System Design: How to Build an API Rate Limiter
System Design: Building API Rate Limiters!
Fixed-window counter rate limiters fail under bursty traffic. Tracking requests per clock minute creates dangerous boundary spikes, allowing a user to fire 100 requests at minute-end and another 100 at minute-start, flooding your API with 200 requests in two seconds.
Never rely on simple counters that watch the clock instead of user behavior. Instead, implement the Token Bucket algorithm (used by Stripe and GitHub). Assign users a bucket of tokens that continuously refills at a fixed rate. Requests consume tokens; empty buckets get throttled. To scale across distributed backend nodes, store the token count and last-refill timestamp in a centralized Redis cache rather than local server memory.
This cleanly balances short-term burst capacity with sustained request rates while preventing edge-case traffic spikes.
#SystemDesign #APIDesign #Redis #Scalability #BackendDevelopment
Fixed-window counter rate limiters fail under bursty traffic. Tracking requests per clock minute creates dangerous boundary spikes, allowing a user to fire 100 requests at minute-end and another 100 at minute-start, flooding your API with 200 requests in two seconds.
Never rely on simple counters that watch the clock instead of user behavior. Instead, implement the Token Bucket algorithm (used by Stripe and GitHub). Assign users a bucket of tokens that continuously refills at a fixed rate. Requests consume tokens; empty buckets get throttled. To scale across distributed backend nodes, store the token count and last-refill timestamp in a centralized Redis cache rather than local server memory.
This cleanly balances short-term burst capacity with sustained request rates while preventing edge-case traffic spikes.
#SystemDesign #APIDesign #Redis #Scalability #BackendDevelopment
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