fix(ssi): merge target and namespace implementations

[betterengineering]

What does this PR do?

When we introduced targets, we created a separate code path to handle the target based configuration. However, the namespace based configuration can also be represented and supported by the target implementation. This commit merges the two implementations.

When I merged these in code, I found bugs in the target implementation surrounding Local SDK Injection and how it was handled with targets defined. This change fixes those bugs to match the expectations around enabledNamespaces.

Motivation

We want to reduce the complexity of the auto instrumentation webhook. There is no need to have two implementations to support our customer facing configuration options and as shown with this change, two implementations open up more chances for bugs.

See SSI Kubernetes | Platform Stability for more details on all changes related to cleanup.

Describe how you validated your changes

I tested this change heavily using injector-dev. For each scenario listed, I ran with and without the --build flag to use the latest agent release vs this branch to ensure there was a seamless migration path.

Broken annotation based injection with target list

This should have injection but it does not currently:

helm:
  apps:
    - name: annotation-example
      namespace: application
      values:
        env:
          - name: DD_TRACE_DEBUG
            value: "true"
          - name: DD_APM_INSTRUMENTATION_DEBUG
            value: "true"
        image:
          repository: registry.ddbuild.io/ci/injector-dev/python
          tag: 2cd78ded
        podLabels:
          admission.datadoghq.com/enabled: "true"
          tags.datadoghq.com/env: local
        podAnnotations:
          admission.datadoghq.com/python-lib.version: "v3"
        service:
          port: "8080"
  versions:
    agent: 7.71.1
    cluster_agent:
      version: 7.71.1
      build: {}
    injector: 0.48.0
  config:
    datadog:
      apm:
        instrumentation:
          enabled: false
          targets:
            - name: python
              podSelector:
                matchLabels:
                  language: python
              ddTraceVersions:
                python: "3"

Annotation based injection

We expect injection:

helm:
  apps:
    - name: annotation-example
      namespace: application
      values:
        env:
          - name: DD_TRACE_DEBUG
            value: "true"
          - name: DD_APM_INSTRUMENTATION_DEBUG
            value: "true"
        image:
          repository: registry.ddbuild.io/ci/injector-dev/python
          tag: 2cd78ded
        podLabels:
          admission.datadoghq.com/enabled: "true"
          tags.datadoghq.com/env: local
        podAnnotations:
          admission.datadoghq.com/python-lib.version: "v3"
        service:
          port: "8080"
  versions:
    agent: 7.71.1
    cluster_agent:
      version: 7.71.1
      build: {}
    injector: 0.48.0
  config:
    datadog:
      apm:
        instrumentation:
          enabled: false

Namespace based selection

We expect injection:

helm:
  apps:
    - name: namespace-selection-example
      namespace: application
      values:
        env:
          - name: DD_TRACE_DEBUG
            value: "true"
          - name: DD_APM_INSTRUMENTATION_DEBUG
            value: "true"
        image:
          repository: registry.ddbuild.io/ci/injector-dev/python
          tag: 2cd78ded
        podLabels:
          tags.datadoghq.com/env: local
        service:
          port: "8080"
  versions:
    agent: 7.71.1
    cluster_agent:
      version: 7.71.1
      build: {}
    injector: 0.48.0
  config:
    datadog:
      apm:
        instrumentation:
          enabled: true
          enabledNamespaces:
            - "application"
          libVersions:
            python: "3"

Workload selection

We expect injection:

helm:
  apps:
    - name: workload-selection-example
      namespace: application
      values:
        env:
          - name: DD_TRACE_DEBUG
            value: "true"
          - name: DD_APM_INSTRUMENTATION_DEBUG
            value: "true"
        image:
          repository: registry.ddbuild.io/ci/injector-dev/python
          tag: 2cd78ded
        podLabels:
          language: python
          tags.datadoghq.com/env: local
        service:
          port: "8080"
  versions:
    agent: 7.71.1
    cluster_agent:
      version: 7.71.1
      build: {}
    injector: 0.48.0
  config:
    datadog:
      apm:
        instrumentation:
          enabled: true
          targets:
            - name: python
              podSelector:
                matchLabels:
                  language: python
              ddTraceVersions:
                python: "3"

Instrumentation disabled

We expect no injection to occur:

helm:
  apps:
    - name: disabled-example
      namespace: application
      values:
        env:
          - name: DD_TRACE_DEBUG
            value: "true"
          - name: DD_APM_INSTRUMENTATION_DEBUG
            value: "true"
        image:
          repository: registry.ddbuild.io/ci/injector-dev/python
          tag: 2cd78ded
        podLabels:
          tags.datadoghq.com/env: local
        service:
          port: "8080"
  versions:
    agent: 7.71.1
    cluster_agent:
      version: 7.71.1
      build: {}
    injector: 0.48.0
  config:
    datadog:
      apm:
        instrumentation:
          enabled: false

Additional Notes

This change opted to not change the behavior of any tests and to keep test changes minimal. A follow up PR will rewrite some of these tests to test the webhook behavior more directly.

