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Getting started with futures

This document will help you learn about futures, a Rust crate with a zero-cost implementation of futures and streams. Futures are available in many other languages like C++, Java, and Scala, and this crate draws inspiration from these libraries. The futures crate, however, distinguishes itself by being both ergonomic as well as adhering to the Rust philosophy of zero-cost abstractions. More concretely, futures do not require allocations to create and compose, and the per-connection Task that drives futures requires only one. Futures are intended to be the foundation for asynchronous, composable, high performance I/O in Rust, and early benchmarks show that a simple HTTP server built on futures is really fast!

This document is split up into a few sections:

If you'd like to help contribute to this document you can find it on GitHub.


Hello, World!

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The futures crate requires Rust 1.10.0 or greater, which can be easily obtained through rustup. Windows, macOS, and Linux are all tested and known to work, but PRs for other platforms are always welcome! You can add futures to your project's Cargo.toml like so:

[dependencies]
futures = "0.1"
tokio-core = "0.1"
tokio-tls = { git = "https://github.com/tokio-rs/tokio-tls" }

Here we're adding a dependency on three crates:

The futures crate itself is a low-level implementation of futures which does not assume any particular runtime or I/O layer. For the examples below we'll be using the concrete implementations available in tokio-core to show how futures and streams can be used to perform sophisticated I/O with zero abstraction overhead.

Now that we've got all that set up, let's write our first program! As a "Hello, World!" for I/O, let's download the Rust home page:

extern crate futures;
extern crate tokio_core;
extern crate tokio_tls;

use std::net::ToSocketAddrs;

use futures::Future;
use tokio_core::reactor::Core;
use tokio_core::net::TcpStream;
use tokio_tls::ClientContext;

fn main() {
    let mut core = Core::new().unwrap();
    let addr = "www.rust-lang.org:443".to_socket_addrs().unwrap().next().unwrap();

    let socket = TcpStream::connect(&addr, &core.handle());

    let tls_handshake = socket.and_then(|socket| {
        let cx = ClientContext::new().unwrap();
        cx.handshake("www.rust-lang.org", socket)
    });
    let request = tls_handshake.and_then(|socket| {
        tokio_core::io::write_all(socket, "\
            GET / HTTP/1.0\r\n\
            Host: www.rust-lang.org\r\n\
            \r\n\
        ".as_bytes())
    });
    let response = request.and_then(|(socket, _)| {
        tokio_core::io::read_to_end(socket, Vec::new())
    });

    let (_, data) = core.run(response).unwrap();
    println!("{}", String::from_utf8_lossy(&data));
}

If you place that file in src/main.rs, and then execute cargo run, you should see the HTML of the Rust home page!

There's a lot to digest here, though, so let's walk through it line-by-line. First up in main():

let mut core = Core::new().unwrap();
let addr = "www.rust-lang.org:443".to_socket_addrs().unwrap().next().unwrap();

Here we create an event loop on which we will perform all our I/O. Then we resolve the "www.rust-lang.org" host name by using the standard library's to_socket_addrs method.

Next up:

let socket = TcpStream::connect(&addr, &core.handle());

We get a handle to our event loop and connect to the host with TcpStream::connect. Note, though, that TcpStream::connect returns a future! This means that we don't actually have the socket yet, but rather it will be fully connected at some later point in time.

Once our socket is available we need to perform three tasks to download the rust-lang.org home page:

  1. Perform a TLS handshake. The home page is only served over HTTPS, so we had to connect to port 443 and we'll have to obey the TLS protocol.
  2. An HTTP 'GET' request needs to be issued. For the purposes of this tutorial we will write the request by hand, though in a serious program you would use an HTTP client built on futures.
  3. Finally, we download the response by reading off all the data on the socket.

Let's take a look at each of these steps in detail, the first being:

let tls_handshake = socket.and_then(|socket| {
    let cx = ClientContext::new().unwrap();
    cx.handshake("www.rust-lang.org", socket)
});

Here we use the and_then method on the Future trait to continue building on the future returned by TcpStream::connect. The and_then method takes a closure which receives the resolved value of this previous future. In this case socket will have type TcpStream. The and_then closure, however, will not run if TcpStream::connect returned an error.

