Learning Redis and Spring Security by Building a Simple Rate Limiter

I’ve always been curious about how APIs prevent overuse. When I started exploring Redis and Spring Security, I decided to learn both by implementing a rate limiter in a Spring Boot application.

The idea was simple: limit how many requests a user can make per minute, using Redis to store request counts. I built a custom Spring Boot filter that intercepted each request, checked the user’s token or IP, and queried Redis for its request count.

Redis made this fast — with atomic increments and expirations, it handled concurrent requests effortlessly. On the security side, I integrated Spring Security to protect endpoints and distinguish between authenticated and anonymous users.

Once implemented, I tested it by hammering the API with scripts. Redis counters kicked in exactly as expected — requests were throttled, and the API stayed stable.

Takeaway:
This small project taught me how caching systems like Redis aren’t just for data—they’re powerful tools for resilience and control. Combining it with Spring Security deepened my understanding of backend reliability.