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Main repository for Datadog Agent
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[workloadmeta/store] Fix filters that match entities #27151

Closed davidor closed 5 days ago

davidor commented 5 days ago

What does this PR do?

Fixes a bug in the workloadmeta store. It was not working correctly for filters that match entities, like workloadmeta.IsNodeMetadata used by the metadata controller.

I've set the no-changelog label because this doesn't affect any released versions.

Describe how to test/QA your changes

Skip. It's a bit difficult to test this case manually, but I added a unit test that should cover it.

davidor commented 5 days ago

/merge

dd-devflow[bot] commented 5 days ago

:steam_locomotive: MergeQueue: waiting for PR to be ready

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dd-devflow[bot] commented 5 days ago

:steam_locomotive: MergeQueue: pull request added to the queue

The median merge time in main is 26m.

Use /merge -c to cancel this operation!

pr-commenter[bot] commented 5 days ago

Regression Detector

Regression Detector Results

Run ID: 88faa51c-0fda-4357-9eb4-ae9b1712dd42 Metrics dashboard Target profiles

Baseline: 1987bff89f063bf8bde58002196f5cd2f58245df Comparison: ea8e2c750b8c7b27d294eaa87d88ade81e965e3c

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 | +2.51 | [+1.61, +3.42] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.61 | [-3.25, +6.47] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&live=false) | | ➖ | idle | memory utilization | +0.39 | [+0.35, +0.42] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&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%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&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%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&live=false) | | ➖ | file_tree | memory utilization | -0.37 | [-0.42, -0.31] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.54 | [-1.35, +0.27] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&live=false) | | ➖ | basic_py_check | % cpu utilization | -1.10 | [-3.78, +1.58] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -5.35 | [-18.04, +7.34] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A88faa51c-0fda-4357-9eb4-ae9b1712dd42&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=1719570558000&to_ts=1719581958000&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".