[dca] Do not load language detection patcher if disabled

[sblumenthal]

What does this PR do?

This PR does not load the language detection patcher in the cluster agent if language_detection.enabled is set to false

Motivation

Follow-up to #23621, the middle point in the QA section was not working as expected

Additional Notes

I have tested this locally and confirmed that the log line no longer exists when language_detection.enabled is set to false, and does show up when language_detection.enabled is set to true

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Same as #23621. Specifically:
Ensure that:

• no cluster agent logs shows that the language detection patcher was activated (e.g. Starting language detection patcher)

... as the other points in PR have been validated

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 30847096 (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-developer-experience

[pr-commenter]

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=30847096 --os-family=ubuntu

[pr-commenter]

Regression Detector

Regression Detector Results

Run ID: b05b9c70-81e5-4296-9cb9-201c5e78c66d
Baseline: f11876e
Comparison: 3807d35

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.

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	+54.06	[+47.21, +60.92]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
❌	file_to_blackhole	% cpu utilization	+54.06	[+47.21, +60.92]
➖	file_tree	memory utilization	+2.08	[+1.96, +2.21]
➖	tcp_syslog_to_blackhole	ingress throughput	+1.55	[+1.46, +1.64]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.30	[-1.61, +4.21]
➖	idle	memory utilization	+0.98	[+0.91, +1.05]
➖	process_agent_standard_check_with_stats	memory utilization	+0.50	[+0.44, +0.56]
➖	otel_to_otel_logs	ingress throughput	+0.06	[-0.38, +0.51]
➖	trace_agent_json	ingress throughput	+0.01	[-0.01, +0.03]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.04, +0.04]
➖	trace_agent_msgpack	ingress throughput	-0.01	[-0.01, -0.00]
➖	uds_dogstatsd_to_api	ingress throughput	-0.02	[-0.22, +0.19]
➖	process_agent_real_time_mode	memory utilization	-0.50	[-0.58, -0.43]
➖	pycheck_1000_100byte_tags	% cpu utilization	-1.21	[-6.05, +3.63]
➖	process_agent_standard_check	memory utilization	-1.71	[-1.78, -1.63]
➖	basic_py_check	% cpu utilization	-1.84	[-4.21, +0.52]

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".

[sblumenthal]

/merge

[dd-devflow]

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