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Main repository for Datadog Agent
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[test-infra-definitions][automated] Bump test-infra-definitions to 22e80bf13972b14260e490ef7b8138f6a06b9f0b #27159

Closed agent-platform-auto-pr[bot] closed 2 days ago

agent-platform-auto-pr[bot] commented 4 days ago

This PR was automatically created by the test-infra-definitions bump task.

This PR bumps the test-infra-definitions submodule to 22e80bf13972b14260e490ef7b8138f6a06b9f0b from 563e1012f405. Here is the full changelog between the two commits: https://github.com/DataDog/test-infra-definitions/compare/563e1012f405...22e80bf13972b14260e490ef7b8138f6a06b9f0b

:warning: This PR is opened with the qa/no-code-change and changelog/no-changelog labels by default. Please make sure this is appropriate

pr-commenter[bot] commented 4 days ago

Regression Detector

Regression Detector Results

Run ID: 83e213de-d011-47c0-9732-e74c11206d6e Metrics dashboard Target profiles

Baseline: 1cf1cce4b5711e0929b4eca8d3fd1c51cf3b8a4c Comparison: d2b035aa1a7e6cd78cfad099c84dd56483e6a40e

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 | |------|----------------------------|--------------------|----------|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | otel_to_otel_logs | ingress throughput | +0.82 | [+0.01, +1.64] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&live=false) | | ➖ | file_tree | memory utilization | +0.54 | [+0.45, +0.63] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&live=false) | | ➖ | idle | memory utilization | +0.37 | [+0.33, +0.41] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&live=false) | | ➖ | basic_py_check | % cpu utilization | +0.31 | [-2.27, +2.88] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&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%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&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%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.96 | [-1.83, -0.08] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -3.22 | [-7.75, +1.31] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -5.39 | [-18.10, +7.33] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A83e213de-d011-47c0-9732-e74c11206d6e&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=1719584530000&to_ts=1719595930000&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".