.821µs 6.828µs 6.829µs 0.31% 0.880 0.584 0.09% 0.000µs 1 200
credit_card/is_card_number/x371413321323331 throughput 146434762.609op/s 146857161.201op/s ± 132782.515op/s 146884550.422op/s ± 89592.788op/s 146962102.027op/s 147021245.270op/s 147059691.838op/s 147081142.446op/s 0.13% -0.875 0.571 0.09% 9389.142op/s 1 200
credit_card/is_card_number_no_luhn/ execution_time 3.902µs 3.917µs ± 0.004µs 3.916µs ± 0.002µs 3.919µs 3.923µs 3.927µs 3.930µs 0.36% 0.530 1.600 0.09% 0.000µs 1 200
credit_card/is_card_number_no_luhn/ throughput 254425626.179op/s 255321832.231op/s ± 242813.441op/s 255350474.781op/s ± 160697.774op/s 255503740.911op/s 255630265.361op/s 255740529.257op/s 256257154.556op/s 0.36% -0.521 1.595 0.09% 17169.503op/s 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time 59.471µs 60.431µs ± 0.644µs 60.461µs ± 0.482µs 60.865µs 61.489µs 62.072µs 62.499µs 3.37% 0.338 -0.409 1.06% 0.046µs 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput 16000136.534op/s 16549611.276op/s ± 175713.030op/s 16539535.491op/s ± 130790.262op/s 16728033.658op/s 16789851.197op/s 16804681.941op/s 16814938.758op/s 1.67% -0.292 -0.499 1.06% 12424.788op/s 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time 53.539µs 53.779µs ± 0.106µs 53.780µs ± 0.071µs 53.855µs 53.939µs 54.031µs 54.208µs 0.80% 0.298 0.677 0.20% 0.007µs 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 throughput 18447578.099op/s 18594697.238op/s ± 36478.659op/s 18594317.338op/s ± 24627.324op/s 18618025.300op/s 18652840.025op/s 18673624.868op/s 18677881.653op/s 0.45% -0.283 0.643 0.20% 2579.431op/s 1 200
credit_card/is_card_number_no_luhn/37828224631 execution_time 3.894µs 3.916µs ± 0.004µs 3.916µs ± 0.002µs 3.919µs 3.923µs 3.927µs 3.932µs 0.39% -0.167 5.516 0.10% 0.000µs 1 200
credit_card/is_card_number_no_luhn/37828224631 throughput 254349181.790op/s 255330788.011op/s ± 256032.579op/s 255350219.477op/s ± 151941.204op/s 255489108.214op/s 255647616.450op/s 255703526.752op/s 256800048.183op/s 0.57% 0.190 5.601 0.10% 18104.237op/s 1 200
credit_card/is_card_number_no_luhn/378282246310005 execution_time 50.238µs 50.365µs ± 0.074µs 50.364µs ± 0.051µs 50.411µs 50.511µs 50.549µs 50.623µs 0.51% 0.523 0.167 0.15% 0.005µs 1 200
credit_card/is_card_number_no_luhn/378282246310005 throughput 19753927.607op/s 19854918.068op/s ± 29135.001op/s 19855396.837op/s ± 20078.109op/s 19876884.189op/s 19899824.484op/s 19904211.851op/s 19905414.273op/s 0.25% -0.515 0.150 0.15% 2060.156op/s 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time 44.750µs 44.979µs ± 0.084µs 44.981µs ± 0.060µs 45.038µs 45.111µs 45.158µs 45.218µs 0.53% -0.012 -0.322 0.19% 0.006µs 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput 22115220.697op/s 22232608.603op/s ± 41580.997op/s 22231561.265op/s ± 29476.897op/s 22262712.739op/s 22297883.934op/s 22323301.844op/s 22346323.930op/s 0.52% 0.022 -0.323 0.19% 2940.220op/s 1 200
credit_card/is_card_number_no_luhn/x371413321323331 execution_time 6.802µs 6.815µs ± 0.007µs 6.814µs ± 0.004µs 6.819µs 6.825µs 6.835µs 6.837µs 0.34% 0.728 0.683 0.10% 0.000µs 1 200
credit_card/is_card_number_no_luhn/x371413321323331 throughput 146269422.122op/s 146738233.105op/s ± 141759.750op/s 146764133.156op/s ± 94193.041op/s 146836506.101op/s 146930977.010op/s 146967815.362op/s 147014180.861op/s 0.17% -0.722 0.668 0.10% 10023.928op/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 [3.917µs; 3.918µs] or [-0.014%; +0.014%] None None None
