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[CWS] Sort replayed events #37845
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[CWS] Sort replayed events #37845
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: a0635df Optimization Goals: ✅ Improvement(s) detected
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perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
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➖ | docker_containers_cpu | % cpu utilization | +3.31 | [+0.25, +6.37] | 1 | Logs |
➖ | quality_gate_logs | % cpu utilization | +0.99 | [-1.79, +3.77] | 1 | Logs bounds checks dashboard |
➖ | quality_gate_idle_all_features | memory utilization | +0.62 | [+0.47, +0.78] | 1 | Logs bounds checks dashboard |
➖ | docker_containers_memory | memory utilization | +0.36 | [+0.29, +0.43] | 1 | Logs |
➖ | otlp_ingest_metrics | memory utilization | +0.33 | [+0.17, +0.50] | 1 | Logs |
➖ | uds_dogstatsd_20mb_12k_contexts_20_senders | memory utilization | +0.26 | [+0.20, +0.33] | 1 | Logs |
➖ | quality_gate_idle | memory utilization | +0.23 | [+0.17, +0.30] | 1 | Logs bounds checks dashboard |
➖ | otlp_ingest_logs | memory utilization | +0.15 | [+0.02, +0.28] | 1 | Logs |
➖ | ddot_metrics | memory utilization | +0.12 | [+0.01, +0.24] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | +0.06 | [-0.52, +0.65] | 1 | Logs |
➖ | file_to_blackhole_100ms_latency | egress throughput | +0.06 | [-0.55, +0.68] | 1 | Logs |
➖ | file_to_blackhole_300ms_latency | egress throughput | +0.06 | [-0.54, +0.66] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | +0.01 | [-0.57, +0.58] | 1 | Logs |
➖ | file_to_blackhole_500ms_latency | egress throughput | +0.01 | [-0.62, +0.63] | 1 | Logs |
➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.02, +0.02] | 1 | Logs |
➖ | uds_dogstatsd_to_api | ingress throughput | -0.01 | [-0.28, +0.27] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.02 | [-0.59, +0.55] | 1 | Logs |
➖ | file_to_blackhole_0ms_latency | egress throughput | -0.03 | [-0.63, +0.57] | 1 | Logs |
➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | -0.05 | [-0.28, +0.18] | 1 | Logs |
➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | -0.73 | [-1.59, +0.13] | 1 | Logs |
➖ | ddot_logs | memory utilization | -0.74 | [-0.88, -0.60] | 1 | Logs |
➖ | tcp_syslog_to_blackhole | ingress throughput | -1.24 | [-1.31, -1.18] | 1 | Logs |
✅ | file_tree | memory utilization | -7.87 | [-8.04, -7.70] | 1 | Logs |
Bounds Checks: ❌ Failed
perf | experiment | bounds_check_name | replicates_passed | links |
---|---|---|---|---|
❌ | quality_gate_logs | memory_usage | 8/10 | bounds checks dashboard |
✅ | 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 |
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
❌ Failed. Some Quality Gates were violated.
- 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_logs, bounds check memory_usage: 8/10 replicas passed. Failed 2 which is > 0. Gate FAILED.
- 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_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.
Static quality checks✅ Please find below the results from static quality gates Successful checksInfo
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What does this PR do?
This PR sorts events that are replayed when a new ruleset is loaded (or on startup).
Motivation
This ensures that we evaluate execution events against our rule set in a consistent manner, regardless of how the execution was is captured (snapshot / runtime).
Describe how you validated your changes
Possible Drawbacks / Trade-offs
Additional Notes