Prevent job failure when github workflows are cancelled

[chouetz]

What does this PR do?

Handle the possible cancelled state raising from github as we will allow workflow cancellation to remove pressure on builds.

• Merge the repack task with the test trigger: we must repack when a tarball is generated but bypass when we have no tarball in case of anticipated cancellation. To pass the information between workflow triggering and tarball generation it's easier to do this in a single task. Besides this task is purely ci-related (no need to launch it manually)
• factorize the different trigger_macos_xxx task to handle the Exit code once for all. And Exit(1) only on failures

Motivation

We want to enable github workflow cancellation. As such we must handle situations where tarball is not generated during the test workflow. And allow cancelled jobs for build or linting.

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: d0f20040-47bd-4b5f-98cd-f54e30826d63 Baseline: f0e5f932ecda5207391ca3770a178d3308f42621 Comparison: 3920ea5cd77adda4ebb9e52b4e541d9a8370af0e 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	-0.07	[-6.64, +6.51]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	file_tree	memory utilization	+1.06	[+0.98, +1.15]
➖	process_agent_real_time_mode	memory utilization	+0.76	[+0.72, +0.81]
➖	process_agent_standard_check_with_stats	memory utilization	+0.56	[+0.52, +0.60]
➖	process_agent_standard_check	memory utilization	+0.53	[+0.48, +0.58]
➖	trace_agent_msgpack	ingress throughput	+0.00	[-0.00, +0.00]
➖	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	-0.00	[-0.04, +0.04]
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.02	[-1.44, +1.41]
➖	file_to_blackhole	% cpu utilization	-0.07	[-6.64, +6.51]
➖	idle	memory utilization	-0.24	[-0.28, -0.19]
➖	otel_to_otel_logs	ingress throughput	-0.24	[-0.87, +0.38]
➖	tcp_syslog_to_blackhole	ingress throughput	-0.78	[-0.84, -0.72]

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".

[chouetz]

/merge

[dd-devflow[bot]]

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