[CONTINT-3896] fix panic on shutdown in long running checks

[AliDatadog]

What does this PR do?

This PR fixes panics on shutdown in long running checks.

SBOM channels can be closed so we need to check for nil entries. We also would like to stop the checks on Cancel rather than stop Stop because it is always called:

	// Stop stops the check if it's running
	Stop()
	// Cancel cancels the check. Cancel is called when the check is unscheduled:
	// - unlike Stop, it is called even if the check is not running when it's unscheduled
	// - if the check is running, Cancel is called after Stop and may be called before the call to Stop completes
	Cancel()

Finally we would like Cancel to be called only once.

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Make sure that container image and sbom checks are still working.

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: b7f9eaee-4ac9-4725-a7d7-398945366603 Baseline: f05ac729b999ca5610490a5f23a2024f63165662 Comparison: 2b1d236930f4aabb00369e27016a6e677daeb1ac

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	+0.24	[-6.31, +6.79]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	otel_to_otel_logs	ingress throughput	+0.89	[+0.24, +1.55]
➖	process_agent_standard_check	memory utilization	+0.49	[+0.46, +0.53]
➖	file_to_blackhole	% cpu utilization	+0.24	[-6.31, +6.79]
➖	process_agent_standard_check_with_stats	memory utilization	+0.08	[+0.05, +0.11]
➖	process_agent_real_time_mode	memory utilization	+0.05	[+0.01, +0.09]
➖	trace_agent_msgpack	ingress throughput	+0.01	[+0.00, +0.02]
➖	tcp_dd_logs_filter_exclude	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	-0.02	[-0.05, +0.02]
➖	idle	memory utilization	-0.20	[-0.24, -0.17]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.22	[-0.27, -0.16]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.26	[-1.71, +1.18]
➖	file_tree	memory utilization	-0.66	[-0.72, -0.60]

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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