feat(ssi): add release note for workload selection #33936

betterengineering · 2025-02-11T16:30:16Z

What does this PR do?

This commit adds a release note for the workload selection changes.

Motivation

See Kubernetes SSI | Workload Selection 🎯.

Additional Notes

This change only includes the release note, though feature was implemented over many changes:

This commit adds a release note for the workload selection changes.

agent-platform-auto-pr · 2025-02-11T16:54:02Z

[Fast Unit Tests Report]

On pipeline 55577111 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

tests_deb-arm64-py3
tests_deb-x64-py3
tests_flavor_dogstatsd_deb-x64
tests_flavor_heroku_deb-x64
tests_flavor_iot_deb-x64
tests_rpm-arm64-py3
tests_rpm-x64-py3
tests_windows-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

maycmlee

Just a couple of small edits, but approving to not block merge.

releasenotes/notes/target-based-workload-selection-a745168b07add397.yaml

domalessi

Just a few minor suggestions and typo fixes!

domalessi · 2025-02-11T17:24:40Z

releasenotes/notes/target-based-workload-selection-a745168b07add397.yaml

+    Target based workload selection is now available for Single Step Instrumentation. This feature allows you to target
+    specific workloads for instrumentation using pod and namespace label selectors. By using user defined labels, a
+    single operator will be able to select workloads for instrumentation without modifying their applications. For
+    example, the following configuration will inject the python tracer with a default version for pods with a
+    `launguage=python`:


Suggested change

Target based workload selection is now available for Single Step Instrumentation. This feature allows you to target

specific workloads for instrumentation using pod and namespace label selectors. By using user defined labels, a

single operator will be able to select workloads for instrumentation without modifying their applications. For

example, the following configuration will inject the python tracer with a default version for pods with a

`launguage=python`:

Target-based workload selection is now available for Single Step Instrumentation. This feature enables you to instrument specific workloads using pod and namespace label selectors. By applying user-defined labels, you can select workloads for instrumentation without modifying applications. For example, the following configuration injects the Python tracer with a default version for pods labeled with `language=python`:

"language" was spelled incorrectly, so that's a must-fix. Everything else is suggested for clarity :)

domalessi · 2025-02-11T17:28:33Z

releasenotes/notes/target-based-workload-selection-a745168b07add397.yaml

+    Targets can also be chained together, with the first matching rule taking precedence. For example, the following
+    configuration will install the python tracer for pods matching the label `language=python` and will install the java:
+    tracer for all pods in a namespace with the label `language=java`. If a pod matches both rules, the first match
+    takes precedence:


Suggested change

Targets can also be chained together, with the first matching rule taking precedence. For example, the following

configuration will install the python tracer for pods matching the label `language=python` and will install the java:

tracer for all pods in a namespace with the label `language=java`. If a pod matches both rules, the first match

takes precedence:

Targets can also be chained together, with the first matching rule taking precedence. For example, the following configuration installs the Python tracer for pods labeled `language=python` and the Java tracer for pods in a namespace labeled `language=java`. If a pod matches both rules, the first match takes precedence:

agent-platform-auto-pr · 2025-02-11T17:52:43Z

Uncompressed package size comparison

Comparison with ancestor 4e8821dc6e792662f876a8b40d74fabe578fd258

Diff per package

package	diff	status	size	ancestor	threshold
datadog-agent-aarch64-rpm	0.00MB	✅	863.87MB	863.87MB	0.50MB
datadog-agent-amd64-deb	0.00MB	✅	865.61MB	865.61MB	0.50MB
datadog-agent-arm64-deb	0.00MB	✅	854.12MB	854.12MB	0.50MB
datadog-dogstatsd-amd64-deb	0.00MB	✅	59.04MB	59.04MB	0.50MB
datadog-dogstatsd-x86_64-rpm	0.00MB	✅	59.12MB	59.12MB	0.50MB
datadog-dogstatsd-x86_64-suse	0.00MB	✅	59.12MB	59.12MB	0.50MB
datadog-dogstatsd-arm64-deb	0.00MB	✅	56.53MB	56.53MB	0.50MB
datadog-heroku-agent-amd64-deb	0.00MB	✅	440.97MB	440.97MB	0.50MB
datadog-iot-agent-amd64-deb	0.00MB	✅	86.36MB	86.36MB	0.50MB
datadog-iot-agent-x86_64-rpm	0.00MB	✅	86.43MB	86.43MB	0.50MB
datadog-iot-agent-x86_64-suse	0.00MB	✅	86.42MB	86.42MB	0.50MB
datadog-iot-agent-arm64-deb	0.00MB	✅	82.62MB	82.62MB	0.50MB
datadog-iot-agent-aarch64-rpm	0.00MB	✅	82.69MB	82.69MB	0.50MB
datadog-agent-x86_64-rpm	-0.00MB	✅	875.38MB	875.38MB	0.50MB
datadog-agent-x86_64-suse	-0.00MB	✅	875.38MB	875.38MB	0.50MB

