Rate Limiting

Rate limiting is an important technique for protecting your APIs and servers from overload. It’s the practice of controlling the rate at which a client can access a resource. Without it, a malicious actor or even a simple surge in legitimate traffic can bring your system to its knees. This post will look at rate limiting, exploring its different approaches, implementation strategies, and best practices.

Why is Rate Limiting Important?

The importance of rate limiting stems from many key factors:

Types of Rate Limiting Algorithms

Several algorithms can be implemented for rate limiting, each with its own strengths and weaknesses:

1. Fixed Window:

This is the simplest approach. Requests are counted within a fixed time window (e.g., 1 second, 1 minute). If the count exceeds a predefined limit within the window, further requests are rejected.

graph LR
    A[Client Request] --> B{Counter < Limit?};
    B -- Yes --> C[Request Allowed];
    B -- No --> D[Request Rejected];
    C --> E[Resource Access];
    E --> F[Window Reset];
    F --> B;

Limitations: This approach can suffer from the “burstiness problem.” A large burst of requests at the end of a window can still exceed the limit, even if the average request rate is acceptable.

2. Sliding Window:

This improves upon the fixed window by allowing requests to be distributed more evenly over time. Requests are counted within a sliding window that moves across time. Requests outside the window are not counted.

graph LR
    A[Client Request] --> B(Sliding Window);
    B -- Request in Window & Count < Limit --> C[Request Allowed];
    B -- Request in Window & Count >= Limit --> D[Request Rejected];
    B -- Request Outside Window --> E[Ignore Request];
    C --> F[Resource Access];
    F --> G[Counter Update];
    G --> B;

3. Leaky Bucket:

This algorithm allows a certain number of requests per unit of time to “leak” through. If the rate of incoming requests exceeds the “leak rate,” requests are queued until space becomes available. This is good for smoothing out bursts of requests.

graph LR
    A[Client Request] --> B(Leaky Bucket);
    B -- Bucket Full --> C[Request Rejected];
    B -- Bucket Not Full --> D[Request Accepted];
    D --> E[Resource Access];
    E --> F[Token Release];

4. Token Bucket:

Similar to the Leaky Bucket, but instead of a bucket filling with requests, a bucket fills with tokens at a regular rate. Each request consumes a token. This approach allows for bursts of requests as long as there are sufficient tokens available.

graph LR
    A[Client Request] --> B{Token Available?};
    B -- Yes --> C[Request Allowed];
    B -- No --> D[Request Rejected];
    C --> E[Resource Access];
    E --> F[Token Consumption];
    F --> G[Token Generation];
    G --> B;

Implementation Strategies

Rate limiting can be implemented at different layers of your application:

Example (Python with ratelimit library):

from ratelimit import limits, RateLimitException

@limits(calls=2, period=60) # 2 calls per minute
def my_api_endpoint(request):
    # Your API logic here
    return "Success!"

try:
  result = my_api_endpoint(request)
  print(result)
except RateLimitException:
  print("Rate limit exceeded")

Best Practices