performance-testing

name	performance-testing
description	Design and execute performance tests to measure response times, throughput, and resource utilization. Use for performance test, load test, JMeter, k6, benchmark, latency testing, and scalability analysis.

Performance Testing

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• 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