new-e2e-containers:TestContainerLifecycleEvents: make pod selection more robust

[alopezz]

What does this PR do?

Tries to make the TestContainerLifecycleEvents test more robust by checking that the agent has seen the candidate pod before making assertions on its lifecycle events.

Motivation

This test flakes sometimes (example, history of failures on main).

Describe how you validated your changes

It's hard to assess whether this solves the issue, as the frequency of the flake is fairly low, but if CI is green, it should at least not have broken anything.

Additional Notes

I'm not familiar with the code at hand, so it's possible that I'm way off base with this one.

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor f368d10

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${+0.12}$$	$${705.16}$$ < $${717.44}$$	$${+0.08}$$	$${172.99}$$ < $${177.37}$$
✅	agent_deb_amd64_fips	$${+0.12}$$	$${700.0}$$ < $${711.66}$$	$${+0.05}$$	$${171.7}$$ < $${174.84}$$
✅	agent_heroku_amd64	$${+0.06}$$	$${326.41}$$ < $${337.57}$$	$${+0}$$	$${87.34}$$ < $${90.78}$$
✅	agent_msi	$${+0.33}$$	$${969.39}$$ < $${1015.38}$$	$${+0.05}$$	$${141.45}$$ < $${149.42}$$
✅	agent_rpm_amd64	$${+0.12}$$	$${705.15}$$ < $${717.41}$$	$${+0.03}$$	$${175.96}$$ < $${178.01}$$
✅	agent_rpm_amd64_fips	$${+0.12}$$	$${699.99}$$ < $${711.64}$$	$${-0}$$	$${173.98}$$ < $${177.26}$$
✅	agent_rpm_arm64	$${+0.11}$$	$${690.82}$$ < $${702.19}$$	$${-0}$$	$${159.74}$$ < $${163.22}$$
✅	agent_rpm_arm64_fips	$${+0.12}$$	$${686.63}$$ < $${697.6}$$	$${+0.02}$$	$${159.09}$$ < $${162.68}$$
✅	agent_suse_amd64	$${+0.12}$$	$${705.15}$$ < $${717.41}$$	$${+0.03}$$	$${175.96}$$ < $${178.01}$$
✅	agent_suse_amd64_fips	$${+0.12}$$	$${699.99}$$ < $${711.64}$$	$${-0}$$	$${173.98}$$ < $${177.26}$$
✅	agent_suse_arm64	$${+0.11}$$	$${690.82}$$ < $${702.19}$$	$${-0}$$	$${159.74}$$ < $${163.22}$$
✅	agent_suse_arm64_fips	$${+0.12}$$	$${686.63}$$ < $${697.6}$$	$${+0.02}$$	$${159.09}$$ < $${162.68}$$
✅	docker_agent_amd64	$${+0.11}$$	$${767.49}$$ < $${769.92}$$	$${+0.02}$$	$${260.61}$$ < $${261.52}$$
✅	docker_agent_arm64	$${+0.11}$$	$${777.44}$$ < $${779.46}$$	$${+0.02}$$	$${250.8}$$ < $${251.72}$$
✅	docker_agent_jmx_amd64	$${+0.11}$$	$${958.37}$$ < $${960.8}$$	$${+0.03}$$	$${329.24}$$ < $${330.15}$$
✅	docker_agent_jmx_arm64	$${+0.11}$$	$${957.04}$$ < $${958.93}$$	$${+0.02}$$	$${315.42}$$ < $${316.35}$$
✅	docker_cluster_agent_amd64	$${+0.03}$$	$${178.87}$$ < $${207.49}$$	$${+0.01}$$	$${63.17}$$ < $${70.2}$$
✅	docker_cluster_agent_arm64	$${+0.06}$$	$${196.39}$$ < $${223.38}$$	$${+0.01}$$	$${59.89}$$ < $${66.42}$$
✅	docker_cws_instrumentation_amd64	$${0}$$	$${7.07}$$ < $${7.12}$$	$${+0}$$	$${2.95}$$ < $${3.29}$$
✅	docker_cws_instrumentation_arm64	$${0}$$	$${6.69}$$ < $${6.92}$$	$${+0}$$	$${2.7}$$ < $${3.07}$$
✅	docker_dogstatsd_amd64	$${+0.02}$$	$${38.6}$$ < $${39.3}$$	$${+0.01}$$	$${14.91}$$ < $${15.76}$$
✅	docker_dogstatsd_arm64	$${+0.06}$$	$${37.3}$$ < $${37.94}$$	$${+0.01}$$	$${14.36}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${+0.02}$$	$${29.82}$$ < $${30.53}$$	$${+0}$$	$${7.86}$$ < $${8.75}$$
✅	dogstatsd_deb_arm64	$${+0.02}$$	$${28.33}$$ < $${29.11}$$	$${+0}$$	$${6.8}$$ < $${7.71}$$
✅	dogstatsd_rpm_amd64	$${+0.02}$$	$${29.82}$$ < $${30.53}$$	$${+0}$$	$${7.88}$$ < $${8.76}$$
✅	dogstatsd_suse_amd64	$${+0.02}$$	$${29.82}$$ < $${30.53}$$	$${+0}$$	$${7.88}$$ < $${8.76}$$
✅	iot_agent_deb_amd64	$${+0.04}$$	$${42.59}$$ < $${43.19}$$	$${+0.01}$$	$${11.12}$$ < $${11.98}$$
✅	iot_agent_deb_arm64	$${+0.04}$$	$${40.33}$$ < $${40.92}$$	$${+0.01}$$	$${9.58}$$ < $${10.45}$$
✅	iot_agent_deb_armhf	$${+0.04}$$	$${40.23}$$ < $${40.86}$$	$${+0.01}$$	$${9.69}$$ < $${10.56}$$
✅	iot_agent_rpm_amd64	$${+0.04}$$	$${42.59}$$ < $${43.19}$$	$${+0.01}$$	$${11.14}$$ < $${12.0}$$
✅	iot_agent_suse_amd64	$${+0.04}$$	$${42.59}$$ < $${43.19}$$	$${+0.01}$$	$${11.14}$$ < $${12.0}$$

