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Engineering: backend-engineering, frontend-engineering, data-engineering, ml-engineering, platform-engineering, qa-methodology Executive: go-to-market, legal-strategy, operational-design, org-design, product-strategy ml-engineering: added missing training-infrastructure.md reference qa-methodology: added test-data-management, performance-testing, security-testing references All frontmatter converted to agent-skills convention. Source: https://github.com/magnus919/hermes-profiles
77 lines
2.9 KiB
Markdown
77 lines
2.9 KiB
Markdown
# Performance Testing
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## Types
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| Type | Question Answered | Tool Examples |
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|------|-------------------|---------------|
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| Load test | Does it handle expected traffic? | k6, Locust, Gatling |
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| Stress test | Where does it break? | k6 (ramping VUs), wrk |
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| Soak test | Does it degrade over time? | k6 (constant load, 4–24h) |
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| Spike test | Does it survive sudden bursts? | k6 (spike scenario) |
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| Benchmark | What's the raw throughput/latency? | wrk, hey, ab, pytest-benchmark |
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## When to Performance Test
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- Before launch (baseline)
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- After architectural changes (new DB, new cache layer, new service boundary)
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- After dependency upgrades (ORM version, driver changes)
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- When latency SLO is at risk (p99 trending up over 2+ sprints)
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**Not** on every PR — that's what unit/integration tests are for.
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## k6 Pattern (Load Test)
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```javascript
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import http from 'k6/http';
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import { check, sleep } from 'k6';
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export const options = {
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stages: [
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{ duration: '30s', target: 20 }, // ramp up
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{ duration: '1m', target: 20 }, // steady state
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{ duration: '10s', target: 0 }, // ramp down
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],
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thresholds: {
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http_req_duration: ['p(95)<500', 'p(99)<1000'],
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http_req_failed: ['rate<0.01'],
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},
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};
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export default function () {
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const res = http.get('https://staging.example.com/api/items');
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check(res, {
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'status 200': (r) => r.status === 200,
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'response time < 200ms': (r) => r.timings.duration < 200,
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});
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sleep(1);
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}
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```
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## Key Metrics
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| Metric | Definition | Target Guidance |
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|--------|-----------|-----------------|
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| p50 latency | Median response time | User-perceived "normal" |
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| p95 latency | 95th percentile | SLO boundary for most APIs |
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| p99 latency | 99th percentile | Tail latency — catches GC pauses, cold starts |
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| Throughput | Requests/sec sustained | Compare against capacity plan |
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| Error rate | 5xx / total | < 0.1% under load |
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| Saturation | CPU/memory/connections at peak | < 80% = headroom |
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## Interpreting Results
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| Symptom | Likely Cause | Next Step |
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|---------|-------------|-----------|
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| Latency climbs linearly with VUs | Single-threaded bottleneck or lock contention | Profile CPU, check for global locks |
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| Latency flat then sudden cliff | Resource exhaustion (connections, memory, file descriptors) | Check pool sizes, `ulimit`, OOM killer |
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| Throughput plateaus early | Downstream dependency is the bottleneck | Test the dependency in isolation |
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| Errors only at high concurrency | Race condition or timeout misconfiguration | Check connection pool, retry storms |
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| Memory grows during soak | Leak — unclosed connections, unbounded cache | Heap dump at intervals, diff allocations |
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## CI Integration
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- Run a **smoke benchmark** (10 VUs, 30s) on PRs that touch hot paths — fast, catches 10× regressions
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- Run **full load test** nightly against staging
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- Alert if p95 regresses > 20% vs 7-day baseline
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- Store results in time-series (k6 Cloud, Grafana, or CSV + script) for trend detection
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