fix(cws): Stop spawning a goroutine per event to capture network namespace handles

[YoannGh]

What does this PR do?

Events now push their network namespace handle request onto a bounded queue drained by the existing TC classifier goroutine, instead of each spawning its own goroutine.

Motivation

Every event spawned a goroutine that blocked on the namespace resolver lock while the periodic flush held it across slow netlink calls, piling up 5 to 16k goroutines on busy hosts.

Describe how you validated your changes

The existing functional tests capture namespace handles from events before checking that classifiers get attached.

Additional Notes

Dropped requests aren't counted as errors because bursts make them routine, and the namespace's next event queues it again.

[github-actions]

AI review by Codex (OpenAI) - workflow run

patch is correct. The bounded queue removes per-event goroutine growth and uses the existing resolver lifecycle without introducing an identifiable correctness defect. Review was static; tests were not run.

[datadog-datadog-prod-us1-2]

🎯 Code Coverage (details)
• Patch Coverage: 36.36%
• Overall Coverage: 58.60% (-0.01%)

_{This comment will be updated automatically if new data arrives.
🔗 Commit SHA: 8698801 | Docs | Give us feedback!}

[dd-octo-sts]

Files inventory check summary

File checks results against ancestor 1e25e036:

Results for datadog-agent_7.86.0~devel.git.305.8698801.pipeline.143730183-1_amd64.deb:

No change detected

Results for datadog-iot-agent_7.86.0~devel.git.305.8698801.pipeline.143730183-1_amd64.deb:

No change detected

[dd-octo-sts]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 1e25e03
📊 Static Quality Gates Dashboard
🔗 SQG Job

33 successful checks with minimal change (< 2 KiB)

	Quality gate	Current Size
✅	agent_deb_amd64	755.264 MiB
✅	agent_deb_amd64_fips	688.274 MiB
✅	agent_heroku_amd64	319.145 MiB
✅	agent_msi	653.830 MiB
✅	agent_rpm_amd64	755.248 MiB
✅	agent_rpm_amd64_fips	688.258 MiB
✅	agent_rpm_arm64	725.784 MiB
✅	agent_rpm_arm64_fips	664.367 MiB
✅	agent_suse_amd64	755.248 MiB
✅	agent_suse_amd64_fips	688.258 MiB
✅	agent_suse_arm64	725.784 MiB
✅	agent_suse_arm64_fips	664.367 MiB
✅	docker_agent_amd64	811.427 MiB
✅	docker_agent_arm64	806.590 MiB
✅	docker_agent_jmx_amd64	1002.297 MiB
✅	docker_agent_jmx_arm64	986.240 MiB
✅	docker_cws_instrumentation_amd64	7.443 MiB
✅	docker_cws_instrumentation_arm64	6.877 MiB
✅	docker_dogstatsd_amd64	39.551 MiB
✅	docker_dogstatsd_arm64	37.629 MiB
✅	docker_host_profiler_amd64	308.722 MiB
✅	docker_host_profiler_arm64	319.864 MiB
✅	dogstatsd_deb_amd64	30.293 MiB
✅	dogstatsd_deb_arm64	28.278 MiB
✅	dogstatsd_rpm_amd64	30.293 MiB
✅	dogstatsd_suse_amd64	30.293 MiB
✅	iot_agent_deb_amd64	47.371 MiB
✅	iot_agent_deb_arm64	43.837 MiB
✅	iot_agent_deb_armhf	44.652 MiB
✅	iot_agent_rpm_amd64	47.371 MiB
✅	iot_agent_suse_amd64	47.370 MiB
✅	docker_cluster_agent_amd64	211.196 MiB
✅	docker_cluster_agent_arm64	224.224 MiB

[cit-pr-commenter-54b7da]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Job ID: f17d1f29-7021-46d6-9920-d83415184d0f

