Design an API rate limiter that caps the number of API calls the service can receive in a given period to avoid an overload. Requirements: Functional Requirements: Limit requests. Configurable. Error or notification if the limit is reached. Non-Functional Requirements: Availability. Scalability. Low latency.

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An API rate limiter is a system that controls how many requests a client can make to an API within a specific time period. This helps prevent service overload and ensures fair usage. The key requirements include limiting requests per time window, configurable limits, error notification when limits are reached, high availability, scalability, and low latency. In our design, client requests first pass through the rate limiter, which checks with Redis to determine if the request should be allowed or rejected before reaching the API server. There are several common rate limiting algorithms, each with its own advantages. The Fixed Window Counter divides time into fixed intervals and counts requests in each window. It's simple but can allow traffic bursts at window boundaries. The Sliding Window Log tracks the timestamp of each request and counts those within the sliding time window. It's more accurate but requires more memory. The Sliding Window Counter combines the previous two approaches for a good balance of accuracy and performance. Finally, the Token Bucket algorithm adds tokens to a bucket at a constant rate and consumes one token per request, allowing for controlled traffic bursts while maintaining an overall rate limit. Let's look at how to implement the Sliding Window Log algorithm using Redis. For each client, we use a Redis Sorted Set or ZSET, with a key like 'rate_limit:client123'. Each request is stored as a member in this set, with the timestamp as both the member value and score. When a new request arrives, we first remove all timestamps older than our window size, for example, older than 60 seconds ago. Then we count the remaining members in the set to determine how many requests the client has made within the current window. If this count is below our configured limit, we add the new request timestamp to the set and allow the request. Otherwise, we reject it with a 429 Too Many Requests response. This approach provides accurate rate limiting with a true sliding window, ensuring fair distribution of requests over time. To build a scalable rate limiter architecture, we need to address several key components. First, we use a distributed Redis cluster with multiple nodes to store our rate limiting data. This provides both horizontal scalability and high availability through data sharding and replication. Client IDs are used as sharding keys to distribute the load evenly across Redis nodes. Second, the rate limiter service itself is designed to be stateless, allowing it to scale horizontally behind a load balancer. Each instance connects to the Redis cluster to perform rate limit checks. Third, we implement a configuration service that allows for dynamic updates to rate limits without requiring service restarts. This enables different rate limits for different clients or API endpoints, and allows for quick adjustments based on system load or business requirements. This architecture ensures our rate limiter can handle high traffic volumes while maintaining low latency and high availability. To summarize our API rate limiter design: First, an API rate limiter is essential for controlling request frequency and preventing service overload. Second, the Sliding Window Log algorithm implemented with Redis provides accurate rate limiting by tracking request timestamps within a sliding time window. Third, Redis Sorted Sets or ZSETs efficiently store and query these timestamps, enabling fast rate limit checks. Fourth, a distributed architecture with Redis clusters and stateless rate limiter services ensures high availability and horizontal scalability. Finally, a dynamic configuration service allows for flexible rate limit policies that can be adjusted based on client needs, API endpoint sensitivity, or system load conditions. This design satisfies all our requirements: limiting requests, configurability, error notification, high availability, scalability, and low latency.