Once we have our socket, we create a client TLS context via ClientContext::new. This type from the tokio-tls crate represents the client half of a TLS connection. Next we call the handshake method to actually perform the TLS handshake. The first argument is the domain name we're connecting to, with the I/O object as the second.

Like with TcpStream::connect from before, the handshake method returns a future. The actual TLS handshake may take some time as the client and server need to perform some I/O, agree on certificates, etc. Once resolved, however, the future will become a TlsStream, similar to our previous TcpStream

The and_then combinator is doing some heavy lifting behind the scenes here by ensuring that it executes futures in the right order and keeping track of the futures in flight. Even better, the value returned from and_then itself implements Future, so we can keep chaining computation!

Next up, we issue our HTTP request:

let request = tls_handshake.and_then(|socket| {
    tokio_core::io::write_all(socket, "\
        GET / HTTP/1.0\r\n\
        Host: www.rust-lang.org\r\n\
        \r\n\
    ".as_bytes())
});

Here we take the future from the previous step, tls_handshake, and use and_then again to continue the computation. The write_all combinator writes the entirety of our HTTP request, issueing multiple writes as necessary. Here we're just doing a simple HTTP/1.0 request, so there's not much we need to write.

The future returned by write_all will complete once all the data has been written to the socket. Note that behind the scenes the TlsStream will actually be encrypting all the data we write before sending it to the underlying socket.

And the third and final piece of our request looks like:

let response = request.and_then(|(socket, _)| {
    tokio_core::io::read_to_end(socket, Vec::new())
});

The previous request future is chained again to the final future, the read_to_end combinator. This future will read all data from the socket provided and place it into the buffer provided (in this case an empty one), and resolve to the buffer itself once the underlying connection hits EOF.

Like before, though, reads from the socket are actually decrypting data received from the server under the covers, so we're just reading the decrypted version!

If we were to return at this point in the program, you might be surprised to see that nothing happens when it's run! That's because all we've done so far is construct a future-based computation, we haven't actually run it. Up to this point in the program we've done no I/O, issued no HTTP requests, etc.

To actually execute our future and drive it to completion we'll need to run the event loop:

let (_, data) = core.run(response).unwrap();
println!("{}", String::from_utf8_lossy(&data));

Here we pass our response future, our entire HTTP request, to the event loop, asking it to resolve the future. The event loop will then run until the future has been resolved, returning the result of the future which in this case is io::Result<(TcpStream, Vec<u8>)>.

Note that this core.run(..) call will block the calling thread until the future can itself be resolved. This means that data here has type Vec<u8>. We then print it out to stdout as usual.

Phew! At this point we've seen futures initiate a TCP connection create a chain of computation, and read data from a socket. But this is only a hint of what futures can do, so let's dive more into the traits themselves!


The Future trait

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The core trait of the futures crate is Future. This trait represents an asynchronous computation which will eventually get resolved. Let's take a look:

trait Future {
    type Item;
    type Error;

    fn poll(&mut self) -> Poll<Self::Item, Self::Error>;

    // ...
}

I'm sure quite a few points jump out immediately about this definition, so let's go through them all in detail!

Item and Error

Back to Future

type Item;
type Error;

The first aspect of the Future trait you'll probably notice is the two associated types it contains. These represent the types of values that the Future can resolve to. Each instance of Future can be thought of as resolving to a Result<Self::Item, Self::Error>.

These two types will show up very frequently in where clauses when consuming futures generically, and type signatures when futures are returned. For example when returning a future you might write:

fn foo() -> Box<Future<Item = u32, Error = io::Error>> {
    // ...
}

Or when taking a future you might write:

fn foo<F>(future: F)
    where F: Future<Error = io::Error>,
          F::Item: Clone,
{
    // ...
}

poll

Back to Future

fn poll(&mut self) -> Poll<Self::Item, Self::Error>;

The entire Future trait is built up around this one method, and it's the only required method. The poll method is the sole entry point for extracting the resolved value of a future as well as registering interest in the future itself. As a consumer of futures you will rarely - if ever - need to call this method directly. Rather, you interact with futures through combinators that create higher-level abstractions around futures. But it's useful to our understanding if we have a sense of how futures work under the hood.

