Token bucket rate limiter with Redis and Java

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

This guide shows you how to implement a distributed token bucket rate limiter using Redis and Lua scripts in Java with the Jedis 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 Java implementation using the Jedis client library, taking advantage of Java's try-with-resources for connection management, JedisPool for connection pooling, and checked exceptions for error handling.

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

Add the Jedis dependency to your project:

  • If you use Maven:

    <dependency>
    <groupId>redis.clients</groupId>
    <artifactId>jedis</artifactId>
    <version>5.2.0</version>
</dependency>

  • If you use Gradle:

    implementation 'redis.clients:jedis:5.2.0'

Using the Java class

The TokenBucket class provides a thread-safe interface for rate limiting (source):

import redis.clients.jedis.JedisPool;

public class Main {
    public static void main(String[] args) {
        // Create a Redis connection pool
        JedisPool jedisPool = new JedisPool("localhost", 6379);

        // Create a rate limiter: 10 requests per second
        TokenBucket limiter = new TokenBucket(10, 1, 1.0, jedisPool);

        // Check if a request should be allowed
        TokenBucket.RateLimitResult result = limiter.allow("user:123");

        if (result.allowed()) {
            System.out.printf("Request allowed. %.0f tokens remaining.%n", result.remaining());
            // Process the request
        } else {
            System.out.println("Request denied. Rate limit exceeded.");
            // Return 429 Too Many Requests
        }
    }
}

Jedis operations are synchronous and thread-safe when using JedisPool, which manages a pool of connections internally. The allow() method returns a RateLimitResult record containing both the decision and the remaining token count.

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 Java 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 class automatically falls back to EVAL and reloads the script. The script loading uses volatile and synchronization to ensure thread safety across multiple threads.

// The class handles script caching automatically.
// First call loads the script, subsequent calls use EVALSHA.
TokenBucket.RateLimitResult result1 = limiter.allow("user:123"); // Uses EVAL + caches
TokenBucket.RateLimitResult result2 = limiter.allow("user:123"); // Uses EVALSHA (faster)

Thread safety

The TokenBucket class is thread-safe. You can share a single instance across your application, including from multiple threads in a servlet container or web framework:

// Create a shared limiter instance
TokenBucket limiter = new TokenBucket(10, 1, 1.0, jedisPool);

// Safe to call from multiple threads
ExecutorService executor = Executors.newFixedThreadPool(10);
for (int i = 0; i < 20; i++) {
    final int id = i;
    executor.submit(() -> {
        TokenBucket.RateLimitResult result = limiter.allow("shared:resource");
        System.out.printf("thread %d: allowed=%b remaining=%.0f%n",
                id, result.allowed(), result.remaining());
    });
}
executor.shutdown();

Running the demo

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

# Compile
javac -cp jedis-5.2.0.jar TokenBucket.java DemoServer.java

# Run the demo server
java -cp .:jedis-5.2.0.jar DemoServer

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:

int capacity = 10;
double refillInterval = 1.0;
TokenBucket limiter = new TokenBucket(capacity, 1, refillInterval, jedisPool);

TokenBucket.RateLimitResult result = limiter.allow("user:" + userId);

// Add standard rate limit headers
response.setHeader("X-RateLimit-Limit", String.valueOf(capacity));
response.setHeader("X-RateLimit-Remaining", String.valueOf((int) result.remaining()));
response.setHeader("X-RateLimit-Reset",
        String.valueOf(System.currentTimeMillis() / 1000 + (long) refillInterval));

if (!result.allowed()) {
    response.setHeader("Retry-After", String.valueOf((int) refillInterval));
    response.setStatus(429); // Too Many Requests
}

Customization

Using as a servlet filter

You can wrap the rate limiter as a servlet filter for use with any Java web framework:

public class RateLimitFilter implements Filter {
    private TokenBucket limiter;

    @Override
    public void init(FilterConfig config) {
        JedisPool jedisPool = new JedisPool("localhost", 6379);
        limiter = new TokenBucket(10, 1, 1.0, jedisPool);
    }

    @Override
    public void doFilter(ServletRequest req, ServletResponse res, FilterChain chain)
            throws IOException, ServletException {
        HttpServletRequest httpReq = (HttpServletRequest) req;
        HttpServletResponse httpRes = (HttpServletResponse) res;

        String key = "ip:" + httpReq.getRemoteAddr();
        TokenBucket.RateLimitResult result = limiter.allow(key);

        httpRes.setHeader("X-RateLimit-Remaining",
                String.valueOf((int) result.remaining()));

        if (!result.allowed()) {
            httpRes.setStatus(429);
            httpRes.getWriter().write("{\"error\": \"Rate limit exceeded\"}");
            return;
        }
        chain.doFilter(req, res);
    }
}

Error handling

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

try {
    TokenBucket.RateLimitResult result = limiter.allow("user:123");
    // Handle result
} catch (JedisException e) {
    System.err.println("Rate limiter error: " + e.getMessage());
    // Fail open: allow the request when Redis is unavailable
    // Or fail closed: deny the request
}

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