Let's design a rate limiter. A rate limiter is a crucial component in many systems to prevent abuse and ensure fair usage. We want to design a rate limiter that can be used to control the number of requests a user can make to an API within a specific time window.

Our rate limiter should support the following core features:

• Request Counting: Accurately track the number of requests made by a user.
• Rate Limiting Enforcement: Block requests exceeding the defined rate limit.
• Configurable Rate Limits: Allow defining different rate limits for different users or API endpoints.

Here are some key use cases we need to support:

• A user should be able to make a maximum of 100 requests per minute.
• A specific API endpoint should only allow 1000 requests per hour from all users.
• Different user tiers (e.g., free, premium) may have different rate limits. The free tier has only 10 req/min while the premium tier has 1000 req/min.

What are your initial thoughts on how you would approach designing this system?

Rate Limiter Design

Let's design a rate limiter. I've worked at Google for 8 years as a Senior Staff Engineer, focusing on distributed systems, so I've seen these implemented at scale. I'm currently based in Mountain View.

1. Requirements

Here's what we need:

• Functional Requirements:
  • Limit the number of requests a user (or IP address or API key) can make to an API within a specific time window (e.g., 100 requests per minute).
  • Accurately track request counts.
  • Handle a large number of users concurrently.
  • Enforce the rate limits consistently across multiple servers.
  • Provide a mechanism to update the rate limits dynamically.
• Non-Functional Requirements:
  • Low latency: The rate limiter should add minimal overhead to the API calls.
  • High availability: The rate limiter should be highly available to avoid impacting the API availability.
  • Scalability: The rate limiter should be able to handle a large number of requests and users.
  • Fault tolerance: The rate limiter should be resilient to failures.

2. High-Level Design

We can use a distributed rate limiting service. The core components are:

1. API Gateway/Proxy: This is the entry point for all API requests. It intercepts each request and forwards it to the Rate Limiter Service to check if the request should be allowed or rejected.
2. Rate Limiter Service: This service stores and manages the request counts for each user/IP/API key and enforces the rate limits. It communicates with a data store to persist the request counts. It returns an "allowed" or "rejected" response to the API Gateway.
3. Data Store: This is used to persist the request counts. We can use a distributed cache like Redis or Memcached for fast read/write access. For persistence and resilience, we might also use a database, either SQL or NoSQL, backing up the cache.

Here's how the request flow works:

1. A client makes a request to the API.
2. The API Gateway receives the request.
3. The API Gateway extracts the user ID (or IP address, API key, etc.) from the request.
4. The API Gateway sends a request to the Rate Limiter Service, including the user ID and API endpoint.
5. The Rate Limiter Service retrieves the current request count for the user from the Data Store.
6. If the request count is below the limit, the Rate Limiter Service increments the request count in the Data Store and returns an "allowed" response to the API Gateway.
7. If the request count is at or exceeds the limit, the Rate Limiter Service returns a "rejected" response to the API Gateway.
8. The API Gateway either forwards the request to the API server (if allowed) or returns an error to the client (if rejected).

3. Data Model

We need to store the following information in the Data Store:

Field	Type	Description
userid	String	The ID of the user (or IP address, API key)
apiendpoint	String	The API endpoint being accessed
requestcount	Integer	The number of requests made by the user within the window
expirytime	Timestamp	Timestamp when the count should be reset

4. API Design

The API Gateway needs to communicate with the Rate Limiter Service. A simple REST API endpoint could be used:

• Endpoint: /check_rate_limit

• Method: POST

• Request Body: json { "user_id": "user123", "api_endpoint": "/users", "rate_limit_key": "user123:/users" // Optional, pre-computed key }

• Response: json { "allowed": true, // or false "remaining_requests": 50, // Optional "retry_after": 10 // Optional (seconds) }

5. Tradeoffs

• Consistency vs. Availability: Using a distributed cache like Redis can improve performance, but it might introduce eventual consistency. This means that the request count might not always be perfectly accurate, especially during failures. We can tune the consistency level of Redis to balance accuracy and performance.
• Complexity: Implementing a distributed rate limiter adds complexity to the system. We need to manage the distributed cache, handle network failures, and ensure data consistency.
• Cost: Using a distributed cache and potentially a database will incur costs for infrastructure and maintenance.

6. Alternative Approaches

• Token Bucket Algorithm: This algorithm uses a bucket that holds tokens. Each request consumes a token. If the bucket is empty, the request is rejected. Tokens are added to the bucket at a fixed rate. This algorithm allows for burst traffic.
• Leaky Bucket Algorithm: This algorithm is similar to the Token Bucket algorithm, but it processes requests at a fixed rate. Requests are added to a queue. If the queue is full, the requests are rejected. This algorithm smooths out burst traffic.
• Fixed Window Counter: A simple approach where you count requests within a fixed time window. Easier to implement, but susceptible to issues if requests cluster at window boundaries (e.g., lots of requests just before and after the minute mark).
• Sliding Window Log: Keeps a timestamped log of every request. To determine if a request should be allowed, it counts the number of requests within the current time window. More accurate than fixed window, but more resource-intensive.
• Client-Side Rate Limiting: The client manages its own request rate. This is less reliable as a malicious client can ignore the rate limits.

The Token Bucket and Leaky Bucket algorithms provide smoother rate limiting than a fixed window counter. The Sliding Window Log is more precise, but can be more resource intensive.

7. Edge Cases

• Network Partitions: If the Rate Limiter Service is partitioned from the API Gateway, requests might be incorrectly allowed or rejected. We can use a fallback mechanism to allow requests during network partitions, potentially with a more lenient rate limit.
• Clock Skew: If the clocks on different servers are not synchronized, the rate limits might not be enforced consistently. We can use NTP to synchronize the clocks.
• Concurrency: When multiple requests are made concurrently, the request count might not be incremented correctly. We can use atomic operations (e.g., INCR command in Redis) to ensure that the request count is incremented atomically.
• Thundering Herd: If a large number of users try to access the API at the same time, the Rate Limiter Service might be overwhelmed. We can use a caching layer to protect the Rate Limiter Service from the thundering herd.

8. Future Considerations

• Dynamic Rate Limits: We can provide an API to update the rate limits dynamically. This would allow us to adjust the rate limits based on the current load or to protect the API from abuse.
• Tiered Rate Limiting: We can offer different rate limits to different users or API clients based on their subscription level or usage patterns.
• Monitoring and Alerting: We can monitor the rate limiting metrics (e.g., request counts, rejection rates) and set up alerts to detect anomalies.
• Integration with Authentication and Authorization: We can integrate the rate limiter with authentication and authorization systems to enforce rate limits based on user roles or permissions.