Futures, Tasks, and Threads

As we saw in the previous chapter, threads provide one approach to concurrency. We have seen another approach to concurrency in this chapter, using async with futures and streams. You might be wondering why you would choose one or the other. The answer is: it depends! And in many cases, it is not threads or async but rather threads and async.

Many operating systems have supplied threading-based concurrency models for decades now, and many programming languages have support for them as a result. However, they are not without their tradeoffs. On many operating systems, they use a fair bit of memory for each thread, and they come with some overhead for starting up and shutting down. Threads are also only an option when your operating system and hardware support them! Unlike mainstream desktop and mobile computers, some embedded systems do not have an OS at all, so they also do not have threads!

The async model provides a different—and ultimately complementary—set of tradeoffs. In the async model, concurrent operations do not require their own threads. Instead, they can run on tasks, as when we used trpl::spawn_task to kick off work from a synchronous function throughout the streams section. A task is a lot like a thread—but instead of being managed by the operating system, it is managed by library-level code: the runtime.

In the previous section, we saw that we could build a Stream by using an async channel and spawning an async task which we could call from synchronous code. We could do the exact same thing with a thread! In Listing 17-40, we used trpl::spawn_task and trpl::sleep. In Listing 17-41, we replace those with the thread::spawn and thread::sleep APIs from the standard library in the get_intervals function.

extern crate trpl; // required for mdbook test

use std::{pin::pin, thread, time::Duration};

use trpl::{ReceiverStream, Stream, StreamExt};

fn main() {
    trpl::run(async {
        let messages = get_messages().timeout(Duration::from_millis(200));
        let intervals = get_intervals()
            .map(|count| format!("Interval #{count}"))
            .throttle(Duration::from_millis(500))
            .timeout(Duration::from_secs(10));
        let merged = messages.merge(intervals).take(20);
        let mut stream = pin!(merged);

        while let Some(result) = stream.next().await {
            match result {
                Ok(item) => println!("{item}"),
                Err(reason) => eprintln!("Problem: {reason:?}"),
            }
        }
    });
}

fn get_messages() -> impl Stream<Item = String> {
    let (tx, rx) = trpl::channel();

    trpl::spawn_task(async move {
        let messages = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"];

        for (index, message) in messages.into_iter().enumerate() {
            let time_to_sleep = if index % 2 == 0 { 100 } else { 300 };
            trpl::sleep(Duration::from_millis(time_to_sleep)).await;

            if let Err(send_error) = tx.send(format!("Message: '{message}'")) {
                eprintln!("Cannot send message '{message}': {send_error}");
                break;
            }
        }
    });

    ReceiverStream::new(rx)
}

fn get_intervals() -> impl Stream<Item = u32> {
    let (tx, rx) = trpl::channel();

    // This is *not* `trpl::spawn` but `std::thread::spawn`!
    thread::spawn(move || {
        let mut count = 0;
        loop {
            // Likewise, this is *not* `trpl::sleep` but `std::thread::sleep`!
            thread::sleep(Duration::from_millis(1));
            count += 1;

            if let Err(send_error) = tx.send(count) {
                eprintln!("Could not send interval {count}: {send_error}");
                break;
            };
        }
    });

    ReceiverStream::new(rx)
}

If you run this, the output is identical. And notice how little changes here from the perspective of the calling code! What is more, even though one of our functions spawned an async task on the runtime and the other spawned an OS thread, the resulting streams were unaffected by the differences.

However, there is a significant difference between these two approaches behave, although we might have a hard time measuring it in this very simple example. We could spawn hundreds of thousands or even millions of async tasks on any modern personal computer. If we tried to do that with threads, we would literally run out of memory!

However, there is a reason these APIs are so similar. Threads act as a boundary for sets of synchronous operations; concurrency is possible between threads. Tasks act as a boundary for sets of asynchronous operations; concurrency is possible both between and within tasks. In that regard, tasks are kind of like lightweight, runtime-managed threads with added capabilities that come from being managed by a runtime instead of by the operating system. Futures are an even more granular unit of concurrency, where each future may represent a tree of other futures. That is, the runtime—specifically, its executor—manages tasks, and tasks manage futures.

However, this does not mean that async tasks are always better than threads, any more than that threads are always better than tasks.

On the one hand, concurrency with threads is in some ways a simpler programming model than concurrency with async. Threads are somewhat “fire and forget,” they have no native equivalent to a future, so they simply run to completion, without interruption except by the operating system itself. That is, they have no intra-task concurrency like futures can. Threads in Rust also have no mechanisms for cancellation—a subject we have not covered in depth in this chapter, but which is implicit in the fact that whenever we ended a future, its state got cleaned up correctly.

These limitations make threads harder to compose than futures. It is much more difficult, for example, to build something like the timeout we built in “Building Our Own Async Abstractions”, or the throttle method we used with streams in “Composing Streams”. The fact that futures are richer data structures means they can be composed together more naturally, as we have seen.

Tasks then give additional control over futures, allowing you to choose where and how to group them. And it turns out that threads and tasks often work very well together, because tasks can (at least in some runtimes) be moved around between threads. We have not mentioned it up until now, but under the hood the Runtime we have been using, including the spawn_blocking and spawn_task functions, is multithreaded by default! Many runtimes use an approach called work stealing to transparently move tasks around between threads based on the current utilization of the threads, with the aim of improving the overall performance of the system. To build that actually requires threads and tasks, and therefore futures.

As a default way of thinking about which to use when:

• If the task is very parallelizable, like processing a bunch of data where each part can be processed separately, threads are a better choice.
• If the task is very concurrent, like handling messages from a bunch of different sources which may come in a different intervals or different rates, async is a better choice.

And if you need some mix of parallelism and concurrency, you do not have to choose between threads and async. You can use them together freely, letting each one serve the part it is best at. For example, Listing 17-TODO shows a fairly common example of this kind of mix in real-world Rust code.

extern crate trpl; // for mdbook test

// ANCHOR: all
use std::{thread, time::Duration};

fn main() {
    let (tx, mut rx) = trpl::channel();

    thread::spawn(move || {
        for i in 1..11 {
            tx.send(i).unwrap();
            thread::sleep(Duration::from_secs(1));
        }
    });

    trpl::run(async {
        while let Some(message) = rx.recv().await {
            println!("{message}");
        }
    });
}
// ANCHOR_END: all

We begin by creating an async channel. Then we spawn a thread which takes ownership of the sender side of the channel. Within the thread, we send the numbers 1 through 10, and sleep for a second in between each. Finally, we run a future created with an async block passed to trpl::run just like we have throughout the chapter. In that future, we await those messages, just like in the other message-passing examples we have seen.

To return to the examples we opened the chapter with: you could imagine running a set of video encoding tasks using a dedicated thread, since video encoding is compute bound, but notifying the UI that those operations are done with an async channel. Examples of this kind of mix abound!

Summary

This isn’t the last you’ll see of concurrency in this book: the project in Chapter 21 will use the concepts in this chapter in a more realistic situation than the smaller examples discussed here—and compare more directly what it looks like to solve these kinds of problems with threading vs. with tasks and futures.

Whether with threads, with futures and tasks, or with the combination of them all, Rust gives you the tools you need to write safe, fast, concurrent code—whether for a high-throughput web server or an embedded operating system.

Next, we’ll talk about idiomatic ways to model problems and structure solutions as your Rust programs get bigger. In addition, we’ll discuss how Rust’s idioms relate to those you might be familiar with from object-oriented programming.