This chapter covers the rate limiter requirements: rules engine scope, user vs IP vs API key granularity, enforcement latency SLA.

This chapter covers key design questions: hard vs soft limits, distributed vs single-node, sync vs async enforcement.

This chapter covers a single-node token bucket implementation and why it breaks in a distributed system.

This chapter covers a distributed rate limiter using Redis with Lua scripts for atomic counter operations.

This chapter covers two approaches: fixed window counter vs sliding window log — trade-offs in accuracy and memory.

This chapter covers how Stripe, Shopify, and GitHub implement rate limiting in their API platforms.

This chapter covers transferable patterns: centralized rules engine, client-side backoff, rate limit headers.

This chapter covers race conditions in distributed counters, Redis as SPOF, and false positives blocking legitimate traffic.