Misc CI fixes

[cataphract]

Description

Related libdatadog changes:

• fix(sidecar): avoid telemetry cache lock inversions libdatadog#2562
• fix(ipc)!: fix windows bugs; support optional handles libdatadog#2567
• fix(sidecar)!: make Windows RC notifications unload-safe libdatadog#2569
• fix(sidecar): idle shutdown for short-lived sidecar clients libdatadog#2573

Now stacked atop #4224

Reviewer checklist

• Test coverage seems ok.
• Appropriate labels assigned.

[datadog-datadog-prod-us1-2]

🎉 All green!

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 68.26% (+0.00%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 6e29d76 | Docs | View more details | Give us feedback!}

[pr-commenter]

Benchmarks [ profiler ]

Benchmark execution time: 2026-09-23 15:37:21

Comparing candidate commit 66f1a60 in PR branch glopes/ci-misc with baseline commit 7646f7f in branch glopes/ci-misc-no-libdatadog.

Found 0 performance improvements and 3 performance regressions! Performance is the same for 24 metrics, 9 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:php-profiler-timeline-memory-control

• 🟥 cpu_user_time [+31.797ms; +41.675ms] or [+4.907%; +6.431%]
• 🟥 execution_time [+33.530ms; +37.950ms] or [+4.810%; +5.444%]

scenario:php-profiler-timeline-memory-with-profiler

• 🟥 execution_time [+26.309ms; +46.228ms] or [+2.258%; +3.967%]

[pr-commenter]

Benchmarks [ tracer ]

Benchmark execution time: 2026-09-30 11:58:23

Comparing candidate commit 6e29d76 in PR branch glopes/ci-misc with baseline commit cc71bd6 in branch master.

📊 Benchmarking dashboard

Found 6 performance improvements and 0 performance regressions! Performance is the same for 188 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:SpanBench/benchDatadogAPI

• 🟩 execution_time [-5.816µs; -4.222µs] or [-7.936%; -5.761%]

scenario:SpanBench/benchDatadogAPI-opcache

• 🟩 execution_time [-5.022µs; -3.611µs] or [-6.964%; -5.007%]

scenario:TraceFlushBench/benchFlushTrace

• 🟩 execution_time [-72.415µs; -64.285µs] or [-20.373%; -18.085%]

scenario:TraceFlushBench/benchFlushTrace-opcache

• 🟩 execution_time [-75.446µs; -62.454µs] or [-19.146%; -15.849%]

scenario:TraceSerializationBench/benchSerializeTrace

• 🟩 execution_time [-88.999µs; -81.601µs] or [-20.462%; -18.761%]

scenario:TraceSerializationBench/benchSerializeTrace-opcache

• 🟩 execution_time [-94.790µs; -83.810µs] or [-23.341%; -20.638%]

[chatgpt-codex-connector]

Codex Review Summary

This comment shows the latest Codex review activity on this pull request.

Review	Status	Commit	Review trigger
📝 Code Review	✅ Completed 2026-09-21T19:18:56.126842Z	9919b0d	Draft marked ready
🔒 Security Review	✅ Completed 2026-09-21T19:22:16.724854Z	9919b0d	Draft marked ready

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[bwoebi]

build: update libdatadog sidecar deadlock fix = DataDog/libdatadog@0e8c8db...d3be017

Seems like the wrong fix to me, can we instead hold the lock the other way round on telemetry shutdown? TelemetryCachedEntry anyway is Arc<Mutex<Option<TelemetryCachedClient>>> - so we can just take() the Option, and then drop the lock. Then acquire the TelemetryCachedSet lock and check whether that entry is still None, and if yes, drop it.

Saves us from duplicating the map every time we want to flush.