System Design - Rate Limiter with Variable User Quotas

System Design - Rate Limiter with Variable User Quotas

Problem Statement

Design a rate limiting system where each user has a different token quota. Unlike traditional rate limiters where all users share the same limits (e.g., 100 requests/minute), this system must handle varying quotas for different users. For example, User A might have a quota of 50 requests/minute, while User B has a quota of 100 requests/minute.

Examples

1. User A makes 30 requests in the first minute and 25 requests in the second minute. No rate limiting should occur.
2. User B makes 120 requests in the first minute. Rate limiting should occur for the 20th request in the first minute.
3. User A and User B make 75 requests each in the first minute. No rate limiting should occur for either user.

Constraints

1. The system should handle a large number of users (e.g., millions).
2. The system should handle a high volume of requests per second.
3. The system should be highly available and fault-tolerant.
4. The system should be able to scale horizontally.

Hints

1. Consider using a token bucket algorithm with a separate bucket for each user.
2. Use a distributed cache (e.g., Redis) to store the token buckets for each user.
3. Implement a centralized service to manage user quotas and distribute them to the rate limiter instances.
4. Consider using a sliding window log algorithm to track request timestamps for each user.

Solution

To design a rate limiter with variable user quotas, we can use the following approach:

1. Data Model: Store user quotas in a centralized database or key-value store. Each user has a unique ID and a corresponding quota.

2. Token Bucket Algorithm: Implement the token bucket algorithm for each user. Each user has a separate bucket with an initial number of tokens equal to their quota. Tokens are replenished at a fixed rate (e.g., 1 token per second).

3. Distributed Cache: Use a distributed cache (e.g., Redis) to store the token buckets for each user. This allows the rate limiter to scale horizontally and handle a large number of users.

4. Request Handling: When a request arrives, check the user's token bucket in the cache. If there are tokens available, decrement the bucket and process the request. If the bucket is empty, rate limit the request.

5. Token Replenishment: Periodically replenish the tokens in each user's bucket based on their quota and the fixed rate. This can be done using a background worker or by piggybacking on request handling.

6. Fault Tolerance: Implement fault tolerance by replicating the cache across multiple nodes. Use a consensus algorithm (e.g., Raft) to ensure consistency across replicas.

7. Scalability: Scale the rate limiter horizontally by adding more instances behind a load balancer. Use a consistent hashing scheme to route requests to the same instance for a given user.

8. Monitoring and Alerting: Monitor the rate limiter's performance and set up alerts for异常 behavior, such as high request rates or quota violations.

By following this approach, we can design a rate limiting system that supports variable user quotas, handles a large number of users and requests, and scales horizontally.