Design a Smooth Per-Customer Calls-Per-Second Controller

Question

Design a system that controls the number of calls per second for each customer, ensuring smooth operation and preventing overload.

Codemia · Accepted Answer

To design a per-customer calls-per-second controller, we first need to identify key requirements:
- **Rate Limiting**: Each customer should be allowed a specific number of API calls per second.
- **Fairness**: Ensure that no single customer can monopolize the system resources.
- **Scalability**: The system should handle an increasing number of customers without degradation in performance.
- **Real-time Feedback**: Customers should receive immediate feedback if they exceed their rate limit.

The architecture can be divided into several key components:
1. **API Gateway**: To route incoming requests and enforce rate limits.
2. **Rate Limiter**: A dedicated service that maintains the state of each customer's call count.
3. **Database**: A persistent store to keep track of customer limits and call counts.
4. **Monitoring Service**: To track usage patterns and system health.

This architecture allows for separation of concerns, making it easier to scale each component independently.

For the rate limiting mechanism, we can use the **Token Bucket** algorithm:
- Each customer is assigned a bucket that fills with tokens at a defined rate.
- Each API call consumes a token from the bucket.
- If the bucket is empty, the call is rejected or delayed until tokens are available.

This approach allows for bursts of traffic while maintaining a steady average rate. We can implement this using in-memory data structures for fast access, and store the state in a distributed cache (like Redis) for persistence.

When designing this system, we must consider trade-offs:
- **In-memory vs. Persistent Storage**: In-memory solutions provide fast access but can lose data on failure. A hybrid approach with a persistent store can mitigate this.
- **Single vs. Distributed Rate Limiter**: A single instance is simpler but may become a bottleneck. A distributed approach increases complexity but improves scalability.

To scale, we can partition customers across multiple instances of the rate limiter, ensuring that each instance handles a subset of customers.

To ensure the system is resilient:
- Implement **Circuit Breakers** to handle downstream failures gracefully.
- Use **Retry Logic** for transient errors with exponential backoff.
- Monitor for anomalies in traffic patterns and set up alerts for unusual spikes in usage.

By incorporating these strategies, we can maintain a smooth operation even under unexpected conditions.

Design a Smooth Per-Customer Calls-Per-Second Controller

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