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Main repository for Datadog Agent
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[DONOT MERGE] Add custom component #27162

Open dineshg13 opened 4 days ago

dineshg13 commented 4 days ago

What does this PR do?

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

pr-commenter[bot] commented 4 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=37890217 --os-family=ubuntu

Note: This applies to commit e54c3dff

pr-commenter[bot] commented 4 days ago

Regression Detector

Regression Detector Results

Run ID: 78c95791-e342-410f-b168-c380cee69f26 Metrics dashboard Target profiles

Baseline: ec632caa83025dae10bc8c5152c978440f7da0be Comparison: e54c3dffa6e81c6487a5bcca1f41341ca7c82956

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 | +5.49 | [-7.79, +18.78] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +0.62 | [-4.16, +5.39] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&live=false) | | ➖ | file_tree | memory utilization | +0.08 | [+0.04, +0.13] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&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%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&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%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&live=false) | | ➖ | idle | memory utilization | -0.08 | [-0.11, -0.05] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.42 | [-1.23, +0.39] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -1.47 | [-2.34, -0.61] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&live=false) | | ➖ | basic_py_check | % cpu utilization | -2.00 | [-4.55, +0.55] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A78c95791-e342-410f-b168-c380cee69f26&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=1719595110000&to_ts=1719606510000&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".