performance-testing

SKILL.md

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

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