Performance Testing
Test application speed, responsiveness, stability, and scalability under various load conditions.
When to use me
Use this skill when:
- Preparing for traffic spikes or seasonal loads
- Testing application scalability
- Identifying performance bottlenecks
- Validating SLAs (Service Level Agreements)
- Comparing performance before/after changes
- Capacity planning and infrastructure sizing
- Ensuring user experience under load
What I do
- Load testing: Simulate expected user traffic
- Stress testing: Find breaking points and limits
- Spike testing: Test sudden traffic surges
- Endurance testing: Check for memory leaks over time
- Scalability testing: Verify horizontal/vertical scaling
- Configuration testing: Optimize performance settings
- Benchmarking: Compare against baseline metrics
Examples
# Load testing tools
npx autocannon -c 100 -d 60 https://app.example.com
wrk -t12 -c400 -d30s https://app.example.com
k6 run script.js # Grafana k6
jmeter -n -t testplan.jmx -l results.jtl
# Performance monitoring
npm run test:perf # Custom performance suite
lighthouse https://app.example.com --output json
webpagetest test https://app.example.com
# Stress testing
artillery run stress.yml
npx loadtest -n 10000 -c 100 https://api.example.com
# Memory and CPU profiling
node --inspect script.js
python -m cProfile script.py
go test -bench=. -benchmemOutput format
Performance Test Results:
──────────────────────────────
Load Test (100 concurrent users, 5 minutes):
✅ Average Response Time: 245ms (< 500ms target)
✅ 95th Percentile: 412ms
✅ Throughput: 1,234 req/sec
✅ Error Rate: 0.1% (< 1% target)
⚠️ CPU Usage: 85% (approaching limit)
Stress Test (Breaking Point):
❌ System fails at 850 concurrent users
⚠️ Database connection pool exhausted at 800 users
✅ Graceful degradation observed
Memory Usage (24-hour endurance):
⚠️ Memory leak detected: +2MB/hour
❌ OutOfMemory after 18 hours
Summary: Meets most performance targets, needs memory leak fixNotes
- Establish performance baselines before testing
- Test in production-like environments
- Monitor system resources during tests
- Consider network latency and geography
- Test with realistic data volumes
- Automate performance regression testing
- Document performance requirements and SLAs
- Use APM (Application Performance Monitoring) tools