search

DataDog / datadog-agent

Main repository for Datadog Agent
https://docs.datadoghq.com/
Apache License 2.0
2.73k stars 1.17k forks source link

[e2e] rollback pulumi-aws dependency #27189

Closed pducolin closed 1 day ago

pducolin commented 2 days ago

What does this PR do?

Rollback pulumi-aws dependency to v6.25.0

Motivation

Align back with the dependency version in test-infra-definitions to avoid the Github rate limit error in e2e tests while trying to download a plugin that is not required.

Additional Notes

This targets the dependency used by the test-infra-definitions version in use at this moment by datadog-agent. test-infra-definitions main was upgraded to try to align to the most recent version, but this breaks e2e tests at fakeintake deployment. This PR is reverting the bump on latest test-infra-definitions too. I will investigate the root cause of the issue after fixing datadog-agent

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

pr-commenter[bot] commented 2 days ago

Regression Detector

Regression Detector Results

Run ID: 63fa7c2b-46f3-4615-9492-0a06515f6513 Metrics dashboard Target profiles

Baseline: d2054c27123125b38fe349616fb80b479233a1c3 Comparison: 8b96aacf069ec5169721c1026abcc0557f88bbff

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 | +4.43 | [-8.84, +17.70] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&live=false) | | ➖ | basic_py_check | % cpu utilization | +1.38 | [-1.34, +4.10] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.52 | [-0.36, +1.39] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&live=false) | | ➖ | idle | memory utilization | +0.06 | [+0.03, +0.09] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&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%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&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%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&live=false) | | ➖ | file_tree | memory utilization | -0.40 | [-0.45, -0.36] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -0.47 | [-5.07, +4.13] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -1.03 | [-1.84, -0.23] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A63fa7c2b-46f3-4615-9492-0a06515f6513&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=1719826924000&to_ts=1719838324000&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".