credit_card/is_card_number/ throughput [255250922.122op/s; 255322064.179op/s] or [-0.014%; +0.014%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 execution_time [77.522µs; 77.692µs] or [-0.109%; +0.109%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 throughput [12872156.588op/s; 12900340.240op/s] or [-0.109%; +0.109%] None None None
credit_card/is_card_number/ 378282246310005 execution_time [67.645µs; 67.672µs] or [-0.020%; +0.020%] None None None
credit_card/is_card_number/ 378282246310005 throughput [14777185.899op/s; 14783083.524op/s] or [-0.020%; +0.020%] None None None
credit_card/is_card_number/37828224631 execution_time [3.917µs; 3.918µs] or [-0.015%; +0.015%] None None None
credit_card/is_card_number/37828224631 throughput [255237939.411op/s; 255314323.014op/s] or [-0.015%; +0.015%] None None None
credit_card/is_card_number/378282246310005 execution_time [64.719µs; 64.746µs] or [-0.020%; +0.020%] None None None
credit_card/is_card_number/378282246310005 throughput [15445058.432op/s; 15451374.285op/s] or [-0.020%; +0.020%] None None None
credit_card/is_card_number/37828224631000521389798 execution_time [44.938µs; 44.960µs] or [-0.025%; +0.025%] None None None
credit_card/is_card_number/37828224631000521389798 throughput [22242015.659op/s; 22253169.062op/s] or [-0.025%; +0.025%] None None None
credit_card/is_card_number/x371413321323331 execution_time [6.808µs; 6.810µs] or [-0.013%; +0.013%] None None None
credit_card/is_card_number/x371413321323331 throughput [146838758.822op/s; 146875563.581op/s] or [-0.013%; +0.013%] None None None
credit_card/is_card_number_no_luhn/ execution_time [3.916µs; 3.917µs] or [-0.013%; +0.013%] None None None
credit_card/is_card_number_no_luhn/ throughput [255288180.623op/s; 255355483.838op/s] or [-0.013%; +0.013%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time [60.342µs; 60.520µs] or [-0.148%; +0.148%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput [16525259.140op/s; 16573963.412op/s] or [-0.147%; +0.147%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time [53.764µs; 53.794µs] or [-0.027%; +0.027%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 throughput [18589641.647op/s; 18599752.830op/s] or [-0.027%; +0.027%] None None None
credit_card/is_card_number_no_luhn/37828224631 execution_time [3.916µs; 3.917µs] or [-0.014%; +0.014%] None None None
credit_card/is_card_number_no_luhn/37828224631 throughput [255295304.358op/s; 255366271.664op/s] or [-0.014%; +0.014%] None None None
credit_card/is_card_number_no_luhn/378282246310005 execution_time [50.355µs; 50.376µs] or [-0.020%; +0.020%] None None None
credit_card/is_card_number_no_luhn/378282246310005 throughput [19850880.237op/s; 19858955.899op/s] or [-0.020%; +0.020%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time [44.967µs; 44.991µs] or [-0.026%; +0.026%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput [22226845.876op/s; 22238371.329op/s] or [-0.026%; +0.026%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 execution_time [6.814µs; 6.816µs] or [-0.013%; +0.013%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 throughput [146718586.567op/s; 146757879.642op/s] or [-0.013%; +0.013%] None None None

