
# 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:

```lua
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`](/docs/latest/commands/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`](/docs/latest/commands/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`](/docs/latest/commands/eval/) or [`EVALSHA`](/docs/latest/commands/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](token_bucket.py)):

```python
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](demo_server.py)):

```bash
# 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:

```python
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))
```

## Learn more

*   [EVAL command](/docs/latest/commands/eval/) - Execute Lua scripts
*   [EVALSHA command](/docs/latest/commands/evalsha/) - Execute cached Lua scripts
*   [Lua scripting](../../programmability/eval-intro/index.md) - Introduction to Redis Lua scripting
*   [HMGET command](/docs/latest/commands/hmget/) - Get multiple hash fields
*   [HMSET command](/docs/latest/commands/hmset/) - Set multiple hash fields
*   [Transactions](/docs/latest/develop/using-commands/transactions/) - Alternative to Lua scripts for atomicity

## On this page
