usm: postgres: tests: Do not use kafka test context

[guyarb]

What does this PR do?

Declares a dedicated test context for postgres

Motivation

Not using the same struct of kafka

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

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

[Fast Unit Tests Report]

On pipeline 38109752 (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=38109752 --os-family=ubuntu

Note: This applies to commit c83f3f0c

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: 61cf9bc1-5b75-4cda-a017-814eebdbf318 Metrics dashboard Target profiles

Baseline: f350ef14a5ecfaf059e313b7d87460aa24460a81 Comparison: c83f3f0c4fff4e3201fa3ded5c08c41816dd501e

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 | |------|----------------------------|--------------------|----------|-----------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | tcp_syslog_to_blackhole | ingress throughput | +4.20 | [-8.88, +17.28] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&live=false) | | ➖ | file_tree | memory utilization | +0.92 | [+0.86, +0.99] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&live=false) | | ➖ | idle | memory utilization | +0.54 | [+0.50, +0.57] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aidle%20run_id%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&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%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&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%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.41 | [-1.22, +0.40] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.72 | [-1.60, +0.17] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&live=false) | | ➖ | basic_py_check | % cpu utilization | -0.89 | [-3.54, +1.77] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&live=false) | | ➖ | pycheck_1000_100byte_tags | % cpu utilization | -4.18 | [-8.77, +0.41] | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_1000_100byte_tags%20run_id%3A61cf9bc1-5b75-4cda-a017-814eebdbf318&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=1719911774000&to_ts=1719923174000&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".

[guyarb]

/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!