[process-agent] explicitly invoke the tagger component to fix container tagging

[wiyu]

What does this PR do?

Invokes the tagger component directly in the process-agent and the process check subcommand. The tagger was migrated to a component in https://github.com/DataDog/datadog-agent/pull/21314 This stopped initializing it explicitly.

This fix was introduced in: https://github.com/DataDog/datadog-agent/pull/22929, but had was bulk reverted in https://github.com/DataDog/datadog-agent/pull/22951

Motivation

Tags were missing from container collection as the tagger is not initialized.

Additional Notes

Future follow-ups would be to componentize the ContainerProvider and have it depend on the tagger, so it is properly invoked/started.

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

In an environment with running containers

• Start the process-agent with process or container checks enabled.
• Check tags show up in Live containers and Live processes
• Run the tagger-list sub-command and verify containers and tags show up process-agent tagger-list

[wiyu]

/trigger-ci --variable RUN_ALL_BUILDS=true --variable RUN_KITCHEN_TESTS=true --variable RUN_E2E_TESTS=auto

[dd-devflow[bot]]

:steam_locomotive: Gitlab pipeline started

Started pipeline #28554611

[pr-commenter[bot]]

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Regression Detector Results

Run ID: d12c504e-c09f-4841-a414-f6ddfb48ef97 Baseline: ebd420a89484caf654a89bcbcc580b6e355a877a Comparison: b82166d9fa960a4b6bd8374c93c0e5986158f6be 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	-0.18	[-6.71, +6.34]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	tcp_syslog_to_blackhole	ingress throughput	+0.33	[+0.27, +0.39]
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_msgpack	ingress throughput	+0.00	[-0.00, +0.00]
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.00, +0.00]
➖	trace_agent_json	ingress throughput	-0.01	[-0.04, +0.03]
➖	process_agent_standard_check	memory utilization	-0.04	[-0.10, +0.01]
➖	idle	memory utilization	-0.06	[-0.08, -0.03]
➖	file_to_blackhole	% cpu utilization	-0.18	[-6.71, +6.34]
➖	process_agent_standard_check_with_stats	memory utilization	-0.32	[-0.35, -0.28]
➖	file_tree	memory utilization	-0.38	[-0.46, -0.30]
➖	process_agent_real_time_mode	memory utilization	-0.46	[-0.51, -0.40]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.24	[-2.64, +0.16]
➖	otel_to_otel_logs	ingress throughput	-1.29	[-1.88, -0.69]

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".

[wiyu]

/merge

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