# What Are Tokio and Async IO All About?

The Rust community lately has been focusing a lot on “async I/O” through the tokio project. This is pretty great!

But for many in the community who haven’t worked with web servers and related things it’s pretty confusing as to what we’re trying to achieve there. When this stuff was being discussed around 1.0, I was pretty lost as well, having never worked with this stuff before.

What’s all this Async I/O business about? What are coroutines? Lightweight threads? Futures? How does this all fit together?

## What problem are we trying to solve?

One of Rust’s key features is “fearless concurrency”. But the kind of concurrency required for handling a large amount of I/O bound tasks – the kind of concurrency found in Go, Elixir, Erlang – is absent from Rust.

Let’s say you want to build something like a web service. It’s going to be handling thousands of requests at any point in time (known as the “c10k problem”). In general, the problem we’re considering is having a huge number of I/O bound (usually network I/O) tasks.

“Handling N things at once” is best done by using threads. But … thousands of threads? That sounds a bit much. Threads can be pretty expensive: Each thread needs to allocate a large stack, setting up a thread involves a bunch of syscalls, and context switching is expensive.

Of course, thousands of threads all doing work at once is not going to work anyway. You only have a fixed number of cores, and at any one time only one thread will be running on a core.

But for cases like web servers, most of these threads won’t be doing work. They’ll be waiting on the network. Most of these threads will either be listening for a request, or waiting for their response to get sent.

With regular threads, when you perform a blocking I/O operation, the syscall returns control to the kernel, which won’t yield control back, because the I/O operation is probably not finished. Instead, it will use this as an opportunity to swap in a different thread, and will swap the original thread back when its I/O operation is finished (i.e. it’s “unblocked”). Without Tokio and friends, this is how you would handle such things in Rust. Spawn a million threads; let the OS deal with scheduling based on I/O.

But, as we already discovered, threads don’t scale well for things like this1.

I think the best way to understand lightweight threading is to forget about Rust for a moment and look at a language that does this well, Go.

Instead of using OS threads, Go has lightweight threads, called “goroutines”. You spawn these with the go keyword. A web server might do something like this:

listener, err = net.Listen(...)
// handle err
for {
conn, err := listener.Accept()
// handle err

// spawn goroutine:
go handler(conn)
}


This is a loop which waits for new TCP connections, and spawns a goroutine with the connection and the function handler. Each connection will be a new goroutine, and the goroutine will shut down when handler finishes. In the meantime, the main loop continues executing, because it’s running in a different goroutine.

So if these aren’t “real” (operating system) threads, what’s going on?

A goroutine is an example of a “lightweight” thread. The operating system doesn’t know about these, it sees N threads owned by the Go runtime, and the Go runtime maps M goroutines onto them2, swapping goroutines in and out much like the operating system scheduler. It’s able to do this because Go code is already interruptible for the GC to be able to run, so the scheduler can always ask goroutines to stop. The scheduler is also aware of I/O, so when a goroutine is waiting on I/O it yields to the scheduler.

Essentialy, a compiled Go function will have a bunch of points scattered throughout it where it tells the scheduler and GC “take over if you want” (and also “I’m waiting on stuff, please take over”).

When a goroutine is swapped on an OS thread, some registers will be saved, and the program counter will switch to the new goroutine.

But what about its stack? OS threads have a large stack with them, and you kinda need a stack for functions and stuff to work.

What Go used to do was segmented stacks. The reason a thread needs a large stack is that most programming languages, including C, expect the stack to be contiguous, and stacks can’t just be “reallocated” like we do with growable buffers since we expect stack data to stay put so that pointers to stack data to continue to work. So we reserve all the stack we think we’ll ever need (~8MB), and hope we don’t need more.

But the expectation of stacks being contiguous isn’t strictly necessary. In Go, stacks are made of tiny chunks. When a function is called, it checks if there’s enough space on the stack for it to run, and if not, allocates a new chunk of stack and runs on it. So if you have thousands of threads doing a small amount of work, they’ll all get thousands of tiny stacks and it will be fine.

These days, Go actually does something different; it copies stacks. I mentioned that stacks can’t just be “reallocated” we expect stack data to stay put. But that’s not necessarily true — because Go has a GC it knows what all the pointers are anyway, and it can rewrite pointers to stack data on demand.

