How would you implement a distributed rate limiter across multiple instances of a microservice?

A rate limiter that only tracks state locally, such as a plain Semaphore or Guava's RateLimiter, only protects the single instance it's running in -- if you deploy ten pods of the same service, each one independently enforces the limit, so the effective total allowed traffic becomes N times the number of pods instead of N overall. To enforce a true global limit, you need shared state, typically kept in Redis using either a sliding-window or token-bucket algorithm. The check-increment-expire sequence needs to be atomic across concurrent requests hitting different pods, which is usually implemented as a single Redis Lua script executed with EVAL, called from Java through a client library such as Lettuce or Jedis. Rather than hand-rolling this, you can also lean on an existing framework, such as Resilience4j's RateLimiter backed by a Redis store, or the rate limiting built into Spring Cloud Gateway. Key design decisions include choosing between a fixed window, which is simple but can allow roughly double the intended burst right at window boundaries, a sliding window, which is more accurate but more expensive to compute, or a token bucket, which naturally smooths out bursts, as well as deciding whether the limit should apply per user, per IP address, or globally across the whole system.