feat(data-pipeline): port dd-trace-rs trace buffer implementation by paullegranddc · Pull Request #1826 · DataDog/libdatadog

paullegranddc · 2026-03-31T09:20:42Z

Motivation

As we implement native spans in ruby in python, we have opportuinities to replace more components of the span pipeline with native code.

One of them i the span buffer which we is before the trace exporter.

Changes

Add a TraceBuffer implementation.
The TraceBuffer is split in two part, a Sender which allows putting trace chunks in the queue and a background task, spawned on the shared runtime which periodically pulls data from the chunk and submit them using the TraceExporter.

github-actions · 2026-03-31T09:22:02Z

📚 Documentation Check Results

⚠️ 935 documentation warning(s) found

📦 `libdd-data-pipeline` - 935 warning(s)

Updated: 2026-04-16 15:15:17 UTC | Commit: a38aa3a | missing-docs job results

github-actions · 2026-03-31T09:23:45Z

Clippy Allow Annotation Report

Comparing clippy allow annotations between branches:

Base Branch: origin/main
PR Branch: origin/paullgdc/data-pipeline/trace_buffer

Summary by Rule

Rule	Base Branch	PR Branch	Change
unwrap_used	2	3	⚠️ +1 (+50.0%)
Total	2	3	⚠️ +1 (+50.0%)

Annotation Counts by File

File	Base Branch	PR Branch	Change
`libdd-data-pipeline/src/trace_buffer/mod.rs`	0	1	⚠️ +1 (N/A)
`libdd-data-pipeline/src/trace_exporter/mod.rs`	2	2	No change (0%)

Annotation Stats by Crate

Crate	Base Branch	PR Branch	Change
`clippy-annotation-reporter`	5	5	No change (0%)
`datadog-ffe-ffi`	1	1	No change (0%)
`datadog-ipc`	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`	56	56	No change (0%)
`libdd-common`	10	10	No change (0%)
`libdd-common-ffi`	12	12	No change (0%)
`libdd-data-pipeline`	4	5	⚠️ +1 (+25.0%)
`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`	1	1	No change (0%)
`libdd-trace-utils`	15	15	No change (0%)
Total	197	198	⚠️ +1 (+0.5%)

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.

github-actions · 2026-03-31T09:24:49Z

🔒 Cargo Deny Results

⚠️ 4 issue(s) found, showing only errors (advisories, bans, sources)

📦 `libdd-data-pipeline` - 4 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 v0.1.0
      │   │   ├── (dev) 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]: Name constraints for URI names were incorrectly accepted
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:230:1
    │
230 │ rustls-webpki 0.103.10 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0098
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0098
    ├ Name constraints for URI names were ignored and therefore accepted.
      
      Note this library does not provide an API for asserting URI names, and URI name constraints are otherwise not implemented.  URI name constraints are now rejected unconditionally.
      
      Since name constraints are restrictions on otherwise properly-issued certificates, this bug is reachable only after signature verification and requires misissuance to exploit.
      
      This vulnerability is identified as [GHSA-965h-392x-2mh5](https://github.com/rustls/webpki/security/advisories/GHSA-965h-392x-2mh5). Thank you to @1seal for the report.
    ├ Solution: Upgrade to >=0.103.12, <0.104.0-alpha.1 OR >=0.104.0-alpha.6 (try `cargo update -p rustls-webpki`)
    ├ rustls-webpki v0.103.10
      └── rustls v0.23.37
          ├── hyper-rustls v0.27.7
          │   └── 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 v0.1.0
          │       │   ├── (dev) 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 (*)
          ├── libdd-common v3.0.2 (*)
          └── tokio-rustls v0.26.0
              ├── hyper-rustls v0.27.7 (*)
              └── libdd-common v3.0.2 (*)

error[vulnerability]: Name constraints were accepted for certificates asserting a wildcard name
    ┌─ /home/runner/work/libdatadog/libdatadog/Cargo.lock:230:1
    │
230 │ rustls-webpki 0.103.10 registry+https://github.com/rust-lang/crates.io-index
    │ ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ security vulnerability detected
    │
    ├ ID: RUSTSEC-2026-0099
    ├ Advisory: https://rustsec.org/advisories/RUSTSEC-2026-0099
    ├ Permitted subtree name constraints for DNS names were accepted for certificates asserting a wildcard name.
      
      This was incorrect because, given a name constraint of `accept.example.com`, `*.example.com` could feasibly allow a name of `reject.example.com` which is outside the constraint.
      This is very similar to [CVE-2025-61727](https://go.dev/issue/76442).
      
      Since name constraints are restrictions on otherwise properly-issued certificates, this bug is reachable only after signature verification and requires misissuance to exploit.
      
      This vulnerability is identified as [GHSA-xgp8-3hg3-c2mh](https://github.com/rustls/webpki/security/advisories/GHSA-xgp8-3hg3-c2mh). Thank you to @1seal for the report.
    ├ Solution: Upgrade to >=0.103.12, <0.104.0-alpha.1 OR >=0.104.0-alpha.6 (try `cargo update -p rustls-webpki`)
    ├ rustls-webpki v0.103.10
      └── rustls v0.23.37
          ├── hyper-rustls v0.27.7
          │   └── 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 v0.1.0
          │       │   ├── (dev) 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 (*)
          ├── libdd-common v3.0.2 (*)
          └── tokio-rustls v0.26.0
              ├── hyper-rustls v0.27.7 (*)
              └── libdd-common v3.0.2 (*)

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

Updated: 2026-04-16 15:16:58 UTC | Commit: a38aa3a | dependency-check job results

datadog-datadog-prod-us1 · 2026-03-31T09:36:40Z

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 89.02%
• Overall Coverage: 71.82% (+0.13%)

_{This comment will be updated automatically if new data arrives.

