Removed test optimization from the raw Kafka tests

[DanielLavie]

What does this PR do?

A continuation on #29143, removed test optimizations from the Kafka monitoring raw tests.

Motivation

Fixing Kafka flaky tests

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 46302836 (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-devx-help

[pr-commenter]

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=46302836 --os-family=ubuntu

Note: This applies to commit 2288c98

[pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: fcecf37a-9116-4c10-95c1-b1582e2e3cbc

Baseline: 81e640e
Comparison: 224d5ed

Regression Detector: ✅

Bounds Checks: ✅

Significant changes in experiment optimization goals

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
✅	pycheck_lots_of_tags	% cpu utilization	-5.88	[-9.35, -2.41]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	basic_py_check	% cpu utilization	+2.15	[-1.85, +6.16]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.83	[+1.10, +2.56]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.33	[+1.27, +1.40]	1	Logs
➖	idle	memory utilization	+0.34	[+0.30, +0.39]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.02	[-0.33, +0.36]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.00	[-0.18, +0.18]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.00	[-0.23, +0.22]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.11, +0.10]	1	Logs
➖	otel_to_otel_logs	ingress throughput	-0.04	[-0.85, +0.77]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.07	[-0.56, +0.43]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.08	[-0.33, +0.17]	1	Logs
➖	idle_all_features	memory utilization	-0.10	[-0.20, +0.01]	1	Logs
➖	file_tree	memory utilization	-0.63	[-0.75, -0.51]	1	Logs
✅	pycheck_lots_of_tags	% cpu utilization	-5.88	[-9.35, -2.41]	1	Logs

Bounds Checks Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_300ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	idle	memory_usage	10/10

Explanation

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

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".