Fix _dd.p.ksr scientific notation for very small sampling rates

[bm1549]

What does this PR do?

Fix _dd.p.ksr span tag formatting for very small sampling rates. Rates below 0.001 were formatted using Ruby's %.6g format which outputs scientific notation (e.g. 0.000001 → "1e-06"). Changed to explicit integer-level rounding and %.6f formatting to always produce decimal notation with up to 6 decimal digits, trailing zeros stripped.

Motivation:

Fixes APMAPI-1869. System tests expect decimal notation for _dd.p.ksr values (see DataDog/system-tests#6466).

Related PRs:

• dd-trace-py: fix(tracing): format _dd.p.ksr with decimal notation instead of scientific notation dd-trace-py#17086
• dd-trace-js: fix(tracing): format _dd.p.ksr with decimal notation instead of scientific notation dd-trace-js#7846
• system-tests: Activate _dd.p.ksr tests for all languages system-tests#6466

Change log entry

Yes. Fix _dd.p.ksr span tag formatting for very small sampling rates to use decimal notation instead of scientific notation.

Additional Notes:

Uses (rate * 1e6).round / 1e6.to_f for consistent rounding at the integer level, avoiding IEEE 754 precision issues with %.6f alone.

How to test the change?

Updated unit tests in spec/datadog/tracing/transport/trace_formatter_spec.rb covering:

• Rate 0.000001 → "0.000001" (was "1e-06")
• Rate 0.0000001 → "0" (rounds to zero, new case)
• Rate 0.0000005 → "0.000001" (rounds up, new case)

[datadog-prod-us1-3]

✅ Tests

🎉 All green!

❄️ No new flaky tests detected
🧪 All tests passed

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 95.14% (-0.01%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 515477f | Docs | Datadog PR Page | Was this helpful? React with 👍/👎 or give us feedback!}

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-03-24 01:15:37

Comparing candidate commit 515477f in PR branch brian.marks/fix-ksr-scientific-notation with baseline commit eadefc7 in branch master.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 45 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 ----------------------------------'