The Rust Programming Language

Putting It All Together: Futures, Tasks, and Threads

As we saw in Chapter 16, threads provide one approach to concurrency. We’ve seen another approach in this chapter: using async with futures and streams. If you‘re wondering when to choose method over the other, the answer is: it depends! And in many cases, the choice isn’t threads or async but rather threads and async.

Many operating systems have supplied threading-based concurrency models for decades now, and many programming languages support them as a result. However, these models 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 don’t have an OS at all, so they also don’t have threads.

The async model provides a different—and ultimately complementary—set of tradeoffs. In the async model, concurrent operations don’t 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 in the streams section. A task is similar to a thread, but instead of being managed by the operating system, it’s 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 we could call from synchronous code. We can 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 code, the output is identical to that of Listing 17-40. And notice how little changes here from the perspective of the calling code. What’s 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.

Despite their similarities, these two approaches behave very differently, although we might have a hard time measuring it in this very simple example. We could spawn 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’s 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, because a task can switch between futures in its body. Finally, futures are Rust’s most granular unit of concurrency, and each future may represent a tree of other futures. The runtime—specifically, its executor—manages tasks, and tasks manage futures. In that regard, tasks are similar to lightweight, runtime-managed threads with added capabilities that come from being managed by a runtime instead of by the operating system.

This doesn’t mean that async tasks are always better than threads (or vice versa). Concurrency with threads is in some ways a simpler programming model than concurrency with async. That can be a strength or a weakness. Threads are somewhat “fire and forget”; they have no native equivalent to a future, so they simply run to completion without being interrupted except by the operating system itself. That is, they have no built-in support for intratask concurrency the way futures do. Threads in Rust also have no mechanisms for cancellation—a subject we haven’t covered explicitly in this chapter but was implied by the fact that whenever we ended a future, its state got cleaned up correctly.

These limitations also make threads harder to compose than futures. It’s much more difficult, for example, to use threads to build helpers such as the timeout and throttle methods we built earlier in this chapter. The fact that futures are richer data structures means they can be composed together more naturally, as we have seen.

Tasks, then, give us additional control over futures, allowing us 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. In fact, under the hood, the runtime we’ve 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 how the threads are currently being utilized, to improve the system’s overall performance. That approach actually requires threads and tasks, and therefore futures.

When thinking about which method to use when, consider these rules of thumb:

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

And if you need both parallelism and concurrency, you don’t have to choose between threads and async. You can use them together freely, letting each one play the part it’s best at. For example, Listing 17-42 shows a fairly common example of this kind of mix in real-world Rust code.

extern crate trpl; // for mdbook test

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}");
        }
    });
}

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

To return to the scenario we opened the chapter with, imagine running a set of video encoding tasks using a dedicated thread (because video encoding is compute-bound) but notifying the UI that those operations are done with an async channel. There are countless examples of these kinds of combinations in real-world use cases.

Summary

This isn’t the last you’ll see of concurrency in this book. The project in Chapter 21 will apply these concepts in a more realistic situation than the simpler examples discussed here and compare problem-solving with threading versus tasks more directly.

No matter which of these approaches you choose, 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.