🔗 Commit SHA: 4342a5d | Docs | Datadog PR Page | Was this helpful? React with 👍/👎 or give us feedback!}

pr-commenter · 2026-03-31T09:40:59Z

Benchmarks

Comparison

Benchmark execution time: 2026-03-31 09:40:19

Comparing candidate commit 5938b42 in PR branch paullgdc/data-pipeline/trace_buffer with baseline commit 41bff26 in branch vianney/implement-shared-runtime.

Found 0 performance improvements and 1 performance regressions! Performance is the same for 61 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:tags/replace_trace_tags

🟥 execution_time [+152.789ns; +159.307ns] or [+6.346%; +6.616%]

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	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	145.162µs	147.194µs ± 1.851µs	146.987µs ± 0.608µs	147.504µs	148.894µs	154.489µs	165.669µs	12.71%	6.214	53.332	1.25%	0.131µs	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	[146.937µs; 147.450µs] or [-0.174%; +0.174%]	None	None	None

Group 2

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	244.115ns	254.188ns ± 13.661ns	247.446ns ± 1.207ns	256.824ns	284.718ns	299.800ns	301.808ns	21.97%	1.954	2.976	5.36%	0.966ns	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	[252.294ns; 256.081ns] or [-0.745%; +0.745%]	None	None	None

Group 3

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	755.490µs	757.180µs ± 0.605µs	757.134µs ± 0.376µs	757.532µs	758.231µs	758.721µs	758.947µs	0.24%	0.326	0.400	0.08%	0.043µs	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	[757.096µs; 757.264µs] or [-0.011%; +0.011%]	None	None	None

Group 4

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	3.425ms	3.453ms ± 0.018ms	3.448ms ± 0.011ms	3.464ms	3.487ms	3.498ms	3.525ms	2.24%	0.984	0.832	0.51%	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	[3.450ms; 3.455ms] or [-0.071%; +0.071%]	None	None	None

Group 5

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	4.372ms	4.377ms ± 0.002ms	4.377ms ± 0.002ms	4.378ms	4.381ms	4.383ms	4.390ms	0.30%	1.067	3.179	0.06%	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	[4.377ms; 4.377ms] or [-0.008%; +0.008%]	None	None	None

Group 6

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	2.536µs	2.564µs ± 0.017µs	2.560µs ± 0.011µs	2.573µs	2.596µs	2.608µs	2.616µs	2.18%	0.735	-0.111	0.65%	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	[2.561µs; 2.566µs] or [-0.091%; +0.091%]	None	None	None

Group 7

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	3.893µs	3.914µs ± 0.004µs	3.913µs ± 0.002µs	3.916µs	3.919µs	3.922µs	3.928µs	0.38%	-0.124	7.013	0.09%	0.000µs	1	200
credit_card/is_card_number/	throughput	254600717.643op/s	255512513.409op/s ± 228583.913op/s	255579476.992op/s ± 124824.793op/s	255664568.087op/s	255737052.045op/s	255777886.821op/s	256886255.168op/s	0.51%	0.148	7.108	0.09%	16163.323op/s	1	200
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	79.549µs	80.012µs ± 0.253µs	79.965µs ± 0.155µs	80.140µs	80.549µs	80.616µs	80.707µs	0.93%	0.751	-0.044	0.32%	0.018µs	1	200
credit_card/is_card_number/ 3782-8224-6310-005	throughput	12390503.878op/s	12498179.354op/s ± 39470.673op/s	12505514.939op/s ± 24184.513op/s	12527126.558op/s	12550250.639op/s	12562153.382op/s	12570921.367op/s	0.52%	-0.738	-0.064	0.32%	2790.998op/s	1	200
credit_card/is_card_number/ 378282246310005	execution_time	73.143µs	73.591µs ± 0.229µs	73.555µs ± 0.142µs	73.725µs	74.000µs	74.257µs	74.528µs	1.32%	0.743	0.958	0.31%	0.016µs	1	200
credit_card/is_card_number/ 378282246310005	throughput	13417733.508op/s	13588742.076op/s ± 42218.429op/s	13595181.912op/s ± 26183.206op/s	13618234.480op/s	13650845.305op/s	13665220.412op/s	13671818.060op/s	0.56%	-0.721	0.894	0.31%	2985.294op/s	1	200
credit_card/is_card_number/37828224631	execution_time	3.894µs	3.917µs ± 0.005µs	3.916µs ± 0.003µs	3.919µs	3.925µs	3.942µs	3.947µs	0.79%	1.898	12.307	0.13%	0.000µs	1	200
credit_card/is_card_number/37828224631	throughput	253347052.827op/s	255293143.117op/s ± 331215.054op/s	255342918.091op/s ± 170479.472op/s	255491747.091op/s	255620352.412op/s	255701130.017op/s	256832818.930op/s	0.58%	-1.856	12.169	0.13%	23420.441op/s	1	200
credit_card/is_card_number/378282246310005	execution_time	69.605µs	69.979µs ± 0.230µs	69.940µs ± 0.142µs	70.124µs	70.418µs	70.558µs	70.840µs	1.29%	0.778	0.470	0.33%	0.016µs	1	200
credit_card/is_card_number/378282246310005	throughput	14116301.742op/s	14290119.381op/s ± 46778.607op/s	14297951.818op/s ± 29155.792op/s	14323720.442op/s	14354096.083op/s	14365366.281op/s	14366723.373op/s	0.48%	-0.759	0.423	0.33%	3307.747op/s	1	200
credit_card/is_card_number/37828224631000521389798	execution_time	51.561µs	52.017µs ± 0.192µs	52.039µs ± 0.140µs	52.163µs	52.296µs	52.358µs	52.389µs	0.67%	-0.308	-0.741	0.37%	0.014µs	1	200
credit_card/is_card_number/37828224631000521389798	throughput	19087893.420op/s	19224691.039op/s ± 71022.559op/s	19216312.118op/s ± 51643.365op/s	19277026.130op/s	19345199.103op/s	19377081.744op/s	19394638.076op/s	0.93%	0.321	-0.731	0.37%	5022.053op/s	1	200
credit_card/is_card_number/x371413321323331	execution_time	5.746µs	5.884µs ± 0.054µs	5.882µs ± 0.035µs	5.915µs	5.977µs	6.011µs	6.015µs	2.27%	0.035	-0.230	0.91%	0.004µs	1	200
credit_card/is_card_number/x371413321323331	throughput	166238848.022op/s	169964129.026op/s ± 1548728.618op/s	170004704.233op/s ± 1015979.168op/s	171110156.247op/s	172437366.587op/s	173319885.316op/s	174046044.711op/s	2.38%	0.013	-0.229	0.91%	109511.651op/s	1	200
credit_card/is_card_number_no_luhn/	execution_time	3.894µs	3.918µs ± 0.004µs	3.917µs ± 0.003µs	3.920µs	3.925µs	3.927µs	3.931µs	0.35%	-0.469	4.445	0.11%	0.000µs	1	200
credit_card/is_card_number_no_luhn/	throughput	254401296.829op/s	255250206.334op/s ± 269425.943op/s	255286703.763op/s ± 171973.284op/s	255432114.207op/s	255586331.382op/s	255684132.656op/s	256796304.225op/s	0.59%	0.489	4.547	0.11%	19051.291op/s	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	64.193µs	64.393µs ± 0.117µs	64.363µs ± 0.057µs	64.435µs	64.631µs	64.801µs	64.873µs	0.79%	1.493	2.753	0.18%	0.008µs	1	200
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	15414672.476op/s	15529724.925op/s ± 28134.668op/s	15536833.450op/s ± 13794.405op/s	15547075.037op/s	15562010.099op/s	15568728.678op/s	15578011.637op/s	0.27%	-1.479	2.700	0.18%	1989.421op/s	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	57.487µs	57.615µs ± 0.092µs	57.599µs ± 0.057µs	57.659µs	57.786µs	57.851µs	57.995µs	0.69%	1.149	1.872	0.16%	0.006µs	1	200
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	17242920.443op/s	17356621.217op/s ± 27547.458op/s	17361332.840op/s ± 17136.325op/s	17376897.436op/s	17391244.900op/s	17393536.631op/s	17395174.579op/s	0.19%	-1.136	1.824	0.16%	1947.899op/s	1	200
credit_card/is_card_number_no_luhn/37828224631	execution_time	3.901µs	3.917µs ± 0.004µs	3.916µs ± 0.003µs	3.919µs	3.924µs	3.928µs	3.931µs	0.39%	0.536	0.951	0.11%	0.000µs	1	200
credit_card/is_card_number_no_luhn/37828224631	throughput	254381684.678op/s	255329191.025op/s ± 274053.523op/s	255373173.071op/s ± 174269.807op/s	255522235.569op/s	255677287.135op/s	255792287.145op/s	256329373.409op/s	0.37%	-0.528	0.950	0.11%	19378.510op/s	1	200
