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feat(sidecar)!: Add stats computation via SHM#1821

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bwoebi wants to merge 11 commits intomainfrom
bob/shm-stats-computation
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feat(sidecar)!: Add stats computation via SHM#1821
bwoebi wants to merge 11 commits intomainfrom
bob/shm-stats-computation

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@bwoebi bwoebi commented Mar 30, 2026

This extracts the StatsExporter to libdd-trace-stats to reuse it outside of the data-pipeline.

Using a two-bucket mechanism, it allows all processes to write stats, while the sidecar drains the other bucket.

@bwoebi bwoebi requested review from a team as code owners March 30, 2026 14:59
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📚 Documentation Check Results

⚠️ 1498 documentation warning(s) found

📦 libdd-data-pipeline - 901 warning(s)

📦 libdd-trace-stats - 597 warning(s)

Updated: 2026-04-14 01:37:03 UTC | Commit: bf354d0 | missing-docs job results

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Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

  • Base Branch: origin/main
  • PR Branch: origin/bob/shm-stats-computation

Summary by Rule

Rule Base Branch PR Branch Change
expect_used 3 3 No change (0%)
panic 2 2 No change (0%)
unwrap_used 22 24 ⚠️ +2 (+9.1%)
Total 27 29 ⚠️ +2 (+7.4%)

Annotation Counts by File

File Base Branch PR Branch Change
datadog-ipc/src/platform/unix/mem_handle.rs 2 2 No change (0%)
datadog-ipc/src/platform/unix/mem_handle_macos.rs 6 6 No change (0%)
datadog-ipc/src/platform/windows/mem_handle.rs 4 4 No change (0%)
datadog-sidecar/src/service/blocking.rs 1 1 No change (0%)
datadog-sidecar/src/service/session_info.rs 1 1 No change (0%)
datadog-sidecar/src/service/sidecar_server.rs 6 6 No change (0%)
datadog-sidecar/src/service/stats_flusher.rs 0 1 ⚠️ +1 (N/A)
datadog-sidecar/src/shm_remote_config.rs 7 7 No change (0%)
libdd-trace-stats/src/stats_exporter.rs 0 1 ⚠️ +1 (N/A)

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 21 21 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 56 ⚠️ +1 (+1.8%)
libdd-common 10 10 No change (0%)
libdd-common-ffi 12 12 No change (0%)
libdd-data-pipeline 4 4 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-stats 0 1 ⚠️ +1 (N/A)
libdd-trace-utils 15 15 No change (0%)
Total 195 197 ⚠️ +2 (+1.0%)

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

⚠️ 3 issue(s) found, showing only errors (advisories, bans, sources)

📦 libdd-data-pipeline - 2 error(s)

Show output 
error[unsound]: Rand is unsound with a custom logger using `rand::rng()`
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:208:1
    │
208 │ rand 0.8.5 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ unsound advisory detected
    │
    ├ ID: RUSTSEC-2026-0097
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0097
    ├ It has been reported (by @lopopolo) that the `rand` library is [unsound](https://rust-lang.github.io/unsafe-code-guidelines/glossary.html#soundness-of-code--of-a-library) (i.e. that safe code using the public API can cause Undefined Behaviour) when all the following conditions are met:
      
      - The `log` and `thread_rng` features are enabled
      - A [custom logger](https://docs.rs/log/latest/log/#implementing-a-logger) is defined
      - The custom logger accesses `rand::rng()` (previously `rand::thread_rng()`) and calls any `TryRng` (previously `RngCore`) methods on `ThreadRng`
      - The `ThreadRng` (attempts to) reseed while called from the custom logger (this happens every 64 kB of generated data)
      - Trace-level logging is enabled or warn-level logging is enabled and the random source (the `getrandom` crate) is unable to provide a new seed
      
      `TryRng` (previously `RngCore`) methods for `ThreadRng` use `unsafe` code to cast `*mut BlockRng<ReseedingCore>` to `&mut BlockRng<ReseedingCore>`. When all the above conditions are met this results in an aliased mutable reference, violating the Stacked Borrows rules. Miri is able to detect this violation in sample code. Since construction of [aliased mutable references is Undefined Behaviour](https://doc.rust-lang.org/stable/nomicon/references.html), the behaviour of optimized builds is hard to predict.
      
      Affected versions of `rand` are `>= 0.7, < 0.9.3` and `0.10.0`.
    ├ Announcement: https://github.com/rust-random/rand/pull/1763
    ├ Solution: Upgrade to >=0.10.1 OR <0.10.0, >=0.9.3 (try `cargo update -p rand`)
    ├ rand v0.8.5
      ├── libdd-common v3.0.2
      │   ├── libdd-capabilities-impl v0.1.0
      │   │   ├── (dev) libdd-data-pipeline v3.0.1
      │   │   ├── libdd-trace-stats v2.0.0
      │   │   │   └── libdd-data-pipeline v3.0.1 (*)
      │   │   └── libdd-trace-utils v3.0.1
      │   │       ├── libdd-data-pipeline v3.0.1 (*)
      │   │       ├── libdd-trace-stats v2.0.0 (*)
      │   │       └── (dev) libdd-trace-utils v3.0.1 (*)
      │   ├── libdd-data-pipeline v3.0.1 (*)
      │   ├── libdd-dogstatsd-client v2.0.0
      │   │   └── libdd-data-pipeline v3.0.1 (*)
      │   ├── libdd-shared-runtime v1.0.0
      │   │   ├── libdd-data-pipeline v3.0.1 (*)
      │   │   ├── libdd-telemetry v4.0.0
      │   │   │   └── libdd-data-pipeline v3.0.1 (*)
      │   │   └── libdd-trace-stats v2.0.0 (*)
      │   ├── libdd-telemetry v4.0.0 (*)
      │   ├── libdd-trace-stats v2.0.0 (*)
      │   └── libdd-trace-utils v3.0.1 (*)
      ├── (dev) libdd-data-pipeline v3.0.1 (*)
      ├── (dev) libdd-trace-normalization v2.0.0
      │   └── libdd-trace-utils v3.0.1 (*)
      ├── (dev) libdd-trace-stats v2.0.0 (*)
      ├── libdd-trace-utils v3.0.1 (*)
      └── proptest v1.5.0
          └── (dev) libdd-tinybytes v1.1.0
              ├── libdd-data-pipeline v3.0.1 (*)
              ├── (dev) libdd-tinybytes v1.1.0 (*)
              └── libdd-trace-utils v3.0.1 (*)