Group 8

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 5.321µs 5.408µs ± 0.027µs 5.405µs ± 0.016µs 5.425µs 5.451µs 5.497µs 5.514µs 2.02% 0.577 1.804 0.50% 0.002µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
write only interface execution_time [5.404µs; 5.412µs] or [-0.069%; +0.069%] None None None

Group 9

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 4.979µs 5.064µs ± 0.048µs 5.064µs ± 0.040µs 5.093µs 5.136µs 5.175µs 5.178µs 2.25% 0.234 -0.584 0.95% 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 [5.057µs; 5.070µs] or [-0.132%; +0.132%] None None None

Group 10

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 3.433ms 3.470ms ± 0.014ms 3.469ms ± 0.009ms 3.478ms 3.498ms 3.511ms 3.521ms 1.51% 0.712 1.150 0.41% 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 [3.468ms; 3.472ms] or [-0.057%; +0.057%] None None None

Group 11

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 190.149ns 192.794ns ± 2.197ns 192.428ns ± 1.572ns 193.977ns 196.897ns 199.116ns 200.931ns 4.42% 1.053 0.835 1.14% 0.155ns 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 [192.490ns; 193.099ns] or [-0.158%; +0.158%] None None None

Group 12

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 245.929ns 258.466ns ± 13.920ns 251.481ns ± 3.047ns 268.603ns 290.310ns 293.362ns 296.143ns 17.76% 1.285 0.257 5.37% 0.984ns 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 [256.537ns; 260.395ns] or [-0.746%; +0.746%] None None None

Group 13

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 161.003µs 161.885µs ± 0.520µs 161.815µs ± 0.189µs 162.015µs 162.577µs 163.482µs 166.897µs 3.14% 4.977 42.494 0.32% 0.037µs 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 [161.813µs; 161.957µs] or [-0.045%; +0.045%] None None None

Group 14

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 2.314µs 2.371µs ± 0.017µs 2.374µs ± 0.007µs 2.379µs 2.398µs 2.407µs 2.427µs 2.24% -0.974 2.960 0.71% 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 [2.369µs; 2.373µs] or [-0.099%; +0.099%] None None None

Group 15

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 34.542µs 35.052µs ± 0.842µs 34.676µs ± 0.056µs 34.748µs 36.827µs 36.855µs 38.236µs 10.27% 1.761 1.384 2.40% 0.060µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
redis/obfuscate_redis_string execution_time [34.935µs; 35.168µs] or [-0.333%; +0.333%] None None None

Group 16

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 14.760ms 14.827ms ± 0.035ms 14.822ms ± 0.018ms 14.840ms 14.892ms 14.947ms 14.999ms 1.19% 1.628 4.085 0.24% 0.003ms 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 [14.822ms; 14.832ms] or [-0.033%; +0.033%] None None None