Let's take a closer look at poll. Notice the &mut self argument, which conveys a number of restrictions and abilities:

  • Futures may only be polled by one thread at a time.
  • During a poll, futures can mutate their own state.
  • When poll'd, futures are owned by another entity.

Next we see that Poll is actually a type alias:

type Poll<T, E> = Result<Async<T>, E>;

Let's follow through and see what Async is as well:

pub enum Async<T> {
    Ready(T),
    NotReady,
}

Through this enum futures can communicate whether the future's value is ready to go. If an error ever happens, then Err is returned immediately. Otherwise, the Async type indicates whether the future is ready with a successful payload or not ready.

The Future trait, like Iterator, doesn't specify what happens after poll is called if the future has already resolved. Many implementations will panic, some may never resolve again, etc. This means that implementors of the Future trait don't need to maintain state to check if poll has already returned successfully.

If a call to poll returns NotReady, then futures still need to know how to figure out when to get poll'd later! To accomplish this, a future must ensure that when NotReady is returned the current task is arranged to receive a notification when the value may be available. We'll talk more about tasks later, and it's a sufficient mental model to understand that NotReady means not only the item isn't ready, but you're now registered to receive a notification when it is ready.

To actually deliver notifications, the park method is the primary entry point. This function returns a threadsafe Task handle with one primary method, unpark. The unpark method, when called, indicates that a future can make progress, and may be able to resolve to a value.

More detailed documentation can be found on the poll methods itself.

Future combinators

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Now that we've seen the poll method, it seems like it may be a bit of a pain to call! What if all you have is a future of String and you want to convert it to a future of u32? For this sort of composition, the Future trait also provides a large number of combinators which can be seen on the Future trait itself.

These combinators are similar to the Iterator combinators in that they all consume the receiving future and return a new future. For example, we could have:

fn parse<F>(future: F) -> Box<Future<Item=u32, Error=F::Error>>
    where F: Future<Item=String> + 'static,
{
    Box::new(future.map(|string| {
        string.parse::<u32>().unwrap()
    }))
}

Here we're using map to transform a future of String to a future of u32, ignoring errors. This example returns a Box, but that's not always necessary, and is discussed in the returning futures section.

The combinators on futures allow expressing concepts like:

  • Change the type of a future (map, map_err)
  • Run another future after one has completed (then, and_then, or_else)
  • Figuring out which of two futures resolves first (select)
  • Waiting for two futures to both complete (join)
  • Defining the behavior of poll after resolution (fuse)

Usage of the combinators should feel very similar to the Iterator trait in Rust or futures in Scala. Most composition of futures ends up being done through these combinators. All combinators are zero-cost, meaning that no memory is allocated internally and the implementation will optimize to what you would have otherwise written by hand.


The Stream trait

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Previously, we've taken a long look at the Future trait, which is useful if we're only producing one value over time. But sometimes computations are best modeled as a stream of values being produced over time. For example, a TCP listener produces a number of TCP socket connections over its lifetime. Let's see how Future and Stream relate to their synchronous equivalents in the standard library:

# items Sync Async Common operations
1 Result Future map, and_then
Iterator Stream map, fold, collect

Let's take a look at the Stream trait in the futures crate:

trait Stream {
    type Item;
    type Error;

    fn poll(&mut self) -> Poll<Option<Self::Item>, Self::Error>;
}

You'll notice that the Stream trait is very similar to the Future trait. The primary difference is that a stream's poll method returns Option<Self::Item> instead of Self::Item.

A Stream produces optionally many values over time, signaling termination of the stream by returning Ready(None). At its heart a Stream represents an asynchronous stream of values being produced in order.

A Stream is actually just a special instance of a Future, and can be converted to a future through the into_future method. The returned future will resolve to the next value on the stream plus the stream itself, allowing more values to later be extracted. This also allows composing streams and other arbitrary futures with the core future combinators.