error[vulnerability]: Denial of Service via Stack Exhaustion
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:270:1
    │
270 │ time 0.3.41 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0009
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0009
    ├ ## Impact
      
      When user-provided input is provided to any type that parses with the RFC 2822 format, a denial of
      service attack via stack exhaustion is possible. The attack relies on formally deprecated and
      rarely-used features that are part of the RFC 2822 format used in a malicious manner. Ordinary,
      non-malicious input will never encounter this scenario.
      
      ## Patches
      
      A limit to the depth of recursion was added in v0.3.47. From this version, an error will be returned
      rather than exhausting the stack.
      
      ## Workarounds
      
      Limiting the length of user input is the simplest way to avoid stack exhaustion, as the amount of
      the stack consumed would be at most a factor of the length of the input.
    ├ Announcement: https://github.com/time-rs/time/blob/main/CHANGELOG.md#0347-2026-02-05
    ├ Solution: Upgrade to >=0.3.47 (try `cargo update -p time`)
    ├ time v0.3.41
      └── tracing-appender v0.2.3
          └── libdd-log v1.0.0
              └── (dev) libdd-data-pipeline v3.0.1

advisories FAILED, bans ok, sources ok

📦 libdd-trace-stats - 1 error(s)

Show output 
error[unsound]: Rand is unsound with a custom logger using `rand::rng()`
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:183:1
    │
183 │ rand 0.8.5 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ unsound advisory detected
    │
    ├ ID: RUSTSEC-2026-0097
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0097
    ├ It has been reported (by @lopopolo) that the `rand` library is [unsound](https://rust-lang.github.io/unsafe-code-guidelines/glossary.html#soundness-of-code--of-a-library) (i.e. that safe code using the public API can cause Undefined Behaviour) when all the following conditions are met:
      
      - The `log` and `thread_rng` features are enabled
      - A [custom logger](https://docs.rs/log/latest/log/#implementing-a-logger) is defined
      - The custom logger accesses `rand::rng()` (previously `rand::thread_rng()`) and calls any `TryRng` (previously `RngCore`) methods on `ThreadRng`
      - The `ThreadRng` (attempts to) reseed while called from the custom logger (this happens every 64 kB of generated data)
      - Trace-level logging is enabled or warn-level logging is enabled and the random source (the `getrandom` crate) is unable to provide a new seed
      
      `TryRng` (previously `RngCore`) methods for `ThreadRng` use `unsafe` code to cast `*mut BlockRng<ReseedingCore>` to `&mut BlockRng<ReseedingCore>`. When all the above conditions are met this results in an aliased mutable reference, violating the Stacked Borrows rules. Miri is able to detect this violation in sample code. Since construction of [aliased mutable references is Undefined Behaviour](https://doc.rust-lang.org/stable/nomicon/references.html), the behaviour of optimized builds is hard to predict.
      
      Affected versions of `rand` are `>= 0.7, < 0.9.3` and `0.10.0`.
    ├ Announcement: https://github.com/rust-random/rand/pull/1763
    ├ Solution: Upgrade to >=0.10.1 OR <0.10.0, >=0.9.3 (try `cargo update -p rand`)
    ├ rand v0.8.5
      ├── (dev) libdd-common v3.0.2
      │   ├── libdd-capabilities-impl v0.1.0
      │   │   ├── libdd-trace-stats v2.0.0
      │   │   └── libdd-trace-utils v3.0.1
      │   │       ├── libdd-trace-stats v2.0.0 (*)
      │   │       └── (dev) libdd-trace-utils v3.0.1 (*)
      │   ├── libdd-shared-runtime v1.0.0
      │   │   └── libdd-trace-stats v2.0.0 (*)
      │   ├── libdd-trace-stats v2.0.0 (*)
      │   └── libdd-trace-utils v3.0.1 (*)
      ├── (dev) libdd-trace-normalization v2.0.0
      │   └── libdd-trace-utils v3.0.1 (*)
      ├── (dev) libdd-trace-stats v2.0.0 (*)
      ├── libdd-trace-utils v3.0.1 (*)
      └── proptest v1.5.0
          └── (dev) libdd-tinybytes v1.1.0
              ├── (dev) libdd-tinybytes v1.1.0 (*)
              └── libdd-trace-utils v3.0.1 (*)

advisories FAILED, bans ok, sources ok

Updated: 2026-04-14 01:38:49 UTC | Commit: bf354d0 | dependency-check job results

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datadog-prod-us1-6 bot commented Mar 30, 2026 •
edited by datadog-datadog-prod-us1 bot
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✅ Tests

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 68.58%
• Overall Coverage: 71.66% (-0.12%)

This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 6145322 | Docs | Datadog PR Page | Was this helpful? React with 👍/👎 or give us feedback!