Either way, Go’s rich runtime lets it handle this stuff well. Goroutines are super cheap, and you can spawn thousands without your computer having problems.

Rust used to support lightweight/”green” threads (I believe it used segmented stacks). However, Rust cares a lot about not paying for things you don’t use, and this imposes a penalty on all your code even if you aren’t using green threads, and it was removed pre-1.0.

## Async I/O

A core building block of this is Async I/O. As mentioned in the previous section, with regular blocking I/O, the moment you request I/O your thread will not be allowed to run (“blocked”) until the operation is done. This is perfect when working with OS threads (the OS scheduler does all the work for you!), but if you have lightweight threads you instead want to replace the lightweight thread running on the OS thread with a different one.

Instead, you use non-blocking I/O, where the thread queues a request for I/O with the OS and continues execution. The I/O request is executed at some later point by the kernel. The thread then needs to ask the OS “Is this I/O request ready yet?” before looking at the result of the I/O.

Of course, repeatedly asking the OS if it’s done can be tedious and consume resources. This is why there are system calls like epoll. Here, you can bundle together a bunch of unfinished I/O requests, and then ask the OS to wake up your thread when any of these completes. So you can have a scheduler thread (a real thread) that swaps out lightweight threads that are waiting on I/O, and when there’s nothing else happening it can itself go to sleep with an epoll call until the OS wakes it up (when one of the I/O requests completes).

(The exact mechanism involved here is probably more complex)

So, bringing this to Rust, Rust has the mio library, which is a platform-agnostic wrapper around non-blocking I/O and tools like epoll/kqueue/etc. It’s a building block; and while those used to directly using epoll in C may find it helpful, it doesn’t provide a nice programming model like Go does. But we can get there.

## Futures

These are another building block. A Future is the promise of eventually having a value (in fact, in Javascript these are called Promises).

So for example, you can ask to listen on a network socket, and get a Future back (actually, a Stream, which is like a future but for a sequence of values). This Future won’t contain the response yet, but will know when it’s ready. You can wait() on a Future, which will block until you have a result, and you can also poll() it, asking it if it’s done yet (it will give you the result if it is).

Futures can also be chained, so you can do stuff like future.then(|result| process(result)). The closure passed to then itself can produce another future, so you can chain together things like I/O operations. With chained futures, poll() is how you make progress; each time you call it it will move on to the next future provided the existing one is ready.

This is a pretty good abstraction over things like non-blocking I/O.

Chaining futures works much like chaining iterators. Each and_then (or whatever combinator) call returns a struct wrapping around the inner future, which may contain an additional closure. Closures themselves carry their references and data with them, so this really ends up being very similar to a tiny stack!

## 🗼 Tokio 🗼

Tokio’s essentially a nice wrapper around mio that uses futures. Tokio has a core event loop, and you feed it closures that return futures. What it will do is run all the closures you feed it, use mio to efficiently figure out which futures are ready to make a step3, and make progress on them (by calling poll()).

This actually is already pretty similar to what Go was doing, at a conceptual level. You have to manually set up the Tokio event loop (the “scheduler”), but once you do you can feed it tasks which intermittently do I/O, and the event loop takes care of swapping over to a new task when one is blocked on I/O. A crucial difference is that Tokio is single threaded, whereas the Go scheduler can use multiple OS threads for execution. However, you can offload CPU-critical tasks onto other OS threads and use channels to coordinate so this isn’t that big a deal.

While at a conceptual level this is beginning to shape up to be similar to what we had for Go, code-wise this doesn’t look so pretty. For the following Go code:

// error handling ignored for simplicity

func foo(...) ReturnType {
data := doIo()
result := compute(data)
moreData = doMoreIo(result)
moreResult := moreCompute(data)
// ...
return someFinalResult
}


The Rust code will look something like

// error handling ignored for simplicity

fn foo(...) -> Future<ReturnType, ErrorType> {
do_io().and_then(|data| do_more_io(compute(data)))
.and_then(|more_data| do_even_more_io(more_compute(more_data)))
// ......
}


Not pretty. The code gets worse if you introduce branches and loops. The problem is that in Go we got the interruption points for free, but in Rust we have to encode this by chaining up combinators into a kind of state machine. Ew.

