Performance Testing Skill

Performance Testing

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

Agent Skills: Performance Testing

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