credit_card/is_card_number_no_luhn/378282246310005	execution_time	54.606µs	54.791µs ± 0.135µs	54.762µs ± 0.061µs	54.827µs	55.113µs	55.255µs	55.452µs	1.26%	2.060	5.210	0.25%	0.010µs	1	200
credit_card/is_card_number_no_luhn/378282246310005	throughput	18033460.050op/s	18251216.346op/s ± 44717.898op/s	18260881.540op/s ± 20178.563op/s	18279949.638op/s	18298739.401op/s	18309197.391op/s	18313075.536op/s	0.29%	-2.038	5.102	0.24%	3162.033op/s	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	51.404µs	52.145µs ± 0.239µs	52.158µs ± 0.186µs	52.359µs	52.443µs	52.482µs	52.532µs	0.72%	-0.713	0.140	0.46%	0.017µs	1	200
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	19035976.714op/s	19177654.413op/s ± 88263.922op/s	19172533.331op/s ± 68386.954op/s	19231939.616op/s	19346709.831op/s	19437837.774op/s	19453846.857op/s	1.47%	0.736	0.199	0.46%	6241.202op/s	1	200
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	5.706µs	5.887µs ± 0.055µs	5.898µs ± 0.035µs	5.922µs	5.969µs	6.001µs	6.050µs	2.57%	-0.367	0.332	0.94%	0.004µs	1	200
credit_card/is_card_number_no_luhn/x371413321323331	throughput	165296818.766op/s	169884924.669op/s ± 1606249.539op/s	169552986.733op/s ± 996677.214op/s	170742116.737op/s	172520793.526op/s	174112874.907op/s	175244568.347op/s	3.36%	0.429	0.392	0.94%	113578.994op/s	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
credit_card/is_card_number/	execution_time	[3.913µs; 3.914µs] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/	throughput	[255480833.877op/s; 255544192.941op/s] or [-0.012%; +0.012%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	execution_time	[79.977µs; 80.048µs] or [-0.044%; +0.044%]	None	None	None
credit_card/is_card_number/ 3782-8224-6310-005	throughput	[12492709.099op/s; 12503649.610op/s] or [-0.044%; +0.044%]	None	None	None
credit_card/is_card_number/ 378282246310005	execution_time	[73.559µs; 73.623µs] or [-0.043%; +0.043%]	None	None	None
credit_card/is_card_number/ 378282246310005	throughput	[13582891.008op/s; 13594593.144op/s] or [-0.043%; +0.043%]	None	None	None
credit_card/is_card_number/37828224631	execution_time	[3.916µs; 3.918µs] or [-0.018%; +0.018%]	None	None	None
credit_card/is_card_number/37828224631	throughput	[255247239.896op/s; 255339046.338op/s] or [-0.018%; +0.018%]	None	None	None
credit_card/is_card_number/378282246310005	execution_time	[69.947µs; 70.011µs] or [-0.045%; +0.045%]	None	None	None
credit_card/is_card_number/378282246310005	throughput	[14283636.316op/s; 14296602.446op/s] or [-0.045%; +0.045%]	None	None	None
credit_card/is_card_number/37828224631000521389798	execution_time	[51.991µs; 52.044µs] or [-0.051%; +0.051%]	None	None	None
credit_card/is_card_number/37828224631000521389798	throughput	[19214847.995op/s; 19234534.082op/s] or [-0.051%; +0.051%]	None	None	None
credit_card/is_card_number/x371413321323331	execution_time	[5.877µs; 5.892µs] or [-0.126%; +0.126%]	None	None	None
credit_card/is_card_number/x371413321323331	throughput	[169749490.134op/s; 170178767.917op/s] or [-0.126%; +0.126%]	None	None	None
credit_card/is_card_number_no_luhn/	execution_time	[3.917µs; 3.918µs] or [-0.015%; +0.015%]	None	None	None
credit_card/is_card_number_no_luhn/	throughput	[255212866.489op/s; 255287546.178op/s] or [-0.015%; +0.015%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	execution_time	[64.377µs; 64.409µs] or [-0.025%; +0.025%]	None	None	None
credit_card/is_card_number_no_luhn/ 3782-8224-6310-005	throughput	[15525825.730op/s; 15533624.119op/s] or [-0.025%; +0.025%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	execution_time	[57.602µs; 57.628µs] or [-0.022%; +0.022%]	None	None	None
credit_card/is_card_number_no_luhn/ 378282246310005	throughput	[17352803.404op/s; 17360439.030op/s] or [-0.022%; +0.022%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	execution_time	[3.916µs; 3.917µs] or [-0.015%; +0.015%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631	throughput	[255291209.843op/s; 255367172.208op/s] or [-0.015%; +0.015%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	execution_time	[54.772µs; 54.810µs] or [-0.034%; +0.034%]	None	None	None
credit_card/is_card_number_no_luhn/378282246310005	throughput	[18245018.876op/s; 18257413.817op/s] or [-0.034%; +0.034%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	execution_time	[52.112µs; 52.178µs] or [-0.064%; +0.064%]	None	None	None
credit_card/is_card_number_no_luhn/37828224631000521389798	throughput	[19165421.883op/s; 19189886.944op/s] or [-0.064%; +0.064%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	execution_time	[5.879µs; 5.895µs] or [-0.131%; +0.131%]	None	None	None
credit_card/is_card_number_no_luhn/x371413321323331	throughput	[169662313.931op/s; 170107535.407op/s] or [-0.131%; +0.131%]	None	None	None

