Spike Testing is a type of performance testing where the system is subjected to sudden and extreme increases in load to observe how it handles a sharp rise or fall in user activity.
Purpose:
- To evaluate system stability under abrupt changes in traffic.
- To check if the application recovers quickly after a sudden load increase.
- To identify bottlenecks and scalability issues before real-world high-traffic events.
How to Perform Spike Testing?
Step 1: Define Normal Load (e.g., 1,000 users per second).
Step 2: Plan a sudden spike (e.g., 100,000 users in 30 seconds).
Step 3: Monitor CPU, memory, network, and database performance.
Step 4: Observe if the system crashes or slows down.
Step 5: Test recovery time after the load drops.
Tools for Spike Testing:
Apache JMeter – Load testing for websites & APIs.
LoadRunner – Enterprise-level performance testing.
Gatling – Scalable load testing for web apps.
K6 – Open-source performance testing for DevOps.
Locust – Python-based distributed load testing.
Common Issues Found in Spike Testing
Server crashes due to high traffic.
Slow page load times due to overloaded database.
API failures because of unoptimized backend logic.
Memory leaks that degrade system performance over time.
Session timeouts and login failures during high load.
Benefits of Spike Testing:
Prepares for real-world traffic spikes (Black Friday, viral campaigns).
Ensures system stability under unexpected surges.
Identifies weak points in database, APIs, and infrastructure.
Improves auto-scaling strategies for cloud-based applications.