Support Kafka actions via remote config with one-shot check execution #43325

piochelepiotr · 2025-11-21T04:19:41Z

What does this PR do?

Adds support for one-shot Kafka actions via remote configuration and introduces the RunOnce() interface for checks that should be descheduled after their first execution.

Key changes:

New kafka_actions check scheduled via remote config (DSM_KAFKA_ACTIONS product) for one-off Kafka operations
RunOnce() method added to check.Check interface to support one-shot check execution
Scheduler automatically removes checks that return true from RunOnce() after first run
kafka_messages controller retained for backwards compatibility

Motivation

The existing kafka_messages feature mutates kafka_consumer configs to add live message collection. This approach has limitations, as it modifies the existing kafka_consumer integration with a config that is only used once (it for example makes the kafka_consumer integration drop its cache, since the integration digest changes).

kafka_actions replaces this with a more flexible approach: it schedules dedicated one-shot checks that execute a single action and are automatically removed from the scheduler. The check receives authentication details by matching bootstrap_servers from existing kafka_consumer integrations, ensuring correct cluster targeting.

We keep kafka_messages until the frontend switches to kafka_actions.

Describe how you validated your changes

Added comprehensive tests for kafka_actions controller (matching, auth extraction, config parsing)
Added scheduler tests for RunOnce() behavior
Verified checks are removed from job queue after first execution
Ran locally using the DEBUG remote configuration product, and verified that the kafka_actions check is scheduled, and runs only once

Additional Notes

The RunOnce() interface is generic and can be used by any check needing one-shot execution semantics (e.g., migration tasks, one-time configuration changes, etc.).

agent-platform-auto-pr · 2025-11-21T04:38:04Z

Go Package Import Differences

Baseline: f42d155
Comparison: a1ae377

binary	os	arch	change
agent	linux	amd64	+1, -0 +github.com/DataDog/datadog-agent/pkg/trace/traceutil/normalize
agent	linux	arm64	+1, -0 +github.com/DataDog/datadog-agent/pkg/trace/traceutil/normalize
agent	darwin	amd64	+1, -0 +github.com/DataDog/datadog-agent/pkg/trace/traceutil/normalize
agent	darwin	arm64	+1, -0 +github.com/DataDog/datadog-agent/pkg/trace/traceutil/normalize
iot-agent	linux	amd64	+1, -0 +github.com/DataDog/datadog-agent/pkg/trace/traceutil/normalize
iot-agent	linux	arm64	+1, -0 +github.com/DataDog/datadog-agent/pkg/trace/traceutil/normalize
heroku-agent	linux	amd64	+1, -0 +github.com/DataDog/datadog-agent/pkg/trace/traceutil/normalize

