Asynchronous Programming in Rust

May 18, 2020

Asynchronous programming is a method of programming that can allow multiple different things to be run concurrently (or in parallel). In Rust, it is accomplished using a high-level idea called a Future.

Asynchronous programming in Rust

Asynchronous programming is used a lot for IO because there are many times where you have to wait for something to happen (such as reading from a socket or a file) during which no progress can be made. This is a perfect moment to hand over control to something else so that it can start to make progress. This allows our program to always be making progress, which is the name of the game.

This article will be talking a lot about the implementation of futures in Rust. However, most of the time when programming asynchronous code, you will only ever have to use async/await because everything low level is already implemented.

This article will not talk about how to use async/await in the traditional sense; however, knowing how the underlying concepts work is very useful for programming async/await.

I chose to talk about Rust specifically because Rust is a systems-level language; meaning that, even for a high-level idea, there is almost no hidden magic that is happening behind the scenes. This article does not come close to covering the more specialized workings of futures in Rust, especially when explaining how Rust can guarantee memory safety throughout all of this. While this is very important for Rust to do, it’s also very complicated and best left for more extended readings which I’ve also noted towards the end of the article.

The setup

Let’s think about a very simple fake socket operation. In short, all it does is wait 2 seconds before the data is available and then it sets the data to be a single random i32 (an int in C).

To make it act more like a real socket we’ll have a few functions that we can use to interface with it.

struct MySocket { /* fields omitted */ }
impl MySocket
    fn new() -> Self {
        /* code omitted */
        /* Will start task that waits 2 seconds and then sets data */
    // would be implemented with epoll
    fn has_data_to_read(&mut self) -> bool { /* code omitted */ }

    fn read_data(&self) -> i32 { /* code omitted */ }

    // would be implemted with signal handeling
    fn set_readable_callback(&self, waker: Waker) { /* code omitted */ }

My implementation can be found here at line 43 with comments.

Let’s look at using has_data_to_read and read_data to make a synchronous function to get the data. We’ll have to check if the data is ready in a loop and then when it is, we can return the data.

fn get_num_sync() -> i32
    let s = MySocket::new();
    // busy wait until data is ready to be read
    // will take 2 seconds
    while !s.has_data_to_read() {}
    return s.read_data()

Now, say we wanted to call this function twice, one after another. We would have to wait 4 total seconds to get both results (try it out).

We could also write a function that creates 2 sockets at the same time and then busy waits together. Now we only have to wait 2 seconds for both results.

fn get_2_num_async_hardcode() -> (i32, i32)
    let s1 = MySocket::new();
    let s2 = MySocket::new();

    // busy wait until data is ready to be read
    // will take 2 seconds
    while !s1.has_data_to_read() || !s2.has_data_to_read() {}
    return (s1.read_data(), s2.read_data())

This is asynchronous (try it out).

This is a good start towards understanding how we actually implement asynchronous code; however, there are a few issues:

  • There is no good way to accommodate more sockets without creating a new function.
  • If we wanted to add other unrelated asynchronous operations, like reading from a file, then our code would get very complicated.
  • We are using a busy-wait; while not inherently bad for our program, it won’t scale well with other processes.

How does it really work?

The basic idea is that async works with a trait called Future.

The following is a simplified version of that trait. The real implementation is more complicated but still has the same underlying concepts.

trait SimpleFuture {
    type Output;
    fn poll(&mut self, wake: fn()) -> Poll<Self::Output>;
enum Poll<T> {

This trait requires anything that wants to implement Future to have a definition for poll. This is important because polling is where the magic in asynchronous programming happens.

Before we talk about how we will implement the poll function, lets look into how we use it. When we call poll on a future we are allowing progress to be made on that future.

The return type for poll() is of type Poll, which is an algebraic data type that can be Poll::Ready(data) or Poll::Pending as shown by the enum.

If the future has finished (i.e. our toy socket is done waiting), then we can return that data as Poll::Ready(data).

However, poll must never block. So, as soon it knows it can’t make progress, it should return Poll::Pending (like when has_data_to_read() is false).

Note, a task might have multiple things to wait on, and thus poll may need to return Poll::Pending multiple times before it can eventually return Poll::Ready(data).

Defining an executor

An executor is what takes one or multiple futures and runs them. How to run it, whether that be single-threaded or multi-threaded, with priorities, and so on, is left very open-ended as far as Rust is concerned.

In this article, we will only be talking about how an event loop driven executor works using a single thread.

We can think about how we can implement an executor similar to our first asynchronous function idea: We could iterate over each future and poll it to check if it is ready; and if it is not ready, just poll it again.

Here is some pseudo-code:

fn run(fut_vec)
    let mut ret = vec![];
    while !all_done()
        for fut in fut_vec
            if !is_done(fut)
                // if result of `fut.poll(...)` is `Poll::Ready(d)` then grab data as `d`
                if let Poll::Ready(d) = fut.poll(||) // it expects a wake function but we are not using it yet
                    is_done(fut) = true;
    return ret;

This is a very basic event loop, but we can do better.

This is where the wake part of the poll function comes in.

The point of this closure is to allow the executor to not waste its time continuously checking to see if a future can make progress. Instead, we will require each future to tell us when it can make progress.