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Codecov Report

❌ Patch coverage is 68.58238% with 328 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.65%. Comparing base (252c693) to head (6145322).
⚠️ Report is 6 commits behind head on main.

Additional details and impacted files 
@@            Coverage Diff             @@
##             main    #1821      +/-   ##
==========================================
- Coverage   71.72%   71.65%   -0.08%     
==========================================
  Files         429      432       +3     
  Lines       67887    68890    +1003     
==========================================
+ Hits        48695    49366     +671     
- Misses      19192    19524     +332
Components Coverage Δ
libdd-crashtracker 66.09% <ø> (+0.07%) ⬆️
libdd-crashtracker-ffi 34.86% <ø> (+0.38%) ⬆️
libdd-alloc 98.77% <ø> (ø)
libdd-data-pipeline 85.55% <100.00%> (-0.72%) ⬇️
libdd-data-pipeline-ffi 72.39% <ø> (-0.79%) ⬇️
libdd-common 79.16% <ø> (ø)
libdd-common-ffi 73.87% <ø> (ø)
libdd-telemetry 65.94% <ø> (-0.04%) ⬇️
libdd-telemetry-ffi 16.75% <ø> (ø)
libdd-dogstatsd-client 82.64% <ø> (ø)
datadog-ipc 76.31% <83.78%> (+3.20%) ⬆️
libdd-profiling 81.62% <ø> (ø)
libdd-profiling-ffi 64.94% <ø> (ø)
datadog-sidecar 29.63% <0.90%> (-1.20%) ⬇️
datdog-sidecar-ffi 8.88% <0.00%> (-1.32%) ⬇️
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 89.24% <ø> (+0.52%) ⬆️
datadog-tracer-flare 86.88% <ø> (ø)
libdd-log 74.69% <ø> (ø)
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  • ❄️ 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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pr-commenter bot commented Mar 30, 2026 •
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Benchmarks

Comparison

Benchmark execution time: 2026-03-30 15:37:06

Comparing candidate commit 583679e in PR branch bob/shm-stats-computation with baseline commit 8d2029d in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 62 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 ----------------------------------'

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 583679e 1774884038 bob/shm-stats-computation
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 254.364ns 264.105ns ± 9.336ns 259.870ns ± 2.956ns 268.757ns 285.195ns 291.567ns 296.112ns 13.95% 1.528 1.476 3.53% 0.660ns 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 [262.811ns; 265.399ns] or [-0.490%; +0.490%] None None None

Group 2

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 14.732ms 14.802ms ± 0.032ms 14.800ms ± 0.016ms 14.816ms 14.841ms 14.897ms 14.995ms 1.32% 2.170 11.146 0.22% 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 [14.797ms; 14.806ms] or [-0.030%; +0.030%] None None None

Group 3

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 288.223µs 289.033µs ± 0.466µs 289.001µs ± 0.212µs 289.191µs 289.530µs 291.015µs 292.232µs 1.12% 2.801 13.918 0.16% 0.033µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
sql/obfuscate_sql_string execution_time [288.969µs; 289.098µs] or [-0.022%; +0.022%] None None None

Group 4

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 4.183ms 4.189ms ± 0.008ms 4.188ms ± 0.001ms 4.190ms 4.193ms 4.200ms 4.302ms 2.71% 11.849 153.705 0.20% 0.001ms 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
profile_add_sample_timestamped_x1000 execution_time [4.188ms; 4.190ms] or [-0.028%; +0.028%] None None None

Group 5

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 2.397µs 2.436µs ± 0.019µs 2.432µs ± 0.009µs 2.443µs 2.479µs 2.485µs 2.563µs 5.41% 2.061 9.014 0.80% 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 [2.433µs; 2.439µs] or [-0.111%; +0.111%] None None None

Group 6

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 190.468ns 192.852ns ± 1.765ns 192.784ns ± 1.438ns 194.019ns 196.019ns 197.657ns 199.194ns 3.32% 0.730 0.178 0.91% 0.125ns 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 [192.607ns; 193.096ns] or [-0.127%; +0.127%] None None None

Group 7

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 14.896µs 15.338µs ± 0.301µs 15.273µs ± 0.129µs 15.450µs 15.703µs 16.118µs 18.163µs 18.93% 4.860 39.263 1.96% 0.021µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
two way interface execution_time [15.297µs; 15.380µs] or [-0.272%; +0.272%] None None None

