[CONTINT-4562] Add containers SMP tests #33620
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[Fast Unit Tests Report] On pipeline 54598188 (CI Visibility). The following jobs did not run any unit tests: Jobs:
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 |
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Static quality checks✅ Please find below the results from static quality gates Successful checksInfo
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: de1d350 ❌ Experiments with missing or malformed dataThis is a critical error. No usable optimization goal data was produced by the listed experiments. This may be a result of misconfiguration. Ping #single-machine-performance and we can help out.
Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | docker_containers_cpu | % cpu utilization | +3.71 | [-0.11, +7.53] | 1 | Logs |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +1.78 | [+1.70, +1.86] | 1 | Logs |
| ➖ | docker_containers_memory | memory utilization | +0.20 | [+0.16, +0.24] | 1 | Logs |
| ➖ | otlp_ingest_traces | memory utilization | +0.18 | [-0.10, +0.45] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | +0.08 | [-0.78, +0.95] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | +0.03 | [-0.84, +0.91] | 1 | Logs |
| ➖ | ddot_metrics | memory utilization | +0.02 | [-0.04, +0.08] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | +0.02 | [-0.91, +0.95] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.01 | [-0.27, +0.29] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.00 | [-0.90, +0.90] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.02, +0.02] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.01 | [-0.22, +0.21] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | -0.01 | [-0.85, +0.84] | 1 | Logs |
| ➖ | file_to_blackhole_300ms_latency | egress throughput | -0.01 | [-0.77, +0.75] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.03 | [-0.89, +0.83] | 1 | Logs |
| ➖ | otlp_ingest_metrics | memory utilization | -0.12 | [-0.27, +0.03] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.27 | [-1.12, +0.58] | 1 | Logs |
| ➖ | otlp_ingest_logs | memory utilization | -0.39 | [-0.56, -0.23] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.49 | [-0.56, -0.43] | 1 | Logs bounds checks dashboard |
| ➖ | file_tree | memory utilization | -0.67 | [-0.80, -0.54] | 1 | Logs |
| ➖ | uds_dogstatsd_20mb_12k_contexts_20_senders | memory utilization | -1.06 | [-1.13, -0.99] | 1 | Logs |
| ➖ | ddot_logs | memory utilization | -1.39 | [-1.43, -1.34] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | -3.67 | [-3.78, -3.56] | 1 | Logs bounds checks dashboard |
| ➖ | quality_gate_logs | % cpu utilization | -3.99 | [-6.70, -1.28] | 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_0ms_latency_http1 | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_0ms_latency_http1 | memory_usage | 10/10 | |
| ✅ | file_to_blackhole_0ms_latency_http2 | lost_bytes | 10/10 | |
| ✅ | file_to_blackhole_0ms_latency_http2 | 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 | 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 |
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:
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Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.
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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.
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Its configuration does not mark it "erratic".
CI Pass/Fail Decision
✅ Passed. All Quality Gates passed.
- quality_gate_logs, bounds check intake_connections: 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_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_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.
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| logs_no_ssl: true | ||
| process_config: | ||
| process_dd_url: http://localhost:9093 | ||
| container_image: |
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Is this section missing from our existing experiments? Should we add it to all experiments?
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These settings aim at fixing error logs that the agent emits when it tries to send data to an intake that hasn’t been redirected to a lading black-hole.
Logs are showing that, indeed, some other experiments are emitting those Error while processing transaction: error while sending transaction errors logs because they lack those settings.
All the “idle” experiments are not in this list since, as the agent monitors nothing, it doesn’t even try to send any data on the endpoints that haven’t been configured.
Fixing those errors most probably deserves another dedicated PR.
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Here is the PR to address this issue in other experiments: #36305.
test/regression/cases/docker_containers_cpu/datadog-agent/checks.d/fake_check.py
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What does this PR do?
Add two new SMP experiments:
docker_containers_cpudocker_containers_memoryThey both start an agent which has 200 docker containers to monitor. (Thanks to DataDog/lading#1227)
The containers have many labels that are used by the agent’s
container_labels_as_tagsfeature and many environment variables that are used by the agent’scontainer_env_as_tagsfeature in order populate the workload meta store and the tagger.There’s one simplistic
fake_checkpython custom check in order to trigger auto-discovery.The SMP checks assert a telemetry metric emitted by the
fake_checkcheck in order to validate that the agent has properly discovered the docker containers and has scheduled the check for them.Motivation
Try to detect memory management regressions in workload meta, its docker collector, the tagger, auto-discovery.
Describe how you validated your changes
Possible Drawbacks / Trade-offs
Additional Notes