Like Future, the Stream trait provides a large number of combinators. Many future-like combinators are provided, like then, in addition to stream-specific combinators like fold.

Stream Example

Back to Stream

We saw an example of using futures at the beginning of this tutorial, so let's take a look at an example of streams now, the incoming implementation of Stream on TcpListener. The simple server below will accept connections, write out the word "Hello!" to them, and then close the socket:

extern crate futures;
extern crate tokio_core;

use futures::stream::Stream;
use tokio_core::reactor::Core;
use tokio_core::net::TcpListener;

fn main() {
    let mut core = Core::new().unwrap();
    let address = "127.0.0.1:8080".parse().unwrap();
    let listener = TcpListener::bind(&address, &core.handle()).unwrap();

    let addr = listener.local_addr().unwrap();
    println!("Listening for connections on {}", addr);

    let clients = listener.incoming();
    let welcomes = clients.and_then(|(socket, _peer_addr)| {
        tokio_core::io::write_all(socket, b"Hello!\n")
    });
    let server = welcomes.for_each(|(_socket, _welcome)| {
        Ok(())
    });

    core.run(server).unwrap();
}

Like before, let's walk through this line-by-line:

let mut core = Core::new().unwrap();
let address = "127.0.0.1:8080".parse().unwrap();
let listener = TcpListener::bind(&address, &core.handle()).unwrap();

Here we initialize our event loop, like before, and then we use the TcpListener::bind function to create a TCP listener which will accept sockets.

Now that we've got the TCP listener we can inspect its state:

let addr = listener.local_addr().unwrap();
println!("Listening for connections on {}", addr);

Here we're just calling the local_addr method to print out what address we ended up binding to. We know that at this point the port is actually bound successfully, so clients can now connect.

Next up, we actually create our Stream!

let clients = listener.incoming();

Here the incoming method returns a Stream of TcpListener and SocketAddr pairs. This is similar to libstd's TcpListener and the accept method, only we're receiving all of the events as a stream rather than having to manually accept sockets.

The stream, clients, produces sockets forever. This mirrors how socket servers tend to accept clients in a loop and then dispatch them to the rest of the system for processing.

Now that we've got our stream of clients, we can manipulate it via the standard methods on the Stream trait:

let welcomes = clients.and_then(|(socket, _peer_addr)| {
    tokio_core::io::write_all(socket, b"Hello!\n")
});

Here we use the and_then method on Stream to perform an action over each item of the stream. In this case we're chaining on a computation for each element of the stream (in this case a TcpStream). The computation is the same write_all we saw earlier, where it'll write the entire buffer to the socket provided.

This block means that welcomes is now a stream of sockets which have had "Hello!" written to them. For our purposes we're done with the connection at that point, so we can collapse the entire welcomes stream into a future with the for_each method:

welcomes.for_each(|(_socket, _welcome)| {
    Ok(())
})

Here we take the results of the previous future, write_all, and discard them, closing the socket.

Note that an important limitation of this server is that there is no concurrency! Streams represent in-order processing of data, and in this case the order of the original stream is the order in which sockets are received, which the and_then and for_each combinators preserve. Chaining these therefore has the effect of taking each socket from the stream and processing all chained operations on it before taking the next socket.

If, instead, we want to handle all clients concurrently, we can use the spawn method on [Handle]:

let clients = listener.incoming();
let welcomes = clients.map(|(socket, _peer_addr)| {
    tokio_core::io::write_all(socket, b"hello!\n")
});
let handle = core.handle();
let server = welcomes.for_each(|future| {
    handle.spawn(future.then(|_| Ok(())));
    Ok(())
});

Instead of and_then we're using map here which changes our stream of clients to a stream of futures. We then change our for_each closure to spawn the future, which allows the future to execute concurrently on the event loop. Note that spawn requires the future to have the item/error types as ().

We'll talk a bit more about spawning futures in the tasks and futures section.


Concrete futures and streams

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Alright! At this point we've got a good understanding of the Future and Stream traits, both how they're implemented as well as how they're composed together. But where do all these futures originally come from? Let's take a look at a few concrete implementations of futures and streams.

