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Change OTel top-level spans identification logic to opt-in instead of default #24232

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merged 3 commits into from
Apr 2, 2024

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liustanley
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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.

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@brett0000FF brett0000FF left a comment

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Looks good from Docs team.

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pr-commenter bot commented Mar 29, 2024

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=31210518 --os-family=ubuntu

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pr-commenter bot commented Mar 29, 2024

Regression Detector

Regression Detector Results

Run ID: 9cc8a0e8-748d-4d25-87d4-e0f2a648e890
Baseline: 0d3dcb9
Comparison: 41d9e17

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

No significant changes in experiment optimization goals

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

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

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

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:

  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".

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@mx-psi mx-psi left a comment

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Nice! I think we can also expose this as a feature gate on the exporter :)

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@GustavoCaso GustavoCaso left a comment

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LGTM. I left a comment suggesting to add a test to ensure both functionalities are covered 😄

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/merge

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dd-devflow bot commented Apr 2, 2024

🚂 MergeQueue

Pull request added to the queue.

There are 2 builds ahead! (estimated merge in less than 28m)

Use /merge -c to cancel this operation!

@dd-mergequeue dd-mergequeue bot merged commit 03642f3 into main Apr 2, 2024
183 checks passed
@dd-mergequeue dd-mergequeue bot deleted the stanley.liu/top-level-opt-in branch April 2, 2024 14:49
agent-platform-auto-pr bot pushed a commit that referenced this pull request Apr 2, 2024
… default (#24232)

* Change top-level feature to opt in

* Revert previous tests and add new benchmark

* Add top level metric tests

(cherry picked from commit 03642f3)
dd-mergequeue bot pushed a commit that referenced this pull request Apr 2, 2024
@kacper-murzyn kacper-murzyn modified the milestones: 7.53.0, 7.54.0 Apr 3, 2024
alexgallotta pushed a commit that referenced this pull request May 9, 2024
… default (#24232)

* Change top-level feature to opt in

* Revert previous tests and add new benchmark

* Add top level metric tests
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7 participants