Group 8

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 3.898µs 3.916µs ± 0.004µs 3.916µs ± 0.003µs 3.918µs 3.921µs 3.928µs 3.931µs 0.38% 0.285 3.278 0.10% 0.000µs 1 200
credit_card/is_card_number/ throughput 254410546.503op/s 255356703.693op/s ± 244988.450op/s 255373671.001op/s ± 169867.808op/s 255543142.493op/s 255637833.939op/s 255729420.480op/s 256544161.305op/s 0.46% -0.270 3.301 0.10% 17323.299op/s 1 200
credit_card/is_card_number/ 3782-8224-6310-005 execution_time 79.631µs 80.031µs ± 0.244µs 80.002µs ± 0.156µs 80.167µs 80.518µs 80.729µs 80.989µs 1.23% 1.046 1.491 0.30% 0.017µs 1 200
credit_card/is_card_number/ 3782-8224-6310-005 throughput 12347425.335op/s 12495220.139op/s ± 38049.047op/s 12499677.516op/s ± 24409.434op/s 12522790.576op/s 12542212.464op/s 12556293.821op/s 12557856.557op/s 0.47% -1.024 1.415 0.30% 2690.474op/s 1 200
credit_card/is_card_number/ 378282246310005 execution_time 73.100µs 73.565µs ± 0.205µs 73.538µs ± 0.135µs 73.711µs 73.916µs 74.045µs 74.215µs 0.92% 0.330 -0.245 0.28% 0.014µs 1 200
credit_card/is_card_number/ 378282246310005 throughput 13474455.800op/s 13593527.887op/s ± 37835.182op/s 13598405.589op/s ± 24887.522op/s 13620011.883op/s 13651924.281op/s 13664970.818op/s 13679816.256op/s 0.60% -0.317 -0.259 0.28% 2675.351op/s 1 200
credit_card/is_card_number/37828224631 execution_time 3.905µs 3.917µs ± 0.004µs 3.917µs ± 0.003µs 3.920µs 3.924µs 3.928µs 3.935µs 0.46% 0.675 1.249 0.10% 0.000µs 1 200
credit_card/is_card_number/37828224631 throughput 254132994.542op/s 255285819.844op/s ± 263529.310op/s 255306034.647op/s ± 200405.949op/s 255507517.567op/s 255637993.860op/s 255689671.868op/s 256051420.419op/s 0.29% -0.666 1.224 0.10% 18634.336op/s 1 200
credit_card/is_card_number/378282246310005 execution_time 69.514µs 69.978µs ± 0.241µs 69.964µs ± 0.161µs 70.123µs 70.358µs 70.580µs 71.178µs 1.74% 0.905 2.313 0.34% 0.017µs 1 200
credit_card/is_card_number/378282246310005 throughput 14049323.540op/s 14290397.093op/s ± 49066.669op/s 14293117.154op/s ± 32949.693op/s 14326216.263op/s 14361251.816op/s 14372818.268op/s 14385520.481op/s 0.65% -0.869 2.151 0.34% 3469.537op/s 1 200
credit_card/is_card_number/37828224631000521389798 execution_time 52.284µs 52.385µs ± 0.051µs 52.373µs ± 0.037µs 52.421µs 52.473µs 52.516µs 52.542µs 0.32% 0.544 -0.383 0.10% 0.004µs 1 200
credit_card/is_card_number/37828224631000521389798 throughput 19032466.712op/s 19089536.854op/s ± 18631.247op/s 19093796.485op/s ± 13557.471op/s 19104328.405op/s 19115111.605op/s 19117616.383op/s 19126256.804op/s 0.17% -0.540 -0.390 0.10% 1317.428op/s 1 200
credit_card/is_card_number/x371413321323331 execution_time 5.734µs 5.886µs ± 0.058µs 5.886µs ± 0.041µs 5.932µs 5.971µs 6.001µs 6.002µs 1.96% -0.180 -0.499 0.98% 0.004µs 1 200
credit_card/is_card_number/x371413321323331 throughput 166616233.428op/s 169896656.428op/s ± 1667317.781op/s 169881374.116op/s ± 1203661.970op/s 170968345.777op/s 172614809.080op/s 173510619.475op/s 174393898.988op/s 2.66% 0.224 -0.465 0.98% 117897.171op/s 1 200
credit_card/is_card_number_no_luhn/ execution_time 3.900µs 3.916µs ± 0.004µs 3.915µs ± 0.003µs 3.918µs 3.922µs 3.927µs 3.930µs 0.36% 0.489 1.730 0.10% 0.000µs 1 200
credit_card/is_card_number_no_luhn/ throughput 254482514.192op/s 255380054.752op/s ± 254568.825op/s 255405503.991op/s ± 165728.759op/s 255566738.134op/s 255694210.712op/s 255767470.337op/s 256431881.893op/s 0.40% -0.478 1.734 0.10% 18000.734op/s 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time 64.225µs 64.383µs ± 0.129µs 64.347µs ± 0.060µs 64.432µs 64.678µs 64.813µs 64.984µs 0.99% 1.761 3.531 0.20% 0.009µs 1 200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput 15388398.745op/s 15532103.621op/s ± 31104.747op/s 15540808.170op/s ± 14392.076op/s 15551642.174op/s 15564814.802op/s 15569345.966op/s 15570243.707op/s 0.19% -1.747 3.461 0.20% 2199.438op/s 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time 57.422µs 57.545µs ± 0.089µs 57.527µs ± 0.050µs 57.587µs 57.712µs 57.823µs 57.945µs 0.73% 1.303 2.411 0.15% 0.006µs 1 200
credit_card/is_card_number_no_luhn/ 378282246310005 throughput 17257888.194op/s 17377683.199op/s ± 26919.900op/s 17383075.837op/s ± 15203.473op/s 17396811.599op/s 17409412.412op/s 17414493.386op/s 17414922.847op/s 0.18% -1.290 2.354 0.15% 1903.524op/s 1 200
credit_card/is_card_number_no_luhn/37828224631 execution_time 3.894µs 3.916µs ± 0.004µs 3.915µs ± 0.003µs 3.919µs 3.923µs 3.925µs 3.926µs 0.27% -0.267 3.472 0.10% 0.000µs 1 200
credit_card/is_card_number_no_luhn/37828224631 throughput 254719059.538op/s 255355197.208op/s ± 260220.944op/s 255397394.969op/s ± 182413.760op/s 255545264.715op/s 255660937.780op/s 255727142.094op/s 256787190.080op/s 0.54% 0.284 3.555 0.10% 18400.399op/s 1 200
