"It works on my machine" is not a scalability strategy. You don't know your server's breaking point until you find it. Load testing involves simulating real-world traffic to identify bottlenecks before your marketing campaign launches.
Types of Stress
- Load Testing: Simulating expected normal traffic to verify performance.
- Stress Testing: Pushing beyond limits to see how it fails. Does it slow down gracefully? Or does it crash and corrupt data?
- Spike Testing: Sudden surges (e.g., tickets go on sale). Can your auto-scaler react fast enough?
The Tools
- Apache JMeter: The classic. Powerful, UI-based, Java. Can simulate complex user flows (Login -> Add to Cart -> Checkout). Heavily scriptable but steep learning curve.
- K6: Modern, developer-friendly. Write test scripts in JavaScript. excellent CLI integration for CI/CD pipelines.
- Locust: Python-based code. Define user behavior in Python. Great for simulating thousands of concurrent users with simple code.
Interpreting Results
Don't just look at "Average Response Time." Look at Percentiles (p95, p99).
- Avg: 200ms (Sounds good!)
- p95: 200ms (95% of users are fine)
- p99: 5000ms (1% of your users—often the most active ones—are waiting 5 seconds).
If your p99 is terrible, you have a blocking issue—likely a locked database row or a single slow API call holding up the queue. Fix the p99 to fix scalability.
TestingPerformanceQA
Share: