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=. -benchmem

Output 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 fix

Notes

• 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