agent-platform-auto-pr · 2025-11-21T05:56:48Z

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 4e29f89

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${-1.38}$$	$${704.92}$$ < $${717.44}$$	$${-0.23}$$	$${172.43}$$ < $${177.37}$$
✅	agent_deb_amd64_fips	$${-1.63}$$	$${698.95}$$ < $${711.66}$$	$${-0.23}$$	$${171.19}$$ < $${174.84}$$
✅	agent_heroku_amd64	$${-1.53}$$	$${327.34}$$ < $${337.57}$$	$${-0.19}$$	$${87.34}$$ < $${90.78}$$
✅	agent_msi	$${-1.02}$$	$${999.58}$$ < $${1015.38}$$	$${-0.07}$$	$${144.38}$$ < $${149.42}$$
✅	agent_rpm_amd64	$${-1.38}$$	$${704.91}$$ < $${717.41}$$	$${-0.07}$$	$${175.68}$$ < $${178.01}$$
✅	agent_rpm_amd64_fips	$${-1.63}$$	$${698.94}$$ < $${711.64}$$	$${-0.16}$$	$${173.35}$$ < $${177.26}$$
✅	agent_rpm_arm64	$${-1.39}$$	$${690.18}$$ < $${702.19}$$	$${-0.19}$$	$${159.43}$$ < $${163.22}$$
✅	agent_rpm_arm64_fips	$${-1.62}$$	$${685.37}$$ < $${697.6}$$	$${-0.28}$$	$${158.74}$$ < $${162.68}$$
✅	agent_suse_amd64	$${-1.38}$$	$${704.91}$$ < $${717.41}$$	$${-0.07}$$	$${175.68}$$ < $${178.01}$$
✅	agent_suse_amd64_fips	$${-1.63}$$	$${698.94}$$ < $${711.64}$$	$${-0.16}$$	$${173.35}$$ < $${177.26}$$
✅	agent_suse_arm64	$${-1.39}$$	$${690.18}$$ < $${702.19}$$	$${-0.19}$$	$${159.43}$$ < $${163.22}$$
✅	agent_suse_arm64_fips	$${-1.62}$$	$${685.37}$$ < $${697.6}$$	$${-0.28}$$	$${158.74}$$ < $${162.68}$$
✅	docker_agent_amd64	$${-1.38}$$	$${767.3}$$ < $${769.92}$$	$${-0.17}$$	$${260.33}$$ < $${261.52}$$
✅	docker_agent_arm64	$${-1.39}$$	$${776.93}$$ < $${779.46}$$	$${-0.18}$$	$${250.58}$$ < $${251.72}$$
✅	docker_agent_jmx_amd64	$${-1.38}$$	$${958.17}$$ < $${960.8}$$	$${-0.17}$$	$${328.96}$$ < $${330.15}$$
✅	docker_agent_jmx_arm64	$${-1.39}$$	$${956.4}$$ < $${958.93}$$	$${-0.18}$$	$${315.19}$$ < $${316.35}$$
✅	docker_cluster_agent_amd64	$${-0}$$	$${179.2}$$ < $${207.49}$$	$${+0}$$	$${63.22}$$ < $${70.2}$$
✅	docker_cluster_agent_arm64	$${-0}$$	$${196.69}$$ < $${223.38}$$	$${-0}$$	$${59.95}$$ < $${66.42}$$
✅	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	$${-0}$$	$${38.54}$$ < $${39.3}$$	$${-0}$$	$${14.89}$$ < $${15.76}$$
✅	docker_dogstatsd_arm64	$${-0}$$	$${37.18}$$ < $${37.94}$$	$${-0}$$	$${14.34}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${-0}$$	$${29.76}$$ < $${30.53}$$	$${+0}$$	$${7.85}$$ < $${8.75}$$
✅	dogstatsd_deb_arm64	$${-0}$$	$${28.27}$$ < $${29.11}$$	$${+0}$$	$${6.79}$$ < $${7.71}$$
✅	dogstatsd_rpm_amd64	$${-0}$$	$${29.76}$$ < $${30.53}$$	$${+0}$$	$${7.86}$$ < $${8.76}$$
✅	dogstatsd_suse_amd64	$${-0}$$	$${29.76}$$ < $${30.53}$$	$${+0}$$	$${7.86}$$ < $${8.76}$$
✅	iot_agent_deb_amd64	$${-0.06}$$	$${42.41}$$ < $${43.19}$$	$${-0.02}$$	$${11.07}$$ < $${11.98}$$
✅	iot_agent_deb_arm64	$${-0.06}$$	$${40.14}$$ < $${40.92}$$	$${-0.01}$$	$${9.54}$$ < $${10.45}$$
✅	iot_agent_deb_armhf	$${-0.06}$$	$${40.07}$$ < $${40.86}$$	$${-0.01}$$	$${9.65}$$ < $${10.56}$$
✅	iot_agent_rpm_amd64	$${-0.06}$$	$${42.41}$$ < $${43.19}$$	$${-0.02}$$	$${11.09}$$ < $${12.0}$$
✅	iot_agent_suse_amd64	$${-0.06}$$	$${42.41}$$ < $${43.19}$$	$${-0.02}$$	$${11.09}$$ < $${12.0}$$

cit-pr-commenter · 2025-11-21T06:49:24Z

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: bcd8d2ab-1a3f-41ce-acfb-b96801e4ec46

