[test-infra-definitions][automated] Bump test-infra-definitions to 7e55b9e3279af35d88e4965820114d00e659942b

[agent-platform-auto-pr]

This PR was automatically created by the test-infra-definitions bump task.

This PR bumps the test-infra-definitions submodule to 7e55b9e3279af35d88e4965820114d00e659942b from 08b9d8e048c1.
Here is the full changelog between the two commits: DataDog/test-infra-definitions@08b9d8e...7e55b9e

⚠️ This PR is opened with the qa/no-code-change and changelog/no-changelog labels by default. Please make sure this is appropriate

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 46306540 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• 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

[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=46306540 --os-family=ubuntu

Note: This applies to commit d881dbf

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Run ID: addd7a3d-36de-44c0-9c7a-12f113f95fe1 Metrics dashboard Target profiles

Baseline: ad37c41
Comparison: d881dbf

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
➖	idle_all_features	memory utilization	+2.62	[+2.48, +2.75]	1	Logs
➖	file_tree	memory utilization	+0.74	[+0.62, +0.87]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.64	[-0.17, +1.45]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.58	[+0.48, +0.67]	1	Logs
➖	basic_py_check	% cpu utilization	+0.47	[-2.21, +3.14]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	+0.13	[-0.05, +0.30]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.09	[-0.39, +0.57]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.32, +0.34]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.11, +0.11]	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
➖	file_to_blackhole_500ms_latency	egress throughput	-0.06	[-0.31, +0.18]	1	Logs
➖	idle	memory utilization	-0.26	[-0.32, -0.21]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.41	[-1.13, +0.31]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	-1.09	[-3.49, +1.32]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	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

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".

[L3n41c]

This looks as if we were JSON-marshaling a structured data line 81 to unmarshal it line 84.

Wasn’t it possible to extract cluster name directly from the original structured data without having to serialize and deserialize it ?

[pducolin]

I considered it initially, it requires few more lines of code to extract and cast the items from the map. This is a temporary fix, as @KevinFairise2 is working on migrating this test to the provisioner based suite. I can change it back to the extraction from the map to improve performance - indeed extracting + casting would be a 0(1) operation.

[pducolin]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 26m.

Use /merge -c to cancel this operation!