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 30c57492-2db7-4a7d-ba3d-773a44bc9e3a

Baseline: c806836
Comparison: c3b64e1
Diff

Optimization Goals: ❌ Regression(s) detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	docker_containers_memory	memory utilization	+7.89	[+7.41, +8.36]	1	Logs

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	-11.84	[-13.40, -10.27]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	docker_containers_memory	memory utilization	+7.89	[+7.41, +8.36]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	+0.70	[+0.51, +0.90]	1	Logs bounds checks dashboard
➖	file_tree	memory utilization	+0.48	[+0.42, +0.54]	1	Logs
➖	otlp_ingest_metrics	memory utilization	+0.17	[+0.05, +0.29]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.17	[+0.03, +0.30]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	+0.13	[+0.01, +0.25]	1	Logs
➖	ddot_logs	memory utilization	+0.08	[+0.01, +0.15]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	+0.06	[-0.54, +0.67]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.21, +0.23]	1	Logs
➖	ddot_metrics	memory utilization	+0.01	[-0.16, +0.18]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.00	[-0.61, +0.61]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.01, +0.01]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	-0.02	[-0.22, +0.18]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.04	[-0.64, +0.56]	1	Logs
➖	quality_gate_idle	memory utilization	-0.04	[-0.09, +0.00]	1	Logs bounds checks dashboard
➖	file_to_blackhole_100ms_latency	egress throughput	-0.09	[-0.69, +0.50]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.27	[-0.31, -0.22]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-0.61	[-0.66, -0.55]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	-0.71	[-0.87, -0.56]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-1.52	[-1.59, -1.44]	1	Logs
➖	quality_gate_logs	% cpu utilization	-1.63	[-4.42, +1.15]	1	Logs bounds checks dashboard
✅	docker_containers_cpu	% cpu utilization	-11.84	[-13.40, -10.27]	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	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_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 cpu_usage: 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 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.

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor bbded9b

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${0}$$	$${676.47}$$ < $${702.15}$$	$${-0.01}$$	$${165.12}$$ < $${177.79}$$
✅	agent_deb_amd64_fips	$${0}$$	$${670.98}$$ < $${696.65}$$	$${+0.03}$$	$${164.68}$$ < $${176.53}$$
✅	agent_heroku_amd64	$${0}$$	$${336.47}$$ < $${340.18}$$	$${-0}$$	$${89.8}$$ < $${91.08}$$
✅	agent_msi	$${0}$$	$${1013.73}$$ < $${1015.38}$$	$${+0.02}$$	$${148.44}$$ < $${150.78}$$
✅	agent_rpm_amd64	$${0}$$	$${676.46}$$ < $${702.14}$$	$${-0.03}$$	$${167.37}$$ < $${180.53}$$
✅	agent_rpm_amd64_fips	$${0}$$	$${670.97}$$ < $${696.64}$$	$${-0}$$	$${166.02}$$ < $${178.79}$$
✅	agent_rpm_arm64	$${0}$$	$${666.3}$$ < $${686.31}$$	$${-0.02}$$	$${153.29}$$ < $${161.22}$$
✅	agent_rpm_arm64_fips	$${0}$$	$${661.83}$$ < $${681.84}$$	$${-0.07}$$	$${152.61}$$ < $${160.18}$$
✅	agent_suse_amd64	$${0}$$	$${676.46}$$ < $${702.14}$$	$${-0.03}$$	$${167.37}$$ < $${180.53}$$
✅	agent_suse_amd64_fips	$${0}$$	$${670.97}$$ < $${696.64}$$	$${-0}$$	$${166.02}$$ < $${178.79}$$
✅	agent_suse_arm64	$${0}$$	$${666.3}$$ < $${686.31}$$	$${-0.02}$$	$${153.29}$$ < $${161.22}$$
✅	agent_suse_arm64_fips	$${0}$$	$${661.83}$$ < $${681.84}$$	$${-0.07}$$	$${152.61}$$ < $${160.18}$$
✅	docker_agent_amd64	$${-0}$$	$${747.02}$$ < $${773.59}$$	$${-0}$$	$${251.83}$$ < $${266.06}$$
✅	docker_agent_arm64	$${-0}$$	$${760.81}$$ < $${781.7}$$	$${-0}$$	$${242.45}$$ < $${251.85}$$
✅	docker_agent_jmx_amd64	$${+0}$$	$${937.89}$$ < $${964.45}$$	$${+0}$$	$${320.47}$$ < $${334.68}$$
✅	docker_agent_jmx_arm64	$${0}$$	$${940.27}$$ < $${961.17}$$	$${+0}$$	$${307.06}$$ < $${316.44}$$
✅	docker_cluster_agent_amd64	$${-0.01}$$	$${213.03}$$ < $${213.74}$$	$${-0}$$	$${72.27}$$ < $${73.14}$$
✅	docker_cluster_agent_arm64	$${-0}$$	$${228.99}$$ < $${229.68}$$	$${-0.01}$$	$${68.53}$$ < $${69.41}$$
✅	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	$${0}$$	$${38.45}$$ < $${39.3}$$	$${+0}$$	$${14.84}$$ < $${15.76}$$
✅	docker_dogstatsd_arm64	$${0}$$	$${37.12}$$ < $${37.94}$$	$${+0}$$	$${14.29}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${0}$$	$${29.67}$$ < $${30.53}$$	$${+0}$$	$${7.82}$$ < $${8.75}$$
✅	dogstatsd_deb_arm64	$${0}$$	$${28.25}$$ < $${29.11}$$	$${-0}$$	$${6.77}$$ < $${7.71}$$
✅	dogstatsd_rpm_amd64	$${0}$$	$${29.67}$$ < $${30.53}$$	$${-0}$$	$${7.83}$$ < $${8.76}$$
✅	dogstatsd_suse_amd64	$${0}$$	$${29.67}$$ < $${30.53}$$	$${-0}$$	$${7.83}$$ < $${8.76}$$
✅	iot_agent_deb_amd64	$${0}$$	$${41.93}$$ < $${54.97}$$	$${-0}$$	$${10.9}$$ < $${14.45}$$
✅	iot_agent_deb_arm64	$${0}$$	$${39.75}$$ < $${51.9}$$	$${-0}$$	$${9.42}$$ < $${12.63}$$
✅	iot_agent_deb_armhf	$${0}$$	$${39.62}$$ < $${51.84}$$	$${-0}$$	$${9.5}$$ < $${12.74}$$
✅	iot_agent_rpm_amd64	$${0}$$	$${41.93}$$ < $${54.97}$$	$${+0}$$	$${10.92}$$ < $${14.47}$$
✅	iot_agent_suse_amd64	$${0}$$	$${41.93}$$ < $${54.97}$$	$${+0}$$	$${10.92}$$ < $${14.47}$$