Baseline: f42d155
Comparison: a1ae377
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	+3.61	[+0.62, +6.60]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	docker_containers_cpu	% cpu utilization	+3.61	[+0.62, +6.60]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	+1.37	[+1.30, +1.45]	1	Logs
➖	ddot_metrics	memory utilization	+1.00	[+0.80, +1.20]	1	Logs
➖	docker_containers_memory	memory utilization	+0.55	[+0.42, +0.69]	1	Logs
➖	ddot_logs	memory utilization	+0.38	[+0.32, +0.44]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.29	[+0.19, +0.38]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	+0.20	[+0.05, +0.34]	1	Logs
➖	file_tree	memory utilization	+0.17	[+0.12, +0.22]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	+0.15	[-0.05, +0.36]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	+0.12	[-0.09, +0.32]	1	Logs bounds checks dashboard
➖	file_to_blackhole_100ms_latency	egress throughput	+0.05	[-0.00, +0.10]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	+0.04	[-0.20, +0.28]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	+0.03	[-0.35, +0.41]	1	Logs
➖	otlp_ingest_metrics	memory utilization	+0.02	[-0.12, +0.16]	1	Logs
➖	quality_gate_idle	memory utilization	+0.01	[-0.04, +0.06]	1	Logs bounds checks dashboard
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.01	[-0.07, +0.08]	1	Logs
➖	uds_dogstatsd_to_api_v3	ingress throughput	+0.01	[-0.11, +0.12]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	+0.00	[-0.12, +0.12]	1	Logs
➖	file_to_blackhole_1000ms_latency	egress throughput	-0.05	[-0.46, +0.36]	1	Logs
➖	file_to_blackhole_500ms_latency	egress throughput	-0.12	[-0.49, +0.26]	1	Logs
➖	quality_gate_logs	% cpu utilization	-0.17	[-1.61, +1.28]	1	Logs bounds checks dashboard
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	-0.38	[-0.43, -0.34]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.42	[-0.47, -0.37]	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_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_logs, bounds check intake_connections: 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_metrics_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
quality_gate_metrics_logs, bounds check lost_bytes: 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.

jesssu7 · 2025-11-21T16:33:05Z

pkg/remoteconfig/state/products.go

 	ProductNDMDeviceProfilesCustom:      {},
 	ProductMetricControl:                {},
 	ProductDataStreamsLiveMessages:      {},
+	ProductDataStreamsKafkaActions:      {},


Out of an abundance of caution before the code freeze, let's please use the DEBUG product in this PR since the ProductDataStreamsKafkaActions staging keys haven't been created

Ah okay, I see these don't affect the repo verification

piochelepiotr · 2025-11-21T16:55:53Z

/merge

dd-devflow-routing-codex · 2025-11-21T16:55:57Z

View all feedbacks in Devflow UI.

2025-11-21 16:55:57 UTC ℹ️ Start processing command /merge

2025-11-21 16:56:02 UTC ℹ️ MergeQueue: pull request added to the queue

The expected merge time in main is approximately 37m (p90).

2025-11-21 17:25:58 UTC ℹ️ MergeQueue: This merge request was merged

### What does this PR do? Follow up to #43325 ### Motivation Events generated by the kafka_actions can easily be correlated with the action taken in the UI ### Describe how you validated your changes QA locally ### Additional Notes Co-authored-by: piotr.wolski <[email protected]>

