Fix the cluster name injected by the admission controller in sidecar

[L3n41c]

What does this PR do?

Fix the injection of the cluster name on agent sidecar container.

Motivation

Current code was working if the cluster name was explicitly defined in the configuration but not if the cluster agent had been able to find it on its own.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Follow the instructions of #22535 to configure agent sidecar injection on an EKS cluster. Take care of letting the cluster agent determine the EKS cluster name automatically via the EC2 API and do not explicitly set it through the config. Before this PR, the DD_CLUSTER_NAME environment variable was injected in the agent sidecar container without any value. With this PR, the cluster agent should properly inject the cluster name.

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 96608bcc-602c-4631-8e7f-efe7f28f483d Baseline: 3d1baa4230919f73dca0ea575ad65a9b26eec9e7 Comparison: 7f221350b6a929ad313a766f42971c3e7442a921 Total CPUs: 7

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

Experiments with missing or malformed data

• basic_py_check

Usually, this warning means that there is no usable optimization goal data for that experiment, which could be a result of misconfiguration.

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.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_to_blackhole	% cpu utilization	+0.11	[-6.43, +6.65]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	process_agent_real_time_mode	memory utilization	+0.30	[+0.27, +0.34]
➖	idle	memory utilization	+0.12	[+0.09, +0.15]
➖	file_to_blackhole	% cpu utilization	+0.11	[-6.43, +6.65]
➖	process_agent_standard_check	memory utilization	+0.09	[+0.05, +0.13]
➖	file_tree	memory utilization	+0.06	[-0.03, +0.15]
➖	tcp_syslog_to_blackhole	ingress throughput	+0.05	[-0.02, +0.11]
➖	trace_agent_json	ingress throughput	+0.02	[-0.02, +0.05]
➖	trace_agent_msgpack	ingress throughput	+0.01	[-0.01, +0.02]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	otel_to_otel_logs	ingress throughput	-0.24	[-0.84, +0.36]
➖	process_agent_standard_check_with_stats	memory utilization	-0.53	[-0.56, -0.50]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.97	[-3.39, -0.56]

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".

[L3n41c]

/merge

[dd-devflow[bot]]

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