fix(cluster-agent): Guard against re-admission for APM auto-instrumentation in image_volume mode by mtoffl01 · Pull Request #46743 · DataDog/datadog-agent

mtoffl01 · 2026-02-20T18:49:20Z

What does this PR do?

Avoid double-injection by returning early if the pod already has image_volume mode's init containers.
Init_container mode was already guarded by checking for per-language init containers (e.g. datadog-lib-python-init). This change adds the same style of guard for image_volume mode by checking for the datadog-apm-inject-preload init container.

Motivation

The webhook may be run twice, but we do not want to inject twice. CSI mode needs a guard in the future as well.

Describe how you validated your changes

Tests in target_mutator_test.go were added for both re-admission cases: one for init_container mode and one for image_volume mode. The test asserts that the pod is not changed ("mutated") at all in the case that the representative init container(s) are present.

Additional Notes

agent-platform-auto-pr · 2026-02-20T19:33:49Z

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor b9bcae1
📊 Static Quality Gates Dashboard
🔗 SQG Job

31 successful checks with minimal change (< 2 KiB)

	Quality gate	Current Size
✅	agent_deb_amd64	755.988 MiB
✅	agent_deb_amd64_fips	715.077 MiB
✅	agent_heroku_amd64	323.754 MiB
✅	agent_msi	622.074 MiB
✅	agent_rpm_amd64	755.972 MiB
✅	agent_rpm_amd64_fips	715.061 MiB
✅	agent_rpm_arm64	734.126 MiB
✅	agent_rpm_arm64_fips	696.201 MiB
✅	agent_suse_amd64	755.972 MiB
✅	agent_suse_amd64_fips	715.061 MiB
✅	agent_suse_arm64	734.126 MiB
✅	agent_suse_arm64_fips	696.201 MiB
✅	docker_agent_amd64	817.022 MiB
✅	docker_agent_arm64	819.911 MiB
✅	docker_agent_jmx_amd64	1007.933 MiB
✅	docker_agent_jmx_arm64	999.605 MiB
✅	docker_cluster_agent_amd64	192.342 MiB
✅	docker_cluster_agent_arm64	207.644 MiB
✅	docker_cws_instrumentation_amd64	7.135 MiB
✅	docker_cws_instrumentation_arm64	6.689 MiB
✅	docker_dogstatsd_amd64	38.500 MiB
✅	docker_dogstatsd_arm64	36.812 MiB
✅	dogstatsd_deb_amd64	29.720 MiB
✅	dogstatsd_deb_arm64	27.881 MiB
✅	dogstatsd_rpm_amd64	29.720 MiB
✅	dogstatsd_suse_amd64	29.720 MiB
✅	iot_agent_deb_amd64	42.617 MiB
✅	iot_agent_deb_arm64	39.723 MiB
✅	iot_agent_deb_armhf	40.447 MiB
✅	iot_agent_rpm_amd64	42.618 MiB
✅	iot_agent_suse_amd64	42.618 MiB

On-wire sizes (compressed)

