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Main repository for Datadog Agent
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usm: Enable event monitor if USM needs it #31420

Closed vitkyrka closed 4 days ago

vitkyrka commented 4 days ago

What does this PR do?

If all options other than USM which need the event stream (including network_process.enabled) are explicitly disabled, the event monitor module is not enabled even if USM needs it. Add the USM event stream configuration as a condition for enabling the event monitor module.

Motivation

Reported as found during manual experimentation during investigation of an incident.

Describe how to test/QA your changes

Automated tests have been updated.

Possible Drawbacks / Trade-offs

Additional Notes

agent-platform-auto-pr[bot] commented 4 days ago

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=49853544 --os-family=ubuntu

Note: This applies to commit 61bdab21

cit-pr-commenter[bot] commented 4 days ago

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 515e61a9-1e8c-4778-b84d-daef5d5ac3b6

Baseline: 5d4eb27b9cf32660ddfec393be517531f8d0499d Comparison: 61bdab21eb7c47cecb79c9aa82d0f8002799ef8f Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links | |------|----------------------------------------------|--------------------|----------|----------------|--------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | file_tree | memory utilization | +0.82 | [+0.68, +0.96] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | basic_py_check | % cpu utilization | +0.55 | [-3.24, +4.35] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.38 | [-0.36, +1.12] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | file_to_blackhole_300ms_latency | egress throughput | +0.02 | [-0.61, +0.65] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_300ms_latency%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.01 | [-0.76, +0.78] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_500ms_latency%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.01, +0.01] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_dd_logs_filter_exclude%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | -0.00 | [-0.10, +0.10] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | file_to_blackhole_100ms_latency | egress throughput | -0.01 | [-0.74, +0.72] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_100ms_latency%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.02 | [-0.47, +0.44] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency_linear_load%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.03 | [-0.81, +0.76] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.08 | [-0.86, +0.70] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | quality_gate_idle | memory utilization | -0.72 | [-0.77, -0.68] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&tpl_var_run-id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&view=spans&from_ts=1732541966000&to_ts=1732542566000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | -0.73 | [-1.40, -0.06] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | -0.83 | [-0.91, -0.76] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) | | ➖ | quality_gate_idle_all_features | memory utilization | -1.38 | [-1.49, -1.26] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle_all_features%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&tpl_var_run-id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&view=spans&from_ts=1732541966000&to_ts=1732542566000&live=false) | | ➖ | pycheck_lots_of_tags | % cpu utilization | -1.69 | [-5.12, +1.73] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_lots_of_tags%20run_id%3A515e61a9-1e8c-4778-b84d-daef5d5ac3b6&agg_m=count&agg_m_source=base&agg_q=%40span.url&agg_q_source=base&agg_t=count&fromUser=true&index=single-machine-performance-target-logs&messageDisplay=inline&refresh_mode=paused&storage=hot&stream_sort=time%2Cdesc&top_n=100&top_o=top&viz=stream&x_missing=true&from_ts=1732534766000&to_ts=1732546166000&live=false) |

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_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 | memory_usage | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle&tpl_var_job_id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&tpl_var_run-id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&view=spans&from_ts=1732541966000&to_ts=1732542566000&live=false) | | ✅ | quality_gate_idle_all_features | memory_usage | 10/10 | [bounds checks dashboard](https://app.datadoghq.com/dashboard/vz3-jd5-bdi?fromUser=true&refresh_mode=paused&tpl_var_experiment%5B0%5D=quality_gate_idle_all_features&tpl_var_job_id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&tpl_var_run-id%5B0%5D=515e61a9-1e8c-4778-b84d-daef5d5ac3b6&view=spans&from_ts=1732541966000&to_ts=1732542566000&live=false) |

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.

vitkyrka commented 4 days ago

/merge

dd-devflow[bot] commented 4 days ago

Devflow running: /merge

View all feedbacks in Devflow UI.

2024-11-25 16:42:28 UTC :information_source: MergeQueue: pull request added to the queue

The median merge time in main is 23m.