x/upstash_ratelimit@v0.1.3

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Upstash Ratelimit

An HTTP/REST based Redis client built on top of Upstash REST API. Upstash REST API.

Tests npm (scoped)

It is the only connectionless (HTTP based) ratelimiter and designed for:

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

Quick Start

Install

npm

npm install @upstash/ratelimit

Deno

import { Ratelimit } from "https://deno.land/x/upstash_ratelimit/mod.ts";

Create database

Create a new redis database on upstash

Use it

See here for documentation on how to create a redis instance.

import { Ratelimit } from "@upstash/ratelimit"; // for deno: see above
import { Redis } from "@upstash/redis";

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

// 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!";

Here's a complete nextjs example

The limit method returns some more metadata that might be useful to you:

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>;
};

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({
  redis: Redis.fromEnv(),
  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!";

MultiRegionly replicated ratelimiting

Using a single redis instance has the downside of providing low latencies to the part of your userbase closest to the deployed db. That's why we also built MultiRegionRatelimit which replicates the state across multiple redis databases as well as offering lower latencies to more of your users.

MultiRegionRatelimit does this by checking the current limit in the closest db and returning immediately. Only afterwards will the state be asynchronously replicated to the other datbases leveraging CRDTs. Due to the nature of distributed systems, there is no way to guarantee the set ratelimit is not exceeded by a small margin. This is the tradeoff for reduced global latency.

Usage

The api is the same, except for asking for multiple redis instances:

import { MultiRegionRatelimit } from "@upstash/ratelimit"; // for deno: see above
import { Redis } from "@upstash/redis";

// Create a new ratelimiter, that allows 10 requests per 10 seconds
const ratelimit = new MultiRegionRatelimit({
  redis: [
    new Redis({
      /* auth */
    }),
    new Redis({
      /* auth */
    }),
    new Redis({
      /* auth */
    }),
  ],
  limiter: Ratelimit.slidingWindow(10, "10 s"),
});

// 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);

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);

Example

Let's assume you have customers in the US and Europe. In this case you can create 2 regional redis databases on Upastash and your users will enjoy the latency of whichever db is closest to them.

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({
  redis: Redis.fromEnv(),
  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({
  redis: Redis.fromEnv(),
  limiter: Ratelimit.slidingWindow(10, "10 s"),
});

Token Bucket

Not yet supported for MultiRegionRatelimit

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),
});

Contributing

Install Deno

Database

Create a new redis database on upstash and copy the url and token.

Running tests

UPSTASH_REDIS_REST_URL=".." UPSTASH_REDIS_REST_TOKEN=".." deno test -A