chore(ci): add tag filtering correctness tests

[rayz]

Summary

PR title

Change Type

• Bug fix
• New feature
• Non-functional (chore, refactoring, docs)
• Performance

How did you test this PR?

CI

References

[Copilot]

Pull request overview

This PR adds a new correctness test case for DogStatsD tag filtering. The test verifies that tag filtering rules are correctly applied by the Datadog Agent and the ADP (Adaptive Data Plane) implementation. The PR introduces test configuration files, updates the CI/CD pipeline, and refactors the metrics normalization logic to ensure that metrics with the same tags in different orders are correctly recognized and merged as a single metric.

Changes:

• Added a new correctness test case for DogStatsD tag filtering (dsd-tag-filterlist)
• Refactored metrics normalization to normalize context tags before grouping metrics, ensuring tag order independence
• Added unit test to verify correct metric deduplication when tags are in different order
• Added Makefile target and GitLab CI job to execute the new test

Reviewed changes

Copilot reviewed 5 out of 6 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
test/correctness/dsd-tag-filterlist/config.yaml	Test orchestration configuration defining baseline Agent (public Datadog Agent v7.76.2) and comparison target (ADP)
test/correctness/dsd-tag-filterlist/datadog.yaml	Agent configuration with specific tag filtering rules (exclude/include/miss patterns)
test/correctness/dsd-tag-filterlist/millstone.yaml	Metric generation configuration with fixed metric names and tag values to test filtering behavior
Makefile	Added new test-correctness-dsd-tag-filterlist target following existing pattern
.gitlab/e2e.yml	Added GitLab CI job for the new test, correctly not extending .test-correctness-adp-baseline since it uses a different baseline image
bin/correctness/ground-truth/src/analysis/metrics/types.rs	Refactored normalization logic and added unit test for tag order normalization

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[pr-commenter]

Binary Size Analysis (Agent Data Plane)

Target: 5612418 (baseline) vs dadad04 (comparison) diff
Analysis Type: Stripped binaries (debug symbols excluded)
Baseline Size: 26.47 MiB
Comparison Size: 26.47 MiB
Size Change: +0 B (+0.00%)
Pass/Fail Threshold: +5%
Result: PASSED ✅

Changes by Module

Module	File Size	Symbols

Detailed Symbol Changes

    FILE SIZE        VM SIZE    
 --------------  -------------- 
  [ = ]       0  [ = ]       0    TOTAL

[pr-commenter]

Regression Detector (Agent Data Plane)

Regression Detector Results

Run ID: 26e9fd2d-9d17-4c3d-8cd6-7bb6353ab586

Baseline: 5612418
Comparison: dadad04
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	otlp_ingest_logs_5mb_memory	memory utilization	+13.77	[+13.34, +14.20]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_logs_5mb_throughput	ingress throughput	+0.02	[-0.11, +0.15]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_logs_5mb_cpu	% cpu utilization	-0.14	[-5.24, +4.95]	1	(metrics) (profiles) (logs)

