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[CWS] fix container context resolver #27220

Closed safchain closed 3 days ago

safchain commented 3 days ago

What does this PR do?

ContainerContext can't be nil, use the resolved value attribute instead. This remove the serialization of a process context with an invalid date when there is no container context.

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

safchain commented 3 days ago

/merge

dd-devflow[bot] commented 3 days 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!

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=38113972 --os-family=ubuntu

Note: This applies to commit 95bf19d8

dd-devflow[bot] commented 3 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!

pr-commenter[bot] commented 3 days ago

Regression Detector

Regression Detector Results

Run ID: 814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c Metrics dashboard Target profiles

Baseline: ca763b05d9cb19d166a8c073fd6f13a503016e86 Comparison: 95bf19d884c7f083a0bb8d0330e703fe16ed002c

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 | |------|----------------------------|--------------------|----------|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.20 | [-3.62, +6.02] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&live=false) | | ➖ | file_tree | memory utilization | +0.12 | [+0.01, +0.24] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&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%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&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%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&live=false) | | ➖ | idle | memory utilization | -0.21 | [-0.25, -0.17] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.38 | [-3.07, +2.30] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.52 | [-1.33, +0.29] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -0.87 | [-13.56, +11.81] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.98 | [-1.86, -0.09] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A814e39f5-f0cb-4d1b-b65b-3bf6cd65ec3c&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=1719913636000&to_ts=1719925036000&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".