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AMLII-1830 Fix Flaky UDP tests #27167

Open DDuongNguyen opened 4 days ago

DDuongNguyen commented 4 days ago

What does this PR do?

This PR add retries for some flaky tests in case a server or listener is not ready yet before testing.

Motivation

We got some reports on flaky tests here: TestUDPReceive

TestUDPForward

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=37897805 --os-family=ubuntu

Note: This applies to commit 62de1cda

pr-commenter[bot] commented 4 days ago

Regression Detector

Regression Detector Results

Run ID: 1da03171-06c7-4513-9fc0-e8e25836dc5f Metrics dashboard Target profiles

Baseline: aa64500157741181fbc4d8cc9c4f28079966b776 Comparison: 62de1cdacbbe45ccdd3ef1100273a500311caf3c

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 | +1.53 | [+0.64, +2.43] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | +1.18 | [-11.81, +14.17] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +0.99 | [-3.66, +5.63] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&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%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&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%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&live=false) | | ➖ | file_tree | memory utilization | -0.11 | [-0.16, -0.06] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&live=false) | | ➖ | idle | memory utilization | -0.17 | [-0.20, -0.13] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.62 | [-3.26, +2.02] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.64 | [-1.45, +0.16] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A1da03171-06c7-4513-9fc0-e8e25836dc5f&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=1719598526000&to_ts=1719609926000&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".