enhancement: Improve Tagging Reliability for Namespace Tags

[gabedos]

What does this PR do?

Caches last seen namespace metadata (namespaceLabels and namespaceAnnotations) within the Agent to be applied on newly created pods in previously seen namespaces. This aims to decrease the gap in which namespace based tagging is applied to workloads.

Instead of going worst case 60 seconds without having the KubeMetadata enriched information on a pod, we will instead use the last seen namespace metadata. It is better to use potentially 1min stale data than no data. Additionally, we don't expect namespace metadata to be frequently changing either.

Motivation

Increase the reliability of namespaceLabelsAsTags and namespaceAnnotationsAsTags for pre-existing namespaces on running Agents.

There still exists gaps in tags during Agent restarts (deployments) and for workloads in freshly created namespaces.

Describe how you validated your changes

The tag gabe_team is derived from the namespace label. Configured the Agent following the documentation

datadog:
  tlsVerify:
    false
  namespaceLabelsAsTags:
    "*": gabe_%%label%%

Before the fix, a portion of a pod's first few metric points likely would miss kubemetadata based tags. After 1 min, they were always there. This is problematic though for short-lived jobs. This workload depicted here is for a cron job scheduled every 1 minute for a 2 minute sleeping busybox container.

After the fix, we can see that kubemetadata based tags are never missing for workloads in previously collected namespaces.

Comparing going from the new vs old, you can see the new version always guarantees the tag presence whereas the old version eventually has all the tags present.

Additional Notes

Should have minimal memory impacts on the node agent. Each pod was already storing the reference to the same map of labels and annotations. We are now just creating another struct that references that same underlying map.

[cit-pr-commenter-54b7da]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: f3a76d1f-80d2-493e-9929-f4f412d22f63

Baseline: 0134072
Comparison: 6a7c2ee
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	-0.30	[-3.41, +2.82]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_logs	% cpu utilization	+1.40	[-0.11, +2.91]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	+0.98	[+0.93, +1.04]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.93	[+0.83, +1.02]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.86	[+0.76, +0.95]	1	Logs
➖	ddot_metrics	memory utilization	+0.77	[+0.55, +0.99]	1	Logs
➖	docker_containers_memory	memory utilization	+0.39	[+0.32, +0.46]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	+0.38	[+0.22, +0.54]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.25	[+0.22, +0.29]	1	Logs bounds checks dashboard
➖	otlp_ingest_metrics	memory utilization	+0.25	[+0.09, +0.41]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	+0.20	[-0.03, +0.43]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.06	[-0.41, +0.54]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.06	[-0.36, +0.48]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	+0.01	[-0.13, +0.14]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.13, +0.12]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.01	[-0.10, +0.09]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.05	[-0.43, +0.33]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-0.06	[-0.12, -0.01]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.06	[-0.11, -0.02]	1	Logs
➖	quality_gate_idle	memory utilization	-0.08	[-0.12, -0.04]	1	Logs bounds checks dashboard
➖	docker_containers_cpu	% cpu utilization	-0.30	[-3.41, +2.82]	1	Logs
➖	ddot_logs	memory utilization	-0.58	[-0.64, -0.52]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	-0.81	[-1.01, -0.60]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	-1.68	[-1.90, -1.47]	1	Logs bounds checks dashboard

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

[maycmlee]

Small edits, but approving to not block merge

[maycmlee]

Suggested change

	Increases the reliability of namespaceLabelsAsTags and
	namespaceAnnotationsAsTags for new pods by caching the
	Increases the reliability of `namespaceLabelsAsTags` and
	`namespaceAnnotationsAsTags` for new pods by caching the