[CONTINT-3524] Wrap contimage and contlifecycle checks as long running checks

[AliDatadog]

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What does this PR do?

This PR wraps the contimage and contlfiecycle checks as long running checks to retrieve their stats in agent status output.

As part of https://github.com/DataDog/datadog-agent/pull/22313, we introduced a long running check wrapper to collect long running checks metrics as part of the agent status and agent flare command (that runs agent status). We successfully converted SBOM collection to a long running check. Now we would like to do the same for contimage and contlifecycle.

Motivation

Make investigations easier

Additional Notes

N/A

Possible Drawbacks / Trade-offs

N/A

Describe how to test/QA your changes

1. Health check We can verify that the checks are still working with the new-e2e test framework (TestContainerImage). We can also check the container-images page or the output of agent stream-event-platform --type container-image # (or container-lifecycle).

2. Agent status QA Deploy the agent on Kubernetes and make sure agent status shows the long running checks metrics. Similar to:

    container_image
    ---------------
      Instance ID: container_image [OK]
      Long Running Check: true
      Configuration Source: file:/etc/datadog-agent/conf.d/container_image.yaml
      Total Metric Samples: 0
      Total Events: 0
      Total container-images: 147
      Total Service Checks: 0


    container_lifecycle
    -------------------
      Instance ID: container_lifecycle [OK]
      Long Running Check: true
      Configuration Source: file:/etc/datadog-agent/conf.d/container_lifecycle.d/conf.yaml.default
      Total Metric Samples: 0
      Total Events: 0
      Total container-lifecycle: 5
      Total Service Checks: 0

[AliDatadog]

/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 #29007739

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: 126ec670-87a1-439d-b460-004d67cf5e45 Baseline: 291dd37ff5b861a25c3972701321c18ec551acb5 Comparison: 3136f773348dd3a0b560e1e7d6629bf38cd8114b 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	-0.00	[-6.57, +6.57]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_tree	memory utilization	+0.79	[+0.70, +0.88]
➖	process_agent_standard_check_with_stats	memory utilization	+0.10	[+0.07, +0.14]
➖	trace_agent_json	ingress throughput	+0.00	[-0.04, +0.04]
➖	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]
➖	file_to_blackhole	% cpu utilization	-0.00	[-6.57, +6.57]
➖	idle	memory utilization	-0.01	[-0.04, +0.02]
➖	process_agent_real_time_mode	memory utilization	-0.10	[-0.13, -0.07]
➖	process_agent_standard_check	memory utilization	-0.20	[-0.24, -0.16]
➖	otel_to_otel_logs	ingress throughput	-0.21	[-0.83, +0.41]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.41	[-1.82, +1.00]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.74	[-0.80, -0.68]

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".

[AliDatadog]

/merge

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[AliDatadog]

/merge

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