Design a fault-tolerant Rate Limiting System

Question

Design a fault-tolerant rate limiting system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Codemia · Accepted Answer

### Functional Requirements
1. **Rate Limiting**: Implement strict rate limits per user/API key to prevent abuse and ensure fair usage.
2. **Global Distribution**: Provide rate limiting that works across multiple regions to support ServiceNow's global customer base.
3. **Dynamic Configuration**: Allow administrators to dynamically adjust rate limits and rules without downtime.
4. **Real-time Monitoring**: Offer real-time metrics on API usage and rate limit status.

### Non-Functional Requirements
1. **Fault Tolerance**: The system must continue to operate correctly under various failure conditions, including partial network failures and service outages.
2. **Scalability**: Handle millions of requests per minute with the ability to scale horizontally.
3. **Low Latency**: Ensure responses to API requests are returned within 100 milliseconds under normal conditions.
4. **Data Consistency**: Maintain consistency of rate limit data across distributed nodes, adhering to eventual consistency where applicable.

To estimate the capacity needed, consider the following:
1. **User Base**: Assume 1 million active users.
2. **Requests Per User**: Each user makes an average of 60 requests per minute (1 request per second).
3. **Total Requests**: 1,000,000 users * 60 requests/user/min = 60 million requests/min = 1 million requests/sec.
4. **Rate Limiting Operations**: Each request will involve checking the rate limit, which requires accessing the database and potentially updating it.
5. **Database Operations**: Assuming each request requires one read and one write operation, that results in 2 million operations per second.
6. **Scaling Factor**: Apply a safety margin of 3x to ensure we can handle spikes, leading to a target of 180 million operations per minute.

### Component Diagram
- **API Gateway**: Handles incoming requests and routes them to the rate limiting service.
- **Rate Limiting Service**: Implements the core logic for rate limiting, scales horizontally, and utilizes a distributed cache.
- **Distributed Cache (Redis)**: Stores rate limit counters and configurations, providing low-latency access.
- **Database (Cassandra)**: For persistent storage of user data and rate limit configurations, ensuring high availability and scalability.
- **Monitoring Service**: Collects metrics and logs for real-time monitoring and alerting (e.g., Prometheus, Grafana).
- **Load Balancer**: Distributes incoming requests across multiple instances of the rate-limiting service.

### Technology Choices
- **Redis** for caching to provide fast access to rate limit data.
- **Cassandra** for its high write throughput and ability to scale horizontally.

### Schema Design
1. **User Table** (Cassandra):  
   - **user_id** (Primary Key)  
   - **rate_limit** (int)  
   - **window_size** (int)  
   - **last_request_time** (timestamp)

2. **Request Logs Table** (Cassandra):  
   - **user_id** (Partition Key)  
   - **request_time** (Clustering Key)
   - **api_endpoint** (text)  
   - **status_code** (int)  
   - **region** (text)

### Access Patterns
- **Read**: Check current rate limit status and last request time for a user.
- **Write**: Update rate limit counters and log request details.

### Architectural Decisions
1. **Consistency vs. Availability**: Choosing Cassandra allows for eventual consistency which aligns with our need for high availability under load. This means during partitions or outages, some requests might succeed while others fail, but overall system availability is prioritized.
2. **Caching Strategy**: Using Redis for cache helps reduce the load on the primary data store (Cassandra) but introduces complexity in ensuring cache invalidation strategies are correctly implemented to avoid stale data.
3. **Latency vs. Scalability**: By using distributed caching, we achieve low latency but must manage cache coherence across multiple nodes carefully, especially during configuration updates.

### Operational Concerns
- **Deployment Strategy**: Use blue-green deployments for rolling out new features with minimal downtime.
- **Chaos Engineering**: Implement chaos testing to simulate failures in components to ensure the system remains resilient and to test the rate limit enforcement under stress.
- **SLOs/SLIs**: Set SLOs for request latency (99% of requests within 100ms) and availability (99.9% uptime) to ensure operational excellence.

Design a fault-tolerant Rate Limiting System

ServiceNow

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