[IGNORE] Community PR 5961

[Strech]

⚠️ DO NOT MERGE ⚠️

[dd-octo-sts]

👋 Hey @Strech, please fill "Change log entry" section in the pull request description.

If changes need to be present in CHANGELOG.md you can state it this way

**Change log entry**

Yes. A brief summary to be placed into the CHANGELOG.md

(possible answers Yes/Yep/Yeah)

Or you can opt out like that

**Change log entry**

None.

(possible answers No/Nope/None)

^{Visited at: 2026-07-01 14:06:35 UTC}

[dd-octo-sts]

Typing analysis

This PR does not change typing compared to the base branch.

[datadog-datadog-prod-us1-2]

✨ Fix all issues with BitsAI

⚠️ Warnings

❄️ 4 New flaky tests detected

ActiveJob with active_job instrumentation Data Streams Monitoring when DSM is disabled does not set a consume checkpoint when deserializing from rspec  

super: no superclass method \`deserialize' for #<ExampleJob:0x00005641aca5a048>
Did you mean?  serialize

Failure/Error: super

NoMethodError:
  super: no superclass method \`deserialize' for #<ExampleJob:0x00005641aca5a048>
  Did you mean?  serialize
./lib/datadog/tracing/contrib/active_job/data_streams.rb:38:in \`deserialize'
./spec/datadog/tracing/contrib/rails/rails_active_job_spec.rb:265:in \`block (5 levels) in <top (required)>'
...

New test introduced in this PR is flaky.

ActiveJob with active_job instrumentation Data Streams Monitoring when DSM is enabled sets a consume checkpoint and extracts the pathway context when deserializing from rspec  

super: no superclass method \`deserialize' for #<ExampleJob:0x00005641ac51bf50>
Did you mean?  serialize

Failure/Error: super

NoMethodError:
  super: no superclass method \`deserialize' for #<ExampleJob:0x00005641ac51bf50>
  Did you mean?  serialize
./lib/datadog/tracing/contrib/active_job/data_streams.rb:38:in \`deserialize'
./spec/datadog/tracing/contrib/rails/rails_active_job_spec.rb:238:in \`block (5 levels) in <top (required)>'
...

New test introduced in this PR is flaky.

View in Flaky Test Management

ℹ️ Info

No other issues found (see more)

🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 90.03% (+0.01%)

Useful? React with 👍 / 👎

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 251ce66 | Docs | Datadog PR Page | Give us feedback!}

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-07-03 09:36:39

Comparing candidate commit 251ce66 in PR branch community/pr-5961 with baseline commit b85232d in branch master.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 48 metrics, 1 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 ----------------------------------'