feat(dsm): add kafka_cluster_id to segmentio/kafka-go integration

[robcarlan-datadog]

Summary

• Adds kafka_cluster_id support to the segmentio/kafka-go DSM integration, building on feat(dsm): add kafka_cluster_id to confluent-kafka-go #4470
• Auto-fetches the Kafka cluster ID via metadata request in WrapReader/WrapWriter — no external API changes required
• Plumbs cluster ID through DSM edge tags, offset tracking, and span tags

Changes

• KafkaConfig.ClusterID field + WithClusterID option (for manual override)
• fetchClusterID() using kafka.Client.Metadata() — called automatically from WrapReader/WrapWriter
• kafka_cluster_id added to DSM checkpoint edge tags (produce + consume)
• TrackKafkaCommitOffsetWithCluster used for cluster-aware lag tracking
• messaging.kafka.cluster_id span tag on produce and consume spans
• Existing tests updated to account for cluster ID; new TestReadMessageFunctionalWithClusterID

Test plan

• Existing integration tests pass with cluster ID auto-detected
• TestReadMessageFunctionalWithClusterID validates cluster ID in spans and DSM pathway hashes
• go vet ./contrib/segmentio/kafka-go/... passes

🤖 Generated with Claude Code

[pr-commenter]

Benchmarks

Benchmark execution time: 2026-03-05 21:21:38

Comparing candidate commit e39a9ab in PR branch rob.carlan/kafka-go-cluster-id with baseline commit f98f0a4 in branch rob.carlan/dsm-kafka-cluster-id.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 157 metrics, 7 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 ----------------------------------'

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 59.86%. Comparing base (f98f0a4) to head (e39a9ab).

Additional details and impacted files

see 12 files with indirect coverage changes

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.
• 📦 JS Bundle Analysis: Save yourself from yourself by tracking and limiting bundle sizes in JS merges.