Design a high-throughput Rate Limiting System

Question

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

Codemia · Accepted Answer

### Functional Requirements
1. **Rate Limiting**: The system should limit the number of requests a user can make to a specified number within a time window (e.g., 100 requests per minute).
2. **User Identification**: The system must identify users based on their API keys, IP addresses, or user accounts.
3. **Flexible Policies**: Different rate limits should be configurable for different user tiers (e.g., free vs. premium users).
4. **Burst Handling**: The system should allow for short bursts of traffic to exceed the limits temporarily without immediate rejection.

### Non-Functional Requirements
1. **High Availability**: The system must be available 99.99% of the time to ensure users can access services without interruptions.
2. **Low Latency**: The response time for rate limit checks should be less than 10 ms under normal load.
3. **Scalability**: The system should handle millions of requests per second as user demand grows.
4. **Consistency**: The system should ensure that rate limit data is consistent across distributed components.

To estimate capacity, let's assume:
- **Total Users**: 10 million active users.
- **Requests per User**: On average, each user makes 100 requests per minute.

### Calculation:
Total Requests per Minute = 10,000,000 users * 100 requests/user = 1,000,000,000 requests/minute

To convert this to requests per second:
1,000,000,000 requests/minute / 60 seconds = ~16,666,667 requests/second

Assuming a conservative 15% burst capacity increase, the system should handle up to 19,166,667 requests/second to accommodate spikes.

The architecture consists of the following components:
1. **API Gateway**: Acts as a single entry point for client requests and routes them to the rate limiting service. Technologies: AWS API Gateway.
2. **Rate Limiting Service**: Central component that enforces rate limits. Technologies: Node.js with Redis for in-memory data store.
3. **Data Store (Redis)**: Used for maintaining user request counts and limits. Redis is chosen for its high-speed access and support for TTL (Time-To-Live).
4. **Load Balancer**: Distributes incoming requests among multiple instances of the Rate Limiting Service. Technology: AWS Elastic Load Balancer.
5. **Monitoring & Logging**: Tools like Prometheus and Grafana for monitoring performance and alerting on anomalies.

The schema design for Redis:
- **User Rate Limit Table**:
  - `user_id` (String): Unique identifier for each user.
  - `request_count` (Integer): Number of requests made in the current window.
  - `window_start` (Timestamp): Start time of the current rate limit window.
  - `rate_limit` (Integer): Maximum allowed requests for the user tier.

### Access Patterns:
- **Incrementing Request Count**: Increment request counts atomically.
- **Resetting Count**: Every minute, check if the current timestamp has exceeded `window_start` and reset the count accordingly.

1. **Consistency vs. Availability**: Using Redis with eventual consistency allows for quick reads and writes, but there may be scenarios where updates are not immediately reflected across replicas. This is acceptable given the high-availability requirement.
2. **Performance vs. Memory Usage**: In-memory storage (Redis) provides low latency but at a higher memory cost. Trade-offs must be made in terms of the maximum number of users that can be supported concurrently.
3. **Burst Handling**: Allowing for burst requests can lead to temporarily exceeding rate limits. This requires careful monitoring to ensure that overall system stability is not compromised.
4. **Scaling Strategy**: Horizontal scaling of the Rate Limiting Service may introduce challenges with maintaining state across instances. Solutions like sticky sessions or a distributed cache strategy must be considered.

Design a high-throughput Rate Limiting System

Apple

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