Group 17

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 534.918µs 536.254µs ± 1.079µs 535.955µs ± 0.513µs 536.697µs 538.311µs 540.135µs 541.303µs 1.00% 1.796 4.248 0.20% 0.076µs 1 200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput 1847392.753op/s 1864794.548op/s ± 3738.765op/s 1865828.083op/s ± 1784.512op/s 1867345.634op/s 1868719.858op/s 1869278.344op/s 1869443.971op/s 0.19% -1.778 4.151 0.20% 264.371op/s 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time 380.349µs 381.125µs ± 0.364µs 381.088µs ± 0.228µs 381.346µs 381.760µs 382.056µs 382.206µs 0.29% 0.496 -0.057 0.10% 0.026µs 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput 2616390.114op/s 2623811.712op/s ± 2501.586op/s 2624067.480op/s ± 1568.338op/s 2625526.118op/s 2627486.743op/s 2628327.505op/s 2629162.464op/s 0.19% -0.491 -0.064 0.10% 176.889op/s 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time 189.407µs 190.066µs ± 0.377µs 189.994µs ± 0.196µs 190.230µs 190.788µs 191.315µs 191.852µs 0.98% 1.474 3.422 0.20% 0.027µs 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput 5212351.187op/s 5261357.397op/s ± 10395.554op/s 5263328.367op/s ± 5437.233op/s 5268492.154op/s 5274200.013op/s 5276837.823op/s 5279636.631op/s 0.31% -1.455 3.333 0.20% 735.077op/s 1 200
normalization/normalize_service/normalize_service/[empty string] execution_time 37.830µs 37.948µs ± 0.059µs 37.937µs ± 0.037µs 37.983µs 38.072µs 38.104µs 38.119µs 0.48% 0.656 0.058 0.16% 0.004µs 1 200
normalization/normalize_service/normalize_service/[empty string] throughput 26233517.261op/s 26351637.898op/s ± 41094.586op/s 26359276.516op/s ± 25901.902op/s 26381428.144op/s 26408841.731op/s 26422775.031op/s 26434222.790op/s 0.28% -0.649 0.047 0.16% 2905.826op/s 1 200
normalization/normalize_service/normalize_service/test_ASCII execution_time 45.828µs 46.013µs ± 0.117µs 45.993µs ± 0.074µs 46.090µs 46.255µs 46.344µs 46.391µs 0.87% 0.869 0.367 0.25% 0.008µs 1 200
normalization/normalize_service/normalize_service/test_ASCII throughput 21555760.681op/s 21733104.226op/s ± 55309.471op/s 21742559.878op/s ± 35220.870op/s 21774145.293op/s 21799680.365op/s 21816732.288op/s 21820906.125op/s 0.36% -0.856 0.336 0.25% 3910.970op/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 [536.105µs; 536.404µs] or [-0.028%; +0.028%] None None None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput [1864276.391op/s; 1865312.705op/s] or [-0.028%; +0.028%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time [381.075µs; 381.176µs] or [-0.013%; +0.013%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput [2623465.016op/s; 2624158.408op/s] or [-0.013%; +0.013%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time [190.014µs; 190.118µs] or [-0.027%; +0.027%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput [5259916.673op/s; 5262798.121op/s] or [-0.027%; +0.027%] None None None
normalization/normalize_service/normalize_service/[empty string] execution_time [37.940µs; 37.957µs] or [-0.022%; +0.022%] None None None
normalization/normalize_service/normalize_service/[empty string] throughput [26345942.584op/s; 26357333.213op/s] or [-0.022%; +0.022%] None None None
normalization/normalize_service/normalize_service/test_ASCII execution_time [45.997µs; 46.029µs] or [-0.035%; +0.035%] None None None
normalization/normalize_service/normalize_service/test_ASCII throughput [21725438.865op/s; 21740769.587op/s] or [-0.035%; +0.035%] None None None

Group 18

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 49.391ms 49.759ms ± 1.235ms 49.604ms ± 0.073ms 49.681ms 49.859ms 53.428ms 64.666ms 30.37% 10.179 111.808 2.48% 0.087ms 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 [49.588ms; 49.930ms] or [-0.344%; +0.344%] None None None

Group 19

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 4.165ms 4.170ms ± 0.008ms 4.169ms ± 0.001ms 4.171ms 4.174ms 4.180ms 4.277ms 2.57% 11.840 153.442 0.19% 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 [4.169ms; 4.171ms] or [-0.027%; +0.027%] None None None

Group 20

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 727.700µs 728.774µs ± 0.491µs 728.754µs ± 0.274µs 729.023µs 729.529µs 729.934µs 731.380µs 0.36% 1.150 4.519 0.07% 0.035µs 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 [728.706µs; 728.842µs] or [-0.009%; +0.009%] None None None

Group 21

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 0f58a30 1774872757 yannham/apmsp-2722-agent-client-layer
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 4.217ms 4.224ms ± 0.003ms 4.224ms ± 0.002ms 4.225ms 4.228ms 4.231ms 4.248ms 0.58% 2.012 12.728 0.08% 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 [4.223ms; 4.224ms] or [-0.011%; +0.011%] None None None

Baseline

Omitted due to size.

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codecov-commenter commented Mar 26, 2026 •
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Codecov Report

❌ Patch coverage is 0% with 102 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.21%. Comparing base (b49a17e) to head (0f58a30).

