Rate Limiting • Distributed Rate LimitingEasy⏱️ ~2 min
What is Distributed Rate Limiting and Why is it Needed?
Distributed rate limiting enforces usage quotas across many stateless application servers by centralizing the state about how many requests or units have been consumed in the current time window. Without this shared state, each server would maintain its own counters, leading to inconsistent enforcement. If you have 10 servers each allowing 100 requests per minute locally, your actual system limit becomes 1,000 requests per minute instead of the intended 100.
The fundamental challenge is balancing accuracy against performance overhead. Every rate limit decision requires consulting a shared, low latency datastore, adding network round trips to your request path. In production systems, this typically adds single digit milliseconds at the 99th percentile (p99) when the shared store is in the same region. Amazon API Gateway and Google Cloud Endpoints both use this pattern, enforcing token bucket based quotas from edge locations while maintaining centralized configuration.
The architecture decomposes into three parts: a configuration store holding limits per key (API key, user identifier (ID), tenant), a request store maintaining counters or tokens for each active key, and a decision engine implementing the algorithm. Keys often compose multiple dimensions, such as combining tenant ID with endpoint path, to prevent abuse while apportioning shared capacity fairly. Good designs optimize for the worst 1% of load scenarios and failure cases, not just average conditions.
💡 Key Takeaways
•Without shared state, 10 servers each allowing 100 requests per minute actually permit 1,000 requests per minute system wide, making limits meaningless
•Production systems see single digit millisecond p99 latency overhead for in region rate limit decisions, requiring one atomic read modify write operation per request
•Amazon API Gateway and Google Cloud Endpoints enforce token bucket quotas at edge locations with centralized configuration, returning 429 status codes when limits are exceeded
•Keys are often composite dimensions like tenant ID plus endpoint to enforce fair capacity allocation across multiple tenants and prevent abuse from specific users
📌 Examples
An API serving 10,000 requests per second across 50 servers needs distributed rate limiting to enforce a global 100 requests per second limit per API key. Each server would locally see only 200 requests per second on average, making local only enforcement impossible.
Google Cloud Endpoints combines user ID, project ID, and API method to create composite rate limit keys, enabling limits like "1000 requests per minute per user per API method" while protecting shared backend services.