So, now when a future’s poll returns Poll::Pending, we know that that future can’t make progress and that it will have arranged for itself to be “woken up” (by something else calling the wake() function) when it can make progress.

This wake() function is created by the executor so that it can keep track of which futures can be polled.

Let’s try to add this to our pseudo executor. We’ll keep a list of futures that can make progress and make the wake() function add the current future back to the list.

Here is the pseudo implementation:

fn run2(fut_vec)
    let mut ret = vec![];
    let mut can_make_progress_vec = fut_vec.clone();
    while !all_done()
        while let Some(fut) = can_make_progress_vec.pop()
            if let Poll::Ready(d) = fut.poll(|| can_make_progress_vec.push(self))
                is_done(fut) = true;
    return ret;

This is pseudo-code (strictly as an example); it breaks a lot of Rust’s rules. If you would like to see the real implementation look here at line 227

This pseudo-code is sufficient to understand how an executor works at a high level. Implementing an executor is more of a Rust challenge than it is an understanding futures challenge; you can play around with one by using the one in the futures crate.

Defining poll

We know now what the executor expects of the poll function. That means 2 things for our toy socket.

  1. Return Poll::Ready(data) if data is ready.
  2. Set up wake function and return Poll::Pending otherwise.

This is fairly straightforward:

// let's make a new struct for the future
struct FutSocket
    inner: MySocket
impl Future for FutSocket
    type Output = i32;
    fn poll(self: Pin<&mut Self>, cx: &mut Context<'_>) -> Poll<Self::Output>
        if self.inner.has_data_to_read() {
            // data is ready so we are done
            return Poll::Ready(self.inner.read_data());
        } else {
            // Set waker so that the inner MySocket can wake up the
            // current task when the 2-sec timer has completed.
            return Poll::Pending;

The full implementation can be found here at line 79.

The main thing you will notice is that we are no longer using our SimpleFuture trait anymore. We are now using the standard Future trait.

The first difference is that we are taking a Pin to &mut Self. This is just a very elegant way that Rust can guarantee memory safety (which is outside the scope of this article). Just think of it as a regular mutable reference to self, like in SimpleFuture, as it will be optimized, and in the poll function, we treat it as such.

The second difference is that we are not passing a wake() closure anymore. Now it’s a Context, which is just an abstraction to a virtual function pointer table (which is not the focus for this article). Just note that cx.waker() will get the previously used wake() closure.

Now, with that out of the way, let’s look at what is happening here. If the data is ready, we return the data as a Poll::Ready(data). Otherwise, we arrange to be woken by using set_readable_callback and then return Poll::Pending.

With that, we now have everything needed to use futures for asynchronous code. You can experience all of this working together here.


It’s easy to see how writing out a poll function can get more complicated as more things need to be done within them.

For example, assume we wanted to write an asynchronous read_file function that opens a file and reads it. We must do this in two steps. First, we need to open it, which takes time, as we will have to ask the OS to give us a file descriptor. Then, once that has finished, we can read the file which will also take time to do.

Both steps can be run asynchronously, but in order to work together, they must happen synchronously (one after another).

If we imagined what the poll function would look like for this operation, we would have to know where we are within the context of running the future (opening and then reading the file).

This would be necessary so that every time poll is called we could continue execution where we left off.

This is heavily simplified by the async and .await keywords.

The following is all we have to write to have the functionality mentioned above.

pub async fn read_file(file_path: &str) -> io::Result<String> {
    let mut file = File::open(file_path).await?;
    let mut buffer = String::new();
    file.read_to_string(&mut buffer).await?;

What does this do for us?

In short, it returns a future with the poll function implemented for us at compile-time.

This poll function would work in the following way:

  1. There would be a state variable that stores where it is throughout this function and any relevant data (like a file descriptor).
  2. The first would be an uninitialized state, which is when we just start executing the program.
  3. On our first poll, it would run until we reach the first await. At this point, we would poll the future created by File::open(...) and most likely won’t be able to make progress, so the poll would return Poll::Pending.
  4. Once the File::open(...) future wakes back up and returns Poll::Ready(data), then we can progress to the next state with our new file variable set.
  5. This next state would be defined for everything in between the two awaits. This general idea continues until the end of the function.

One thing to note is that we can’t replace our FutSocket’s poll implementation with async/await because we have to do special things like set the wake() function and interface with the OS. Async/await is intended to take existing future implementation (like File::open(...) and File::read_to_string(...)) and chain them together to make more complicated (and useful) asynchronous code easily.

How is it used in production?

Due to their construction, Futures in Rust are zero-cost. This effort to implement zero-cost futures into Rust has taken over 4 years.

That time paid off because it means that performance is directly tied to executor design. And, because Rust guarantees memory and thread safety, the implementation for executors can be very complicated and yet very safe.

One of the more popular executors (or in this case a runtime) is called tokio. It is a multithreaded, work-stealing, task scheduler tuned for async networking workloads.

By using a runtime like tokio, the actor-based web framework called actix, written in Rust, can handle 54% more fortunes responses per second than the next best web framework written in C, according to this TechEmpower benchmark.

Further Reading