Introduce fuzz tests for docker file logs parsing - #38733
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Approved with one suggested fix.
Regression DetectorRegression Detector ResultsMetrics dashboard Baseline: 83af8f5 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 | -2.50 | [-5.55, +0.54] | 1 | Logs |
Fine details of change detection per experiment
| perf | experiment | goal | Δ mean % | Δ mean % CI | trials | links |
|---|---|---|---|---|---|---|
| ➖ | quality_gate_logs | % cpu utilization | +2.98 | [+0.21, +5.75] | 1 | Logs bounds checks dashboard |
| ➖ | tcp_syslog_to_blackhole | ingress throughput | +1.00 | [+0.93, +1.06] | 1 | Logs |
| ➖ | ddot_logs | memory utilization | +0.58 | [+0.49, +0.67] | 1 | Logs |
| ➖ | quality_gate_idle_all_features | memory utilization | +0.48 | [+0.37, +0.58] | 1 | Logs bounds checks dashboard |
| ➖ | uds_dogstatsd_to_api_cpu | % cpu utilization | +0.41 | [-0.45, +1.27] | 1 | Logs |
| ➖ | docker_containers_memory | memory utilization | +0.41 | [+0.36, +0.47] | 1 | Logs |
| ➖ | otlp_ingest_metrics | memory utilization | +0.14 | [-0.00, +0.28] | 1 | Logs |
| ➖ | file_to_blackhole_100ms_latency | egress throughput | +0.11 | [-0.48, +0.71] | 1 | Logs |
| ➖ | otlp_ingest_logs | memory utilization | +0.06 | [-0.06, +0.18] | 1 | Logs |
| ➖ | file_to_blackhole_500ms_latency | egress throughput | +0.04 | [-0.59, +0.68] | 1 | Logs |
| ➖ | file_to_blackhole_1000ms_latency | egress throughput | +0.02 | [-0.57, +0.61] | 1 | Logs |
| ➖ | uds_dogstatsd_to_api | ingress throughput | +0.00 | [-0.27, +0.28] | 1 | Logs |
| ➖ | uds_dogstatsd_20mb_12k_contexts_20_senders | memory utilization | +0.00 | [-0.05, +0.05] | 1 | Logs |
| ➖ | tcp_dd_logs_filter_exclude | ingress throughput | -0.00 | [-0.02, +0.01] | 1 | Logs |
| ➖ | ddot_metrics | memory utilization | -0.02 | [-0.13, +0.10] | 1 | Logs |
| ➖ | file_to_blackhole_0ms_latency | egress throughput | -0.12 | [-0.70, +0.47] | 1 | Logs |
| ➖ | file_tree | memory utilization | -0.20 | [-0.25, -0.15] | 1 | Logs |
| ➖ | quality_gate_idle | memory utilization | -0.29 | [-0.36, -0.21] | 1 | Logs bounds checks dashboard |
| ➖ | docker_containers_cpu | % cpu utilization | -2.50 | [-5.55, +0.54] | 1 | Logs |
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_1000ms_latency | 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_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_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_logs, bounds check lost_bytes: 10/10 replicas passed. Gate passed.
- quality_gate_logs, bounds check memory_usage: 10/10 replicas passed. Gate passed.
- quality_gate_logs, 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.
- quality_gate_idle_all_features, bounds check intake_connections: 10/10 replicas passed. Gate passed.
Signed-off-by: Brian L. Troutwine <brian.troutwine@datadoghq.com>
…49a4d99016.yaml Co-authored-by: Michael Cretzman <58786311+michaelcretzman@users.noreply.github.com>
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This merge request is not mergeable yet, because of pending checks/missing approvals. It will be added to the queue as soon as checks pass and/or get approvals.
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Static quality checks✅ Please find below the results from static quality gates Successful checksInfo
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What does this PR do?
This commit introduces new fuzz tests for
pkg/logs/internal/parsers/dockerfile. I have not found any panics or crashesbut I have clarified the newline stripping around examples like
\n\n.Peer to:
Motivation
Increase fuzz coverage of Agent.