@hubsign/ratelimit v0.0.11

Rate Limit

This implements a rate limiting functionality using in-memory storage to track request counts and timestamps for enforcing thresholds over time windows.

This library is based on the package @upstash/ratelimit, but uses in-memory storage instead of Redis. It provides rate limiting functionality for controlling the incoming traffic to your application.

It is the only connectionless (HTTP based) rate limiting library and designed for:

• Serverless functions (AWS Lambda, Vercel ....)
• Cloudflare Workers & Pages
• Vercel Edge
• Fastly Compute@Edge
• Next.js, Jamstack ...
• Client side web/mobile applications
• WebAssembly
• and other environments where HTTP is preferred over TCP.

• Rate Limit
  • Quick Start
    • Install
      • npm
    • Use it
    • Timeout
    • Block until ready
    • Ephemeral Cache
  • Using multiple limits
    • Asynchronous synchronization between databases
  • Ratelimiting algorithms
    • Fixed Window
      • Pros
      • Cons
      • Usage
    • Sliding Window
      • Pros
      • Cons
      • Usage
    • Token Bucket
      • Pros
      • Cons
      • Usage
  • Contributing

Quick Start

Install

npm

npm install @hubsign/ratelimit

Use it

import { Ratelimit } from "@hubsign/ratelimit";

// Create a new ratelimiter, that allows 10 requests per 10 seconds
const ratelimit = new Ratelimit({
  limiter: Ratelimit.slidingWindow(10, "10 s"),
  /**
   * Optional prefix for the keys used in redis. This is useful if you want to share a redis
   * instance with other applications and want to avoid key collisions. The default prefix is
   * "@hubsign/ratelimit"
   */
  prefix: "@hubsign/ratelimit",
});

// Use a constant string to limit all requests with a single ratelimit
// Or use a userID, apiKey or ip address for individual limits.
const identifier = "api";
const { success } = await ratelimit.limit(identifier);

if (!success) {
  return "Unable to process at this time";
}
doExpensiveCalculation();
return "Here you go!";

export type RatelimitResponse = {
  /**
   * Whether the request may pass(true) or exceeded the limit(false)
   */
  success: boolean;
  /**
   * Maximum number of requests allowed within a window.
   */
  limit: number;
  /**
   * How many requests the user has left within the current window.
   */
  remaining: number;
  /**
   * Unix timestamp in milliseconds when the limits are reset.
   */
  reset: number;

  /**
   * For the MultiRegion setup we do some synchronizing in the background, after returning the current limit.
   * In most case you can simply ignore this.
   *
   * On Vercel Edge or Cloudflare workers, you need to explicitely handle the pending Promise like this:
   *
   * **Vercel Edge:**
   * https://nextjs.org/docs/api-reference/next/server#nextfetchevent
   *
   * ```ts
   * const { pending } = await ratelimit.limit("id")
   * event.waitUntil(pending)
   * ```
   *
   * **Cloudflare Worker:**
   * https://developers.cloudflare.com/workers/runtime-apis/fetch-event/#syntax-module-worker
   *
   * ```ts
   * const { pending } = await ratelimit.limit("id")
   * context.waitUntil(pending)
   * ```
   */
  pending: Promise<unknown>;
};

Timeout

You can define an optional timeout in milliseconds, after which the request will be allowed to pass regardless of what the current limit is. This can be useful if you don't want network issues to cause your application to reject requests.

const ratelimit = new Ratelimit({
  limiter: Ratelimit.slidingWindow(10, "10 s"),
  timeout: 1000, // 1 second
});

Block until ready

In case you don't want to reject a request immediately but wait until it can be processed, we also provide

ratelimit.blockUntilReady(identifier: string, timeout: number): Promise<RatelimitResponse>

It is very similar to the limit method and takes an identifier and returns the same response. However if the current limit has already been exceeded, it will automatically wait until the next window starts and will try again. Setting the timeout parameter (in milliseconds) will cause the returned Promise to resolve in a finite amount of time.

// Create a new ratelimiter, that allows 10 requests per 10 seconds
const ratelimit = new Ratelimit({
  limiter: Ratelimit.slidingWindow(10, "10 s"),
});

// `blockUntilReady` returns a promise that resolves as soon as the request is allowed to be processed, or after 30 seconds
const { success } = await ratelimit.blockUntilReady("id", 30_000);

if (!success) {
  return "Unable to process, even after 30 seconds";
}
doExpensiveCalculation();
return "Here you go!";

Ephemeral Cache

For extreme load or denial of service attacks, it might be too expensive to call redis for every incoming request, just to find out it should be blocked because they have exceeded the limit.

