Rate limiting algorithms

A rate limiter is a system that helps to prevent the excessive use of resources or a server crash by limiting the number of requests that can be made in a certain period of time. It is a common technique used by web services, APIs, and applications.

Here is a high-level design of a rate limiter:

  • Incoming requests are first sent to a distributed queue or message broker, such as Apache Kafka, RabbitMQ, or AWS SQS.
  • A group of worker threads constantly read from the queue and process the requests.
  • A rate limiter module keeps track of the rate of incoming requests by maintaining a rolling window of the number of requests made in the last N seconds.
  • If the number of requests in the rolling window exceeds a certain threshold, the rate limiter returns an error response to the client.
  • If the rate is within the limit, the request is forwarded to the appropriate handler or service for further processing.
  • Successful responses are then returned to the client.

The rate limiter can be configured to use various algorithms for tracking the number of requests, such as token bucket, leaky bucket, or fixed window. The system can also have the capability to dynamically adjust the rate limit based on traffic patterns and the load on the servers.

To implement a rate limiter in Java, we can use libraries such as Guava RateLimiter or Netflix’s token-bucket library. We can also use Spring AOP to create an aspect that intercepts incoming requests and applies rate limiting before forwarding the request to the appropriate handler.

There are several algorithms that can be used to implement a Rate Limiter. Some popular ones are:

Token Bucket Algorithm:

In this algorithm, tokens are added to a bucket at a fixed rate. When a user wants to use a service, a token is taken from the bucket. If there are no tokens left in the bucket, the user has to wait until a new token is added. This algorithm is simple and efficient, but it can be inaccurate if the token generation rate does not match the service usage rate.

Leaky Bucket Algorithm:

In this algorithm, a bucket with a fixed capacity is used to store requests. Each request is represented as a drop, which is added to the bucket. If the bucket is full, any new drops are discarded. The drops are released from the bucket at a fixed rate, representing the rate at which the service can handle requests. This algorithm can be more accurate than the token bucket algorithm, but it can also be more complex to implement.

Fixed Window Algorithm:

In this algorithm, the number of requests made in a fixed time window is limited. For example, a user may only be allowed to make 10 requests in a 1-minute window. After the window expires, the user can make 10 more requests. This algorithm is easy to implement, but it can be less flexible than the other algorithms.

Sliding Window Algorithm:

This algorithm is similar to the fixed window algorithm, but it uses a sliding window instead. The window moves forward in time as requests are made, and the number of requests made in the window is limited. This allows for more flexibility than the fixed window algorithm, but it can also be more complex to implement.

Adaptive Rate Limiting:

In this algorithm, the rate limit is adjusted dynamically based on the current load on the service. For example, if the service is under heavy load, the rate limit may be reduced to ensure that the service remains responsive. This algorithm can be more complex to implement, but it can provide better performance and user experience in dynamic environments.

The choice of algorithm depends on the specific requirements of the system and the expected usage patterns of the service.