chore: use libdatadog's trace filter implementation

[Eldolfin]

Description

Removes the current trace filter implementation and use libdatadog's implementation instead which more closely matches the agent's behavior.

• Currently the submodule points to an unmerged branch. This will be changed once feat(data-pipeline)!: CSS Trace Filters libdatadog#1985 is merged
• I still have to figure out the resource normalization stuff (implies writing some C...) Done

Reviewer checklist

• Test coverage seems ok.
• Appropriate labels assigned.

[Copilot]

Pull request overview

This PR replaces the project’s custom trace-filtering logic with libdatadog’s TraceFilterer implementation to better match agent behavior, and updates the C/Rust FFI surface accordingly.

Changes:

• Swap components-rs/trace_filter.rs from an in-tree regex/tag filter implementation to libdd_trace_utils::trace_filter::TraceFilterer.
• Remove the slow-path meta-iterator callback from the FFI API and update call sites and headers.
• Update agent-info config plumbing to pass the filter lists into the concentrator config application.

Reviewed changes

Copilot reviewed 6 out of 7 changed files in this pull request and generated 3 comments.

Show a summary per file

File	Description
tracer/trace_filter.c	Updates the C-side call into ddog_check_stats_trace_filter to the new signature (no iterator).
components-rs/trace_filter.rs	Replaces custom filter compilation/evaluation with TraceFilterer and adapts the span lookup interface.
components-rs/datadog.h	Updates the exported C header signature/documentation for ddog_check_stats_trace_filter.
components-rs/common.h	Removes the no-longer-used meta-iteration callback typedefs from the public header.
components-rs/agent_info.rs	Adjusts how filter config fields are extracted and passed into apply_concentrator_config.
Cargo.lock	Adds a new Rust dependency entry related to trace normalization.

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[datadog-prod-us1-5]

 

✨ Fix all issues with BitsAI

⚠️ Warnings

🚦 8 Pipeline jobs failed

DataDog/apm-reliability/dd-trace-php | ASAN test_c with multiple observers: [8.3]     

DataDog/apm-reliability/dd-trace-php | test_web_codeigniter_31: [7.4, cgi-fcgi]     

DataDog/apm-reliability/dd-trace-php | test_web_custom: [7.4, fpm-fcgi]     

View all 8 failed jobs.

ℹ️ Info

No other issues found (see more)

🧪 All tests passed
❄️ No new flaky tests detected

🎯 Code Coverage (details)
• Patch Coverage: 100.00%
• Overall Coverage: 54.08% (-0.04%)

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🔗 Commit SHA: 2854817 | Docs | Datadog PR Page | Give us feedback!}

[pr-commenter]

Benchmarks [ tracer ]

Benchmark execution time: 2026-06-17 12:26:08

Comparing candidate commit 2854817 in PR branch oscarld/use-libdatadog-trace-filter-implementation with baseline commit 941af06 in branch master.

Found 1 performance improvements and 0 performance regressions! Performance is the same for 192 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 ----------------------------------'

scenario:MessagePackSerializationBench/benchMessagePackSerialization

• 🟩 execution_time [-4.195µs; -2.685µs] or [-4.015%; -2.570%]