[clusteragent/autoscaling] Fix flaky DCA target test

[jennchenn]

What does this PR do?

Check that DPA is added to store before continuing to test that the proper error status is set.

Motivation

This test has flaked a few times. The error seems to be caused by the object not being found "in kubernetes". This moves the call to add the object to f.Objects earlier, better matching the logic used in the TestLeaderCreateDeleteLocal test.

Describe how to test/QA your changes

Check that test does not flake.

Possible Drawbacks / Trade-offs

Additional Notes

It has been quite difficult to test this change as I am not able to reproduce the same failure locally, so not 100% sure this will fix the flake but probably a change we would want to make anyway.

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 45964698 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-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

[agent-platform-auto-pr]

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=45964698 --os-family=ubuntu

Note: This applies to commit 70c67ab

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 21fa370f-6534-4939-a778-d5d739e3a74f Metrics dashboard Target profiles

Baseline: 73a3f63
Comparison: 70c67ab

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	trials	links
➖	basic_py_check	% cpu utilization	+2.93	[+0.17, +5.69]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+0.75	[-1.77, +3.28]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.34	[+0.29, +0.40]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.13	[-0.67, +0.94]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.12, +0.11]	1	Logs
➖	idle_all_features	memory utilization	-0.19	[-0.31, -0.07]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.26	[-0.99, +0.48]	1	Logs
➖	idle	memory utilization	-0.34	[-0.39, -0.28]	1	Logs
➖	file_tree	memory utilization	-1.19	[-1.31, -1.07]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	idle	memory_usage	10/10

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".

[jennchenn]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 22m.

Use /merge -c to cancel this operation!