Group 8

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	534.633µs	535.331µs ± 0.508µs	535.168µs ± 0.278µs	535.624µs	536.259µs	536.822µs	537.367µs	0.41%	1.130	1.071	0.09%	0.036µs	1	200
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	1860925.180op/s	1868003.766op/s ± 1770.247op/s	1868573.345op/s ± 970.945op/s	1869242.578op/s	1870152.146op/s	1870372.134op/s	1870441.555op/s	0.10%	-1.125	1.052	0.09%	125.175op/s	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	381.913µs	382.847µs ± 0.462µs	382.768µs ± 0.306µs	383.111µs	383.632µs	384.109µs	384.633µs	0.49%	0.821	1.012	0.12%	0.033µs	1	200
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	2599877.693op/s	2612015.951op/s ± 3149.675op/s	2612545.626op/s ± 2091.495op/s	2614298.777op/s	2615896.217op/s	2617295.626op/s	2618394.105op/s	0.22%	-0.812	0.987	0.12%	222.716op/s	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	194.544µs	195.514µs ± 0.494µs	195.557µs ± 0.254µs	195.778µs	196.104µs	196.446µs	199.155µs	1.84%	1.821	13.424	0.25%	0.035µs	1	200
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	5021211.978op/s	5114754.570op/s ± 12878.646op/s	5113610.637op/s ± 6643.531op/s	5121482.960op/s	5135117.765op/s	5138075.153op/s	5140234.387op/s	0.52%	-1.731	12.663	0.25%	910.658op/s	1	200
normalization/normalize_service/normalize_service/[empty string]	execution_time	37.963µs	38.315µs ± 0.130µs	38.306µs ± 0.082µs	38.406µs	38.534µs	38.604µs	38.736µs	1.12%	0.080	0.277	0.34%	0.009µs	1	200
normalization/normalize_service/normalize_service/[empty string]	throughput	25815922.272op/s	26099776.312op/s ± 88862.624op/s	26105454.639op/s ± 56048.329op/s	26153468.819op/s	26245179.056op/s	26322381.104op/s	26341483.420op/s	0.90%	-0.057	0.269	0.34%	6283.536op/s	1	200
normalization/normalize_service/normalize_service/test_ASCII	execution_time	45.801µs	45.943µs ± 0.080µs	45.923µs ± 0.039µs	45.982µs	46.106µs	46.191µs	46.281µs	0.78%	1.323	2.228	0.17%	0.006µs	1	200
normalization/normalize_service/normalize_service/test_ASCII	throughput	21607035.251op/s	21766200.000op/s ± 37815.879op/s	21775786.351op/s ± 18332.460op/s	21790339.048op/s	21810108.906op/s	21827075.042op/s	21833399.036op/s	0.26%	-1.310	2.181	0.17%	2673.986op/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	[535.261µs; 535.402µs] or [-0.013%; +0.013%]	None	None	None
normalization/normalize_service/normalize_service/A0000000000000000000000000000000000000000000000000...	throughput	[1867758.427op/s; 1868249.105op/s] or [-0.013%; +0.013%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	execution_time	[382.783µs; 382.911µs] or [-0.017%; +0.017%]	None	None	None
normalization/normalize_service/normalize_service/Data🐨dog🐶 繋がっ⛰てて	throughput	[2611579.437op/s; 2612452.466op/s] or [-0.017%; +0.017%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	execution_time	[195.446µs; 195.583µs] or [-0.035%; +0.035%]	None	None	None
normalization/normalize_service/normalize_service/Test Conversion 0f Weird !@#$%^&**() Characters	throughput	[5112969.714op/s; 5116539.427op/s] or [-0.035%; +0.035%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	execution_time	[38.297µs; 38.333µs] or [-0.047%; +0.047%]	None	None	None
normalization/normalize_service/normalize_service/[empty string]	throughput	[26087460.807op/s; 26112091.817op/s] or [-0.047%; +0.047%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	execution_time	[45.932µs; 45.954µs] or [-0.024%; +0.024%]	None	None	None
normalization/normalize_service/normalize_service/test_ASCII	throughput	[21760959.083op/s; 21771440.917op/s] or [-0.024%; +0.024%]	None	None	None

Group 9

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	14.987µs	15.554µs ± 0.201µs	15.537µs ± 0.120µs	15.665µs	15.953µs	16.031µs	16.179µs	4.13%	0.347	0.427	1.29%	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	[15.526µs; 15.581µs] or [-0.179%; +0.179%]	None	None	None

Group 10

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	14.724ms	14.797ms ± 0.030ms	14.793ms ± 0.017ms	14.813ms	14.846ms	14.863ms	14.944ms	1.01%	1.270	4.631	0.20%	0.002ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
benching serializing traces from their internal representation to msgpack	execution_time	[14.793ms; 14.801ms] or [-0.028%; +0.028%]	None	None	None

Group 11

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	191.119ns	193.821ns ± 2.150ns	193.349ns ± 1.179ns	194.677ns	197.961ns	201.344ns	203.578ns	5.29%	1.519	3.216	1.11%	0.152ns	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	[193.523ns; 194.119ns] or [-0.154%; +0.154%]	None	None	None

Group 12

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	5.222µs	5.418µs ± 0.037µs	5.416µs ± 0.026µs	5.445µs	5.477µs	5.492µs	5.500µs	1.54%	-0.476	2.764	0.68%	0.003µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
write only interface	execution_time	[5.413µs; 5.424µs] or [-0.095%; +0.095%]	None	None	None

Group 13

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	14.862ms	14.910ms ± 0.017ms	14.909ms ± 0.010ms	14.919ms	14.936ms	14.960ms	14.985ms	0.51%	0.492	2.000	0.12%	0.001ms	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
concentrator/add_spans_to_concentrator	execution_time	[14.907ms; 14.912ms] or [-0.016%; +0.016%]	None	None	None

Group 14

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	288.634µs	289.526µs ± 0.576µs	289.438µs ± 0.190µs	289.612µs	290.412µs	291.836µs	293.627µs	1.45%	3.600	20.030	0.20%	0.041µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
sql/obfuscate_sql_string	execution_time	[289.446µs; 289.605µs] or [-0.028%; +0.028%]	None	None	None

