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usm: Add support for Kafka Produce v9/v10 #27176

Closed vitkyrka closed 1 day ago

vitkyrka commented 2 days ago

What does this PR do?

Adds support for v9 and v10 of the Kafka Produce API to USM.

Motivation

https://datadoghq.atlassian.net/browse/USMON-828

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

pr-commenter[bot] commented 1 day 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=38105222 --os-family=ubuntu

Note: This applies to commit 4c4988ac

pr-commenter[bot] commented 1 day ago

Regression Detector

Regression Detector Results

Run ID: d25e33a6-f4cf-464f-ba6a-578f000d89b2 Metrics dashboard Target profiles

Baseline: 6ff7054fa07f015fddec313d2d6f4bc381cb2705 Comparison: 4c4988ac04f60faa6b422eef945ac39b07347538

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 | |------|----------------------------|--------------------|----------|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | otel_to_otel_logs | ingress throughput | +0.73 | [-0.08, +1.54] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.53 | [-0.37, +1.42] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&live=false) | | ➖ | idle | memory utilization | +0.30 | [+0.27, +0.34] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&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%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&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%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.07 | [-2.67, +2.53] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&live=false) | | ➖ | file_tree | memory utilization | -0.54 | [-0.58, -0.49] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -1.54 | [-6.41, +3.33] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -2.13 | [-14.94, +10.67] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3Ad25e33a6-f4cf-464f-ba6a-578f000d89b2&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=1719908735000&to_ts=1719920135000&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".
vitkyrka commented 1 day ago

/merge

dd-devflow[bot] commented 1 day 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!