Learning Redis by Building a Rate Limiter in Spring Boot

Rate limiting is one of the simplest and most effective ways to protect APIs from abuse. I learned Redis by building a distributed rate limiter in Spring Boot using a Spring Security filter, and the experience gave me a strong understanding of caching, counters, and how Redis handles high-speed operations.

Why Redis?

Redis is built for speed. Its in-memory data store and atomic increment operations make it perfect for rate limiting. Storing counters in Redis ensures limits remain consistent across multiple server instances.

How I Built It

1. Created a Spring Security filter that runs before each request
2. Generated a key like rate_limit:{ip} or rate_limit:{userId}
3. Incremented a Redis counter with expiration
4. If count exceeded the limit, returned 429 Too Many Requests

What I Learned

• Redis atomicity makes rate limiting reliable
• Using TTL simplifies window resets
• A simple filter can completely secure endpoints

This project helped me deeply understand Redis operations and real-world API protection.