Rocket.jl 🚀 - Reactive Programming in Julia

Documentation	Build Status

Welcome to Rocket.jl - a fast, powerful, and intuitive reactive programming package for Julia! Rocket.jl makes it easy to work with asynchronous data streams and handle real-time events with style.

Built for both performance and developer happiness, Rocket.jl combines the elegance of Observer pattern, the power of Actor model, and the expressiveness of Functional programming.

Inspired by RxJS and ReactiveX communities.

Why Rocket.jl?

• 🏃 High Performance: Designed from the ground up for speed and efficiency
• 🎯 Type Safety: Leverage Julia's type system for robust applications
• 🔧 Modular Design: Build complex reactive systems from simple components
• 🎨 Expressive API: Write clean, readable code that's a joy to maintain

Essential Concepts

Rocket.jl is built on five powerful concepts:

• Observable: represents a collection of future messages (data or/and events).
• Actor: is an object that knows how to react on incoming messages delivered by the Observable.
• Subscription: represents a teardown logic which might be useful for cancelling the execution of an Observable.
• Operators: are objects that enable a functional programming style to dealing with collections with operations like map, filter, reduce, etc.
• Subject: the way of multicasting a message to multiple Observers.

See It In Action!

Let's create a fun bouncing ball animation to demonstrate Rocket.jl's reactive capabilities:

using Rocket, Compose, IJulia ; set_default_graphic_size(35cm, 2cm)

function draw_ball(t)
    IJulia.clear_output(true)
    x = -exp(-0.01t) + 1                     # x coordinate
    y = -abs(exp(-0.04t)*(cos(0.1t))) + 0.83 # y coordinate
    display(compose(context(), circle(x, y, 0.01)))
end

source = interval(20) |> take(200) # Take only first 200 emissions

subscription = subscribe!(source, draw_ball)

unsubscribe!(subscription) # It is possible to unsubscribe before the stream ends    
IJulia.clear_output(false);

Documentation

Want to learn more? Check out our documentation website.

It is also possible to build a documentation locally. Just execute

$ julia make.jl

in the docs/ directory to build a local version of the documentation.

First example

Normally you use an arrays for processing some data.

for value in array_of_values
    doSomethingWithMyData(value)
end

In Rocket.jl you will use an observable.

subscription = subscribe!(source_of_values, lambda(
    on_next     = (data)  -> doSomethingWithMyData(data),
    on_error    = (error) -> doSomethingWithAnError(error),
    on_complete = ()      -> println("Completed!")
))

At some point of time you may decide to stop listening for new messages.

unsubscribe!(subscription)

Actors

To process messages from an observable you have to define an Actor that know how to react on incoming messages.

struct MyActor <: Rocket.Actor{Int} end

Rocket.on_next!(actor::MyActor, data::Int) = doSomethingWithMyData(data)
Rocket.on_error!(actor::MyActor, error)    = doSomethingWithAnError(error)
Rocket.on_complete!(actor::MyActor)        = println("Completed!")

Actor can also have its own local state

struct StoreActor{D} <: Rocket.Actor{D}
    values :: Vector{D}

    StoreActor{D}() where D = new(Vector{D}())
end

Rocket.on_next!(actor::StoreActor{D}, data::D) where D = push!(actor.values, data)
Rocket.on_error!(actor::StoreActor, error)             = doSomethingWithAnError(error)
Rocket.on_complete!(actor::StoreActor)                 = println("Completed: $(actor.values)")

For debugging purposes you can use a general LambdaActor actor or just pass a function object as an actor in subscribe! function.

Operators

What makes Rocket.jl powerful is its ability to help you process, transform and modify the messages flow through your observables using Operators.

List of all available operators can be found in the documentation (link).

squared_int_values = source_of_int_values |> map(Int, (d) -> d ^ 2)
subscribe!(squared_int_values, lambda(
    on_next = (data) -> println(data)
))

Rocket.jl is fast

Rocket.jl has been designed with a focus on efficiency, scalability and maximum performance. Below is a benchmark comparison between Rocket.jl, Signals.jl, Reactive.jl and Observables.jl in Julia v1.11.3 (see versioninfo below).

We test map and filter operators latency in application to a finite stream of integers. Code is available in demo folder.

Rocket.jl outperforms Observables.jl, Reactive.jl and Signals.jl significantly in terms of execution times and memory consumption both in synchronous and asynchronous modes.

versioninfo()

Julia Version 1.11.3
Commit d63adeda50d (2025-01-21 19:42 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: macOS (arm64-apple-darwin24.0.0)
  CPU: 11 × Apple M3 Pro
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, apple-m2)
Threads: 1 default, 0 interactive, 1 GC (on 5 virtual cores)

] status

  [510215fc] Observables v0.5.5
  [a223df75] Reactive v0.8.3
  [df971d30] Rocket v1.8.1
  [6303bc30] Signals v1.2.0

License

MIT License Copyright (c) 2021-2024 BIASlab, 2024-present ReactiveBayes