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Change OTel top-level spans identification logic to opt-in instead of default #24232
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Looks good from Docs team.
Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv create-vm --pipeline-id=31210518 --os-family=ubuntu |
Regression DetectorRegression Detector ResultsRun ID: 9cc8a0e8-748d-4d25-87d4-e0f2a648e890 Performance changes are noted in the perf column of each table:
No significant changes in experiment optimization goalsConfidence level: 90.00% There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.
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perf | experiment | goal | Δ mean % | Δ mean % CI |
---|---|---|---|---|
➖ | file_to_blackhole | % cpu utilization | -1.26 | [-7.41, +4.89] |
Fine details of change detection per experiment
perf | experiment | goal | Δ mean % | Δ mean % CI |
---|---|---|---|---|
➖ | file_tree | memory utilization | +4.64 | [+4.53, +4.74] |
➖ | process_agent_standard_check | memory utilization | +0.45 | [+0.39, +0.51] |
➖ | idle | memory utilization | +0.28 | [+0.24, +0.32] |
➖ | otel_to_otel_logs | ingress throughput | +0.23 | [-0.22, +0.69] |
➖ | process_agent_real_time_mode | memory utilization | +0.17 | [+0.12, +0.22] |
➖ | basic_py_check | % cpu utilization | +0.11 | [-2.41, +2.64] |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.03 | [-0.01, +0.06] |
➖ | trace_agent_json | ingress throughput | +0.02 | [-0.01, +0.05] |
➖ | trace_agent_msgpack | ingress throughput | +0.01 | [+0.00, +0.01] |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.20, +0.20] |
➖ | pycheck_1000_100byte_tags | % cpu utilization | -0.37 | [-5.27, +4.53] |
➖ | process_agent_standard_check_with_stats | memory utilization | -0.42 | [-0.48, -0.37] |
➖ | tcp_syslog_to_blackhole | ingress throughput | -0.49 | [-0.57, -0.41] |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.93 | [-3.77, +1.91] |
➖ | file_to_blackhole | % cpu utilization | -1.26 | [-7.41, +4.89] |
Explanation
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:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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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.
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Its configuration does not mark it "erratic".
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Nice! I think we can also expose this as a feature gate on the exporter :)
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LGTM. I left a comment suggesting to add a test to ensure both functionalities are covered 😄
/merge |
🚂 MergeQueue Pull request added to the queue. There are 2 builds ahead! (estimated merge in less than 28m) Use |
… default (#24232) (#24301) Co-authored-by: liustanley <[email protected]>
… default (#24232) * Change top-level feature to opt in * Revert previous tests and add new benchmark * Add top level metric tests
What does this PR do?
Changes new OTel top-level spans identification logic from #22163 to opt-in instead of default.
Motivation
The OTel top-level spans identification changes have been delayed until we have a new policy for releasing breaking changes for OTel customers. Changing this feature to opt-in prevents breaking changes.
Additional Notes
Possible Drawbacks / Trade-offs
Describe how to test/QA your changes
Add APM feature flag "enable_otlp_compute_top_level_by_span_kind". Send OTLP spans of varying span kinds and verify that root spans and server/consumer spans are marked as top-level in Datadog. Also verify that client/producer spans are marked as measured and have stats computed, and internal spans are not marked as top-level or measured.