feat(tracing): load stable configuration through libdatadog

[pawelchcki]

Summary

• Pin libdatadog at 4fab32380890733100870a9a87db631a53f9e4ef and call libdd-library-config through a small Rust static library with a manual C ABI.
• Add an opt-in C++ stable configuration loader for tracing. Values resolve from defaults, local stable configuration, environment variables, fleet stable configuration, and code, in that order. Configuration telemetry records the stable source and config ID.
• Add CMake packaging and Bazel builds using rules_rs, with tests and usage documentation.

Dependency

Depends on DataDog/libdatadog#1770 for the libdd-library-config reader API used by the pinned submodule.

Testing

• C++ test suite: 137 test cases, 199879 assertions passed.
• Bazel: //:stable_config_loader_test passed.
• cargo check --offline --locked and cargo fmt --check passed for the FFI crate.
• git diff --check passed.

[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 2026-09-29T08:33:37.422953Z	0f97c5e	PR opened
🔒 Security Review	✅ Completed 2026-09-29T08:30:17.328782Z	0f97c5e	PR opened

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[chatgpt-codex-connector]

💡 Codex Review

Here are some automated review suggestions for this pull request.

Reviewed commit: 0f97c5e10b

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[pr-commenter]

Benchmarks

Benchmark execution time: 2026-09-29 09:35:11

Comparing candidate commit 8187422 in PR branch stable-config with baseline commit df496e9 in branch main.

Found 1 performance improvements and 1 performance regressions! Performance is the same for 6 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:BM_HexPadded_uint64/WorstCasePadding

• 🟥 execution_time [+1.144µs; +1.370µs] or [+2.482%; +2.974%]

scenario:BM_TraceTinyCCSource

• 🟩 execution_time [-3.280ms; -2.840ms] or [-3.966%; -3.435%]