Import conductor configuration from k8s-datadog-agent-ops

[chouquette]

What does this PR do?

Import the conductor settings for agent beta builds & deployments.

Motivation

Avoiding starting the deployment through a downstream pipeline, and better leverage conductor's features, some of which are tied to the repository initiating the run (such as build impact analysis)

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[chouquette]

For simplicity, here's the source from the other repo: https://github.com/DataDog/k8s-datadog-agent-ops/blob/11f60d0e3df1fb2e724db3518473facf999648e0/service.datadog.yaml

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

[Fast Unit Tests Report]

On pipeline 39258756 (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]]

Regression Detector

Regression Detector Results

Run ID: c613268d-4017-43f3-be97-61ac7579e134 Metrics dashboard Target profiles

Baseline: 0fa9f1c0a0b016bfef6d6892f18f8f1e0e7f651f Comparison: 65358dd8072a263ee47d82cc54a18c1663857af1

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
➖	basic_py_check	% cpu utilization	+1.14	[-1.40, +3.69]	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.99	[-11.97, +13.95]	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.00, +0.00]	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	Logs
➖	idle	memory utilization	-0.11	[-0.14, -0.07]	Logs
➖	file_tree	memory utilization	-0.48	[-0.55, -0.41]	Logs
➖	otel_to_otel_logs	ingress throughput	-0.61	[-1.43, +0.20]	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.67	[-1.55, +0.21]	Logs
➖	pycheck_1000_100byte_tags	% cpu utilization	-1.31	[-5.99, +3.38]	Logs

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".

[chouquette]

/merge

[dd-devflow[bot]]

:steam_locomotive: MergeQueue: waiting for PR to be ready

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Use /merge -c to cancel this operation!

[dd-devflow[bot]]

:steam_locomotive: MergeQueue: pull request added to the queue

The median merge time in main is 28m.

Use /merge -c to cancel this operation!