SKILL.md
Performance Testing
Table of Contents
- [Overview](#overview)
- [When to Use](#when-to-use)
- [Quick Start](#quick-start)
- [Reference Guides](#reference-guides)
- [Best Practices](#best-practices)
Overview
Performance testing measures how systems behave under various load conditions, including response times, throughput, resource utilization, and scalability. It helps identify bottlenecks, validate performance requirements, and ensure systems can handle expected loads.
When to Use
- Validating response time requirements
- Measuring API throughput and latency
- Testing database query performance
- Identifying performance bottlenecks
- Comparing algorithm efficiency
- Benchmarking before/after optimizations
- Validating caching effectiveness
- Testing concurrent user capacity
Quick Start
Minimal working example:
// load-test.js
import http from "k6/http";
import { check, sleep } from "k6";
import { Rate, Trend } from "k6/metrics";
// Custom metrics
const errorRate = new Rate("errors");
const orderDuration = new Trend("order_duration");
// Test configuration
export const options = {
stages: [
{ duration: "2m", target: 10 }, // Ramp up to 10 users
{ duration: "5m", target: 10 }, // Stay at 10 users
{ duration: "2m", target: 50 }, // Ramp up to 50 users
{ duration: "5m", target: 50 }, // Stay at 50 users
{ duration: "2m", target: 0 }, // Ramp down to 0
],
thresholds: {
http_req_duration: ["p(95)<500"], // 95% of requests under 500ms
http_req_failed: ["rate<0.01"], // Error rate under 1%
errors: ["rate<0.1"], // Custom error rate under 10%
},
};
// ... (see reference guides for full implementation)Reference Guides
Detailed implementations in the references/ directory:
| Guide | Contents |
|---|---|
| k6 for API Load Testing | k6 for API Load Testing |
| Apache JMeter | Apache JMeter |
| pytest-benchmark for Python | pytest-benchmark for Python |
| JMH for Java Benchmarking | JMH for Java Benchmarking |
| Database Query Performance | Database Query Performance |
| Real-Time Monitoring | Real-Time Monitoring |
Best Practices
✅ DO
- Define clear performance requirements (SLAs)
- Test with realistic data volumes
- Monitor resource utilization
- Test caching effectiveness
- Use percentiles (P95, P99) over averages
- Warm up before measuring
- Run tests in production-like environment
- Identify and fix N+1 query problems
❌ DON'T
- Test only with small datasets
- Ignore memory leaks
- Test in unrealistic environments
- Focus only on average response times
- Skip database indexing analysis
- Test only happy paths
- Ignore network latency
- Compare without statistical significance