## Generators and async/await

This is where generators (also called coroutines) come in.

Generators are an experimental feature in Rust. Here’s an example:

let mut generator = || {
let i = 0;
loop {
yield i;
i += 1;
}
};
assert_eq!(generator.resume(), GeneratorState::Yielded(0));
assert_eq!(generator.resume(), GeneratorState::Yielded(1));
assert_eq!(generator.resume(), GeneratorState::Yielded(2));


Functions are things which execute a task and return once. On the other hand, generators return multiple times; they pause execution to “yield” some data, and can be resumed at which point they will run until the next yield. While my example doesn’t show this, generators can also finish executing like regular functions.

Generators are similar, except they implement the Generator trait4, and usually store an enum representing various states.

The unstable book has some examples on what the generator state machine enum will look like.

This is much closer to what we were looking for! Now our code can look like this:

fn foo(...) -> Future<ReturnType, ErrorType> {
let generator = || {
let mut future = do_io();
let data;
loop {
// poll the future, yielding each time it fails,
// but if it succeeds then move on
match future.poll() {
Ok(Async::Ready(d)) => { data = d; break },
Err(..) => ...
};
yield future.polling_info();
}
let result = compute(data);
// do the same thing for doMoreIo(), etc
}

futurify(generator)
}


where futurify is a function that takes a generator and returns a future which on each poll call will resume() the generator, and return NotReady until the generator finishes executing.

But wait, this is even more ugly! What was the point of converting our relatively clean callback-chaining code into this mess?

Well, if you look at it, this code now looks linear. We’ve converted our callback code to the same linear flow as the Go code, however it has this weird loop-yield boilerplate and the futurify function and is overall not very neat.

And that’s where futures-await comes in. futures-await is a procedural macro that does the last-mile work of packaging away this boilerplate. It essentially lets you write the above function as

#[async]
fn foo(...) -> Result<ReturnType, ErrorType> {
let data = await!(do_io());
let result = compute(data);
let more_data = await!(do_more_io());
// ....


Nice and clean. Almost as clean as the Go code, just that we have explicit await!() calls. These await calls are basically providing the same function as the interruption points that Go code gets implicitly.

And, of course, since it’s using a generator under the hood, you can loop and branch and do whatever else you want as normal, and the code will still be clean.

## Tying it together

So, in Rust, futures can be chained together to provide a lightweight stack-like system. With async/await, you can neatly write these future chains, and await provides explicit interruption points on each I/O operation. Tokio provides an event loop “scheduler” abstraction, which you can feed async functions to, and under the hood it uses mio to abstract over low level non-blocking I/O primitives.

These are components which can be used independently — you can use tokio with futures without using async/await. You can use async/await without using Tokio. For example, I think this would be useful for Servo’s networking stack. It doesn’t need to do much parallel I/O (not at the order of thousands of threads), so it can just use multiplexed OS threads. However, we’d still want to pool threads and pipeline data well, and async/await would help here.

Put together, all these components get something almost as clean as the Go stuff, with a little more explicit boilerplate. Because generators (and thus async/await) play nice with the borrow checker (they’re just enum state machines under the hood), Rust’s safety guarantees are all still in play, and we get to have “fearless concurrency” for programs having a huge quantity of I/O bound tasks!

Thanks to Arshia Mufti, Steve Klabnik, Zaki Manian, and Kyle Huey for reviewing drafts of this post

1. Note that this isn’t necessarily true for all network server applications. For example, Apache uses OS threads. OS threads are often the best tool for the job.

3. In general future combinators aren’t really aware of tokio or even I/O, so there’s no easy way to ask a combinator “hey, what I/O operation are you waiting for?”. Instead, with Tokio you use special I/O primitives that still provide futures but also register themselves with the scheduler in thread local state. This way when a future is waiting for I/O, Tokio can check what the recentmost I/O operation was, and associate it with that future so that it can wake up that future again when epoll tells it that that I/O operation is ready. (Edit Dec 2018: This has changed, futures now have a built in Waker concept that handles passing things up the stack
4. The Generator trait has a resume() function which you can call multiple times, and each time it will return any yielded data or tell you that the generator has finished running.