You can use an ephemeral in memory cache by passing the ephemeralCache option:

const cache = new Map(); // must be outside of your serverless function handler

// ...

const ratelimit = new Ratelimit({
  // ...
  ephemeralCache: cache,
});

If enabled, the ratelimiter will keep a global cache of identifiers and their reset timestamps, that have exhausted their ratelimit. In serverless environments this is only possible if you create the cache or ratelimiter instance outside of your handler function. While the function is still hot, the ratelimiter can block requests without having to request data from redis, thus saving time and money.

Using multiple limits

Sometimes you might want to apply different limits to different users. For example you might want to allow 10 requests per 10 seconds for free users, but 60 requests per 10 seconds for paid users.

Here's how you could do that:

import { Ratelimit } from "@hubsign/ratelimit";

const ratelimit = {
  free: new Ratelimit({
    prefix: "ratelimit:free",
    limiter: Ratelimit.slidingWindow(10, "10s"),
  }),
  paid: new Ratelimit({
    prefix: "ratelimit:paid",
    limiter: Ratelimit.slidingWindow(60, "10s"),
  }),
};

await ratelimit.free.limit(ip);
// or for a paid user you might have an email or userId available:
await ratelimit.paid.limit(userId);

Asynchronous synchronization between databases

The MultiRegion setup will do some synchronization between databases after returning the current limit. This can lead to problems on Cloudflare Workers and therefore Vercel Edge functions, because dangling promises must be taken care of:

Vercel Edge: docs

const { pending } = await ratelimit.limit("id");
event.waitUntil(pending);

Cloudflare Worker: docs

const { pending } = await ratelimit.limit("id");
context.waitUntil(pending);

Ratelimiting algorithms

We provide different algorithms to use out of the box. Each has pros and cons.

Fixed Window

This algorithm divides time into fixed durations/windows. For example each window is 10 seconds long. When a new request comes in, the current time is used to determine the window and a counter is increased. If the counter is larger than the set limit, the request is rejected.

Pros

• Very cheap in terms of data size and computation
• Newer requests are not starved due to a high burst in the past

Cons

• Can cause high bursts at the window boundaries to leak through
• Causes request stampedes if many users are trying to access your server, whenever a new window begins

Usage

Create a new ratelimiter, that allows 10 requests per 10 seconds.

const ratelimit = new Ratelimit({
  limiter: Ratelimit.fixedWindow(10, "10 s"),
});

Sliding Window

Builds on top of fixed window but instead of a fixed window, we use a rolling window. Take this example: We have a rate limit of 10 requests per 1 minute. We divide time into 1 minute slices, just like in the fixed window algorithm. Window 1 will be from 00:00:00 to 00:01:00 (HH:MM:SS). Let's assume it is currently 00:01:15 and we have received 4 requests in the first window and 5 requests so far in the current window. The approximation to determine if the request should pass works like this:

limit = 10

// 4 request from the old window, weighted + requests in current window
rate = 4 * ((60 - 15) / 60) + 5 = 8

return rate < limit // True means we should allow the request

Pros

• Solves the issue near boundary from fixed window.

Cons

• More expensive in terms of storage and computation
• Is only an approximation, because it assumes a uniform request flow in the previous window, but this is fine in most cases

Usage

Create a new ratelimiter, that allows 10 requests per 10 seconds.

const ratelimit = new Ratelimit({
  limiter: Ratelimit.slidingWindow(10, "10 s"),
});

Token Bucket

Consider a bucket filled with {maxTokens} tokens that refills constantly at {refillRate} per {interval}. Every request will remove one token from the bucket and if there is no token to take, the request is rejected.

Pros

• Bursts of requests are smoothed out and you can process them at a constant rate.
• Allows to set a higher initial burst limit by setting maxTokens higher than refillRate

Cons

• Expensive in terms of computation

Usage

Create a new bucket, that refills 5 tokens every 10 seconds and has a maximum size of 10.

const ratelimit = new Ratelimit({
  redis: Redis.fromEnv(),
  limiter: Ratelimit.tokenBucket(5, "10 s", 10),
});

If you are using a custom prefix, you need to use the same in the dashboard's top right corner.

Contributing