First, any value already available is trivially a future that is immediately ready. For this, the done, failed, and finished functions suffice. The done variant takes a Result<T, E> and returns a Future<Item=T, Error=E>. The failed and finished variants then specify either T or E and leave the other associated type as a wildcard.

For streams, the equivalent of an "immediately ready" stream is the iter function which creates a stream that yields the same items as the underlying iterator.

In situations though where a value isn't immediately ready, there are also more general implementations of Future and Stream that are available in the futures crate, the first of which is oneshot. Let's take a look:

extern crate futures;

use std::thread;
use futures::Future;

fn expensive_computation() -> u32 {
    // ...
    200
}

fn main() {
    let (tx, rx) = futures::oneshot();

    thread::spawn(move || {
        tx.complete(expensive_computation());
    });

    let rx = rx.map(|x| x + 3);
}

Here we can see that the oneshot function returns two halves (like mpsc::channel). The first half, tx ("transmitter"), is of type Complete and is used to complete the oneshot, providing a value to the future on the other end. The Complete::complete method will transmit the value to the receiving end.

The second half, rx ("receiver"), is of type Oneshot which is a type that implements the Future trait. The Item type is T, the type of the oneshot. The Error type is Canceled, which happens when the Complete half is dropped without completing the computation.

This concrete implementation of Future can be used (as shown here) to communicate values across threads. Each half implements the Send trait and is a separately owned entity to get passed around. It's generally not recommended to make liberal use of this type of future, however; the combinators above or other forms of base futures should be preferred wherever possible.

For the Stream trait, a similar primitive is available, channel. This type also has two halves, where the sending half is used to send messages and the receiving half implements Stream.

The channel's Sender type differs from the standard library's in an important way: when a value is sent to the channel it consumes the sender, returning a future that will resolve to the original sender only once the sent value is consumed. This creates backpressure so that a producer won't be able to make progress until the consumer has caught up.


Returning futures

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When working with futures, one of the first things you're likely to need to do is to return a Future! Like with the Iterator trait, however, this isn't (yet) the easiest thing to do. Let's walk through your options:

Trait objects

Back to returning future

First, what you can do is return a boxed trait object:

fn foo() -> Box<Future<Item = u32, Error = io::Error>> {
    // ...
}

The upside of this strategy is that it's easy to write down (just a Box) and easy to create. This is also maximally flexible in terms of future changes to the method as any type of future can be returned as an opaque, boxed Future.

Note that the boxed method actually returns a BoxFuture, but this is just a type alias for Box<Future + Send>:

fn foo() -> BoxFuture<u32, u32> {
    finished(1).boxed()
}

The downside of this approach is that it requires a runtime allocation when the future is constructed. The Box needs to be allocated on the heap and the future itself is then placed inside. Note, though that this is the only allocation here, otherwise while the future is being executed no allocations will be made. Furthermore, the cost is not always that high in the end because internally there are no boxed futures (i.e. chains of combinators do not generally introduce allocations), it's only at the fringe that a Box comes into effect.

Custom types

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If you'd like to not return a Box, however, another option is to name the return type explicitly. For example:

struct MyFuture {
    inner: Oneshot<i32>,
}

fn foo() -> MyFuture {
    let (tx, rx) = oneshot();
    // ...
    MyFuture { inner: tx }
}

impl Future for MyFuture {
    // ...
}

In this example we're returning a custom type, MyFuture, and we implement the Future trait directly for it. This implementation leverages an underlying Oneshot<i32>, but any other kind of protocol can also be implemented here as well.

The upside to this approach is that it won't require a Box allocation and it's still maximally flexible. The implementation details of MyFuture are hidden to the outside world so it can change without breaking others.

The downside to this approach, however, is that it's not always ergonomically viable. Defining new types gets cumbersome after awhile, and if you're very frequently returning futures it may be too much.

Named types

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The next possible alternative is to name the return type directly:

fn add_10<F>(f: F) -> Map<F, fn(i32) -> i32>
    where F: Future<Item = i32>,
{
    fn do_map(i: i32) -> i32 { i + 10 }
    f.map(do_map)
}

Here we name the return type exactly as the compiler sees it. The map function returns the Map struct which internally contains the future and the function to perform the map.