Additional details and impacted files 
@@                                 Coverage Diff                                  @@
##           ekump/APMSP-2516-implement-http-common-component    #1806      +/-   ##
====================================================================================
- Coverage                                             71.29%   71.21%   -0.09%     
====================================================================================
  Files                                                   423      427       +4     
  Lines                                                 66609    66711     +102     
====================================================================================
+ Hits                                                  47492    47506      +14     
- Misses                                                19117    19205      +88
Components Coverage Δ
libdd-crashtracker 65.24% <ø> (ø)
libdd-crashtracker-ffi 34.47% <ø> (ø)
libdd-alloc 98.77% <ø> (ø)
libdd-data-pipeline 87.05% <ø> (ø)
libdd-data-pipeline-ffi 72.91% <ø> (ø)
libdd-common 80.19% <ø> (ø)
libdd-common-ffi 73.87% <ø> (ø)
libdd-telemetry 62.48% <ø> (ø)
libdd-telemetry-ffi 16.75% <ø> (ø)
libdd-dogstatsd-client 82.64% <ø> (ø)
datadog-ipc 72.48% <ø> (ø)
libdd-profiling 81.61% <ø> (ø)
libdd-profiling-ffi 64.94% <ø> (ø)
datadog-sidecar 30.18% <ø> (ø)
datdog-sidecar-ffi 6.52% <ø> (ø)
spawn-worker 54.69% <ø> (ø)
libdd-tinybytes 93.16% <ø> (ø)
libdd-trace-normalization 81.71% <ø> (ø)
libdd-trace-obfuscation 87.24% <ø> (ø)
libdd-trace-protobuf 68.25% <ø> (ø)
libdd-trace-utils 88.72% <ø> (ø)
datadog-tracer-flare 86.88% <ø> (ø)
libdd-log 74.69% <ø> (ø)
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datadog-prod-us1-5 bot commented Mar 26, 2026 •
edited by datadog-datadog-prod-us1-2 bot
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✅ Tests

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 0.00%
• Overall Coverage: 71.21% (-0.06%)

This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 0f58a30 | Docs | Datadog PR Page | Was this helpful? React with 👍/👎 or give us feedback!

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dd-octo-sts bot commented Mar 26, 2026 •
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Artifact Size Benchmark Report

aarch64-alpine-linux-musl
Artifact Baseline Commit Change
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.so 8.76 MB 8.76 MB 0% (0 B) 👌
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a 101.91 MB 101.93 MB +.01% (+19.18 KB) 🔍
aarch64-unknown-linux-gnu
Artifact Baseline Commit Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a 118.86 MB 118.83 MB --.02% (-30.56 KB) 💪
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so 11.36 MB 11.36 MB -0% (-88 B) 👌
libdatadog-x64-windows
Artifact Baseline Commit Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll 27.38 MB 27.38 MB +0% (+2.50 KB) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib 80.69 KB 80.69 KB 0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb 187.11 MB 187.08 MB --.01% (-24.00 KB) 💪
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib 925.04 MB 925.19 MB +.01% (+155.56 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll 9.06 MB 9.06 MB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib 80.69 KB 80.69 KB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb 26.98 MB 26.99 MB +.02% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib 61.28 MB 61.30 MB +.02% (+17.40 KB) 🔍
libdatadog-x86-windows
Artifact Baseline Commit Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll 23.19 MB 23.24 MB +.22% (+52.50 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib 81.94 KB 81.94 KB 0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb 191.47 MB 191.64 MB +.08% (+176.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib 908.71 MB 910.20 MB +.16% (+1.48 MB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll 6.90 MB 6.90 MB +.01% (+1.00 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib 81.94 KB 81.94 KB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb 29.11 MB 29.11 MB 0% (0 B) 👌
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib 57.68 MB 57.67 MB --.01% (-10.66 KB) 💪
x86_64-alpine-linux-musl
Artifact Baseline Commit Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a 88.72 MB 88.77 MB +.04% (+45.07 KB) 🔍
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so 10.32 MB 10.32 MB +.03% (+4.00 KB) 🔍
x86_64-unknown-linux-gnu
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/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a 111.55 MB 111.54 MB -0% (-8.09 KB) 👌
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so 12.08 MB 12.07 MB --.03% (-4.48 KB) 💪

