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DataDog / datadog-agent

Main repository for Datadog Agent
https://docs.datadoghq.com/
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Allow impl folder with suffix #27199

Closed dustmop closed 3 months ago

dustmop commented 3 months ago

What does this PR do?

Allow components to pass the linter if they're using multiple implementations without a "primary" impl folder.

Motivation

The docs explicitly allow components to use "non-primary" implementations, such as "impl-a" and "impl-b", but the linter doesn't take this use case into account: https://datadoghq.dev/datadog-agent/components/creating-components/#file-hierarchy

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

dustmop commented 3 months ago

/merge

dd-devflow[bot] commented 3 months ago

:steam_locomotive: MergeQueue: waiting for PR to be ready

This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals. Note: if you pushed new commits since the last approval, you may need additional approval. You can remove it from the waiting list with /remove command.

Use /merge -c to cancel this operation!

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

[Fast Unit Tests Report]

On pipeline 38027524 (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

dd-devflow[bot] commented 3 months ago

:steam_locomotive: MergeQueue: pull request added to the queue

The median merge time in main is 24m.

Use /merge -c to cancel this operation!

pr-commenter[bot] commented 3 months ago

Regression Detector

Regression Detector Results

Run ID: 8d8c1c25-23cd-4941-a698-126820af716d Metrics dashboard Target profiles

Baseline: ddbc83ccd58e53fd370620254b5ad27b4de6f3a2 Comparison: 1b8563cf880e4d25e441f5a76c115541b60c61fd

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 | |------|----------------------------|--------------------|----------|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | file_tree | memory utilization | +1.77 | [+1.67, +1.88] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.29 | [-3.60, +6.18] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&live=false) | | ➖ | idle | memory utilization | +0.16 | [+0.13, +0.19] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&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%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&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%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.09 | [-0.97, +0.80] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.69 | [-3.27, +1.89] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -1.09 | [-1.90, -0.29] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -1.16 | [-13.94, +11.63] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A8d8c1c25-23cd-4941-a698-126820af716d&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=1719850621000&to_ts=1719862021000&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".