zwitschi
cc11082ea7
CI / lint-test-build (push) Failing after 19s
- Added synthetic latency profiler scenarios and CLI scripts for baseline generation and regression checks. - Introduced latency baseline and threshold artifacts for CI enforcement. - Enhanced CI workflow with latency guardrail checks. - Updated documentation to include latency profiling commands and performance metrics. - Added unit tests for latency guardrail evaluation.
49 lines
1.4 KiB
Markdown
49 lines
1.4 KiB
Markdown
# Performance Hardening
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This folder contains latency profiling baselines and guardrail thresholds used in CI.
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## Scenarios
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The profiler covers representative load patterns:
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- `book_update_burst`: rapid market-data deltas with moderate detection load.
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- `execution_spike`: heavier detection/execution pressure.
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- `reconnect_storm`: frequent reconnect/reset behavior.
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## Profiling Commands
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Generate a fresh profile:
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```powershell
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python scripts/profile_latency.py --iterations 600 --output ops/performance/latency_baseline.json
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```
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Check current performance against the baseline and thresholds:
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```powershell
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python scripts/check_latency_regression.py \
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--baseline ops/performance/latency_baseline.json \
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--thresholds ops/performance/latency_thresholds.json \
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--iterations 600
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```
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CI executes the same guardrail check.
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## Baseline Snapshot (2026-06-01)
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Key end-to-end latency baselines from `latency_baseline.json`:
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- `book_update_burst`: p95 = 0.0132 ms, p99 = 0.0198 ms
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- `execution_spike`: p95 = 0.0139 ms, p99 = 0.0177 ms
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- `reconnect_storm`: p95 = 0.0114 ms, p99 = 0.0134 ms
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## Optimization Note
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`MetricsCalculator.compute()` was optimized to use DuckDB SQL aggregations and quantiles, reducing Python-side row scans.
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Measured benchmark (`scripts/benchmark_metrics_compute.py`):
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- Python scan baseline: 12.623 ms
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- SQL aggregate implementation: 11.039 ms
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- Speedup: 1.14x
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