A rate limiter restricts the number of requests a client can make within a specific time window, typically identified by IP address or user ID. This prevents abuse and protects backend services from overload. Key design considerations include:
- Algorithm Choice: Common algorithms include Token Bucket, Leaky Bucket, and Fixed Window Counter. Token Bucket is often preferred for its flexibility in handling bursts.
- State Management: The current request count and timestamps need to be stored. A distributed cache like Redis is ideal for high availability and low latency, supporting concurrent access.
- Thresholds and Expiration: Define limits (e.g., 100 requests per minute) and implement mechanisms to track and reset counts. Expiration policies in the cache are crucial.
- Blocking Strategy: When a limit is exceeded, the client's requests should be rejected (e.g., with a
429 Too Many Requests HTTP status). Temporary blocking of IPs or users can be managed in the cache.
- Scalability and Availability: The rate limiter itself must be highly available and scalable to handle the traffic it's protecting. This often involves deploying multiple instances and using a distributed data store.
- Data Persistence (Optional): While not always necessary for real-time limiting, a NoSQL database could be used for long-term storage of blocked IPs or historical data, with periodic cleanup.
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