[DCA][Flare] Adds DatadogAgent user values

[tbavelier]

What does this PR do?

• Same as [DCA][Flare] Adds Helm user values, cluster Agent manifest, node Agent manifest v2 #16313 but with the operator: adds redacted (using the flare redaction system)DatadogAgent user values by retrieving it from the API server
• Renames the environment variables that are not Helm specific

Motivation

Same motivation, help with troubleshooting:

As containers support team, I would like to get all the info needed directly from the flare. We usually ask customer to provide the helm value files and the daemonset|deployment manifests
As customer, I would like that my support ticket require the least possible roundtrip iterations to speedup the resolution.

Describe how to test/QA your changes

Using a kind cluster with the Operator built from below PR:

1. Deploy a DatadogAgent manifest
2. Run the flare command in the DCA
3. Retrieve the archive, unzip it and assert the presence of:
   • datadog-agent-cr.yaml: the DatadogAgent resource
   • agent-daemonset.yaml: the node Agent daemonset manifest
   • cluster-agent-deployment.yaml: the DCA deployment manifest

Possible Drawbacks / Trade-offs

N/A

Additional Notes

• This requires [DatadogAgent][Flare] Additional environment variables and RBAC to permit user manifests retrieval datadog-operator#1477 that adds the necessary env vars on the cluster Agent to function

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: ad289fec-86cb-46b5-8f57-790b80813f3b

Baseline: 01da4b5
Comparison: 1411c89
Diff

Optimization Goals: ❌ Significant changes detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+5.92	[+1.86, +9.99]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	basic_py_check	% cpu utilization	+5.92	[+1.86, +9.99]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.77	[+0.71, +0.82]	1	Logs
➖	idle_all_features	memory utilization	+0.67	[+0.56, +0.77]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	+0.48	[+0.34, +0.62]	1	Logs
➖	quality_gate_idle	memory utilization	+0.39	[+0.34, +0.44]	1	Logs bounds checks dashboard
➖	idle	memory utilization	+0.28	[+0.23, +0.33]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+0.19	[-0.54, +0.92]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.04	[-0.45, +0.53]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.00	[-0.25, +0.25]	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	[-0.22, +0.22]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.09, +0.08]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.01	[-0.34, +0.32]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.05	[-0.22, +0.13]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.46	[-0.56, -0.36]	1	Logs bounds checks dashboard
➖	pycheck_lots_of_tags	% cpu utilization	-1.52	[-4.99, +1.94]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	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	bounds checks dashboard
✅	idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

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

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".

[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=48321804 --os-family=ubuntu

Note: This applies to commit 1411c89

[jhgilbert]

Approved with very minor grammar suggestion, thanks!

[pgimalac]

Hard to review the code added in manifest as it's really out of my knowledge base, but LGTM

[tbavelier]

/merge

[dd-devflow]

🚂 MergeQueue: pull request added to the queue

The median merge time in main is 23m.

Use /merge -c to cancel this operation!