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Fix AAS Profiling Billing#40371

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Sep 4, 2025
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Fix AAS Profiling Billing#40371
dd-mergequeue[bot] merged 5 commits into
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nicholas.hulston/fix-aas-profiling-billing

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@nhulston

@nhulston nhulston commented Aug 28, 2025 •
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What does this PR do?

Motivation

https://datadoghq.atlassian.net/browse/SLES-2442

Describe how you validated your changes (if not by through tests)

Tested manually, and I see the tags as expected:
Screenshot 2025-08-28 at 5 36 46 PM

Possible Drawbacks / Trade-offs

@github-actions github-actions Bot added team/agent-apm trace-agent medium review PR review might take time labels Aug 28, 2025
@nhulston nhulston added team/serverless-azure-gcp qa/done QA done before merge and regressions are covered by tests labels Aug 28, 2025
@nhulston
nhulston marked this pull request as ready for review August 28, 2025 22:14
@nhulston
nhulston requested review from a team as code owners August 28, 2025 22:14
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cit-pr-commenter Bot commented Aug 28, 2025 •
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Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 3f278009-3490-46c0-961f-a95c71152d21

Baseline: 7b57d49
Comparison: bca7113
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 +0.28 [-2.90, +3.46] 1 Logs

Fine details of change detection per experiment

perf experiment goal Δ mean % Δ mean % CI trials links
➖ docker_containers_memory memory utilization +2.79 [+2.61, +2.98] 1 Logs
➖ quality_gate_metrics_logs memory utilization +1.87 [+1.51, +2.23] 1 Logs bounds checks dashboard
➖ file_tree memory utilization +0.47 [+0.43, +0.51] 1 Logs
➖ ddot_logs memory utilization +0.45 [+0.34, +0.55] 1 Logs
➖ ddot_metrics memory utilization +0.43 [+0.23, +0.62] 1 Logs
➖ uds_dogstatsd_20mb_12k_contexts_20_senders memory utilization +0.33 [+0.29, +0.37] 1 Logs
➖ docker_containers_cpu % cpu utilization +0.28 [-2.90, +3.46] 1 Logs
➖ file_to_blackhole_500ms_latency egress throughput +0.03 [-0.59, +0.65] 1 Logs
➖ uds_dogstatsd_to_api ingress throughput +0.00 [-0.04, +0.05] 1 Logs
➖ file_to_blackhole_1000ms_latency egress throughput +0.00 [-0.60, +0.60] 1 Logs
➖ quality_gate_idle memory utilization -0.02 [-0.07, +0.04] 1 Logs bounds checks dashboard
➖ tcp_dd_logs_filter_exclude ingress throughput -0.03 [-0.06, -0.00] 1 Logs
➖ file_to_blackhole_100ms_latency egress throughput -0.05 [-0.65, +0.56] 1 Logs
➖ file_to_blackhole_0ms_latency egress throughput -0.09 [-0.69, +0.50] 1 Logs
➖ otlp_ingest_logs memory utilization -0.10 [-0.24, +0.05] 1 Logs
➖ otlp_ingest_metrics memory utilization -0.15 [-0.33, +0.03] 1 Logs
➖ quality_gate_idle_all_features memory utilization -0.21 [-0.24, -0.17] 1 Logs bounds checks dashboard
➖ tcp_syslog_to_blackhole ingress throughput -1.45 [-1.51, -1.39] 1 Logs
➖ quality_gate_logs % cpu utilization -3.29 [-6.05, -0.54] 1 Logs bounds checks dashboard

Bounds Checks: ❌ Failed

perf experiment bounds_check_name replicates_passed links
❌ docker_containers_cpu simple_check_run 7/10
✅ docker_containers_memory memory_usage 10/10
❌ docker_containers_memory simple_check_run 9/10
✅ 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, 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_metrics_logs, bounds check memory_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 lost_bytes: 10/10 replicas passed. Gate passed.
  • quality_gate_metrics_logs, bounds check cpu_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 intake_connections: 10/10 replicas passed. Gate passed.
  • quality_gate_logs, bounds check lost_bytes: 10/10 replicas passed. Gate 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.

@agent-platform-auto-pr

agent-platform-auto-pr Bot commented Aug 28, 2025 •
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Static quality checks

✅ Please find below the results from static quality gates
Comparison made with ancestor 6016e14