credit_card/is_card_number_no_luhn/378282246310005 execution_time 54.604µs 54.774µs ± 0.132µs 54.744µs ± 0.057µs 54.808µs 55.010µs 55.137µs 55.606µs 1.57% 2.480 10.145 0.24% 0.009µs 1 200
credit_card/is_card_number_no_luhn/378282246310005 throughput 17983723.845op/s 18256933.219op/s ± 43770.772op/s 18266797.357op/s ± 18906.944op/s 18283331.087op/s 18302495.181op/s 18306859.179op/s 18313682.152op/s 0.26% -2.437 9.803 0.24% 3095.061op/s 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time 52.302µs 52.390µs ± 0.049µs 52.382µs ± 0.031µs 52.422µs 52.476µs 52.528µs 52.547µs 0.32% 0.758 0.183 0.09% 0.003µs 1 200
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput 19030657.991op/s 19087682.736op/s ± 17665.513op/s 19090640.813op/s ± 11381.199op/s 19100978.448op/s 19110084.490op/s 19115445.949op/s 19119733.477op/s 0.15% -0.754 0.173 0.09% 1249.140op/s 1 200
credit_card/is_card_number_no_luhn/x371413321323331 execution_time 5.725µs 5.894µs ± 0.056µs 5.895µs ± 0.038µs 5.936µs 5.981µs 6.006µs 6.021µs 2.14% -0.105 -0.364 0.94% 0.004µs 1 200
credit_card/is_card_number_no_luhn/x371413321323331 throughput 166088698.212op/s 169665494.993op/s ± 1603997.112op/s 169643267.308op/s ± 1098086.049op/s 170598169.208op/s 172402379.580op/s 173224290.350op/s 174684654.591op/s 2.97% 0.152 -0.324 0.94% 113419.724op/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 [3.916µs; 3.917µs] or [-0.013%; +0.013%] None None None
credit_card/is_card_number/ throughput [255322750.650op/s; 255390656.736op/s] or [-0.013%; +0.013%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 execution_time [79.997µs; 80.065µs] or [-0.042%; +0.042%] None None None
credit_card/is_card_number/ 3782-8224-6310-005 throughput [12489946.907op/s; 12500493.371op/s] or [-0.042%; +0.042%] None None None
credit_card/is_card_number/ 378282246310005 execution_time [73.537µs; 73.593µs] or [-0.039%; +0.039%] None None None
credit_card/is_card_number/ 378282246310005 throughput [13588284.295op/s; 13598771.479op/s] or [-0.039%; +0.039%] None None None
credit_card/is_card_number/37828224631 execution_time [3.917µs; 3.918µs] or [-0.014%; +0.014%] None None None
credit_card/is_card_number/37828224631 throughput [255249297.216op/s; 255322342.472op/s] or [-0.014%; +0.014%] None None None
credit_card/is_card_number/378282246310005 execution_time [69.944µs; 70.011µs] or [-0.048%; +0.048%] None None None
credit_card/is_card_number/378282246310005 throughput [14283596.925op/s; 14297197.262op/s] or [-0.048%; +0.048%] None None None
credit_card/is_card_number/37828224631000521389798 execution_time [52.378µs; 52.392µs] or [-0.014%; +0.014%] None None None
credit_card/is_card_number/37828224631000521389798 throughput [19086954.743op/s; 19092118.966op/s] or [-0.014%; +0.014%] None None None
credit_card/is_card_number/x371413321323331 execution_time [5.879µs; 5.894µs] or [-0.136%; +0.136%] None None None
credit_card/is_card_number/x371413321323331 throughput [169665582.219op/s; 170127730.637op/s] or [-0.136%; +0.136%] None None None
credit_card/is_card_number_no_luhn/ execution_time [3.915µs; 3.916µs] or [-0.014%; +0.014%] None None None
credit_card/is_card_number_no_luhn/ throughput [255344773.961op/s; 255415335.542op/s] or [-0.014%; +0.014%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 execution_time [64.365µs; 64.401µs] or [-0.028%; +0.028%] None None None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005 throughput [15527792.802op/s; 15536414.440op/s] or [-0.028%; +0.028%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 execution_time [57.533µs; 57.558µs] or [-0.022%; +0.022%] None None None
credit_card/is_card_number_no_luhn/ 378282246310005 throughput [17373952.359op/s; 17381414.038op/s] or [-0.021%; +0.021%] 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 [255319133.088op/s; 255391261.328op/s] or [-0.014%; +0.014%] None None None
credit_card/is_card_number_no_luhn/378282246310005 execution_time [54.756µs; 54.792µs] or [-0.033%; +0.033%] None None None
credit_card/is_card_number_no_luhn/378282246310005 throughput [18250867.011op/s; 18262999.427op/s] or [-0.033%; +0.033%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 execution_time [52.383µs; 52.397µs] or [-0.013%; +0.013%] None None None
credit_card/is_card_number_no_luhn/37828224631000521389798 throughput [19085234.465op/s; 19090131.006op/s] or [-0.013%; +0.013%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 execution_time [5.887µs; 5.902µs] or [-0.131%; +0.131%] None None None
credit_card/is_card_number_no_luhn/x371413321323331 throughput [169443196.420op/s; 169887793.566op/s] or [-0.131%; +0.131%] None None None

