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fix(tracer): pass prioritySampler in agentTraceWriter benchmarks to prevent nil panic#4601

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gh-worker-dd-mergequeue-cf854d[bot] merged 1 commit into
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mtoff/fix-writer-bench
Mar 25, 2026
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fix(tracer): pass prioritySampler in agentTraceWriter benchmarks to prevent nil panic#4601
gh-worker-dd-mergequeue-cf854d[bot] merged 1 commit into
mainfrom
mtoff/fix-writer-bench

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@mtoffl01

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What does this PR do?

Provides the agentTraceWriters used in writer_bench_test.go with a real prioritySampler, instead of nil.

Motivation

I noticed BenchmarkAgentTraceWriterFlush panicking with a nil pointer dereference because newAgentTraceWriter was called with a nil prioritySampler. This panics because, after a successful flush, the writer calls prioritySampling.readRatesJSON, which dereferences the nil. Passing newPrioritySampler() in all benchmark constructors avoids the panic and matches how the writer is created in production.

Reviewer's Checklist

  • Changed code has unit tests for its functionality at or near 100% coverage.
  • System-Tests covering this feature have been added and enabled with the va.b.c-dev version tag.
  • There is a benchmark for any new code, or changes to existing code.
  • If this interacts with the agent in a new way, a system test has been added.
  • New code is free of linting errors. You can check this by running make lint locally.
  • New code doesn't break existing tests. You can check this by running make test locally.
  • Add an appropriate team label so this PR gets put in the right place for the release notes.
  • All generated files are up to date. You can check this by running make generate locally.
  • Non-trivial go.mod changes, e.g. adding new modules, are reviewed by @DataDog/dd-trace-go-guild. Make sure all nested modules are up to date by running make fix-modules locally.

Unsure? Have a question? Request a review!

@mtoffl01
mtoffl01 requested a review from a team as a code owner March 25, 2026 20:54
@datadog-prod-us1-4

datadog-prod-us1-4 Bot commented Mar 25, 2026

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✅ Tests

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
Patch Coverage: -1.00%
Overall Coverage: 60.04% (+4.03%)

This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 271c00b | Docs | Datadog PR Page | Was this helpful? React with 👍/👎 or give us feedback!

@pr-commenter

pr-commenter Bot commented Mar 25, 2026

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Benchmarks

Benchmark execution time: 2026-03-25 21:18:30

Comparing candidate commit 271c00b in PR branch mtoff/fix-writer-bench with baseline commit 5d005fa in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 215 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 ----------------------------------'

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2 participants