performance-testing

spjoshis's avatarfrom spjoshis

Master performance testing with load testing, stress testing, JMeter, k6, and performance benchmarking.

⭐1stars🔀0forks📁View on GitHub🕐Updated Dec 30, 2025
[Testing and QA]

When & Why to Use This Skill

This Claude skill empowers developers and QA engineers to master performance testing using industry-standard tools like k6 and JMeter. It provides comprehensive guidance on load testing, stress testing, and performance benchmarking to ensure application scalability, responsiveness, and stability under high-traffic conditions, helping teams identify bottlenecks before they impact production.

Use Cases

  • Simulating high-concurrency traffic to validate application scalability and responsiveness before major product launches or marketing events.
  • Automating performance regression tests using k6 scripts to ensure new code deployments do not degrade system latency.
  • Conducting stress tests to identify the system's breaking point and inform infrastructure capacity planning and auto-scaling configurations.
  • Generating detailed performance reports to validate Service Level Agreements (SLAs) by measuring P95/P99 response times and error rates.
  • Identifying specific architectural bottlenecks, such as slow database queries or CPU-intensive processes, through systematic load testing.
nameperformance-testing
descriptionMaster performance testing with load testing, stress testing, JMeter, k6, and performance benchmarking.

Performance Testing

Conduct performance testing to ensure applications meet scalability, responsiveness, and stability requirements under load.

When to Use This Skill

  • Load testing
  • Stress testing
  • Scalability validation
  • Performance benchmarking
  • Capacity planning
  • Performance regression testing
  • SLA validation
  • Bottleneck identification

Core Concepts

1. k6 Load Test Script

// load-test.js
import http from 'k6/http';
import { check, sleep } from 'k6';

export const options = {
  stages: [
    { duration: '2m', target: 100 }, // Ramp up to 100 users
    { duration: '5m', target: 100 }, // Stay at 100 users
    { duration: '2m', target: 200 }, // Ramp up to 200 users
    { duration: '5m', target: 200 }, // Stay at 200 users
    { duration: '2m', target: 0 },   // Ramp down to 0
  ],
  thresholds: {
    http_req_duration: ['p(95)<500'], // 95% requests < 500ms
    http_req_failed: ['rate<0.01'],   // Error rate < 1%
  },
};

export default function () {
  const res = http.get('https://api.example.com/products');
  
  check(res, {
    'status is 200': (r) => r.status === 200,
    'response time < 500ms': (r) => r.timings.duration < 500,
  });
  
  sleep(1);
}

2. Performance Test Report

# Performance Test Report

**Test Date**: 2024-01-15
**Application**: E-Commerce API
**Tool**: k6

## Test Configuration
- Virtual Users: 100 → 200
- Duration: 16 minutes
- Ramp-up: 2 minutes per stage

## Results Summary

| Metric | Target | Actual | Status |
|--------|--------|--------|--------|
| Avg Response Time | <300ms | 245ms | ✅ Pass |
| P95 Response Time | <500ms | 412ms | ✅ Pass |
| P99 Response Time | <1000ms | 876ms | ✅ Pass |
| Throughput | >100 req/s | 125 req/s | ✅ Pass |
| Error Rate | <1% | 0.3% | ✅ Pass |

## Bottlenecks Identified
1. Database queries slow at 200+ users
2. Image processing CPU-intensive

## Recommendations
1. Add database indexing on product_id
2. Implement CDN for images
3. Add caching layer (Redis)

Best Practices

  1. Define objectives - Response time, throughput, concurrent users
  2. Realistic scenarios - Match production patterns
  3. Gradual load increase - Ramp up slowly
  4. Monitor system - CPU, memory, database
  5. Baseline first - Know current performance
  6. Isolate environment - Dedicated test environment
  7. Analyze results - Identify bottlenecks
  8. Continuous testing - Performance regression tests

Resources