	Quality gate	Change	Size (prev → curr → max)
✅	agent_deb_amd64	+14.38 KiB (0.01% increase)	185.452 → 185.466 → 186.090
✅	agent_deb_amd64_fips	+38.7 KiB (0.02% increase)	176.271 → 176.308 → 180.330
✅	agent_heroku_amd64	+5.43 KiB (0.01% increase)	87.099 → 87.104 → 88.440
✅	agent_msi	+44.0 KiB (0.03% increase)	149.207 → 149.250 → 154.470
✅	agent_rpm_amd64	-6.77 KiB (0.00% reduction)	187.337 → 187.330 → 189.170
✅	agent_rpm_amd64_fips	+17.95 KiB (0.01% increase)	178.406 → 178.424 → 181.060
✅	agent_rpm_arm64	+5.52 KiB (0.00% increase)	169.721 → 169.727 → 170.020
✅	agent_rpm_arm64_fips	+9.93 KiB (0.01% increase)	162.465 → 162.475 → 164.130
✅	agent_suse_amd64	-6.77 KiB (0.00% reduction)	187.337 → 187.330 → 189.170
✅	agent_suse_amd64_fips	+17.95 KiB (0.01% increase)	178.406 → 178.424 → 181.060
✅	agent_suse_arm64	+5.52 KiB (0.00% increase)	169.721 → 169.727 → 170.020
✅	agent_suse_arm64_fips	+9.93 KiB (0.01% increase)	162.465 → 162.475 → 164.130
✅	docker_agent_amd64	+6.9 KiB (0.00% increase)	277.758 → 277.765 → 279.410
✅	docker_agent_arm64	neutral	265.054 MiB → 267.960
✅	docker_agent_jmx_amd64	neutral	346.404 MiB → 348.040
✅	docker_agent_jmx_arm64	neutral	329.697 MiB → 332.560
✅	docker_cluster_agent_amd64	neutral	67.202 MiB → 68.000
✅	docker_cluster_agent_arm64	neutral	63.187 MiB → 63.640
✅	docker_cws_instrumentation_amd64	neutral	2.995 MiB → 3.330
✅	docker_cws_instrumentation_arm64	neutral	2.726 MiB → 3.090
✅	docker_dogstatsd_amd64	neutral	14.900 MiB → 15.820
✅	docker_dogstatsd_arm64	neutral	14.239 MiB → 14.830
✅	dogstatsd_deb_amd64	neutral	7.852 MiB → 8.790
✅	dogstatsd_deb_arm64	neutral	6.741 MiB → 7.710
✅	dogstatsd_rpm_amd64	neutral	7.865 MiB → 8.800
✅	dogstatsd_suse_amd64	neutral	7.865 MiB → 8.800
✅	iot_agent_deb_amd64	-2.18 KiB (0.02% reduction)	11.239 → 11.237 → 12.040
✅	iot_agent_deb_arm64	+2.68 KiB (0.03% increase)	9.601 → 9.603 → 10.450
✅	iot_agent_deb_armhf	+2.73 KiB (0.03% increase)	9.803 → 9.806 → 10.620
✅	iot_agent_rpm_amd64	neutral	11.254 MiB → 12.060
✅	iot_agent_suse_amd64	neutral	11.254 MiB → 12.060

gabedos

Avoiding double mutation when the image_volume mode LDPreload init container is present looks good to me

cit-pr-commenter-54b7da · 2026-02-20T19:51:30Z

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 2594282b-a4f4-4ff5-871b-34890c74c6b9

Baseline: 68b27ba
Comparison: e5a370e
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	+2.64	[-0.49, +5.77]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	+2.64	[-0.49, +5.77]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.16	[+1.08, +1.23]	1	Logs
➖	docker_containers_memory	memory utilization	+0.95	[+0.87, +1.02]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.58	[+0.54, +0.62]	1	Logs bounds checks dashboard
➖	quality_gate_idle	memory utilization	+0.52	[+0.48, +0.56]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	+0.41	[+0.36, +0.47]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.34	[+0.23, +0.45]	1	Logs
➖	ddot_metrics	memory utilization	+0.19	[-0.02, +0.40]	1	Logs
➖	file_tree	memory utilization	+0.14	[+0.09, +0.20]	1	Logs
➖	otlp_ingest_metrics	memory utilization	+0.12	[-0.04, +0.27]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.05	[-0.45, +0.55]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.05	[-0.34, +0.43]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.01	[-0.12, +0.14]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.09, +0.10]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.00	[-0.04, +0.05]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	-0.01	[-0.14, +0.11]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.04	[-0.47, +0.38]	1	Logs
➖	ddot_logs	memory utilization	-0.14	[-0.20, -0.08]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	-0.18	[-0.38, +0.02]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	-0.19	[-0.36, -0.03]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	-0.21	[-0.44, +0.02]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	-0.37	[-0.58, -0.17]	1	Logs bounds checks dashboard
➖	quality_gate_logs	% cpu utilization	-1.90	[-3.42, -0.39]	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:

Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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.
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 memory_usage: 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_metrics_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 intake_connections: 10/10 replicas passed. Gate passed.
quality_gate_logs, 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 lost_bytes: 10/10 replicas passed. Gate passed.

urseberry

Left a non-blocking capitalization nit suggestion.

releasenotes/notes/guard-readmission-ssi-imgvol-e9dfef7c4b5b8b0d.yaml

…0d.yaml Co-authored-by: Ursula Chen <58821586+urseberry@users.noreply.github.com>

mtoffl01 · 2026-02-23T14:58:55Z

/merge

gh-worker-devflow-routing-ef8351 · 2026-02-23T14:59:00Z

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2026-02-23 14:58:59 UTC ℹ️ Start processing command /merge

2026-02-23 14:59:05 UTC ℹ️ MergeQueue: pull request added to the queue

The expected merge time in main is approximately 1h (p90).