Group 15

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	185.650µs	186.487µs ± 0.660µs	186.242µs ± 0.295µs	186.951µs	187.750µs	188.363µs	188.613µs	1.27%	1.102	0.272	0.35%	0.047µs	1	200
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	5301872.025op/s	5362364.084op/s ± 18900.401op/s	5369366.076op/s ± 8519.453op/s	5376292.601op/s	5381953.072op/s	5385146.412op/s	5386485.013op/s	0.32%	-1.091	0.237	0.35%	1336.460op/s	1	200
normalization/normalize_name/normalize_name/bad-name	execution_time	17.850µs	17.979µs ± 0.065µs	17.977µs ± 0.035µs	18.007µs	18.102µs	18.168µs	18.229µs	1.40%	0.898	1.787	0.36%	0.005µs	1	200
normalization/normalize_name/normalize_name/bad-name	throughput	54857142.857op/s	55620139.313op/s ± 200755.788op/s	55626646.928op/s ± 107684.306op/s	55755093.011op/s	55924799.103op/s	55994131.933op/s	56020865.905op/s	0.71%	-0.866	1.698	0.36%	14195.578op/s	1	200
normalization/normalize_name/normalize_name/good	execution_time	9.864µs	9.929µs ± 0.072µs	9.885µs ± 0.015µs	9.978µs	10.073µs	10.122µs	10.129µs	2.47%	1.090	-0.146	0.72%	0.005µs	1	200
normalization/normalize_name/normalize_name/good	throughput	98726648.921op/s	100717184.066op/s ± 725984.940op/s	101166649.247op/s ± 148775.520op/s	101249768.524op/s	101322506.300op/s	101361822.768op/s	101379910.161op/s	0.21%	-1.075	-0.189	0.72%	51334.887op/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	[186.396µs; 186.579µs] or [-0.049%; +0.049%]	None	None	None
normalization/normalize_name/normalize_name/Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Long-.Too-Lo...	throughput	[5359744.670op/s; 5364983.498op/s] or [-0.049%; +0.049%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	execution_time	[17.970µs; 17.988µs] or [-0.050%; +0.050%]	None	None	None
normalization/normalize_name/normalize_name/bad-name	throughput	[55592316.492op/s; 55647962.135op/s] or [-0.050%; +0.050%]	None	None	None
normalization/normalize_name/normalize_name/good	execution_time	[9.919µs; 9.939µs] or [-0.101%; +0.101%]	None	None	None
normalization/normalize_name/normalize_name/good	throughput	[100616569.535op/s; 100817798.596op/s] or [-0.100%; +0.100%]	None	None	None

Group 16

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	4.374ms	4.381ms ± 0.009ms	4.379ms ± 0.002ms	4.382ms	4.385ms	4.393ms	4.501ms	2.78%	11.444	145.811	0.21%	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	[4.379ms; 4.382ms] or [-0.029%; +0.029%]	None	None	None

Group 17

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
redis/obfuscate_redis_string	execution_time	33.647µs	34.231µs ± 0.984µs	33.772µs ± 0.056µs	33.889µs	36.332µs	36.419µs	37.277µs	10.38%	1.703	1.004	2.87%	0.070µs	1	200

scenario	metric	95% CI mean	Shapiro-Wilk pvalue	Ljung-Box pvalue (lag=1)	Dip test pvalue
redis/obfuscate_redis_string	execution_time	[34.094µs; 34.367µs] or [-0.399%; +0.399%]	None	None	None

Group 18

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	934.320µs	937.751µs ± 2.304µs	937.143µs ± 1.085µs	938.459µs	941.935µs	945.090µs	951.054µs	1.48%	1.823	5.759	0.25%	0.163µs	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	[937.431µs; 938.070µs] or [-0.034%; +0.034%]	None	None	None

Group 19

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

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	4.929µs	5.007µs ± 0.052µs	5.013µs ± 0.055µs	5.046µs	5.091µs	5.093µs	5.097µs	1.68%	0.231	-1.377	1.03%	0.004µs	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	[5.000µs; 5.014µs] or [-0.143%; +0.143%]	None	None	None

Group 20

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching string interning on wordpress profile	execution_time	160.220µs	161.100µs ± 0.614µs	161.078µs ± 0.227µs	161.283µs	161.677µs	162.973µs	165.895µs	2.99%	4.142	27.671	0.38%	0.043µs	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	[161.015µs; 161.185µs] or [-0.053%; +0.053%]	None	None	None

Group 21

cpu_model	git_commit_sha	git_commit_date	git_branch
Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz	`5938b42`	1774948956	paullgdc/data-pipeline/trace_buffer

scenario	metric	min	mean ± sd	median ± mad	p75	p95	p99	max	peak_to_median_ratio	skewness	kurtosis	cv	sem	runs	sample_size
benching deserializing traces from msgpack to their internal representation	execution_time	47.873ms	48.280ms ± 0.975ms	48.127ms ± 0.095ms	48.260ms	48.438ms	53.542ms	58.876ms	22.33%	8.559	79.737	2.01%	0.069ms	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	[48.145ms; 48.416ms] or [-0.280%; +0.280%]	None	None	None

Baseline

Omitted due to size.

bantonsson · 2026-03-31T14:25:52Z

+        if res.timed_out() {
+            return Err(TraceBufferError::TimedOut(timeout));
+        }
+        Ok(())


~~Or am I missing something?~~

Doesn't this always return Ok, even if the last flush didn't happen because of shut down?

I think we need to look at the state.last_flush_generation

res is a WaitTimeoutResult so I cannot just return it

Sorry for the confusion. I was editing my question while you answered.

Doesn't this always return Ok, even if the last flush didn't happen because of shut down?

I think we need to look at the state.last_flush_generation

yannham · 2026-03-31T12:49:31Z

@@ -0,0 +1,851 @@
+// Copyright 2025-Present Datadog, Inc. https://www.datadoghq.com/
+// SPDX-License-Identifier: Apache-2.0


Nit: a module-level doc, even if short, is always nice to get an idea of what happens here quickly.

