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[cluster-agent][cws-instrumentation] Disable mutate unlabelled by default #27224

Closed Gui774ume closed 3 days ago

Gui774ume commented 3 days ago

What does this PR do?

This PR disables "mutate_unlabelled" by default.

Motivation

As "remote_copy" is now the default parameter for cws-instrumentation, we do not need to call the pod admission hook point for all pods creation. Users that wish to use a shared volume to support read only filesystems (i.e. admission_controller.cws_instrumentation.remote_copy.mount_volume) will need to enable mutate_unlabelled as well if they want all they pods to be modified with the shared volume.

Gui774ume 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.

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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=38115105 --os-family=ubuntu

Note: This applies to commit 65c70f55

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: 24cb903f-fd4f-4496-9245-e763e7d11cd2 Metrics dashboard Target profiles

Baseline: 671f875b21bf84ec8b0d44cdd8cf1d450d808dc4 Comparison: 65c70f555144f433a1c06142ae71dee5861fb917

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 | +1.51 | [+1.39, +1.63] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +1.42 | [-3.44, +6.27] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&live=false) | | ➖ | basic_py_check | % cpu utilization | +0.73 | [-1.95, +3.41] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.51 | [-0.38, +1.41] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&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%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&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%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.67 | [-1.48, +0.14] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&live=false) | | ➖ | idle | memory utilization | -1.29 | [-1.34, -1.23] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -6.41 | [-18.94, +6.13] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A24cb903f-fd4f-4496-9245-e763e7d11cd2&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=1719914640000&to_ts=1719926040000&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".