Group 9

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 34.784µs 35.285µs ± 0.840µs 34.891µs ± 0.042µs 35.016µs 37.037µs 37.092µs 38.673µs 10.84% 1.769 1.551 2.37% 0.059µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
redis/obfuscate_redis_string execution_time [35.169µs; 35.402µs] or [-0.330%; +0.330%] None None None

Group 10

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 4.909µs 4.975µs ± 0.036µs 4.978µs ± 0.033µs 5.009µs 5.027µs 5.035µs 5.038µs 1.21% 0.109 -1.383 0.73% 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 [4.970µs; 4.980µs] or [-0.101%; +0.101%] None None None

Group 11

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 159.359µs 161.024µs ± 0.417µs 161.036µs ± 0.218µs 161.224µs 161.508µs 162.694µs 163.020µs 1.23% 1.049 6.231 0.26% 0.029µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
benching string interning on wordpress profile execution_time [160.966µs; 161.082µs] or [-0.036%; +0.036%] None None None

Group 12

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 3.436ms 3.470ms ± 0.019ms 3.466ms ± 0.009ms 3.475ms 3.514ms 3.531ms 3.566ms 2.89% 1.937 5.294 0.55% 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 [3.467ms; 3.472ms] or [-0.077%; +0.077%] None None None

Group 13

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 185.665µs 186.148µs ± 0.277µs 186.107µs ± 0.193µs 186.326µs 186.651µs 186.880µs 187.052µs 0.51% 0.655 0.017 0.15% 0.020µs 1 200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput 5346094.818op/s 5372081.382op/s ± 7976.483op/s 5373266.375op/s ± 5568.314op/s 5378598.543op/s 5382847.746op/s 5385272.999op/s 5386057.087op/s 0.24% -0.648 0.002 0.15% 564.023op/s 1 200
normalization/normalize_name/normalize_name/bad-name execution_time 17.830µs 18.059µs ± 0.095µs 18.067µs ± 0.042µs 18.106µs 18.159µs 18.267µs 18.687µs 3.43% 1.398 10.270 0.52% 0.007µs 1 200
normalization/normalize_name/normalize_name/bad-name throughput 53514076.001op/s 55376283.231op/s ± 287934.298op/s 55351051.376op/s ± 127494.560op/s 55501518.070op/s 55879044.155op/s 56035122.028op/s 56084297.999op/s 1.32% -1.242 9.263 0.52% 20360.029op/s 1 200
normalization/normalize_name/normalize_name/good execution_time 9.864µs 9.921µs ± 0.074µs 9.890µs ± 0.009µs 9.923µs 10.102µs 10.194µs 10.266µs 3.80% 2.468 6.143 0.74% 0.005µs 1 200
normalization/normalize_name/normalize_name/good throughput 97413417.416op/s 100801691.680op/s ± 737641.600op/s 101114771.349op/s ± 93392.033op/s 101192357.955op/s 101256999.121op/s 101319061.533op/s 101377514.620op/s 0.26% -2.423 5.864 0.73% 52159.138op/s 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... execution_time [186.110µs; 186.186µs] or [-0.021%; +0.021%] None None None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo... throughput [5370975.918op/s; 5373186.846op/s] or [-0.021%; +0.021%] None None None
normalization/normalize_name/normalize_name/bad-name execution_time [18.046µs; 18.072µs] or [-0.073%; +0.073%] None None None
normalization/normalize_name/normalize_name/bad-name throughput [55336378.306op/s; 55416188.155op/s] or [-0.072%; +0.072%] None None None
normalization/normalize_name/normalize_name/good execution_time [9.911µs; 9.931µs] or [-0.103%; +0.103%] None None None
normalization/normalize_name/normalize_name/good throughput [100699461.648op/s; 100903921.711op/s] or [-0.101%; +0.101%] None None None

Group 14

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 934.204µs 937.022µs ± 2.044µs 936.554µs ± 1.002µs 937.717µs 940.903µs 943.521µs 948.858µs 1.31% 1.796 5.593 0.22% 0.145µs 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 [936.739µs; 937.305µs] or [-0.030%; +0.030%] None None None

Group 15

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 534.769µs 535.665µs ± 0.720µs 535.465µs ± 0.360µs 535.983µs 536.736µs 537.174µs 541.953µs 1.21% 3.723 27.648 0.13% 0.051µs 1 200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput 1845180.059op/s 1866840.561op/s ± 2495.353op/s 1867535.898op/s ± 1256.310op/s 1868503.259op/s 1869334.302op/s 1869637.773op/s 1869965.873op/s 0.13% -3.661 26.898 0.13% 176.448op/s 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time 380.090µs 381.013µs ± 0.481µs 380.933µs ± 0.313µs 381.375µs 381.866µs 382.286µs 382.358µs 0.37% 0.514 -0.273 0.13% 0.034µs 1 200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput 2615347.038op/s 2624589.723op/s ± 3312.105op/s 2625136.550op/s ± 2155.220op/s 2627049.691op/s 2629050.397op/s 2630785.109op/s 2630955.525op/s 0.22% -0.509 -0.280 0.13% 234.201op/s 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time 194.988µs 195.701µs ± 0.262µs 195.666µs ± 0.166µs 195.850µs 196.159µs 196.479µs 196.688µs 0.52% 0.692 0.922 0.13% 0.018µs 1 200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput 5084192.896op/s 5109855.344op/s ± 6822.856op/s 5110747.204op/s ± 4333.089op/s 5114559.404op/s 5118990.880op/s 5122010.858op/s 5128517.961op/s 0.35% -0.682 0.902 0.13% 482.449op/s 1 200
normalization/normalize_service/normalize_service/[empty string] execution_time 37.925µs 38.296µs ± 0.125µs 38.299µs ± 0.072µs 38.369µs 38.504µs 38.621µs 38.648µs 0.91% -0.132 0.696 0.33% 0.009µs 1 200
normalization/normalize_service/normalize_service/[empty string] throughput 25874584.043op/s 26112402.878op/s ± 85504.167op/s 26110086.695op/s ± 48764.035op/s 26161061.588op/s 26256496.234op/s 26349093.254op/s 26368166.466op/s 0.99% 0.159 0.707 0.33% 6046.058op/s 1 200
normalization/normalize_service/normalize_service/test_ASCII execution_time 45.774µs 45.931µs ± 0.137µs 45.906µs ± 0.041µs 45.954µs 46.066µs 46.229µs 47.431µs 3.32% 7.121 71.894 0.30% 0.010µs 1 200
normalization/normalize_service/normalize_service/test_ASCII throughput 21083094.802op/s 21772007.288op/s ± 63512.730op/s 21783681.747op/s ± 19533.816op/s 21800816.477op/s 21820948.979op/s 21828619.285op/s 21846378.210op/s 0.29% -6.917 68.760 0.29% 4491.028op/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 [535.566µs; 535.765µs] or [-0.019%; +0.019%] None None None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000... throughput [1866494.729op/s; 1867186.393op/s] or [-0.019%; +0.019%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて execution_time [380.946µs; 381.079µs] or [-0.018%; +0.018%] None None None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて throughput [2624130.698op/s; 2625048.749op/s] or [-0.017%; +0.017%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters execution_time [195.664µs; 195.737µs] or [-0.019%; +0.019%] None None None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters throughput [5108909.762op/s; 5110800.926op/s] or [-0.019%; +0.019%] None None None
normalization/normalize_service/normalize_service/[empty string] execution_time [38.279µs; 38.314µs] or [-0.045%; +0.045%] None None None
normalization/normalize_service/normalize_service/[empty string] throughput [26100552.822op/s; 26124252.933op/s] or [-0.045%; +0.045%] None None None
normalization/normalize_service/normalize_service/test_ASCII execution_time [45.912µs; 45.950µs] or [-0.041%; +0.041%] None None None
normalization/normalize_service/normalize_service/test_ASCII throughput [21763205.034op/s; 21780809.542op/s] or [-0.040%; +0.040%] None None None

