Scaling to 8 instances accidentally arms abusers with 8x firepower—devs duct-tape redis lua scripts to save the melting database

A recent article highlights the importance of implementing a production-ready distributed rate limiter to prevent API abuse and protect backend services. The traditional approach of using in-memory rate limiting on each instance can lead to a multiplication effect, where the effective limit is amplified by the number of instances, making it ineffective. A centralized rate limiting store using Redis can eliminate this problem by providing atomic operations and low latency. The article discusses three algorithms for production rate limiting: token bucket, sliding window log, and sliding window counter, each with its tradeoffs between precision, memory usage, and implementation complexity. It also emphasizes the need for observability, metrics, and alerting to detect abuse and tune limits effectively. By using Redis Lua scripts and implementing fail-open with local probabilistic fallback, circuit breakers, and hierarchical quotas, developers can build a robust rate limiting system that protects their APIs from abuse and ensures fair resource allocation.