Disable zypper repos on suse step-by-step e2e tests

[alopezz]

What does this PR do?

Deletes all repo files for step-by-step installation e2e tests on Suse, effectively disabling any repos not under our control

Motivation

Addressing this type of failure: https://gitlab.ddbuild.io/DataDog/datadog-agent/-/jobs/540198262

Additional Notes

zypper doesn't seem to honor --no-refresh and still goes ahead and tries to refresh a bunch of repositories that we don't really care about. This approach is heavy-handed but it's the most direct and I see no potential drawbacks.

I launched the test from my machine and it does pass, while it fails pretty consistently without this change.

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 36495053 (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

[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=36495053 --os-family=ubuntu

Note: This applies to commit 8bcf5b4

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: 27afd32b-d94f-44bf-ab01-917ebb43a9bd
Baseline: 4cda34a
Comparison: 8bcf5b4

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
➖	basic_py_check	% cpu utilization	+1.72	[-1.12, +4.57]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.08	[-1.97, +4.14]
➖	idle	memory utilization	+0.17	[+0.13, +0.21]
➖	pycheck_1000_100byte_tags	% cpu utilization	+0.17	[-4.66, +4.99]
➖	file_tree	memory utilization	+0.10	[+0.01, +0.18]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.20, +0.21]
➖	otel_to_otel_logs	ingress throughput	+0.00	[-0.36, +0.37]
➖	trace_agent_msgpack	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.02	[-0.03, +0.00]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.03	[-0.07, +0.01]
➖	tcp_syslog_to_blackhole	ingress throughput	-2.70	[-23.34, +17.94]

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".

[alopezz]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 36m.

Use /merge -c to cancel this operation!