### What does this PR do? Refactors the `kafka_actions` one-shot check implementation to use `run_once: true` + `Interval() == 0` instead of the `RunOnce()` interface, and removes all `RunOnce()` infrastructure that was added in #43325. **Key changes:** * Removed `RunOnce()` method from `check.Check` interface and all implementations * Python checks now support `run_once: true` in instance config, which sets `Interval()` to `0` * Checks with `Interval() == 0` use existing `enqueueOnce()` for immediate execution * Removed scheduler logic for de-scheduling `RunOnce()` checks * Fixed numeric precision issue: use `json.Decoder.UseNumber()` to prevent scientific notation (e.g., `1e+06`) in YAML ### Motivation After implementing #43325, I realized that the agent already had a mechanism for one-shot check execution: `Interval() == 0` triggers `enqueueOnce()` for immediate, single-run execution. The `RunOnce()` interface I added was: 1. **Redundant** - duplicated existing functionality 2. **Slower** - caused up to a 15-second delays due to bucket scheduling (checks were added to job queues instead of running immediately) 3. **More complex** - required interface changes across ~15 files ### Describe how you validated your changes * Manually tested with local agent - verified check executes immediately and only once (no more 15-second delay) Co-authored-by: piotr.wolski <[email protected]>

### What does this PR do? Refactors the `kafka_actions` one-shot check implementation to use `run_once: true` + `Interval() == 0` instead of the `RunOnce()` interface, and removes all `RunOnce()` infrastructure that was added in DataDog#43325. **Key changes:** * Removed `RunOnce()` method from `check.Check` interface and all implementations * Python checks now support `run_once: true` in instance config, which sets `Interval()` to `0` * Checks with `Interval() == 0` use existing `enqueueOnce()` for immediate execution * Removed scheduler logic for de-scheduling `RunOnce()` checks * Fixed numeric precision issue: use `json.Decoder.UseNumber()` to prevent scientific notation (e.g., `1e+06`) in YAML ### Motivation After implementing DataDog#43325, I realized that the agent already had a mechanism for one-shot check execution: `Interval() == 0` triggers `enqueueOnce()` for immediate, single-run execution. The `RunOnce()` interface I added was: 1. **Redundant** - duplicated existing functionality 2. **Slower** - caused up to a 15-second delays due to bucket scheduling (checks were added to job queues instead of running immediately) 3. **More complex** - required interface changes across ~15 files ### Describe how you validated your changes * Manually tested with local agent - verified check executes immediately and only once (no more 15-second delay) Co-authored-by: piotr.wolski <[email protected]> a560f83

github-actions bot added the medium review PR review might take time label Nov 21, 2025

piochelepiotr changed the title ~~Piotr wolski/start one time checks~~ Support Kafka actions via remote config with one-shot check execution Nov 21, 2025

github-actions bot added the team/agent-runtimes label Nov 21, 2025

piochelepiotr force-pushed the piotr-wolski/start-one-time-checks branch from a78cd6e to e60b3c8 Compare November 21, 2025 04:51

github-actions bot added long review PR is complex, plan time to review it and removed medium review PR review might take time labels Nov 21, 2025

piochelepiotr added the qa/done QA done before merge and regressions are covered by tests label Nov 21, 2025

piochelepiotr force-pushed the piotr-wolski/start-one-time-checks branch from e60b3c8 to e82d30c Compare November 21, 2025 05:15

piochelepiotr added 2 commits November 20, 2025 22:19

Add kafka actions check scheduling

1f805f1

Add release note

a1ae377

piochelepiotr force-pushed the piotr-wolski/start-one-time-checks branch from e82d30c to a1ae377 Compare November 21, 2025 05:56

piochelepiotr marked this pull request as ready for review November 21, 2025 06:20

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dd-mergequeue bot merged commit 8c75423 into main Nov 21, 2025
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dd-mergequeue bot deleted the piotr-wolski/start-one-time-checks branch November 21, 2025 17:25

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piochelepiotr mentioned this pull request Nov 21, 2025

Pass down remote config id to kafka actions #43356

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piochelepiotr mentioned this pull request Nov 24, 2025

DSMON-1152: Partially revert RunOnce implementation #43383

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Support Kafka actions via remote config with one-shot check execution #43325

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Experiments ignored for regressions

Fine details of change detection per experiment

Bounds Checks: ✅ Passed

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Support Kafka actions via remote config with one-shot check execution #43325

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piochelepiotr commented Nov 21, 2025

What does this PR do?

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Describe how you validated your changes

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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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