[AGNTR-186] Automatic Agent RC staging deployments - improvement

[kacper-murzyn]

What does this PR do?

This PR is the second step required to utilise changes made in this PR.

After this change the child pipeline in k8s-datadog-agent-ops repository will automatically open the PR for staging deployments, which will make it easier for engineers responsible for RC deployments.

Some of the variables passed to the new pipeline where not required so I cleaned them up.

Motivation

Automation of the Agent RC build and QA process

Describe how to test/QA your changes

This will be tested e2e with next Agent RC build.

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 34924060 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_deb-arm64-py3
• tests_deb-x64-py3
• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64
• tests_rpm-arm64-py3
• tests_rpm-x64-py3
• tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-developer-experience

[kacper-murzyn]

/merge

[dd-devflow]

🚂 MergeQueue

Pull request added to the queue.

This build is going to start soon! (estimated merge in less than 28m)

Use /merge -c to cancel this operation!

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: a74a6359-3b92-4614-99e8-e76ba4acc6ed
Baseline: d80bb36
Comparison: 6fee044

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

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
➖	tcp_syslog_to_blackhole	ingress throughput	+3.18	[-18.73, +25.10]
➖	basic_py_check	% cpu utilization	+0.77	[-1.72, +3.26]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.28	[-2.56, +3.13]
➖	otel_to_otel_logs	ingress throughput	+0.06	[-0.30, +0.42]
➖	trace_agent_json	ingress throughput	-0.00	[-0.01, +0.01]
➖	trace_agent_msgpack	ingress throughput	-0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.20, +0.20]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.03	[-0.07, +0.02]
➖	idle	memory utilization	-0.28	[-0.32, -0.25]
➖	file_tree	memory utilization	-0.42	[-0.53, -0.31]
➖	pycheck_1000_100byte_tags	% cpu utilization	-2.07	[-6.81, +2.68]

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".