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Main repository for Datadog Agent
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[workloadmeta] Fix linter issues #27227

Closed davidor closed 2 days ago

davidor commented 3 days ago

What does this PR do?

Fixes a variety of linter issues in workloadmeta.

All of them are minor. Each one is on a separate commit.

There was a similar PR specific for the collectors: https://github.com/DataDog/datadog-agent/pull/27212

Describe how to test/QA your changes

Skip.

pr-commenter[bot] commented 3 days 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=38118945 --os-family=ubuntu

Note: This applies to commit 65003af8

pr-commenter[bot] commented 3 days ago

Regression Detector

Regression Detector Results

Run ID: c5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803 Metrics dashboard Target profiles

Baseline: f350ef14a5ecfaf059e313b7d87460aa24460a81 Comparison: 65003af8bb7d3e20ad25b5bec678f30db21a6546

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 | |------|----------------------------|--------------------|----------|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | tcp_syslog_to_blackhole | ingress throughput | +2.83 | [-10.11, +15.76] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&live=false) | | ➖ | file_tree | memory utilization | +0.88 | [+0.82, +0.94] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.76 | [-0.14, +1.65] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&live=false) | | ➖ | basic_py_check | % cpu utilization | +0.42 | [-2.29, +3.13] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&live=false) | | ➖ | idle | memory utilization | +0.17 | [+0.12, +0.21] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&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%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&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%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.41 | [-1.22, +0.40] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -1.17 | [-5.97, +3.64] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3Ac5dfd0a9-02b9-4a5c-aa84-7fdad8cd1803&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=1719916612000&to_ts=1719928012000&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".
davidor commented 2 days ago

/merge

dd-devflow[bot] commented 2 days 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!