Decision

✅ Passed

agent-platform-auto-pr · 2025-02-11T17:55:39Z

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	837.37MiB	858.45MiB	202.88MiB	214.3MiB
✅	static_quality_gate_docker_agent_amd64	921.67MiB	942.69MiB	308.65MiB	321.56MiB
✅	static_quality_gate_docker_agent_arm64	934.04MiB	952.69MiB	292.63MiB	351.22MiB
✅	static_quality_gate_docker_agent_jmx_amd64	1.09GiB	1.11GiB	383.74MiB	395.8MiB
✅	static_quality_gate_docker_agent_jmx_arm64	1.09GiB	1.11GiB	363.72MiB	375.5MiB
✅	static_quality_gate_docker_dogstatsd_amd64	47.86MiB	57.88MiB	18.29MiB	28.29MiB
✅	static_quality_gate_docker_dogstatsd_arm64	46.26MiB	56.27MiB	17.04MiB	27.06MiB
✅	static_quality_gate_docker_cluster_agent_amd64	267.69MiB	277.7MiB	107.27MiB	117.28MiB
✅	static_quality_gate_docker_cluster_agent_arm64	283.73MiB	293.73MiB	102.1MiB	112.12MiB

cit-pr-commenter · 2025-02-11T18:17:34Z

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: cc677c28-2452-4117-b511-c1fefc6bd754

Baseline: 4e8821d
Comparison: d7df7f1
Diff

Optimization Goals: ❌ Regression(s) detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	quality_gate_logs	% cpu utilization	+16.93	[+13.33, +20.53]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	quality_gate_logs	% cpu utilization	+16.93	[+13.33, +20.53]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	+0.24	[-0.54, +1.02]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	+0.13	[+0.06, +0.20]	1	Logs bounds checks dashboard
➖	file_to_blackhole_0ms_latency_http2	egress throughput	+0.05	[-0.89, +1.00]	1	Logs
➖	file_to_blackhole_1000ms_latency_linear_load	egress throughput	+0.04	[-0.43, +0.50]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.02	[-0.25, +0.29]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	file_to_blackhole_0ms_latency_http1	egress throughput	-0.01	[-1.01, +1.00]	1	Logs
➖	file_to_blackhole_300ms_latency	egress throughput	-0.01	[-0.64, +0.63]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	-0.02	[-0.81, +0.76]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.05	[-1.05, +0.95]	1	Logs
➖	quality_gate_idle	memory utilization	-0.12	[-0.15, -0.08]	1	Logs bounds checks dashboard
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.22	[-0.99, +0.55]	1	Logs
➖	file_tree	memory utilization	-0.27	[-0.34, -0.20]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-2.04	[-2.96, -1.11]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-2.07	[-2.25, -1.90]	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:

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_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.
quality_gate_idle, bounds check intake_connections: 10/10 replicas passed. Gate passed.
quality_gate_idle, bounds check memory_usage: 10/10 replicas passed. Gate passed.

…dd397.yaml Co-authored-by: May Lee <[email protected]>

betterengineering · 2025-02-11T19:05:47Z

/merge

dd-devflow · 2025-02-11T19:05:57Z

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2025-02-11 19:33:22 UTC ℹ️ MergeQueue: merge request added to the queue

The median merge time in main is 30m.

2025-02-11 20:00:35 UTC ℹ️ MergeQueue: This merge request was merged

davidor · 2025-02-12T07:26:30Z

releasenotes/notes/target-based-workload-selection-a745168b07add397.yaml

+#
+# Each section note must be formatted as reStructuredText.
+---
+features:


This should be in the release notes of the cluster agent: https://github.com/DataDog/datadog-agent/tree/main/releasenotes-dca/notes

Thank you!!! So sorry I missed this

feat(ssi): add release note for workload selection

187fccf

This commit adds a release note for the workload selection changes.

betterengineering requested review from a team February 11, 2025 16:30

betterengineering requested a review from a team as a code owner February 11, 2025 16:30

betterengineering requested review from natitsechanski and removed request for a team February 11, 2025 16:30

github-actions bot added the short review PR is simple enough to be reviewed quickly label Feb 11, 2025

betterengineering added qa/done QA done before merge and regressions are covered by tests team/injection-platform labels Feb 11, 2025

domalessi self-requested a review February 11, 2025 17:15

maycmlee approved these changes Feb 11, 2025

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betterengineering and others added 2 commits February 11, 2025 13:34

Update releasenotes/notes/target-based-workload-selection-a745168b07a…

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…dd397.yaml Co-authored-by: May Lee <[email protected]>

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feat(ssi): add release note for workload selection #33936

feat(ssi): add release note for workload selection #33936

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Fine details of change detection per experiment

Bounds Checks: ✅ Passed

Explanation

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feat(ssi): add release note for workload selection #33936

feat(ssi): add release note for workload selection #33936

Conversation

betterengineering commented Feb 11, 2025 • edited Loading

What does this PR do?

Motivation

Additional Notes

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Uncompressed package size comparison

Decision

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Static quality checks ✅

Info

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Regression Detector

Regression Detector Results

Optimization Goals: ❌ Regression(s) detected

Fine details of change detection per experiment

Bounds Checks: ✅ Passed

Explanation

CI Pass/Fail Decision

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