Capacity Planning, Forecasting, and Distributed Load Testing

Capacity Planning ensures that systems have sufficient compute, memory, disk I/O, and network bandwidth to meet expected demand without over-provisioning infrastructure costs. This guide covers growth forecasting models, stress testing, and running distributed load tests with k6 and Locust.

⚡ Quick Dive

Load Testing Types & Objectives

Test Type Traffic Profile Primary Objective
Baseline / Smoke Minimal constant load (5-10 VUs) Verify script functionality and system sanity
Load Test Normal expected peak load for 1 hour Validate latency percentiles (p95/p99) under SLA
Stress Test Step-up traffic until system breaks Identify exact breaking point and failure modes
Spike Test Instantaneous 10x traffic jump Verify auto-scaling responsiveness and buffer queues
Soak / Endurance High sustained load for 24-48 hours Detect memory leaks and resource exhaustion

📖 Extended Guide

1. Modern Distributed Load Testing with k6

// 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: 500 },  // Spike to 500 users
    { duration: '2m', target: 0 },    // Ramp-down
  ],
  thresholds: {
    http_req_duration: ['p(99)<200'], // 99% of requests must complete under 200ms
    http_req_failed: ['rate<0.01'],   // Error rate must be under 1%
  },
};

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