The upside to this approach is that it's more ergonomic than the custom future type above and it also doesn't have the runtime overhead of Box from before.

The downside, however, is that it's often quite difficult to name the type. Sometimes the types can get quite large or be unnameable altogether. Here we're using a function pointer (fn(i32) -> i32) but we would ideally use a closure. Unfortunately the return type cannot name the closure, for now.

impl Trait

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In an ideal world, however, we can have our cake and eat it too with a new language feature called impl Trait. This language feature will allow, for example:

fn add_10<F>(f: F) -> impl Future<Item = i32, Error = F::Error>
    where F: Future<Item = i32>,
{
    f.map(|i| i + 10)
}

Here we're indicating that the return type is "something that implements Future" with the given associated types. Other than that we just use the future combinators as we normally would.

The upsides to this approach are that it is zero overhead with no Box necessary, it's maximally flexible to future implementations as the actual return type is hidden, and it's ergonomic to write as it's similar to the nice Box example above.

The downside to this approach is only that it's not on stable Rust yet. As of the time of this writing impl Trait has an initial implementation as a PR but it will still take some time to make its way into nightly and then finally the stable channel. The good news, however, is that as soon as impl Trait hits stable Rust all crates using futures can immediately benefit! It should be a backwards-compatible extension to change return types from Box to impl Trait


Task and Future

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Up to this point we've talked a lot about how to build computations by creating futures, but we've barely touched on how to actually run a future. When talking about poll earlier it was mentioned that if poll returns NotReady then it's arranged for a task to be notified, but where did this task come from? Additionally, where'd poll get called from in the first place?

Enter, a Task!

In the futures crate a task drives a computation represented by futures. Tasks can be thought of as being similar to green threads or normal OS threads: they're spawned to represent concurrent computations (which in this case are futures). Any particular instance of a Future may be short-lived, only a part of a larger computation. For example, in our "hello world" example we had a number of futures, but only one actually ran at a time. For the entire program, we had one task that followed the logical "thread of execution" as each future resolved and the overall computation progressed.

When a future is spawned it is fused with a task, and then this structure can be polled for completion. Precisely how and when a poll happens is up to the function which spawned the future. Normally you won't call spawn but rather one of CpuPool::spawn with a thread pool or Handle::spawn with an event loop. These internally use spawn and handle managing calls to poll for you.

The clever implementation of Task is the key to the futures crate's efficiency: when a future is spawned, each of the Futures in the chain of computations is combined into a single state machine structure and moved together from the stack into the heap. This action is the only allocation imposed by the futures library. In effect, the Task behaves as if you had written an efficient state machine by hand while allowing you to express that state machine as straight-line sequence of computations.


Task-local data

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In the previous section we've now seen how each individual future is only one piece of a larger asynchronous computation. This means that futures come and go, but there could also be data that lives for the entire span of a computation that many futures need access to.

Futures themselves are often required to be 'static, so we have two choices to share data between futures:

  • If the data is only ever used by one future at a time we can thread through ownership of the data between each future.
  • If the data needs to be accessed concurrently, however, then we'd have to naively store data in an Arc/Rc or worse, in an Arc<Mutex> if we wanted to mutate it.

But both of these solutions are relatively heavyweight, so let's see if we can do better!

In the Task and Future section we saw how an asynchronous computation has access to a Task for its entire lifetime, and from the signature of poll we also see that it has mutable access to this task. The Task API leverages these facts and allows you to store data inside a Task. Data associated with a Task can be created with two methods:

  • The task_local! macro behaves similarly to the thread_local! macro in the standard library. Data initialized this way is lazily initialized on first access for a task, and then it's destroyed when a task itself is destroyed.
  • The TaskRc structure provides the ability to create a reference-counted piece of data that can only be accessed on an appropriate task. It can be cloned, however, like an Rc.

Note that both of these methods will fuse data with the currently running task, which may not always be desired. These two should likely be used sparingly in general.