Baseline: 1e25e03
Comparison: 8698801
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	quality_gate_security_mean_fs_load	memory utilization	+0.11	[+0.07, +0.15]	1	Logs bounds checks dashboard
➖	quality_gate_metrics_logs	memory utilization	+0.05	[-0.18, +0.27]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	+0.04	[-0.00, +0.09]	1	Logs bounds checks dashboard
➖	quality_gate_private_action_runner	memory utilization	+0.04	[-0.08, +0.17]	1	Logs bounds checks dashboard
➖	quality_gate_security_idle	memory utilization	-0.01	[-0.05, +0.02]	1	Logs bounds checks dashboard
➖	quality_gate_security_no_fs_load	memory utilization	-0.09	[-0.16, -0.02]	1	Logs bounds checks dashboard
➖	python_openmetrics	% cpu utilization	-0.20	[-0.89, +0.49]	1	Logs bounds checks dashboard
➖	quality_gate_idle_all_features	memory utilization	-0.20	[-0.28, -0.12]	1	Logs bounds checks dashboard
➖	dsd_uds_10mb_3k_timestamped_contexts_cpu	% cpu utilization	-0.38	[-0.62, -0.14]	1	Logs
➖	dsd_uds_10mb_3k_timestamped_contexts_memory	memory utilization	-0.53	[-0.75, -0.31]	1	Logs
➖	quality_gate_logs	% cpu utilization	-1.93	[-2.79, -1.06]	1	Logs bounds checks dashboard
➖	dsd_uds_client_drop_detector_cpu	% cpu utilization	-2.88	[-3.40, -2.37]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	observed_value	links
✅	python_openmetrics	checks_execution_time	10/10	79.26 ≤ 100	bounds checks dashboard
✅	python_openmetrics	cpu_usage	10/10	1329.06 ≤ 1500	bounds checks dashboard
✅	python_openmetrics	memory_usage	10/10	4.31GiB ≤ 4.75GiB	bounds checks dashboard
✅	quality_gate_idle	intake_connections	10/10	4 ≤ 5	bounds checks dashboard
✅	quality_gate_idle	memory_usage	10/10	177.33MiB ≤ 181MiB	bounds checks dashboard
✅	quality_gate_idle	total_bytes_received	10/10	762.48KiB ≤ 819.20KiB	bounds checks dashboard
✅	quality_gate_idle_all_features	intake_connections	10/10	2 ≤ 5	bounds checks dashboard
✅	quality_gate_idle_all_features	memory_usage	10/10	469.74MiB ≤ 542MiB	bounds checks dashboard
✅	quality_gate_idle_all_features	total_bytes_received	10/10	1.13MiB ≤ 1.25MiB	bounds checks dashboard
✅	quality_gate_logs	intake_connections	10/10	16 ≤ 40	bounds checks dashboard
✅	quality_gate_logs	memory_usage	10/10	213.14MiB ≤ 228MiB	bounds checks dashboard
✅	quality_gate_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard
✅	quality_gate_logs	total_bytes_received	10/10	263.88MiB ≤ 292MiB	bounds checks dashboard
✅	quality_gate_metrics_logs	cpu_usage	10/10	366.76 ≤ 2000	bounds checks dashboard
✅	quality_gate_metrics_logs	intake_connections	10/10	19 ≤ 40	bounds checks dashboard
✅	quality_gate_metrics_logs	memory_usage	10/10	399.66MiB ≤ 455MiB	bounds checks dashboard
✅	quality_gate_metrics_logs	missed_bytes	10/10	0B = 0B	bounds checks dashboard
✅	quality_gate_metrics_logs	total_bytes_received	10/10	0.95GiB ≤ 1.04GiB	bounds checks dashboard
✅	quality_gate_private_action_runner	memory_usage	10/10	73.66MiB ≤ 77MiB	bounds checks dashboard
✅	quality_gate_security_idle	cpu_usage	10/10	29.24 ≤ 100	bounds checks dashboard
✅	quality_gate_security_idle	memory_usage	10/10	328.02MiB ≤ 357MiB	bounds checks dashboard
✅	quality_gate_security_mean_fs_load	cpu_usage	10/10	46.48 ≤ 200	bounds checks dashboard
✅	quality_gate_security_mean_fs_load	memory_usage	10/10	309.27MiB ≤ 337MiB	bounds checks dashboard
✅	quality_gate_security_no_fs_load	cpu_usage	10/10	23.67 ≤ 100	bounds checks dashboard
✅	quality_gate_security_no_fs_load	memory_usage	10/10	319.12MiB ≤ 348MiB	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 intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
• quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_security_mean_fs_load, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_security_mean_fs_load, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_private_action_runner, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_idle, bounds check total_bytes_received: 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_security_no_fs_load, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_security_no_fs_load, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check missed_bytes: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
• quality_gate_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.
• quality_gate_security_idle, bounds check cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_security_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check total_bytes_received: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check missed_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 cpu_usage: 10/10 replicas passed. Gate passed.
• quality_gate_metrics_logs, bounds check intake_connections: 10/10 replicas passed. Gate passed.

[github-actions]

AI review by Codex (OpenAI) - workflow run

The patch is correct. The bounded, nonblocking queue removes per-event goroutine creation and uses the existing worker and shutdown lifecycle. No actionable regressions were identified in the affected code. Validation was limited to static review; functional tests were not run.