[FA] Fix installer flakiness

[coignetp]

What does this PR do?

Eventually assert catalog was set in each test

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[agent-platform-auto-pr[bot]]

[Fast Unit Tests Report]

On pipeline 38113777 (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[bot]]

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=38113777 --os-family=ubuntu

Note: This applies to commit 09ef6ae4

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: 5c634766-6e55-40b6-8731-20527f9a08a9 Metrics dashboard Target profiles

Baseline: 671f875b21bf84ec8b0d44cdd8cf1d450d808dc4 Comparison: 09ef6ae4ea3994893c8253f64bac4abe88e9c559

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 | links | |------|----------------------------|--------------------|----------|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | pycheck_1000_100byte_tags | % cpu utilization | +3.26 | [-1.72, +8.24] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | basic_py_check | % cpu utilization | +2.07 | [-0.60, +4.73] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | +1.28 | [+0.46, +2.09] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | +1.03 | [-11.92, +13.98] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.10 | [-0.77, +0.98] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | idle | memory utilization | +0.09 | [+0.05, +0.14] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.01] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.00, +0.00] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) | | ➖ | file_tree | memory utilization | -0.96 | [-1.07, -0.85] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A5c634766-6e55-40b6-8731-20527f9a08a9&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1719913120000&to_ts=1719924520000&live=false) |

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".

[coignetp]

/merge

[dd-devflow[bot]]

:steam_locomotive: MergeQueue: pull request added to the queue

The median merge time in main is 25m.

Use /merge -c to cancel this operation!