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Andrew Meredith
Andrew Meredith

Posted on • Originally published at kendru.github.io

Rate Limiting with a Token Bucket

Recently I was looking into options to add rate limiting to a specific endpoint in an application at work. Most endpoints are only exposed internally, and we are careful to not make more requests than the system can handle. However, in one case, the endpoint is open to our customers, and it runs some pretty intensive database operations, so we wanted to limit the rate at which clients can make requests. This functionality is available in pretty much every API gateway out there as well as in a lot of reverse proxies. In our case, application updates are easier to make than config updates, so we opted for a simple solution that we could deploy as part of our Node.js app.

Enter the Token Bucket.

A token bucket is an algorithm that allows tokens to be accumulated over time at a specific rate. These tokens can then be "redeemed" to execute some action. If there are no tokens available, the action cannot be taken. Imagine that we have a bucket that holds some number of balls, say 100. When there are fewer than 100 balls in the bucket, a machine will automatically refill the bucket at a rate of 1 ball per second until it is full again. We can take as many balls as we want as quickly as we want, but once the bucket is empty, we have to wait for it to start filling up again before we can take more.

If we use a token bucket to rate limit an API, then it allows us to set a request rate (the rate at which tokens are added to the bucket) with the ability to burst above this rate for a short period (until we have drained the capacity of the bucket). Let's take a first pass at implementing a token bucket.

Initial TokenBucket Implementation

class TokenBucket {

    constructor(capacity, fillPerSecond) {
        this.capacity = capacity;
        this.tokens = capacity;
        setInterval(() => this.addToken(), 1000 / fillPerSecond);
    }

    addToken() {
        if (this.tokens < this.capacity) {
            this.tokens += 1;
        }
    }

    take() {
        if (this.tokens > 0) {
            this.tokens -= 1;
            return true;
        }

        return false;
    }
}
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We could then use this in a Node.js/express application to limit the number of requests made to a particular endpoint:

Rate Limiting with TokenBucket

const express = require('express');
const app = express();

function limitRequests(perSecond, maxBurst) {
    const bucket = new TokenBucket(maxBurst, perSecond);

    // Return an Express middleware function
    return function limitRequestsMiddleware(req, res, next) {
        if (bucket.take()) {
            next();
        } else {
            res.status(429).send('Rate limit exceeded');
        }
    }
}


app.get('/',
    limitRequests(5, 10), // Apply rate limiting middleware
    (req, res) => {
        res.send('Hello from the rate limited API');
    }
);

app.listen(3000, () => console.log('Server is running'));
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In this example, the / endpoint is restricted to serving 5 requests per second across all clients. If we wanted to have a per-client limit , then we could keep a map of IP address (or API keys) to token bucket, creating a new token bucket every time we encounter a new client, as in the following example:

Rate Limiting by IP

function limitRequests(perSecond, maxBurst) {
    const buckets = new Map();

    // Return an Express middleware function
    return function limitRequestsMiddleware(req, res, next) {
        if (!buckets.has(req.ip)) {
            buckets.set(req.ip, new TokenBucket(maxBurst, perSecond));
        }

        const bucketForIP = buckets.get(req.ip);
        if (bucketForIP.take()) {
            next();
        } else {
            res.status(429).send('Client rate limit exceeded');
        }
    }
}
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Using this approach, we would need to be careful, since a large number of distinct IPs could create quite a bit of overhead in terms of both memory and timers to refill buckets. In practice, we would probably want to remove the token buckets after some time, and we would also want to defer adding tokens until they are requested, which will eliminate the need for JavaScript timers. Here is our new timer-free TokenBucket implementation:

Timer-Free TokenBucket

class TokenBucket {
    constructor(capacity, fillPerSecond) {
        this.capacity = capacity;
        this.fillPerSecond = fillPerSecond;

        this.lastFilled = Math.floor(Date.now() / 1000);
        this.tokens = capacity;
    }

    take() {
        // Calculate how many tokens (if any) should have been added since the last request
        this.refill();

        if (this.tokens > 0) {
            this.tokens -= 1;
            return true;
        }

        return false;
    }

    refill() {
        const now = Math.floor(Date.now() / 1000);
        const rate = (now - this.lastFilled) / this.fillPerSecond;

        this.tokens = Math.min(this.capacity, this.tokens + Math.floor(rate * this.capacity));
        this.lastFilled = now;
    }
}
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This implementation should be have the same, but it only does work when take() is called, which should be more efficient in most cases.


This article was originally posted as Rate Limiting in JavaScript with a Token Bucket on Andrew's blog.

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