Load Testing Mobile App API: k6, JMeter, Gatling

Imagine: your mobile app hits the top of the App Store, and 50,000 new users flood in overnight. In the morning, you find backend downtime—504 errors on every screen. Without load testing, the backend can't handle peak surges from push notifications or ad campaigns. We run full-cycle testing: write scripts in k6, JMeter, or Gatling; set up load profiles; analyze bottlenecks; and provide optimization recommendations. k6 starts scripts three times faster than JMeter at high volumes, and its scripts are compact and readable. Below, we walk through the process using a typical mobile API example.

Why API Load Testing Is Critical for Mobile Apps

Mobile traffic differs from web: devices send many concurrent requests, run in the background, and experience unpredictable network conditions. Without load profiling, the backend may not withstand peak loads. A common issue is that the backend is designed for average traffic, not spikes from push notifications or ad campaigns.

How We Choose the Tool

Three main tools, each for its own purpose:

For most mobile APIs, we choose k6—compact scripts, native Grafana integration, runs in Docker without JVM. Our specialists (5+ years experience) configure the tool for your architecture.

Writing a k6 Script for a Mobile API

A typical mobile API has specifics: JWT authorization with short TTL, refresh tokens, gzip response compression, sometimes GraphQL instead of REST. The script must account for these.

import http from 'k6/http'; import { check, sleep } from 'k6'; import { SharedArray } from 'k6/data'; // Test accounts from CSV—not a single account for all VUs const users = new SharedArray('users', () => open('./test_users.csv').split('\n').map(line => { const [email, password] = line.split(','); return { email, password }; })); export const options = { stages: [ { duration: '2m', target: 100 }, // ramp-up { duration: '5m', target: 500 }, // plateau { duration: '2m', target: 1000 }, // peak { duration: '1m', target: 0 }, // ramp-down ], thresholds: { http_req_duration: ['p(95)<500'], // 95% of requests < 500ms http_req_failed: ['rate<0.01'], // less than 1% errors }, }; export default function () { const user = users[__VU % users.length]; // Login + get token const loginRes = http.post('https://api.example.com/v1/auth/login', JSON.stringify({ email: user.email, password: user.password, }), { headers: { 'Content-Type': 'application/json' }, }); check(loginRes, { 'login 200': (r) => r.status === 200, 'has token': (r) => r.json('data.access_token') !== undefined, }); const token = loginRes.json('data.access_token'); sleep(1); // simulate user behavior // Load feed const feedRes = http.get('https://api.example.com/v1/feed?page=1&limit=20', { headers: { Authorization: `Bearer ${token}` }, }); check(feedRes, { 'feed 200': (r) => r.status === 200, 'feed has items': (r) => r.json('data.items').length > 0, }); sleep(2); } 

Official k6 documentation recommends using SharedArray to distribute test data among virtual users.

SharedArray for users is critical. If all virtual users share one account, the backend might cache the session, skewing results.

What Load Profiles Are Needed?

Mobile traffic is uneven. We define four typical profiles:

k6 run --out influxdb=http://localhost:8086/k6 scenario.js 

- name: Run k6 load test uses: grafana/[email protected] with: filename: tests/load/api_test.js flags: --duration 5m --vus 100 env: K6_CLOUD_TOKEN: ${{ secrets.K6_CLOUD_TOKEN }}