Group 16

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 48.080ms 48.801ms ± 1.251ms 48.650ms ± 0.070ms 48.722ms 48.890ms 57.137ms 60.582ms 24.52% 7.989 64.420 2.56% 0.088ms 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 [48.628ms; 48.974ms] or [-0.355%; +0.355%] None None None

Group 17

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 4.152ms 4.165ms ± 0.005ms 4.166ms ± 0.002ms 4.168ms 4.170ms 4.172ms 4.198ms 0.77% 0.491 7.219 0.12% 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 [4.164ms; 4.165ms] or [-0.017%; +0.017%] None None None

Group 18

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 14.833ms 14.886ms ± 0.024ms 14.880ms ± 0.014ms 14.902ms 14.930ms 14.957ms 14.983ms 0.69% 0.723 0.862 0.16% 0.002ms 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 [14.882ms; 14.889ms] or [-0.023%; +0.023%] None None None

Group 19

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 146.100µs 148.035µs ± 1.810µs 147.742µs ± 0.609µs 148.366µs 149.700µs 154.421µs 166.327µs 12.58% 6.326 55.395 1.22% 0.128µs 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 [147.784µs; 148.286µs] or [-0.169%; +0.169%] None None None

Group 20

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 725.275µs 727.034µs ± 0.596µs 726.997µs ± 0.324µs 727.332µs 728.119µs 728.624µs 728.920µs 0.26% 0.400 0.544 0.08% 0.042µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
profile_add_sample2_frames_x1000 execution_time [726.952µs; 727.117µs] or [-0.011%; +0.011%] None None None

Group 21

cpu_model git_commit_sha git_commit_date git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz 583679e 1774884038 bob/shm-stats-computation
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 5.423µs 5.558µs ± 0.056µs 5.552µs ± 0.044µs 5.612µs 5.637µs 5.662µs 5.706µs 2.78% 0.015 -0.775 1.00% 0.004µs 1 200
scenario metric 95% CI mean Shapiro-Wilk pvalue Ljung-Box pvalue (lag=1) Dip test pvalue
write only interface execution_time [5.551µs; 5.566µs] or [-0.139%; +0.139%] None None None

Baseline

Omitted due to size.

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dd-octo-sts bot commented Mar 30, 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 7.63 MB 7.63 MB 0% (0 B) 👌
/aarch64-alpine-linux-musl/lib/libdatadog_profiling.a 83.35 MB 83.37 MB +.03% (+29.29 KB) 🔍
aarch64-unknown-linux-gnu
Artifact Baseline Commit Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a 99.40 MB 99.44 MB +.03% (+32.50 KB) 🔍
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so 10.17 MB 10.17 MB +.01% (+1.87 KB) 🔍
libdatadog-x64-windows
Artifact Baseline Commit Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll 25.16 MB 25.18 MB +.08% (+22.00 KB) 🔍
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib 78.21 KB 78.21 KB 0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb 184.22 MB 184.33 MB +.06% (+120.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib 916.99 MB 917.23 MB +.02% (+247.83 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll 7.88 MB 7.89 MB +.09% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib 78.21 KB 78.21 KB 0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb 23.64 MB 23.65 MB +.03% (+8.00 KB) 🔍
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib 46.13 MB 46.15 MB +.05% (+24.63 KB) 🔍
libdatadog-x86-windows
Artifact Baseline Commit Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll 21.63 MB 21.66 MB +.10% (+22.50 KB) 🔍
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib 79.42 KB 79.42 KB 0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb 188.31 MB 188.46 MB +.07% (+152.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib 902.01 MB 903.03 MB +.11% (+1.01 MB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll 6.12 MB 6.12 MB +.11% (+7.00 KB) 🔍
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib 79.42 KB 79.42 KB 0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb 25.31 MB 25.34 MB +.12% (+32.00 KB) 🔍
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib 43.60 MB 43.64 MB +.08% (+35.95 KB) 🔍
x86_64-alpine-linux-musl
Artifact Baseline Commit Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a 74.47 MB 74.49 MB +.02% (+19.34 KB) 🔍
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so 8.52 MB 8.53 MB +.09% (+8.00 KB) 🔍
x86_64-unknown-linux-gnu
Artifact Baseline Commit Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a 91.77 MB 91.81 MB +.04% (+44.69 KB) 🔍
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so 10.20 MB 10.20 MB +.05% (+5.53 KB) 🔍

yannham
yannham reviewed Mar 31, 2026
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Can't say I have all the context, but I tried to at least provide a technical review for what I could understand.

