react-native-multithreading

🧵 Fast and easy multithreading for React Native using JSI.

Installation

npm install react-native-multithreading
npx pod-install

Requires a version of react-native-reanimated which includes PR #1790. Either patch it yourself, or wait until that lands in a release.

⚠️ Warning: This is still just a proof of concept - do not use this library in production! ⚠️

Why

Since JSI is becoming more mainstream, there might be functions that are actually blocking and take a while to execute. For example, a storage library like my react-native-mmkv or an SQLite JSI library might take a few milliseconds to execute a complex call. You don't want your entire React-JS thread to freeze when doing that, since users will perceive a noticeable lag or freeze.

That's where react-native-multithreading comes in; you can simply off-load such expensive calculations/blocking calls to a separate thread with almost no overhead while your main React-JS thread can concentrate on running your app's business logic, respond to user input, update state and more. You can also run complex JS calculations such as the Fibonacci number, but that's probably a rare use-case.

Inspired by @karol-bisztyga's Multithreading PR for Reanimated

Usage

To try out the Fibonacci Example, clone the repo and run the following commands:

yarn bootstrap
cd example
yarn ios

See my tweet 🐦

Shoot and Forget

To simply perform an expensive calculation on another thread without caring about the result, use the spawnThread function:

// JS thread
spawnThread(() => {
  'worklet'
  // custom thread
  // expensive calculation
})
// JS thread

The React-JS Thread will continue execution while the custom thread will run the given function on a custom parallel runtime.

Await

Since spawnThread returns a Promise, you can also await the result. The React-JS Thread will not be blocked and will still be able to continue execution elsewhere (timers, callbacks, ...), while the custom thread runs the given function in a custom parallel runtime.

const result = await spawnThread(() => {
  'worklet'
  // expensive calculation
  return ...
})

Fibonacci

This example calculates the Fibonacci Number for the given input. This demonstrates expensive calculation, awaiting the result, as well as using values from "outside". (fibonacci function and input are captured into the new thread and therefore immutable.)

const fibonacci = (num: number): number => {
  'worklet'
  if (num <= 1) return 1
  return fibonacci(num - 1) + fibonacci(num - 2)
}

const input = 50
const result = await spawnThread(() => {
  'worklet'
  console.log(`calculating fibonacci for input: ${input} in JS-Runtime: ${global._LABEL}...`)
  const fib = fibonacci(input)
  console.log("finished calculating fibonacci!")
  return fib
})
console.log(`Fibonacci Result: ${result}`)

What's possible?

• You can use variables from "outside" (e.g. state), but those will be immutable/frozen.
• You can use functions from "outside" if they also contain the 'worklet' directive.
• You can assign Reanimated Shared Values.
• You can call native JSI functions ("Host Functions") from a JSI library, e.g. every function react-native-mmkv provides.
• You can asynchronously dispatch calls to functions from "outside" using runOnJS from react-native-reanimated.

What's not possible?

1. At the moment, only iOS is implemented. I cannot implement Android until react-native-reanimated gets published with source-code (no prebuilt .aar)
2. Since the library uses JSI for synchronous native methods access, remote debugging (e.g. with Chrome) is no longer possible. Instead, you should use Flipper.
3. All functions you are calling inside a custom thread, must be workletized to truly run on a separate thread. So add the 'worklet' directive at the top of every function you're calling in that thread (including the thread callback itself), and don't forget to install the Reanimated babel plugin.

License

MIT

Credits

• react-native-reanimated for Shared Value adapting, essentially allowing JSI multithreading
• Erik the Coder for the Icon
• You, for appreciating my work

Note: Technically this is not multithreading, but rather multiprocessing.