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Main repository for Datadog Agent
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TEST ONLY DO NOT MERGE fail otel agent integration test #31437

Closed jackgopack4 closed 13 hours ago

jackgopack4 commented 16 hours ago

What does this PR do?

test what happens when OTel Agent integration fails on purpose

Motivation

Describe how to test/QA your changes

Possible Drawbacks / Trade-offs

Additional Notes

agent-platform-auto-pr[bot] commented 16 hours ago

[Fast Unit Tests Report]

On pipeline 49892595 (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

agent-platform-auto-pr[bot] commented 16 hours 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=49892595 --os-family=ubuntu

Note: This applies to commit 82a2409d

cit-pr-commenter[bot] commented 15 hours ago

Regression Detector

Regression Detector Results

Metrics dashboard
Target profiles
Run ID: 56f822ed-6bf8-4dea-ac4c-51f11783e7f0

Baseline: a22a1658c9a9ba62aa163c6771523fb36a5a3495 Comparison: 82a2409d504fc2294550f24e64e07ef7c84410a7 Diff

Optimization Goals: ✅ No significant changes detected

Fine details of change detection per experiment

| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links | |------|----------------------------------------------|--------------------|----------|----------------|--------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | ➖ | quality_gate_idle_all_features | memory utilization | +3.04 | [+2.89, +3.20] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle_all_features%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&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=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&tpl_var_run-id%5B0%5D=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&view=spans&from_ts=1732559979000&to_ts=1732560579000&live=false) | | ➖ | tcp_syslog_to_blackhole | ingress throughput | +0.64 | [+0.58, +0.70] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Atcp_syslog_to_blackhole%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | file_tree | memory utilization | +0.39 | [+0.26, +0.52] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_tree%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | otel_to_otel_logs | ingress throughput | +0.36 | [-0.33, +1.05] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aotel_to_otel_logs%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | quality_gate_idle | memory utilization | +0.35 | [+0.30, +0.40] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Aquality_gate_idle%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&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=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&tpl_var_run-id%5B0%5D=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&view=spans&from_ts=1732559979000&to_ts=1732560579000&live=false) | | ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | +0.10 | [-0.36, +0.56] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency_linear_load%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.07 | [-0.70, +0.85] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_500ms_latency%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.05 | [-0.72, +0.82] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_100ms_latency%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | file_to_blackhole_0ms_latency | egress throughput | +0.03 | [-0.78, +0.83] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_0ms_latency%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.02 | [-0.77, +0.81] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_1000ms_latency%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | uds_dogstatsd_to_api | ingress throughput | +0.01 | [-0.09, +0.11] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&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%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | file_to_blackhole_300ms_latency | egress throughput | -0.01 | [-0.64, +0.62] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Afile_to_blackhole_300ms_latency%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.22 | [-0.94, +0.51] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Auds_dogstatsd_to_api_cpu%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | basic_py_check | % cpu utilization | -1.59 | [-5.38, +2.19] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Abasic_py_check%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&live=false) | | ➖ | pycheck_lots_of_tags | % cpu utilization | -2.51 | [-5.95, +0.92] | 1 | [Logs](https://app.datadoghq.com/logs?query=experiment%3Apycheck_lots_of_tags%20run_id%3A56f822ed-6bf8-4dea-ac4c-51f11783e7f0&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=1732552779000&to_ts=1732564179000&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=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&tpl_var_run-id%5B0%5D=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&view=spans&from_ts=1732559979000&to_ts=1732560579000&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=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&tpl_var_run-id%5B0%5D=56f822ed-6bf8-4dea-ac4c-51f11783e7f0&view=spans&from_ts=1732559979000&to_ts=1732560579000&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.