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Main repository for Datadog Agent
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Fixed daily / weekly failure summary notifications #27187

Closed CelianR closed 3 months ago

CelianR commented 3 months ago

What does this PR do?

Fixes notify.failure-summary-send-notifications:

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

agent-platform-auto-pr[bot] commented 3 months ago

[Fast Unit Tests Report]

On pipeline 37968672 (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

CelianR commented 3 months ago

/merge

dd-devflow[bot] commented 3 months ago

:steam_locomotive: MergeQueue: pull request added to the queue

The median merge time in main is 25m.

Use /merge -c to cancel this operation!

pr-commenter[bot] commented 3 months ago

Regression Detector

Regression Detector Results

Run ID: 590ad572-e126-4121-967f-04759fd1b4e9 Metrics dashboard Target profiles

Baseline: 848628afd3cb4a57f7c332f51f679a262fe8b3d8 Comparison: 9e54632e474eef5af7057243ae37cf71f828eae0

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 | +1.58 | [+0.69, +2.47] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.09 | [-3.81, +5.99] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&live=false) | | ➖ | idle | memory utilization | +0.14 | [+0.10, +0.18] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | +0.06 | [-0.75, +0.87] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&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%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&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%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.07 | [-2.72, +2.57] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&live=false) | | ➖ | file_tree | memory utilization | -0.53 | [-0.57, -0.48] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -2.41 | [-15.04, +10.23] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A590ad572-e126-4121-967f-04759fd1b4e9&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=1719826448000&to_ts=1719837848000&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".