Rate Limiting | Use Cases | Redis Tutorial

Introduction

Rate limiting is a technique used to control the rate at which a user can access a particular service. It is essential for preventing abuse, ensuring fair usage, and protecting system resources. In this tutorial, we will explore how to implement rate limiting using Redis.

Why Use Redis for Rate Limiting?

Redis is an in-memory data structure store that can be used as a database, cache, and message broker. It is highly performant, supports various data structures, and is easy to use. These features make Redis an excellent choice for implementing rate limiting.

Basic Concepts of Rate Limiting

Rate limiting can be implemented using several strategies, including:

Token Bucket: Tokens are added to a bucket at a fixed rate, and requests consume tokens. If the bucket is empty, the request is denied.
Leaky Bucket: Similar to the token bucket but with a fixed outflow rate, ensuring a steady rate of request processing.
Fixed Window: Limits the number of requests in a fixed time window.
Sliding Window Log: Maintains a log of request timestamps and counts requests in the current window.
Sliding Window Counter: Uses counters for fixed sub-intervals within the window and sums them up.

Implementing Fixed Window Rate Limiting with Redis

Let's start with a basic fixed window rate limiting implementation using Redis. We'll use Redis commands to track requests and enforce limits.

Example: Fixed Window Rate Limiting

Limit: 5 requests per minute

SET user:1234 0 EX 60 NX

This command sets the initial count to 0 for user 1234 with an expiration of 60 seconds if the key does not exist.

INCR user:1234

This command increments the request count for user 1234.

if redis.call('GET', 'user:1234') > 5 then
  return 'Rate limit exceeded'
else
  redis.call('INCR', 'user:1234')
  return 'Request accepted'
end

Implementing Sliding Window Rate Limiting with Redis

Sliding window rate limiting offers a more flexible approach compared to fixed window. It smooths out bursts of requests by maintaining a more granular count of requests over time.

Example: Sliding Window Rate Limiting

Limit: 5 requests per minute

local current_time = redis.call('TIME')[1]
local window_start = current_time - 60
redis.call('ZREMRANGEBYSCORE', 'user:1234:requests', 0, window_start)
redis.call('ZADD', 'user:1234:requests', current_time, current_time)
local request_count = redis.call('ZCARD', 'user:1234:requests')

if request_count > 5 then
  return 'Rate limit exceeded'
else
  return 'Request accepted'
end

This Lua script for Redis maintains a sorted set of request timestamps and removes old entries to enforce the rate limit.

Conclusion

Rate limiting is a crucial technique for managing traffic, preventing abuse, and ensuring fair usage of resources. Redis provides powerful tools to implement various rate limiting strategies efficiently. By leveraging Redis's capabilities, you can create robust and scalable rate limiting mechanisms for your applications.

Rate Limiting Using Redis