Let’s take a closer look at futures. Tokio is built on top of the futures crate and uses its runtime model. This allows Tokio to interop with other libraries also using the futures crate.

Note: This runtime model is very different than async libraries found in other languages. While, at a high level, APIs can look similar, the way code gets executed differs.

We’ll be taking a closer look at the runtime in the upcoming sections, but a basic understanding of the runtime is necessary to understand futures. To gain this understanding, we’ll first look at the synchronous model that Rust uses by default and see how this differs from Tokio’s asynchronous model.

Synchronous Model

First, let’s talk briefly about the synchronous (or blocking) model that the Rust standard library uses.

# use std::io::prelude::*;
# use std::net::TcpStream;
# fn dox(mut socket: TcpStream) {
// let socket = ...;
let mut buf = [0; 1024];
let n = socket.read(&mut buf).unwrap();

// Do something with &buf[..n];
# }
# fn main() {}

When socket.read is called, either the socket has pending data in its receive buffer or it does not. If there is pending data, the call to read will return immediately and buf will be filled with that data. However, if there is no pending data, the read function will block the current thread until data is received. Once the data is received, buf will be filled with this newly received data and the read function will return.

In order to perform reads on many different sockets concurrently, a thread per socket is required. Using a thread per socket does not scale up very well to large numbers of sockets. This is known as the c10k problem.

Non-blocking sockets

The way to avoid blocking a thread when performing an operation like read is to not block the thread! Non-blocking sockets allow performing operations, like read, without blocking the thread. When the socket has no pending data in its receive buffer, the read function returns immediately, indicating that the socket was “not ready” to perform the read operation.

When using a Tokio TcpStream, a call to read will always immediately return a value (ErrorKind::WouldBlock) even if there is no pending data to read. If there is no pending data, the caller is responsible for calling read again at a later time. The trick is to know when that “later time” is.

Another way to think about a non-blocking read is as ‘polling’ the socket for data to read.

Futures are an abstraction around this polling model. A Future represents a value that will be available at “some point in the future”. We can poll the future and ask if the value is ready or not. Let’s take a look in more detail.

A closer look at futures

A future is a value that represents the completion of an asynchronous computation. Usually, the future completes due to an event that happens elsewhere in the system. While we’ve been looking at things from the perspective of basic I/O, you can use a future to represent a wide range of events, e.g.:

  • A database query, when the query finishes, the future is completed, and its value is the result of the query.

  • An RPC invocation to a server. When the server replies, the future is completed, and its value is the server’s response.

  • A timeout. When time is up, the future is completed, the value is an empty tuple () (also referred to as “unit” or “the unit type”).

  • A long-running CPU-intensive task, running on a thread pool. When the task finishes, the future is completed, and its value is the return value of the task.

  • Reading bytes from a socket. When the bytes are ready, the future is completed – and depending on the buffering strategy, the bytes might be returned directly, or written as a side-effect into some existing buffer.

The entire point of the future abstraction is to allow asynchronous functions, i.e., functions that cannot immediately return a value, to be able to return something.

For example, an asynchronous HTTP client could provide a get function that looks like this:

pub fn get(&self, uri: &str) -> ResponseFuture { ... }

Then, the user of the library would use the function as so:

let response_future = client.get("https://www.example.com");

Now, the response_future isn’t the actual response. It is a future that will complete once the response is received. However, since the caller has a concrete value (the future), they can start to use it. For example, they may chain computations using combinators to perform once the response is received or they might pass the future to a function.

let response_is_ok = response_future
    .map(|response| {


None of those actions taken with the future perform any immediate work. They cannot because they don’t have the actual HTTP response. Instead, they define the work to be done when the response future completes and the actual response is available.

Both the futures crate and Tokio come with a collection of combinator functions that can be used to work with futures. So far we’ve seen and_then which chains two futures together, then which allows to chain a future to a previous one even if the previous one errored, and map which simply maps a future’s value from one type to another.

We’ll be exploring more combinators later in this guide.

Poll based Futures

As hinted at earlier, Rust futures are poll based. This means that instead of a Future being responsible for pushing the data somewhere once it is complete, it relies on being asked whether it is complete or not.

This is a unique aspect of the Rust future library. Most future libraries for other programming languages use a push based model where callbacks are supplied to the future and the computation invokes the callback immediately with the computation result.

Using a poll based model offers many advantages, including being a zero cost abstraction, i.e., using Rust futures has no added overhead compared to writing the asynchronous code by hand.

We’ll take a closer look at this poll based model in the next section.

The Future trait

The Future trait is as follows:

trait Future {
    /// The type of the value returned when the future completes.
    type Item;

    /// The type representing errors that occurred while processing the computation.
    type Error;

    /// The function that will be repeatedly called to see if the future
    /// has completed or not
    fn poll(&mut self) -> Result<Async<Self::Item>, Self::Error>;

For now it’s just important to know that futures have two associated types: Item and Error. Item is the type of the value that the Future will yield when it completes. Error is the type of Error that the Future may yield if there’s an error before that prevents the Future from completing successfully.

Finally, Futures have one method named poll. We won’t go into too much detail about poll in this section since you don’t need to know about poll to use futures with combinators. The only thing to be aware for now is that poll is what the runtime will call in order to see if the Future is complete yet or not. If you’re curious: Async is an enum with values Ready(Item) or NotReady which informs the runtime of if the future is complete or not.

In a future section, we’ll be implementing a Future from scratch including writing a poll function that properly informs the runtime when the future is complete.


Streams are the iterator equivalent of futures. Instead of yielding a value at some point in the future, streams yield a collection of values each at some point in the future. In other words, streams don’t yield just one value at one point in the future like futures do. They rather keep yielding values over time.

Just like futures, you can use streams to represent a wide range of things as long as those things produce discrete values at different points sometime in the future. For instance:

  • UI Events caused by the user interacting with a GUI in different ways. When an event happens the stream yields a different message to your app over time.
  • Push Notifications from a server. Sometimes a request/response model is not what you need. A client can establish a notification stream with a server to be able to receive messages from the server without specifically being requested.
  • Incoming socket connections. As different clients connect to a server, the connections stream will yield socket connections.

Streams are very similar to futures in their implementation:

trait Stream {
    /// The type of the value yielded by the stream.
    type Item;

    /// The type representing errors that occurred while processing the computation.
    type Error;

    /// The function that will be repeatedly called to see if the stream has
    /// another value it can yield
    fn poll(&mut self) -> Poll<Option<Self::Item>, Self::Error>;

Streams come with their own set of combinators and will be covered in more depth in the working with futures section.

Next up: Runtime