add CI visibility links to KMT run jobs #26948
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brycekahle
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team/ebpf-platform
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Regression DetectorRegression Detector ResultsRun ID: d574a32a-fa84-49cb-b9b9-56405edcb8a1 Metrics dashboard Target profiles Baseline: 79aaadb 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 | links |
|---|---|---|---|---|---|
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +1.94 | [-11.12, +15.00] | Logs |
| ➖ | idle | memory utilization | +0.56 | [+0.53, +0.60] | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.00 | [-0.00, +0.00] | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.01, +0.01] | Logs |
| ➖ | file_tree | memory utilization | -0.11 | [-0.15, -0.06] | Logs |
| ➖ | pycheck_1000_100byte_tags | % cpu utilization | -0.42 | [-5.20, +4.36] | Logs |
| ➖ | otel_to_otel_logs | ingress throughput | -0.44 | [-1.25, +0.37] | Logs |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.56 | [-1.45, +0.33] | Logs |
| ➖ | basic_py_check | % cpu utilization | -1.16 | [-3.85, +1.52] | Logs |
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".
tasks/gitlab_helpers.py
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| def get_link_to_job_id(job_id: str): | ||
| def get_link_to_pipeline_job_id(job_id: str): | ||
| query_params = { | ||
| "ci_level": "job", | ||
| "@ci.job.id": job_id, | ||
| "@ci.pipeline.name": "DataDog/datadog-agent", | ||
| } | ||
| query_string = to_query_string(query_params) |
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why don't we use urllib.parse.urlencode here? It's normally already quoted
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I think because the entire string is a query value, so characters like & need to be encoded too.
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From urllib.parse.urlencode
An alternate function that can be passed as quote_via is quote(), which will encode spaces as %20 and not encode ‘/’ characters. For maximum control of what is quoted, use quote and specify a value for safe.
I could use quote(urlencode(query_params)), didn't know it!
tasks/gitlab_helpers.py
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| def get_link_to_test_runs(job_id: str): | ||
| query_params = { | ||
| "test_level": "test", | ||
| "@ci.job.url": f"\"https://gitlab.ddbuild.io/DataDog/datadog-agent/-/jobs/{job_id}\"", |
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Could we use job.name and git.branch instead? It's not strictly similar if you have several executions on the same branch so maybe the commit id. It's then easier to navigate in ci-vis I guess
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If you retry a job in Gitlab, it will have a different ID but the branch, commit, and job name will remain the same.
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CI Test Vis executions miss the ci.job.id, I asked for it
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[Fast Unit Tests Report] On pipeline 37339878 (CI Visibility). The following jobs did not run any unit tests: Jobs:
If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-developer-experience |
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/merge |
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🚂 MergeQueue: pull request added to the queue The median merge time in Use |
What does this PR do?
Motivation
Easier pivot to CI Visibility product
Additional Notes
Possible Drawbacks / Trade-offs
Describe how to test/QA your changes