
# Token bucket rate limiter with Redis and Node.js

Implement a token bucket rate limiter using Redis and Lua scripts in Node.js

This guide shows you how to implement a distributed token bucket rate limiter using Redis and Lua scripts in Node.js with async/await.

## 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 Node.js implementation using the [`node-redis`](../../../clients/nodejs/index.md) client library.

## 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.

## Installation

Install the `redis` package from npm:

```bash
npm install redis
```

## Using the Node.js module

The `TokenBucket` class provides an async interface for rate limiting ([source](tokenBucket.js)):

```javascript
const { createClient } = require('redis');
const { TokenBucket } = require('./tokenBucket');

// Create a Redis connection
const client = createClient({ url: 'redis://localhost:6379' });
await client.connect();

// Create a rate limiter: 10 requests per second
const limiter = new TokenBucket({
    redisClient: client,
    capacity: 10,        // Maximum burst size
    refillRate: 1,       // Add 1 token per interval
    refillInterval: 1.0  // Every 1 second
});

// Check if a request should be allowed
const { allowed, remaining } = await limiter.allow('user:123');

if (allowed) {
    console.log(`Request allowed. ${remaining} tokens remaining.`);
    // Process the request
} else {
    console.log('Request denied. Rate limit exceeded.');
    // Return 429 Too Many Requests
}

// Disconnect when done
await client.disconnect();
```

Because `node-redis` operations are asynchronous, the `allow()` method returns a Promise. Use `await` or `.then()` to handle the result.

### Configuration parameters

*   **capacity**: Maximum number of tokens in the bucket (controls burst size)
*   **refillRate**: Number of tokens added per refill interval
*   **refillInterval**: Time in seconds between refills

For example:

*   `capacity: 10, refillRate: 1, refillInterval: 1.0` allows 10 requests per second with bursts up to 10
*   `capacity: 100, refillRate: 10, refillInterval: 1.0` allows 10 requests per second with bursts up to 100
*   `capacity: 60, refillRate: 1, refillInterval: 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:{userId}` - Limit each user independently
*   **Per IP address**: `ip:{ipAddress}` - Limit by client IP
*   **Per API endpoint**: `api:{endpoint}:{userId}` - Different limits per endpoint
*   **Global**: `global:api` - Single limit shared across all requests

### Script caching with EVALSHA

The Node.js implementation uses [`EVALSHA`](/docs/latest/commands/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`](/docs/latest/commands/eval/) and reloads the script.

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

## Running the demo

A demonstration HTTP server is included to show the rate limiter in action ([source](demoServer.js)):

```bash
# Install dependencies
npm install redis

# Run the demo server
node demoServer.js
```

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:

```javascript
const { allowed, remaining } = await limiter.allow(`user:${userId}`);

// Add standard rate limit headers
res.set('X-RateLimit-Limit', String(limiter.capacity));
res.set('X-RateLimit-Remaining', String(Math.floor(remaining)));
res.set('X-RateLimit-Reset', String(Math.floor(Date.now() / 1000 + limiter.refillInterval)));

if (!allowed) {
    res.set('Retry-After', String(Math.ceil(limiter.refillInterval)));
    res.status(429).json({ error: 'Too Many Requests' });
    return;
}
```

## Customization

### Using with Express middleware

You can wrap the rate limiter as Express middleware for easy integration:

```javascript
function rateLimitMiddleware(limiter, keyFn) {
    return async (req, res, next) => {
        const key = keyFn(req);
        const { allowed, remaining } = await limiter.allow(key);

        res.set('X-RateLimit-Remaining', String(Math.floor(remaining)));

        if (!allowed) {
            res.status(429).json({ error: 'Rate limit exceeded' });
            return;
        }
        next();
    };
}

// Apply per-IP rate limiting
app.use(rateLimitMiddleware(limiter, (req) => `ip:${req.ip}`));
```

### Error handling

The `allow()` method may throw if the Redis connection is lost. Wrap calls in try/catch blocks for production use:

```javascript
try {
    const { allowed, remaining } = await limiter.allow('user:123');
    // Handle result
} catch (err) {
    console.error('Rate limiter error:', err);
    // Fail open or closed depending on your policy
}
```

## 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
*   [Node.js client](../../../clients/nodejs/index.md) - Redis Node.js client documentation

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