Token bucket rate limiter with Redis

Implement a token bucket rate limiter using Redis and Lua scripts

This guide shows you how to implement a distributed token bucket rate limiter using Redis and Lua scripts for atomic operations.

Overview

Rate limiting is a critical technique for controlling the rate at which operations are performed. Common use cases include:

  • Limiting API requests per user or IP address
  • Preventing abuse and protecting against denial-of-service attacks
  • Ensuring fair resource allocation across multiple clients
  • Throttling background jobs or batch operations

The token bucket algorithm is a popular rate limiting approach that allows bursts of traffic while maintaining an average rate limit over time.

How it works

The token bucket algorithm works like a bucket that holds tokens:

  1. Initialization: The bucket starts with a maximum capacity of tokens
  2. Refill: Tokens are added to the bucket at a constant rate (for example, 1 token per second)
  3. Consumption: Each request consumes one token from the bucket
  4. Decision: If tokens are available, the request is allowed; otherwise, it's denied
  5. Capacity limit: The bucket never exceeds its maximum capacity

This approach allows for burst traffic (using accumulated tokens) while enforcing an average rate limit over time.

Why use Redis?

Redis is ideal for distributed rate limiting because:

  • Atomic operations: Lua scripts execute atomically, preventing race conditions
  • Shared state: Multiple application servers can share the same rate limit counters
  • High performance: In-memory operations provide microsecond latency
  • Automatic expiration: Keys can be set to expire automatically (though not used in this implementation)

The Lua script

The core of this implementation is a Lua script that runs atomically on the Redis server. This ensures that checking and updating the token bucket happens in a single operation, preventing race conditions in distributed environments.

Here's how the script works:

local key = KEYS[1]
local capacity = tonumber(ARGV[1])
local refill_rate = tonumber(ARGV[2])
local refill_interval = tonumber(ARGV[3])
local now = tonumber(ARGV[4])

-- Get current state or initialize
local bucket = redis.call('HMGET', key, 'tokens', 'last_refill')
local tokens = tonumber(bucket[1])
local last_refill = tonumber(bucket[2])

-- Initialize if this is the first request
if tokens == nil then
    tokens = capacity
    last_refill = now
end

-- Calculate token refill
local time_passed = now - last_refill
local refills = math.floor(time_passed / refill_interval)

if refills > 0 then
    tokens = math.min(capacity, tokens + (refills * refill_rate))
    last_refill = last_refill + (refills * refill_interval)
end

-- Try to consume a token
local allowed = 0
if tokens >= 1 then
    tokens = tokens - 1
    allowed = 1
end

-- Update state
redis.call('HMSET', key, 'tokens', tokens, 'last_refill', last_refill)

-- Return result: allowed (1 or 0) and remaining tokens
return {allowed, tokens}

Script breakdown

  1. State retrieval: Uses HMGET to fetch the current token count and last refill time from a hash
  2. Initialization: On first use, sets tokens to full capacity
  3. Token refill calculation: Computes how many tokens should be added based on elapsed time
  4. Capacity enforcement: Uses math.min() to ensure tokens never exceed capacity
  5. Token consumption: Decrements the token count if available
  6. State update: Uses HMSET to save the new state
  7. Return value: Returns both the decision (allowed/denied) and remaining tokens

Why atomicity matters

Without atomic execution, race conditions could occur:

  • Double spending: Two requests could read the same token count and both succeed when only one should
  • Lost updates: Concurrent updates could overwrite each other's changes
  • Inconsistent state: Token count and refill time could become desynchronized

Using EVAL or EVALSHA ensures the entire operation executes atomically, making it safe for distributed systems.

Using the Python module

The TokenBucket class provides a simple interface for rate limiting (source):

import redis
from token_bucket import TokenBucket

# Create a Redis connection
r = redis.Redis(host='localhost', port=6379, decode_responses=True)

# Create a rate limiter: 10 requests per second
limiter = TokenBucket(
    redis_client=r,
    capacity=10,        # Maximum burst size
    refill_rate=1,      # Add 1 token per interval
    refill_interval=1.0 # Every 1 second
)

# Check if a request should be allowed
allowed, remaining = limiter.allow('user:123')

if allowed:
    print(f"Request allowed. {remaining} tokens remaining.")
    # Process the request
else:
    print("Request denied. Rate limit exceeded.")
    # Return 429 Too Many Requests

Configuration parameters

  • capacity: Maximum number of tokens in the bucket (controls burst size)
  • refill_rate: Number of tokens added per refill interval
  • refill_interval: Time in seconds between refills

For example:

  • capacity=10, refill_rate=1, refill_interval=1.0 allows 10 requests per second with bursts up to 10
  • capacity=100, refill_rate=10, refill_interval=1.0 allows 10 requests per second with bursts up to 100
  • capacity=60, refill_rate=1, refill_interval=60.0 allows 1 request per minute with bursts up to 60

Rate limit keys

The key parameter identifies what you're rate limiting. Common patterns:

  • Per user: user:{user_id} - Limit each user independently
  • Per IP address: ip:{ip_address} - Limit by client IP
  • Per API endpoint: api:{endpoint}:{user_id} - Different limits per endpoint
  • Global: global:api - Single limit shared across all requests

Running the demo

A demonstration web server is included to show the rate limiter in action (source):

# Install dependencies
pip install redis

# Run the demo server
python demo_server.py

The demo provides an interactive web interface where you can:

  • Submit requests and see them allowed or denied in real-time
  • View the current token count
  • Adjust rate limit parameters dynamically
  • Test different rate limiting scenarios

The demo assumes Redis is running on localhost:6379 but you can easily change the host and port in the demo_server.py script. Visit http://localhost:8080 in your browser to try it out.

Response headers

It's common to include rate limit information in HTTP response headers:

allowed, remaining = limiter.allow(f'user:{user_id}')

# Add standard rate limit headers
response.headers['X-RateLimit-Limit'] = str(limiter.capacity)
response.headers['X-RateLimit-Remaining'] = str(int(remaining))
response.headers['X-RateLimit-Reset'] = str(int(time.time() + limiter.refill_interval))

if not allowed:
    response.status_code = 429  # Too Many Requests
    response.headers['Retry-After'] = str(int(limiter.refill_interval))

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