Added a doc module

yannham · 2026-03-31T13:47:48Z

+    }
+
+    fn reset(&mut self) {
+        *self = Self::new(self.max_buffered_spans);


Not sure if it is worth it (is this remotely perf-sensitive?), but clear()ing chunks instead of rea might avoid dropping the old vector to just re-allocate a new one.

I changed to reset each field, and clear the Vec.
Right now this is not perf sensitive as this method is only used after a process forks but better make the code a bit nicer just in case

yannham · 2026-03-31T14:10:27Z

+    pub spans_queued: usize,
+}
+
+fn channel<T>(


We discussed that orally, but I'm writing it down for the record: I'm not sure about the trade-off of going with a custom channel implementation here. It seems what we do is simple enough that we should be able to piggy-back on existing, well-tested, well-optimized channel impls. It's also a non trivial part of the code of this module. It might be the case that the custom channel is actually the right solution, but using a standard bounded channel or whatnot should be the default approach IMHO, only moving to a custom solution if we find good reasons to do so in the process. And in this case, the trade-offs should be properly documented here.

yannham · 2026-04-01T09:44:21Z

+        let gen = state.batch.batch_gen;
+        if state.batch.span_count() > self.flush_trigger_number_of_spans || self.synchronous_write {
+            state.flush_needed = true;
+            self.waiter.notify_receiver(state);


I wonder if we can avoid notifying if state.flush_needed was already set to true here. I think, but it must be double checked that this won't let a waiter be stuck forever. I'm saying that because notifying is basically a futex syscall, and a syscall is costly, so usually we try to avoid unnecessary wait or notify. That being said, it's a bit of a detail and it might be for something to think about later, or be ignored entirely if this part is not perf sensitive.

yannham · 2026-04-01T09:54:03Z

+            // Enable the notify future BEFORE acquiring the lock to avoid lost wakeups:
+            // any notify_waiters() call that fires between enable() and .await is captured.


TIL you can lose a notify_one() notification between notified() and enable(). Tokio/async-rust footgun #1989495 I guess...

yannham · 2026-04-01T10:42:55Z

+/// This allows mapping from the buffered spans to another type, and
+/// calling any method on the trace exporter to send traces
+pub trait Export<T>: Send + Debug {
+    fn export_trace_chunks<'a: 'c, 'b: 'c, 'c>(


Do 'a and 'b need to be different here?

No, refactored to have only one lifetime

# Motivation As we implement native spans in ruby in python, we have opportuinities to replace more components of the span pipeline with native code. One of them i the span buffer which we is before the trace exporter. # Changes Add a TraceBuffer implementation. The TraceBuffer is split in two part, a Sender which allows putting trace chunks in the queue and a background task, spawned on the shared runtime which periodically pulls data from the chunk and submit them using the TraceExporter.

dd-octo-sts · 2026-04-16T15:00:52Z

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	83.31 MB	83.31 MB	0% (0 B) 👌

aarch64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.a	99.38 MB	99.38 MB	0% (0 B) 👌
/aarch64-unknown-linux-gnu/lib/libdatadog_profiling.so	10.10 MB	10.10 MB	0% (0 B) 👌

libdatadog-x64-windows

Artifact	Baseline	Commit	Change
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.dll	25.18 MB	25.18 MB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.lib	79.61 KB	79.61 KB	0% (0 B) 👌
/libdatadog-x64-windows/debug/dynamic/datadog_profiling_ffi.pdb	184.33 MB	184.40 MB	+.03% (+64.00 KB) 🔍
/libdatadog-x64-windows/debug/static/datadog_profiling_ffi.lib	917.67 MB	917.69 MB	+0% (+19.62 KB) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.dll	7.89 MB	7.89 MB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.lib	79.61 KB	79.61 KB	0% (0 B) 👌
/libdatadog-x64-windows/release/dynamic/datadog_profiling_ffi.pdb	23.66 MB	23.66 MB	0% (0 B) 👌
/libdatadog-x64-windows/release/static/datadog_profiling_ffi.lib	46.17 MB	46.17 MB	0% (0 B) 👌

libdatadog-x86-windows

Artifact	Baseline	Commit	Change
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.dll	21.66 MB	21.66 MB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.lib	80.84 KB	80.84 KB	0% (0 B) 👌
/libdatadog-x86-windows/debug/dynamic/datadog_profiling_ffi.pdb	188.48 MB	188.55 MB	+.03% (+72.00 KB) 🔍
/libdatadog-x86-windows/debug/static/datadog_profiling_ffi.lib	903.03 MB	903.05 MB	+0% (+20.82 KB) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.dll	6.13 MB	6.13 MB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.lib	80.84 KB	80.84 KB	0% (0 B) 👌
/libdatadog-x86-windows/release/dynamic/datadog_profiling_ffi.pdb	25.35 MB	25.35 MB	0% (0 B) 👌
/libdatadog-x86-windows/release/static/datadog_profiling_ffi.lib	43.66 MB	43.66 MB	0% (0 B) 👌

x86_64-alpine-linux-musl

Artifact	Baseline	Commit	Change
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.a	74.41 MB	74.41 MB	0% (0 B) 👌
/x86_64-alpine-linux-musl/lib/libdatadog_profiling.so	8.52 MB	8.52 MB	0% (0 B) 👌

x86_64-unknown-linux-gnu

Artifact	Baseline	Commit	Change
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.a	91.76 MB	91.76 MB	0% (0 B) 👌
/x86_64-unknown-linux-gnu/lib/libdatadog_profiling.so	10.20 MB	10.20 MB	0% (0 B) 👌

codecov-commenter · 2026-04-16T15:29:21Z

Codecov Report

❌ Patch coverage is 89.01515% with 58 lines in your changes missing coverage. Please review.
✅ Project coverage is 71.82%. Comparing base (46469a5) to head (4342a5d).

