Rust Inside Other Languages

For our third project, we’re going to choose something that shows off one of Rust’s greatest strengths: a lack of a substantial runtime.

As organizations grow, they increasingly rely on a multitude of programming languages. Different programming languages have different strengths and weaknesses, and a polyglot stack lets you use a particular language where its strengths make sense and a different one where it’s weak.

A very common area where many programming languages are weak is in runtime performance of programs. Often, using a language that is slower, but offers greater programmer productivity, is a worthwhile trade-off. To help mitigate this, they provide a way to write some of your system in C and then call that C code as though it were written in the higher-level language. This is called a ‘foreign function interface’, often shortened to ‘FFI’.

Rust has support for FFI in both directions: it can call into C code easily, but crucially, it can also be called into as easily as C. Combined with Rust’s lack of a garbage collector and low runtime requirements, this makes Rust a great candidate to embed inside of other languages when you need that extra oomph.

There is a whole chapter devoted to FFI and its specifics elsewhere in the book, but in this chapter, we’ll examine this particular use-case of FFI, with examples in Ruby, Python, and JavaScript.

The problem

There are many different projects we could choose here, but we’re going to pick an example where Rust has a clear advantage over many other languages: numeric computing and threading.

Many languages, for the sake of consistency, place numbers on the heap, rather than on the stack. Especially in languages that focus on object-oriented programming and use garbage collection, heap allocation is the default. Sometimes optimizations can stack allocate particular numbers, but rather than relying on an optimizer to do its job, we may want to ensure that we’re always using primitive number types rather than some sort of object type.

Second, many languages have a ‘global interpreter lock’ (GIL), which limits concurrency in many situations. This is done in the name of safety, which is a positive effect, but it limits the amount of work that can be done at the same time, which is a big negative.

To emphasize these two aspects, we’re going to create a little project that uses these two aspects heavily. Since the focus of the example is to embed Rust into other languages, rather than the problem itself, we’ll just use a toy example:

Start ten threads. Inside each thread, count from one to five million. After all ten threads are finished, print out ‘done!’.

I chose five million based on my particular computer. Here’s an example of this code in Ruby:

threads = []

10.times do
  threads << do
    count = 0

    5_000_000.times do
      count += 1


threads.each do |t|
  puts "Thread finished with count=#{t.value}"
puts "done!"

Try running this example, and choose a number that runs for a few seconds. Depending on your computer’s hardware, you may have to increase or decrease the number.

On my system, running this program takes 2.156 seconds. And, if I use some sort of process monitoring tool, like top, I can see that it only uses one core on my machine. That’s the GIL kicking in.

While it’s true that this is a synthetic program, one can imagine many problems that are similar to this in the real world. For our purposes, spinning up a few busy threads represents some sort of parallel, expensive computation.

A Rust library

Let’s rewrite this problem in Rust. First, let’s make a new project with Cargo:

$ cargo new embed
$ cd embed

This program is fairly easy to write in Rust:

fn main() { use std::thread; fn process() { let handles: Vec<_> = (0..10).map(|_| { thread::spawn(|| { let mut x = 0; for _ in 0..5_000_000 { x += 1 } x }) }).collect(); for h in handles { println!("Thread finished with count={}", h.join().map_err(|_| "Could not join a thread!").unwrap()); } } }
use std::thread;

fn process() {
    let handles: Vec<_> = (0..10).map(|_| {
        thread::spawn(|| {
            let mut x = 0;
            for _ in 0..5_000_000 {
                x += 1

    for h in handles {
        println!("Thread finished with count={}",
        h.join().map_err(|_| "Could not join a thread!").unwrap());

Some of this should look familiar from previous examples. We spin up ten threads, collecting them into a handles vector. Inside of each thread, we loop five million times, and add one to x each time. Finally, we join on each thread.