@yannham yannham force-pushed the yannham/apmsp-2722-agent-client-layer branch from 929a466 to 6c0054d Compare March 26, 2026 14:55
ekump and others added 18 commits March 30, 2026 12:06
@ekump @yannham
add core API types and crate scaffold
c5a1fb6 
New HTTP client crate for use by external language clients via FFI.
Defines HttpClient, HttpRequest, HttpResponse, HttpClientError, and a
builder API with configurable timeout, transport, and retry.
@ekump @yannham
implement reqwest backend with integration tests
d007f58 
ReqwestBackend sends requests via reqwest::Client with connection
pooling, error mapping, and per-request timeout. Integration tests
cover HTTP round-trips, timeout behavior, and connection pooling.
@ekump @yannham
add UDS, Windows Named Pipe, and FIPS TLS support
7bab547 
Builder API supports unix_socket() and windows_named_pipe() for
platform-specific transports. FIPS TLS via rustls-no-provider with
init_fips_crypto() helper for callers to install the crypto provider.
@ekump @yannham
add configurable retry with exponential backoff
46c89df 
Opt-in retry via RetryConfig on the builder. Supports configurable max
retries, initial delay, exponential backoff, and jitter. All errors
except InvalidConfig are retried.
@ekump @yannham
add CODEOWNERS entry for libdd-http-client
7d20890
@ekump @yannham
update license file
b3ba5f0
@ekump @yannham
add tests for windows named pipes
2c24e79
@ekump @yannham
add README
3f1e660
@ekump @yannham
add miri ignore for tests involving hickorydns
1e48b62
@ekump @yannham
add multipart form-data upload support
8e44854 
Add MultipartPart type and multipart_parts field on HttpRequest.
When parts are present, the request is sent as multipart/form-data
with reqwest handling Content-Type and boundary automatically. Setting
both multipart parts and a body returns an error.
@ekump @yannham
add hyper backend and shared multipart encoder
9276a38 
Add a hyper-backend feature as a lighter alternative to reqwest-backend
for consumers where binary size matters. The hyper backend uses
libdd-common's Connector and a new shared multipart encoder in
libdd-common. CI runs tests against both backends.
@ekump @yannham
fix(libdd-http-client): resolve --all-features conflict between backends
9e6ac69 
When both reqwest-backend and hyper-backend are enabled (e.g. CI with
--all-features), reqwest takes precedence. Hyper backend only activates
when reqwest-backend is not enabled.
@ekump @yannham
add response and fips tests
13486a5
@yannham
chore: move new() to Backend trait
9b6601e
@yannham
chore: improve compile-time directive in http client
2bb1959
@yannham
chore: add checks in http client tests
f67520e
@yannham
chore: update libdd-common deps of libdd-http-client
15b51a4
@yannham
chore: improve HTTP client README example
3e69678
@yannham yannham force-pushed the yannham/apmsp-2722-agent-client-layer branch from 6c0054d to f6111ed Compare March 30, 2026 10:21
@yannham yannham force-pushed the ekump/APMSP-2516-implement-http-common-component branch from fc06596 to 97cfd8f Compare March 30, 2026 10:26
@yannham yannham force-pushed the ekump/APMSP-2516-implement-http-common-component branch from 97cfd8f to b49a17e Compare March 30, 2026 11:51
@yannham yannham force-pushed the yannham/apmsp-2722-agent-client-layer branch from f6111ed to dafaef0 Compare March 30, 2026 12:10
@yannham yannham force-pushed the yannham/apmsp-2722-agent-client-layer branch from dafaef0 to 0f58a30 Compare March 30, 2026 12:12
Base automatically changed from ekump/APMSP-2516-implement-http-common-component to main March 30, 2026 15:24
brettlangdon
brettlangdon reviewed Apr 14, 2026
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some initial thoughts/questions, in general lgtm