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #1826      +/-   ##
==========================================
+ Coverage   71.69%   71.82%   +0.13%     
==========================================
  Files         431      432       +1     
  Lines       69142    69668     +526     
==========================================
+ Hits        49569    50039     +470     
- Misses      19573    19629      +56

Components	Coverage Δ
libdd-crashtracker	`66.05% <ø> (+0.01%)`	⬆️
libdd-crashtracker-ffi	`34.47% <ø> (ø)`
libdd-alloc	`98.77% <ø> (ø)`
libdd-data-pipeline	`85.87% <89.01%> (+0.39%)`	⬆️
libdd-data-pipeline-ffi	`71.94% <ø> (ø)`
libdd-common	`79.16% <ø> (ø)`
libdd-common-ffi	`73.87% <ø> (ø)`
libdd-telemetry	`68.07% <ø> (ø)`
libdd-telemetry-ffi	`19.37% <ø> (ø)`
libdd-dogstatsd-client	`82.64% <ø> (ø)`
datadog-ipc	`74.84% <ø> (ø)`
libdd-profiling	`81.63% <ø> (-0.02%)`	⬇️
libdd-profiling-ffi	`64.53% <ø> (ø)`
datadog-sidecar	`30.53% <ø> (ø)`
datdog-sidecar-ffi	`13.20% <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	`89.24% <ø> (ø)`
datadog-tracer-flare	`86.88% <ø> (ø)`
libdd-log	`74.69% <ø> (ø)`

🚀 New features to boost your workflow:

❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.

paullegranddc requested a review from a team as a code owner March 31, 2026 09:20

github-actions bot added the data-pipeline label Mar 31, 2026

paullegranddc force-pushed the paullgdc/data-pipeline/trace_buffer branch from 3ede0c0 to 5938b42 Compare March 31, 2026 09:22

paullegranddc requested a review from a team as a code owner March 31, 2026 10:03

bantonsson reviewed Mar 31, 2026

View reviewed changes

yannham reviewed Apr 1, 2026

View reviewed changes

Base automatically changed from vianney/implement-shared-runtime to main April 8, 2026 15:17

gh-worker-dd-mergequeue-cf854d bot requested a review from a team as a code owner April 8, 2026 15:17

paullegranddc force-pushed the paullgdc/data-pipeline/trace_buffer branch from b08bedf to 7252dcb Compare April 16, 2026 14:22

paullegranddc added 2 commits April 16, 2026 16:34

fix: adress style and doc comments

d2fdb74

fix: remove explicit trace exporter and put it in dyn Exporter trait

9c0e2e8

fix: reuse capacity on reset

4342a5d

		@@ -0,0 +1,851 @@
		// Copyright 2025-Present Datadog, Inc. https://www.datadoghq.com/
		// SPDX-License-Identifier: Apache-2.0

		// Enable the notify future BEFORE acquiring the lock to avoid lost wakeups:
		// any notify_waiters() call that fires between enable() and .await is captured.

Conversation

paullegranddc commented Mar 31, 2026

Motivation

Changes

Uh oh!

github-actions bot commented Mar 31, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

📚 Documentation Check Results

📦 libdd-data-pipeline - 935 warning(s)

Uh oh!

github-actions bot commented Mar 31, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Clippy Allow Annotation Report

Summary by Rule

Annotation Counts by File

Annotation Stats by Crate

About This Report

Uh oh!

github-actions bot commented Mar 31, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

🔒 Cargo Deny Results

📦 libdd-data-pipeline - 4 error(s)

Uh oh!

datadog-datadog-prod-us1 bot commented Mar 31, 2026 • edited by datadog-prod-us1-3 bot Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

pr-commenter bot commented Mar 31, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Benchmarks

Comparison

Explanation

More details about the CI and significant changes

scenario:tags/replace_trace_tags

Candidate

Group 1

Group 2

Group 3

Group 4

Group 5

Group 6

Group 7

Group 8

Group 9

Group 10

Group 11

Group 12

Group 13

Group 14

Group 15

Group 16

Group 17

Group 18

Group 19

Group 20

Group 21

Baseline

Uh oh!

bantonsson Mar 31, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

bantonsson Mar 31, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Uh oh!

Uh oh!

yannham Mar 31, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

github-actions bot commented Mar 31, 2026 •

edited

Loading

📦 `libdd-data-pipeline` - 935 warning(s)

github-actions bot commented Mar 31, 2026 •

edited

Loading

github-actions bot commented Mar 31, 2026 •

edited

Loading

📦 `libdd-data-pipeline` - 4 error(s)

datadog-datadog-prod-us1 bot commented Mar 31, 2026 •

edited by datadog-prod-us1-3 bot

Loading

pr-commenter bot commented Mar 31, 2026 •

edited

Loading

bantonsson Mar 31, 2026 •

edited

Loading

bantonsson Mar 31, 2026 •

edited

Loading

yannham Mar 31, 2026 •

edited

Loading

dd-octo-sts bot commented Apr 16, 2026 •

edited

Loading