Reduce apm internal metrics overhead (CSI-1279)

[ajgajg1134]

What does this PR do?

Skip zero value metrics that are rarely used, these metrics provide some debugging value, but don't provide any signal when 0, thus we can stop sending these and reduce the overhead here a small amount. DM me for exact cost calculations

Motivation

Reduce overhead

Describe how you validated your changes

Ran locally and observed these metrics do not appear, but the other ones still do. Also unit tests

Additional Notes

[git-thuerk-done]

Hi @ajgajg1134 just a small suggestion but approved!

[dustmop]

LGTM, with one suggestion

[cit-pr-commenter]

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: dfaa6347-ac93-4fd2-912f-469526d68776

Baseline: 18cb39c
Comparison: c80b94b
Diff

Optimization Goals: ❌ Regression(s) detected

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	docker_containers_memory	memory utilization	+5.98	[+5.53, +6.43]	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	+32.20	[+30.07, +34.34]	1	Logs

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
❌	docker_containers_cpu	% cpu utilization	+32.20	[+30.07, +34.34]	1	Logs
❌	docker_containers_memory	memory utilization	+5.98	[+5.53, +6.43]	1	Logs
➖	quality_gate_logs	% cpu utilization	+1.78	[-1.04, +4.61]	1	Logs bounds checks dashboard
➖	ddot_metrics	memory utilization	+0.74	[+0.57, +0.91]	1	Logs
➖	ddot_logs	memory utilization	+0.34	[+0.28, +0.40]	1	Logs
➖	otlp_ingest_logs	memory utilization	+0.28	[+0.15, +0.41]	1	Logs
➖	uds_dogstatsd_20mb_12k_contexts_20_senders	memory utilization	+0.21	[+0.17, +0.25]	1	Logs
➖	ddot_metrics_sum_cumulativetodelta_exporter	memory utilization	+0.19	[-0.01, +0.39]	1	Logs
➖	quality_gate_idle	memory utilization	+0.18	[+0.12, +0.24]	1	Logs bounds checks dashboard
➖	file_to_blackhole_500ms_latency	egress throughput	+0.09	[-0.52, +0.70]	1	Logs
➖	file_to_blackhole_100ms_latency	egress throughput	+0.07	[-0.53, +0.66]	1	Logs
➖	file_to_blackhole_1000ms_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
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.25, +0.23]	1	Logs
➖	file_to_blackhole_0ms_latency	egress throughput	-0.02	[-0.62, +0.59]	1	Logs
➖	ddot_metrics_sum_cumulative	memory utilization	-0.10	[-0.23, +0.02]	1	Logs
➖	ddot_metrics_sum_delta	memory utilization	-0.14	[-0.28, +0.01]	1	Logs
➖	file_tree	memory utilization	-0.20	[-0.24, -0.16]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.35	[-0.42, -0.28]	1	Logs
➖	quality_gate_idle_all_features	memory utilization	-0.48	[-0.52, -0.43]	1	Logs bounds checks dashboard
➖	otlp_ingest_metrics	memory utilization	-0.84	[-0.97, -0.71]	1	Logs
➖	quality_gate_metrics_logs	memory utilization	-1.77	[-1.95, -1.58]	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	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_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_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 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_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.

[agent-platform-auto-pr]

Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 18cb39c

Successful checks

Info

	Quality gate	Delta	On disk size (MiB)	Delta	On wire size (MiB)
✅	agent_deb_amd64	$${+0}$$	$${674.74}$$ < $${702.15}$$	$${-0.03}$$	$${164.63}$$ < $${177.79}$$
✅	agent_deb_amd64_fips	$${+0}$$	$${669.25}$$ < $${696.65}$$	$${+0.07}$$	$${163.9}$$ < $${176.53}$$
✅	agent_heroku_amd64	$${0}$$	$${336.55}$$ < $${340.18}$$	$${-0.01}$$	$${89.82}$$ < $${91.08}$$
✅	agent_msi	$${+0.01}$$	$${1013.56}$$ < $${1015.38}$$	$${+0.02}$$	$${148.41}$$ < $${150.78}$$
✅	agent_rpm_amd64	$${+0}$$	$${674.73}$$ < $${702.14}$$	$${-0.02}$$	$${166.95}$$ < $${180.53}$$
✅	agent_rpm_amd64_fips	$${+0}$$	$${669.24}$$ < $${696.64}$$	$${+0.02}$$	$${165.55}$$ < $${178.79}$$
✅	agent_rpm_arm64	$${+0}$$	$${664.33}$$ < $${686.31}$$	$${+0}$$	$${153.22}$$ < $${161.22}$$
✅	agent_rpm_arm64_fips	$${+0}$$	$${659.85}$$ < $${681.84}$$	$${+0.04}$$	$${152.74}$$ < $${160.18}$$
✅	agent_suse_amd64	$${+0}$$	$${674.73}$$ < $${702.14}$$	$${-0.02}$$	$${166.95}$$ < $${180.53}$$
✅	agent_suse_amd64_fips	$${+0}$$	$${669.24}$$ < $${696.64}$$	$${+0.02}$$	$${165.55}$$ < $${178.79}$$
✅	agent_suse_arm64	$${+0}$$	$${664.33}$$ < $${686.31}$$	$${+0}$$	$${153.22}$$ < $${161.22}$$
✅	agent_suse_arm64_fips	$${+0}$$	$${659.85}$$ < $${681.84}$$	$${+0.04}$$	$${152.74}$$ < $${160.18}$$
✅	docker_agent_amd64	$${+0}$$	$${746.17}$$ < $${773.59}$$	$${+0}$$	$${251.8}$$ < $${266.06}$$
✅	docker_agent_arm64	$${+0}$$	$${759.72}$$ < $${781.7}$$	$${+0}$$	$${242.42}$$ < $${251.85}$$
✅	docker_agent_jmx_amd64	$${+0}$$	$${937.04}$$ < $${964.45}$$	$${-0}$$	$${320.42}$$ < $${334.68}$$
✅	docker_agent_jmx_arm64	$${+0}$$	$${939.19}$$ < $${961.17}$$	$${-0}$$	$${307.01}$$ < $${316.44}$$
✅	docker_cluster_agent_amd64	$${-0}$$	$${213.01}$$ < $${213.74}$$	$${-0}$$	$${72.26}$$ < $${73.14}$$
✅	docker_cluster_agent_arm64	$${-0}$$	$${228.92}$$ < $${229.68}$$	$${+0}$$	$${68.54}$$ < $${69.41}$$
✅	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	$${+0}$$	$${38.44}$$ < $${39.3}$$	$${-0}$$	$${14.84}$$ < $${15.76}$$
✅	docker_dogstatsd_arm64	$${0}$$	$${37.13}$$ < $${37.94}$$	$${+0}$$	$${14.29}$$ < $${14.83}$$
✅	dogstatsd_deb_amd64	$${0}$$	$${29.66}$$ < $${30.53}$$	$${-0}$$	$${7.82}$$ < $${8.75}$$
✅	dogstatsd_deb_arm64	$${0}$$	$${28.24}$$ < $${29.11}$$	$${+0}$$	$${6.77}$$ < $${7.71}$$
✅	dogstatsd_rpm_amd64	$${0}$$	$${29.66}$$ < $${30.53}$$	$${-0}$$	$${7.83}$$ < $${8.76}$$
✅	dogstatsd_suse_amd64	$${0}$$	$${29.66}$$ < $${30.53}$$	$${-0}$$	$${7.83}$$ < $${8.76}$$
✅	iot_agent_deb_amd64	$${0}$$	$${41.89}$$ < $${54.97}$$	$${-0}$$	$${10.9}$$ < $${14.45}$$
✅	iot_agent_deb_arm64	$${0}$$	$${39.71}$$ < $${51.9}$$	$${-0}$$	$${9.41}$$ < $${12.63}$$
✅	iot_agent_deb_armhf	$${0}$$	$${39.58}$$ < $${51.84}$$	$${+0}$$	$${9.49}$$ < $${12.74}$$
✅	iot_agent_rpm_amd64	$${0}$$	$${41.89}$$ < $${54.97}$$	$${+0}$$	$${10.91}$$ < $${14.47}$$
✅	iot_agent_suse_amd64	$${0}$$	$${41.89}$$ < $${54.97}$$	$${+0}$$	$${10.91}$$ < $${14.47}$$

[github-actions]

Serverless Benchmark Results

BenchmarkStartEndInvocation comparison between d54f5f9 and 0247ee2.

tl;dr

Use these benchmarks as an insight tool during development.

1. Skim down the vs base column in each chart. If there is a ~, then there was no statistically significant change to the benchmark. Otherwise, ensure the estimated percent change is either negative or very small.

2. The last row of each chart is the geomean. Ensure this percentage is either negative or very small.

What is this benchmarking?

The BenchmarkStartEndInvocation compares the amount of time it takes to call the start-invocation and end-invocation endpoints. For universal instrumentation languages (Dotnet, Golang, Java, Ruby), this represents the majority of the duration overhead added by our tracing layer.

The benchmark is run using a large variety of lambda request payloads. In the charts below, there is one row for each event payload type.

How do I interpret these charts?

The charts below comes from benchstat. They represent the statistical change in duration (sec/op), memory overhead (B/op), and allocations (allocs/op).

The benchstat docs explain how to interpret these charts.

Before the comparison table, we see common file-level configuration. If there are benchmarks with different configuration (for example, from different packages), benchstat will print separate tables for each configuration.

The table then compares the two input files for each benchmark. It shows the median and 95% confidence interval summaries for each benchmark before and after the change, and an A/B comparison under "vs base". ... The p-value measures how likely it is that any differences were due to random chance (i.e., noise). The "~" means benchstat did not detect a statistically significant difference between the two inputs. ...

Note that "statistically significant" is not the same as "large": with enough low-noise data, even very small changes can be distinguished from noise and considered statistically significant. It is, of course, generally easier to distinguish large changes from noise.

Finally, the last row of the table shows the geometric mean of each column, giving an overall picture of how the benchmarks changed. Proportional changes in the geomean reflect proportional changes in the benchmarks. For example, given n benchmarks, if sec/op for one of them increases by a factor of 2, then the sec/op geomean will increase by a factor of ⁿ√2.

I need more help

First off, do not worry if the benchmarks are failing. They are not tests. The intention is for them to be a tool for you to use during development.

If you would like a hand interpreting the results come chat with us in #serverless-agent in the internal DataDog slack or in #serverless in the public DataDog slack. We're happy to help!

Benchmark stats

[ajgajg1134]

/merge

[dd-devflow-routing-codex]

View all feedbacks in Devflow UI.

2025-10-22 19:45:13 UTC ℹ️ Start processing command /merge

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2025-10-22 19:45:19 UTC ℹ️ MergeQueue: pull request added to the queue

The expected merge time in main is approximately 40m (p90).

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2025-10-22 20:14:11 UTC ℹ️ MergeQueue: This merge request was merged