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 68b9492f-cf9b-440d-9c83-d9b009150d74

Baseline: 67324f0
Comparison: 35fd06d
Diff

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	+1.13	[-1.80, +4.07]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	otlp_ingest_logs	memory utilization	+1.25	[+1.14, +1.35]	1	Logs
➖	docker_containers_cpu	% cpu utilization	+1.13	[-1.80, +4.07]	1	Logs
➖	ddot_logs	memory utilization	+0.88	[+0.81, +0.95]	1	Logs
➖	otlp_ingest_metrics	memory utilization	+0.53	[+0.39, +0.67]	1	Logs
➖	ddot_metrics	memory utilization	+0.50	[+0.29, +0.72]	1	Logs
➖	quality_gate_idle	memory utilization	+0.33	[+0.28, +0.39]	1	Logs bounds checks dashboard
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	+0.32	[+0.08, +0.56]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.25	[+0.20, +0.30]	1	Logs bounds checks dashboard
➖	docker_containers_memory	memory utilization	+0.15	[+0.06, +0.24]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	+0.10	[-0.11, +0.32]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.04	[-0.37, +0.45]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.01	[-0.39, +0.41]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	+0.00	[-0.12, +0.13]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.07, +0.07]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.15, +0.13]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.01	[-0.07, +0.04]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-0.01	[-0.07, +0.04]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.06	[-0.42, +0.31]	1	Logs
➖	file_tree	memory utilization	-0.11	[-0.15, -0.06]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	-0.12	[-0.27, +0.03]	1	Logs
➖	quality_gate_logs	% cpu utilization	-0.28	[-1.72, +1.16]	1	Logs bounds checks dashboard
➖	quality_gate_metrics_logs	memory utilization	-0.36	[-0.58, -0.14]	1	Logs bounds checks dashboard
➖	tcp_syslog_to_blackhole	ingress throughput	-0.69	[-0.76, -0.62]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	docker_containers_cpu	simple_check_run	10/10
✅	docker_containers_memory	memory_usage	10/10
✅	docker_containers_memory	simple_check_run	10/10
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	lost_bytes	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_100ms_latency	lost_bytes	10/10
✅	file_to_blackhole_100ms_latency	memory_usage	10/10
✅	file_to_blackhole_500ms_latency	lost_bytes	10/10
✅	file_to_blackhole_500ms_latency	memory_usage	10/10
✅	quality_gate_idle	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_logs	lost_bytes	10/10	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	cpu_usage	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	intake_connections	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	lost_bytes	10/10	bounds checks dashboard
✅	quality_gate_metrics_logs	memory_usage	10/10	bounds checks dashboard

Explanation

Confidence level: 90.00%
Effect size tolerance: |Δ mean %| ≥ 5.00%

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

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".

CI Pass/Fail Decision

✅ Passed. All Quality Gates passed.

• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.

[rdesgroppes]

I think your proposal makes sense, but then why not iterate (still favoring the oldest pod) rather than considering only one?