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	otlp_ingest_logs_5mb_memory	memory utilization	+13.77	[+13.34, +14.20]	1	(metrics) (profiles) (logs)
➖	dsd_uds_10mb_3k_contexts_cpu	% cpu utilization	+3.55	[-28.51, +35.61]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_5mb_cpu	% cpu utilization	+2.31	[+0.19, +4.43]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_ottl_filtering_5mb_cpu	% cpu utilization	+2.22	[-0.13, +4.58]	1	(metrics) (profiles) (logs)
➖	dsd_uds_1mb_3k_contexts_cpu	% cpu utilization	+1.31	[-53.48, +56.09]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_ottl_transform_5mb_cpu	% cpu utilization	+0.42	[-1.73, +2.56]	1	(metrics) (profiles) (logs)
➖	dsd_uds_10mb_3k_contexts_memory	memory utilization	+0.31	[+0.13, +0.49]	1	(metrics) (profiles) (logs)
➖	quality_gates_rss_dsd_medium	memory utilization	+0.31	[+0.11, +0.50]	1	(metrics) (profiles) (logs)
➖	dsd_uds_512kb_3k_contexts_cpu	% cpu utilization	+0.24	[-56.89, +57.36]	1	(metrics) (profiles) (logs)
➖	dsd_uds_500mb_3k_contexts_throughput	ingress throughput	+0.19	[+0.05, +0.32]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_metrics_5mb_throughput	ingress throughput	+0.04	[-0.09, +0.16]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_logs_5mb_throughput	ingress throughput	+0.02	[-0.11, +0.15]	1	(metrics) (profiles) (logs)
➖	dsd_uds_1mb_3k_contexts_throughput	ingress throughput	+0.00	[-0.05, +0.06]	1	(metrics) (profiles) (logs)
➖	dsd_uds_512kb_3k_contexts_throughput	ingress throughput	+0.00	[-0.05, +0.05]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_5mb_throughput	ingress throughput	+0.00	[-0.02, +0.02]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_ottl_filtering_5mb_throughput	ingress throughput	-0.00	[-0.02, +0.02]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_ottl_transform_5mb_throughput	ingress throughput	-0.00	[-0.02, +0.02]	1	(metrics) (profiles) (logs)
➖	dsd_uds_100mb_3k_contexts_throughput	ingress throughput	-0.00	[-0.04, +0.03]	1	(metrics) (profiles) (logs)
➖	dsd_uds_10mb_3k_contexts_throughput	ingress throughput	-0.00	[-0.13, +0.12]	1	(metrics) (profiles) (logs)
➖	dsd_uds_1mb_3k_contexts_memory	memory utilization	-0.01	[-0.18, +0.15]	1	(metrics) (profiles) (logs)
➖	quality_gates_rss_dsd_heavy	memory utilization	-0.03	[-0.17, +0.10]	1	(metrics) (profiles) (logs)
➖	dsd_uds_500mb_3k_contexts_memory	memory utilization	-0.06	[-0.23, +0.11]	1	(metrics) (profiles) (logs)
➖	dsd_uds_100mb_3k_contexts_memory	memory utilization	-0.09	[-0.27, +0.10]	1	(metrics) (profiles) (logs)
➖	quality_gates_rss_idle	memory utilization	-0.10	[-0.13, -0.08]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_5mb_memory	memory utilization	-0.11	[-0.35, +0.14]	1	(metrics) (profiles) (logs)
➖	dsd_uds_500mb_3k_contexts_cpu	% cpu utilization	-0.13	[-1.59, +1.33]	1	(metrics) (profiles) (logs)
➖	dsd_uds_512kb_3k_contexts_memory	memory utilization	-0.13	[-0.30, +0.04]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_logs_5mb_cpu	% cpu utilization	-0.14	[-5.24, +4.95]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_ottl_filtering_5mb_memory	memory utilization	-0.18	[-0.51, +0.16]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_traces_ottl_transform_5mb_memory	memory utilization	-0.38	[-0.64, -0.13]	1	(metrics) (profiles) (logs)
➖	quality_gates_rss_dsd_low	memory utilization	-0.40	[-0.59, -0.21]	1	(metrics) (profiles) (logs)
➖	quality_gates_rss_dsd_ultraheavy	memory utilization	-0.61	[-0.74, -0.48]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_metrics_5mb_cpu	% cpu utilization	-0.64	[-6.78, +5.50]	1	(metrics) (profiles) (logs)
➖	otlp_ingest_metrics_5mb_memory	memory utilization	-1.01	[-1.24, -0.78]	1	(metrics) (profiles) (logs)
➖	dsd_uds_100mb_3k_contexts_cpu	% cpu utilization	-1.93	[-7.92, +4.06]	1	(metrics) (profiles) (logs)

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	observed_value	links
✅	quality_gates_rss_dsd_heavy	memory_usage	10/10	115.48MiB ≤ 140MiB	(metrics) (profiles) (logs)
✅	quality_gates_rss_dsd_low	memory_usage	10/10	33.98MiB ≤ 50MiB	(metrics) (profiles) (logs)
✅	quality_gates_rss_dsd_medium	memory_usage	10/10	54.18MiB ≤ 75MiB	(metrics) (profiles) (logs)
✅	quality_gates_rss_dsd_ultraheavy	memory_usage	10/10	169.87MiB ≤ 200MiB	(metrics) (profiles) (logs)
✅	quality_gates_rss_idle	memory_usage	10/10	21.28MiB ≤ 40MiB	(metrics) (profiles) (logs)

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[webern]

Diff looks good now, only test code 👍