Successful checks

Info

Quality gate Delta On disk size (MiB) Delta On wire size (MiB)
✅ agent_deb_amd64 $${+0.01}$$ $${701.81}$$ < $${709.39}$$ $${-0.03}$$ $${177.61}$$ < $${178.58}$$
✅ agent_deb_amd64_fips $${+0.01}$$ $${695.48}$$ < $${703.09}$$ $${-0.04}$$ $${176.17}$$ < $${178.12}$$
✅ agent_heroku_amd64 $${0}$$ $${339.91}$$ < $${355.37}$$ $${+0}$$ $${90.46}$$ < $${95.72}$$
✅ agent_msi $${+0.04}$$ $${984.53}$$ < $${986.02}$$ $${+0.02}$$ $${151.64}$$ < $${152.67}$$
✅ agent_rpm_amd64 $${+0.01}$$ $${701.8}$$ < $${709.38}$$ $${+0.02}$$ $${179.86}$$ < $${181.22}$$
✅ agent_rpm_amd64_fips $${+0.01}$$ $${695.47}$$ < $${703.08}$$ $${+0.04}$$ $${177.79}$$ < $${179.85}$$
✅ agent_rpm_arm64 $${+0.01}$$ $${688.6}$$ < $${695.74}$$ $${+0.01}$$ $${161.35}$$ < $${163.96}$$
✅ agent_rpm_arm64_fips $${+0.01}$$ $${683.03}$$ < $${693.05}$$ $${-0.01}$$ $${160.0}$$ < $${163.0}$$
✅ agent_suse_amd64 $${+0.01}$$ $${701.8}$$ < $${709.38}$$ $${+0.02}$$ $${179.86}$$ < $${181.22}$$
✅ agent_suse_amd64_fips $${+0.01}$$ $${695.47}$$ < $${703.08}$$ $${+0.04}$$ $${177.79}$$ < $${179.85}$$
✅ agent_suse_arm64 $${+0.01}$$ $${688.6}$$ < $${695.74}$$ $${+0.01}$$ $${161.35}$$ < $${163.96}$$
✅ agent_suse_arm64_fips $${+0.01}$$ $${683.03}$$ < $${693.05}$$ $${-0.01}$$ $${160.0}$$ < $${163.0}$$
✅ docker_agent_amd64 $${+0.01}$$ $${773.22}$$ < $${788.65}$$ $${-0}$$ $${265.78}$$ < $${272.01}$$
✅ docker_agent_arm64 $${+0.01}$$ $${783.98}$$ < $${802.0}$$ $${+0.01}$$ $${252.23}$$ < $${259.7}$$
✅ docker_agent_jmx_amd64 $${+0.01}$$ $${964.09}$$ < $${979.84}$$ $${-0}$$ $${334.42}$$ < $${340.95}$$
✅ docker_agent_jmx_arm64 $${+0.01}$$ $${963.45}$$ < $${981.8}$$ $${+0.02}$$ $${316.83}$$ < $${324.65}$$
✅ docker_cluster_agent_amd64 $${0}$$ $${212.68}$$ < $${214.5}$$ $${+0}$$ $${72.21}$$ < $${73.51}$$
✅ docker_cluster_agent_arm64 $${+0}$$ $${228.6}$$ < $${230.33}$$ $${+0}$$ $${68.49}$$ < $${69.77}$$
✅ docker_cws_instrumentation_amd64 $${0}$$ $${7.07}$$ < $${7.12}$$ $${-0}$$ $${2.95}$$ < $${3.29}$$
✅ docker_cws_instrumentation_arm64 $${0}$$ $${6.69}$$ < $${6.92}$$ $${-0}$$ $${2.71}$$ < $${3.07}$$
✅ docker_dogstatsd_amd64 $${0}$$ $${38.32}$$ < $${39.57}$$ $${+0}$$ $${14.81}$$ < $${15.76}$$
✅ docker_dogstatsd_arm64 $${0}$$ $${36.99}$$ < $${38.2}$$ $${+0}$$ $${14.26}$$ < $${14.83}$$
✅ dogstatsd_deb_amd64 $${0}$$ $${29.56}$$ < $${31.4}$$ $${-0}$$ $${7.8}$$ < $${8.95}$$
✅ dogstatsd_deb_arm64 $${0}$$ $${28.15}$$ < $${29.97}$$ $${+0}$$ $${6.76}$$ < $${7.89}$$
✅ dogstatsd_rpm_amd64 $${0}$$ $${29.56}$$ < $${31.4}$$ $${-0}$$ $${7.81}$$ < $${8.96}$$
✅ dogstatsd_suse_amd64 $${0}$$ $${29.56}$$ < $${31.4}$$ $${-0}$$ $${7.81}$$ < $${8.96}$$
✅ iot_agent_deb_amd64 $${0}$$ $${54.58}$$ < $${54.97}$$ $${-0}$$ $${13.76}$$ < $${14.45}$$
✅ iot_agent_deb_arm64 $${0}$$ $${51.88}$$ < $${51.9}$$ $${+0}$$ $${11.9}$$ < $${12.63}$$
✅ iot_agent_deb_armhf $${0}$$ $${51.45}$$ < $${51.84}$$ $${-0}$$ $${11.97}$$ < $${12.74}$$
✅ iot_agent_rpm_amd64 $${0}$$ $${54.58}$$ < $${54.97}$$ $${+0}$$ $${13.78}$$ < $${14.47}$$
✅ iot_agent_suse_amd64 $${0}$$ $${54.58}$$ < $${54.97}$$ $${+0}$$ $${13.78}$$ < $${14.47}$$

@nhulston

nhulston commented Sep 3, 2025

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/merge

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2025-09-03 13:10:28 UTC ℹ️ Start processing command /merge

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Serverless Benchmark Results

BenchmarkStartEndInvocation comparison between 9e7aa42 and 6a32bf32d4868030ae0176882b1023d43f766537.

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

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/merge

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/merge

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The expected merge time in main is approximately 31m (p90).

2025-09-04 13:28:02 UTC ℹ️ MergeQueue: This merge request was merged

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dd-mergequeue Bot deleted the nicholas.hulston/fix-aas-profiling-billing branch September 4, 2025 13:28
@github-actions github-actions Bot added this to the 7.71.0 milestone Sep 4, 2025
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@ichinaski ichinaski ichinaski approved these changes
@hestonhoffman hestonhoffman hestonhoffman approved these changes
@duncanista duncanista duncanista approved these changes
@duncanpharvey duncanpharvey duncanpharvey approved these changes

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medium review PR review might take time qa/done QA done before merge and regressions are covered by tests team/agent-apm trace-agent team/serverless-azure-gcp

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