[Serverless] Increase time to wait for metric samples in tests. #32409
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Decision✅ Passed |
Test changes on VMUse this command from test-infra-definitions to manually test this PR changes on a VM: inv aws.create-vm --pipeline-id=51624469 --os-family=ubuntuNote: This applies to commit dbf840a |
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Serverless Benchmark Results
tl;drUse these benchmarks as an insight tool during development.
What is this benchmarking?The 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 The benchstat docs explain how to interpret these charts.
I need more helpFirst 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 Benchmark stats |
| @@ -164,7 +164,7 @@ func TestGenerateEnhancedMetricsFromReportLogNoColdStart(t *testing.T) { | |||
| } | |||
| go GenerateEnhancedMetricsFromReportLog(args) | |||
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| generatedMetrics, timedMetrics := demux.WaitForNumberOfSamples(6, 0, 0100*time.Millisecond) | |||
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Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: f5d99e9 Optimization Goals: ✅ No significant changes detected
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| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gate_logs | % cpu utilization | +2.15 | [-1.13, +5.43] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +1.91 | [+1.22, +2.60] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | +0.18 | [+0.10, +0.27] | 1 | Logs bounds checks dashboard |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.10 | [-0.64, +0.85] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency_linear_load | egress throughput | +0.10 | [-0.35, +0.56] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http2 | egress throughput | +0.10 | [-0.78, +0.98] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.01 | [-0.78, +0.79] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | +0.00 | [-0.01, +0.02] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.01 | [-0.84, +0.83] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency_http1 | egress throughput | -0.01 | [-0.85, +0.83] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | -0.01 | [-0.13, +0.10] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | -0.04 | [-0.82, +0.74] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.06 | [-0.10, -0.02] | 1 | Logs bounds checks dashboard |
| ➖ | file_to_blackhole_300ms_latency | egress throughput | -0.10 | [-0.74, +0.55] | 1 | Logs |
| ➖ | otel_to_otel_logs | ingress throughput | -0.20 | [-0.87, +0.47] | 1 | Logs |
| ➖ | file_tree | memory utilization | -0.62 | [-0.75, -0.49] | 1 | Logs |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | -0.91 | [-0.98, -0.85] | 1 | Logs |
Bounds Checks: ❌ Failed
| perf | experiment | bounds_check_name | replicates_passed | links |
|---|---|---|---|---|
| ❌ | file_to_blackhole_100ms_latency | lost_bytes | 9/10 | |
| ❌ | file_to_blackhole_300ms_latency | lost_bytes | 9/10 | |
| ✅ | 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 | memory_usage | 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 |
| ✅ | quality_gate_idle_all_features | memory_usage | 10/10 | bounds checks dashboard |
| ✅ | quality_gate_logs | lost_bytes | 10/10 | |
| ✅ | quality_gate_logs | memory_usage | 10/10 |
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 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 memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_idle_all_features, bounds check memory_usage: 10/10 replicas passed. Gate passed.
What does this PR do?
Increase time to wait for metric samples from 100ms to 125ms in tests.
Motivation
We have seen a few of flaky test failures recently. While I'm not certain increasing the wait time will fix these failures, it's a good place to start since each of the failures was due to a timeout to wait for metric samples.
Related flaky test failures:
Describe how you validated your changes
Possible Drawbacks / Trade-offs
Additional Notes