Token bucket rate limiter with Redis and Ruby

Implement a token bucket rate limiter using Redis and Lua scripts in Ruby

This guide shows you how to implement a distributed token bucket rate limiter using Redis and Lua scripts in Ruby with the redis-rb client library.

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. This guide covers the Ruby implementation using the redis-rb gem.

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.

Installation

Install the redis gem:

gem install redis

Or add it to your Gemfile:

gem 'redis', '~> 5.0'

Then run:

bundle install

Using the Ruby module

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

require 'redis'
require_relative 'token_bucket'

# Create a Redis connection
redis = Redis.new(host: 'localhost', port: 6379)

# Create a rate limiter: 10 requests per second
limiter = TokenBucket.new(
  redis: redis,
  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
result = limiter.allow('user:123')

if result[:allowed]
  puts "Request allowed. #{result[:remaining]} tokens remaining."
  # Process the request
else
  puts 'Request denied. Rate limit exceeded.'
  # Return 429 Too Many Requests
end

Ruby's keyword arguments make the constructor parameters self-documenting, and the allow method returns a Hash with :allowed and :remaining keys.

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

Script caching with EVALSHA

The Ruby implementation uses EVALSHA for optimal performance. On first use, the Lua script is loaded into Redis with SCRIPT LOAD, and subsequent calls use the cached SHA1 hash. If the script is evicted from the cache, the module automatically falls back to EVAL and reloads the script.

# The module handles script caching automatically.
# First call loads the script, subsequent calls use EVALSHA.
result1 = limiter.allow('user:123') # Uses EVAL + caches
result2 = limiter.allow('user:123') # Uses EVALSHA (faster)

Running the demo

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

# Install dependencies
gem install redis webrick

# Run the demo server
ruby demo_server.rb

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 specify a different host and port using the --redis-host HOST and --redis-port PORT command-line arguments. 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:

result = limiter.allow("user:#{user_id}")

# Add standard rate limit headers
response['X-RateLimit-Limit'] = limiter.capacity.to_s
response['X-RateLimit-Remaining'] = result[:remaining].to_i.to_s
response['X-RateLimit-Reset'] = (Time.now.to_i + limiter.refill_interval).to_s

unless result[:allowed]
  response.status = 429 # Too Many Requests
  response['Retry-After'] = limiter.refill_interval.ceil.to_s
end

Customization

Using with Rack middleware

You can wrap the rate limiter as Rack middleware for easy integration with any Rack-based framework (Rails, Sinatra, Hanami):

class RateLimitMiddleware
  def initialize(app, limiter:, key_proc:)
    @app = app
    @limiter = limiter
    @key_proc = key_proc
  end

  def call(env)
    key = @key_proc.call(env)
    result = @limiter.allow(key)

    if result[:allowed]
      status, headers, body = @app.call(env)
      headers['X-RateLimit-Remaining'] = result[:remaining].to_i.to_s
      [status, headers, body]
    else
      [429, { 'Content-Type' => 'application/json', 'Retry-After' => @limiter.refill_interval.ceil.to_s },
       ['{"error":"Rate limit exceeded"}']]
    end
  end
end

# Apply per-IP rate limiting
use RateLimitMiddleware, limiter: limiter, key_proc: ->(env) { "ip:#{env['REMOTE_ADDR']}" }

Error handling

The allow method may raise an error if the Redis connection is lost. Wrap calls in a begin/rescue block for production use:

begin
  result = limiter.allow('user:123')
  # Handle result
rescue Redis::BaseError => e
  puts "Rate limiter error: #{e.message}"
  # Fail open or closed depending on your policy
end

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