In general there's quite a bit of undocumented unsafe and memory ordering questions in the concentrator implementation. Since it's basically C-like code, I understand that one wouldn't want to add a hundred // SAFETY comment before each and any pointer operations, which would be verbose and unhelpful. I still wonder if some middle-ground can be found to explicitly document unsafe non-trivial invariants and justification for the ordering of atomic operations.

datadog-ipc/src/shm_stats.rs
}
}
} else {
let old_active = hdr.active_idx.fetch_xor(1, AcqRel);
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Same naive question: what is the Rel part of the ordering here doing?

@bwoebi bwoebi force-pushed the bob/shm-stats-computation branch 3 times, most recently from f1d9b36 to b42be6c Compare March 31, 2026 17:16
yannham
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There are still a few remaining comments, but they are mostly to bring attention and non-blocking. I can't say I have enough context to validate all the parts of the change, but the in-mem concurrent data structure looks reasonable, albeit complex.

bwoebi added 5 commits April 10, 2026 18:09
@bwoebi
Add stats computation via SHM
e00c9d6 
This extraces the StatsExporter to libdd-trace-stats to reuse it outside of the data-pipeline.

Using a two-bucket mechanism, it allows all processes to write stats, while the sidecar drains the other bucket.

Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>

# Conflicts:
#	libdd-data-pipeline/Cargo.toml
#	libdd-data-pipeline/src/lib.rs
#	libdd-data-pipeline/src/trace_exporter/mod.rs
#	libdd-data-pipeline/src/trace_exporter/stats.rs
#	libdd-trace-stats/src/stats_exporter.rs
@bwoebi
clippy
e0ea995 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi
PR comments
21b7ed7 
is_multiple_of is still unstable in MSRV 1.84.1

Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi
Fix ftruncate() causing SIGBUS on /dev/shm full
a102d48 
Add dogstatsd stats
Add hostname
Reduce flush mutex contention

Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi
Add tag filters to /info
db03e5c 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi bwoebi force-pushed the bob/shm-stats-computation branch from 3bbae7f to db03e5c Compare April 10, 2026 16:17
@bwoebi
Fixup after rebase
d4cde3e 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi bwoebi force-pushed the bob/shm-stats-computation branch from a80a877 to d4cde3e Compare April 10, 2026 17:15
@bwoebi
Actually reuse the exporter
67e6d3a 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi bwoebi force-pushed the bob/shm-stats-computation branch 2 times, most recently from 73fcb57 to 34a2a05 Compare April 13, 2026 12:47
@bwoebi
fix compile
1837fb9 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi bwoebi force-pushed the bob/shm-stats-computation branch from 34a2a05 to 1837fb9 Compare April 13, 2026 12:56
@bwoebi bwoebi changed the title Add stats computation via SHM feat(sidecar): Add stats computation via SHM Apr 13, 2026
@bwoebi
Stats exporter in README
9e69e0f 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi bwoebi changed the title feat(sidecar): Add stats computation via SHM feat(sidecar)!: Add stats computation via SHM Apr 13, 2026
VianneyRuhlmann
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Few comments and questions but looking good regarding stats

libdd-trace-stats/src/stats_exporter.rs Outdated
Comment on lines +46 to +48
lang_version: &m.lang_version,
lang_interpreter: &m.lang_interpreter,
lang_vendor: &m.lang_vendor,
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According to the spec we don't need these

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And it's also wrong, the headers are named differently to those the stats endpoint uses (arghs).
Will fix.

.github/workflows/lint.yml
fi
# shellcheck disable=SC2046
cargo clippy --workspace --all-targets --all-features -- -D warnings
cargo clippy --workspace --all-targets --all-features -- -D warnings $([ ${{ matrix.rust_version }} = 1.84.1 ] || echo -Aclippy::manual_is_multiple_of)
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I don't have a strong opinion but this should probably be discussed in #libdatadog

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We've done that in the past already for cases where our MSRV did not support APIs which became the only solution to satisfy a clippy lint in future. It misses a comment though, when we'll be able to drop this.

datadog-sidecar/src/service/stats_flusher.rs

//! Periodic stats flusher for the SHM span concentrator.
//!
//! The sidecar maintains one `SpanConcentratorState` per (env, version, service) triple
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Is it because different env and version of the app can the same sidecar instance ? Can the env and version be modified in the app ? Is this something that can happen in other tracers/ should address at the client-side stats spec level ?

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Yes, you can have multiple versions (and envs) of the same app running, so yes.
They can also be modified in app, but that's just a detail, the other case is more important.

I don't know about other tracers here though.

@bwoebi
Fix data races with concentrator shutdown
1159d3a 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi bwoebi requested a review from a team as a code owner April 13, 2026 20:45
@bwoebi bwoebi force-pushed the bob/shm-stats-computation branch 2 times, most recently from 57fae29 to ec23fce Compare April 14, 2026 01:00
@bwoebi
Avoid TracerHeaderMap
6145322 
Signed-off-by: Bob Weinand <bob.weinand@datadoghq.com>
@bwoebi bwoebi force-pushed the bob/shm-stats-computation branch from ec23fce to 6145322 Compare April 14, 2026 01:36
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