[betterengineering]

We had three versions of this test initially 😅

[stanistan]

Great cleanup! Have a few suggestions/questions on the implementation wrt annotations/target simplification

[stanistan]

Right now we add an annotation to the pod that says which target this is coming from and what the configuration is.

Is that consistent for the behavior from namespaces? We'll have an applied-target with this JSON format?

What happens for the annotation? I'm not sure we should be adding an applied-target annotation there

[betterengineering]

For enabledNamespaces, the generated target applies. I updated this change to no longer apply an annotation for local lib injection.

[stanistan]

Thanks!

[stanistan]

We should consolidate to using this in the common package:

datadog-agent/pkg/clusteragent/admission/mutate/common/filter.go

Lines 135 to 163 in 8ff7142

	type podMutationLabelFlag int
	const (
	podMutationUnspecified podMutationLabelFlag = iota
	podMutationEnabled
	podMutationDisabled
	)
	// getPodMutationLabelFlag returns podMutationUnspecified if the label is not
	// set or if the label is set to an invalid value.
	func getPodMutationLabelFlag(pod *corev1.Pod) podMutationLabelFlag {
	if val, found := pod.GetLabels()[common.EnabledLabelKey]; found {
	switch val {
	case "true":
	return podMutationEnabled
	case "false":
	return podMutationDisabled
	default:
	log.Warnf(
	"Invalid label value '%s=%s' on pod %s should be either 'true' or 'false', ignoring it",
	common.EnabledLabelKey,
	val,
	PodString(pod),
	)
	}
	}
	return podMutationUnspecified
	}

[betterengineering]

Created this task as a follow up. I'd like to give these methods some love as part of that change and don't want to touch too much as part of this change: https://datadoghq.atlassian.net/browse/INPLAT-813

[buraizu]

Requesting a few updates for branding, style, and clarity

[betterengineering]

/merge

[dd-devflow-routing-codex]

View all feedbacks in Devflow UI.

2025-10-22 11:19:19 UTC ℹ️ Start processing command /merge

---

2025-10-22 11:19:25 UTC ℹ️ MergeQueue: waiting for PR to be ready

This merge request is not mergeable according to GitHub. Common reasons include pending required checks, missing approvals, or merge conflicts — but it could also be blocked by other repository rules or settings.
It will be added to the queue as soon as checks pass and/or get approvals.
Note: if you pushed new commits since the last approval, you may need additional approval.
You can remove it from the waiting list with /remove command.

---

2025-10-22 12:21:31 UTC ℹ️ MergeQueue: merge request added to the queue

The expected merge time in main is approximately 39m (p90).

---

2025-10-22 12:42:52 UTC ℹ️ MergeQueue: This merge request was merged