feat(ssi): add namespace support for labels/expressions

[betterengineering]

What does this PR do?

This commit adds the ability to use a namespace selector to match against namespaces defined in the targets list. For example, this config will match a pod in a namespace with tracing: yes and env: prod:

    - name: "Enabled Prod Namespaces"
        namespaceSelector:
          matchLabels:
            tracing: "yes"
          matchExpressions:
            - key: "env"
              operator: "In"
              values:
                - "prod"
        ddTraceVersions:
          python: "default" 

This PR maintains the ability to use matchNames as a convenience method. For example, this config would match the billing-service namespace:

      - name: "Billing Service"
        namespaceSelector:
          matchNames:
          - "billing-service"
        ddTraceVersions:
          python: "default"       

Motivation

This change is adding the full support for namespaces defined in Kubernetes SSI | Workload Selection 🎯. It was not added to the change in feat(ssi): add target based filtering as it required a bit more effort to get correct.

Describe how you validated your changes

This change is validated with the addition of unit tests at the moment. We will be adding more tests for this feature before launch.

Possible Drawbacks / Trade-offs

Additional Notes

One thing to note, this config/filter is not yet active or exposed. It's simply modifying a struct with unit tests that's not wired in anywhere. We're working hard to get this feature ready for the next agent release milestone and will include release notes, additional testing, etc before that happens.

[betterengineering]

@davidor is this the best way to do this? How does this store work? Is it safe to assume the namespace will be there by the time we get a mutating webhook?

[agent-platform-auto-pr]

Uncompressed package size comparison

Comparison with ancestor 3d3ac19974a60cdd73d270207fce061a35190abe

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-arm64-deb	0.00MB	✅	861.55MB	861.54MB	0.50MB
datadog-agent-aarch64-rpm	0.00MB	✅	871.27MB	871.27MB	0.50MB
datadog-agent-amd64-deb	0.00MB	✅	873.67MB	873.67MB	0.50MB
datadog-agent-x86_64-rpm	0.00MB	✅	883.41MB	883.41MB	0.50MB
datadog-agent-x86_64-suse	0.00MB	✅	883.41MB	883.41MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	59.10MB	59.10MB	0.50MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	59.18MB	59.18MB	0.50MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	59.18MB	59.18MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.57MB	56.57MB	0.50MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	446.05MB	446.05MB	0.50MB
datadog-iot-agent-amd64-deb	0.00MB	✅	86.48MB	86.48MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	86.55MB	86.55MB	0.50MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	86.55MB	86.55MB	0.50MB
datadog-iot-agent-arm64-deb	0.00MB	✅	82.74MB	82.74MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.00MB	✅	82.81MB	82.81MB	0.50MB

Decision

✅ Passed

[agent-platform-auto-pr]

[Fast Unit Tests Report]

On pipeline 55157309 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[agent-platform-auto-pr]

Static quality checks ✅

Please find below the results from static quality gates

Info

Result	Quality gate	On disk size	On disk size limit	On wire size	On wire size limit
✅	static_quality_gate_agent_deb_amd64	844.99MiB	858.45MiB	203.61MiB	214.3MiB
✅	static_quality_gate_docker_agent_amd64	929.33MiB	942.69MiB	310.73MiB	321.56MiB

[agent-platform-auto-pr]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv aws.create-vm --pipeline-id=55157309 --os-family=ubuntu

Note: This applies to commit 8ef8b1f

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 9ac7b087-d58f-4961-8ad2-acc37648135b

Baseline: 3d3ac19
Comparison: 8ef8b1f
Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.53	[-0.26, +1.32]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+0.41	[+0.33, +0.50]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.06	[-0.65, +0.77]	1	Logs
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.05	[-0.87, +0.96]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.00	[-0.64, +0.64]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.30, +0.30]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.01	[-0.03, +0.01]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.01	[-0.96, +0.93]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.03	[-0.90, +0.83]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	-0.04	[-0.50, +0.42]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.19	[-0.98, +0.60]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.25	[-0.31, -0.18]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	-0.51	[-0.55, -0.48]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	-0.63	[-0.70, -0.57]	1	Logs
➖	quality_gate_logs	% cpu utilization	-1.00	[-4.01, +2.02]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-1.63	[-2.49, -0.78]	1	Logs

Bounds Checks: ✅ Passed

perf	experiment	bounds_check_name	replicates_passed	links
✅	file_to_blackhole_0ms_latency	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http1	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http1	memory_usage	10/10
✅	file_to_blackhole_0ms_latency_http2	lost_bytes	10/10
✅	file_to_blackhole_0ms_latency_http2	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency	memory_usage	10/10
✅	file_to_blackhole_1000ms_latency_linear_load	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_300ms_latency	lost_bytes	10/10
✅	file_to_blackhole_300ms_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
✅	quality_gate_logs	lost_bytes	10/10
✅	quality_gate_logs	memory_usage	10/10

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.