Right now, however, this is a Rust library, and it doesn’t expose anything that’s callable from C. If we tried to hook this up to another language right now, it wouldn’t work. We only need to make two small changes to fix this, though. The first is to modify the beginning of our code:

fn main() { #[no_mangle] pub extern fn process() { }
pub extern fn process() {

We have to add a new attribute, no_mangle. When you create a Rust library, it changes the name of the function in the compiled output. The reasons for this are outside the scope of this tutorial, but in order for other languages to know how to call the function, we can’t do that. This attribute turns that behavior off.

The other change is the pub extern. The pub means that this function should be callable from outside of this module, and the extern says that it should be able to be called from C. That’s it! Not a whole lot of change.

The second thing we need to do is to change a setting in our Cargo.toml. Add this at the bottom:

name = "embed"
crate-type = ["dylib"]

This tells Rust that we want to compile our library into a standard dynamic library. By default, Rust compiles an ‘rlib’, a Rust-specific format.

Let’s build the project now:

$ cargo build --release
   Compiling embed v0.1.0 (file:///home/steve/src/embed)

We’ve chosen cargo build --release, which builds with optimizations on. We want this to be as fast as possible! You can find the output of the library in target/release:

$ ls target/release/
build  deps  examples  native

That is our ‘shared object’ library. We can use this file just like any shared object library written in C! As an aside, this may be embed.dll (Microsoft Windows) or libembed.dylib (Mac OS X), depending on your operating system.

Now that we’ve got our Rust library built, let’s use it from our Ruby.


Open up an embed.rb file inside of our project, and do this:

require 'ffi'

module Hello
  extend FFI::Library
  ffi_lib 'target/release/'
  attach_function :process, [], :void


puts 'done!'

Before we can run this, we need to install the ffi gem:

$ gem install ffi # this may need sudo
Fetching: ffi-1.9.8.gem (100%)
Building native extensions.  This could take a while...
Successfully installed ffi-1.9.8
Parsing documentation for ffi-1.9.8
Installing ri documentation for ffi-1.9.8
Done installing documentation for ffi after 0 seconds
1 gem installed

And finally, we can try running it:

$ ruby embed.rb
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000
Thread finished with count=5000000

Whoa, that was fast! On my system, this took 0.086 seconds, rather than the two seconds the pure Ruby version took. Let’s break down this Ruby code:

require 'ffi'

We first need to require the ffi gem. This lets us interface with our Rust library like a C library.

module Hello
  extend FFI::Library
  ffi_lib 'target/release/'

The Hello module is used to attach the native functions from the shared library. Inside, we extend the necessary FFI::Library module and then call ffi_lib to load up our shared object library. We just pass it the path that our library is stored, which, as we saw before, is target/release/

attach_function :process, [], :void

The attach_function method is provided by the FFI gem. It’s what connects our process() function in Rust to a Ruby function of the same name. Since process() takes no arguments, the second parameter is an empty array, and since it returns nothing, we pass :void as the final argument.


This is the actual call into Rust. The combination of our module and the call to attach_function sets this all up. It looks like a Ruby function but is actually Rust!

puts 'done!'

Finally, as per our project’s requirements, we print out done!.

That’s it! As we’ve seen, bridging between the two languages is really easy, and buys us a lot of performance.

Next, let’s try Python!


Create an file in this directory, and put this in it:

from ctypes import cdll

lib = cdll.LoadLibrary("target/release/")



Even easier! We use cdll from the ctypes module. A quick call to LoadLibrary later, and we can call process().

On my system, this takes 0.017 seconds. Speedy!


Node isn’t a language, but it’s currently the dominant implementation of server-side JavaScript.

In order to do FFI with Node, we first need to install the library:

$ npm install ffi

After that installs, we can use it:

var ffi = require('ffi');

var lib = ffi.Library('target/release/libembed', {
  'process': ['void', []]



It looks more like the Ruby example than the Python example. We use the ffi module to get access to ffi.Library(), which loads up our shared object. We need to annotate the return type and argument types of the function, which are void for return and an empty array to signify no arguments. From there, we just call it and print the result.

On my system, this takes a quick 0.092 seconds.


As you can see, the basics of doing this are very easy. Of course, there's a lot more that we could do here. Check out the FFI chapter for more details.