2026-02-23 15:52:57 UTC ℹ️ MergeQueue: This merge request was merged

…tation in image_volume mode (#46743) ### What does this PR do? Avoid double-injection by returning early if the pod already has image_volume mode's init containers. Init_container mode was already guarded by checking for per-language init containers (e.g. datadog-lib-python-init). This change adds the same style of guard for image_volume mode by checking for the datadog-apm-inject-preload init container. ### Motivation The webhook may be run twice, but we do not want to inject twice. CSI mode needs a guard in the future as well. ### Describe how you validated your changes Tests in target_mutator_test.go were added for both re-admission cases: one for init_container mode and one for image_volume mode. The test asserts that the pod is not changed ("mutated") at all in the case that the representative init container(s) are present. ### Additional Notes Co-authored-by: mikayla.toffler <mikayla.toffler@datadoghq.com> (cherry picked from commit 2d33331) ___ Co-authored-by: Mikayla Toffler <46911781+mtoffl01@users.noreply.github.com>

…APM auto-instrumentation in image_volume mode (#46819) Backport 2d33331 from #46743. ___ ### What does this PR do? Avoid double-injection by returning early if the pod already has image_volume mode's init containers. Init_container mode was already guarded by checking for per-language init containers (e.g. datadog-lib-python-init). This change adds the same style of guard for image_volume mode by checking for the datadog-apm-inject-preload init container. ### Motivation The webhook may be run twice, but we do not want to inject twice. CSI mode needs a guard in the future as well. ### Describe how you validated your changes Tests in target_mutator_test.go were added for both re-admission cases: one for init_container mode and one for image_volume mode. The test asserts that the pod is not changed ("mutated") at all in the case that the representative init container(s) are present. ### Additional Notes Co-authored-by: sabrina.lu <sabrina.lu@datadoghq.com>

mtoffl01 added 2 commits February 20, 2026 13:32

add image-volume mode guard against re-admission

959b11d

Add tests for re-admission in init-container and img-vol mode

6b4e532

dd-octo-sts bot added the internal Identify a non-fork PR label Feb 20, 2026

github-actions bot added the short review PR is simple enough to be reviewed quickly label Feb 20, 2026

dd-octo-sts bot added team/container-platform The Container Platform Team team/injection-platform labels Feb 20, 2026

mtoffl01 added the qa/done QA done before merge and regressions are covered by tests label Feb 20, 2026

release note

ddb6c5b

mtoffl01 marked this pull request as ready for review February 20, 2026 19:14

mtoffl01 requested review from a team as code owners February 20, 2026 19:14

gabedos approved these changes Feb 20, 2026

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urseberry approved these changes Feb 20, 2026

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…0d.yaml Co-authored-by: Ursula Chen <58821586+urseberry@users.noreply.github.com>

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gh-worker-dd-mergequeue-cf854d bot merged commit 2d33331 into main Feb 23, 2026
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[Backport 7.77.x] fix(cluster-agent): Guard against re-admission for APM auto-instrumentation in image_volume mode #46819

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fix(cluster-agent): Guard against re-admission for APM auto-instrumentation in image_volume mode#46743

fix(cluster-agent): Guard against re-admission for APM auto-instrumentation in image_volume mode#46743
gh-worker-dd-mergequeue-cf854d[bot] merged 4 commits intomainfrom
mtoff/ssi-imgvol-readmit-guard

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Experiments ignored for regressions

Fine details of change detection per experiment

Bounds Checks: ✅ Passed

Explanation

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mtoffl01 commented Feb 20, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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What does this PR do?

Motivation

Describe how you validated your changes

Additional Notes

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Static quality checks

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Regression Detector

Regression Detector Results

Optimization Goals: ✅ No significant changes detected

Experiments ignored for regressions

Fine details of change detection per experiment

Bounds Checks: ✅ Passed

Explanation

CI Pass/Fail Decision

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