libdd-agent-client/src/lib.rs
Comment on lines +6 to +24
//! This crate is **Milestone 2** of APMSP-2721 (libdd-http-client: Common HTTP Client for
//! Language Clients). It sits on top of the basic `libdd-http-client` primitives (Milestone 1)
//! and encapsulates all Datadog-agent-specific concerns, making them the **default** rather than
//! opt-in boilerplate that every subsystem must repeat.
//!
//! # What it replaces in dd-trace-py
//!
//! | Concern | dd-trace-py location |
//! |---------|----------------------|
//! | Language metadata headers | `writer.py:638-644`, `stats.py:113-117`, `datastreams/processor.py:128-133` |
//! | Container/entity-ID headers | `http.py:32-37`, `container.py:157-183` |
//! | Retry logic (fibonacci backoff) | `writer.py:245-249`, `stats.py:123-126`, `datastreams/processor.py:140-143` |
//! | Trace send with `X-Datadog-Trace-Count` | `writer.py:749-752` |
//! | `rate_by_service` parsing | `writer.py:728-734` |
//! | Stats send | `stats.py:204-228` |
//! | Pipeline stats send (always gzip) | `datastreams/processor.py:204-210` |
//! | Telemetry send with per-request headers | `telemetry/writer.py:111-129` |
//! | EVP event send | `openfeature/writer.py:114-117` |
//! | `GET /info` with typed result | `agent.py:17-46` |
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probably want to cut this 😅

libdd-agent-client/src/lib.rs
//! use libdd_agent_client::{AgentClient, LanguageMetadata};
//!
//! let client = AgentClient::builder()
//! .http("localhost", 8126)
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should we bake in the behaviors for env var parsing and etc so this is all picked up automatically for users, unless they want to manually set?

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same below for api key/intake url, etc

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oh, I see in the builder having an "auto detect" option, I guess that is this. nice

libdd-agent-client/src/builder.rs
///
/// When set, `x-datadog-test-session-token: <token>` is injected on every request.
/// Replaces dd-trace-py's `AgentWriter.set_test_session_token` (`writer.py:754-755`).
pub fn test_token(self, token: impl Into<String>) -> Self {
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🙌🏻

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Suggested change
pub fn test_token(self, token: impl Into<String>) -> Self {
pub fn test_agent_session_token(self, token: impl Into<String>) -> Self {

? not super important, but might make it more clear to users

libdd-agent-client/src/builder.rs
Comment on lines +84 to +92
/// Connection mode for the underlying HTTP client.
///
/// # Correctness note
///
/// The Datadog agent has a low keep-alive timeout that causes "pipe closed" errors on every
/// second connection when connection reuse is enabled. [`ClientMode::Periodic`] (the default)
/// disables connection pooling and is **correct** for all periodic-flush writers (traces, stats,
/// data streams). Only high-frequency continuous senders (e.g. a streaming profiling exporter)
/// should opt into [`ClientMode::Persistent`].
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oh....... we do keep-alive by default in Python, should we not be?

libdd-agent-client/src/builder.rs
/// data streams). Only high-frequency continuous senders (e.g. a streaming profiling exporter)
/// should opt into [`ClientMode::Persistent`].
#[derive(Debug, Clone, Default, PartialEq, Eq)]
pub enum ClientMode {
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instead of ClientMode, what about just a builder method for use_keep_alive(enabled: bool) default of false

?

libdd-agent-client/src/lib.rs
//! # async fn example() -> Result<(), libdd_agent_client::BuildError> {
//! use libdd_agent_client::{AgentClient, LanguageMetadata};
//!
//! let client = AgentClient::builder()
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I wonder if we should explicitly split an agentless client from a normal agent client.

Since the requirements for building are probably different, and can we enforce which API endpoints are available and the expected payload formats/responses/etc

we might be able to better control from the builder requirements as well, like agentless MUST have an API key and be using a public intake endpoint.

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@yannham @codecov-commenter @brettlangdon @ekump