Did you say you want a distributed rate limiter?

Rate-limiting is crucial for maintaining the health and quality of services by preventing resource overuse and potential system failures, particularly in distributed systems and multi-tenant applications. Stateless rate limiters, while simple, often fail in dynamic environments due to their inability to adapt to fluctuating demands, leading to inefficiencies. The blog post explores two approaches for creating a distributed rate limiter using Momento Cache. The first method, recommended for its accuracy and efficiency, uses Momento’s increment and updateTTL APIs to manage user transactions per minute, setting a time-to-live for requests. The second approach, inspired by traditional methods such as those used with Redis, relies on get and increment APIs but struggles with accuracy under high demand due to race conditions. Testing reveals that the first approach is superior in maintaining accuracy and managing latency in high contention scenarios, making it a robust solution for distributed rate-limiting.