Load Testing with Locust

+15 Mana ✨
Locust(DOCUMENTATION)

Introduction

Load testing reveals performance issues before they affect users. Locust makes it easy to write Python-based load tests.

Key Concepts

Virtual Users: Simulated concurrent users.

RPS: Requests per second.

Deep Dive

Basic Locust File

python
# locustfile.py
from locust import HttpUser, task, between

class WebsiteUser(HttpUser):
    wait_time = between(1, 5)  # Wait 1-5s between tasks
    
    @task(3)  # Weight: 3x more likely
    def view_articles(self):
        self.client.get('/articles/')
    
    @task(1)
    def view_article(self):
        self.client.get('/articles/sample-article/')
    
    @task(2)
    def search(self):
        self.client.get('/search/', params={'q': 'django'})
    
    def on_start(self):
        # Login once at start
        self.client.post('/login/', {
            'username': 'testuser',
            'password': 'testpass'
        })

Running Locust

bash
# Start with web UI
locust -f locustfile.py --host=http://localhost:8000

# Headless mode for CI
locust -f locustfile.py --headless \
    --users 100 \
    --spawn-rate 10 \
    --run-time 1m \
    --host=http://localhost:8000

Testing Authenticated Endpoints

python
class AuthenticatedUser(HttpUser):
    token = None
    
    def on_start(self):
        response = self.client.post('/api/token/', {
            'username': 'test',
            'password': 'pass'
        })
        self.token = response.json()['access']
    
    @task
    def get_profile(self):
        self.client.get('/api/profile/', headers={
            'Authorization': f'Bearer {self.token}'
        })

Best Practices

  1. Test against staging, not production: Use production-like data.
  2. Start small: Gradually increase load to find breaking points.
  3. Monitor during tests: Watch server metrics alongside Locust.

Summary

Use Locust to simulate real user load. Define realistic user scenarios with weights. Monitor server metrics during tests to find bottlenecks.

✓ Completed