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[FA] Test we support start after stop #27193

Open coignetp opened 1 day ago

coignetp commented 1 day ago

What does this PR do?

Test we can start & promote experiment after a stop

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

agent-platform-auto-pr[bot] commented 1 day ago

[Fast Unit Tests Report]

On pipeline 38088466 (CI Visibility). The following jobs did not run any unit tests:

Jobs: - tests_deb-arm64-py3 - tests_deb-x64-py3 - tests_flavor_dogstatsd_deb-x64 - tests_flavor_heroku_deb-x64 - tests_flavor_iot_deb-x64 - tests_rpm-arm64-py3 - tests_rpm-x64-py3 - tests_windows-x64

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-devx-help

pr-commenter[bot] commented 1 day ago

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=38088466 --os-family=ubuntu

Note: This applies to commit 59ff5a27

pr-commenter[bot] commented 1 day ago

Regression Detector

Regression Detector Results

Run ID: 39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb Metrics dashboard Target profiles

Baseline: ff3269e00c93678d2a3dd7df167bd272a6071fcf Comparison: 59ff5a2758e711ada7d30feb2b1691829bec035c

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

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.

Fine details of change detection per experiment

| perf | experiment | goal | Δ mean % | Δ mean % CI | links | |------|----------------------------|--------------------|----------|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +2.64 | [+1.74, +3.53] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | file_tree | memory utilization | +0.70 | [+0.63, +0.76] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | +0.00 | [-0.00, +0.00] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.09 | [-2.69, +2.50] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | idle | memory utilization | -0.19 | [-0.23, -0.15] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -0.34 | [-5.10, +4.43] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.38 | [-1.19, +0.43] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -2.93 | [-15.75, +9.90] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A39b3eb8c-71e9-494d-ac40-e2f87e7fa8cb&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719900734000&to_ts=1719912134000&live=false) |

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".